Deep learning-derived spatial organization features on histology images predicts prognosis in colorectal liver metastasis patients after hepatectomy

Lin Qi; Jie-Ying Liang; Zhong-Wu Li; Shao-Yan Xi; Yu-Ni Lai; Feng Gao; Xian-Rui Zhang; De-Shen Wang; Ming-Tao Hu; Yi Cao; Li-Jian Xu; Ronald C K Chan; Bao-Cai Xing; Xin Wang; Yu-Hong Li

doi:10.1016/j.isci.2023.107702

Deep learning-derived spatial organization features on histology images predicts prognosis in colorectal liver metastasis patients after hepatectomy

iScience. 2023 Aug 23;26(10):107702. doi: 10.1016/j.isci.2023.107702. eCollection 2023 Oct 20.

Authors

Lin Qi^{1

2

3}, Jie-Ying Liang^{4

5

6}, Zhong-Wu Li⁷, Shao-Yan Xi^{5

8}, Yu-Ni Lai², Feng Gao⁹, Xian-Rui Zhang^{1

2

3}, De-Shen Wang^{5

6}, Ming-Tao Hu^{5

6}, Yi Cao^{1

2

3}, Li-Jian Xu¹⁰, Ronald C K Chan¹¹, Bao-Cai Xing¹², Xin Wang^{1

2

3}, Yu-Hong Li^{5

6}

Affiliations

¹ Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China.
² Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China.
³ Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China.
⁴ Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Medical Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
⁵ State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
⁶ Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou, China.
⁷ Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing, China.
⁸ Department of Pathology, Sun Yat-Sen University Cancer Center, Guangzhou, China.
⁹ Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Disease, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
¹⁰ Centre for Perceptual and Interactive Intelligence, The Chinese University of Hong Kong, Hong Kong SAR, China.
¹¹ Department of Pathology, The Chinese University of Hong Kong, Hong Kong SAR, China.
¹² Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Hepatopancreatobiliary Surgery Department I, Peking University Cancer Hospital & Institute, Beijing, China.

Abstract

Histopathological images of colorectal liver metastases (CRLM) contain rich morphometric information that may predict patients' outcomes. However, to our knowledge, no study has reported any practical deep learning framework based on the histology images of CRLM, and their direct association with prognosis remains largely unknown. In this study, we developed a deep learning-based framework for fully automated tissue classification and quantification of clinically relevant spatial organization features (SOFs) in H&E-stained images of CRLM. The SOFs based risk-scoring system demonstrated a strong and robust prognostic value that is independent of the current clinical risk score (CRS) system in independent clinical cohorts. Our framework enables fully automated tissue classification of H&E images of CRLM, which could significantly reduce assessment subjectivity and the workload of pathologists. The risk-scoring system provides a time- and cost-efficient tool to assist clinical decision-making for patients with CRLM, which could potentially be implemented in clinical practice.

Keywords: Artificial intelligence; Bioinformatics; Computer science applications; Gastroenterology.