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Buccal fat pad removal is an effective technique for refining the facial silhouette that should be reserved only for patients with increased buccal fat pad volume. Removal of only the excessive portion of the fat pad is important because this structure provides significant volume in the midface that can be difficult to restore once aging takes its toll on the surrounding soft and bony tissue.

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.

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Background: Dynamic contrast-enhanced (DCE) MRI is useful for diagnosis and assessment of treatment response in breast cancer. Fast DCE MRI offers a higher sampling rate of contrast enhancement curves in comparison to conventional DCE MRI, potentially characterizing tumor perfusion kinetics more accurately for measurement of functional tumor volume (FTV) as a predictor of treatment response. Purpose: To investigate FTV by fast DCE MRI as a predictor of neoadjuvant systemic therapy (NAST) response in triplenegative breast cancer (TNBC). Study Type: Prospective. Population/Subjects: Sixty patients with biopsy-confirmed TNBC between December 2016 and September 2020. Field Strength/Sequence: A 3.0 T/3D fast spoiled gradient echo-based DCE MRI Assessment: Patients underwent MRI at baseline and after four cycles (C4) of NAST, followed by definitive surgery. DCE subtraction images were analyzed in consensus by two breast radiologists with 5 (A.H.A.) and 2 (H.S.M.) years of experience. Tumor volumes (TV) were measured on early and late subtractions. Tumors were segmented on 1 and 2.5-minute early phases subtractions and FTV was determined using optimized signal enhancement thresholds. Interpolated enhancement curves from segmented voxels were used to determine optimal early phase timing. Statistical Tests: Tumor volumes were compared between patients who had a pathologic complete response (pCR) and those who did not using the area under the receiver operating curve (AUC) and Mann-Whitney U test. Results: About 26 of 60 patients (43%) had pCR. FTV at 1 minute after injection at C4 provided the best discrimination between pCR and non-pCR, with AUC (95% confidence interval [CI]) = 0.85 (0.74,0.95) (P < 0.05). The 1-minute timing was optimal for FTV measurements at C4 and for the change between C4 and baseline. TV from the early phase at C4 also yielded a good AUC (95%CI) of 0.82 (0.71,0.93) (P < 0.05).

T riple-negative breast cancer (TNBC) accounts for about 10%-20% of all breast cancers, and it is negative for estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (1). Compared with other types of breast cancer, TNBC is considered more aggressive, with a higher recurrence rate and decreased overall survival (1,2). Within the TNBC group, pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) is strongly correlated with improved disease-free survival and overall survival (3). Because only about 20%-50% of participants with TNBC will achieve pCR, early assessment of the treatment response is beneficial (4-6). For example, participants with predicted non-pCR may be directed toward more aggressive or potentially more effective novel therapies at an early stage.The effectiveness of NAST is most commonly assessed by the change in tumor size based on conventional breast imaging (eg, mammography, US) and clinical examination (7). The appropriateness criteria of the American

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients achieving pathologic complete response (pCR). We investigated ability of deep learning (DL) on dynamic contrast enhanced (DCE) MRI and diffusion weighted imaging acquired early during NAST to predict TNBC patients’ pCR status in the breast. During the development phase using the images of 130 TNBC patients, the DL model achieved areas under the receiver operating characteristic curves (AUCs) of 0.97 ± 0.04 and 0.82 ± 0.10 for the training and the validation, respectively. The model achieved an AUC of 0.86 ± 0.03 when evaluated in the independent testing group of 32 patients. In an additional prospective blinded testing group of 48 patients, the model achieved an AUC of 0.83 ± 0.02. These results demonstrated that DL based on multiparametric MRI can potentially differentiate TNBC patients with pCR or non-pCR in the breast early during NAST.

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