A Novel Scoring System for Response of Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer Using Early-Treatment Blood Features Derived From Machine Learning

Jaesik Kim; Kyung-Ah Sohn; Jung-Hak Kwak; Min Jung Kim; Seung-Bum Ryoo; Seung-Yong Jeong; Kyu Joo Park; Hyun-Cheol Kang; Eui Kyu Chie; Sang-Hyuk Jung; Dokyoon Kim; Ji Won Park

doi:10.3389/fonc.2021.790894

A Novel Scoring System for Response of Preoperative Chemoradiotherapy in Locally Advanced Rectal Cancer Using Early-Treatment Blood Features Derived From Machine Learning

Front Oncol. 2021 Nov 29:11:790894. doi: 10.3389/fonc.2021.790894. eCollection 2021.

Authors

Jaesik Kim^{1

2

3}, Kyung-Ah Sohn^{1

4}, Jung-Hak Kwak⁵, Min Jung Kim^{5

6}, Seung-Bum Ryoo⁵, Seung-Yong Jeong^{5

6}, Kyu Joo Park⁵, Hyun-Cheol Kang⁷, Eui Kyu Chie^{7

8}, Sang-Hyuk Jung^{2

3

9}, Dokyoon Kim^{2

3}, Ji Won Park^{2

3

5

6}

Affiliations

¹ Department of Computer Engineering, Ajou University, Suwon, South Korea.
² Department of Biostatistics, Epidemiology & Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
³ Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States.
⁴ Department of Artificial Intelligence, Ajou University, Suwon, South Korea.
⁵ Department of Surgery, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea.
⁶ Cancer Research Institute, Seoul National University, Seoul, South Korea.
⁷ Department of Radiation Oncology, Seoul National University College of Medicine, Seoul, South Korea.
⁸ Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, South Korea.
⁹ Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea.

Abstract

Background: Preoperative chemoradiotherapy (CRT) is a standard treatment for locally advanced rectal cancer (LARC). However, individual responses to preoperative CRT vary from patient to patient. The aim of this study is to develop a scoring system for the response of preoperative CRT in LARC using blood features derived from machine learning.

Methods: Patients who underwent total mesorectal excision after preoperative CRT were included in this study. The performance of machine learning models using blood features before CRT (pre-CRT) and from 1 to 2 weeks after CRT (early-CRT) was evaluated. Based on the best model, important features were selected. The scoring system was developed from the selected model and features. The performance of the new scoring system was compared with those of systemic inflammatory indicators: neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and the prognostic nutritional index.

Results: The models using early-CRT blood features had better performances than those using pre-CRT blood features. Based on the ridge regression model, which showed the best performance among the machine learning models (AUROC 0.6322 and AUPRC 0.5965), a novel scoring system for the response of preoperative CRT, named Response Prediction Score (RPS), was developed. The RPS system showed higher predictive power (AUROC 0.6747) than single blood features and systemic inflammatory indicators and stratified the tumor regression grade and overall downstaging clearly.

Conclusion: We discovered that we can more accurately predict CRT response by using early-treatment blood data. With larger data, we can develop a more accurate and reliable indicator that can be used in real daily practices. In the future, we urge the collection of early-treatment blood data and pre-treatment blood data.

Keywords: early-treatment blood features; machine learning; pathologic response; prediction; preoperative chemoradiotherapy; rectal cancer.