Dataset Information

3T DCE-MRI Radiomics Improves Predictive Models of Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.

ABSTRACT:

Objectives

To test whether 3T MRI radiomics of breast malignant lesions improves the performance of predictive models of complete response to neoadjuvant chemotherapy when added to other clinical, histological and radiological information.

Methods

Women who consecutively had pre-neoadjuvant chemotherapy (NAC) 3T DCE-MRI between January 2016 and October 2019 were retrospectively included in the study. 18F-FDG PET-CT and histological information obtained through lesion biopsy were also available. All patients underwent surgery and specimens were analyzed. Subjects were divided between complete responders (Pinder class 1i or 1ii) and non-complete responders to NAC. Geometric, first order or textural (higher order) radiomic features were extracted from pre-NAC MRI and feature reduction was performed. Five radiomic features were added to other available information to build predictive models of complete response to NAC using three different classifiers (logistic regression, support vector machines regression and random forest) and exploring the whole set of possible feature selections.

Results

The study population consisted of 20 complete responders and 40 non-complete responders. Models including MRI radiomic features consistently showed better performance compared to combinations of other clinical, histological and radiological information. The AUC (ROC analysis) of predictors that did not include radiomic features reached up to 0.89, while all three classifiers gave AUC higher than 0.90 with the inclusion of radiomic information (range: 0.91-0.98).

Conclusions

Radiomic features extracted from 3T DCE-MRI consistently improved predictive models of complete response to neo-adjuvant chemotherapy. However, further investigation is necessary before this information can be used for clinical decision making.

SUBMITTER: Montemezzi S

PROVIDER: S-EPMC8093630 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The purpose of this study was to evaluate breast MRI radiomics in predicting, prior to any treatment, the response to neoadjuvant chemotherapy (NAC) in patients with invasive lymph node (LN)-positive breast cancer for two tasks: (1) prediction of pathologic complete response and (2) prediction of post-NAC LN status. Our study included 158 patients, with 19 showing post-NAC complete pathologic response (pathologic TNM stage T0,N0,MX) and 139 showing incomplete response. Forty-two patients were post-NAC LN-negative, and 116 were post-NAC LN-positive. We further analyzed prediction of response by hormone receptor subtype of the primary cancer (77 hormone receptor-positive, 39 HER2-enriched, 38 triple negative, and 4 cancers with unknown receptor status). Only pre-NAC MRIs underwent computer analysis, initialized by an expert breast radiologist indicating index cancers and metastatic axillary sentinel LNs on DCE-MRI images. Forty-nine computer-extracted radiomics features were obtained, both for the primary cancers and for the metastatic sentinel LNs. Since the dataset contained MRIs acquired at 1.5 T and at 3.0 T, we eliminated features affected by magnet strength using the Mann-Whitney U-test with the null-hypothesis that 1.5 T and 3.0 T samples were selected from populations having the same distribution. Bootstrapping and ROC analysis were used to assess performance of individual features in the two classification tasks. Eighteen features appeared unaffected by magnet strength. Pre-NAC tumor features generally appeared uninformative in predicting response to therapy. In contrast, some pre-NAC LN features were able to predict response: two pre-NAC LN features were able to predict pathologic complete response (area under the ROC curve (AUC) up to 0.82 [0.70; 0.88]), and another two were able to predict post-NAC LN-status (AUC up to 0.72 [0.62; 0.77]), respectively. In the analysis by a hormone receptor subtype, several potentially useful features were identified for predicting response to therapy in the hormone receptor-positive and HER2-enriched cancers.