Intratumoral and Peritumoral Ultrasoundomics Models for Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.
Authors
Affiliations (3)
Affiliations (3)
- Zhejiang Provincial People's Hospital Bijie Hospital (The First People's Hospital of Bijie), Bijie, Guizhou, China.
- Department of Ultrasound, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, China.
- Zhejiang Provincial People's Hospital Bijie Hospital (The First People's Hospital of Bijie), Bijie, Guizhou, China; Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China. Electronic address: [email protected].
Abstract
This study investigated the clinical utility of a machine learning (ML) model based on ultrasoundomics features (UFs) from primary foci to predict pathological complete response (pCR) in patients with breast cancer (BC) undergoing neoadjuvant chemotherapy (NAC). Ultrasound images from 333 patients with BC treated with NAC were retrospectively analyzed. Patients in the internal training cohort and external validation cohort were from two different hospitals, respectively. Tumor regions of interest (ROI) were delineated, and outward segmentation of the peritumor area was performed, using the ROI edge as a baseline and expanding it by 3 mm and 5 mm, respectively. This resulted in three distinct segmentation images: ROI (intratumoral region), ROI (intratumoral region + 3-mm peritumoral region), and ROI (intratumoral region + 5-mm peritumoral region). Three sets of UFs were extracted, normalized using the Z-score method, and further refined through Spearman correlation analysis and LASSO for feature selection. Eight ML models (R0), based on key UFs in ROI (intratumoral region), were compared: support vector machine, K-nearest neighbor, random forest, Naïve Bayes, logistic regression, AdaBoost (AB), multilayer perceptron, and light gradient boosting machine learning. The optimal ML models were evaluated using diagnostic performance metrics, including AUC, accuracy, sensitivity, and specificity. The best-performing algorithm was then applied to construct ML models (R3 and R5) incorporating ROI (intratumoral region + 3-mm peritumoral region) and ROI (intratumoral region + 5-mm peritumoral region). Predictive efficacy and clinical utility were assessed through decision curve analysis (DCA) and Shapley Additive exPlanation (SHAP), which quantified feature importance and facilitated model visualization and interpretation. Among the 333 patients, 119 (35.7%) achieved pCR, while 214 (64.3%) did not. Among the eight ML algorithms, the R0 model based on AB demonstrated the best performance, with an AUC of 0.811 (Internal training cohort) and 0.632 (External validation cohort). The R3 model also showed improved performance, with an AUC of 0.830 (Internal training cohort) and 0.699 (External validation cohort). The R5 model yielded the highest predictive accuracy, with an AUC of 0.871 (Internal training cohort) and 0.779 (External validation cohort). SHAP analysis was conducted for R5, and SHAP waterfall charts enable single-sample predictions to validate the model's clinical utility. DCA revealed that the combination of intratumoral region and 5-mm peritumoral region in R5 offered superior clinical benefit. ML models integrating intratumoral and peritumoral ultrasoundomics offer valuable assistance in assessing NAC efficacy in patients with BC.