Background: The assessment of the International Society of Urological Pathology (ISUP) nuclear grade is crucial for the management and treatment of clear cell renal cell carcinoma (ccRCC). This study aimed to explore the value of using integrated multimodal information for ISUP grading and prognostic stratification in ccRCC patients, to guide postoperative adjuvant therapy. Methods: This retrospective study analyzed a total of 729 patients from three cohorts, utilizing whole slide i-mages and computed tomography (CT) images. Artificial intelligence algorithms were used to extract morphological and textural features from whole slide images and CT images separately, creating single-modality predictive models for ISUP grading. By combining the CT and pathology single-modality predictive features, a multimodal predictive signature (MPS) was developed. The prognostic performance of the MPS model was further validated in two independent cohorts. Results: The single-modality predictive models for CT and pathology performed well in predicting ISUP grade for ccRCC. The MPS model achieved higher area under the curve values of 0.95, 0.93, and 0.95 across three independent patient cohorts. Additionally, the MPS model was able to distinguish patients with poorer overall survival. In the external validation cohort, uni- and multivariate analyses showed hazard ratios of 2.542 (95% confidence interval [CI]: 1.363–4.741, P < 0.0001) and 1.723 (95% CI: 0.888–3.357, P = 0.003), respectively. The C-index values for the two cohorts were 0.75 and 0.71. Furthermore, the MPS outperformed single-modality models, providing a complementary tool for current risk stratification in ccRCC adjuvant therapy. Conclusion: Our novel MPS model demonstrated high accuracy in ISUP grading for ccRCC patients. With further validation across multiple centers, the MPS model could be used for precise detection of nuclear grading in ccRCC, serving as an effective tool for assisting clinical decision-making.

The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and prognosis in clear cell renal cell carcinoma (ccRCC). In this work, single-cell RNA sequencing (scRNA-seq) based deconvolution was utilized to create a malignant cell hierarchy with metabolic differences and to investigate the relationship between metabolic biomarkers and prognosis. Simultaneously, we created a machine learning-based approach for creating metabolism-related prognostic signature (MRPS). Gamma-glutamyltransferase 6 (GGT6) was further explored for deep biological insights through in vitro experiments. Compared to 51 published signatures and conventional clinical features, MRPS showed substantially higher accuracy. Meanwhile, high MRPS-risk samples demonstrated an immunosuppressive phenotype with more infiltrations of regulatory T cell (Treg) and tumour-associated macrophage (TAM). Following the administration of immune checkpoint inhibitors (ICIs), MRPS showed consistent and strong performance and was an independent risk factor for overall survival. GGT6, an essential metabolic indicator and component of MRPS, has been proven to support proliferation and invasion in ccRCC. MRPS has the potential to be a highly effective tool in improving the clinical results of patients with ccRCC.

Purpose: Exploring the value of predicting the WHO/ISUP grade of clear cell renal cell carcinoma (ccRCC) using computed tomography urography (CTU) images, providing valuable recommendations for the treatment of ccRCC.

Background: CDK6 is linked to tumor progression and metastasis, although its molecular mechanism and prognostic value are unclear in bladder cancer.

Background: It can be difficult to decide clinically whether males with prostate-specific antigen (PSA) levels between 4 and 10 ng/mL should be suggested for a biopsy. This study aimed to develop a fully-automated magnetic resonance imaging (MRI) based prediction model for patients with PSA levels of 4–10 ng/mL to predict prostate cancer (PCa) preoperatively and reduce unnecessary biopsies. Methods: A retrospective study of 574 patients with PSA of 4–10 ng/mL was conducted, split into training (n=434) and testing (n=108) groups. A no-new-Net (nnU-net) model was trained for three-dimensional (3D) prostate segmentation on T2-weighted fast spin echo (T2FSE) MRI sequences and 1,595 radiomics features were extracted with PyRadiomics. There were 113 machine learning approaches compared to construct a radiomics model after features selection. The diagnostic performance of the model was compared with PSA and PSA density (PSAD). Results: The nnU-net model achieved relatively higher accuracy of segmentation for the prostate region in various datasets. The average dice was 95.33%, the average relative volume error (RVE) was 1.57%, and the average 95% Hausdorff distance (HD95) value was 2.73 mm. The radiomics model [area under the curve (AUC): 0.938; 95% confidence interval (CI): 0.916–0.960] shows superior accuracy to PSA (AUC: 0.542; 95% CI: 0.474–0.611) and PSAD (AUC: 0.718; 95% CI: 0.659–0.777) in predicting PCa (P<0.05). Conclusions: The automated 3D radiomics model holds the potential to reduce unnecessary biopsies and aid urologists in managing patients with PSA levels of 4–10 ng/mL.