Deep Learning Predicts Lymphovascular Invasion Status in Muscle Invasive Bladder Cancer Histopathology.

Background: Lymphovascular invasion (LVI) is linked to poor prognosis in patients with muscle-invasive bladder cancer (MIBC). Accurately identifying the LVI status in MIBC patients is crucial for effective risk stratification and precision treatment. We aim to develop a deep learning model to identify the LVI status in whole-slide images (WSIs) of MIBC patients.

Prediction of HER2 Status Based on Deep Learning in H&E-Stained Histopathology Images of Bladder Cancer.

Epidermal growth factor receptor 2 () has been widely recognized as one of the targets for bladder cancer immunotherapy. The key to implementing personalized treatment for bladder cancer patients lies in achieving rapid and accurate diagnosis. To tackle this challenge, we have pioneered the application of deep learning techniques to predict expression status from H&E-stained pathological images of bladder cancer, bypassing the need for intricate IHC staining or high-throughput sequencing methods.

Overcoming the compensatory increase in NRF2 induced by NPL4 inhibition enhances disulfiram/copper-induced oxidative stress and ferroptosis in renal cell carcinoma.

Renal cell carcinoma (RCC) is the most common type of kidney cancer, and it appears to be highly susceptible to ferroptosis. Disulfiram, an alcoholism drug, has been shown to have anticancer properties in various studies, including those on RCC. However, the mechanism of the anticancer effect of disulfiram/copper on RCC remains unclear.

A Weakly Supervised Deep Learning Model and Human-Machine Fusion for Accurate Grading of Renal Cell Carcinoma from Histopathology Slides.

(1) Background: The Fuhrman grading (FG) system is widely used in the management of clear cell renal cell carcinoma (ccRCC). However, it is affected by observer variability and irreproducibility in clinical practice. We aimed to use a deep learning multi-class model called SSL-CLAM to assist in diagnosing the FG status of ccRCC patients using digitized whole slide images (WSIs).

Predicting Lymph Node Metastasis Status from Primary Muscle-Invasive Bladder Cancer Histology Slides Using Deep Learning: A Retrospective Multicenter Study.

Background: Accurate prediction of lymph node metastasis (LNM) status in patients with muscle-invasive bladder cancer (MIBC) before radical cystectomy can guide the use of neoadjuvant chemotherapy and the extent of pelvic lymph node dissection. We aimed to develop and validate a weakly-supervised deep learning model to predict LNM status from digitized histopathological slides in MIBC.

Methods: We trained a multiple instance learning model with an attention mechanism (namely SBLNP) from a cohort of 323 patients in the TCGA cohort.

Machine Learning Quantified Tumor-Stroma Ratio Is an Independent Prognosticator in Muscle-Invasive Bladder Cancer.

Although the tumor-stroma ratio (TSR) has prognostic value in many cancers, the traditional semi-quantitative visual assessment method has inter-observer variability, making it impossible for clinical practice. We aimed to develop a machine learning (ML) algorithm for accurately quantifying TSR in hematoxylin-and-eosin (H&E)-stained whole slide images (WSI) and further investigate its prognostic effect in patients with muscle-invasive bladder cancer (MIBC). We used an optimal cell classifier previously built based on QuPath open-source software and ML algorithm for quantitative calculation of TSR.

Quantitative Assessment of Tumor-Infiltrating Lymphocytes Using Machine Learning Predicts Survival in Muscle-Invasive Bladder Cancer.

(1) Purpose: Although assessment of tumor-infiltrating lymphocytes (TILs) has been acknowledged to have important predictive prognostic value in muscle-invasive bladder cancer (MIBC), it is limited by inter- and intra-observer variability, hampering widespread clinical application. We aimed to evaluate the prognostic value of quantitative TILs score based on a machine learning (ML) algorithm to identify MIBC patients who might benefit from immunotherapy or the de-escalation of therapy. (2) Methods: We constructed an artificial neural network classifier for tumor cells, lymphocytes, stromal cells, and “ignore” cells from hematoxylin-and-eosin-stained slide images using the QuPath open source software.

Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides.

(1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort.

Development and validation of a deep learning-based laparoscopic system for improving video quality.

Purpose: A clear surgical field of view is a prerequisite for successful laparoscopic surgery. Surgical smoke, image blur, and lens fogging can affect the clarity of laparoscopic imaging. We aimed to develop a real-time assistance system (namely LVQIS) for removing these interfering factors during laparoscopic surgery, thereby improving laparoscopic video quality.

Development and validation of a deep-learning based assistance system for enhancing laparoscopic control level.

Background And Aims: Inter-operator variations in the level of intraoperative laparoscope control by surgeons influence surgical outcomes. We aimed to construct a laparoscopic surgery quantification system (LSQS) for real-time evaluation of the surgeon's laparoscope control to improve intraoperative manipulation of the laparoscope.

Methods: Using 1888 images from 80 laparoscopic videos for training, the U-Net, PSPNet, LinkNet, and DeepLabv3+ models were used to segment surgical instruments.