130 362

Cited 0 times in

LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer

Authors
 Jeonghyun Kang  ;  Yoon Jung Choi  ;  Im-Kyung Kim  ;  Hye Sun Lee  ;  Hogeun Kim  ;  Seung Hyuk Baik  ;  Nam Kyu Kim  ;  Kang Young Lee 
Citation
 CANCER RESEARCH AND TREATMENT, Vol.53(3) : 773-783, 2021-07 
Journal Title
CANCER RESEARCH AND TREATMENT
ISSN
 1598-2998 
Issue Date
2021-07
Keywords
LASSO ; Lymph node ; Machine learning ; T1 colorectal cancer ; Tumor-infiltrating lymphocytes
Abstract
Purpose: The role of tumor-infiltrating lymphocytes (TILs) in predicting lymph node metastasis (LNM) in patients with T1 colorectal cancer (CRC) remains unclear. Furthermore, clinical utility of a machine learning-based approach has not been widely studied.

Materials and methods: Immunohistochemistry for TILs against CD3, CD8, and forkhead box P3 in both center and invasive margin of the tumor were performed using surgically resected T1 CRC slides. Three hundred and sixteen patients were enrolled and categorized into training (n=221) and validation (n=95) sets via random sampling. Using clinicopathologic variables including TILs, the least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection and predictive signature building in the training set. The predictive accuracy of our model and the Japanese criteria were compared using area under the receiver operating characteristic (AUROC), net reclassification improvement (NRI)/integrated discrimination improvement (IDI), and decision curve analysis (DCA) in the validation set.

Results: LNM was detected in 29 (13.1%) and 12 (12.6%) patients in training and validation sets, respectively. Nine variables were selected and used to generate the LASSO model. Its performance was similar in training and validation sets (AUROC, 0.795 vs. 0.765; p=0.747). In the validation set, the LASSO model showed better outcomes in predicting LNM than Japanese criteria, as measured by AUROC (0.765 vs. 0.518, p=0.003) and NRI (0.447, p=0.039)/IDI (0.121, p=0.034). DCA showed positive net benefits in using our model.

Conclusion: Our LASSO model incorporating histopathologic parameters and TILs showed superior performance compared to conventional Japanese criteria in predicting LNM in patients with T1 CRC.
Files in This Item:
T202102766.pdf Download
DOI
10.4143/crt.2020.974
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Yonsei Biomedical Research Center (연세의생명연구원) > 1. Journal Papers
Yonsei Authors
Kang, Jeonghyun(강정현) ORCID logo https://orcid.org/0000-0001-7311-6053
Kim, Nam Kyu(김남규) ORCID logo https://orcid.org/0000-0003-0639-5632
Kim, Im Kyung(김임경) ORCID logo https://orcid.org/0000-0001-8505-5307
Kim, Hogeun(김호근)
Baik, Seung Hyuk(백승혁) ORCID logo https://orcid.org/0000-0003-4183-2332
Lee, Kang Young(이강영)
Lee, Hye Sun(이혜선) ORCID logo https://orcid.org/0000-0001-6328-6948
Choi, Yoon Jung(최윤정) ORCID logo https://orcid.org/0000-0002-5701-8864
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/184299
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links