Performance of a HER2 testing algorithm tailored for urothelial bladder cancer: A Bi-centre study.

Aims: This study aimed to develop an AI algorithm for automated HER2 scoring in urothelial bladder cancer (UBCa) and assess the interobserver agreement using both manual and AI-assisted methods based on breast cancer criteria.

Methods And Results: We utilized 330 slides from two institutions for initial AI development and selected 200 slides for the ring study, involving six pathologists (3 senior, 3 junior). Our AI algorithm achieved high accuracy in two independent tests, with accuracies of 0.

Correlation analysis of Ki67 changes with survival outcomes in breast cancer before and after neoadjuvant therapy based on residual cancer Burden grade.

Purpose: This study aims to investigate the change of Ki67 value pre- and post-neoadjuvant therapy (NAT) and evaluate its potential value in predicting survival outcomes in different molecular subtypes of breast cancer.

Methods: A total of 257 breast cancer patients who underwent NAT at Renmin Hospital of Wuhan University from July 2019 to Sep 2023 were included in this study. The Ki67 index of the patients was re-interpreted by two attending physicians, and the changes of Ki67 value pre- and post-NAT were compared.

Comparison of clinicopathological characteristics, efficacy of neoadjuvant therapy, and prognosis in HER2-low and HER2-ultralow breast cancer.

Background: This study aims to analyze potential differences in clinicopathology, efficacy of neoadjuvant therapy (NAT), and clinical outcome among HER2-null, HER2-ultralow and HER2-low breast cancers.

Methods: Consecutive cases of HER2-negative breast cancer that received NAT were included. They were classified as HER2-null (no staining), HER2-ultralow (incomplete faint staining in ≤ 10% of tumour cells) and HER2-low (HER2-1 + or HER2-2+, in situ hybridisation negative).

Artificial intelligence: illuminating the depths of the tumor microenvironment.

Artificial intelligence (AI) can acquire characteristics that are not yet known to humans through extensive learning, enabling to handle large amounts of pathology image data. Divided into machine learning and deep learning, AI has the advantage of handling large amounts of data and processing image analysis, consequently it also has a great potential in accurately assessing tumour microenvironment (TME) models. With the complex composition of the TME, in-depth study of TME contributes to new ideas for treatment, assessment of patient response to postoperative therapy and prognostic prediction.

Advancements in technology for characterizing the tumor immune microenvironment.

Immunotherapy plays a key role in cancer treatment, however, responses are limited to a small number of patients. The biological basis for the success of immunotherapy is the complex interaction between tumor cells and tumor immune microenvironment (TIME). Historically, research on tumor immune constitution was limited to the analysis of one or two markers, more novel technologies are needed to interpret the complex interactions between tumor cells and TIME.