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.
We have previously developed a computerized decision support system for bladder cancer treatment response assessment (CDSS-T) in CT urography (CTU). In this work, we conducted an observer study to evaluate the diagnostic accuracy and intra-observer variability with and without the CDSS-T system. One hundred fifty-seven pre- and posttreatment lesion pairs were identified in pre- and post- chemotherapy CTU scans of 123 patients. Forty lesion pairs had T0 stage (complete response) after chemotherapy. Multi-disciplinary observers from 4 different institutions participated in reading the lesion pairs, including 5 abdominal radiologists, 4 radiology residents, 5 oncologists, 1 urologist, and 1 medical student. Each observer provided estimates of the T0 likelihood after treatment without and then with the CDSST aid for each lesion. To assess the intra-observer variability, 51 cases were evaluated two times – the original and the repeated evaluation. The average area under the curve (AUC) of 16 observers for estimation of T0 disease after treatment increased from 0.73 without CDSS-T to 0.77 with CDSS-T (p = 0.003). For the evaluation with CDSS-T, the average AUC performance for different institutions was similar. The performance with CDSS-T was improved significantly and the AUC standard deviations were slightly smaller showing potential trend of more accurate and uniform performance with CDSS-T. There was no significant difference between the original and repeated evaluation. This study demonstrated that our CDSS-T system has the potential to improve treatment response assessment of physicians from different specialties and institutions, and reduce the inter- and intra-observer variabilities of the assessments.
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