A PERSONALIZED ONCOLOGY MOBILE APPLICATION INTEGRATING CLINICAL AND GENOMIC FEATURES TO PREDICT THE RISK STRATIFICATION OF LUNG CANCER PATIENTS VIA MACHINE LEARNING

Authors : Mehmet Cihan Sakman, Talip Zengin, Deniz Kurşun, Tuğba Süzek

Pages : 90-99

Doi:10.22531/muglajsci.1165634

View : 15 | Download : 8

Publication Date : 2022-12-30

Article Type : Research

Abstract :Predicting lung adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC) risk status is a crucial step in precision oncology. In current clinical practice, clinicians, and patients are informed about the patient\'s risk group only with cancer staging. Several machine learning approaches for stratifying LUAD and LUSC patients have recently been described, however, there has yet to be a study that compares the integrated modeling of clinical and genetic data from these two lung cancer types. In our work, we used a prognostic prediction model based on clinical and somatically altered gene features from 1026 patients to assess the relevance of features based on their impact on risk classification. By integrating the clinical features and somatically mutated genes of patients, we achieved the highest accuracy; 93% for LUAD and 89% for LUSC, respectively. Our second finding is that new prognostic genes such as KEAP1 for LUAD and CSMD3 for LUSC and new clinical factors such as the site of resection are significantly associated with the risk stratification and can be integrated into clinical decision making. We validated the most important features found on an independent RNAseq dataset from NCBI GEO with survival information (GSE81089) and integrated our model into a user-friendly mobile application. Using this machine learning model and mobile application, clinicians and patients can assess the survival risk of their patients using each patient’s own clinical and molecular feature set.
Keywords : Machine learning, lung adenocarcinoma, lung squamous cell carcinoma, prognosis prediction model, the cancer genome atlas, multi-omics, data integration, electronic health records, Machine learning, lung adenocarcinoma, lung squamous cell carcinoma, prognosis predict