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

Autor: Hogeun Kim, Nam Kyu Kim, Yoon Jung Choi, Seung Hyuk Baik, Hye Sun Lee, Jeonghyun Kang, Im-kyung Kim, Kang Young Lee
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Male
Cancer Research
Colorectal cancer
Feature selection
Lymph node metastasis
LASSO
Machine learning
computer.software_genre
Risk Assessment
Tumor-infiltrating lymphocytes
Machine Learning
03 medical and health sciences
0302 clinical medicine
Lymphocytes
Tumor-Infiltrating
Lasso (statistics)
Margin (machine learning)
Gastrointestinal Cancer
medicine
Humans
Prospective Studies
Lymph node
Aged
Neoplasm Staging
Retrospective Studies
Receiver operating characteristic
business.industry
Regression analysis
medicine.disease
Prognosis
Immunohistochemistry
medicine.anatomical_structure
Oncology
ROC Curve
030220 oncology & carcinogenesis
Lymphatic Metastasis
T1 colorectal cancer
030211 gastroenterology & hepatology
Original Article
Female
Artificial intelligence
Lymph Nodes
business
Colorectal Neoplasms
computer
Zdroj: Cancer Research and Treatment : Official Journal of Korean Cancer Association
ISSN: 2005-9256
1598-2998
Popis: 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.
Databáze: OpenAIRE
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