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BACKGROUND Cholangiocarcinoma is a type of hepatobiliary tumor. For perihilar cholangiocarcinoma (pCCA), patients who experience early recurrence (ER) have a poor prognosis. Preoperative accurate prediction of postoperative ER can avoid unnecessary operation; however, prediction is challenging. PURPOSE To develop a novel signature based on clinical and/or MRI radiomics features of pCCA to preoperatively predict ER. STUDY TYPE Retrospective. POPULATION One hundred eighty-four patients (median age, 61.0 years; interquartile range: 53.0-66.8 years) including 115 men and 69 women. FIELD STRENGTH/SEQUENCE A 1.5 T; volumetric interpolated breath-hold examination (VIBE) sequence. ASSESSMENT The models were developed from the training set (128 patients) and validated in a separate testing set (56 patients). The contrast-enhanced arterial and portal vein phase MR images of hepatobiliary system were used for extracting radiomics features. The correlation analysis, least absolute shrinkage and selection operator (LASSO) logistic regression (LR), backward stepwise LR were mainly used for radiomics feature selection and modeling (Modelradiomic ). The univariate and multivariate backward stepwise LR were used for preoperative clinical predictors selection and modeling (Modelclinic ). The radiomics and preoperative clinical predictors were combined by multivariate LR method to construct clinic-radiomics nomogram (Modelcombine ). STATISTICAL TESTS Chi-squared (χ2 ) test or Fisher's exact test, Mann-Whitney U-test or t-test, Delong test. Two tailed P

We aimed to explore the feasibility of 2D and 3D radiomics signature based on the unenhanced computed tomography (CT) images to predict BRCA1-associated protein 1 (BAP1) gene mutation status for malignant pleural mesothelioma (MPM) patients.74 patients with MPM were retrospectively enrolled (22 mutant BAP1, 52 wild-type BAP1 demonstrated by Sanger sequencing). The radiomic features were extracted respectively from the 2D and 3D segmentation of unenhanced pre-treatment CT images, and the dataset was randomly divided into training (n = 51) and test (n = 23) sets for radiomics model development and internal validation. The synthetic minority over-sampling technique (SMOTE) was used for data balancing in the training set. 2D or 3D features were sequentially selected by ICC 0.8, correlation analysis (cut-value 0.7), univariate analysis or univariate logistic regression (LR), which were involved into multivariate LR for LR model construction. Following the comparison of the 2D and 3D models by the ROC analysis and Delong test for AUC, the calibration and clinical utility of 2D and 3D models were evaluated.3D radiomic features showed better ICCs compared with 2D in both intra- (P0.001) and inter-observer (P 0.001) analysis. 3D radiomic model based on selected features developed from a balanced training dataset presented a favorable predictive performance with AUC of 0.786 and 0.768 in the training and test sets, respectively. The predictive performance of 3D model was superior to 2D model (1 feature) both in the training (AUC 0.786 vs. 0.683, P = 0.036) and the test (AUC 0.768 vs.0.652, P = 0.441) set. The calibration curve and decision curves also indicate a better BAP1 prediction performance and clinical benefit for 3D model than that of 2D model.The developed unenhanced CT-based 3D radiomics signature is potential as a noninvasive marker for predicting BAP1 mutation status.

The most common mesenchymal tumors are gastrointestinal stromal tumors (GISTs), which have malignant potential and can occur anywhere along the gastrointestinal system. Imaging methods are important and indispensable of GISTs in diagnosis, risk staging, therapy, and follow-up. The recommended imaging method for staging and follow-up is computed tomography (CT) according to current guidelines. Artificial intelligence (AI) applies and elaborates theses, procedures, modes, and utilization systems for simulating, enlarging, and stretching the intellectual capacity of humans. Recently, researchers have done a few studies to explore AI applications in GIST imaging. This article reviews the present AI studies in GISTs imaging, including preoperative diagnosis, risk stratification and prediction of prognosis, gene mutation, and targeted therapy response.

The biological behavior of carcinoma of the esophagogastric junction (CEGJ) is different from that of gastric or esophageal cancer. Differentiating squamous cell carcinoma of the esophagogastric junction (SCCEG) from adenocarcinoma of the esophagogastric junction (AEG) can indicate Siewert stage and whether the surgical route for patients with CEGJ is transthoracic or transabdominal, as well as aid in determining the extent of lymph node dissection. With the development of neoadjuvant therapy, preoperative determination of pathological type can help in the selection of neoadjuvant radiotherapy and chemotherapy regimens.To establish and evaluate computed tomography (CT)-based multiscale and multiphase radiomics models to distinguish SCCEG and AEG preoperatively.We retrospectively analyzed the preoperative contrasted-enhanced CT imaging data of single-center patients with pathologically confirmed SCCEG (The 2D-venous model (5 features, AUC: 0.849) performed better than 2D-arterial (5 features, AUC: 0.808). The 2D-arterial-venous combined model could further enhance the performance (AUC: 0.869). The 3D-venous model (7 features, AUC: 0.877) performed better than 3D-arterial (10 features, AUC: 0.876). And the 3D-arterial-venous combined model (AUC: 0.904) outperformed other single-phase-based models. The venous model showed a positive improvement compared with the arterial model (NRI0, IDI0), and the 3D-venous and combined models showed a significant positive improvement compared with the 2D-venous and combined models (The combined arterial-venous CT radiomics model based on 3D segmentation can improve the performance in differentiating EGJ squamous cell carcinoma from adenocarcinoma.

To investigate the diagnostic performance of the apparent diffusion coefficient (ADC) derived from intratumoral and peritumoral zones for assessing pathologic prognostic factors in rectal cancer.One hundred forty-six patients with rectal cancer who underwent preoperative MRI were prospectively enrolled. Two radiologists independently placed free-hand regions of interest (ROIs) in the largest tumor cross section and three small ROIs on the peritumoral zone adjacent to the tumor contour. Maximum values of tumor ADC (ADCInterobserver agreement was excellent for ADCThe ADC ratio could provide better predictive performance for assessing preoperative prognostic factors in resectable rectal cancer.• Both the peritumor/tumor ADC ratio and ADC

To assess the role of region of interest (ROI) selection of intravoxel incoherent motion (IVIM) for predicting lymph node metastases (LNM) and tumor response after chemoradiation therapy (CRT) in locally advanced rectal cancer.Seventy-nine patients with biopsy-proven rectal adenocarcinoma who underwent pre- and post-CRT MRI and surgery were prospectively enrolled. The exclusion criteria included nonresectable and/or metastatic disease and loss of follow-up. Pathological stage was determined using ypTNM stage and tumor regression grade. Slow diffusion coefficient (D), fast diffusion coefficient (D*), perfusion-related diffusion fraction (f), apparent diffusion coefficient (ADC) and their percentage changes (Δ%) were evaluated by two readers using whole-volume, single-slice and small samples ROI methods. Risk factors including carcinoembryonic antigen, post-CRT T-staging, extramural venous invasion and IVIM parameters were evaluated through multivariate analyses. Areas under the receiver operating characteristic curves (AUCs) were calculated to evaluate diagnostic performance. Duration of follow-up was two-year. Recurrence-free survival of patients with LNM and tumor response was estimated using Kaplan-Meier analysis.Interobserver agreement were good for pre- and post-CRT three ROI methods (intraclass correlation coefficient [ICC], 0.581-0.953). Whole-volume ROI-derived Δ%D was an independent risk factor for LNM, non-pathological complete response (non-pCR) and poor response (odds ratio, 0.940, 0.952, 0.805, respectively; all p 0.001). Whole-volume ROI-derived Δ%D showed best AUC of 0.810, 0.851 and 0.903 for LNM, non-pCR and poor response (cutoff value, 31.8%, 54.5%, 52.8%, respectively). Patients with post-CRT LNM showed reduction in 2-year recurrence-free survival (hazard ratio, 3.253).Whole-volume ROI-derived Δ%D provided high diagnostic performance for evaluating post-CRT LNM and tumor response. Patients with post-CRT LNM showed earlier recurrence.

Purpose We aim to develop survival predictive tools to inform clinical decision-making in perihilar cholangiocarcinoma (pCCA). Materials and methods A total of 184 patients who had curative resection and magnetic resonance imaging (MRI) examination for pCCA between January 2010 and December 2018 were enrolled. 110 patients were randomly selected for model development, while the other 74 patients for model testing. Preoperative clinical, laboratory, and imaging data were analyzed. Preoperative clinical predictors were used independently or integrated with radiomics signatures to construct different preoperative models through the multivariable Cox proportional hazards method. The nomograms were constructed to predict overall survival (OS), and the performance of which was evaluated by the discrimination ability, time-dependent receiver operating characteristic curve (ROC), calibration curve, and decision curve. Results The clinical model (Modelclinic) was constructed based on three independent variables including preoperative CEA, cN stage, and invasion of hepatic artery in images. The model yield the best performance (Modelclinic&AP&PVP) was build using three independent variables, SignatureAP and SignaturePVP. In training and testing cohorts, the concordance indexes (C-indexes) of Modelclinic were 0.846 (95 % CI, 0.735−0.957) and 0.755 (95 % CI, 0.540–969), and Modelclinic&AP&PVP achieved C-indexes of 0.962 (95 % CI, 0.905−1) and 0.814 (95 % CI, 0.569−1). Both Modelclinic and Modelclinic&AP&PVP outperformed the TNM staging system. Good agreement was observed in the calibration curves, and favorable clinical utility was validated using the decision curve analysis for Modelclinic and Modelclinic&AP&PVP. Conclusion Two preoperative nomograms were constructed to predict 1-, 3-, and 5-years survival for individual pCCA patients, demonstrating the potential for clinical application to assist decision-making.

Clinical evidence suggests that the response to immune checkpoint blockade depends on the immune status in the tumor microenvironment. This study aims to predict the immunophenotyping (IP) and overall survival (OS) of intrahepatic cholangiocarcinoma (ICC) patients using preoperative magnetic resonance imaging (MRI) texture analysis. A total of 78 ICC patients were included and divided into inflamed (n = 26) or non-inflamed (n = 52) immunophenotyping based on the density of CD8+ T cells. The enhanced T1-weighted MRI in the arterial phase was employed with texture analysis. The logistic regression analysis was applied to select the significant features related to IP. The OS-related feature was determined by Cox proportional-hazards model and Kaplan-Meier analysis. IP and OS predictive models were developed using the selected features, respectively. Three wavelets and one 3D feature have favorable ability to discriminate IP, a combination of which performed best with an AUC of 0.919. The inflamed immunophenotyping had a better prognosis than the non-inflamed one. The 5-year survival rates of the two groups were 48.5% and 25.3%, respectively (p < 0.05). The only wavelet-HLH_firstorder_Median feature was associated with OS and used to build the OS predictive model with a C-index of 0.70 (95% CI, 0.57, 0.82), which could well stratify ICC patients into high- and low-risk groups. The 1-, 3-, and 5-year survival probabilities of the stratified groups were 62.5%, 30.0%, and 24.2%, and 89.5%, 62.2%, and 42.1%, respectively (p < 0.05). The MRI texture signature could serve as a potential predictive biomarker for the IP and OS of ICC patients. • The MRI texture signature, including three wavelets and one 3D feature, showed significant associations with immunophenotyping of ICC, and all have favorable ability to discriminate immunophenotyping; a combination of the above features performed best with an AUC of 0.919. • The only wavelet-HLH_firstorder_Median feature was associated with the OS of ICC and used to build the OS predictive model, which could well stratify ICC patients into high- and low-risk groups.

Background Both the 7th and 8th editions of the American Joint Committee on Cancer (AJCC) staging system for perihilar cholangiocarcinoma (pCCA) had the same definition for T2a and T2b. But the value of this classification as prognostic factor remains unclear. Methods 178 patients with stage T2a or T2b who underwent curative intent resection for pCCA between Jan 2010 and Dec 2018 were enrolled. Relationships between survival and clinicopathological factors including patient demographics and tumor characteristics were evaluated using univariate and multivariate Cox regression analysis. The overall survival (OS) were calculated by Kaplan-Meier method. Results There was no significant difference in OS between T2a and T2b groups, and the median OS duration were 37 and 31 months (P = 0.354). Both the 7th and 8th edition of the AJCC TNM staging demonstrated a poor prognostic predictive performance. High level of preoperative AST (≥85.0 IU/L) and CA19–9 (≥1000 U/mL), vascular resection and lower pathological differentiation of the tumor were the independent predictors for poor survival after resection. Conclusion The newly released 8th edition of AJCC staging system demonstrated a poor ability to discriminate the prognosis of patients with stage T2a and T2b pCCA after resection.

To investigate whether Liver Imaging Reporting and Data System (LI-RADS) imaging features at preoperative gadoxetic acid-enhanced MRI can predict microvascular invasion (MVI) and histologic grade of hepatocellular carcinoma (HCC) and to evaluate their associations with recurrence after curative resection of single HCC.From July 2015 to September 2018, 111 consecutive patients with pathologically confirmed HCC who underwent gadoxetic acid-enhanced MRI within 1 month before surgery were included in this retrospective study. Significant MRI findings and clinical parameters for predicting MVI, high-grade HCCs and postoperative recurrence were identified by logistic regression model and Cox proportional hazards model.Twenty-six of 111 (23.4 %) patients had MVI and 36 of 111 (32.4 %) patients had high-grade HCCs, whereas 44 of 95 (46.3 %) patients experienced recurrence. Tumor size5 cm (OR = 9.852; p 0.001) and absence of nodule-in-nodule architecture (OR = 8.302; p = 0.001) were independent predictors of MVI. Enhancing capsule (OR = 4.396; p = 0.004) and corona enhancement (OR = 3.765; p = 0.021) were independent predictors of high-grade HCCs. Blood products in mass (HR = 2.275; p = 0.009), corona enhancement (HR = 4.332; p 0.001), and serum AFP level400 ng/mL (HR = 2.071; p = 0.023) were independent predictors of recurrence.LI-RADS imaging features can be used as potential biomarkers for predicting aggressive pathologic features and recurrence of HCC. The identification of prognostic LI-RADS imaging features may facilitate the selection of surgical candidates and optimize the management of HCC patients.

Objective To investigate the preoperative predictive value of non-invasive imaging biomarkers for programmed cell death protein 1/programmed cell death protein ligand 1 (PD-1/PD-L1) expression and outcome in intrahepatic cholangiocarcinoma (ICC) using machine learning.Methods PD-1/PD-L1 expression in 98 ICC patients was assessed by immunohistochemistry, and their prognostic effects were analysed using Cox regression and Kaplan-Meier analysis. Radiomic features were extracted from MRI in the arterial and portal vein phases, and three sets of Radiomics score (Radscore) with good performance were derived respectively as biomarkers for predicting PD-1, PD-L1 expression and overall survival (OS). PD-1 and PD-L1 expression models were developed using the Radscore (arterial phase), clinico-radiological factors and clinical factors, individually and in combination. The imaging-based OS predictive model was constructed by combining independent predictors among clinico-radiological, clinical factors and OS Radscore. Pathology-based OS model using pathological and clinical factors was also constructed and compared with imaging-based OS model.Results The highest area under the curves of the models predicting PD-1 and PD-L1 expression was 0.897 and 0.890, respectively. PD-1+ and PD-L1+ cases had worse outcomes than negative cases. The 5-year survival rates of PD-1+ and PD-1− cases were 12.5% and 48.3%, respectively (p