Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study

Lili Feng; Zhenyu Liu; Chaofeng Li; Zhenhui Li; Xiaoying Lou; Lizhi Shao; Yunlong Wang; Yan Huang; Haiyang Chen; Xiaolin Pang; Shuai Liu; Fang He; Jian Zheng; Xiaochun Meng; Peiyi Xie; Guanyu Yang; Yi Ding; Mingbiao Wei; Jingping Yun; Mien-Chie Hung; Weihua Zhou; Daniel R Wahl; Ping Lan; Jie Tian; Xiangbo Wan

doi:10.1016/S2589-7500(21)00215-6

Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study

Lancet Digit Health. 2022 Jan;4(1):e8-e17. doi: 10.1016/S2589-7500(21)00215-6.

Authors

Lili Feng¹, Zhenyu Liu², Chaofeng Li³, Zhenhui Li⁴, Xiaoying Lou⁵, Lizhi Shao⁶, Yunlong Wang¹, Yan Huang⁵, Haiyang Chen⁷, Xiaolin Pang⁷, Shuai Liu⁷, Fang He⁷, Jian Zheng⁷, Xiaochun Meng⁸, Peiyi Xie⁸, Guanyu Yang⁹, Yi Ding¹⁰, Mingbiao Wei¹, Jingping Yun¹¹, Mien-Chie Hung¹², Weihua Zhou¹³, Daniel R Wahl¹⁴, Ping Lan¹⁵, Jie Tian¹⁶, Xiangbo Wan¹⁷

Affiliations

¹ Department of Radiation Oncology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangzhou, China.
² CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
³ State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
⁴ Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China.
⁵ Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
⁶ CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Computer Science and Engineering, Southeast University, Nanjing, China.
⁷ Department of Radiation Oncology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
⁸ Department of Radiology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
⁹ School of Computer Science and Engineering, Southeast University, Nanjing, China.
¹⁰ Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
¹¹ Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China.
¹² Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA; Graduate Institute of Biomedical Sciences and Research Centers for Cancer Biology and Molecular Medicine, China Medical University, Taichung, Taiwan; Department of Biotechnology, Asia University, Taichung, Taiwan.
¹³ Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA.
¹⁴ Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA; Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA.
¹⁵ Department of Colorectal Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangzhou, China.
¹⁶ CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China. Electronic address: jie.tian@ia.ac.cn.
¹⁷ Department of Radiation Oncology, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangzhou, China. Electronic address: wanxbo@mail.sysu.edu.cn.

PMID: 34952679
DOI: 10.1016/S2589-7500(21)00215-6

Abstract

Background: Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides.

Methods: In this multicentre observational study, eligible participants who had undergone neoadjuvant chemoradiotherapy followed by radical surgery were recruited, with their pretreatment pelvic MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) and whole slide images of H&E-stained biopsy sections collected for annotation and feature extraction. The RAdioPathomics Integrated preDiction System (RAPIDS) was constructed by machine learning on the basis of three feature sets associated with pathological complete response: radiomics MRI features, pathomics nucleus features, and pathomics microenvironment features from a retrospective training cohort. The accuracy of RAPIDS for the prediction of pathological complete response in locally advanced rectal cancer was verified in two retrospective external validation cohorts and further validated in a multicentre, prospective observational study (ClinicalTrials.gov, NCT04271657). Model performances were evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Findings: Between Sept 25, 2009, and Nov 3, 2017, 303 patients were retrospectively recruited in the training cohort, 480 in validation cohort 1, and 150 in validation cohort 2; 100 eligible patients were enrolled in the prospective study between Jan 10 and June 10, 2020. RAPIDS had favourable accuracy for the prediction of pathological complete response in the training cohort (AUC 0·868 [95% CI 0·825-0·912]), and in validation cohort 1 (0·860 [0·828-0·892]) and validation cohort 2 (0·872 [0·810-0·934]). In the prospective validation study, RAPIDS had an AUC of 0·812 (95% CI 0·717-0·907), sensitivity of 0·888 (0·728-0·999), specificity of 0·740 (0·593-0·886), NPV of 0·929 (0·862-0·995), and PPV of 0·512 (0·313-0·710). RAPIDS also significantly outperformed single-modality prediction models (AUC 0·630 [0·507-0·754] for the pathomics microenvironment model, 0·716 [0·580-0·852] for the radiomics MRI model, and 0·733 [0·620-0·845] for the pathomics nucleus model; all p<0·0001).

Interpretation: RAPIDS was able to predict pathological complete response to neoadjuvant chemoradiotherapy based on pretreatment radiopathomics images with high accuracy and robustness and could therefore provide a novel tool to assist in individualised management of locally advanced rectal cancer.

Funding: National Natural Science Foundation of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences.

Publication types

Multicenter Study
Observational Study
Research Support, Non-U.S. Gov't
Validation Study

MeSH terms

Aged
Artificial Intelligence / standards*
Female
Humans
Male
Middle Aged
Neoadjuvant Therapy / methods*
Prospective Studies
Rectal Neoplasms / therapy*
Retrospective Studies

Associated data

ClinicalTrials.gov/NCT04271657