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A cost-effective, machine learning-based new unified risk-classification score (NU-CATS) for patients with
Shuhua Zheng1, Yilin Wu2, Eric D Donnelly1
1Department of Radiation Oncology, Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL 60611, United States of America.
Introduction:
Treatment for endometrial cancer (EC) is increasingly guided by molecular risk classifications. Here, we aimed at using machine learning (ML) to incorporate clinical and molecular risk factors to optimize risk assessment.
Methods:
The Cancer Genome Atlas-Uterine Corpus Endometrial Carcinoma (n = 596), Memorial Sloan Kettering-Metastatic Events and Tropisms (n = 1315) and the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange (n = 4561) datasets were used to identify genetic alterations and clinicopathological features. Software packages including Keras, Pytorch, and Scikit Learn were tested to build artificial neural networks (ANNs) with a binary output as either intra-abdominal metastatic progression ('1') vs. non-metastatic ('0').
Results:
Black patients with EC have worse prognosis than White patients, adjusting for TP53 or POLE mutation status. Over 75% of Black patients carry TP53 mutations as compared to approximately 40% of White patients. Older age is associated with an increasing likelihood of TP53 mutation, high risk histology, and distant metastasis. For patients above age 70, 91% of Black and 60% of White EC patients carry TP53 mutations. A ML-based New Unified classifiCATion Score (NU-CATS) that incorporates age, race, histology, mismatch repair status, and TP53 mutation status showed 75% accuracy in prognosticating intra-abdominal progression. A higher NU-CATS is associated with an increasing risk of having positive pelvic or para-aortic lymph nodes and distant metastasis. NU-CATS was shown to outperform Leiden/TransPORTEC model for estimating risk of FIGO Stage I/II disease progression and survival in Black EC patients.
Conclusion:
The NU-CATS, a ML-based, cost-effective algorithm, incorporates diverse clinicopathologic and molecular variables of EC and yields superior prognostication of the risk of nodal involvement, distant metastasis, disease progression, and overall survival.
