Biyue Zhu

Ph.D.

Children's Hospital of Chongqing Medical University, China

Associate Professor

PLCO (Learn more about this study)

PLCOI-1341

Sep 28, 2023

Precision Diagnosis of Cancers Based on Artificial Intelligence Pathology Analysis

Cancer is one of the main causes of death. Due to the high risk and high incidence of the disease, there is an urgent need for rapid, accurate, and cheap diagnosis of cancer. Pathological diagnosis is one of the important methods of clinical diagnosis and provides crucial information for prognosis and treatment. Traditionally, pathological diagnosis requires a pathologist to observe glass slides and give suggestions. Two decades ago, whole slide imaging (WSI) technology was developed. WSI scans the slide under a slide scanner microscope and generates a digital image of the whole field of view of a pathology slide, which can permanently store all the tissue information on the tissue slide. Compared with traditional glass slides, WSI has many advantages in slide preservation and management, such as teaching, remote diagnosis, image repeatability, etc., and effectively solves the problems of traditional glass slides that could be easily damaged, faded, lost, and difficult to retrieve. Also, WSI technology facilitates the easy collaboration of a multidisciplinary team at any time, place, and professionals to give quantitative analysis and annotation of the slides, and finally give an accurate and timely diagnosis for patients.

Artificial intelligence (AI) is expected to play a vital role in the new era of precision pathology diagnosis. Combining cutting-edge AI and WSI technology can integrate patient clinical information, multi-omics data, and tissue pathology images to facilitate precision diagnosis. Several studies have demonstrated the power of AI by developing an automated histopathological evaluation system for tumors. This tool can improve the accuracy of adult tumor pathology and clinical diagnosis, save time and cost, and also open up a new way to predict patient prognosis and molecular changes. Despite the rapid development of AI and WSI, powerful AI models are still in high demand.

Therefore, to improve the effectiveness and accuracy of clinical diagnosis, facilitate disease prediction and timely treatment of tumors, and save time and costs for the family, it is imperative to develop powerful AI tools for various cancer pathological diagnoses. To develop a smart AI model, large numbers of pathology images are highly required for model training.

This project will first collect the WSI images and clinical information of cancer patients, and use these data to train and develop an automated AI system. We envision that this new technology can be a versatile tool to improve the accuracy of clinical diagnosis, facilitate disease prediction and timely treatment of tumors, and save time and costs for the family. This tool can also be used to assist in the training of cancer pathologists. In addition, our results may facilitate the cancer etiology elucidation and drug discovery, and potentially improve the quality of life and survival rate of cancer patients.