The extracellular matrix (ECM) is one of the major components of tumors that plays multiple crucial roles, including mechanical support, modulation of the microenvironment, and a source of signaling molecules. The quantity and cross-linking status of ECM components are major factors determining tissue stiffness. During tumorigenesis, the interplay between cancer cells and the tumor microenvironment (TME) often results in the stiffness of the ECM, leading to aberrant mechanotransduction and further malignant transformation. Therefore, a comprehensive understanding of ECM dysregulation in the TME would contribute to the discovery of promising therapeutic targets for cancer treatment. Herein, we summarized the knowledge concerning the following: (1) major ECM constituents and their functions in both normal and malignant conditions; (2) the interplay between cancer cells and the ECM in the TME; (3) key receptors for mechanotransduction and their alteration during carcinogenesis; and (4) the current therapeutic strategies targeting aberrant ECM for cancer treatment.

Purpose: The purpose of this study is to develop and validate a nomogram model combing radiomics features and clinical characteristics to preoperatively differentiate grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (pNET). Experimental Design: A total of 137 patients who underwent contrast-enhanced CT from two hospitals were included in this study. The patients from the second hospital (n ¼ 51) were selected as an independent validation set. The arterial phase in contrast-enhanced CT was selected for radiomics feature extraction. The Mann-Whitney U test and least absolute shrinkage and selection operator regression were applied for feature selection and radiomics signature construction. A combined nomogram model was developed by incorporating the radiomics signature with clinical factors. The association between the nomogram model and the Ki-67 index and rate of nuclear mitosis were also investigated respectively. The utility of the proposed model was evaluated using the ROC, area under ROC curve (AUC), calibration curve, and decision curve analysis (DCA). The Kaplan-Meier (KM) analysis was used for survival analysis. Results: An eight-feature-combined radiomics signature was constructed as a tumor grade predictor. The nomogram model combining the radiomics signature with clinical stage showed the best performance (training set: AUC ¼ 0.907; validation set: AUC ¼ 0.891). The calibration curve and DCA demonstrated the clinical usefulness of the proposed nomogram. A significant correlation was observed between the developed nomogram and Ki-67 index and rate of nuclear mitosis, respectively. The KM analysis showed a significant difference between the survival of predicted grade 1 and grade 2/3 groups (P ¼ 0.002). Conclusions: The combined nomogram model developed could be useful in differentiating grade 1 and grade 2/3 tumor in patients with pNETs.

Introduction: The emerging field of “radiomics” has considerable potential in disease diagnosis, pathologic grading, prognosis evaluation, and prediction of treatment response. We aimed to develop a novel radiomics nomogram based on radiomics features and clinical characteristics that could preoperatively predict early recurrence (ER) of intrahepatic cholangiocarcinoma (ICC) after partial hepatectomy.Methods: A predictive model was developed from a training cohort comprising 139 ICC patients diagnosed between January 2010 and June 2014. Radiomics features were extracted from arterial-phase image of contrast-enhanced magnetic resonance imaging. Feature selection and construction of a “radiomics signature” were through Spearman's rank correlation and least absolute shrinkage and selection operator (LASSO) logistic regression. Combined with clinical characteristics, a radiomics nomogram was developed with multivariable logistic regression. Performance of the nomogram was evaluated with regard to discrimination, calibration, and clinical utility. An independent validation cohort involving 70 patients recruited from July 2014 to March 2016 was used to evaluate the utility of the nomogram developed.Results: The radiomics signature, consisting of nine features, differed significantly between ER patients and non-ER patients in training and validation cohorts. The area under the curve (AUC) of the radiomics signature in training and validation cohorts was 0.82 (confidence interval [CI], 0.74–0.88) and 0.77 (95% CI, 0.65–0.86), respectively. The AUC of the radiomics nomogram combining the radiomics signature and clinical stage in the two cohorts was 0.90 (95%CI, 0.83–0.94) and 0.86 (95% CI, 0.76–0.93), respectively. Decision curve analysis confirmed the clinical usefulness of the radiomics nomogram.Conclusion: The non-invasive radiomics nomogram developed using the radiomics signature and clinical stage could be used to predict ER of ICC after partial hepatectomy.

Background The present study constructed and validated the use of contrast‐enhanced computed tomography (CT)‐based radiomics to preoperatively predict microvascular invasion (MVI) status (positive vs negative) and risk (low vs high) in patients with hepatocellular carcinoma (HCC). Methods We enrolled 637 patients from two independent institutions. Patients from Institution I were randomly divided into a training cohort of 451 patients and a test cohort of 111 patients. Patients from Institution II served as an independent validation set. The LASSO algorithm was used for the selection of 798 radiomics features. Two classifiers for predicting MVI status and MVI risk were developed using multivariable logistic regression. We also performed a survival analysis to investigate the potentially prognostic value of the proposed MVI classifiers. Results The developed radiomics signature predicted MVI status with an area under the receiver operating characteristic curve (AUC) of .780, .776, and .743 in the training, test, and independent validation cohorts, respectively. The final MVI status classifier that integrated two clinical factors (age and α‐fetoprotein level) achieved AUC of .806, .803, and .796 in the training, test, and independent validation cohorts, respectively. For MVI risk stratification, the AUCs of the radiomics signature were .746, .664, and .700 in the training, test, and independent validation cohorts, respectively, and the AUCs of the final MVI risk classifier‐integrated clinical stage were .783, .778, and .740, respectively. Survival analysis showed that our MVI status classifier significantly stratified patients for short overall survival or early tumor recurrence. Conclusions Our CT radiomics‐based models were able to predict MVI status and MVI risk of HCC and might serve as a reliable preoperative evaluation tool.