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Radiomics and Machine Learning in Medical Imaging / Breast Cancer Treatment Studies / MRI in Cancer Diagnosis · Journal article
Diagnostics · August 8, 2026
Early or partial results. Treat as a signal, not a conclusion.
This prospective cohort study evaluated whether multiparametric ultrasound radiomic features (quantitative ultrasound and shear wave elastography) acquired serially at baseline, week 1, and week 3 during neoadjuvant chemotherapy could predict pathologic response in 135 women with stage II–III breast cancer. A stacked machine learning ensemble achieved an internal cross-validated AUC of 0.93 (95% CI 0.88–0.97), substantially higher than a clinical-only model (AUC 0.71), but the finding remains unvalidated externally and requires confirmation before clinical deployment.
Prospective observational cohort study with machine learning model development. Women with biopsy-proven stage II–III breast cancer enrolled within the multidisciplinary breast pathway shared between Vasile Goldis Western University of Arad and Victor Babes University of Medicine and Pharmacy Timisoara (Pius Brinzeu County Emergency Hospital), Romania.. Intervention: Multi-parametric ultrasound imaging (quantitative ultrasound and shear wave elastography) acquisitions at baseline, week 1, and week 3 during neoadjuvant chemotherapy; radiomic feature extraction and machine learning ensemble model. Compared with: Clinical-only baseline model (performance on same cohort); pathologic response (residual cancer burden class 0/I vs II/III) as reference standard. n = 135. Two university medical centres in Romania: Vasile Goldis Western University of Arad and Victor Babes University of Medicine and Pharmacy Timisoara.
Responders showed greater week 3 increases in mid-band fit (3.4 ± 0.9 vs. 1.2 ± 0.8 dB, p < 0.001) compared to non-responders Responders showed greater week 3 increases in entropy (0.7 ± 0.2 vs. 0.2 ± 0.2, p < 0.001) Responders showed more pronounced SWE mean stiffness reduction (−44.1 ± 9.7 vs. −12.1 ± 8.6 kPa, p < 0.001)
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If externally validated, this multiparametric ultrasound radiomic approach could enable early identification of NAC non-responders by week 3, potentially allowing earlier treatment switching and avoiding unnecessary toxicity. However, internal cross-validation alone does not establish clinical readiness; prospective external validation in independent cohorts is essential before considering implementation.
Single-centre prospective cohort with internal cross-validation only, no external validation, using a complex machine learning ensemble on a surrogate radiomic endpoint (week 3 imaging changes) rather than a direct clinical outcome; promising technical result but requires external validation before clinical use.
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If externally validated, this multiparametric ultrasound radiomic approach could enable early identification of NAC non-responders by week 3, potentially allowing earlier treatment switching and avoiding unnecessary toxicity. However, internal cross-validation alone does not establish clinical readiness; prospective external validation in independent cohorts is essential before considering implementation.
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.
Background/Objectives: Early identification of breast cancer patients unlikely to benefit from neoadjuvant chemotherapy (NAC) remains a pressing clinical problem because ineffective therapy delays definitive surgery and exposes patients to unnecessary toxicity. Quantitative ultrasound (QUS) and shear wave elastography (SWE) probe complementary tissue properties—scatterer microstructure and mechanical stiffness—that may change before macroscopic tumor shrinkage. This study aimed to evaluate whether multi-parametric ultrasound (mpUS) radiomic kinetics, analyzed with a machine learning ensemble and interpreted with SHAP, could predict pathologic response to NAC. Methods: A prospective observational cohort enrolled 135 women with biopsy-proven stage II–III breast cancer treated with NAC within the multidisciplinary breast pathway shared between Vasile Goldis Western University of Arad and Victor Babes University of Medicine and Pharmacy Timisoara (Pius Brinzeu County Emergency Hospital). All patients underwent standardized QUS and SWE acquisitions at baseline, week 1, and week 3. Response was defined pathologically at surgery as residual cancer burden (RCB) class 0/I versus II/III. Group comparisons used Welch’s t-test, Mann–Whitney U, chi-square, and Fisher’s exact tests; correlations used Spearman’s rho. A stacked machine learning ensemble (four base learners—XGBoost, random forest, support vector machine, and L2-penalized logistic regression—combined by a separate second-stage logistic meta-learner) was trained with nested 10-fold cross-validation, bootstrap stability assessment, SHAP-based interpretability, and decision curve analysis. Results: Sixty patients (44.4%) were responders and 75 (55.6%) were non-responders. Responders showed greater week 3 increases in mid-band fit (3.4 ± 0.9 vs. 1.2 ± 0.8 dB, p < 0.001), entropy (0.7 ± 0.2 vs. 0.2 ± 0.2, p < 0.001), and more pronounced SWE mean stiffness reduction (−44.1 ± 9.7 vs. −12.1 ± 8.6 kPa, p < 0.001). The stacked ensemble integrating clinical, QUS, and SWE kinetic features reached an AUC of 0.93 (95% CI 0.88–0.97) versus 0.71 for the clinical-only model (all reported performance figures represent internal cross-validation only). SHAP analysis identified Δ MBF and Δ entropy at week 3 as the dominant features, with high bootstrap stability. Conclusions: Multi-parametric ultrasound radiomic kinetics integrated through a machine learning ensemble may provide an interpretable early-response biomarker for NAC in breast cancer, pending external validation.
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