MRI radiomics in head and neck cancer from reproducibility to combined approaches.
Cluster analysis
Head and neck squamous cell carcinoma
Magnetic resonance imaging
Overall survival
Prognostic models
Radiomic features
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
24 Apr 2024
24 Apr 2024
Historique:
received:
30
12
2023
accepted:
17
04
2024
medline:
25
4
2024
pubmed:
25
4
2024
entrez:
24
4
2024
Statut:
epublish
Résumé
The clinical applicability of radiomics in oncology depends on its transferability to real-world settings. However, the absence of standardized radiomics pipelines combined with methodological variability and insufficient reporting may hamper the reproducibility of radiomic analyses, impeding its translation to clinics. This study aimed to identify and replicate published, reproducible radiomic signatures based on magnetic resonance imaging (MRI), for prognosis of overall survival in head and neck squamous cell carcinoma (HNSCC) patients. Seven signatures were identified and reproduced on 58 HNSCC patients from the DB2Decide Project. The analysis focused on: assessing the signatures' reproducibility and replicating them by addressing the insufficient reporting; evaluating their relationship and performances; and proposing a cluster-based approach to combine radiomic signatures, enhancing the prognostic performance. The analysis revealed key insights: (1) despite the signatures were based on different features, high correlations among signatures and features suggested consistency in the description of lesion properties; (2) although the uncertainties in reproducing the signatures, they exhibited a moderate prognostic capability on an external dataset; (3) clustering approaches improved prognostic performance compared to individual signatures. Thus, transparent methodology not only facilitates replication on external datasets but also advances the field, refining prognostic models for potential personalized medicine applications.
Identifiants
pubmed: 38658630
doi: 10.1038/s41598-024-60009-6
pii: 10.1038/s41598-024-60009-6
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
9451Subventions
Organisme : National Plan for NRRP Complementary Investments
ID : PNC0000003
Informations de copyright
© 2024. The Author(s).
Références
Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. Cancer J. Clin. 68, 394–424 (2018).
doi: 10.3322/caac.21492
pubmed: 30207593
Mody, M. D., Rocco, J. W., Yom, S. S., Haddad, R. I. & Saba, N. F. Head and neck cancer. Lancet 398, 2289–2299 (2021).
doi: 10.1016/S0140-6736(21)01550-6
pubmed: 34562395
Machiels, J.-P. et al. Squamous cell carcinoma of the oral cavity, larynx, oropharynx and hypopharynx: EHNS–ESMO–ESTRO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann. Oncol. 31, 1462–1475 (2020).
doi: 10.1016/j.annonc.2020.07.011
pubmed: 33239190
Zanoni, D. K., Patel, S. G. & Shah, J. P. Changes in the 8th Edition of the American Joint Committee on Cancer (AJCC) staging of head and neck cancer: Rationale and implications. Curr. Oncol. Rep. 21, 52 (2019).
doi: 10.1007/s11912-019-0799-x
pubmed: 30997577
pmcid: 6528815
Bera, K., Braman, N., Gupta, A., Velcheti, V. & Madabhushi, A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat. Rev. Clin. Oncol. 19, 132–146 (2022).
doi: 10.1038/s41571-021-00560-7
pubmed: 34663898
Bruixola, G. et al. Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges. Cancer Treat. Rev. 99, 102263 (2021).
doi: 10.1016/j.ctrv.2021.102263
pubmed: 34343892
Tanadini-Lang, S. et al. Radiomic biomarkers for head and neck squamous cell carcinoma. Strahlenther. Onkol. 196, 868–878 (2020).
doi: 10.1007/s00066-020-01638-4
pubmed: 32495038
Peng, Z. et al. Application of radiomics and machine learning in head and neck cancers. Int. J. Biol. Sci. 17, 475–486 (2021).
doi: 10.7150/ijbs.55716
pubmed: 33613106
pmcid: 7893590
Tortora, M. et al. Radiomics applications in head and neck tumor imaging: A narrative review. Cancers Basel. 15, 1174 (2023).
doi: 10.3390/cancers15041174
pubmed: 36831517
pmcid: 9954362
Pfaehler, E. et al. A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features. Phys. Imaging Radiat. Oncol. 20, 69–75 (2021).
doi: 10.1016/j.phro.2021.10.007
pubmed: 34816024
pmcid: 8591412
Traverso, A., Wee, L., Dekker, A. & Gillies, R. Repeatability and reproducibility of radiomic features: A systematic review. Int. J. Radiat. Oncol. Biol. Phys. 102, 1143–1158 (2018).
doi: 10.1016/j.ijrobp.2018.05.053
pubmed: 30170872
pmcid: 6690209
Zwanenburg, A. et al. The image biomarker standardization initiative: Standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295, 328–338 (2020).
doi: 10.1148/radiol.2020191145
pubmed: 32154773
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, g7594 (2015).
doi: 10.1136/bmj.g7594
pubmed: 25569120
Clark, K. et al. The Cancer Imaging Archive (TCIA): Maintaining and operating a public information repository. J. Digit. Imaging 26, 1045–1057 (2013).
doi: 10.1007/s10278-013-9622-7
pubmed: 23884657
pmcid: 3824915
Lambin, P. et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14, 749–762 (2017).
doi: 10.1038/nrclinonc.2017.141
pubmed: 28975929
Park, J. E. et al. Quality of science and reporting of radiomics in oncologic studies: Room for improvement according to radiomics quality score and TRIPOD statement. Eur. Radiol. 30, 523–536 (2020).
doi: 10.1007/s00330-019-06360-z
pubmed: 31350588
Cavalieri, S. et al. Development of a multiomics database for personalized prognostic forecasting in head and neck cancer: The Big Data to Decide EU Project. Head Neck 43, 601–612 (2021).
doi: 10.1002/hed.26515
pubmed: 33107152
Corti, A. et al. MRI-based radiomic prognostic signature for locally advanced oral cavity squamous cell carcinoma: Development, testing and comparison with genomic prognostic signatures. Biomark. Res. 11, 69 (2023).
doi: 10.1186/s40364-023-00494-5
pubmed: 37455307
pmcid: 10350277
Bologna, M. et al. Prognostic radiomic signature for head and neck cancer: Development and validation on a multi-centric MRI dataset. Radiother. Oncol. J. Eur. Soc. Ther. Radiol. Oncol. 183, 109638 (2023).
doi: 10.1016/j.radonc.2023.109638
Jung, F., Steger, S., Knapp, O., Noll, M. & Wesarg, S. COSMO—coupled shape model for radiation therapy planning of head and neck cancer. In Clinical Image-Based Procedures. Translational Research in Medical Imaging. CLIP 2014. Lecture Notes in Computer Science (ed. Linguraru, M. et al.) 25–32 (Springer, 2014).
Tustison, N. J., Cook, P. A. & Gee, J. C. N4ITK: Improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010).
doi: 10.1109/TMI.2010.2046908
pubmed: 20378467
pmcid: 3071855
Leijenaar, R. T. et al. Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: A multicenter study. Br. J. Radiol. 91, 20170498 (2018).
doi: 10.1259/bjr.20170498
pubmed: 29451412
pmcid: 6223271
Pyradiomics features description. Available online: https://pyradiomics.readthedocs.io/en/v3.1.0/features.html .
van Griethuysen, J. J. M. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77, e104–e107 (2017).
doi: 10.1158/0008-5472.CAN-17-0339
pubmed: 29092951
pmcid: 5672828
Kaplan, E. L. & Meier, P. Nonparametric estimation from incomplete samples. J. Am. Stat. Assoc. 73, 457–481 (1958).
doi: 10.1080/01621459.1958.10501452
Peto, R. & Peto, J. Asymptotically efficient rank invariant test procedures. J. R. Stat. Soc. 135, 185–207 (1972).
Harrell, F. E., Kerry, L. L. & Mark, D. B. Tutorial in biostatistics multivariable prognostic models: Issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 15, 361–387 (1996).
doi: 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4
pubmed: 8668867
Park, H.-S. & Jun, C.-H. A simple and fast algorithm for K-medoids clustering. Expert Syst. Appl. 36, 3336–3341 (2009).
doi: 10.1016/j.eswa.2008.01.039
Bos, P. et al. Improved outcome prediction of oropharyngeal cancer by combining clinical and MRI features in machine learning models. Eur. J. Radiol. 139, 109701 (2021).
doi: 10.1016/j.ejrad.2021.109701
pubmed: 33865064
Chen, J. et al. An MRI-based radiomics-clinical nomogram for the overall survival prediction in patients with hypopharyngeal squamous cell carcinoma: A multi-cohort study. Eur. Radiol. 32, 1548–1557 (2022).
doi: 10.1007/s00330-021-08292-z
pubmed: 34665315
Alfieri, S. et al. Prognostic role of pre-treatment magnetic resonance imaging (MRI)-based radiomic analysis in effectively cured head and neck squamous cell carcinoma (HNSCC) patients. Acta Oncol. 60, 1192–1200 (2021).
doi: 10.1080/0284186X.2021.1924401
pubmed: 34038324
Siow, T. Y. et al. MRI radiomics for predicting survival in patients with locally advanced hypopharyngeal cancer treated with concurrent chemoradiotherapy. Cancers Basel. 14, 6119 (2022).
doi: 10.3390/cancers14246119
pubmed: 36551604
pmcid: 9775984
Mossinelli, C. et al. The role of radiomics in tongue cancer: A new tool for prognosis prediction. Head Neck 45, 849–861 (2023).
doi: 10.1002/hed.27299
pubmed: 36779382
Lawrence, M. S. et al. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517, 576–582 (2015).
doi: 10.1038/nature14129
Tonella, L., Giannoccaro, M., Alfieri, S., Canevari, S. & De Cecco, L. Gene expression signatures for head and neck cancer patient stratification: Are results ready for clinical application?. Curr. Treat. Options Oncol. 18, 32 (2017).
doi: 10.1007/s11864-017-0472-2
pubmed: 28474265
Sun, F., Sun, J. & Zhao, Q. A deep learning method for predicting metabolite-disease associations via graph neural network. Brief. Bioinform. 23, 4 (2022).
doi: 10.1093/bib/bbac266
Liu, Z. et al. Radiogenomics: A key component of precision cancer medicine. Br. J. Cancer 129, 741–753 (2023).
doi: 10.1038/s41416-023-02317-8
pubmed: 37414827
Li, X. et al. Caspase-1 and gasdermin D afford the optimal targets with distinct switching strategies in NLRP1b inflammasome-induced cell death. Res. Washington, D. C. 2022, 9838341 (2022).
Philip, M. M., Welch, A., McKiddie, F. & Nath, M. A systematic review and meta-analysis of predictive and prognostic models for outcome prediction using positron emission tomography radiomics in head and neck squamous cell carcinoma patients. Cancer Med. 12, 16181–16194 (2023).
doi: 10.1002/cam4.6278
pubmed: 37353996
pmcid: 10469753
Li, L. et al. A meta-analysis of MRI-based radiomic features for predicting lymph node metastasis in patients with cervical cancer. Eur. J. Radiol. 151, 110243 (2022).
doi: 10.1016/j.ejrad.2022.110243
pubmed: 35366583
Spadarella, G. et al. Systematic review of the radiomics quality score applications: An EuSoMII Radiomics Auditing Group Initiative. Eur. Radiol. 33, 1884–1894 (2023).
doi: 10.1007/s00330-022-09187-3
pubmed: 36282312