Magnetic resonance imaging-based radiomics nomogram for the evaluation of therapeutic responses to neoadjuvant chemohormonal therapy in high-risk non-metastatic prostate cancer.

PURPOSE: The aim of this study was to assess the potential application of a radiomics features-based nomogram for predicting therapeutic responses to neoadjuvant chemohormonal therapy (NCHT) in patients with high-risk non-metastatic prostate cancer (PCa). METHODS: Clinicopathologic information was retrospectively collected from 162 patients with high-risk non-metastatic PCa receiving NCHT and radical prostatectomy at our center. The postoperative pathological findings were used as the gold standard for evaluating the efficacy of NCHT. The least absolute shrinkage and selection operator (LASSO) was conducted to develop radiomics signature. Multivariate logistic regression analyses were conducted to identify the predictors of a positive pathological response to NCHT, and a nomogram was constructed based on these predictors. RESULTS: Sixty-three patients (38.89%) experienced positive pathological response to NCHT. Receiver operating characteristic analyses showed that the area under the curve (AUC) of periprostatic fat (PPF) radiomics signature was 0.835 (95% CI, 0.754-0.898), while the AUC of intratumoral radiomics signature was 0.822 (95% CI, 0.739-0.888). Multivariate logistic regression analysis revealed that PSA level, PPF radiomics signature and intratumoral radiomics signature were independent predictors of positive pathological response. A nomogram based on these three predictors was constructed. The AUC was 0.908 (95% CI, 0.839-0.954). The Hosmer-Lemeshow goodness-of-fit test showed that the nomogram was well calibrated. Decision curve analysis revealed the favorable clinical practicability of the nomogram. The nomogram was successfully validated in the validation cohort. Kaplan-Meier analyses showed that nomogram and positive pathological response were significantly related with survival of PCa. CONCLUSION: The radiomics-clinical nomogram based on mpMRI radiomics features exhibited superior predictive ability for positive pathological response to NCHT in high-risk non-metastatic PCa.

Ubiquitin-specific protease 5 promotes bladder cancer progression through stabilizing Twist1.

Aberrant activation of the epithelial-mesenchymal transition (EMT) pathway drives the development of solid tumors, which is precisely regulated by core EMT-related transcription factors, including Twist1. However, the expression pattern and regulatory mechanism of Twist1 in the progression of bladder cancer is still unclear. In this study, we explore the role of Twist1 in the progression of bladder cancer. We discovered that the EMT regulon Twist1 protein, but not Twist1 mRNA, is overexpressed in bladder cancer samples using RT-qPCR, western blot and immunohistochemistry (IHC). Mechanistically, co-immunoprecipitation (Co-IP) coupled with liquid chromatography and tandem mass spectrometry identified USP5 as a binding partner of Twist1, and the binding of Twist1 to ubiquitin-specific protease 5 (USP5) stabilizes Twist through its deubiquitinase activity to activate the EMT. Further studies found that USP5 depletion reduces cell proliferation, invasion and the EMT in bladder cancer cells, and ectopic expression of Twist1 rescues the adverse effects of USP5 loss on cell invasion and the EMT. A xenograft tumor model was used to reconfirmed the inhibitor effect of silencing USP5 expression on tumorigenesis in vivo. In addition, USP5 protein levels are significantly elevated and positively associated with Twist1 levels in clinical bladder cancer samples. Collectively, our study revealed that USP5-Twist1 axis is a novel regulatory mechanism driving bladder cancer progression and that approaches targeting USP5 may become a promising cancer treatment strategy.

A computed tomography-based comprehensive standardized adrenal tumor scoring model for predicting the perioperative outcomes of retroperitoneal laparoscopic adrenal surgery.

Background: Many imaging scoring models have been developed for tumor surgery to provide critical guidance for the selection of surgical methods. However, little research has been aimed at developing scoring models for adrenal tumors and retroperitoneal laparoscopic adrenal surgery (RLAS), which has become the primary technique for treating adrenal tumors. The study set out to establish a computed tomography (CT)-based adrenal tumor scoring model for predicting perioperative outcomes in patients with adrenal tumors who have undergone RLAS. Methods: The retrospective analysis included 306 patients with adrenal tumors diagnosed by preoperative unenhanced or enhanced CT from January 2014 to August 2018 in the First Affiliated Hospital of Fujian Medical University. CT images were used to quantify the tumor location and size; the relationships of the tumors with the surrounding organs and tissues, the large abdominal blood vessels, and the upper poles of the kidneys and renal hila; the adhesion of periadrenal fat (PF); and the tumor CT enhancement value. We conducted multivariate ordinal logistic regression analysis to screen variables and performed principal component analysis to construct a novel scoring model for RLAS. The perioperative outcomes of RLAS were evaluated according to postoperative length of stay, operative time (OT), intraoperative blood loss (IBL), and postoperative complications. Results: The final scoring model included tumor size; the relationships of the tumors with the surrounding organs and tissues, the large abdominal blood vessels, and the upper poles of the kidneys and renal hila; the tumor CT enhancement value; the adhesion of the PF; and the functional status of adrenal tumors. The total score had positive correlations with the OT (rs=0.431), IBL (rs=0.446), and postoperative length (rs=0.180) (all P values <0.001). Compared to any single metric, the total score provided better prediction of OT and IBL. The grading system for RLAS based on the scoring model also performed well in predicting the complexity and difficulty of RLAS. The coincidence rate for these factors was good (all P values <0.001). Conclusions: The developed model is feasible and repeatable in the prediction of the perioperative outcomes, complexity, and difficulty of RLAS.