A distributed feature selection pipeline for survival analysis using radiomics in non-small cell lung cancer patients.
Distributed learning
Feature selection
NSCLC
Radiomics
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
03 Apr 2024
03 Apr 2024
Historique:
received:
12
12
2023
accepted:
27
03
2024
medline:
4
4
2024
pubmed:
4
4
2024
entrez:
3
4
2024
Statut:
epublish
Résumé
Predictive modelling of cancer outcomes using radiomics faces dimensionality problems and data limitations, as radiomics features often number in the hundreds, and multi-institutional data sharing is ()often unfeasible. Federated learning (FL) and feature selection (FS) techniques combined can help overcome these issues, as one provides the means of training models without exchanging sensitive data, while the other identifies the most informative features, reduces overfitting, and improves model interpretability. Our proposed FS pipeline based on FL principles targets data-driven radiomics FS in a multivariate survival study of non-small cell lung cancer patients. The pipeline was run across datasets from three institutions without patient-level data exchange. It includes two FS techniques, Correlation-based Feature Selection and LASSO regularization, and Cox Proportional-Hazard regression with Overall Survival as endpoint. Trained and validated on 828 patients overall, our pipeline yielded a radiomic signature comprising "intensity-based energy" and "mean discretised intensity". Validation resulted in a mean Harrell C-index of 0.59, showcasing fair efficacy in risk stratification. In conclusion, we suggest a distributed radiomics approach that incorporates preliminary feature selection to systematically decrease the feature set based on data-driven considerations. This aims to address dimensionality challenges beyond those associated with data constraints and interpretability concerns.
Identifiants
pubmed: 38570606
doi: 10.1038/s41598-024-58241-1
pii: 10.1038/s41598-024-58241-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
7814Subventions
Organisme : Dutch Research Council
ID : TRAIN (629.002.212)
Pays : Netherlands
Informations de copyright
© 2024. The Author(s).
Références
Kumar, V. et al. Radiomics: The process and the challenges. Magn. Reson. Imaging 30, 1234–1248 (2012).
pubmed: 22898692
pmcid: 3563280
doi: 10.1016/j.mri.2012.06.010
Chen, M., Copley, S. J., Viola, P., Lu, H. & Aboagye, E. O. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin. Cancer Biol. 93, 97–113 (2023).
pubmed: 37211292
doi: 10.1016/j.semcancer.2023.05.004
Lu, L. et al. Radiomics prediction of EGFR status in lung cancer: Our experience in using multiple feature extractors and the cancer imaging archive data. Tomography 6, 223–230 (2020).
pubmed: 32548300
pmcid: 7289249
doi: 10.18383/j.tom.2020.00017
Francesco, E. et al. PET radiomics and response to immunotherapy in lung cancer: A systematic review of the literature. Cancers 15, 3258 (2023).
doi: 10.3390/cancers15123258
Wu, X., Kong, N., Xu, M., Gao, C. & Lou, L. Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review. Eur. Radiol. 33, 2105–2117 (2022).
pubmed: 36307554
pmcid: 9935659
doi: 10.1007/s00330-022-09174-8
Maniar, A. Z. et al. Novel biomarkers in NSCLC: Radiomic analysis, kinetic analysis, and circulating tumor DNA. Semin. Oncol. 49, 298–305 (2022).
doi: 10.1053/j.seminoncol.2022.06.002
Martina, K. et al. Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer. Eur. J. Nucl. Med. Mol. Imaging 48, 3643–3655 (2021).
doi: 10.1007/s00259-021-05371-7
Wojciech, B., Paweł, B. & Joanna, P. Radiomics and artificial intelligence in lung cancer screening. Transl. Lung Cancer Res. 10, 1186–1199 (2021).
doi: 10.21037/tlcr-20-708
Akinci D’Antonoli, T. et al. CT radiomics signature of tumor and peritumoral lung parenchyma to predict nonsmall cell lung cancer postsurgical recurrence risk. Acad. Radiol. 27, 497–507 (2020).
pubmed: 31285150
doi: 10.1016/j.acra.2019.05.019
Rita, F. A. et al. Exploring technical issues in personalized medicine: NSCLC survival prediction by quantitative image analysis: Usefulness of density correction of volumetric CT data. Radiol. Med. 125, 625–635 (2020).
doi: 10.1007/s11547-020-01157-3
Zhou, L., Pan, S., Wang, J. & Vasilakos, A. V. Machine learning on big data: Opportunities and challenges. Neurocomputing 237, 350–361 (2017).
doi: 10.1016/j.neucom.2017.01.026
Kadir, S. N., Goodman, D. F. M. & Harris, K. D. High-dimensional cluster analysis with the masked EM algorithm. Neural Comput. 26, 2379–2394 (2014).
pubmed: 25149694
pmcid: 4298163
doi: 10.1162/NECO_a_00661
Wu, Y. et al. Robust feature selection method of radiomics for grading glioma. in Proceedings of the 2nd International Conference on Healthcare Science and Engineering (2018).
Ge, G. & Zhang, J. Feature selection methods and predictive models in CT lung cancer radiomics. J. Appl. Clin. Med. Phys. 24, 13869 (2023).
doi: 10.1002/acm2.13869
Rong, D. & Gao, X.-Z. Feature selection and its use in big data: Challenges, methods, and trends. IEEE Access 7, 19709–19725 (2019).
doi: 10.1109/ACCESS.2019.2894366
Sugai, Y. et al. Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients. Radiat. Oncol. 16, 80 (2021).
pubmed: 33931085
pmcid: 8086112
doi: 10.1186/s13014-021-01810-9
Schaefer, M., Schepers, J., Prasser, F. & Thun, S. The use of machine learning in rare diseases: A scoping review. Orphanet. J. Rare Dis. 15, 1–10 (2020).
doi: 10.1186/s13023-020-01424-6
Chowdhury, A., Kassem, H., Padoy, N., Umeton, R. & Karargyris, A. A review of medical federated learning: Applications in oncology and cancer research. Brainlesion 1, 3–24 (2022).
Castillo, T. J. M. et al. A multi-center, multi-vendor study to evaluate the generalizability of a radiomics model for classifying prostate cancer: High grade vs low grade. Diagnostics 11, 369 (2021).
doi: 10.3390/diagnostics11020369
Damiani, A. et al. Distributed learning to protect privacy in multi-centric clinical studies. In Artificial Intelligence in Medicine (eds Holmes, J. H. et al.) 65–75 (Springer, 2015).
doi: 10.1007/978-3-319-19551-3_8
European Commission. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA Relevance). https://eur-lex.europa.eu/eli/reg/2016/679/oj (2016).
Tuladhar, D. & Forkert, N. R. Distributed learning in healthcare. Trends Artif. Intell. Big Data E-Health 1, 183–212 (2022).
doi: 10.1007/978-3-031-11199-0_10
Xu, B. S., Su, C., Walker, P. B., Bian, J.-G. & Wang, F. Federated learning for healthcare informatics. J. Healthc. Inform. Res. 5, 1–19 (2020).
pubmed: 33204939
pmcid: 7659898
doi: 10.1007/s41666-020-00082-4
Choudhury, A. et al. Predicting outcomes in anal cancer patients using multi-centre data and distributed learning: A proof-of-concept study. Radiother. Oncol. 159, 183–189 (2021).
pubmed: 33753156
doi: 10.1016/j.radonc.2021.03.013
Lu, S. et al. WebDISCO: A web service for distributed cox model learning without patient-level data sharing. J. Am. Med. Inform. Assoc. 22, 1212–1219 (2015).
pubmed: 26159465
pmcid: 5009917
doi: 10.1093/jamia/ocv083
Gouthamchand, V. et al. FAIR-ification of structured head and neck cancer clinical data for multi-institutional collaboration and federated learning. J. Am. Med. Inform. Assoc. https://doi.org/10.21203/rs.3.rs-2705743/v1 (2023).
doi: 10.21203/rs.3.rs-2705743/v1
Deist, T. M. et al. Distributed learning on 20000+ lung cancer patients: The personal health train. Radiother. Oncol. 144, 189–200 (2020).
pubmed: 31911366
doi: 10.1016/j.radonc.2019.11.019
Shi, Z. et al. Distributed radiomics as a signature validation study using the personal health train infrastructure. Sci. Data 6, 241 (2019).
doi: 10.1038/s41597-019-0241-0
Wang, L. et al. A prognostic model of non-small cell lung cancer with a radiomics nomogram in an eastern Chinese population. Front. Oncol. 12, 766 (2022).
Mak, K. S. et al. Defining a standard set of patient-centred outcomes for lung cancer. European Respiratory Journal 48, 852–860 (2016).
pubmed: 27390281
doi: 10.1183/13993003.02049-2015
Hall, M. A. Correlation-Based Feature Selection for Machine Learning (The University of Waikato, 1999).
Muthukrishnan, R. & Rohini, R. LASSO: A feature selection technique in predictive modeling for machine learning. in 2016 IEEE International Conference on Advances in Computer Applications (ICACA), 18–20 (2016). https://doi.org/10.1109/ICACA.2016.7887916 .
Bogowicz, M. et al. Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer. Sci. Rep. 10, 1297 (2020).
doi: 10.1038/s41598-020-61297-4
Zhang, W., Guo, Y. & Jin, Q. Radiomics and its feature selection: A review. Symmetry 15, 1834 (2023).
doi: 10.3390/sym15101834
Asad, M. et al. Limitations and future aspects of communication costs in federated learning: A survey. Sensors 23, 7358 (2023).
pubmed: 37687814
pmcid: 10490700
doi: 10.3390/s23177358
Welch, M. L. et al. Vulnerabilities of radiomic signature development: The need for safeguards. Radiother. Oncol. 130, 2–9 (2019).
pubmed: 30416044
doi: 10.1016/j.radonc.2018.10.027
Nazari, M., Shiri, I. & Zaidi, H. Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients. Comput. Biol. Med. 129, 104135 (2021).
pubmed: 33254045
doi: 10.1016/j.compbiomed.2020.104135
Ibrahim, A. et al. The effects of in-plane spatial resolution on CT-based radiomic features’ stability with and without combat harmonization. Cancers 13, 1848 (2021).
pubmed: 33924382
pmcid: 8103509
doi: 10.3390/cancers13081848
Royston, P. Tools for checking calibration of a Cox model in external validation: Approach based on individual event probabilities. Stat. J. 14, 738–755 (2014).
doi: 10.1177/1536867X1401400403
Grambsch, P. M. & Therneau, T. M. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 81, 515–526 (1994).
doi: 10.1093/biomet/81.3.515
Froelicher, D. et al. Truly privacy-preserving federated analytics for precision medicine with multiparty homomorphic encryption. Nat. Commun. 12, 5910 (2021).
pubmed: 34635645
pmcid: 8505638
doi: 10.1038/s41467-021-25972-y
Aerts, H. J. W. L. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014).
pubmed: 24892406
doi: 10.1038/ncomms5006
van Griethuysen, A. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77, e104–e107 (2017).
pubmed: 29092951
pmcid: 5672828
doi: 10.1158/0008-5472.CAN-17-0339
Zwanenburg, A. et al. The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295, 328–338 (2020).
pubmed: 32154773
doi: 10.1148/radiol.2020191145
Moncada-Torres, A., Martin, F., Sieswerda, M., Van Soest, J. & Geleijnse, G. VANTAGE6: An open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange. AMIA Annu. Symp. Proc. 2020, 870–877 (2020).
pubmed: 33936462
Damiani, C. et al. Building an artificial intelligence laboratory based on real world data: The experience of gemelli generator. Front. Comput. Sci. 3, 768266 (2021).
doi: 10.3389/fcomp.2021.768266
Simon, N., Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for Cox’s proportional hazards model via coordinate descent. J. Stat. Softw. 39, 1–13 (2011).
pubmed: 27065756
pmcid: 4824408
doi: 10.18637/jss.v039.i05
Masciocchi, C. et al. Federated Cox Proportional Hazards Model with multicentric privacy-preserving LASSO feature selection for survival analysis from the perspective of personalized medicine. in 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS) (IEEE, 2022).
Uno, T., Pencina, M. J., D’Agostino, R. B. & Wei, L.-J. On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Stat. Med. 30, 1105–1117 (2011).
pubmed: 21484848
pmcid: 3079915
doi: 10.1002/sim.4154
Collins, G. S., Reitsma, J. B., Altman, D. G. & Moons, K. G. M. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. BMJ 350, 7594 (2015).
doi: 10.1136/bmj.g7594