Preoperative Prediction of Perineural Invasion and Prognosis in Gastric Cancer Based on Machine Learning through a Radiomics–Clinicopathological Nomogram
1
Department of General Surgery, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China
2
Department of Endoscopic Center, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing 210031, China
3
Department of General Surgery, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, China
4
Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2024, 16(3), 614; https://doi.org/10.3390/cancers16030614
Submission received: 28 December 2023
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Revised: 26 January 2024
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Accepted: 27 January 2024
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Published: 31 January 2024
(This article belongs to the Special Issue Application of Multi-Omics Analysis in Cancer Diagnosis, Treatment and Prognosis)
Simple Summary
Gastric cancer remains the world’s fifth most lethal malignancy. Perineural invasion (PNI) is a common growth pattern of gastric cancer. Currently, the diagnosis of PNI relies on postoperative pathology, which is an invasive approach. In this study, we built a radiomics–clinicopathological model based on logistic regression analysis to preoperatively predict PNI. The radiomics–clinicopathological model yielded AUC values of 0.851 (95%CI: 0.769–0.933) in the training set, 0.842 (95%CI: 0.713–0.970) in the testing set and 0.813 (95%CI: 0.672–0.954) in the validation set. This proposed model may help clinicians make clinical decisions and provide personalized treatment to gastric cancer patients. In this research, the value of perineural invasion (PNI) in predicting prognoses for gastric cancer patients was also studied.
Abstract
Purpose: The aim of this study was to construct and validate a nomogram for preoperatively predicting perineural invasion (PNI) in gastric cancer based on machine learning, and to investigate the impact of PNI on the overall survival (OS) of gastric cancer patients. Methods: Data were collected from 162 gastric patients and analyzed retrospectively, and radiomics features were extracted from contrast-enhanced computed tomography (CECT) scans. A group of 42 patients from the Cancer Imaging Archive (TCIA) were selected as the validation set. Univariable and multivariable analyses were used to analyze the risk factors for PNI. The t-test, Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select radiomics features. Radscores were calculated and logistic regression was applied to construct predictive models. A nomogram was developed by combining clinicopathological risk factors and the radscore. The area under the curve (AUC) values of receiver operating characteristic (ROC) curves, calibration curves and clinical decision curves were employed to evaluate the performance of the models. Kaplan–Meier analysis was used to study the impact of PNI on OS. Results: The univariable and multivariable analyses showed that the T stage, N stage and radscore were independent risk factors for PNI (p < 0.05). A nomogram based on the T stage, N stage and radscore was developed. The AUC of the combined model yielded 0.851 in the training set, 0.842 in the testing set and 0.813 in the validation set. The Kaplan–Meier analysis showed a statistically significant difference in OS between the PNI group and the non-PNI group (p < 0.05). Conclusions: A machine learning-based radiomics–clinicopathological model could effectively predict PNI in gastric cancer preoperatively through a non-invasive approach, and gastric cancer patients with PNI had relatively poor prognoses.
Keywords:
machine learning; radiomics; gastric cancer; perineural invasion
