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Radiomics and Machine Learning in Medical Imaging / Endometrial and Cervical Cancer Treatments · Journal article
BMC Cancer · August 18, 2026
Encouraging direction, but not yet definitive.
This retrospective study developed and internally validated a clinico-radiomic nomogram for preoperative prediction of adjuvant therapy eligibility in early-stage cervical cancer, achieving good discrimination (AUC 0.865 in validation). While the integrated model showed statistically significant incremental discrimination improvement (IDI) and decision curve benefit over clinical variables alone, the absolute gain in AUC was modest and not statistically significant, limiting its practice-changing potential without external validation.
Retrospective cohort study with internal training-validation split. Patients with FIGO stage IB–IIA cervical cancer who underwent radical hysterectomy; setting not specified beyond retrospective cohort structure.. Intervention: Clinico-radiomic nomogram incorporating preoperative CT radiomic features and clinical variables. Compared with: Clinical model alone; radiomics model alone. n = 200.
Combined clinico-radiomic model achieved AUC 0.865 (95% CI 0.765–0.965) in validation cohort Incremental AUC gain over clinical model was 0.033 in validation cohort, not statistically significant (P = 0.253) Integrated discrimination improvement (IDI) favored combined model in validation cohort (IDI = 0.095, P = 0.019)
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The nomogram may support preoperative risk stratification for adjuvant therapy eligibility, but the modest incremental improvement over clinical judgment alone suggests it should be used as a decision-support tool rather than a replacement for clinical assessment. External validation in independent cohorts is needed before routine clinical adoption.
A well-designed retrospective validation study of a diagnostic nomogram with good discrimination (AUC 0.865 in validation), but modest incremental gain over clinical variables alone and modest sample size requiring external confirmation.
As stated by the source record.
Quoted from the source exactly as published.
The nomogram may support preoperative risk stratification for adjuvant therapy eligibility, but the modest incremental improvement over clinical judgment alone suggests it should be used as a decision-support tool rather than a replacement for clinical assessment. External validation in independent cohorts is needed before routine clinical adoption.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
To develop and validate an integrated clinico-radiomic nomogram for the preoperative prediction of guideline-defined indications for postoperative adjuvant therapy in patients with FIGO stage IB–IIA cervical cancer. A total of 200 patients who underwent radical hysterectomy were retrospectively included and divided into training ( n = 141) and validation ( n = 59) cohorts. Radiomic features were extracted from preoperative contrast-enhanced CT images and selected through a multistep process including reproducibility assessment, correlation analysis, and LASSO regression. Clinical variables were identified using univariable and multivariable analyses. Three models—a clinical model, a radiomics model, and a combined model—were developed using multivariable logistic regression. Model performance was evaluated in terms of discrimination, calibration, and clinical utility. The combined model achieved AUCs of 0.901 (95% CI, 0.851–0.952) in the training cohort and 0.865 (95% CI, 0.765–0.965) in the validation cohort. Although these AUCs were higher than those of the clinical model, the differences were not statistically significant in either cohort (training: ΔAUC = 0.024, P = 0.079; validation: ΔAUC = 0.033, P = 0.253). However, integrated discrimination improvement (IDI) favored the combined model in both the training cohort (IDI = 0.061, P = 0.002) and the validation cohort (IDI = 0.095, P = 0.019), while decision curve analysis suggested favorable net benefit across a broad range of evaluated threshold probabilities. The clinico-radiomic nomogram showed favorable overall predictive performance for preoperative estimation of guideline-defined indications for postoperative adjuvant therapy. However, the incremental improvement in AUC over the clinical model was modest and not statistically significant. The added value of radiomics may lie primarily in refining individual risk classification and decision support, as suggested by IDI and decision curve analysis, and requires confirmation in external cohorts.
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