Radiomic analysis reveals diverse prognostic and molecular insights into the response of breast cancer to neoadjuvant chemotherapy: a multicohort study.

BACKGROUND: Breast cancer patients exhibit various response patterns to neoadjuvant chemotherapy (NAC). However, it is uncertain whether diverse tumor response patterns to NAC in breast cancer patients can predict survival outcomes. We aimed to develop and validate radiomic signatures indicative of tumor shrinkage and therapeutic response for improved survival analysis. METHODS: This retrospective, multicohort study included three datasets. The development dataset, consisting of preoperative and early NAC DCE-MRI data from 255 patients, was used to create an imaging signature-based multitask model for predicting tumor shrinkage patterns and pathological complete response (pCR). Patients were categorized as pCR, nonpCR with concentric shrinkage (CS), or nonpCR with non-CS, with prediction performance measured by the area under the curve (AUC). The prognostic validation dataset (n = 174) was used to assess the prognostic value of the imaging signatures for overall survival (OS) and recurrence-free survival (RFS) using a multivariate Cox model. The gene expression data (genomic validation dataset, n = 112) were analyzed to determine the biological basis of the response patterns. RESULTS: The multitask learning model, utilizing 17 radiomic signatures, achieved AUCs of 0.886 for predicting tumor shrinkage and 0.760 for predicting pCR. Patients who achieved pCR had the best survival outcomes, while nonpCR patients with a CS pattern had better survival than non-CS patients did, with significant differences in OS and RFS (p = 0.00012 and p = 0.00063, respectively). Gene expression analysis highlighted the involvement of the IL-17 and estrogen signaling pathways in response variability. CONCLUSIONS: Radiomic signatures effectively predict NAC response patterns in breast cancer patients and are associated with specific survival outcomes. The CS pattern in nonpCR patients indicates better survival.

Magnetic resonance imaging tumor regression shrinkage patterns after neoadjuvant chemotherapy in patients with locally advanced breast cancer: Correlation with tumor biological subtypes and pathological response after therapy.

The objective of this study is to analyze magnetic resonance imaging shrinkage pattern of tumor regression after neoadjuvant chemotherapy and to evaluate its relationship with biological subtypes and pathological response. We reviewed the magnetic resonance imaging studies of 51 patients with single mass-enhancing lesions (performed at time 0 and at the II and last cycles of neoadjuvant chemotherapy). Tumors were classified as Luminal A, Luminal B, HER2+, and Triple Negative based on biological and immunohistochemical analysis after core needle biopsy. We classified shrinkage pattern, based on tumor regression morphology on magnetic resonance imaging at the II cycle, as concentric, nodular, and mixed. We assigned a numeric score (0: none; 1: low; 2: medium; 3: high) to the enhancement intensity decrease. Pathological response on the surgical specimen was classified as complete (grade 5), partial (grades 4-3), and non-response (grades 1-2) according to Miller and Payne system. Fisher test was used to relate shrinkage pattern with biological subtypes and final pathological response. Seventeen patients achieved complete response, 25 partial response, and 9 non-response. A total of 13 lesions showed nodular pattern, 20 concentric, and 18 mixed. We found an association between concentric pattern and HER2+ (p < 0.001) and mixed pattern and Luminal A lesions (p < 0.001). We observed a statistical significant correlation between concentric pattern and complete response (p < 0.001) and between mixed pattern and non-response (p = 0.005). Enhancement intensity decrease 3 was associated with complete response (p < 0.001). Shrinkage pattern and enhancement intensity decrease may serve as early response indicators after neoadjuvant chemotherapy. Shrinkage pattern correlates with tumor biological subtypes.

Tumor enhancement and shrinkage pattern in dynamic contrast-enhanced magnetic resonance imaging for predicting pathologic complete response after human epidermal growth factor receptor 2 (HER2)-targeted therapy in breast cancer.

Background: Targeted therapy with neoadjuvant chemotherapy for patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer has increased the rates of pathological complete response (pCR) and breast preservation surgery and improved the overall disease-free survival rate. This study aimed to determine whether tumor enhancement and shrinkage patterns in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the efficacy of targeted therapy in patients with HER2-positive breast cancer and differentiate pCR from non-pCR. Methods: The data of 64 patients with HER2-positive breast cancer who received targeted therapy prior to surgery were retrospectively collected. All patients had complete postoperative pathological data. The pretreatment evaluation of the tumor enhancement pattern and the shrinkage pattern after two treatment cycles were assessed. The difference in the enhancement and shrinkage patterns between the pCR and non-pCR groups was evaluated via the χ2 test. Logistic regression analysis was used to assess the value of enhancement and shrinkage patterns for predicting pCR in patients with HER2-positive breast cancer. Results: There were statistically significant differences in tumor size, estrogen receptor (ER) status, lymph node metastasis, enhancement pattern, and shrinkage pattern between the pCR and non-pCR cases. Patients with a tumor size ≤20 mm were likely to achieve pCR. ER status, lymph node metastasis, and enhancement and shrinkage patterns each had good precision for predicting pCR, and the combination of enhancement and shrinkage patterns had the highest prediction accuracy. Multivariate logistic regression analysis indicated that only enhancement pattern had a significant predictive value. Conclusions: Among patients with HER2-positive breast cancer, those with tumor size ≤20 mm, ER-negative status, no lymph node metastases, and mass enhancement and concentric shrinkage patterns are more likely to achieve pCR. Mass enhancement combined with concentric shrinkage had the highest accuracy in predicting pCR, indicating that preoperative imaging may be useful for guiding clinical decisions regarding targeted treatments.

Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer.

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer. Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer. Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2-: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811). Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

Magnetic resonance imaging shrinkage patterns following neoadjuvant chemotherapy for breast carcinomas with an emphasis on the radiopathological correlations.

Preoperative neoadjuvant chemotherapy (NAC) is considered to be the standard treatment for locally-advanced breast carcinomas. Obtaining precise information regarding the tumor extent and distribution by imaging modalities to assess the success of breast-conserving surgery following NAC is extremely important. Analysis of the detailed radiopathological correlation of magnetic resonance imaging (MRI) following NAC has not been reported previously. The MRI and histopathological shrinkage patterns of residual breast carcinomas in 27 consecutive cases were analyzed following NAC and classified into five categories: Types I and II (concentric shrinkage with and without surrounding lesions, respectively); type III (shrinkage with residual multinodular lesions); type IV (diffuse contrast enhancement in whole quadrant); and non-visualization. The present study clearly demonstrated that the most common MRI shrinkage pattern was type I (11 cases), followed by type II and non-visualization, and the most common histopathological shrinkage pattern was type II (11 cases), followed by type III (8 cases). The concordance rate between MRI and pathological patterns was 48% and the worst concordance MRI pattern was type I. MRI is considered to be a useful method for evaluation of the residual carcinoma following NAC. However, the concordance rate was low in the MRI pattern I cases and tiny foci of residual carcinoma were present in half of the non-visualization cases, as shown by MRI. Therefore, the tumor extent must be completely resected for patients who undergo NAC, and postoperative radiation may be important for preventing local recurrence of breast carcinoma.