MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

Abstract

Triple-negative breast cancer (TNBC) is persistently refractory to therapy, and methods to improve targeting and evaluation of responses to therapy in this disease are needed. Here, we integrate quantitative magnetic resonance imaging (MRI) data with biologically-based mathematical modeling to accurately predict the response of TNBC to neoadjuvant systemic therapy (NAST) on an individual basis. Specifically, 56 TNBC patients enrolled in the ARTEMIS trial (NCT02276443) underwent standard-of-care doxorubicin/cyclophosphamide (A/C) and then paclitaxel for NAST, where dynamic contrast-enhanced MRI and diffusion-weighted MRI were acquired before treatment and after two and four cycles of A/C. A biologically-based model was established to characterize tumor cell movement, proliferation, and treatment-induced cell death. Two evaluation frameworks were investigated using: 1) images acquired before and after two cycles of A/C for calibration and predicting tumor status after A/C, and 2) images acquired before, after two cycles, and after four cycles of A/C for calibration and predicting response following NAST. For Framework 1, the concordance correlation coefficients between the predicted and measured patient-specific, post-A/C changes in tumor cellularity and volume were 0.95 and 0.94, respectively. For Framework 2, the biologically-based model achieved an area under the receiver operator characteristic curve of 0.89 (sensitivity/specificity = 0.72/0.95) for differentiating pathological complete response (pCR) from non-pCR, which is statistically superior (P < 0.05) to the value of 0.78 (sensitivity/specificity = 0.72/0.79) achieved by tumor volume measured after four cycles of A/C. Overall, this model successfully captured patient-specific, spatiotemporal dynamics of TNBC response to NAST, providing highly accurate predictions of NAST response.

Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study

“…In contrast, previous studies only used conventional clinical and radiologic characteristics and did not perform precise subtypes analysis, or only used radiomics method. 49 , 50 , 51 , 52 Secondly, we collected longitudinal MRI data, including pre-NAC and post-NAC sequences, which contained the tumor change information during NAC, rather than single-modality models. 53 Previous study also reported that delta-radiomics had higher performance.…”

Imaging‐proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer

“…According to a comparative study, the performance of radiomic‐based models, which employ MRI features, was superior to that of biologically based models using tumor cell features in predicting the response to neoadjuvant chemotherapy in triple‐negative breast cancer. The radiomic models achieved an impressive AUC of 0.89 37 . Nonetheless, the majority of the studies have predominantly utilized handcrafted radiomic features as the input for their models.…”

Development and Assessment of a Novel Core Biopsy-Based Prediction Model for Pathological Complete Response to Neoadjuvant Chemotherapy in Women with Breast Cancer

“…In addition, the pCR of NAC can be predicted from different perspectives, including whole-slide biopsy images [ 24 ], genomic features [ 25 ], and even radiological features from pre- [ 26 ] and mid-treatment magnetic resonance imaging (MRI) [ 27 ]. Based on these parameters, quite a few prediction models have been built [ 28 , 29 ]; however, the prognostic biomarkers involved in most models are not accessible to every patient for economic reasons or due to the cumbersome steps involved. These reasons limited the assessment of which of the NAC-treated patients would have a good response.…”