2026-10-22

Background: Breast-conserving surgery (BCS) is the standard care for early breast cancer. However, positive surgical margins lead to high recurrence and require reoperation, remaining a clinical challenge. This study aims to develop an ensemble model to preoperatively predict positive margins using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods: A total of 887 patients from three Chinese medical centers were included. Patients from the Yunnan (581 patients) and Fujian (51 patients) provinces were used for model development, with external testing on 188 patients from Guangdong and prospective testing on 67 patients from Yunnan. Three base models were developed: the clinical model based on 32 features, the deep learning (DL) model using ResNet-34, and the radiomics model utilizing radiomics features. The ensemble model combined predictions from these three base models using weighted voting. Performance metrics (accuracy, AUC, sensitivity, specificity) were computed, and prognostic analyses were conducted with a public dataset. Results: The ensemble model outperformed each of the three base models, with the AUC of 0.931, accuracy of 0.872, sensitivity of 0.871, and specificity of 0.880 in the validation set. Robustness was further confirmed in independent test cohorts, yielding AUCs of 0.762 and 0.861. Furthermore, patients identified at high-risk by the model exhibited poor recurrence-free survival (RFS, HR = 8.117, p < 0.0001) and overall survival (OS, HR = 5.748, p < 0.0001), underscoring its prognostic value. Conclusions: The ensemble model based on DCE-MRI effectively predicts positive margins and prognosis for BCS patients, facilitating the clinical decision-making on the surgery. Trial registration This study was approved by the Ethics Committee of the Yunnan Cancer Hospital (KYLX2023-134), Xiang'an Hospital of Xiamen University (XDYX202305K26), and Cancer Hospital of Shantou University Medical College (2024059). The study was also registered with the Chinese Clinical Trial Registry (ChiCTR2400083298, Registered 19 April 2024, https://www.chictr.org.cn/showproj.html?%20proj=207948 ).

Purpose: In urological practice, the decision to resect or preserve a kidney with obstruction, tumor or atrophy is based on split renal function, although SPECT renal dynamic scintigraphy as the reference standard is resource-intensive. We developed and externally validated a non-contrast CT radiomics model to identify nonfunctioning kidneys and support urological decision-making in this setting. Methods: In this three-center retrospective study (TRIPOD+AI), adults underwent non-contrast CT and SPECT renal dynamic scintigraphy within 72 h. IBSI-compliant radiomics features were extracted with PyRadiomics, and between-kidney asymmetry features were constructed. After three-stage selection (Spearman, recursive feature elimination, and LASSO), 12 machine learning models were developed and externally validated in 2 independent cohorts. A Rad-score and clinical nomogram were also derived. Results: Among 1029 patients, the internal cohort included 418 patients. Twenty-three of features were retained. The random forest model yielded an internal test AUC of 0.9426, sensitivity of 85.5%, specificity of 88.3%, and negative predictive value of 95.6%. Its external AUCs were 0.875 and 0.913. The nomogram attained external AUCs of 0.920 and 0.881. At the thresholds fixed in the training data, the random forest achieved external sensitivity of 66.0 and 76.4% with negative predictive values of 94.8 and 93.3%, and the nomogram 92.5 and 93.7% with 98.3 and 97.2%. Both radiomics models outperformed logistic-regression baselines built from clinical variables and simple CT size and asymmetry measurements, although the margin over the combined baseline was not statistically significant in one external cohort. The Rad-score was correlated with single-kidney GFR (ρ = -0.738, P < 0.001) and facilitated risk stratification. Conclusion: Non-contrast CT radiomics identified nonfunctioning kidneys without contrast agents or radiotracers. Because non-contrast CT is already part of routine urological workup, this externally validated rule-out tool may reduce unnecessary SPECT examinations and support decisions between nephrectomy and nephron-sparing management.