Machine learning model predicting factors for incisional infection following right hemicolectomy for colon cancer

• Published in: BMC surgery Vol. 24; no. 1; pp. 279 - 9
• Main Authors: Li, Jiatong, Yan, Zhaopeng
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 01.10.2024; BioMed Central; BMC
• Subjects: Accuracy; Aged; Body mass index; Body size; Cancer; Cancer surgery; Cancer therapies; Chemotherapy; Colectomy - adverse effects; Colon; Colon cancer; Colonic Neoplasms - surgery; Colorectal cancer; Colorectal carcinoma; Complications; Data collection; Decision trees; Deep learning; Demographic variables; Demographics; Feature selection; Female; Health aspects; Health services; Hospitals; Humans; Incisional infection; Infection; Laparoscopy; Learning algorithms; Logistic Models; Logistics; Machine Learning; Machine learning models; Male; Malignancy; Medical records; Medical research; Medicine, Experimental; Middle Aged; Multidisciplinary teams; Patients; Performance prediction; Perioperative care; Postoperative; Radiation therapy; Random variables; Regression analysis; Regression models; Retrospective Studies; Right hemicolectomy; Risk assessment; Risk Factors; Support Vector Machine; Support vector machines; Surgical site infections; Surgical Wound Infection - diagnosis; Surgical Wound Infection - epidemiology; Surgical Wound Infection - etiology; Sutures; Taiwan; China; Deep learning; Machine learning models; Right hemicolectomy; Colorectal cancer; Incisional infection
• ISSN: 1471-2482; 1471-2482
• DOI: 10.1186/s12893-024-02543-8

Colorectal cancer is a prevalent malignancy worldwide, and right hemicolectomy is a common surgical procedure for its treatment. However, postoperative incisional infections remain a significant complication, leading to prolonged hospital stays, increased healthcare costs, and patient discomfort. Therefore, this study aims to utilize machine learning models, including random forest, support vector machine, deep learning models, and traditional logistic regression, to predict factors associated with incisional infection following right hemicolectomy for colon cancer. Clinical data were collected from 322 patients undergoing right hemicolectomy for colon cancer, including demographic information, preoperative chemotherapy status, body mass index (BMI), operative time, and other relevant variables. These data are divided into training and testing sets in a ratio of 7:3. Machine learning models, including random forest, support vector machine, and deep learning, were trained using the training set and evaluated using the testing set. The deep learning model exhibited the highest performance in predicting incisional infection, followed by random forest and logistic regression models. Specifically, the deep learning model demonstrated higher area under the receiver operating characteristic curve (ROC-AUC) and F1 score compared to other models. These findings suggest the efficacy of machine learning models in predicting risk factors for incisional infection following right hemicolectomy for colon cancer. Machine learning models, particularly deep learning models, offer a promising approach for predicting the risk of incisional infection following right hemicolectomy for colon cancer. These models can provide valuable decision support for clinicians, facilitating personalized treatment strategies and improving patient outcomes.