{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T11:11:47Z","timestamp":1788174707816,"version":"build-2803163510"},"reference-count":58,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:00:00Z","timestamp":1759363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["PJT-173231"],"award-info":[{"award-number":["PJT-173231"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"crossref","award":["DH-2025-00119"],"award-info":[{"award-number":["DH-2025-00119"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: Cervical cancer remains a major global health concern, with high recurrence rates in advanced stages. [18F]FDG PET\/CT provides prognostic biomarkers such as SUV, MTV, and TLG, though these are not routinely integrated into clinical protocols. Radiomics offers quantitative analysis of tumor heterogeneity, supporting risk stratification. Purpose: To evaluate the prognostic value of clinical and radiomic features for disease-free survival (DFS) in locoregionally advanced cervical cancer using machine learning (ML). Methods: Sixty-three patients (mean age 47.9 \u00b1 14.5 years) were diagnosed between 2015 and 2020. Radiomic features were extracted from pre-treatment PET\/CT (IBSI-compliant PyRadiomics). Clinical variables included age, T-stage, Dmax, lymph node involvement, SUVmax, and TMTV. Forty-two models were built by combining six feature-selection techniques (UCI, MD, MI, VH, VH.VIMP, IBMA) with seven ML algorithms (CoxPH, CB, GLMN, GLMB, RSF, ST, EV) using nested 3-fold cross-validation with bootstrap resampling. External validation was performed on 95 patients (mean age 50.6 years, FIGO IIB\u2013IIIB) from an independent cohort with different preprocessing protocols. Results: Recurrence occurred in 31.7% (n = 20). SUVmax of lymph nodes, lymph node involvement, and TMTV were the most predictive individual features (C-index \u2264 0.77). The highest performance was achieved by UCI + EV\/GLMB on combined clinical + radiomic features (C-index = 0.80, p &lt; 0.05). For single feature sets, IBMA + RSF performed best for clinical (C-index = 0.72), and VH.VIMP + GLMN for radiomics (C-index = 0.71). External validation confirmed moderate generalizability (best C-index = 0.64). Conclusions: UCI-based feature selection with GLMB or EV yielded the best predictive accuracy, while VH.VIMP + GLMN offered superior external generalizability for radiomics-only models. These findings support the feasibility of integrating radiomics and ML for individualized DFS risk stratification in cervical cancer.<\/jats:p>","DOI":"10.3390\/cancers17193218","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T15:07:48Z","timestamp":1759417668000},"page":"3218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Pre-Treatment PET Radiomics for Prediction of Disease-Free Survival in Cervical Cancer"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5261-6163","authenticated-orcid":false,"given":"Fereshteh","family":"Yousefirizi","sequence":"first","affiliation":[{"name":"Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, BC V5Z 0B4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5359-2407","authenticated-orcid":false,"given":"Ghasem","family":"Hajianfar","sequence":"additional","affiliation":[{"name":"Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maziar","family":"Sabouri","sequence":"additional","affiliation":[{"name":"Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, BC V5Z 0B4, Canada"},{"name":"Department of Physics & Astronomy, University of British Columbia, Vancouver, BC V6T 1N4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caroline","family":"Holloway","sequence":"additional","affiliation":[{"name":"BC Cancer\u2014Victoria, Victoria, BC V8R 6V5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2712-7189","authenticated-orcid":false,"given":"Pete","family":"Tonseth","sequence":"additional","affiliation":[{"name":"BC Cancer\u2014Victoria, Victoria, BC V8R 6V5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abraham","family":"Alexander","sequence":"additional","affiliation":[{"name":"BC Cancer\u2014Victoria, Victoria, BC V8R 6V5, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tahir I.","family":"Yusufaly","sequence":"additional","affiliation":[{"name":"Russell H. Morgan Department of Radiology and Radiological Sciences, School of Medicine, Johns Hopkins University, Baltimore, MD 21287, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Loren K.","family":"Mell","sequence":"additional","affiliation":[{"name":"Center for Precision Radiation Medicine, La Jolla, CA 92037, USA"},{"name":"Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6196-6982","authenticated-orcid":false,"given":"Sara","family":"Harsini","sequence":"additional","affiliation":[{"name":"Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, BC V5Z 0B4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7995-3581","authenticated-orcid":false,"given":"Fran\u00e7ois","family":"B\u00e9nard","sequence":"additional","affiliation":[{"name":"BC Cancer, Vancouver, BC V5Z 4E6, Canada"},{"name":"Department of Radiology, University of British Columbia, Vancouver, BC V5Z 1M9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7559-5297","authenticated-orcid":false,"given":"Habib","family":"Zaidi","sequence":"additional","affiliation":[{"name":"Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos","family":"Uribe","sequence":"additional","affiliation":[{"name":"BC Cancer, Vancouver, BC V5Z 4E6, Canada"},{"name":"Department of Radiology, University of British Columbia, Vancouver, BC V5Z 1M9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9980-2403","authenticated-orcid":false,"given":"Arman","family":"Rahmim","sequence":"additional","affiliation":[{"name":"Department of Basic and Translational Research, BC Cancer Research Institute, Vancouver, BC V5Z 0B4, Canada"},{"name":"Department of Physics & Astronomy, University of British Columbia, Vancouver, BC V6T 1N4, Canada"},{"name":"Department of Radiology, University of British Columbia, Vancouver, BC V5Z 1M9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"E359","DOI":"10.1002\/ijc.29210","article-title":"Cancer Incidence and Mortality Worldwide: Sources, Methods and Major Patterns in GLOBOCAN 2012","volume":"136","author":"Ferlay","year":"2015","journal-title":"Int. J. Cancer"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1002\/ijc.1440","article-title":"Estimating the World Cancer Burden: Globocan 2000","volume":"94","author":"Parkin","year":"2001","journal-title":"Int. J. Cancer"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1007\/s00428-018-2362-9","article-title":"The European Society of Gynaecological Oncology\/European Society for Radiotherapy and Oncology\/European Society of Pathology Guidelines for the Management of Patients with Cervical Cancer","volume":"472","author":"Cibula","year":"2018","journal-title":"Virchows Arch."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2067","DOI":"10.1007\/s00259-024-06689-8","article-title":"\u201cMetabolic fingerprints\u201d of cachexia in lung cancer patients","volume":"51","author":"Frille","year":"2024","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"995","DOI":"10.2967\/jnumed.123.267183","article-title":"Is automatic tumor segmentation on whole-body 18F-FDG PET images a clinical reality?","volume":"65","author":"Sundar","year":"2024","journal-title":"J. Nucl. Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yousefirizi, F., Gowdy, C., Klyuzhin, I.S., Sabouri, M., Tonseth, P., Hayden, A.R., Wilson, D., Sehn, L.H., Scott, D.W., and Steidl, C. (2024). Evaluating Outcome Prediction via Baseline, End-of-Treatment, and Delta Radiomics on PET-CT Images of Primary Mediastinal Large B-Cell Lymphoma. Cancers, 16.","DOI":"10.3390\/cancers16061090"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.cpet.2021.09.010","article-title":"AI-based detection, classification and prediction\/prognosis in medical imaging: Towards radiophenomics","volume":"17","author":"Yousefirizi","year":"2022","journal-title":"PET Clin."},{"key":"ref_8","first-page":"73","article-title":"Pattern of Failure with Locally Advanced Cervical Cancer\u2013 A Retrospective Audit and Analysis of Contributory Factors. Asian Pac","volume":"19","author":"Bandyopadhyay","year":"2018","journal-title":"J. Cancer Prev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e7804","DOI":"10.7717\/peerj.7804","article-title":"Correlation between Subsets of Tumor-Infiltrating Immune Cells and Risk Stratification in Patients with Cervical Cancer","volume":"7","author":"Chen","year":"2019","journal-title":"PeerJ"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1053\/j.semnuclmed.2019.06.007","article-title":"FDG-PET Imaging in Cervical Cancer","volume":"49","author":"Gandy","year":"2019","journal-title":"Semin. Nucl. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-Based Phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"20190948","DOI":"10.1259\/bjr.20190948","article-title":"Radiomics: From Qualitative to Quantitative Imaging","volume":"93","author":"Rogers","year":"2020","journal-title":"Br. J. Radiol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s00259-012-2247-0","article-title":"Quantifying Tumour Heterogeneity in 18F-FDG PET\/CT Imaging by Texture Analysis","volume":"40","author":"Chicklore","year":"2013","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"38","DOI":"10.2967\/jnumed.114.144055","article-title":"18F-FDG PET Uptake Characterization through Texture Analysis: Investigating the Complementary Nature of Heterogeneity and Functional Tumor Volume in a Multi-Cancer Site Patient Cohort","volume":"56","author":"Hatt","year":"2015","journal-title":"J. Nucl. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.ejmp.2017.10.009","article-title":"Investigating Multi-Radiomic Models for Enhancing Prediction Power of Cervical Cancer Treatment Outcomes","volume":"46","author":"Altazi","year":"2018","journal-title":"Phys. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"6741","DOI":"10.1007\/s00330-019-06265-x","article-title":"Prediction of Local Relapse and Distant Metastasis in Patients with Definitive Chemoradiotherapy-Treated Cervical Cancer by Deep Learning from [18F]-Fluorodeoxyglucose Positron Emission Tomography\/Computed Tomography","volume":"29","author":"Shen","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7680","DOI":"10.21037\/qims-23-163","article-title":"Tensor Radiomics: Paradigm for Systematic Incorporation of Multi-Flavoured Radiomics Features","volume":"13","author":"Rahmim","year":"2023","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4111","DOI":"10.1007\/s00259-024-06805-8","article-title":"The Role of Biomarkers and Dosimetry Parameters in Overall and Progression Free Survival Prediction for Patients Treated with Personalized 90Y Glass Microspheres SIRT: A Preliminary Machine Learning Study","volume":"51","author":"Mansouri","year":"2024","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1007\/s11547-023-01725-3","article-title":"Time-to-Event Overall Survival Prediction in Glioblastoma Multiforme Patients Using Magnetic Resonance Imaging Radiomics","volume":"128","author":"Hajianfar","year":"2023","journal-title":"Radiol. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1097\/IGC.0000000000000616","article-title":"Predictive Value of Standardized Intratumoral Metabolic Heterogeneity in Locally Advanced Cervical Cancer Treated with Chemoradiation","volume":"26","author":"Yang","year":"2016","journal-title":"Int. J. Gynecol. Cancer"},{"key":"ref_21","first-page":"166","article-title":"A Preliminary Investigation into Textural Features of Intratumoral Metabolic Heterogeneity in (18)F-FDG PET for Overall Survival Prognosis in Patients with Bulky Cervical Cancer Treated with Definitive Concurrent Chemoradiotherapy","volume":"6","author":"Ho","year":"2016","journal-title":"Am. J. Nucl. Med. Mol. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e190218","DOI":"10.1148\/ryai.2020190218","article-title":"18F-FDG PET\/CT Habitat Radiomics Predicts Outcome of Patients with Cervical Cancer Treated with Chemoradiotherapy","volume":"2","author":"Mu","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3432","DOI":"10.1007\/s00259-021-05303-5","article-title":"[18F]FDG PET Radiomics to Predict Disease-Free Survival in Cervical Cancer: A Multi-Scanner\/Center Study with External Validation","volume":"48","author":"Ferreira","year":"2021","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"de Alencar, N.R.G., Machado, M.A.D., Mourato, F.A., de Oliveira, M.L., Moraes, T.F., Mattos Junior, L.A.R., Chang, T.-M.C., de Azevedo, C.R.A.S., and Brand\u00e3o, S.C.S. (2022). Exploratory Analysis of Radiomic as Prognostic Biomarkers in 18F-FDG PET\/CT Scan in Uterine Cervical Cancer. Front. Med., 9.","DOI":"10.3389\/fmed.2022.1046551"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s00259-016-3427-0","article-title":"Characterization of PET\/CT Images Using Texture Analysis: The Past, the Present\u2026 Any Future?","volume":"44","author":"Hatt","year":"2017","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"43169","DOI":"10.18632\/oncotarget.17856","article-title":"Prediction of Cervical Cancer Recurrence Using Textural Features Extracted from 18F-FDG PET Images Acquired with Different Scanners","volume":"8","author":"Orlhac","year":"2017","journal-title":"Oncotarget"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"962","DOI":"10.2967\/jnumed.123.267044","article-title":"Is PET Radiomics Useful to Predict Pathologic Tumor Response and Prognosis in Locally Advanced Cervical Cancer?","volume":"65","author":"Collarino","year":"2024","journal-title":"J. Nucl. Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1102\/1470-7330.2008.0022","article-title":"The Prognostic Value of PET and PET\/CT in Cervical Cancer","volume":"8","author":"Grigsby","year":"2008","journal-title":"Cancer Imaging"},{"key":"ref_29","unstructured":"Yousefirizi, F., Bloise, I., Martineau, P., Wilson, D., Benard, F., Bradshaw, T.B., Arman, R., and Uribe, C. (2021). Reproducibility of a Semi-Automatic Gradient-Based Segmentation Approach for Lymphoma PET. Proceedings of the EANM Abstract Book, a Supplement of the European Journal of Nuclear Medicine and Molecular Imaging (EJNMMI), Springer Science & Business Media."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"707","DOI":"10.2967\/jnumed.120.247999","article-title":"Repeatability of 18F-FDG PET Radiomic Features in Cervical Cancer","volume":"62","author":"Crandall","year":"2021","journal-title":"J. Nucl. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1016\/j.cpet.2021.06.007","article-title":"Radiomics in PET Imaging: A Practical Guide for Newcomers","volume":"16","author":"Orlhac","year":"2021","journal-title":"PET Clin."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational Radiomics System to Decode the Radiographic Phenotype","volume":"77","author":"Fedorov","year":"2017","journal-title":"Cancer Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1093\/bioinformatics\/btt383","article-title":"MRMRe: An R Package for Parallelized MRMR Ensemble Feature Selection","volume":"29","author":"Olsen","year":"2013","journal-title":"Bioinformatics"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1002\/sam.10103","article-title":"Random Survival Forests for High-Dimensional Data","volume":"4","author":"Ishwaran","year":"2011","journal-title":"Stat. Anal. Data Min."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Annest, A., Bumgarner, R.E., Raftery, A.E., and Yeung, K.Y. (2009). Iterative Bayesian Model Averaging: A Method for the Application of Survival Analysis to High-Dimensional Microarray Data. BMC Bioinform., 10.","DOI":"10.1186\/1471-2105-10-72"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1198\/jasa.2009.tm08622","article-title":"High-Dimensional Variable Selection for Survival Data","volume":"105","author":"Ishwaran","year":"2010","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_37","unstructured":"Fox, J., and Weisberg, S. (2002). Cox Proportional-Hazards Regression for Survival Data. An R and S-PLUS Companion to Applied Regression, Sage."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1093\/bioinformatics\/btp088","article-title":"Boosting for High-Dimensional Time-to-Event Data with Competing Risks","volume":"25","author":"Binder","year":"2009","journal-title":"Bioinformatics"},{"key":"ref_39","unstructured":"Hastie, T., and Qian, J. (2016, July 25). Glmnet Vignette. Available online: https:\/\/hastie.su.domains\/Papers\/Glmnet_Vignette.pdf."},{"key":"ref_40","first-page":"841","article-title":"Random Survival Forests","volume":"2","author":"Ishwaran","year":"2008","journal-title":"Aoas"},{"key":"ref_41","first-page":"2109","article-title":"Model-Based Boosting 2.0","volume":"11","author":"Hothorn","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1002\/widm.8","article-title":"Classification and Regression Trees: Classification and Regression Trees","volume":"1","author":"Loh","year":"2011","journal-title":"Wiley Interdiscip. Rev. Data Min. Knowl. Discov."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.2967\/jnumed.121.262618","article-title":"Improved Prognosis of Treatment Failure in Cervical Cancer with Nontumor PET\/CT Radiomics","volume":"63","author":"Yusufaly","year":"2022","journal-title":"J. Nucl. Med."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Li, H., Zhu, M., Jian, L., Bi, F., Zhang, X., Fang, C., Wang, Y., Wang, J., Wu, N., and Yu, X. (2021). Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients with Cervical Cancer. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.706043"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Tian, X., Sun, C., Liu, Z., Li, W., Duan, H., Wang, L., Fan, H., Li, M., Li, P., and Wang, L. (2020). Prediction of Response to Preoperative Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer Using Multicenter CT-Based Radiomic Analysis. Front. Oncol., 10.","DOI":"10.3389\/fonc.2020.00077"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liu, H., Cui, Y., Chang, C., Zhou, Z., Zhang, Y., Ma, C., Yin, Y., and Wang, R. (2024). Development and Validation of a 18F-FDG PET\/CT Radiomics Nomogram for Predicting Progression Free Survival in Locally Advanced Cervical Cancer: A Retrospective Multicenter Study. BMC Cancer, 24.","DOI":"10.1186\/s12885-024-11917-3"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"249","DOI":"10.3233\/CBM-210201","article-title":"Song Radiomics Model of 18F-FDG PET\/CT Imaging for Predicting Disease-Free Survival of Early-Stage Uterine Cervical Squamous Cancer","volume":"33","author":"Liu","year":"2022","journal-title":"Cancer Biomark."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Han, L., Wang, Q., Zhao, L., Feng, X., Wang, Y., Zou, Y., and Li, Q. (2021). A Systematic Review and Meta-Analysis of the Prognostic Impact of Pretreatment Fluorodeoxyglucose Positron Emission Tomography\/Computed Tomography Parameters. Diagnostics, 11.","DOI":"10.3390\/diagnostics11071258"},{"key":"ref_49","first-page":"17","article-title":"Prognostic Significance of Metabolic Tumor Volume and Total Lesion Glycolysis in Patients with Advanced Cervical Carcinoma","volume":"38","author":"M","year":"2019","journal-title":"Rev. Esp. Med. Nucl. Imagen Mol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, S.-W., Shen, W.-C., Hsieh, T.-C., Liang, J.-A., Hung, Y.-C., Yeh, L.-S., Chang, W.-C., Lin, W.-C., Yen, K.-Y., and Kao, C.-H. (2018). Textural Features of Cervical Cancers on FDG-PET\/CT Associate with Survival and Local Relapse in Patients Treated with Definitive Chemoradiotherapy. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-30336-6"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2540","DOI":"10.1007\/s00330-021-08326-6","article-title":"Multiparametric Magnetic Resonance Imaging-Derived Radiomics for the Prediction of Disease-Free Survival in Early-Stage Squamous Cervical Cancer","volume":"32","author":"Zhou","year":"2022","journal-title":"Eur. Radiol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.ygyno.2016.08.317","article-title":"Is Age a Prognostic Biomarker for Survival among Women with Locally Advanced Cervical Cancer Treated with Chemoradiation? An NRG Oncology\/Gynecologic Oncology Group Ancillary Data Analysis","volume":"143","author":"Moore","year":"2016","journal-title":"Gynecol. Oncol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s13244-023-01572-w","article-title":"METhodological RadiomICs Score (METRICS): A Quality Scoring Tool for Radiomics Research Endorsed by EuSoMII","volume":"15","author":"Kocak","year":"2024","journal-title":"Insights Imaging"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e220232","DOI":"10.1148\/ryai.220232","article-title":"A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging","volume":"5","author":"Bradshaw","year":"2023","journal-title":"Radiol. Artif. Intell."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.7150\/thno.37429","article-title":"Association of MRI-Derived Radiomic Biomarker with Disease-Free Survival in Patients with Early-Stage Cervical Cancer","volume":"10","author":"Fang","year":"2020","journal-title":"Theranostics"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1007\/s00259-023-06180-w","article-title":"Multicentric Development and Evaluation of 18F-FDG PET\/CT and MRI Radiomics Models to Predict Para-Aortic Lymph Node Involvement in Locally Advanced Cervical Cancer","volume":"50","author":"Lucia","year":"2023","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1007\/s00259-017-3898-7","article-title":"Prediction of Outcome Using Pretreatment 18F-FDG PET\/CT and MRI Radiomics in Locally Advanced Cervical Cancer Treated with Chemoradiotherapy","volume":"45","author":"Lucia","year":"2018","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1007\/s00259-018-4231-9","article-title":"External Validation of a Combined PET and MRI Radiomics Model for Prediction of Recurrence in Cervical Cancer Patients Treated with Chemoradiotherapy","volume":"46","author":"Lucia","year":"2019","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/19\/3218\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T04:27:47Z","timestamp":1759552067000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/17\/19\/3218"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,2]]},"references-count":58,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["cancers17193218"],"URL":"https:\/\/doi.org\/10.3390\/cancers17193218","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,2]]}}}