Biomedicines · 2024
GastricAITool: A Clinical Decision Support Tool for the Diagnosis and Prognosis of Gastric Cancer.
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Aznar-Gimeno Rocío, García-González María Asunción, Muñoz-Sierra Rubén, Carrera-Lasfuentes Patricia, Rodrigálvarez-Chamarro María de la Vega, González-Muñoz Carlos, Meléndez-Estrada Enrique, Lanas Ángel, Del Hoyo-Alonso Rafael
Аннотация
BACKGROUND/OBJECTIVE: Gastric cancer (GC) is a complex disease representing a significant global health concern. Advanced tools for the early diagnosis and prediction of adverse outcomes are crucial. In this context, artificial intelligence (AI) plays a fundamental role. The aim of this work was to develop a diagnostic and prognostic tool for GC, providing support to clinicians in critical decision-making and enabling personalised strategies.
METHODS: Different machine learning and deep learning techniques were explored to build diagnostic and prognostic models, ensuring model interpretability and transparency through explainable AI methods. These models were developed and cross-validated using data from 590 Spanish Caucasian patients with primary GC and 633 cancer-free individuals. Up to 261 variables were analysed, including demographic, environmental, clinical, tumoral, and genetic data. Variables such as Helicobacter pylori infection, tobacco use, family history of GC, TNM staging, metastasis, tumour location, treatment received, gender, age, and genetic factors (single nucleotide polymorphisms) were selected as inputs due to their association with the risk and progression of the disease.
RESULTS: The XGBoost algorithm (version 1.7.4) achieved the best performance for diagnosis, with an AUC value of 0.68 using 5-fold cross-validation. As for prognosis, the Random Survival Forest algorithm achieved a C-index of 0.77. Of interest, the incorporation of genetic data into the clinical-demographics models significantly increased discriminatory ability in both diagnostic and prognostic models.
CONCLUSIONS: This article presents GastricAITool, a simple and intuitive decision support tool for the diagnosis and prognosis of GC.
METHODS: Different machine learning and deep learning techniques were explored to build diagnostic and prognostic models, ensuring model interpretability and transparency through explainable AI methods. These models were developed and cross-validated using data from 590 Spanish Caucasian patients with primary GC and 633 cancer-free individuals. Up to 261 variables were analysed, including demographic, environmental, clinical, tumoral, and genetic data. Variables such as Helicobacter pylori infection, tobacco use, family history of GC, TNM staging, metastasis, tumour location, treatment received, gender, age, and genetic factors (single nucleotide polymorphisms) were selected as inputs due to their association with the risk and progression of the disease.
RESULTS: The XGBoost algorithm (version 1.7.4) achieved the best performance for diagnosis, with an AUC value of 0.68 using 5-fold cross-validation. As for prognosis, the Random Survival Forest algorithm achieved a C-index of 0.77. Of interest, the incorporation of genetic data into the clinical-demographics models significantly increased discriminatory ability in both diagnostic and prognostic models.
CONCLUSIONS: This article presents GastricAITool, a simple and intuitive decision support tool for the diagnosis and prognosis of GC.