The following publications are possibly variants of this publication:
- Local tuning of radiomics-based model for predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancerBin Tang, Jacopo Lenkowicz, Qian Peng, Luca Boldrini, Qing Hou, Nicola Dinapoli, Vincenzo Valentini, Peng Diao, Gang Yin, Lucia Clara Orlandini. bmcmi, 22(1):44, 2022. [doi]
- A Novel Effectiveness Assessment Framework for Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer Based on Multi-modal IntelligenceXiao Tian, Dong Sui, Weifeng Liu, Maozu Guo 0001, Gongning Luo, Kuanquan Wang. bibm 2023: 1-6 [doi]
- MLDRL: Multi-loss disentangled representation learning for predicting esophageal cancer response to neoadjuvant chemoradiotherapy using longitudinal CT imagesHailin Yue, Jin Liu, Junjian Li, Hulin Kuang, Jinyi Lang, Jianhong Cheng, Lin Peng, Yongtao Han, Harrison Bai, Yuping Wang, Qifeng Wang, Jianxin Wang 0001. mia, 79:102423, 2022. [doi]