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For: Nardone V, Reginelli A, Grassi R, Boldrini L, Vacca G, D'Ippolito E, Annunziata S, Farchione A, Belfiore MP, Desideri I, Cappabianca S. Delta radiomics: a systematic review. Radiol Med 2021;126:1571-83. [PMID: 34865190 DOI: 10.1007/s11547-021-01436-7] [Cited by in Crossref: 24] [Cited by in F6Publishing: 28] [Article Influence: 12.0] [Reference Citation Analysis]
Number Citing Articles
1 Sansone M, Fusco R, Grassi F, Gatta G, Belfiore MP, Angelone F, Ricciardi C, Ponsiglione AM, Amato F, Galdiero R, Grassi R, Granata V, Grassi R. Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography. Curr Oncol 2023;30:839-53. [PMID: 36661713 DOI: 10.3390/curroncol30010064] [Reference Citation Analysis]
2 Huang EP, O'Connor JPB, McShane LM, Giger ML, Lambin P, Kinahan PE, Siegel EL, Shankar LK. Criteria for the translation of radiomics into clinically useful tests. Nat Rev Clin Oncol 2023;20:69-82. [PMID: 36443594 DOI: 10.1038/s41571-022-00707-0] [Reference Citation Analysis]
3 Bang C, Bernard G, Le WT, Lalonde A, Kadoury S, Bahig H. Artificial Intelligence to Predict Outcomes of Head and Neck Radiotherapy. Clinical and Translational Radiation Oncology 2023. [DOI: 10.1016/j.ctro.2023.100590] [Reference Citation Analysis]
4 Farhadi F, Rajagopal JR, Veziroglu EM, Abdollahi H, Shiri I, Nikpanah M, Morris MA, Zaidi H, Rahmim A, Saboury B. Multi-Scale Temporal Imaging: From Micro- and Meso- to Macro-scale-time Nuclear Medicine. PET Clinics 2023;18:135-148. [DOI: 10.1016/j.cpet.2022.09.008] [Reference Citation Analysis]
5 Belfiore MP, Sansone M, Monti R, Marrone S, Fusco R, Nardone V, Grassi R, Reginelli A. Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer. J Pers Med 2022;13. [PMID: 36675744 DOI: 10.3390/jpm13010083] [Reference Citation Analysis]
6 Belfiore MP, Nardone V, D’onofrio I, Salvia AAH, D’ippolito E, Gallo L, Caliendo V, Gatta G, Fasano M, Grassi R, Angrisani A, Guida C, Reginelli A, Cappabianca S. Diffusion-weighted imaging and apparent diffusion coefficient mapping of head and neck lymph node metastasis: a systematic review. Exploration of Targeted Anti-tumor Therapy 2022. [DOI: 10.37349/etat.2022.00110] [Reference Citation Analysis]
7 Peng Z, Lin Z, He A, Yi L, Jin M, Chen Z, Tao Y, Yang Y, Cui C, Liu Y, Zuo M. Development and Validation of a Comprehensive Model for Predicting Distant Metastasis of Solid Lung Adenocarcinoma: 3D Radiomics, 2D Radiomics and Clinical Features. CMAR 2022;Volume 14:3437-3448. [DOI: 10.2147/cmar.s393058] [Reference Citation Analysis]
8 He P, Wan H, Wan J, Jiang H, Yang Y, Xie K, Wu H. Systemic therapies in hepatocellular carcinoma: Existing and emerging biomarkers for treatment response. Front Oncol 2022;12. [DOI: 10.3389/fonc.2022.1015527] [Reference Citation Analysis]
9 Spadarella G, Stanzione A, Akinci D’antonoli T, Andreychenko A, Fanni SC, Ugga L, Kotter E, Cuocolo R. Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative. Eur Radiol 2022. [DOI: 10.1007/s00330-022-09187-3] [Reference Citation Analysis]
10 Du G, Zeng Y, Chen D, Zhan W, Zhan Y. Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer. Jpn J Radiol 2022. [PMID: 36260211 DOI: 10.1007/s11604-022-01352-4] [Reference Citation Analysis]
11 Jia L, Zheng Q, Tian J, He D, Zhao J, Zhao L, Huang G. Artificial intelligence with magnetic resonance imaging for prediction of pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer: A systematic review and meta-analysis. Front Oncol 2022;12:1026216. [DOI: 10.3389/fonc.2022.1026216] [Reference Citation Analysis]
12 Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Prospective clinical research of radiomics and deep learning in oncology: A translational review. Crit Rev Oncol Hematol 2022;179:103823. [PMID: 36152912 DOI: 10.1016/j.critrevonc.2022.103823] [Reference Citation Analysis]
13 De Robertis R, Geraci L, Tomaiuolo L, Bortoli L, Beleù A, Malleo G, D’onofrio M. Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT texture analysis. Radiol med 2022;127:1079-1084. [DOI: 10.1007/s11547-022-01548-8] [Reference Citation Analysis]
14 Granata V, De Muzio F, Cutolo C, Dell’aversana F, Grassi F, Grassi R, Simonetti I, Bruno F, Palumbo P, Chiti G, Danti G, Fusco R. Structured Reporting in Radiological Settings: Pitfalls and Perspectives. JPM 2022;12:1344. [DOI: 10.3390/jpm12081344] [Reference Citation Analysis]
15 Vicini S, Bortolotto C, Rengo M, Ballerini D, Bellini D, Carbone I, Preda L, Laghi A, Coppola F, Faggioni L. A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers. Radiol med 2022;127:819-836. [DOI: 10.1007/s11547-022-01512-6] [Reference Citation Analysis]
16 De Muzio F, Grassi F, Dell’aversana F, Fusco R, Danti G, Flammia F, Chiti G, Valeri T, Agostini A, Palumbo P, Bruno F, Cutolo C, Grassi R, Simonetti I, Giovagnoni A, Miele V, Barile A, Granata V. A Narrative Review on LI-RADS Algorithm in Liver Tumors: Prospects and Pitfalls. Diagnostics 2022;12:1655. [DOI: 10.3390/diagnostics12071655] [Reference Citation Analysis]
17 Nardone V, Reginelli A, Grassi R, Vacca G, Giacobbe G, Angrisani A, Clemente A, Danti G, Correale P, Carbone SF, Pirtoli L, Bianchi L, Vanzulli A, Guida C, Grassi R, Cappabianca S. Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery. Cancers (Basel) 2022;14:3004. [PMID: 35740669 DOI: 10.3390/cancers14123004] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
18 Granata V, Fusco R, Belli A, Danti G, Bicci E, Cutolo C, Petrillo A, Izzo F. Diffusion weighted imaging and diffusion kurtosis imaging in abdominal oncological setting: why and when. Infect Agent Cancer 2022;17:25. [PMID: 35681237 DOI: 10.1186/s13027-022-00441-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
19 Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Dell'Aversana F, Grassi F, Belli A, Silvestro L, Ottaiano A, Nasti G, Avallone A, Flammia F, Miele V, Tatangelo F, Izzo F, Petrillo A. Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases. Radiol Med 2022. [PMID: 35653011 DOI: 10.1007/s11547-022-01501-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
20 Bicci E, Nardi C, Calamandrei L, Pietragalla M, Cavigli E, Mungai F, Bonasera L, Miele V. Role of Texture Analysis in Oropharyngeal Carcinoma: A Systematic Review of the Literature. Cancers (Basel) 2022;14:2445. [PMID: 35626048 DOI: 10.3390/cancers14102445] [Reference Citation Analysis]
21 Borgheresi A, De Muzio F, Agostini A, Ottaviani L, Bruno A, Granata V, Fusco R, Danti G, Flammia F, Grassi R, Grassi F, Bruno F, Palumbo P, Barile A, Miele V, Giovagnoni A. Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective. J Clin Med 2022;11:2599. [PMID: 35566723 DOI: 10.3390/jcm11092599] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
22 Granata V, Fusco R, De Muzio F, Cutolo C, Mattace Raso M, Gabelloni M, Avallone A, Ottaiano A, Tatangelo F, Brunese MC, Miele V, Izzo F, Petrillo A. Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern. Diagnostics 2022;12:1115. [DOI: 10.3390/diagnostics12051115] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
23 Fusco R, Granata V, Grazzini G, Pradella S, Borgheresi A, Bruno A, Palumbo P, Bruno F, Grassi R, Giovagnoni A, Grassi R, Miele V, Barile A. Radiomics in medical imaging: pitfalls and challenges in clinical management. Jpn J Radiol 2022. [PMID: 35344132 DOI: 10.1007/s11604-022-01271-4] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
24 Granata V, Fusco R, Setola SV, De Muzio F, Dell' Aversana F, Cutolo C, Faggioni L, Miele V, Izzo F, Petrillo A. CT-Based Radiomics Analysis to Predict Histopathological Outcomes Following Liver Resection in Colorectal Liver Metastases. Cancers (Basel) 2022;14:1648. [PMID: 35406419 DOI: 10.3390/cancers14071648] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 9.0] [Reference Citation Analysis]
25 Palumbo P, Cannizzaro E, Di Cesare A, Bruno F, Arrigoni F, Splendiani A, Barile A, Masciocchi C, Di Cesare E. Stress Perfusion Cardiac Magnetic Resonance in Long-Standing Non-Infarcted Chronic Coronary Syndrome with Preserved Systolic Function. Diagnostics 2022;12:786. [DOI: 10.3390/diagnostics12040786] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Castello A, Castellani M, Florimonte L, Urso L, Mansi L, Lopci E. The Role of Radiomics in the Era of Immune Checkpoint Inhibitors: A New Protagonist in the Jungle of Response Criteria. J Clin Med 2022;11:1740. [PMID: 35330068 DOI: 10.3390/jcm11061740] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
27 Committeri U, Fusco R, Di Bernardo E, Abbate V, Salzano G, Maglitto F, Dell’aversana Orabona G, Piombino P, Bonavolontà P, Arena A, Perri F, Maglione MG, Setola SV, Granata V, Iaconetta G, Ionna F, Petrillo A, Califano L. Radiomics Metrics Combined with Clinical Data in the Surgical Management of Early-Stage (cT1–T2 N0) Tongue Squamous Cell Carcinomas: A Preliminary Study. Biology 2022;11:468. [DOI: 10.3390/biology11030468] [Reference Citation Analysis]
28 Granata V, Fusco R, Setola SV, Simonetti I, Cozzi D, Grazzini G, Grassi F, Belli A, Miele V, Izzo F, Petrillo A. An update on radiomics techniques in primary liver cancers. Infect Agent Cancer 2022;17:6. [PMID: 35246207 DOI: 10.1186/s13027-022-00422-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Dell'Aversana F, Ottaiano A, Nasti G, Grassi R, Pilone V, Miele V, Brunese MC, Tatangelo F, Izzo F, Petrillo A. EOB-MR Based Radiomics Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases. Cancers (Basel) 2022;14:1239. [PMID: 35267544 DOI: 10.3390/cancers14051239] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 8.0] [Reference Citation Analysis]
30 Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Dell' Aversana F, Ottaiano A, Avallone A, Nasti G, Grassi F, Pilone V, Miele V, Brunese L, Izzo F, Petrillo A. Contrast MR-Based Radiomics and Machine Learning Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases: A Preliminary Study. Cancers (Basel) 2022;14:1110. [PMID: 35267418 DOI: 10.3390/cancers14051110] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 12.0] [Reference Citation Analysis]
31 Boldrini L, Mahmood F, Romano A, Cusumano D. Radiomics for MR-Linacs: State of the art and future directions. Advances in Magnetic Resonance Technology and Applications 2022. [DOI: 10.1016/b978-0-323-91689-9.00026-1] [Reference Citation Analysis]
32 Mazaheri Y, Thakur SB, Bitencourt AG, Lo Gullo R, Hötker AM, Bates DDB, Akin O. Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods. BJR|Open 2022;4. [DOI: 10.1259/bjro.20210072] [Reference Citation Analysis]
33 Cè M, Caloro E, Pellegrino ME, Basile M, Sorce A, Fazzini D, Oliva G, Cellina M. Artificial intelligence in breast cancer imaging: risk stratification, lesion detection and classification, treatment planning and prognosis-a narrative review. Explor Target Antitumor Ther 2022;3:795-816. [PMID: 36654817 DOI: 10.37349/etat.2022.00113] [Reference Citation Analysis]