Conference proceeding
Survival prediction for patients with metastatic urothelial cancer after immunotherapy using machine learning
Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol.12465, pp.124653A-124653A-6
04/07/2023
DOI: 10.1117/12.2655482
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.
Details
- Title: Subtitle
- Survival prediction for patients with metastatic urothelial cancer after immunotherapy using machine learning
- Creators
- Rain Tarango - Univ. of Michigan (United States)Lubomir Hadjiiski - Univ. of Michigan (United States)Ajjai Alva - Univ. of Michigan (United States)Heang-Ping Chan - Univ. of Michigan (United States)Richard H. Cohan - Univ. of Michigan (United States)Elaine M. Caoili - Univ. of Michigan (United States)Kenny H. Cha - Ctr. for Devices and Radiological Health (United States)Ravi K. Samala - Ctr. for Devices and Radiological Health (United States)Alon Z. Weizer - Univ. of Michigan (United States)Chuan Zhou - Univ. of Michigan (United States)Monika Joshi - Pennsylvania State UniversityYousef Zakharia - University of Iowa
- Contributors
- Khan M. Iftekharuddin (Editor) - Old Dominion UniversityWeijie Chen (Editor) - United States Food and Drug Administration
- Resource Type
- Conference proceeding
- Publication Details
- Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol.12465, pp.124653A-124653A-6
- Publisher
- SPIE
- DOI
- 10.1117/12.2655482
- ISSN
- 1605-7422
- Language
- English
- Date published
- 04/07/2023
- Academic Unit
- Hematology, Oncology, and Blood & Marrow Transplantation; Internal Medicine
- Record Identifier
- 9984544951402771
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