{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T14:04:32Z","timestamp":1789999472960,"version":"4.0.1"},"reference-count":38,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Systemic inflammatory indices derived from routine complete blood counts have been proposed as inexpensive biomarkers in gynecological malignancies. Their ability to discriminate tumor grade in endometrial cancer, however, has rarely been evaluated against routinely available clinical variables under rigorous validation. Methods: We retrospectively analyzed 225 women with endometrial cancer (166 low-grade, G1\u2013G2; 59 high-grade, G3). Nine inflammatory indices were computed. Four feature sets were compared using a prespecified elastic-net logistic regression under repeated nested cross-validation (5 outer \u00d7 5 inner folds, 20 repetitions) with 5000 stratified bootstrap confidence intervals: a collinearity-reduced inflammatory panel, the full inflammatory panel, preoperative clinical variables (age, CA-125 and preoperative albumin), and their combination. Selection among nine classifiers was retained as an exploratory analysis. Because 66% of high-grade events were non-endometrioid, every analysis was repeated in three cohorts: the full cohort, endometrioid tumors only, and the subgroup with assessed hormone receptor status. Histologic subtype was deliberately excluded because serous, clear-cell and undifferentiated carcinomas are high-grade by definition. Model behavior was interpreted with TreeSHAP and cross-checked against permutation importance. Results: Of nine inflammatory indices, only the eosinophil-to-lymphocyte ratio remained statistically associated with grade after false discovery rate correction (AUC 0.626, 95% CI 0.545\u20130.707; q = 0.036), with all others showing negligible effect sizes (|r| &lt; 0.08); its discrimination was nonetheless modest and insufficient for individual-level prediction. The inflammatory panel achieved AUC 0.533 (95% CI 0.443\u20130.621), within the range attainable by chance in this design (permutation p = 0.254). Preoperative clinical variables reached AUC 0.687 (95% CI 0.590\u20130.772) in the full cohort but 0.390 (0.257\u20130.524) in endometrioid tumors alone and 0.627 (0.485\u20130.765) in the receptor-assessed subgroup. Adding inflammatory indices to clinical variables did not improve discrimination (\u0394AUC = \u22120.025, 95% CI \u22120.062 to +0.012; one-sided upper bound +0.007 against a prespecified margin of +0.05). The results were unchanged under an alternative grade dichotomization and after excluding patients with inconsistent blood-count entries. Conclusions: Most inflammatory indices did not demonstrate clinically useful discrimination between G3 and G1\u2013G2 disease in this cohort, and did not add incremental discriminative value beyond the prespecified margin over routinely available preoperative clinical variables. The apparent discrimination of the clinical variables in the full cohort did not survive restriction to endometrioid histology, indicating that it reflected histologic composition rather than grade. Decision curve analysis on recalibrated probabilities showed that no model exceeded the treat-all strategy at thresholds below the prevalence of high-grade disease, and that, above it, any advantage over treating no one was small in the full cohort and absent within endometrioid tumors.<\/jats:p>","DOI":"10.3390\/diagnostics16182987","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T13:33:42Z","timestamp":1789479222000},"page":"2987","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hematological Versus Clinical Predictors of Tumor Grade in Endometrial Cancer: A Comparative Machine Learning Study with SHAP-Based Explainability"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0978-3091","authenticated-orcid":false,"given":"Esra","family":"Akayd\u0131n G\u00fclt\u00fcrk","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Faculty of Medicine, Sivas Cumhuriyet University, 58140 Sivas, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9811-2726","authenticated-orcid":false,"given":"\u015eerife \u00d6zlem","family":"Gen\u00e7","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Faculty of Medicine, Sivas Cumhuriyet University, 58140 Sivas, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"ref_1","first-page":"7","article-title":"Cancer statistics, 2020","volume":"70","author":"Siegel","year":"2020","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e631","DOI":"10.1016\/j.ijrobp.2011.04.013","article-title":"Fifteen-year radiotherapy outcomes of the PORTEC-1 trial for endometrial carcinoma","volume":"81","author":"Creutzberg","year":"2011","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/S0140-6736(05)67063-8","article-title":"Endometrial cancer","volume":"366","author":"Amant","year":"2005","journal-title":"Lancet"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"106","DOI":"10.5152\/jtgga.2012.12","article-title":"Comparison of preoperative endometrial biopsy grade and final pathologic diagnosis in patients with endometrioid endometrial cancer","volume":"13","author":"Sencan","year":"2012","journal-title":"J. Turk. Ger. Gynecol. Assoc."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104630","DOI":"10.1016\/j.ijgc.2026.104630","article-title":"Clinical behavior of FIGO stage I endometrioid endometrial adenocarcinoma diagnosed as high grade on pre-operative biopsy and low grade on hysterectomy specimen","volume":"36","author":"Sia","year":"2026","journal-title":"Int. J. Gynecol. Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Leng, J., Wu, F., and Zhang, L. (2022). Prognostic significance of pretreatment neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, or monocyte-to-lymphocyte ratio in endometrial neoplasms: A systematic review and meta-analysis. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.734948"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1007\/s12672-024-00956-8","article-title":"The predictive value of NLR, PLR and MLR in the differential diagnosis of benign uterine diseases and endometrial malignant tumors","volume":"15","author":"Qin","year":"2024","journal-title":"Discov. Oncol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"345","DOI":"10.2217\/bmm-2023-0629","article-title":"Prognostic value of pretreatment systemic immune-inflammation index in patients with endometrial cancer: A meta-analysis","volume":"18","author":"Ji","year":"2024","journal-title":"Biomark. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e70170","DOI":"10.1111\/aji.70170","article-title":"Prognostic value of systemic immune-inflammation index in patients with gynecological tumors: A systematic review and meta-analysis","volume":"94","author":"Guo","year":"2025","journal-title":"Am. J. Reprod. Immunol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kirsch-Mangu, A.T., \u021a\u00eepcu, A., G\u00e2ta, V.A., Pop, D.C., Fekete, Z., Irimie, A., and Kubelac, P.M. (2025). Neutrophil to lymphocyte ratio a prognostic tool in endometrial cancer among classical prognostic factors. Diagnostics, 15.","DOI":"10.3390\/diagnostics15172172"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"64","DOI":"10.22514\/ejgo.2025.121","article-title":"Effects of neutrophil-to-lymphocyte ratio, tumor-infiltrating lymphocyte and neutrophil scores on prognosis in low-risk endometrial cancer","volume":"46","author":"Pulur","year":"2025","journal-title":"Eur. J. Gynaecol. Oncol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1007\/s13193-025-02228-5","article-title":"Additional risk factors lead to a measurable inflammatory response in stage I endometrial cancer\u2014A prospective multicentric observational study","volume":"16","author":"Ronsini","year":"2025","journal-title":"Indian J. Surg. Oncol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bhardwaj, V., Sharma, A., Parambath, S.V., Gul, I., Zhang, X., Lobie, P.E., Qin, P., and Pandey, V. (2022). Machine learning for endometrial cancer prediction and prognostication. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.852746"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1002\/jum.70082","article-title":"Development of a high-performance ultrasound prediction model for the diagnosis of endometrial cancer: An interpretable XGBoost algorithm utilizing SHAP","volume":"45","author":"Lai","year":"2026","journal-title":"J. Ultrasound Med."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yasar, S., Yagin, F.H., Melekoglu, R., and Ardig\u00f2, L.P. (2024). Integrating proteomics and explainable artificial intelligence: A comprehensive analysis of protein biomarkers for endometrial cancer diagnosis and prognosis. Front. Mol. Biosci., 11.","DOI":"10.3389\/fmolb.2024.1389325"},{"key":"ref_16","unstructured":"WHO Classification of Tumours Editorial Board (2020). Female Genital Tumours, International Agency for Research on Cancer. [5th ed.]. WHO Classification of Tumours Series."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1016\/S0140-6736(07)61602-X","article-title":"The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: Guidelines for reporting observational studies","volume":"370","author":"Altman","year":"2007","journal-title":"Lancet"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6212","DOI":"10.1158\/1078-0432.CCR-14-0442","article-title":"Systemic immune-inflammation index predicts prognosis of patients after curative resection for hepatocellular carcinoma","volume":"20","author":"Hu","year":"2014","journal-title":"Clin. Cancer Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1038\/s41416-020-0894-7","article-title":"The pan-immune-inflammation value is a new prognostic biomarker in metastatic colorectal cancer","volume":"123","author":"Guarini","year":"2020","journal-title":"Br. J. Cancer"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning, Springer. [2nd ed.].","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: A practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"837","DOI":"10.2307\/2531595","article-title":"Comparing the areas under two or more correlated receiver operating characteristic curves: A nonparametric approach","volume":"44","author":"DeLong","year":"1988","journal-title":"Biometrics"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., and Lemeshow, S. (2000). Applied Logistic Regression, Wiley. [2nd ed.].","DOI":"10.1002\/0471722146"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1214\/ss\/1032280304","article-title":"Bioequivalence trials, intersection-union tests and equivalence confidence sets","volume":"11","author":"Berger","year":"1996","journal-title":"Stat. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"van der Ploeg, T., Austin, P.C., and Steyerberg, E.W. (2014). Modern modelling techniques are data hungry: A simulation study for predicting dichotomous endpoints. BMC Med. Res. Methodol., 14.","DOI":"10.1186\/1471-2288-14-137"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2200302","DOI":"10.1002\/bimj.202200302","article-title":"Stability of clinical prediction models developed using statistical or machine learning methods","volume":"65","author":"Riley","year":"2023","journal-title":"Biom. J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Van Calster, B., McLernon, D.J., van Smeden, M., Wynants, L., and Steyerberg, E.W. (2019). Calibration: The Achilles heel of predictive analytics. BMC Med., 17.","DOI":"10.1186\/s12916-019-1466-7"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision curve analysis: A novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med. Decis. Mak."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The meaning and use of the area under a receiver operating characteristic (ROC) curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e423","DOI":"10.1016\/S1470-2045(25)00167-6","article-title":"ESGO\u2013ESTRO\u2013ESP guidelines for the management of patients with endometrial carcinoma: Update 2025","volume":"26","author":"Concin","year":"2025","journal-title":"Lancet Oncol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s41512-019-0064-7","article-title":"A simple, step-by-step guide to interpreting decision curve analysis","volume":"3","author":"Vickers","year":"2019","journal-title":"Diagn. Progn. Res."},{"key":"ref_33","first-page":"4765","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1002\/sim.7992","article-title":"Minimum sample size for developing a multivariable prediction model: PART II\u2014Binary and time-to-event outcomes","volume":"38","author":"Riley","year":"2019","journal-title":"Stat. Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"m441","DOI":"10.1136\/bmj.m441","article-title":"Calculating the sample size required for developing a clinical prediction model","volume":"368","author":"Riley","year":"2020","journal-title":"BMJ"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"111675","DOI":"10.1016\/j.jclinepi.2025.111675","article-title":"Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: Methodological systematic review","volume":"180","author":"Tsegaye","year":"2025","journal-title":"J. Clin. Epidemiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"112013","DOI":"10.1016\/j.jclinepi.2025.112013","article-title":"In humble defense of unexplainable black box prediction models in healthcare","volume":"189","author":"Weerts","year":"2026","journal-title":"J. Clin. Epidemiol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1643","DOI":"10.1016\/j.spinee.2021.02.024","article-title":"Decision curve analysis to evaluate the clinical benefit of prediction models","volume":"21","author":"Vickers","year":"2021","journal-title":"Spine J."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/18\/2987\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T13:18:03Z","timestamp":1789996683000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/18\/2987"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,15]]},"references-count":38,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["diagnostics16182987"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16182987","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,15]]}}}