Survival prediction for patients with metastatic urothelial cancer after immunotherapy using machine learning

Rain Tarango; Lubomir Hadjiiski; Ajjai Alva; Heang-Ping Chan; Richard H. Cohan; Elaine M. Caoili; Kenny H. Cha; Ravi K. Samala; Alon Z. Weizer; Chuan Zhou; Monika Joshi; Yousef Zakharia

doi:10.1117/12.2655482

7 April 2023 Survival prediction for patients with metastatic urothelial cancer after immunotherapy using machine learning

Rain Tarango, Lubomir Hadjiiski, Ajjai Alva, Heang-Ping Chan, Richard H. Cohan, Elaine M. Caoili, Kenny H. Cha, Ravi K. Samala, Alon Z. Weizer, Chuan Zhou, Monika Joshi, Yousef Zakharia

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 124653A (2023) https://doi.org/10.1117/12.2655482
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

We studied the feasibility of developing a machine learning model to predict the survival of patients with metastatic urothelial cancer after immunotherapy. CT scans of 363 metastatic tumors in 49 patients undergoing immunotherapy were collected at every treatment time point. 1040 temporal triplets of metastatic cancers were formed. At every time point, a radiologist measured the tumor diameter. The patient survival data was collected from clinical records. Using the tumor diameters at prior time points as inputs, we built a model to predict patient survival after immunotherapy using artificial neural networks (PSNN). The PSNN used 3 prior time points to predict patient survival at a future time point: PS(t₄)=PSNN(d(t₁), d(t₂), d(t₃)). Specifically, PSNN was trained to predict patient survival at 4 years from the beginning of treatment (t₄=4) using 3 prior time points within 3 years from the beginning of treatment (0<t₁<t₂<t₃<3). We split the dataset into training (53 tumors, 13 patients, 335 triplets) and independent test (310 lesions, 36 patients, 705 triplets) sets. The final patient-based survival prediction scores were obtained by averaging PSNN scores of all triplets for a given patient. Area under the ROC curve (AUC) and Kaplan-Meier analysis were used for performance evaluation. The training and test AUCs for survival prediction at 4 years were 0.77±0.13 and 0.73±0.09, respectively. Using a decision threshold determined by the training set, the test set was stratified into two subgroups of longer and shorted survival. Median survival time for the 2 test subgroups estimated by the PSNN was 5 and 2 years, respectively (p=0.025). The PSNN shows promise for predicting patient survival after immunotherapy.

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Rain Tarango, Lubomir Hadjiiski, Ajjai Alva, Heang-Ping Chan, Richard H. Cohan, Elaine M. Caoili, Kenny H. Cha, Ravi K. Samala, Alon Z. Weizer, Chuan Zhou, Monika Joshi, and Yousef Zakharia "Survival prediction for patients with metastatic urothelial cancer after immunotherapy using machine learning", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 124653A (7 April 2023); https://doi.org/10.1117/12.2655482

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