Predicting 5-year recurrence risk in colorectal cancer: development and validation of a histology-based deep learning approach.

Background: Accurate estimation of the long-term risk of recurrence in patients with non-metastatic colorectal cancer (CRC) is crucial for clinical management. Histology-based deep learning is expected to provide more abundant information for risk stratification.

Methods: We developed and validated a weakly supervised deep-learning model for predicting 5-year relapse-free survival (RFS) to stratify patients with different risks based on histological images from three hospitals of 614 cases with non-metastatic CRC.

Predicting T Cell-Inflamed Gene Expression Profile in Hepatocellular Carcinoma Based on Dynamic Contrast-Enhanced Ultrasound Radiomics.

Purpose: The T cell-inflamed gene expression profile (GEP) quantifies 18 genes' expression indicative of a T-cell immune tumor microenvironment, playing a crucial role in the immunotherapy of hepatocellular carcinoma (HCC). Our study aims to develop a radiomics-based machine learning model using contrast-enhanced ultrasound (CEUS) for predicting T cell-inflamed GEP in HCC.

Methods: The primary cohort of HCC patients with preoperative CEUS and RNA sequencing data of tumor tissues at the single center was used to construct the model.

Personalized drug screening in patient-derived organoids of biliary tract cancer and its clinical application.

Patients with biliary tract cancer (BTC) show different responses to chemotherapy, and there is no effective way to predict chemotherapeutic response. We have generated 61 BTC patient-derived organoids (PDOs) from 82 tumors (74.4%) that show similar histological and genetic characteristics to the corresponding primary BTC tissues.

Numerical study of a DoFP polarimeter based on the self-organized nanograting array.

The self-organized nanograting manufactured by irradiating the transparent materials with the femtosecond laser has aroused wide interests in photonic applications in recent years. Although the mechanism of nanograting formatting has not yet been fully understood, the essential property of the optical birefringence can be precisely acquired by controlling the energy fluence of the femtosecond laser. In this paper, we proposed a novel application of the self-organized nanograting in a division-of-focal-plane polarimeter.