SwarmDeepSurv: Swarm Intelligence Advances Deep Survival Network for Prognostic Radiomics Signatures in Four Solid Cancers

Overview

Journal Patterns (N Y)

Date 2023 Aug 21

PMID 37602223

Authors

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

Affiliations

Soon will be listed here.

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.

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References

Zhang N, Liang R, Gensheimer M, Guo M, Zhu H, Yu J . Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer. Theranostics. 2020; 10(25):11707-11718. PMC: 7546006. DOI: 10.7150/thno.50565. View

Ren J, Liu D, Li G, Duan J, Dong J, Liu Z . Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients. Front Cardiovasc Med. 2022; 9:923549. PMC: 9263287. DOI: 10.3389/fcvm.2022.923549. View

Lao J, Chen Y, Li Z, Li Q, Zhang J, Liu J . A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme. Sci Rep. 2017; 7(1):10353. PMC: 5583361. DOI: 10.1038/s41598-017-10649-8. View

Faraggi D, Simon R . A neural network model for survival data. Stat Med. 1995; 14(1):73-82. DOI: 10.1002/sim.4780140108. View

Al-Tashi Q, Saad M, Muneer A, Qureshi R, Mirjalili S, Sheshadri A . Machine Learning Models for the Identification of Prognostic and Predictive Cancer Biomarkers: A Systematic Review. Int J Mol Sci. 2023; 24(9). PMC: 10178491. DOI: 10.3390/ijms24097781. View

Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K . A guide to deep learning in healthcare. Nat Med. 2019; 25(1):24-29. DOI: 10.1038/s41591-018-0316-z. View

Hong L, Aminu M, Li S, Lu X, Petranovic M, Saad M . Efficacy and clinicogenomic correlates of response to immune checkpoint inhibitors alone or with chemotherapy in non-small cell lung cancer. Nat Commun. 2023; 14(1):695. PMC: 9908867. DOI: 10.1038/s41467-023-36328-z. View

Yin Q, Chen W, Zhang C, Wei Z . A convolutional neural network model for survival prediction based on prognosis-related cascaded Wx feature selection. Lab Invest. 2022; 102(10):1064-1074. DOI: 10.1038/s41374-022-00801-y. View

Saeys Y, Inza I, Larranaga P . A review of feature selection techniques in bioinformatics. Bioinformatics. 2007; 23(19):2507-17. DOI: 10.1093/bioinformatics/btm344. View

10.

Boehm K, Khosravi P, Vanguri R, Gao J, Shah S . Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer. 2021; 22(2):114-126. PMC: 8810682. DOI: 10.1038/s41568-021-00408-3. View

11.

Wu J, Li C, Gensheimer M, Padda S, Kato F, Shirato H . Radiological tumor classification across imaging modality and histology. Nat Mach Intell. 2021; 3:787-798. PMC: 8612063. DOI: 10.1038/s42256-021-00377-0. View

12.

Liu R, Rizzo S, Waliany S, Garmhausen M, Pal N, Huang Z . Systematic pan-cancer analysis of mutation-treatment interactions using large real-world clinicogenomics data. Nat Med. 2022; 28(8):1656-1661. DOI: 10.1038/s41591-022-01873-5. View

13.

Chen Z, Pang M, Zhao Z, Li S, Miao R, Zhang Y . Feature selection may improve deep neural networks for the bioinformatics problems. Bioinformatics. 2019; 36(5):1542-1552. DOI: 10.1093/bioinformatics/btz763. View

14.

Wu J, Gensheimer M, Zhang N, Guo M, Liang R, Zhang C . Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer. J Nucl Med. 2019; 61(3):327-336. PMC: 7067523. DOI: 10.2967/jnumed.119.230037. View

15.

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

16.

Leung K, Elashoff R, Afifi A . Censoring issues in survival analysis. Annu Rev Public Health. 1997; 18:83-104. DOI: 10.1146/annurev.publhealth.18.1.83. View

17.

Kirkpatrick S, Gelatt Jr C, Vecchi M . Optimization by simulated annealing. Science. 1983; 220(4598):671-80. DOI: 10.1126/science.220.4598.671. View

18.

Siegel R, Miller K, Fuchs H, Jemal A . Cancer statistics, 2022. CA Cancer J Clin. 2022; 72(1):7-33. DOI: 10.3322/caac.21708. View

19.

Wang J, Li S . An Improved Grey Wolf Optimizer Based on Differential Evolution and Elimination Mechanism. Sci Rep. 2019; 9(1):7181. PMC: 6509275. DOI: 10.1038/s41598-019-43546-3. View

20.

Spooner A, Chen E, Sowmya A, Sachdev P, Kochan N, Trollor J . A comparison of machine learning methods for survival analysis of high-dimensional clinical data for dementia prediction. Sci Rep. 2020; 10(1):20410. PMC: 7683682. DOI: 10.1038/s41598-020-77220-w. View