ArticleQuantitative imaging in medicine and surgery2026
Contrast-enhanced ultrasound-based energy response phenotyping in thyroid nodule ablation: identifying heterogeneous subgroups via latent class analysis.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Thermal ablation of benign thyroid nodules shows substantial inter-patient heterogeneity, yet no pre-procedural tool exists to stratify treatment response. This study aims to identify energy response phenotypes in thyroid nodule thermal ablation using latent class analysis (LCA) and develop a pre-ablation predictive model. Methods: This retrospective study analyzed 157 patients undergoing thermal ablation for benign thyroid nodules. LCA integrated pre-ablation contrast-enhanced ultrasound (CEUS) parameters [peak intensity (PI), time to peak (TTP), area under the curve (AUC)], ablation energy, and volume reduction rates (VRRs) at 3, 6, and 12 months to identify distinct phenotypes. A multinomial logistic regression model using baseline CEUS and clinical variables predicted phenotype membership, validated in a held-out testing set (n=47, 30%). Results: Three energy response phenotypes were identified with adequate classification certainty (entropy =0.833): class 1 (delayed-response, n=56, 35.7%) exhibited the highest PI and prolonged TTP (both P<0.001) with gradual improvement (VRR 89.86% at 12 months); class 2 (energy-resistant, n=33, 21.0%) demonstrated reduced PI, rapid washout, required 7.6-fold higher energy (P<0.001), yet achieved poorest outcomes (12-month VRR 86.56%, P<0.001); class 3 (optimal-response, n=68, 43.3%) showed balanced perfusion achieving the highest VRR (97.65% at 12 months, P<0.001). Pre-ablation TTP emerged as the dominant predictor (100% relative importance). The discriminant model achieved good discrimination for class 1 (AUC =0.878) and class 2 (AUC =0.916), but limited discrimination for class 3 (AUC =0.726). Ten-fold cross-validation showed moderate stability (accuracy 59.73%±15.93%). Sensitivity analyses excluding energy from LCA confirmed phenotype stability (κ=0.82). Conclusions: CEUS-based phenotyping successfully stratifies thyroid nodule ablation candidates, enabling personalized treatment planning. Pre-ablation prediction identifies energy-resistant patients requiring alternative therapeutic strategies.
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