Open AccessThe Effectiveness of Machine Learning in Predicting Lateral Lymph Node Metastasis From Lower Rectal Cancer: A Single Center Development and Validation Study
Author(s) -
Kasai Shunsuke,
Shiomi Akio,
Kagawa Hiroyasu,
Hino Hitoshi,
Manabe Shoichi,
Yamaoka Yusuke,
Chen Kai,
Nanishi Kenji,
Kinugasa Yusuke
Publication year - 2022
Publication title -
annals of gastroenterological surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.308
H-Index - 15
ISSN - 2475-0328
DOI - 10.1002/ags3.12504
Subject(s) - medicine , receiver operating characteristic , cohort , colorectal cancer , lymph node , machine learning , magnetic resonance imaging , cutoff , single center , retrospective cohort study , lymph node metastasis , dissection (medical) , radiology , cancer , artificial intelligence , surgery , metastasis , computer science , physics , quantum mechanics
Aim Accurate preoperative diagnosis of lateral lymph node metastasis (LLNM) from lower rectal cancer is important to identify patients who require lateral lymph node dissection (LLND). We aimed to create an effective prediction model for LLNM using machine learning by combining preoperative information. Methods We retrospectively examined patients who underwent primary rectal cancer surgery with unilateral or bilateral LLND between April 2010 and March 2020 at a single institution. Using the machine learning software “Prediction One” (Sony Network Communications), we developed a prediction model in the training cohort that included 267 consecutive patients (500 sides) from April 2010. Clinicopathological data obtained from the preoperative examinations were used as the learning items. In the validation cohort that included subsequent patients until March 2020, we compared the discriminating powers of the prediction model and the conventional method using the short‐axis diameter of the largest lateral lymph node, as detected on magnetic resonance imaging. Results The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.903 in the validation cohort comprising 56 patients (107 sides). This indicated significantly higher predictive power than that of the conventional method (AUC = 0.754; P = .022). Using the cutoff values defined in the training cohort, the accuracy, sensitivity, and specificity of the prediction model were 80.4%, 90.0%, and 79.4%, respectively. The model was able to correctly predict four of five sides comprising LLNM with the short‐axis diameters ≤4 mm. Conclusion Machine learning contributed to the creation of an effective prediction model for LLNM.