Background The Prostate Imaging Reporting and Data System (PI‐RADS) provides guidelines for risk stratification of lesions detected on multiparametric MRI (mpMRI) of the prostate but suffers from high intra/interreader variability. Purpose To develop an artificial intelligence (AI) solution for PI‐RADS classification and compare its performance with an expert radiologist using targeted biopsy results. Study Type Retrospective study including data from our institution and the publicly available ProstateX dataset. Population In all, 687 patients who underwent mpMRI of the prostate and had one or more detectable lesions (PI‐RADS score >1) according to PI‐RADSv2. Field Strength/Sequence T2‐weighted, diffusion‐weighted imaging (DWI; five evenly spaced b values between b = 0–750 s/mm2) for apparent diffusion coefficient (ADC) mapping, high b‐value DWI (b = 1500 or 2000 s/mm2), and dynamic contrast‐enhanced T1‐weighted series were obtained at 3.0T. Assessment PI‐RADS lesions were segmented by a radiologist. Bounding boxes around the T2/ADC/high‐b value segmentations were stacked and saved as JPEGs. These images were used to train a convolutional neural network (CNN). The PI‐RADS scores obtained by the CNN were compared with radiologist scores. The cancer detection rate was measured from a subset of patients who underwent biopsy. Statistical Tests Agreement between the AI and the radiologist‐driven PI‐RADS scores was assessed using a kappa score, and differences between categorical variables were assessed with a Wald test. Results For the 1034 detection lesions, the kappa score for the AI system vs. the expert radiologist was moderate, at 0.40. However, there was no significant difference in the rates of detection of clinically significant cancer for any PI‐RADS score in 86 patients undergoing targeted biopsy (P = 0.4–0.6). Data Conclusion We developed an AI system for assignment of a PI‐RADS score on segmented lesions on mpMRI with moderate agreement with an expert radiologist and a similar ability to detect clinically significant cancer. Level of Evidence 4 Technical Efficacy Stage 2

Head and neck squamous cell carcinoma (HNSCC) affects 650,000 people worldwide and has a dismal 50% 5-year survival rate. Recurrence and metastasis are believed the two most important factors causing this high mortality. Understanding the biological process and the underlying mechanisms of recurrence and metastasis is critical to develop novel and effective treatment, which is expected to improve patients’ survival of HNSCC. MicroRNAs are small, non-coding nucleotides that regulate gene expression at the transcriptional and post-transcriptional level. Oncogenic and tumor-suppressive microRNAs have shown to regulate nearly every step of recurrence and metastasis, ranging from migration and invasion, epithelial-mesenchymal transition (EMT), anoikis, to gain of cancer stem cell property. This review encompasses an overview of microRNAs involved in these processes. The recent advances of utilizing microRNA as biomarkers and targets for treatment, particularly on controlling recurrence and metastasis are also reviewed.

PURPOSE To develop an artificial intelligence (AI)–based model for identifying patients with lymph node (LN) metastasis based on digital evaluation of primary tumors and train the model using cystectomy specimens available from The Cancer Genome Atlas (TCGA) Project; patients from our institution were included for validation of the leave-out test cohort. METHODS In all, 307 patients were identified for inclusion in the study (TCGA, n = 294; in-house, n = 13). Deep learning models were trained from image patches at 2.5×, 5×, 10×, and 20× magnifications, and spatially resolved prediction maps were combined with microenvironment (lymphocyte infiltration) features to derive a final patient-level AI score (probability of LN metastasis). Training and validation included 219 patients (training, n = 146; validation, n = 73); 89 patients (TCGA, n = 75; in-house, n = 13) were reserved as an independent testing set. Multivariable logistic regression models for predicting LN status based on clinicopathologic features alone and a combined model with AI score were fit to training and validation sets. RESULTS Several patients were determined to have positive LN metastasis in TCGA (n = 105; 35.7%) and in-house (n = 3; 23.1%) cohorts. A clinicopathologic model that considered using factors such as age, T stage, and lymphovascular invasion demonstrated an area under the curve (AUC) of 0.755 (95% CI, 0.680 to 0.831) in the training and validation cohorts compared with the cross validation of the AI score (likelihood of positive LNs), which achieved an AUC of 0.866 (95% CI, 0.812 to 0.920; P = .021). Performance in the test cohort was similar, with a clinicopathologic model AUC of 0.678 (95% CI, 0.554 to 0.802) and an AI score of 0.784 (95% CI, 0.702 to 0.896; P = .21). In addition, the AI score remained significant after adjusting for clinicopathologic variables ( P = 1.08 × 10−9), and the combined model significantly outperformed clinicopathologic features alone in the test cohort with an AUC of 0.807 (95% CI, 0.702 to 0.912; P = .047). CONCLUSION Patients who are at higher risk of having positive LNs during cystectomy can be identified on primary tumor samples using novel AI-based methodologies applied to digital hematoxylin and eosin–stained slides.

urinary test was high enough to apply this assay to the clinical setting. Liquid biopsy analysis of TERT promoter mutation in urinary cfDNA could be a novel diagnostic biomarker for both UBC and UTUC.