Predictive Analysis of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Using Multi-Region Ultrasound Imaging Features Combined With Pathological Parameters.

Authors

Abstract

This study aimed to analyze the correlation between the ultrasonographic radiomic features of multiple regions within and surrounding the primary tumor in breast cancer patients prior to receiving neoadjuvant chemotherapy (NAC) and the efficacy of NAC. By integrating clinical and pathological parameters, a predictive model was constructed to provide an accurate basis for personalized treatment and precise prognosis in breast cancer patients. This retrospective study included 321 breast cancer patients who underwent NAC treatment at the Second Hospital of Lanzhou University from January 2019 to December 2024. According to post-operative pathological results, the patients were divided into pathological complete response (PCR) and non-pathological complete response (non-PCR) groups. Regions of interest were outlined on 2-D ultrasound images using Itk-snap software. The intra-tumor (Intra) region and 5 mm (Peri-5 mm), 10 mm (Peri-10 mm) and 15 mm (Peri-15 mm) the peri-tumoralregions were demarcated, with radiomics features extracted from each region. Patients were randomly divided into a training set (n = 224) and a validation set (n = 97) in a 7:3 ratio. All features underwent Z-score normalization followed by dimensionality reduction using t-tests, Pearson correlation coefficients and least absolute shrinkage and selection operator. Radiomics models for Intra, Peri-5 mm, Peri-10 mm, Peri-15 mm and the combined intra-tumoral and peri-tumoral regions (Intra-tumoral, Peri-tumoral, IntraPeri) were constructed using a random forest machine-learning classifier. The predictive performance of the models was assessed by plotting receiver operating characteristic curves and calculating the area under the curve (AUC). Additionally, calibration curves and decision curve analysis were plotted to evaluate the model's goodness of fit and clinical net benefit RESULTS: A total of 214 radiomics features were extracted from the intra-tumoral and multi-region peri-tumoral areas. Using the least absolute shrinkage and selection operator regression model, eight intra-tumoral radiomics features, eight peri-10 mm radiomics features and nine IntraPeri-10 mm radiomics features were selected as being closely associated with PCR. The AUC of the intra-tumoral model was 0.860 and 0.823 in the training and validation sets, respectively. The AUCs of the peri-5 mm, Peri-10 mm and Peri-15 mm models were 0.836, 0.854 and 0.822 in the training set, and 0.793, 0.799 and 0.792 in the validation set. Among them, the AUC of the IntraPeri-10 mm model in the validation set was 0.842 (95% confidence interval [CI]: 0.764-0.921), which was superior to the AUC of the IntraPeri-5 mm model (0.831; 95% CI: 0.758-0.914) and the IntraPeri-15 mm model (0.838; 95% CI: 0.761-0.917). The combined model based on IntraPeri-10 mm and clinical pathological parameters (HER-2, Ki-67) achieved an AUC of 0.869 (95% CI: 0.800-0.937). The Delong test showed that the AUC of the combined model was significantly superior to that of the other models. The calibration curve indicated that the combined model had a good fit, and decision curve analysis demonstrated that the combined model provided a better clinical net benefit. The peri-10 mm region is the optimal predictive area for the tumor's surrounding tissue after NAC in breast cancer. The IntraPeri-10 mm model, incorporating clinical pathological parameters, performs better at predicting the efficacy of NAC in breast cancer and can accurately assess treatment response, offering valuable guidance for subsequent treatment decisions.

Tags

Topics