Reference Citation Analysis: Find an Article, Find a Category, Find a Journal, Find a Scholar

For: Nguyen MM, Gill IS, Ellison LM. The evolving presentation of renal carcinoma in the United States: trends from the Surveillance, Epidemiology, and End Results program. J Urol 2006;176:2397-400; discussion 2400. [PMID: 17085111 DOI: 10.1016/j.juro.2006.07.144] [Citation(s) in RCA: 245] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2005] [Indexed: 12/23/2022]

For:	Nguyen MM, Gill IS, Ellison LM. The evolving presentation of renal carcinoma in the United States: trends from the Surveillance, Epidemiology, and End Results program. J Urol 2006;176:2397-400; discussion 2400. [PMID: 17085111 DOI: 10.1016/j.juro.2006.07.144] [Citation(s) in RCA: 245] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2005] [Indexed: 12/23/2022]

Number

Cited by Other Article(s)

Bachanek S, Wuerzberg P, Biggemann L, Janssen TY, Nietert M, Lotz J, Zeuschner P, Maßmann A, Uhlig A, Uhlig J. Renal tumor segmentation, visualization, and segmentation confidence using ensembles of neural networks in patients undergoing surgical resection. Eur Radiol 2025;35:2147-2156. [PMID: 39177855 PMCID: PMC11913914 DOI: 10.1007/s00330-024-11026-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 05/29/2024] [Accepted: 08/02/2024] [Indexed: 08/24/2024]

Abstract

OBJECTIVES

To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated confidence to promote clinical applicability.

MATERIALS AND METHODS

The training dataset included solid renal tumor patients from two tertiary centers undergoing surgical resection and receiving CT in the corticomedullary or nephrogenic contrast media (CM) phase. Manual tumor segmentation was performed on all axial CT slices serving as reference standard for automatic segmentations. Independent testing was performed on the publicly available KiTS 2019 dataset. Ensembles of neural networks (ENN, DeepLabV3) were used for automatic renal tumor segmentation, and their performance was quantified with DICE score. ENN average foreground entropy measured segmentation confidence (binary: successful segmentation with DICE score > 0.8 versus inadequate segmentation ≤ 0.8).

RESULTS

N = 639/n = 210 patients were included in the training and independent test dataset. Datasets were comparable regarding age and sex (p > 0.05), while renal tumors in the training dataset were larger and more frequently benign (p < 0.01). In the internal test dataset, the ENN model yielded a median DICE score = 0.84 (IQR: 0.62-0.97, corticomedullary) and 0.86 (IQR: 0.77-0.96, nephrogenic CM phase), and the segmentation confidence an AUC = 0.89 (sensitivity = 0.86; specificity = 0.77). In the independent test dataset, the ENN model achieved a median DICE score = 0.84 (IQR: 0.71-0.97, corticomedullary CM phase); and segmentation confidence an accuracy = 0.84 (sensitivity = 0.86 and specificity = 0.81). ENN segmentations were visualized with color-coded voxelwise tumor probabilities and thresholds superimposed on clinical CT images.

CONCLUSIONS

ENN-based renal tumor segmentation robustly performs in external test data and might aid in renal tumor classification and treatment planning.

CLINICAL RELEVANCE STATEMENT

Ensembles of neural networks (ENN) models could automatically segment renal tumors on routine CTs, enabling and standardizing downstream image analyses and treatment planning. Providing confidence measures and segmentation overlays on images can lower the threshold for clinical ENN implementation.

KEY POINTS

Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds. ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset. ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.

Collapse

Barrera-Juarez E, Halun-Trevino AN, Ruelas-Martinez M, Madero-Frech A, Camacho-Trejo V, Estrada-Bujanos M, Bojorquez D, Uribe-Montoya J, Rodriguez-Covarrubias F, Villarreal-Garza C. Prognosis impact and clinical findings in renal cancer patients: comparative analysis between public and private health coverage in a cross-sectional and multicenter context. Cancer Causes Control 2025;36:265-273. [PMID: 39514146 PMCID: PMC11928398 DOI: 10.1007/s10552-024-01891-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 05/13/2024] [Indexed: 11/16/2024]

Abstract

PURPOSE

Research on disparities in prognosis and clinical characteristics between public and private healthcare sectors in developing countries remains limited. The study aimed to determine whether patients with public health coverage (1) have a greater mean tumor size at diagnosis compared to those with private health coverage; (2) exhibit differences in clinical staging and TNM classification between groups; and (3) show variations in demographic, clinical characteristics, histopathological findings, and surgical approaches among cohorts.

METHODS

A cross-sectional, multicenter study was conducted on 629 patients from both private and public healthcare sectors, all histologically confirmed and surgically treated for Renal Cell Carcinoma (RCC), between 2011 and 2021 in high-volume hospitals in Monterrey, Mexico. To compare variables between groups, we employed independent samples t-tests, Mann Whitney U nonparametric test, along with Pearson's chi-square test complemented by post hoc analyses.

RESULTS

Mean tumor size in the public group was 1.9 cm greater than in the private group (7.39 vs. 5.51 cm, p < 0.001). Patients in the public sector more frequently presented with larger tumors, a higher prevalence of risk factors (excluding BMI and hypertension), advanced disease (OR 2.12, 95% CI 1.43-3.16, p < 0.001), presence of symptoms, elevated TNM, lymphovascular invasion and a lower prevalence of minimally invasive surgery. A male-to-female ratio of 2.6:1 was noted in the private coverage group.

CONCLUSIONS

This study highlights a notable association between public health coverage and a higher prevalence of advanced RCC, with tumors in private coverage patients being smaller yet larger than commonly reported. There is a crucial need to develop new health policies for early detection of renal cancer in developing countries.

Collapse

Kishitani K, Taguchi S, Tanaka K, Danno T, Oshina T, Fujii Y, Kamei J, Akiyama Y, Kakutani S, Sato Y, Yamada Y, Niimi A, Yamada D, Kume H. Survival improvement over time in renal cell carcinoma treated with nephrectomy: A longitudinal propensity score-matched study. Int J Urol 2025;32:145-150. [PMID: 39467021 PMCID: PMC11803181 DOI: 10.1111/iju.15610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Accepted: 10/03/2024] [Indexed: 10/30/2024]

Thomsen FF, Petersson RD, Schou-Jensen KS, Rashu BS, Niebuhr MH, Azawi NH. Transperitoneal robot-assisted partial nephrectomy: a comparison of operative and oncological outcomes between posterior and anterolateral tumours. Int Urol Nephrol 2025:10.1007/s11255-025-04372-x. [PMID: 39808377 DOI: 10.1007/s11255-025-04372-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2024] [Accepted: 01/07/2025] [Indexed: 01/16/2025]

Sawczyn G, Brambilla C, Rodrigues GJ, Pereira MWAP, Cardili L, de Carvalho PA, Gallucci FP, Sarkis ÁS, Nahas WC, Cordeiro MD. Clinical, pathological, and oncological outcomes in unclassified renal cell carcinoma compared to clear cell renal cell carcinoma. Indian J Urol 2025;41:51-58. [PMID: 39886631 PMCID: PMC11778684 DOI: 10.4103/iju.iju_256_24] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2024] [Revised: 08/15/2024] [Accepted: 09/25/2024] [Indexed: 02/01/2025] Open

Shinohara M, Hata S, Nishida H, Mimata H, Shin T. Small Renal Cell Carcinoma Presenting With Testicular Metastasis: A Rare Case of pT1a Disease With an Aggressive Clinical Course. Cureus 2025;17:e77304. [PMID: 39935928 PMCID: PMC11811579 DOI: 10.7759/cureus.77304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2025] [Indexed: 02/13/2025] Open

Urraro F, Piscopo M, Giordano N, Russo GM, Gallo L, Magliocchetti S, Giordano DS, Patanè V, Arcaniolo D, Cozzolino I, Nardone V, Cappabianca S, Reginelli A. Diagnostic Value of Contrast-Enhanced Ultrasound in Differentiating Malignant from Benign Small Renal Masses After CT/MRI. J Clin Med 2024;13:6478. [PMID: 39518616 PMCID: PMC11545930 DOI: 10.3390/jcm13216478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2024] [Revised: 10/18/2024] [Accepted: 10/27/2024] [Indexed: 11/16/2024] Open

Affiliation(s)

Fabrizio Urraro Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (F.U.); (V.P.); (S.C.); (A.R.)
Marco Piscopo Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (F.U.); (V.P.); (S.C.); (A.R.)
Nicoletta Giordano Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (F.U.); (V.P.); (S.C.); (A.R.)
Gaetano Maria Russo Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (F.U.); (V.P.); (S.C.); (A.R.)
Luigi Gallo Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (F.U.); (V.P.); (S.C.); (A.R.)
Simona Magliocchetti Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (F.U.); (V.P.); (S.C.); (A.R.)
Diego Sandro Giordano Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (F.U.); (V.P.); (S.C.); (A.R.)
Vittorio Patanè Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (F.U.); (V.P.); (S.C.); (A.R.)
Davide Arcaniolo Urology Unit, Department of Woman, Child and General and Specialized Surgery, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy
Immacolata Cozzolino Pathology Unit, Mental and Ohysical Health and Preventive Medicine Department, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy
Valerio Nardone Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (F.U.); (V.P.); (S.C.); (A.R.)
Salvatore Cappabianca Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (F.U.); (V.P.); (S.C.); (A.R.)
Alfonso Reginelli Department of Precision Medicine, University of Campania “Luigi Vanvitelli”, 80138 Naples, Italy; (F.U.); (V.P.); (S.C.); (A.R.)

Collapse

Uhlig A, Uhlig J, Leha A, Biggemann L, Bachanek S, Stöckle M, Reichert M, Lotz J, Zeuschner P, Maßmann A. Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting. Eur Radiol 2024;34:6254-6263. [PMID: 38634876 PMCID: PMC11399155 DOI: 10.1007/s00330-024-10731-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 02/14/2024] [Accepted: 03/06/2024] [Indexed: 04/19/2024]

Abstract

OBJECTIVES

To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT).

MATERIAL AND METHODS

Patients who underwent surgical treatment for renal tumors at two tertiary centers from 2012 to 2022 were included retrospectively. Preoperative arterial (corticomedullary) and venous (nephrogenic) phase CT scans from these centers, as well as from external imaging facilities, were manually segmented, and standardized radiomic features were extracted. Following preprocessing and addressing the class imbalance, a ML algorithm based on extreme gradient boosting trees (XGB) was employed to predict renal tumor subtypes using 10-fold cross-validation. The evaluation was conducted using the multiclass area under the receiver operating characteristic curve (AUC). Algorithms were trained on data from one center and independently tested on data from the other center.

RESULTS

The training cohort comprised n = 297 patients (64.3% clear cell renal cell cancer [RCC], 13.5% papillary renal cell carcinoma (pRCC), 7.4% chromophobe RCC, 9.4% oncocytomas, and 5.4% angiomyolipomas (AML)), and the testing cohort n = 121 patients (56.2%/16.5%/3.3%/21.5%/2.5%). The XGB algorithm demonstrated a diagnostic performance of AUC = 0.81/0.64/0.8 for venous/arterial/combined contrast phase CT in the training cohort, and AUC = 0.75/0.67/0.75 in the independent testing cohort. In pairwise comparisons, the lowest diagnostic accuracy was evident for the identification of oncocytomas (AUC = 0.57-0.69), and the highest for the identification of AMLs (AUC = 0.9-0.94) CONCLUSION: Radiomic feature analyses can distinguish renal tumor subtypes on routinely acquired CTs, with oncocytomas being the hardest subtype to identify.

CLINICAL RELEVANCE STATEMENT

Radiomic feature analyses yield robust results for renal tumor assessment on routine CTs. Although radiologists routinely rely on arterial phase CT for renal tumor assessment and operative planning, radiomic features derived from arterial phase did not improve the accuracy of renal tumor subtype identification in our cohort.

Collapse

Toide M, Tanaka H, Kobayashi M, Fujiwara M, Nakamura Y, Fukuda S, Kimura K, Waseda Y, Yoshida S, Tateishi U, Fujii Y. Stepwise algorithm using computed tomography and magnetic resonance imaging for differential diagnosis of fat-poor angiomyolipoma in small renal masses: A prospective validation study. Int J Urol 2024;31:778-784. [PMID: 38632863 DOI: 10.1111/iju.15464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 03/28/2024] [Indexed: 04/19/2024]

Bodard S, Dariane C, Bibault JE, Boudhabhay I, Delavaud C, Timsit MO, Verkarre V, Méjean A, Hélénon O, Guinebert S, Correas JM. [Nephron sparing in the management of localized solid renal mass]. Bull Cancer 2024;111:720-732. [PMID: 37169604 DOI: 10.1016/j.bulcan.2023.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Revised: 03/30/2023] [Accepted: 04/13/2023] [Indexed: 05/13/2023]

Affiliation(s)

Sylvain Bodard AP-HP, hôpital Necker-Enfants-Malades, service d'imagerie adulte, 75015 Paris, France; Université de Paris-Cité, 75006 Paris, France; Sorbonne université, laboratoire d'imagerie biomédicale, CNRS, Inserm, Paris, France; Groupe de recherche interdisciplinaire francophone en onco-néphrologie (GRIFON), Paris, France.
Charles Dariane Université de Paris-Cité, 75006 Paris, France; AP-HP, hôpital européen Georges-Pompidou, service d'urologie, 75015 Paris, France
Jean-Emmanuel Bibault Université de Paris-Cité, 75006 Paris, France; AP-HP, hôpital européen Georges-Pompidou, service de radiothérapie, 75015 Paris, France
Idris Boudhabhay Université de Paris-Cité, 75006 Paris, France; AP-HP, hôpital Necker-Enfants-Malades, service de néphrologie et transplantation rénale adulte, 75015 Paris, France
Christophe Delavaud AP-HP, hôpital Necker-Enfants-Malades, service d'imagerie adulte, 75015 Paris, France
Marc-Olivier Timsit Université de Paris-Cité, 75006 Paris, France; AP-HP, hôpital européen Georges-Pompidou, service d'urologie, 75015 Paris, France
Virginie Verkarre Université de Paris-Cité, 75006 Paris, France; AP-HP, hôpital européen Georges-Pompidou, service d'anatomie pathologie, 75015 Paris, France
Arnaud Méjean Université de Paris-Cité, 75006 Paris, France; AP-HP, hôpital européen Georges-Pompidou, service d'urologie, 75015 Paris, France
Olivier Hélénon AP-HP, hôpital Necker-Enfants-Malades, service d'imagerie adulte, 75015 Paris, France; Université de Paris-Cité, 75006 Paris, France
Sylvain Guinebert AP-HP, hôpital Necker-Enfants-Malades, service d'imagerie adulte, 75015 Paris, France; Université de Paris-Cité, 75006 Paris, France
Jean-Michel Correas AP-HP, hôpital Necker-Enfants-Malades, service d'imagerie adulte, 75015 Paris, France; Université de Paris-Cité, 75006 Paris, France; Sorbonne université, laboratoire d'imagerie biomédicale, CNRS, Inserm, Paris, France

Collapse

Petersson RD, Fode M, Niebuhr MH, Rashu BS, Thomsen FF. Robot-assisted partial nephrectomy in patients aged 75 years or older - comparing the risk of complications with their younger counterparts. Aging Clin Exp Res 2024;36:107. [PMID: 38714631 PMCID: PMC11076407 DOI: 10.1007/s40520-024-02751-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 03/28/2024] [Indexed: 05/10/2024]

Uhlig J, Uhlig A, Deshpande H, Ströbel P, Trojan L, Lotz J, Hurwitz M, Hafez O, Humphrey P, Grünwald V, Kim HS. Epidemiology, treatment and outcomes of primary renal sarcomas in adult patients. Sci Rep 2024;14:10038. [PMID: 38693188 PMCID: PMC11063157 DOI: 10.1038/s41598-024-60174-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Accepted: 04/19/2024] [Indexed: 05/03/2024] Open

Peak T, Tian Y, Patel A, Shaw T, Obermayer A, Laborde J, Kim Y, Johnson J, Stewart P, Fang B, Teer JK, Koomen J, Berglund A, Marchion D, Francis N, Echevarria PR, Dhillon J, Clark N, Chang A, Sexton W, Zemp L, Chahoud J, Wang L, Manley B. Pathogenic Roles for RNASET2 in Clear Cell Renal Cell Carcinoma. J Transl Med 2024;104:102041. [PMID: 38431116 DOI: 10.1016/j.labinv.2024.102041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 02/03/2024] [Accepted: 02/07/2024] [Indexed: 03/05/2024] Open

Affiliation(s)

Taylor Peak Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.
Yijun Tian Department of Tumor Biology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Aman Patel Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Tim Shaw Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Alyssa Obermayer Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Jose Laborde Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Youngchul Kim Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Joseph Johnson Analytic Microcopy Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
Paul Stewart Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Bin Fang Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Jamie K Teer Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
John Koomen Molecular Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
Anders Berglund Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Doug Marchion Tissue Core Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
Natasha Francis Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Paola Ramos Echevarria Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Jasreman Dhillon Department of Pathology, H. Lee Moffitt Cancer Center, Tampa, Florida
Noel Clark Tissue Core Shared Resource, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
Andrew Chang Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Wade Sexton Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Logan Zemp Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Jad Chahoud Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Liang Wang Department of Tumor Biology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida
Brandon Manley Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida.

Collapse

Serhal M, Rangwani S, Seedial SM, Thornburg B, Riaz A, Nemcek AA, Sato KT, Perry KT, Choy B, Lewandowski RJ, Gordon AC. Safety and Diagnostic Efficacy of Image-Guided Biopsy of Small Renal Masses. Cancers (Basel) 2024;16:835. [PMID: 38398226 PMCID: PMC10887197 DOI: 10.3390/cancers16040835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 02/06/2024] [Accepted: 02/13/2024] [Indexed: 02/25/2024] Open

Abstract

INTRODUCTION

Image-guided renal mass biopsy is gaining increased diagnostic acceptance, but there are limited data concerning the safety and diagnostic yield of biopsy for small renal masses (≤4 cm). This study evaluated the safety, diagnostic yield, and management after image-guided percutaneous biopsy for small renal masses.

METHODS

A retrospective IRB-approved study was conducted on patients who underwent renal mass biopsy for histopathologic diagnosis at a single center from 2015 to 2021. Patients with a prior history of malignancy or a renal mass >4 cm were excluded. Descriptive statistics were used to summarize patient demographics, tumor size, the imaging modality used for biopsy, procedure details, complications, pathological diagnosis, and post-biopsy management. A biopsy was considered successful when the specimen was sufficient for diagnosis without need for a repeat biopsy. Complications were graded according to the SIR classification of adverse events. A chi-squared test (significance level set at p ≤ 0.05) was used to compare the success rate of biopsies in different lesion size groups.

RESULTS

A total of 167 patients met the inclusion criteria. The median age was 65 years (range: 26-87) and 51% were male. The median renal mass size was 2.6 cm (range: one-four). Ultrasound was solely employed in 60% of procedures, CT in 33%, a combination of US/CT in 6%, and MRI in one case. With on-site cytopathology, the median number of specimens obtained per procedure was four (range: one-nine). The overall complication rate was 5%. Grade A complications were seen in 4% (n = 7), consisting of perinephric hematoma (n = 6) and retroperitoneal hematoma (n = 1). There was one grade B complication (0.5%; pain) and one grade D complication (0.5%; pyelonephritis). There was no patient mortality within 30 days post-biopsy. Biopsy was successful in 88% of cases. A sub-group analysis showed a success rate of 85% in tumors <3 cm and 93% in tumors ≥3 cm (p = 0.01). Pathological diagnoses included renal cell carcinoma (65%), oncocytoma (18%), clear cell papillary renal cell tumors (9%), angiomyolipoma (4%), xanthogranulomatous pyelonephritis (1%), lymphoma (1%), high-grade papillary urothelial carcinoma (1%), and metanephric adenoma (1%), revealing benign diagnosis in 30% of cases. The most common treatment was surgery (40%), followed by percutaneous cryoablation (22%). In total, 37% of patients were managed conservatively, and one patient received chemotherapy.

CONCLUSION

This study demonstrates the safety and diagnostic efficacy of image-guided biopsy of small renal masses. The diagnostic yield was significantly higher for masses 3-4 cm in size compared to those <3 cm. The biopsy results showed a high percentage of benign diagnoses and informed treatment decisions in most patients.

Collapse

Affiliation(s)

Muhamad Serhal Section of Interventional Radiology, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; (M.S.); (S.M.S.); (B.T.); (A.R.); (A.A.N.J.); (K.T.S.); (R.J.L.)
Sean Rangwani Northwestern University Feinberg School of Medicine, Chicago Campus, Chicago, IL 60611, USA;
Stephen M. Seedial Section of Interventional Radiology, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; (M.S.); (S.M.S.); (B.T.); (A.R.); (A.A.N.J.); (K.T.S.); (R.J.L.)
Bartley Thornburg Section of Interventional Radiology, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; (M.S.); (S.M.S.); (B.T.); (A.R.); (A.A.N.J.); (K.T.S.); (R.J.L.)
Ahsun Riaz Section of Interventional Radiology, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; (M.S.); (S.M.S.); (B.T.); (A.R.); (A.A.N.J.); (K.T.S.); (R.J.L.)
Albert A. Nemcek Section of Interventional Radiology, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; (M.S.); (S.M.S.); (B.T.); (A.R.); (A.A.N.J.); (K.T.S.); (R.J.L.)
Kent T. Sato Section of Interventional Radiology, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; (M.S.); (S.M.S.); (B.T.); (A.R.); (A.A.N.J.); (K.T.S.); (R.J.L.)
Kent T. Perry Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA;
Bonnie Choy Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA;
Robert J. Lewandowski Section of Interventional Radiology, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; (M.S.); (S.M.S.); (B.T.); (A.R.); (A.A.N.J.); (K.T.S.); (R.J.L.)
Andrew C. Gordon Section of Interventional Radiology, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; (M.S.); (S.M.S.); (B.T.); (A.R.); (A.A.N.J.); (K.T.S.); (R.J.L.)

Collapse

Jin DD, Lin JH, Li SH, Zhuang BW, Xie XY, Xie XH, Wang Y. Ultrasound findings and clinical characteristics in differentiating renal urothelial carcinoma from endophytic clear cell renal cell carcinoma. Clin Hemorheol Microcirc 2024;88:309-323. [PMID: 38995769 DOI: 10.3233/ch-242119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2024]

Liang M, Qiu H, Ou B, Wu J, Zhao X, Luo B. Evaluation of contrast-enhanced ultrasound for predicting tumor grade in small (≤4 cm) clear cell renal cell carcinoma: Qualitative and quantitative analysis. Clin Hemorheol Microcirc 2024;88:351-362. [PMID: 39031342 DOI: 10.3233/ch-231990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2024]

Gao H, Nowroozizadeh B, Zepeda JP, Landman J, Farzaneh T, Johnson C, Hosseini H, Han M. The success rate of small renal mass core needle biopsy and its impact on lowering benign resection rate. BMC Urol 2023;23:189. [PMID: 37980518 PMCID: PMC10657570 DOI: 10.1186/s12894-023-01363-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 11/09/2023] [Indexed: 11/20/2023] Open

Gao Y, Wang X, Zhao X, Zhu C, Li C, Li J, Wu X. Multiphase CT radiomics nomogram for preoperatively predicting the WHO/ISUP nuclear grade of small (< 4 cm) clear cell renal cell carcinoma. BMC Cancer 2023;23:953. [PMID: 37814228 PMCID: PMC10561466 DOI: 10.1186/s12885-023-11454-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 09/27/2023] [Indexed: 10/11/2023] Open

Patel AK, Butaney M, Lane BR, Wilder S, Johnson A, Qi J, Wang Y, DiBianco J, Herrel L, Maatman T, Peabody J, Rosenberg B, Seifman B, Semerjian A, Shetty S, Schervish E, Collins J, Tandogdu Z, Rogers CG. Building a Roadmap for Surveillance of Renal Masses Using a Modified Delphi Method to Help Achieve Consensus. Urology 2023;180:168-175. [PMID: 37353086 DOI: 10.1016/j.urology.2023.06.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 06/07/2023] [Accepted: 06/12/2023] [Indexed: 06/25/2023]

Toffoli T, Saut O, Etchegaray C, Jambon E, Le Bras Y, Grenier N, Marcelin C. Differentiation of Small Clear Renal Cell Carcinoma and Oncocytoma through Magnetic Resonance Imaging-Based Radiomics Analysis: Toward the End of Percutaneous Biopsy. J Pers Med 2023;13:1444. [PMID: 37888055 PMCID: PMC10608459 DOI: 10.3390/jpm13101444] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/13/2023] [Accepted: 09/21/2023] [Indexed: 10/28/2023] Open

Abstract

PURPOSE

The aim of this study was to ascertain whether radiomics data can assist in differentiating small (<4 cm) clear cell renal cell carcinomas (ccRCCs) from small oncocytomas using T2-weighted magnetic resonance imaging (MRI).

MATERIAL AND METHODS

This retrospective study incorporated 48 tumors, 28 of which were ccRCCs and 20 were oncocytomas. All tumors were less than 4 cm in size and had undergone pre-biopsy or pre-surgery MRI. Following image pre-processing, 102 radiomics features were evaluated. A univariate analysis was performed using the Wilcoxon rank-sum test with Bonferroni correction. We compared multiple radiomics pipelines of normalization, feature selection, and machine learning (ML) algorithms, including random forest (RF), logistic regression (LR), AdaBoost, K-nearest neighbor, and support vector machine, using a supervised ML approach.

RESULTS

No statistically significant features were identified via the univariate analysis with Bonferroni correction. The most effective algorithm was identified using a pipeline incorporating standard normalization, RF-based feature selection, and LR, which achieved an area under the curve (AUC) of 83%, accuracy of 73%, sensitivity of 79%, and specificity of 65%. Subsequently, the most significant features were identified from this algorithm, and two groups of uncorrelated features were established based on Pearson correlation scores. Using these features, an algorithm was established after a pipeline of standard normalization and LR, achieving an AUC of 90%, an accuracy of 77%, sensitivity of 83%, and specificity of 69% for distinguishing ccRCCs from oncocytomas.

CONCLUSIONS

Radiomics analysis based on T2-weighted MRI can aid in distinguishing small ccRCCs from small oncocytomas. However, it is not superior to standard multiparameter renal MRI and does not yet allow us to dispense with percutaneous biopsy.

Collapse

Hong Z, Chen X, Wang L, Zhou X, He H, Zou G, Liu Q, Wang Y. ROCK2-RNA interaction map reveals multiple biological mechanisms underlying tumor progression in renal cell carcinoma. Hum Cell 2023;36:1790-1803. [PMID: 37418232 DOI: 10.1007/s13577-023-00947-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 06/24/2023] [Indexed: 07/08/2023]

Khaleel S, Truong H, Jiang S, K-Lee P, Davelman B, Gordon D, Benfante N, Arora A, Ostrovnaya I, Tickoo S, Coleman J, Hakimi AA, Russo P. Adverse pathologic features impact survival outcomes for small renal masses following nephrectomy. Urol Oncol 2023;41:391.e5-391.e11. [PMID: 37423816 PMCID: PMC11042782 DOI: 10.1016/j.urolonc.2023.06.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 04/30/2023] [Accepted: 06/18/2023] [Indexed: 07/11/2023]

Abstract

PURPOSE

While most small renal masses (SRM) < 4 cm have an excellent prognosis following resection, the impact of adverse T3a pathologic features on oncologic outcomes of SRMs remains unclear. We sought to compare clinical outcomes for surgically resected pT3a versus pT1a SRMs at our institution.

MATERIALS AND METHODS

We retrospectively reviewed records of patients who underwent radical or partial nephrectomy (RN, PN) for renal tumors <4 cm at our institution between 2010 and 2020. We compared features and outcomes of pT3a vs pT1a SRMs. Continuous and categorical variables were compared using Student's t and Pearson's chi-squared tests, respectively. Postoperative outcomes of interest including overall, cancer-specific, and recurrence-free survival (OS, CSS, and RFS) were analyzed using Kaplan-Meier method, Cox proportional hazard regression, and competing risk analysis. Analyses were performed using R statistical package (R Foundation, v4.0).

RESULTS

We identified 1,837 patients with malignant SRMs. Predictors of postoperative pT3a upstaging included higher renal score, larger tumor size, and presence of radiologic features concerning for T3a disease (odds ratio [OR] = 5.45, 95% confidence interval [CI] 3.92-7.59, P < 0.001). On univariable modeling, pT3a SRMs had higher positive margin rates (9.6% vs 4.1%, P < 0.001), worse OS (hazard ratio [HR] = 2.9, 95% CI 1.6-5.3, P = 0.002), RFS (HR 9.32, 95% CI 2-40.1, P = 0.003), and CSS (HR = 3.6, 95% CI 1.5-8.2, P = 0.003). On multivariable modeling, pT3a status remained associated with worse RFS (HR = 2.7, 95% CI 1.04-7, P = 0.04), but not OS (HR 1.6, 95% CI = 0.83-3.1, P = 0.2); multivariable modeling was deferred for CSS due to low event rates.

CONCLUSIONS

Adverse T3a pathologic features portend worse outcomes for SRMs, highlighting the crucial role of pre-operative planning and case selection. These patients have relatively poor prognosis, and should be monitored more closely and counseled for consideration of adjuvant therapy or clinical trials.

Collapse

Dehghani Firouzabadi F, Gopal N, Hasani A, Homayounieh F, Li X, Jones EC, Yazdian Anari P, Turkbey E, Malayeri AA. CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis. PLoS One 2023;18:e0287299. [PMID: 37498830 PMCID: PMC10374097 DOI: 10.1371/journal.pone.0287299] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 06/03/2023] [Indexed: 07/29/2023] Open

Abstract

PURPOSE

Differentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic confirmation. However, radiomics has the potential to characterize renal masses without the need for invasive procedures. Here, we conducted a systematic review on the accuracy of CT radiomics in distinguishing fp-AMLs from RCCs.

METHODS

We conducted a search using PubMed/MEDLINE, Google Scholar, Cochrane Library, Embase, and Web of Science for studies published from January 2011-2022 that utilized CT radiomics to discriminate between fp-AMLs and RCCs. A random-effects model was applied for the meta-analysis according to the heterogeneity level. Furthermore, subgroup analyses (group 1: RCCs vs. fp-AML, and group 2: ccRCC vs. fp-AML), and quality assessment were also conducted to explore the possible effect of interstudy differences. To evaluate CT radiomics performance, the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were assessed. This study is registered with PROSPERO (CRD42022311034).

RESULTS

Our literature search identified 10 studies with 1456 lesions in 1437 patients. Pooled sensitivity was 0.779 [95% CI: 0.562-0.907] and 0.817 [95% CI: 0.663-0.910] for groups 1 and 2, respectively. Pooled specificity was 0.933 [95% CI: 0.814-0.978]and 0.926 [95% CI: 0.854-0.964] for groups 1 and 2, respectively. Also, our findings showed higher sensitivity and specificity of 0.858 [95% CI: 0.742-0.927] and 0.886 [95% CI: 0.819-0.930] for detecting ccRCC from fp-AML in the unenhanced phase of CT scan as compared to the corticomedullary and nephrogenic phases of CT scan.

CONCLUSION

This study suggested that radiomic features derived from CT has high sensitivity and specificity in differentiating RCCs vs. fp-AML, particularly in detecting ccRCCs vs. fp-AML. Also, an unenhanced CT scan showed the highest specificity and sensitivity as compared to contrast CT scan phases. Differentiating between fp-AML and RCC often is not possible without biopsy or surgery; radiomics has the potential to obviate these invasive procedures due to its high diagnostic accuracy.

Collapse

Distante A, Marandino L, Bertolo R, Ingels A, Pavan N, Pecoraro A, Marchioni M, Carbonara U, Erdem S, Amparore D, Campi R, Roussel E, Caliò A, Wu Z, Palumbo C, Borregales LD, Mulders P, Muselaers CHJ. Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives. Diagnostics (Basel) 2023;13:2294. [PMID: 37443687 DOI: 10.3390/diagnostics13132294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/01/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023] Open

Affiliation(s)

Alfredo Distante Department of Urology, Catholic University of the Sacred Heart, 00168 Roma, Italy Department of Urology, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen, The Netherlands
Laura Marandino Department of Medical Oncology, IRCCS Ospedale San Raffaele, 20132 Milan, Italy
Riccardo Bertolo Department of Urology, San Carlo Di Nancy Hospital, 00165 Rome, Italy
Alexandre Ingels Department of Urology, University Hospital Henri Mondor, APHP (Assistance Publique-Hôpitaux de Paris), 94000 Créteil, France
Nicola Pavan Department of Surgical, Oncological and Oral Sciences, Section of Urology, University of Palermo, 90133 Palermo, Italy
Angela Pecoraro Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, 10043 Turin, Italy
Michele Marchioni Department of Medical, Oral and Biotechnological Sciences, G. d'Annunzio University of Chieti, 66100 Chieti, Italy
Umberto Carbonara Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation-Urology, University of Bari, 70121 Bari, Italy
Selcuk Erdem Division of Urologic Oncology, Department of Urology, Istanbul University Istanbul Faculty of Medicine, Istanbul 34093, Turkey
Daniele Amparore Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, 10043 Turin, Italy
Riccardo Campi Urological Robotic Surgery and Renal Transplantation Unit, Careggi Hospital, University of Florence, 50121 Firenze, Italy
Eduard Roussel Department of Urology, University Hospitals Leuven, 3000 Leuven, Belgium
Anna Caliò Section of Pathology, Department of Diagnostic and Public Health, University of Verona, 37134 Verona, Italy
Zhenjie Wu Department of Urology, Changhai Hospital, Naval Medical University, Shanghai 200433, China
Carlotta Palumbo Division of Urology, Maggiore della Carità Hospital of Novara, Department of Translational Medicine, University of Eastern Piedmont, 13100 Novara, Italy
Leonardo D Borregales Department of Urology, Well Cornell Medicine, New York-Presbyterian Hospital, New York, NY 10032, USA
Peter Mulders Department of Urology, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen, The Netherlands
Constantijn H J Muselaers Department of Urology, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen, The Netherlands

Collapse

Wu X, Uhlig J, Shuch BM, Uhlig A, Kim HS. Cost-effectiveness of minimally invasive partial nephrectomy and percutaneous cryoablation for cT1a renal cell carcinoma. Eur Radiol 2023;33:1801-1811. [PMID: 36329348 DOI: 10.1007/s00330-022-09211-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 08/12/2022] [Accepted: 09/30/2022] [Indexed: 11/06/2022]

Abstract

BACKGROUND

There is growing evidence that partial nephrectomy (PN) and percutaneous cryoablation (PCA) yield comparable outcomes for patients with cT1a renal cell carcinoma (RCC), although the cost-effectiveness of both treatments still needs to be assessed.

PURPOSE

To perform a cost-effectiveness analysis of PN and PCA for patients with cT1a RCC.

MATERIALS AND METHODS

A decision analysis was created over a 5-year span from a healthcare payer's perspective computing expected costs and outcomes of PN and PCA in terms of quality-adjusted life-years (QALYs) and incremental cost-effectiveness (ICER). After each treatment, the following states were modelled using data from the recent literature: procedural complications, no evidence of disease (NED), local recurrence, metastases, and death from RCC- or non-RCC-related causes. Probabilistic and deterministic sensitivity analyses were performed.

RESULTS

PCA and PN yielded health benefits of 3.68 QALY and 3.67 QALY. Overall expected costs were $20,491 and $26,478 for PCA and PN. On probabilistic sensitivity analysis, PCA was more cost-effective than PN in 84.78% of Monte Carlo simulations. PCA was more cost-effective until its complication risk was at least 38% higher than PN. PCA was more cost-effective than PN when (i) PCAs annual local recurrence risk was < 3.5% higher than that of PN in absolute values; (ii) PCAs annual metastatic risk was < 1.0% higher than that of PN; or (iii) PCAs annual cancer-specific mortality risk < 0.65% higher than that of PN. PCA remained cost-effective until its procedural cost is above $13,875.

CONCLUSION

PCA appears to be more cost-effective than PN for the treatment of cT1a RCC, although the currently available evidence is of limited quality. PCA may be the better treatment strategy in the majority of scenarios varying procedural complications, recurrence, metastatic risk, and RCC-mortality in clinically plausible ranges.

KEY POINTS

• For patients with cT1a RCCs, PCA yields a comparable health benefit at lower costs compared to PN, making PCA the dominant and therefore more cost-effective treatment strategy over PN. • PCA was more cost-effective than PN when (i) PCAs annual local recurrence risk was < 3.5% higher than PN in absolute values; (ii) PCAs annual metastatic risk was < 1.0% higher than PN; or (iii) PCAs annual cancer-specific mortality risk < 0.65% higher than PN. • PCA is more cost-effective than PN for the treatment of cT1a RCC, and it remained so in the majority of scenarios varying procedural complications, recurrence, metastatic risk, and RCC mortality.

Collapse

Feng S, Gong M, Zhou D, Yuan R, Kong J, Jiang F, Zhang L, Chen W, Li Y. A CT-based radiomics nomogram for differentiation of benign and malignant small renal masses (≤4 cm). Transl Oncol 2023;29:101627. [PMID: 36731307 PMCID: PMC9937807 DOI: 10.1016/j.tranon.2023.101627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/26/2022] [Accepted: 01/15/2023] [Indexed: 02/04/2023] Open

Abstract

RATIONALE AND OBJECTIVES

Based on radiomics signature and clinical data, to develop and verify a radiomics nomogram for preoperative distinguish between benign and malignant of small renal masses (SRM).

MATERIALS AND METHODS

One hundred and fifty-six patients with malignant (n = 92) and benign (n = 64) SRM were divided into the following three categories: category A, typical angiomyolipoma (AML) with visible fat; category B, benign SRM without visible fat, including fat-poor angiomyolipoma (fp-AML), and other rare benign renal tumors; category C, malignant renal tumors. At the same time, one hundred and fifty-six patients included in the study were divided into the training set (n = 108) and test set (n = 48). Respectively from corticomedullary phase (CP), nephrogram phase (NP) and excretory phase (EP) CT images to extract the radiomics features, and the optimal features were screened to establish the logistic regression model and decision tree model, and computed the radiomics score (Rad-score). Demographics and CT findings were evaluated and statistically significant factors were selected to construct a clinical factors model. The radiomics nomogram was established by merging Rad-score and selected clinical factors. The Akaike information criterion (AIC) values and the area under the curve (AUC) were used to compare model discriminant performance, and decision curve analysis (DCA) was used to assess clinical usefulness.

RESULTS

Seven, fifteen, nineteen, and seventeen distinguishing features were obtained in the CP, NP, EP, and three-phase joint, respectively, and the logistic regression and decision tree models were built based on this features. In the training set, the logistic regression model works better than the decision tree model for distinguishing categories A and B from category C, with the AUC of CP, NP, EP and three-phase joint were 0.868, 0.906, 0.937 and 0.975, respectively. The radiomics nomogram constructed based on the three-phase joint Rad-score and selected clinical factor performed well on the training set (AUC, 0.988; 95% CI, 0.974-1.000) for differentiation of categories A and B from category C. In the test set, the AUC of clinical factors model, radiomics signature and radiomics nomogram for discriminating categories A and B from category C were 0.814, 0.954 and 0.968, respectively; for the identification of category A from category C, the AUC of the three models were 0.789, 0.979, 0.985, respectively; for discriminating category B from category C, the AUC of the three models were 0.853, 0.915, 0.946, respectively. The radiomics nomogram had better discriminative than the clinical factors model in both training and test sets (P < 0.05). The radiomics nomogram (AIC = 40.222) with the lowest AIC value was considered the best model compared with that of the clinical factors model (AIC = 106.814) and the radiomics signature (AIC = 44.224). The DCA showed that the radiomics nomogram have better clinical utility than the clinical factors model and radiomics signature.

CONCLUSIONS

The logistic regression model has better discriminative performance than the decision tree model, and the radiomics nomogram based on Rad-score of three-phase joint and clinical factors has a good predictive effect in differentiating benign from malignant of SRM, which may help clinicians develop accurate and individualized treatment strategies.

Collapse

Tang Y, Wu K, Hu X, Liu Y, Yang W, Li X. Survival benefit stratification of partial nephrectomy versus non-surgical treatment in elderly patients with T1a renal cell carcinoma. Cancer Med 2023;12:7974-7981. [PMID: 36629133 PMCID: PMC10134256 DOI: 10.1002/cam4.5580] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 11/14/2022] [Accepted: 12/17/2022] [Indexed: 01/12/2023] Open

He M, Gao Q, Xiang J, Mao Q, Jiang T. Diagnostic Value of Qualitative and Quantitative Contrast-Enhanced Ultrasound for Pathological Subtypes of Small Solid Renal Masses. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023. [PMID: 36591796 DOI: 10.1002/jum.16169] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 12/16/2022] [Accepted: 12/19/2022] [Indexed: 06/17/2023]

Chen WG, Shan GD, Zhu HT, Chen LH, Xu GQ. Gastric metastasis presenting as submucosa tumors from renal cell carcinoma: A case report. World J Clin Cases 2022;10:9805-9813. [PMID: 36186204 PMCID: PMC9516902 DOI: 10.12998/wjcc.v10.i27.9805] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 06/16/2022] [Accepted: 08/12/2022] [Indexed: 02/05/2023] Open

Kim HM, Byun SS, Kim JK, Jeong CW, Kwak C, Hwang EC, Kang SH, Chung J, Kim YJ, Ha YS, Hong SH. Machine learning-based prediction model for late recurrence after surgery in patients with renal cell carcinoma. BMC Med Inform Decis Mak 2022;22:241. [PMID: 36100881 PMCID: PMC9472380 DOI: 10.1186/s12911-022-01964-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 07/21/2022] [Indexed: 11/24/2022] Open

Cao H, Fang L, Chen L, Zhan J, Diao X, Liu Y, Lu C, Zhang Z, Chen Y. The Value of Contrast-Enhanced Ultrasound in Diagnosing Small Renal Cell Carcinoma Subtypes and Angiomyolipoma. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2022;41:1415-1423. [PMID: 34499770 DOI: 10.1002/jum.15824] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 08/10/2021] [Accepted: 08/12/2021] [Indexed: 06/13/2023]

Identification of co-expression hub genes for ferroptosis in kidney renal clear cell carcinoma based on weighted gene co-expression network analysis and The Cancer Genome Atlas clinical data. Sci Rep 2022;12:4821. [PMID: 35314744 PMCID: PMC8938444 DOI: 10.1038/s41598-022-08950-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 03/15/2022] [Indexed: 12/14/2022] Open

Mileto A, Potretzke TA. Standardized Evaluation of Small Renal Masses Using the MRI Clear Cell Likelihood Score. Radiology 2022;303:600-602. [PMID: 35289666 DOI: 10.1148/radiol.220054] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]

Kapur P, Setoodeh S, Araj E, Yan J, Malladi V, Cadeddu JA, Christie A, Brugarolas J. Improving Renal Tumor Biopsy Prognostication With BAP1 Analyses. Arch Pathol Lab Med 2022;146:154-165. [PMID: 34019633 PMCID: PMC9812366 DOI: 10.5858/arpa.2020-0413-oa] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/08/2021] [Indexed: 01/07/2023]

Abstract

CONTEXT.—

Active surveillance of small renal masses highlights the need for accurate prognostication of biopsies.

OBJECTIVE.—

To comprehensively evaluate the accuracy of biopsies in assessing known prognostic parameters including histologic subtype by comparison with subsequent nephrectomy samples.

DESIGN.—

We retrospectively identified patients at University of Texas Southwestern Medical Center, Dallas, Texas, who had a biopsy for a renal mass between 2004-2018. Biopsy samples were evaluated for known prognostic factors such as tumor grade, necrosis, sarcomatoid/rhabdoid change, and BRCA1-associated protein-1 (BAP1) status, which we previously showed is an independent prognostic factor for clear cell renal cell carcinoma. Accuracy was determined by comparison with subsequent analyses of nephrectomy specimens. Statistical analyses were performed to assess biopsy accuracy and correlation with tumor size and pathologic stage.

RESULTS.—

From 805 biopsies with a diagnosis of renal neoplasm, 178 had subsequent resection of the biopsied tumor. Concordance rate for histologic subtype was 96.9% (κ [w], 0.90; 95% CI, 0.82-0.99) and excellent for small renal masses (98.8%; κ [w], 0.97; 95% CI, 0.90-1). Amongst the prognostic variables evaluated, BAP1 immunohistochemistry in clear cell renal cell carcinoma had the highest agreement (94.8%; κ [w], 0.83; 95% CI, 0.66-0.99). The presence of 1 or more aggressive features (grade 3-4, tumor necrosis, BAP1 loss, sarcomatoid/rhabdoid change) in a biopsy significantly correlated with pT stage (P = .004).

CONCLUSIONS.—

Biopsy analyses showed high accuracy for subtyping renal tumors, but it underestimated several poor prognostic features. Addition of BAP1 for clear cell renal cell carcinoma may increase prognostic accuracy. If validated, routine incorporation of BAP1 immunohistochemistry in clear cell renal cell carcinoma biopsies may refine prognosis and aid in the selection of patients for active surveillance.

Collapse

Affiliation(s)

Payal Kapur Department of Pathology, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390,Department of Urology, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390,Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390
Sasan Setoodeh Department of Pathology, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390
Ellen Araj Department of Pathology, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390
Jingsheng Yan Department of Population and Data Sciences, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390
Venkat Malladi Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390,Lyda Hill Department of Bioinformatics, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390
Jeffrey A Cadeddu Department of Urology, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390,Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390
Alana Christie Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390
James Brugarolas Internal Medicine, Hematology-Oncology Division, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390,Kidney Cancer Program, Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390

Collapse

Effect of Aberrant Long Noncoding RNA on the Prognosis of Clear Cell Renal Cell Carcinoma. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021;2021:6533049. [PMID: 34512796 PMCID: PMC8433025 DOI: 10.1155/2021/6533049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 08/07/2021] [Indexed: 11/17/2022]

Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC). Abdom Radiol (NY) 2021;46:4278-4288. [PMID: 33855609 DOI: 10.1007/s00261-021-03083-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 03/22/2021] [Accepted: 03/31/2021] [Indexed: 12/11/2022]

Prediction of histologic grade and type of small (< 4 cm) papillary renal cell carcinomas using texture and neural network analysis: a feasibility study. Abdom Radiol (NY) 2021;46:4266-4277. [PMID: 33813624 DOI: 10.1007/s00261-021-03044-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 03/01/2021] [Accepted: 03/05/2021] [Indexed: 10/21/2022]

Abstract

OBJECTIVE

To predict the histologic grade and type of small papillary renal cell carcinomas (pRCCs) using texture analysis and machine learning algorithms.

METHODS

This was a retrospective HIPAA-compliant study. 24 noncontrast (NC), 22 corticomedullary (CM) phase, and 24 nephrographic (NG) phase CTs of small (< 4 cm) surgically resected pRCCs were identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade and type 1 or 2. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (20 gray-level co-occurrences and 11 gray-level run-lengths) features were calculated for each tumor in each phase. Feature values in low- versus high-grade and type 1 versus 2 pRCCs were compared. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of histologic grade and type of pRCCs in each phase. Histogram, texture, and combined histogram and texture feature sets were used to train and test three classification algorithms (support vector machine (SVM), random forest, and histogram-based gradient boosting decision tree (HGBDT)) with stratified shuffle splits and threefold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of histologic grade and type of pRCCs.

RESULTS

Individual histogram and texture features did not have statistically significant differences between low- and high-grade or type 1 and type 2 pRCCs across all phases. Individual features had low predictive power for tumor grade or type in all phases (AUC < 0.70). HGBDT was highly accurate at predicting pRCC histologic grade and type using histogram, texture or combined histogram and texture feature data from the CM phase (AUCs = 0.97-1.0). All algorithms had highest AUCs using CM phase feature data sets; AUCs decreased using feature sets from NC or NG phases.

CONCLUSIONS

The histologic grade and type of small pRCCs can be predicted with classification algorithms using CM histogram and texture features, which outperform NC and NG phase image data. The accurate prediction of pRCC histologic grade and type may be able to further guide management of patients with small (< 4 cm) pRCCs being considered for active surveillance.

Collapse

Masic S, Strother M, Kidd LC, Egleston B, Braun A, Srivastava A, Smaldone M, Milestone B, Parsons R, Viterbo R, Greenberg R, Chen D, Kutikov A, Uzzo R. Feasibility and Outcomes of Renal Mass Biopsy for Anatomically Complex Renal Tumors. Urology 2021;158:125-130. [PMID: 34380055 DOI: 10.1016/j.urology.2021.07.026] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 07/06/2021] [Accepted: 07/25/2021] [Indexed: 11/29/2022]

Saba K, Högger DC, Hötker AM, Rupp NJ, Sulser T, Hermanns T. [Dignity of Small Renal Masses: Implications for Diagnostics and Therapy]. PRAXIS 2021;110:565-570. [PMID: 34344187 DOI: 10.1024/1661-8157/a003709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]

Lavi A, Breau RH, Mallick R, Kapoor A, Finelli A, So A, Pouliot F, Tanguay S, Lavallée LT, Rendon R, Fairey A, Drachenberg DE, Lattouf JB, Maloni R, Power NE. Adrenalectomy During Radical Nephrectomy- Incidence and Oncologic Outcomes From the Canadian Kidney Cancer Information System (CKCis) -A Modern Era, Nationwide, Multicenter Cohort. Urology 2021;157:168-173. [PMID: 34129893 DOI: 10.1016/j.urology.2021.05.053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 04/20/2021] [Accepted: 05/28/2021] [Indexed: 01/20/2023]

Abstract

OBJECTIVE

To characterize proportion of patients receiving adrenalectomy, adrenal involvement prevalence and oncologic outcomes of routine adrenalectomy in contemporary practice. Ipsilateral adrenalectomy was once standard during radical nephrectomy. However, benefit of routine adrenalectomy has been questioned because adrenal involvement of renal cell carcinoma (RCC) is low.

METHODS

All patients receiving radical nephrectomy in the Canadian Kidney Cancer information system, a collaborative prospective cohort populated by 14 major Canadian centers, between January 2011 to February 2020 were included. Patients were excluded if they had non-RCC histology, multiple tumors, contralateral tumors, metastatic disease or previous history of RCC. Patient demographic, clinical, and surgical information were summarized and compared. Cox-proportional hazards was used for multivariable analysis.

RESULTS

During study period, 2759 patients received radical nephrectomy, of these, 831(30.1%) had concomitant adrenalectomy. Pathological adrenal involvement was identified in 102 (3.7%overall; 12.3%of adrenalectomy). Median follow-up was 21.6months (Interquartile range 7.0-46.5). Patients with adrenalectomy had higher venous tumor thrombus (30.3% vs 9.6%; P <.0001), higher T stage (71.1% vs 43.4% pT3/4; P <.0001), lymph node metastases (17.6% vs 10.7%; P = .0035), Fuhrman grades (71.4% of Fuhrman grades 3/4 vs 56.2%; P <.0001) and increased proportion of clear cell histology (79.3% vs 74.5%; P = .0074) compared to the no adrenalectomy group. Adrenalectomy patients had higher risk of recurrence (HR 1.23; 95% CI 1.04-1.47; P = .019) and no difference in survival (HR 1.09, 95% CI 0.86-1.38, P = .48).

CONCLUSION

Adrenalectomy is not associated with better oncological outcome of recurrence/survival. Adrenalectomy should be reserved for patients with radiographic adrenal involvement and/or intra-operative adrenal involvement.

Collapse

Zhang HM, Wen DG, Wang Y, Bao YG, Yuan Y, Chen YT, Song B. Arterial Spin Labeling MRI for Predicting Microvascular Invasion of T1 Staging Renal Clear Cell Carcinoma Preoperatively. Front Oncol 2021;11:644975. [PMID: 34084743 PMCID: PMC8168533 DOI: 10.3389/fonc.2021.644975] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 04/09/2021] [Indexed: 02/05/2023] Open

Abstract

BACKGROUND

Microvascular invasion (MVI) is a valuable factor for T1 staging renal clear cell carcinoma (ccRCC) operation strategy decision, which is confirmed histopathologically post-operation. This study aimed to prospectively evaluate the performance of arterial spin labeling (ASL) MRI for predicting MVI of T1 staging ccRCC preoperatively.

METHODS

16 volunteers and 39 consecutive patients were enrolled. MRI examinations consisted of ASL (three post label delays separately) of the kidney, followed by T1 and T2-weighted imaging. Two sessions of ASL were used to evaluate the reproducibility on volunteers. Renal blood flow of renal cortex, medulla, the entire and solid part of the tumor were measured on ASL images. Conventional imaging features were extracted. MVI and WHO/ISUP classification were evaluated histopathologically. A paired t-test was used to compare the renal cortex and medulla between ASL 1 and ASL 2. The reproducibility was assessed using the intraclass correlation. Differences in mean perfusion between the entire and the solid parts of tumors with or without MVI were assessed separately using Student's t test. The diagnostic performance was assessed. Logistic regression analysis was used to indicate the independent prediction index for MVI.

RESULTS

The two sessions of ASL showed no significant difference between the mean cortex values of RBF. The cortical RBF measurements demonstrated good agreement. 12 ccRCCs presented with MVI histopathologically. Mean perfusion of the solid part of tumors with MVI were 536.4 ± 154.8 ml/min/100 g (PLD1), 2912.5 ± 939.3 ml/min/100 g (PLD2), 3280.3 ± 901.2 ml/min/100 g (PLD3). Mean perfusion of the solid part of tumors without MVI were 453.5 ± 87.2 ml/min/100 g (PLD1), 1043.6 ± 695.8 ml/min/100 g (PLD2), 1577.6 ± 1085.8 ml/min/100 g (PLD3). These two groups have significant difference at all the PLDs (p < 0.05). The RBF of PLD1 of the solid part of tumor perfusion showed well diagnostic performance for predicting MVI: sensitivity 75%, specificity 100%, positive predictive value 66.7%, and negative predictive value 95.7%. The maximum diameter of the tumor, ill-defined margin, and the solid part of tumor perfusion were the independent prediction index for MVI.

CONCLUSION

ASL MR imaging has good reproducibility for renal cortex, and good diagnostic performance for predicting MVI for ccRCC.

Collapse

Zuaiter M, Axelrod JH, Pizov G, Gofrit ON. Hyper-Interleukin-6 Protects Against Renal Ischemic-Reperfusion Injury-A Mouse Model. Front Surg 2021;8:605675. [PMID: 34055865 PMCID: PMC8155529 DOI: 10.3389/fsurg.2021.605675] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 04/15/2021] [Indexed: 01/20/2023] Open

Michel KF, Spaulding A, Jemal A, Yabroff KR, Lee DJ, Han X. Associations of Medicaid Expansion With Insurance Coverage, Stage at Diagnosis, and Treatment Among Patients With Genitourinary Malignant Neoplasms. JAMA Netw Open 2021;4:e217051. [PMID: 34009349 PMCID: PMC8134994 DOI: 10.1001/jamanetworkopen.2021.7051] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open

Abstract

IMPORTANCE

Health insurance coverage is associated with improved outcomes in patients with cancer. However, it is unknown whether Medicaid expansion through the Patient Protection and Affordable Care Act (ACA) was associated with improvements in the diagnosis and treatment of patients with genitourinary cancer.

OBJECTIVE

To assess the association of Medicaid expansion with health insurance status, stage at diagnosis, and receipt of treatment among nonelderly patients with newly diagnosed kidney, bladder, or prostate cancer.

DESIGN, SETTING, AND PARTICIPANTS

This case-control study included adults aged 18 to 64 years with a new primary diagnosis of kidney, bladder, or prostate cancer, selected from the National Cancer Database from January 1, 2011, to December 31, 2016. Patients in states that expanded Medicaid were the case group, and patients in nonexpansion states were the control group. Data were analyzed from January 2020 to March 2021.

EXPOSURES

State Medicaid expansion status.

MAIN OUTCOMES AND MEASURES

Insurance status, stage at diagnosis, and receipt of cancer and stage-specific treatments. Cases and controls were compared with difference-in-difference analyses.

RESULTS

Among a total of 340 552 patients with newly diagnosed genitourinary cancers, 94 033 (27.6%) had kidney cancer, 25 770 (7.6%) had bladder cancer, and 220 749 (64.8%) had prostate cancer. Medicaid expansion was associated with a net decrease in uninsured rate of 1.1 (95% CI, -1.4 to -0.8) percentage points across all incomes and a net decrease in the low-income population of 4.4 (95% CI, -5.7 to -3.0) percentage points compared with nonexpansion states. Expansion was also associated with a significant shift toward early-stage diagnosis in kidney cancer across all income levels (difference-in-difference, 1.4 [95% CI, 0.1 to 2.6] percentage points) and among individuals with low income (difference-in-difference, 4.6 [95% CI, 0.3 to 9.0] percentage points) and in prostate cancer among individuals with low income (difference-in-difference, 3.0 [95% CI, 0.3 to 5.7] percentage points). Additionally, there was a net increase associated with expansion compared with nonexpansion in receipt of active surveillance for low-risk prostate cancer of 4.1 (95% CI, 2.9 to 5.3) percentage points across incomes and 4.5 (95% CI, 0 to 9.0) percentage points among patients in low-income areas.

CONCLUSIONS AND RELEVANCE

These findings suggest that Medicaid expansion was associated with decreases in uninsured status, increases in the proportion of kidney and prostate cancer diagnosed in an early stage, and higher rates of active surveillance in the appropriate, low-risk prostate cancer population. Associations were concentrated in population residing in low-income areas and reinforce the importance of improving access to care to all patients with cancer.

Collapse

Hu SL. The Nephrologist's Management of Renal Cell Carcinoma After Kidney Surgery. Semin Nephrol 2021;40:59-68. [PMID: 32130967 DOI: 10.1016/j.semnephrol.2019.12.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]

Houshyar R, Glavis-Bloom J, Bui TL, Chahine C, Bardis MD, Ushinsky A, Liu H, Bhatter P, Lebby E, Fujimoto D, Grant W, Tran-Harding K, Landman J, Chow DS, Chang PD. Outcomes of Artificial Intelligence Volumetric Assessment of Kidneys and Renal Tumors for Preoperative Assessment of Nephron Sparing Interventions. J Endourol 2021;35:1411-1418. [PMID: 33847156 DOI: 10.1089/end.2020.1125] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open

Abstract

Background Renal cell carcinoma is the most common kidney cancer and the 13th most common cause of cancer death worldwide. Partial nephrectomy and percutaneous ablation, increasingly utilized to treat small renal masses and preserve renal parenchyma, require precise preoperative imaging interpretation. We sought to develop and evaluate a convolutional neural network (CNN), a type of deep learning artificial intelligence, to act as a surgical planning aid by determining renal tumor and kidney volumes via segmentation on single-phase computed tomography (CT). Materials and Methods After institutional review board approval, the CT images of 319 patients were retrospectively analyzed. Two distinct CNNs were developed for (1) bounding cube localization of the right and left hemi-abdomen and (2) segmentation of the renal parenchyma and tumor within each bounding cube. Training was performed on a randomly selected cohort of 269 patients. CNN performance was evaluated on a separate cohort of 50 patients using Sorensen-Dice coefficients (which measures the spatial overlap between the manually segmented and neural network derived segmentations) and Pearson correlation coefficients. Experiments were run on a GPU-optimized workstation with a single NVIDIA GeForce GTX Titan X (12GB, Maxwell architecture). Results Median Dice coefficients for kidney and tumor segmentation were 0.970 and 0.816, respectively; Pearson correlation coefficients between CNN-generated and human-annotated estimates for kidney and tumor volume were 0.998 and 0.993 (p < 0.001), respectively. End-to-end trained CNNs were able to perform renal parenchyma and tumor segmentation on a new test case in an average of 5.6 seconds. Conclusions Initial experience with automated deep learning artificial intelligence demonstrates that it is capable of rapidly and accurately segmenting kidneys and renal tumors on single-phase contrast-enhanced CT scans and calculating tumor and renal volumes.

Collapse

Affiliation(s)

Roozbeh Houshyar University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States;
Justin Glavis-Bloom University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States;
Thanh-Lan Bui University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States;
Chantal Chahine University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States;
Michelle D Bardis University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States.,University of California Irvine Center for Artificial Intelligence in Diagnostic Medicine, Irvine, California, United States;
Alexander Ushinsky Washington University in St Louis School of Medicine, 12275, Mallinckrodt Institute of Radiology, St Louis, Missouri, United States;
Hanna Liu University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States;
Param Bhatter University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States;
Elliott Lebby University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States;
Dylann Fujimoto University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States;
William Grant University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States;
Karen Tran-Harding University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States;
Jaime Landman University of California Irvine, Urology, 333 City Blvd West, Orange, California, United States, 92868;
Daniel S Chow University of California Irvine School of Medicine, 12219, Radiological Sciences, 101 The City Dr S, Orange, California, United States, 92697-3950.,University of California Irvine Center for Artificial Intelligence in Diagnostic Medicine, 4100 E. Peltason Dr., Irvine, California, United States, 92617;
Peter D Chang University of California Irvine School of Medicine, 12219, Radiological Sciences, Orange, California, United States.,University of California Irvine Center for Artificial Intelligence in Diagnostic Medicine, Irvine, California, United States;

Collapse

Herbert A, Barclay ME, Koo MM, Rous B, Greenberg DC, Abel G, Lyratzopoulos G. Stage-specific incidence trends of renal cancers in the East of England, 1999-2016. Cancer Epidemiol 2021;71:101883. [PMID: 33493782 PMCID: PMC7988458 DOI: 10.1016/j.canep.2020.101883] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/16/2020] [Accepted: 12/22/2020] [Indexed: 11/20/2022]

Wan L, Tu C, Qi L, Li Z. Survivorship and prognostic factors for pleomorphic liposarcoma: a population-based study. J Orthop Surg Res 2021;16:175. [PMID: 33663547 PMCID: PMC7931523 DOI: 10.1186/s13018-021-02327-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 02/24/2021] [Indexed: 02/03/2023] Open

Abstract

Background

Pleomorphic liposarcoma is the least common but most aggressive subtype of liposarcoma. Very few studies have presented data on pleomorphic liposarcoma specifically, often including a limited number of cases and short-term follow-up. As a result, the survivorship and prognostic characteristics of this tumor remain incompletely identified.

Study design and setting

Cross-sectional analysis of the Surveillance Epidemiology and End Results database (1996–2015).

Results

Overall survival for the entire series was 54% (95% confidence interval [CI], 49–58%) and 40% (95% CI, 35–45%) at 5 and 10 years, respectively. Disease-specific survival for the entire series was 60% (95% CI, 56–65%) and 53% (95% CI, 48–58%) at 5 and 10 years, respectively. Patients who survived 10 years or more were more likely to die of events unrelated to pleomorphic liposarcoma. Univariate and multivariate analysis demonstrated that not receiving cancer-directed surgery was an independent poor prognostic factor. Older age (≥ 65 years old) was associated with worse overall survival but not disease-specific survival. Tumor stage and radiotherapy showed different impact on survival depending on tumor size. In comparison to localized staged tumors, regional stage only predicts poor survival in patients with tumor size less than 5 cm, while distant stage is an independent worse prognosis factor. Radiotherapy only benefits patients with tumor size larger than 10 cm. These results were confirmed in competing risk analysis.

Conclusion

Survival rates of patients with pleomorphic liposarcoma has not changed over the past 20 years. Patients with distant stage have poor prognosis; regional stage indicates worse survival in patients with tumor size less than 5 cm. Receiving surgery could prolong the survival, while radiotherapy only benefits patients with large tumor size (> 10 cm). Older age is associated with poor overall survival but not disease-specific survival. Routine patient surveillance following initial diagnosis should at least be 10 years for pleomorphic liposarcoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-021-02327-3.

Collapse

Zhong J, Gallagher M, Hounslow C, Iball G, Wah T. Radiation dose reduction in CT-guided cryoablation of renal tumors. Diagn Interv Radiol 2021;27:244-248. [PMID: 33517258 DOI: 10.5152/dir.2021.19548] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]

Abstract

PURPOSE

We aimed to evaluate the effect on the radiation dose to the patient by reducing the tube current during the placement of the ablation needles (reduced dose group) compared with the patient doses delivered when scanning at the standard fully diagnostic level (full dose group) in computed tomography (CT)-guided percutaneous cryoablation.

METHODS

We conducted a retrospective study of 103 patients undergoing cryoablation in a tertiary cancer center. Overall, 62 patients were scanned with standard exposure parameters (full dose group) set on a 64-slice multidetector CT scanner, while 41 patients were scanned on a reduced dose protocol. Dose levels were retrieved from the hospital picture and archiving communication system including the volumetric CT dose index (CTDIvol), total dose length product (DLP), length of cryoablation procedure, number of cryoablation needles and patient size. Wilcoxon Mann-Whitney (rank-sum) tests were used to compare the median DLP, CTDIvol and skin dose between the two groups.

RESULTS

Median total DLP for the full dose group was 6025 mGy•cm (1909-13353 mGy•cm) compared with 3391 mGy•cm (1683-6820 mGy•cm) for the reduced dose group. The reduced dose group had a 44% reduction in total DLP and 42% reduction in total CTDIvol (p < 0.001). The estimated skin doses were 384 mGy for the full dose group and 224 mGy for the reduced dose group (42% reduction) (p < 0.001). At 12-month follow-up, the technical success for the full dose (n=62) was 97% with 2 patients requiring a further cryoablation treatment for residual tumor. The technical success for the reduced dose group (n=41) was 100%.

CONCLUSION

CT dose reduction technique during image-guided cryoablation treatment of renal tumors can achieve significant radiation dose reduction whilst maintaining sufficient image quality.

Collapse

Devlin CM, Fowler S, Biyani CS, Forster JA. Changes in UK renal oncological surgical practice from 2008 to 2017: implications for cancer service provision and surgical training. BJU Int 2021;128:206-217. [PMID: 33249738 DOI: 10.1111/bju.15310] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]

Abstract

OBJECTIVE

To determine and analyse the temporal changes in oncological nephrectomy practice and training opportunities using data obtained from the UK British Association of Urological Surgeons nephrectomy register from 2008 to 2017.

PATIENT AND METHODS

All nephrectomies within the dataset for this time period were analysed (n = 54 251). Cases were divided into radical nephrectomy (RN), partial nephrectomy (PN) and nephroureterectomy (NU). Simple nephrectomy, donor nephrectomy and benign PN were excluded. The annual frequencies for each oncological nephrectomy method, surgical approach, grade of surgeon, hospital caseload numbers and short-term surgical outcomes were determined.

RESULTS

Reported annual nephrectomy numbers increased by 2.5-fold in the 9-year time period. The number of hospitals performing nephrectomies decreased by 22%, however, more than 40% of centres performed more than 70 cases a year. There was a trend towards a decrease in overall length of hospital stay (9 vs 5 days; P < 0.01) and decreased transfusion rates. The proportion of minimally invasive procedures increased from 57% to 75%, with nephron-sparing rates increasing from 8.9% overall to 24.8%. With regard to surgical technique, robot-assisted surgery saw a mean annual increase of 222%. Overall, there was a 10% decrease in the proportion of PNs performed by trainee surgeons.

CONCLUSIONS

Renal surgery has changed considerably with regard to volume and also surgical approach, with rates of nephron-sparing surgery and minimally invasive surgery significantly increasing. Increasing hospital centralization and institutional experience, and a shift to robot-assisted surgery appear to have contributed to the observed improved patient outcomes. The increasing utilization of robot-assisted surgery has potential implications and challenges for the training of future urology surgeons.

Collapse

Rosiello G, Pecoraro A, Luzzago S, Deuker M, Stolzenbach LF, Tian Z, Larcher A, Capitanio U, Montorsi F, Shariat SF, Kapoor A, Saad F, Briganti A, Karakiewicz PI. Prognostic factors in patients with small renal masses: a comparison between <2 vs. 2.1-4 cm renal cell carcinomas. Cancer Causes Control 2020;32:119-126. [PMID: 33169306 DOI: 10.1007/s10552-020-01364-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Accepted: 11/02/2020] [Indexed: 01/31/2023]

Affiliation(s)

Giuseppe Rosiello Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada. .,Department of Urology and Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy.
Angela Pecoraro Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada.,Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Turin, Italy
Stefano Luzzago Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada.,Department of Urology, European Institute of Oncology, Milan, Italy
Marina Deuker Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada.,Department of Urology, University Hospital Frankfurt, Frankfurt am Main, Germany
Lara Franziska Stolzenbach Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada.,Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany
Zhe Tian Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada
Alessandro Larcher Department of Urology and Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
Umberto Capitanio Department of Urology and Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
Francesco Montorsi Department of Urology and Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
Shahrokh F Shariat Department of Urology, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria.,Institute of Urology and Reproductive Health, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
Anil Kapoor Division of Urology, Department of Surgery, Juravinski Cancer Centre, McMaster University, Hamilton, ON, Canada
Fred Saad Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada
Alberto Briganti Department of Urology and Division of Experimental Oncology, URI, Urological Research Institute, IRCCS San Raffaele Scientific Institute, Via Olgettina, 60, 20132, Milan, Italy
Pierre I Karakiewicz Cancer Prognostics and Health Outcomes Unit, Division of Urology, University of Montreal Health Center, Montreal, Quebec, Canada

Collapse