Deep learning‐based survival analysis for brain metastasis patients with the national cancer database. Issue 9 (13th August 2020)

• Record Type: Journal Article
• Authors: Bice, Noah; Kirby, Neil; Bahr, Tyler; Rasmussen, Karl; Saenz, Daniel; Wagner, Timothy; Papanikolaou, Niko; Fakhreddine, Mohamad
• Is Part Of: Journal of applied clinical medical physics. Volume 21:Issue 9(2020)
• Page Start: 187
• Page End: 192
• Publication Date: 2020-08-13
• Subjects: brain metastasis -- deep learning -- machine learning -- survival; Medical physics -- Periodicals; Clinical medicine -- Periodicals; Health Physics; Clinical Medicine; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; 610.153
• DOI: 10.1002/acm2.12995
• Languages: English
• ISSNs: 1526-9914

Abstract

Abstract: Purpose: Prognostic indices such as the Brain Metastasis Graded Prognostic Assessment have been used in clinical settings to aid physicians and patients in determining an appropriate treatment regimen. These indices are derivative of traditional survival analysis techniques such as Cox proportional hazards (CPH) and recursive partitioning analysis (RPA). Previous studies have shown that by evaluating CPH risk with a nonlinear deep neural network, DeepSurv, patient survival can be modeled more accurately. In this work, we apply DeepSurv to a test case: breast cancer patients with brain metastases who have received stereotactic radiosurgery. Methods: Survival times, censorship status, and 27 covariates including age, staging information, and hormone receptor status were provided for 1673 patients by the NCDB. Monte Carlo cross‐validation with 50 samples of 1400 patients was used to train and validate the DeepSurv, CPH, and RPA models independently. DeepSurv was implemented with L2 regularization, batch normalization, dropout, Nesterov momentum, and learning rate decay. RPA was implemented as a random survival forest (RSF). Concordance indices of test sets of 140 patients were used for each sample to assess the generalizable predictive capacity of each model. Results: Following hyperparameter tuning, DeepSurv was trained at 32 min per sample on a 1.33 GHz quad‐core CPU. Test set concordance indices of 0.7488 ± 0.0049, 0.6251 ± 0.0047, and 0.7368 ± 0.0047, were found for DeepSurv, CPH, and RSF, respectively. A Tukey HSD test demonstrates a statistically significant difference between the mean concordance indices of the three models. Conclusion: Our results suggest that deep learning‐based survival prediction can outperform traditional models, specifically in a case where an accurate prognosis is highly clinically relevant. We recommend that where appropriate data are available, deep learning‐based prognostic indicators should be used to supplement classical statistics.