Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis

Jinlian Jin, Haiyan Zhou, Shulin Sun, Zhe Tian, Haibing Ren, Jinwu Feng Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People’s Republic of ChinaCorrespondence: Jinlian JinDepartment...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jin J, Zhou H, Sun S, Tian Z, Ren H, Feng J
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	colorectal cancer pt1nxm0 lymph nodes metastasis prediction model machine learning random forest classifier generalized linear model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/32f6a6438eff4a2b97f87e0354dc31b7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:32f6a6438eff4a2b97f87e0354dc31b7
record_format	dspace
spelling	oai:doaj.org-article:32f6a6438eff4a2b97f87e0354dc31b72021-12-02T19:17:36ZSupervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis1179-1322https://doaj.org/article/32f6a6438eff4a2b97f87e0354dc31b72021-12-01T00:00:00Zhttps://www.dovepress.com/supervised-learning-based-systemic-inflammatory-markers-enable-accurat-peer-reviewed-fulltext-article-CMARhttps://doaj.org/toc/1179-1322Jinlian Jin, Haiyan Zhou, Shulin Sun, Zhe Tian, Haibing Ren, Jinwu Feng Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People’s Republic of ChinaCorrespondence: Jinlian JinDepartment of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, No. 60, Qiaohu 1st Road, Xiling District, Yichang, Hubei, 443002, People’s Republic of ChinaTel +8613986746553Email jjl7475@163.comPurpose: Predicting lymph node metastasis (LNM) after endoscopic resection is crucial in determining whether patients with pT1NxM0 colorectal cancer (CRC) should undergo additional surgery. This study was aimed to develop a predictive model that can be used to reduce the current likelihood of overtreatment.Patients and Methods: We recruited a total of 1194 consecutive CRC patients with pT1NxM0 who underwent endoscopic or surgical resection at the Gezhouba Central Hospital of Sinopharm between January 1, 2006, and August 31, 2021. The random forest classifier (RFC) and generalized linear algorithm (GLM) were used to screen out the variables that greatly affected the LNM prediction, respectively. The area under the curve (AUC) and decision curve analysis (DCA) were applied to assess the accuracy of predictive models.Results: Analysis identified the top 10 candidate factors including depth of submucosal invasion, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), platelet-to-neutrophil ratio(PNR), venous invasion, poorly differentiated clusters, tumor budding, grade, lymphatic vascular invasion, and background adenoma. The performance of the GLM achieved the highest AUC of 0.79 (95% confidence interval [CI]: 0.30 to 1.28) in the training cohort and robust AUC of 0.80 (95% confidence interval [CI]: 0.36 to 1.24) in the validation cohort. Meanwhile, the RFC exhibited a robust AUC of 0.84 (95% confidence interval [CI]: 0.40 to 1.28) in the training cohort and a high AUC of 0.85 (95% CI: 0.41 to 1.29) in the validation cohort. DCAs also showed that the RFC had superior predictive ability.Conclusion: Our supervised learning-based model incorporating histopathologic parameters and inflammatory markers showed a more accurate predictive performance compared to the GLM. This newly supervised learning-based predictive model can be used to determine an individually tailored treatment strategy.Keywords: colorectal cancer, pT1NxM0, lymph nodes metastasis, prediction model, machine learning, random forest classifier, generalized linear modelJin JZhou HSun STian ZRen HFeng JDove Medical Pressarticlecolorectal cancerpt1nxm0lymph nodes metastasisprediction modelmachine learningrandom forest classifiergeneralized linear modelNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancer Management and Research, Vol Volume 13, Pp 8967-8977 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	colorectal cancer pt1nxm0 lymph nodes metastasis prediction model machine learning random forest classifier generalized linear model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	colorectal cancer pt1nxm0 lymph nodes metastasis prediction model machine learning random forest classifier generalized linear model Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Jin J Zhou H Sun S Tian Z Ren H Feng J Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis
description	Jinlian Jin, Haiyan Zhou, Shulin Sun, Zhe Tian, Haibing Ren, Jinwu Feng Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People’s Republic of ChinaCorrespondence: Jinlian JinDepartment of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, No. 60, Qiaohu 1st Road, Xiling District, Yichang, Hubei, 443002, People’s Republic of ChinaTel +8613986746553Email jjl7475@163.comPurpose: Predicting lymph node metastasis (LNM) after endoscopic resection is crucial in determining whether patients with pT1NxM0 colorectal cancer (CRC) should undergo additional surgery. This study was aimed to develop a predictive model that can be used to reduce the current likelihood of overtreatment.Patients and Methods: We recruited a total of 1194 consecutive CRC patients with pT1NxM0 who underwent endoscopic or surgical resection at the Gezhouba Central Hospital of Sinopharm between January 1, 2006, and August 31, 2021. The random forest classifier (RFC) and generalized linear algorithm (GLM) were used to screen out the variables that greatly affected the LNM prediction, respectively. The area under the curve (AUC) and decision curve analysis (DCA) were applied to assess the accuracy of predictive models.Results: Analysis identified the top 10 candidate factors including depth of submucosal invasion, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), platelet-to-neutrophil ratio(PNR), venous invasion, poorly differentiated clusters, tumor budding, grade, lymphatic vascular invasion, and background adenoma. The performance of the GLM achieved the highest AUC of 0.79 (95% confidence interval [CI]: 0.30 to 1.28) in the training cohort and robust AUC of 0.80 (95% confidence interval [CI]: 0.36 to 1.24) in the validation cohort. Meanwhile, the RFC exhibited a robust AUC of 0.84 (95% confidence interval [CI]: 0.40 to 1.28) in the training cohort and a high AUC of 0.85 (95% CI: 0.41 to 1.29) in the validation cohort. DCAs also showed that the RFC had superior predictive ability.Conclusion: Our supervised learning-based model incorporating histopathologic parameters and inflammatory markers showed a more accurate predictive performance compared to the GLM. This newly supervised learning-based predictive model can be used to determine an individually tailored treatment strategy.Keywords: colorectal cancer, pT1NxM0, lymph nodes metastasis, prediction model, machine learning, random forest classifier, generalized linear model
format	article
author	Jin J Zhou H Sun S Tian Z Ren H Feng J
author_facet	Jin J Zhou H Sun S Tian Z Ren H Feng J
author_sort	Jin J
title	Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis
title_short	Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis
title_full	Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis
title_fullStr	Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis
title_full_unstemmed	Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis
title_sort	supervised learning based systemic inflammatory markers enable accurate additional surgery for pt1nxm0 colorectal cancer: a comparative analysis of two practical prediction models for lymph node metastasis
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/32f6a6438eff4a2b97f87e0354dc31b7
work_keys_str_mv	AT jinj supervisedlearningbasedsystemicinflammatorymarkersenableaccurateadditionalsurgeryforpt1nxm0colorectalcanceracomparativeanalysisoftwopracticalpredictionmodelsforlymphnodemetastasis AT zhouh supervisedlearningbasedsystemicinflammatorymarkersenableaccurateadditionalsurgeryforpt1nxm0colorectalcanceracomparativeanalysisoftwopracticalpredictionmodelsforlymphnodemetastasis AT suns supervisedlearningbasedsystemicinflammatorymarkersenableaccurateadditionalsurgeryforpt1nxm0colorectalcanceracomparativeanalysisoftwopracticalpredictionmodelsforlymphnodemetastasis AT tianz supervisedlearningbasedsystemicinflammatorymarkersenableaccurateadditionalsurgeryforpt1nxm0colorectalcanceracomparativeanalysisoftwopracticalpredictionmodelsforlymphnodemetastasis AT renh supervisedlearningbasedsystemicinflammatorymarkersenableaccurateadditionalsurgeryforpt1nxm0colorectalcanceracomparativeanalysisoftwopracticalpredictionmodelsforlymphnodemetastasis AT fengj supervisedlearningbasedsystemicinflammatorymarkersenableaccurateadditionalsurgeryforpt1nxm0colorectalcanceracomparativeanalysisoftwopracticalpredictionmodelsforlymphnodemetastasis
_version_	1718376845906280448

Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis

Ejemplares similares