Multitask machine learning-based tumor-associated collagen signatures predict peritoneal recurrence and disease-free survival in gastric cancer.

Journal: Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
PMID: 39271552

Abstract

BACKGROUND: Accurate prediction of peritoneal recurrence for gastric cancer (GC) is crucial in clinic. The collagen alterations in tumor microenvironment affect the migration and treatment response of cancer cells. Herein, we proposed multitask machine learning-based tumor-associated collagen signatures (TACS), which are composed of quantitative collagen features derived from multiphoton imaging, to simultaneously predict peritoneal recurrence (TACS) and disease-free survival (TACS).

Authors

  • Meiting Fu
    Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China.
  • Yuyu Lin
    Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China.
  • Junyao Yang
    Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China.
  • Jiaxin Cheng
    Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, People's Republic of China.
  • Liyan Lin
  • Guangxing Wang
    School of Civil Engineering, The University of Queensland, Brisbane St. Lucia, QLD 4072, Australia. guangxing.wang@uq.edu.au.
  • Chenyan Long
    Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, China.
  • Shuoyu Xu
  • Jianping Lu
    Department of Radiology, Changhai Hospital.
  • Guoxin Li
    Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China. gzliguoxin@163.com caishirong@yeah.net ehbhltj@hotmail.com keekee77@126.com.
  • Jun Yan
    Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.
  • Gang Chen
    Department of Orthopedics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Shuangmu Zhuo
    Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education and Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, P.R. China.
  • Dexin Chen
    Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.

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