Abstract

Backgroud

To predict the malignancy of 1–5 cm gastric gastrointestinal stromal tumors (GISTs) by machine learning (ML) on CT images using three models - Logistic Regression (LR), Decision Tree (DT) and Gradient Boosting Decision Tree (GBDT).

Methods

231 patients from Center 1 were randomly assigned into the training cohort (n = 161) and the internal validation cohort (n = 70) in a 7:3 ratio. The other 78 patients from Center 2 served as the external test cohort. Scikit-learn software was used to build three classifiers. The performance of the three models were evaluated by sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC). Diagnostic differences between ML models and radiologists were compared in the external test cohort. Important features of LR and GBDT were analyzed and compared.

Results

GBDT outperformed LR and DT with the largest AUC values (0.981 and 0.815) in the training and internal validation cohorts and the greatest accuracy (0.923, 0.833 and 0.844) across all three cohorts. However, LR was found to have the largest AUC value (0.910) in the external test cohort. DT yielded the worst accuracy (0.790 and 0.727) and AUC values (0.803 and 0.700) in both the internal validation cohort and the external test cohort. GBDT and LR performed better than radiologists. Long diameter was demonstrated to be the same and most important CT feature for GBDT and LR.

Conclusions

ML classifiers, especially GBDT and LR with high accuracy and strong robustness, were considered to be promising in risk classification of 1–5 cm gastric GISTs based on CT. Long diameter was found the most important feature for risk stratification.

Details

Title
Machine learning for predicting the risk stratification of 1–5 cm gastric gastrointestinal stromal tumors based on CT
Author
Zhang, Cui; Wang, Jian; Dai, Yang Yangiling; Xu, Zhihua; Zhu, Fangmei; Yu, Huajun
Pages
1-11
Section
Research
Publication year
2023
Publication date
2023
Publisher
BioMed Central
e-ISSN
14712342
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2838768100
Copyright
© 2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.