SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers

Qasem Al-Tashi; Maliazurina B Saad; Ajay Sheshadri; Carol C Wu; Joe Y Chang; Bissan Al-Lazikani; Christopher Gibbons; Natalie I Vokes; Jianjun Zhang; J Jack Lee; John V Heymach; David Jaffray; Seyedali Mirjalili; Jia Wu

doi:10.1016/j.patter.2023.100777

SwarmDeepSurv: swarm intelligence advances deep survival network for prognostic radiomics signatures in four solid cancers

Patterns (N Y). 2023 Jun 28;4(8):100777. doi: 10.1016/j.patter.2023.100777. eCollection 2023 Aug 11.

Authors

Qasem Al-Tashi¹, Maliazurina B Saad¹, Ajay Sheshadri², Carol C Wu³, Joe Y Chang⁴, Bissan Al-Lazikani⁵, Christopher Gibbons⁶, Natalie I Vokes⁷, Jianjun Zhang⁷, J Jack Lee⁸, John V Heymach⁷, David Jaffray⁹, Seyedali Mirjalili^{10

11

12}, Jia Wu^{1

7}

Affiliations

¹ Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
² Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
³ Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁴ Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁵ Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁶ Section of Patient-Centered Analytics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁷ Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁸ Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
⁹ Office of the Chief Technology and Digital Officer, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
¹⁰ Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006, Australia.
¹¹ Yonsei Frontier Lab, Yonsei University, Seoul 03722, Korea.
¹² University Research and Innovation Center, Obuda University, 1034 Budapest, Hungary.

Abstract

Survival models exist to study relationships between biomarkers and treatment effects. Deep learning-powered survival models supersede the classical Cox proportional hazards (CoxPH) model, but substantial performance drops were observed on high-dimensional features because of irrelevant/redundant information. To fill this gap, we proposed SwarmDeepSurv by integrating swarm intelligence algorithms with the deep survival model. Furthermore, four objective functions were designed to optimize prognostic prediction while regularizing selected feature numbers. When testing on multicenter sets (n = 1,058) of four different cancer types, SwarmDeepSurv was less prone to overfitting and achieved optimal patient risk stratification compared with popular survival modeling algorithms. Strikingly, SwarmDeepSurv selected different features compared with classical feature selection algorithms, including the least absolute shrinkage and selection operator (LASSO), with nearly no feature overlapping across these models. Taken together, SwarmDeepSurv offers an alternative approach to model relationships between radiomics features and survival endpoints, which can further extend to study other input data types including genomics.

Keywords: CoxPH; SwarmDeepSurv; cancer; deepsurv; feature selection; meta-heuristic; prognostic biomarkers; radiomics signatures; survival analysis; swarm intelligence.

Grants and funding

R00 CA218667/CA/NCI NIH HHS/United States