A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients.

Journal: Annals of surgical oncology
PMID:

Abstract

BACKGROUND: The relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients.

Authors

  • Wei Jiang
    Department of Civil Engineering, Johns Hopkins System Institute, Johns Hopkins University, Baltimore, Maryland.
  • Min Li
    Hubei Provincial Institute for Food Supervision and Test, Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test, Wuhan 430075, China.
  • Jie Tan
    Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA.
  • Mingyuan Feng
    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.
  • Jixiang Zheng
    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.
  • 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.
  • Zhangyuanzhu Liu
    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.
  • Botao Yan
    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.
  • Guangxing Wang
    School of Civil Engineering, The University of Queensland, Brisbane St. Lucia, QLD 4072, Australia. guangxing.wang@uq.edu.au.
  • Shuoyu Xu
  • Weiwei Xiao
    Department of Radiation Oncology, Sun Yat sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, People's Republic of China.
  • Yuanhong Gao
    Department of Radiation Oncology, Sun Yat sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, People's Republic of China. gaoyh@sysucc.org.cn.
  • 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.
  • Jun Yan
    Department of Statistics, University of Connecticut, Storrs, CT 06269, USA.