Cholangiocarcinoma (CCA) is a highly aggressive and heterogeneous malignancy arising from the epithelial cells of the biliary tract. The limitations of the current methods in the diagnosis of CCA highlight the urgent need for new, accurate tools for early cancer detection, better prognostication and patient monitoring. Liquid biopsy (LB) is a modern and non-invasive technique comprising a diverse group of methodologies aiming to detect tumour biomarkers from body fluids. These biomarkers include circulating tumour cells, cell-free DNA, circulating tumour DNA, RNA and extracellular vesicles. The aim of this review is to explore the current and potential future applications of LB in CCA management, with a focus on diagnosis, prognostication and monitoring. We examine both its significant potential and the inevitable limitations associated with this technology. We conclude that LB holds considerable promise, but further research is necessary to fully integrate it into precision oncology for CCA.

This study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).

The study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation cohort (n = 71). An external validation cohort comprising 40 patients from Center B also was included. Computed tomography (CT) radiomics features were extracted from the internal area of the tumor (IAT) and IAT combined peritumoral areas of the tumor at 3 mm (PAT 3 mm) and 5 mm (PAT 5 mm). Independent predictors from both clinical and radiomics scores (Radscore) were used to construct a radiomics nomogram. Kaplan-Meier analysis with a log-rank test was performed to evaluate the correlation between factors and OS.

The PAT 5-mm radiomics model (RM) exhibited exceptional predictive capability for grading, achieving an area under the curves of 0.80, 0.80, and 0.90 in the training, internal validation, and external validation cohorts. The nomogram and RM gained from the PAT 5-mm region were more clinically useful than the clinical model. The association between OS and predicted nuclear grade derived from the PAT 5-mm Radscore and the nomogram-predicted score was statistically significant (p < 0.05).

The CT-based radiomics and nomograms showed valuable predictive capabilities for the World Health Organization/International Society of Urological Pathology grade and OS in patients with ccRCC.

The intratumoral and peritumoral radiomics are feasible and promising to predict nuclear grade and overall survival in patients with clear cell renal cell carcinoma, which can contribute to the development of personalized preoperative treatment strategies.

The multi-regional radiomics features are associated with clear cell renal cell carcinoma (ccRCC) grading and prognosis. The combination of intratumoral and peritumoral 5 mm regional features demonstrated superior predictive performance for grading. The nomogram and radiomics models have a broad range of clinical applications.

In inoperable hepatocellular carcinoma (HCC), chemotherapy is a common treatment strategy. However, there is a lack of reliable methods to predict the prognosis of patients with inoperable HCC after chemotherapy. Therefore, the aim of this study was to identify the clinical characteristics of patients with inoperable HCC and to establish and validate nomogram models for predicting the survival outcomes in this patient group following chemotherapy.

The data of patients diagnosed with HCC from the Surveillance, Epidemiology, and End Results (SEER) database were retrospectively collected. Logistic regression analyses were used to identify potential factors for inoperability in patients with HCC. Kaplan-Meier analyses were applied to evaluate the impact of chemotherapy on prognosis. Additionally, Cox regression analyses were performed to identify the potential risk factors associated with overall survival (OS) and cancer-specific survival (CSS) in patients with inoperable HCC treated with chemotherapy. Finally, we constructed prognostic nomograms for predicting the 1- and 3-year survival probabilities.

A total of 3,519 operable patients with HCC and 4,656 patients with inoperable HCC were ultimately included in this study. Logistic regression analyses revealed a significant association between patient age, gender, race, tumor, node, metastasis (TNM) stage, tumor size, pretreatment alpha fetoprotein (AFP) levels, and marital status with inoperability. Moreover, Kaplan-Meier analyses revealed a significant improvement in both OS and CSS with the administration of chemotherapy. Moreover, 1,456 patients with inoperable HCC were enrolled in the training group and 631 patients with inoperable HCC were enrolled in the validation group to develop and validate the prognostic models. Cox regression models indicated that TNM stage, tumor size, and pretreatment AFP were independent risk factors for predicting OS and CSS in patients with inoperable HCC receiving chemotherapy. These factors were subsequently integrated into the predictive nomograms.

We preliminarily developed survival models with strong predictive capabilities for estimating survival probabilities in patients with HCC following chemotherapy. These models hold potential for clinical application and warrant further exploration through additional studies.

We aimed to investigate the dysregulation of the microRNAs(miRNAs) in cholangiocarcinoma (CCA), including its impact on the homeostasis of the transcriptome and cellular behavior. MiRNAs serve as potent epigenetic regulators of transcriptional output, targeting various signaling pathways. This study aimed to investigate the expression level, epigenetic mechanism and function of miR-125a-3 in CCA. The study data showed that the expression level of miR125a-3p was decreased in CCA tissue samples and cell lines, and it was closely related to lymph node metastasis, tissue differentiation and TNM stage. The data demonstrate a strong association between decreased miR-125a-3p expression and poorer prognosis in cholangiocarcinoma patients. miR-125a-3p acts as a tumor suppressor by inhibiting the viability, migration and invasion of CCA cells. There are CpG islands in the promoter region of miR-125a-3p gene, and the methylation of the promoter region of miR-125a-3p gene leads to the transcriptional repression of miR-125a-3p. In addition, miR125a-3p can target and regulate CAC1 mRNA and protein expression in the downstream mechanism, and the high expression of CAC1 can promote the proliferation, migration and invasion of cholangiocarcinoma cells. These data demonstrate that miR-125a-3p promoter methylation leads to silencing of its expression. Mechanically, miR-125a-3p acts as a tumor suppressor and participates in the occurrence and development of CCA through targeting CAC1 gene expression. Therefore, miR-125a-3p may serve as a new target for the diagnosis, prognostic assessment or molecular therapy of CCA.

Purpose: Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) are primary liver cancers with different therapeutic methods and prognoses. This study aims to investigate the ultrasonography and enhanced computed tomography (CT) features of these cancers and improve the early diagnosis rate. Methods: We retrospectively analyzed the clinical and imaging data of 319 patients diagnosed with HCC and 124 patients diagnosed with ICC, confirmed by pathology. Results: A total of 443 patients were eligible in this study. From the perspective of clinical data, between HCC and ICC patients existed significant differences in age, gender, hepatic background, serum tumor markers of AFP and CA19.9, chronic hepatitis B/C and lymph node infiltration (p<0.05), but not in tumor size, microvascular invasion, serum tumor markers of CEA and CA125 (P>0.05). With respect to ultrasonography features, HCC patients had a higher proportion than ICC patients in splenomegaly (p=0.001), while ICC patients had a higher proportion than HCC patients in absence/not rich vascularity and intrahepatic bile duct dilatation (p<0.05). With respect to CT features, HCC patients were significantly different from ICC patients in the three-phase enhanced CT value mean, enhanced intensity and homogeneity of nodules (P<0.05). A multivariate logistic regression analysis was performed to further clarify the correlation of these indices. However, only age≤60 years (OR=1.861, P=0.045), male (OR=3.850, P<0.001), AFP>7ng/ml (OR=0.119, P<0.001), lymph node infiltration (OR=5.968, P<0.001), intrahepatic bile duct dilatation (OR=2.414, P=0.04), splenomegaly (OR=0.081, P<0.001), rim APHE (OR=3.109, P=0.002), and iso- or hyper enhancement (OR=0.188, P<0.001) were independent risk factors. Conclusions: While there are overlapping ultrasonography and CT features between HCC and ICC, the integration of tumor markers and specific imaging characteristics can be beneficial in distinguishing between the two.

Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies.

To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS).

A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05.

One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072).

Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.

Combined hepatocellular carcinoma and cholangiocarcinoma (CHCC-CC) is a rare subtype of primary liver malignancy and has been treated equally as intra-hepatic cholangiocarcinoma (IHCC) according to the 8th AJCC staging system. Owing to its rarity, its prognostic factors have been rarely explored and defined.

PubMed, EMBASE, the Cochrane Library and Web of Science were searched up till January 1st, 2023 and eligible studies were restricted to studies reported prognostic factors of resected CHCC-CC. Standard Parmar modifications were used to determine pooled univariable hazard ratios (HRs).

A total of eleven studies with 1286 patients with resected classical CHCC-CC were finally included. Pooled results indicated that serum tumor biomarkers, including AFP, CA199, and CEA, were prognostic factors for postoperative overall survival (OS) and disease-free survival (DFS). Moreover, liver cirrhosis (P = 0.010), HBV infection (P = 0.030), and HCV infection (P < 0.001) were prognostic factors for OS. Age (HR = 1.03, P = 0.005) was a prognostic factor for DFS. Tumor size (OS: HR = 2, P < 0.001, DFS: HR = 2.15, P < 0.001), tumor number (OS: HR = 2.05, P < 0.001; DFS: HR = 1.96, P = 0.006), surgical margin (OS: HR = 2.33, <0.001001; DFS: HR = 2.35, P < 0.001), node metastasis (OS: HR = 2.96, P < 0.001; DFS: HR = 2.1, P < 0.001), vascular invasion (OS: HR = 2.17, P < 0.001; DFS: HR = 2.64, P < 0.001), and postoperative prophylactic trans-arterial chemotherapy embolization (PPTACE) (OS: HR = 1.67, P = 0.04; DFS: HR = 2.31, P < 0.001) were common prognostic factors for OS and DFS.

Various risk factors unmentioned in the 8th AJCC staging system were identified. These promising findings would facilitate a more personalized predictive model and help clinicians to stratify patients with different survival outcomes.

Hepatocellular carcinoma (HCC) is a widespread and impactful cancer which has pertinent implications worldwide. Although most cases of HCC are typically diagnosed in individuals aged ≥60 years, there has been a notable rise in the occurrence of HCC among younger patients. However, there is a scarcity of precise prognostic models available for predicting outcomes in these younger patients. A retrospective analysis was conducted to investigate early-onset hepatocellular carcinoma (EO-LIHC) using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2018. The analysis included 1392 patients from the SEER database and our hospital. Among them, 1287 patients from the SEER database were assigned to the training cohort (n = 899) and validation cohort 1 (n = 388), while 105 patients from our hospital were assigned to validation cohort 2. A Cox regression analysis showed that age, sex, AFP, grade, stage, tumor size, surgery, and chemotherapy were independent risk factors. The nomogram developed in this study demonstrated its discriminatory ability to predict the 1-, 3-, and 5-year overall survival (OS) rates in EO-LIHC patients based on individual characteristics. Additionally, a web-based OS prediction model specifically tailored for EO-LIHC patients was created and validated. Overall, these advancements contribute to improved decision-making and personalized care for individuals with EO-LIHC.

This study aimed to differentiate temporal-plus epilepsy (TPE) from temporal lobe epilepsy (TLE) using extraction of radiomics features from three-dimensional magnetization-prepared rapid acquisition gradient echo (3D-MPRAGE) imaging data.

Data from patients with TLE or TPE who underwent epilepsy surgery between January 2019 and January 2021 were retrospectively analyzed. Thirty-three regions of interest in the affected hemisphere of each patient were defined on 3D-MPRAGE images. A total of 3531 image features were extracted from each patient. Four feature selection methods and 10 machine learning algorithms were used to build 40 differentiation models. Model performance was evaluated using receiver operating characteristic analysis.

Eighty-two patients were included for analysis, 47 with TLE and 35 with TPE. The model combining logistic regression and the relief selection method had the best performance (area under the receiver operating characteristic curve, .779; accuracy, .875; sensitivity, .800; specificity, .929; positive predictive value, .889; negative predictive value, .867).

Radiomics analysis can differentiate TPE from TLE. The logistic regression classifier trained with radiomics features extracted from 3D-MPRAGE images had the highest accuracy and best performance.

Using noninvasive radiomics to predict pathological biomarkers is an innovative work worthy of exploration. This retrospective cohort study aimed to analyze the correlation between NAD(P)H quinone oxidoreductase 1 (NQO1) expression levels and the prognosis of patients with hepatocellular carcinoma (HCC) and to construct radiomic models to predict the expression levels of NQO1 prior to surgery. Data of patients with HCC from The Cancer Genome Atlas (TCGA) and the corresponding arterial phase-enhanced CT images from The Cancer Imaging Archive were obtained for prognosis analysis, radiomic feature extraction, and model development. In total, 286 patients with HCC from TCGA were included. According to the cut-off value calculated using R, patients were divided into high-expression (n = 143) and low-expression groups (n = 143). Kaplan-Meier survival analysis showed that higher NQO1 expression levels were significantly associated with worse prognosis in patients with HCC (p = 0.017). Further multivariate analysis confirmed that high NQO1 expression was an independent risk factor for poor prognosis (HR = 1.761, 95% CI: 1.136-2.73, p = 0.011). Based on the arterial phase-enhanced CT images, six radiomic features were extracted, and a new bi-regional radiomics model was established, which could noninvasively predict higher NQO1 expression with good performance. The area under the curve (AUC) was 0.9079 (95% CI 0.8127-1.0000). The accuracy, sensitivity, and specificity were 0.86, 0.88, and 0.84, respectively, with a threshold value of 0.404. The data verification of our center showed that this model has good predictive efficiency, with an AUC of 0.8791 (95% CI 0.6979-1.0000). In conclusion, there existed a significant correlation between the CT image features and the expression level of NQO1, which could indirectly reflect the prognosis of patients with HCC. The predictive model based on arterial phase CT imaging features has good stability and diagnostic efficiency and is a potential means of identifying the expression level of NQO1 in HCC tissues before surgery.

The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.

Determining the absence or presence of peripancreatic lymph nodal metastasis (PLNM) is important to the pathologic staging, prognostication, and guidance of treatment in pancreatic ductal adenocarcinoma (PDAC) patients. Computed tomography and MRI had a poor sensitivity and diagnostic accuracy in the assessment of PLNM.

To develop and validate a 3 T MRI primary tumor radiomics-based nomogram from multicenter datasets for pretreatment prediction of the PLNM in PDAC patients.

Retrospective.

A total of 251 patients (156 men and 95 women; mean age, 60.85 ± 8.23 years) with histologically confirmed pancreatic ductal adenocarcinoma from three hospitals.

A 3.0 T and fat-suppressed T1-weighted imaging.

Quantitative imaging features were extracted from fat-suppressed T1-weighted (FS T1WI) images at the arterial phase.

Normally distributed data were compared by using t-tests, while the Mann-Whitney U test was used to evaluate non-normally distributed data. The diagnostic performances of the preoperative and postoperative nomograms were assessed in the external validation cohort with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA). AUCs were compared with the De Long test. A p value below 0.05 was considered to be statistically significant.

The AUCs of magnetic resonance imaging (MRI) Rad-score were 0.868 (95% confidence level [CI]: 0.613-0.852) and 0.772 (95% CI: 0.659-0.879) in the training and internal validation cohort, respectively. The preoperative and postoperative nomograms could accurately predict PLNM in the training cohort (AUC = 0.909 and 0.851) and were validated in both the internal and external cohorts (AUC = 0.835 and 0.805, 0.808 and 0.733, respectively). DCA indicated that the two novel nomograms are of similar clinical usefulness.

Pre-/postoperative nomograms and the constructed radiomics signature from primary tumor based on FS T1WI of arterial phase could serve as a potential tool to predict PLNM in patients with PDAC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.