Fusing Radiomics and Ultrasonic Features: A Superior Nomogram for Diagnosing Cervical Tuberculous Lymphadenitis

This study aims to develop and validate a radiomics-based nomogram derived from ultrasound (US) images for differentiating cervical tuberculous lymphadenitis (CTL) from non-CTL in the pre-biopsy setting.

A retrospective analysis was conducted on 287 patients with biopsy-confirmed cervical lymphadenopathy (116 CTL and 171 non-CTL), all of whom underwent conventional US examinations. The US images were randomly split into training and validation cohorts at a 7:3 ratio. Radiomics features were extracted from manually delineated regions of interest (ROIs) using PyRadiomics. Features with intra- and inter-observer ICC > 0.75 were retained. Significant features were selected via t-test and LASSO regression. A radiomics score (radscore) was calculated, and a nomogram was constructed by integrating the radscore with US features (necrosis, heterogeneous, and posterior enhancement). Diagnostic performance was evaluated using ROC curves, calibration curves, decision curve analysis (DCA), and the Hosmer-Lemeshow goodness-of-fit test.

The nomogram demonstrated discriminative performance, with AUCs of 0.798 (95% CI: 0.738-0.858) in the training cohort and 0.756 (95% CI: 0.654-0.858) in the validation cohort. Its AUC was higher than that of the clinical US model (AUC: 0.764 (95% CI: 0.700-0.829) and 0.744 (95% CI: 0.640-0.847), respectively) and the radiomics-only model (AUC: 0.693 (95% CI: 0.621-0.765) and 0.611 (95% CI: 0.492-0.730), respectively). The AUC difference between the nomogram and the clinical US model was not statistically significant, but calibration and DCA confirmed its favorable clinical applicability and goodness of fit.

The US-based radiomics nomogram may help differentiate CTL from non-CTL and serve as an adjunct to pre-biopsy assessment. This approach shows promise for improving the pre-biopsy diagnostic workup of tuberculous lymphadenitis, although formal subgroup validation in pathologically early-stage cases is warranted in future studies. Unlike "black-box" machine learning models, this nomogram provides a clear display of each predictor's contribution, which makes the model easier to interpret.