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This study aimed to investigate mid-treatment breast tumor ultrasound characteristics that may predict eventual pathologic complete response (pCR) in triple-negative breast cancer; specifically, we examined associations between pCR and two parameters: tumor response pattern and tumor appearance. Ultrasound was performed at mid-treatment, defined as the completion of four cycles of anthracycline-based chemotherapy and before receiving taxane-based chemotherapy. Consensus imaging review was performed while blinded to pathology results (i.e., pCR/non-pCR) from surgery. Tumor response pattern was described as "complete," "concentric," "fragmented," "stable" or "progression." Tumor appearance was designated as "mass," "architectural distortion," "flat tumor bed" or "clip only." Univariate and multivariate regression analyses of 144 participants showed significant associations between mid-treatment response pattern and pCR (p = 0.0348 and p = 0.0173, respectively), with complete and concentric response patterns more likely to achieve pCR than other patterns. Univariate and multivariate regression analyses further showed significant associations between mid-treatment tumor appearance and pCR (p0.0001 for both), with persistent appearance of mass less likely than other appearances to achieve pCR. To conclude, our study demonstrated strong associations between pCR and both tumor response pattern and tumor appearance, thereby suggesting that these parameters have potential as qualitative imaging biomarkers of pCR in triple-negative breast cancer.

MRI-guided breast biopsy is often necessary to distinguish between benign and malignant lesions depicted at MRI, and meticulous preparation and radiologic-pathologic correlation aid in definitive diagnosis.

Background and Purpose: There is currently lack of recognized imaging criteria for prediction of treatment response to NAST in breast cancer patients. And early identification of treatment response to neoadjuvant systemic therapy (NAST) in Triple Negative Breast Cancer (TNBC) patients is important for appropriate treatment selection and response monitoring. A novel MRI sequence, Magnetic Resonance Image Compilation (MagIC) is capable of simultaneous quantitation of several tissue water properties including longitudinal (T1), transverse (T2) relaxation times, and proton density (PD). In this study we evaluated the ability of a radiomic model extracted from a novel MagIC sequence acquired early during NAST to predict pathologic complete response to NAST in TNBC. Materials and Methods: This IRB approved prospective ARTEMIS trial (NCT02276443) included 184 women (122 training dataset, 62 testing dataset) diagnosed with stage I-III TNBC. All patients were scanned with MagIC on a 3T MRI scanner at baseline (184 patients), and after 4 cycles (156 Patients) of NAST. T1, T2 and PD maps were generated from the source images using SyMRI (SyntheticMR, Linkoping, Sweden). Histopathology at surgery was used to determine pathologic complete response (pCR) which was defined as absence of the invasive cancer in the breast and axillary lymph nodes. 3D contouring of the tumors was performed using an in-house toolbox. 310 (10 first-order, 300 GLCM) textural features were extracted from each map, with total of 930 features/patient. Radiomic features were compared between pCR and non-pCR using Wilcoxon Rank Sum test and Fisher’s exact test. To build a multivariate, predictive model, logistic regression with elastic net regularization was performed for texture feature selection. The tuning parameter was optimized using 5-fold cross-validation based on the average area under curve (AUC) of each fold of a cross-validation using training data. Then the testing data were used to compare model’s performance by AUC. Results: Univariate analysis found 23 PD, 17 T1 and 10 T2 radiomic features at C4 time point to be able to predict pCR status with AUC >70% in both training and testing cohort. The top performing radiomic features were Entropy, Variance, Homogeneity and Energy (Tables1-2). Multivariate radiomics models from C4-PD, and C4-T1 maps showed best performance during both cross validation and independent testing. The radiomic signature of C4-T1 map that included 27features had best performance, with an AUC of 0.77, 0.70 (95% CI: 0.571-0.868) in training and testing cohort respectively. C4-PD map radiomic signature that included 6features was able to predict the pCR status with AUC of 0.73, 0.72 (95% CI: 0.571-0.868) in training and testing cohort respectively. Conclusion: Our data found that MagIC-based radiomics signature could potentially predict pathologic complete response in TNBC early during NAST. This data shows the potential application of MagIC radiomic model for improvement of response assessment in TNBC. Table 1.Best performing radiomic features from PD map after 4 cycles of NAST in TNBC patients.FeatureTraining CohortTraining CohortTraining CohortTesting CohortTesting CohortTesting CohortNAUC95% CINAUC95% CIP-valuePD-mapAngular Variance of Sum entropy1060.73820.6437-0.8328500.73240.5895-0.8752 Table 2.Best performing radiomic features from T1-T2 maps after 4 cycles of NAST in TNBC patients.FeatureTraining CohortTraining CohortTraining CohortTesting CohortTesting CohortTesting CohortNAUC95% CINAUC95% CIP-valueT1-mapAngular Variance of Sum entropy1060.76530.6762-0.8544500.70510.5524-0.8579 Citation Format: Nabil Elshafeey, Ken-Pin Hwang, Beatriz Elena Adrada, Rosalind Pitpitan Candelaria, Medine Boge, Rania M Mahmoud, Huiqin Chen, Jia Sun, Wei Yang, Aikaterini Kotrotsou, Benjamin C Musall, Jong Bum Son, Gary J Whitman, Jessica Leung, Huong Le-Petross, Lumarie Santiago, Deanna Lynn Lane, Marion Elizabeth Scoggins, David Allen Spak, Mary Saber Guirguis, Miral Mahesh Patel, Frances Perez, Abeer H Abdelhafez, Jason B White, Lei Huo, Elizabeth Ravenberg, Wei Peng, Alastair Thompson, Senthil Damodaran, Debu Tripathy, Stacey L Moulder, Clinton Yam, Mark David Pagel, Jingfei Ma, Gaiane Margishvili Rauch. Radiomics model based on magnetic resonance image compilation (MagIC) as early predictor of pathologic complete response to neoadjuvant systemic therapy in triple-negative breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD11-06.

Introduction: Neoadjuvant chemotherapy (NACT) is becoming standard of care for presurgical treatment of triple negative breast cancer (TNBC) patients. Achievement of pathological complete response (pCR) after NACT is associated with improved outcomes. There is currently an unmet need in development of imaging and clinical tools for prediction of pCR to NACT in TNBC. We investigated use of deep learning convolution neural networks (CNNs) for early prediction of pCR in a TNBC cohort on the basis of MRI acquired before the initiation and at the midpoint, after completion of four cycles of NACT (C4). Materials and Methods: Baseline and C4 MRIs of 112 TNBC patients were collected from an ongoing prospective clinical trial (NCT02276443). Four patients were excluded because they underwent different treatment for the second regimen. Among the 108 patients, 52 patients (48%) had pCR confirmed at surgery. Positive enhancement integral (PEI) derived from the early phases of DCE MRI, and apparent diffusion coefficients (ADC) derived from DWI MRI (b = 100 and 800 s/mm2), were used for our investigation. The images were aligned and the tumor regions were cropped from all images. All tumor patches were normalized between [0, 1], and were padded to form matrices of the same size of 192×192×64 for PEI, or the size of 192×192×16 for ADC. The CNN was constructed using stacked 3D convolution and MaxPooling layers. It consisted of up to four channels for the inputs (baseline and C4 PEI and ADC). Features extracted from each channel were concatenated and regressed for pCR prediction via three densely connected layers. Binary cross-entropy was used as the loss function for CNN training, and the loss was optimized using an Adam optimizer with the initial learning rate of 0.0001. Because of the currently limited sample size, four-fold cross-validation was used for CNN training and evaluation. The patients were divided into four groups, each group had 27 patients and the pCR:non-pCR ratio was controlled as 13:14. For each fold, one group was reserved as the independent testing group, and the other three groups were combined for network training and internal validation. Receiver operating characteristic (ROC) curve was plotted for each fold of testing, and area under the curve (AUC) was calculated. Final performance of the CNN was determined by averaging the AUCs of the four testing folds. Additionally, to test the prediction efficacy of each input, we trained the CNN under the same settings but used PEI or ADC only as input, and the results were compared. Results: The CNN trained with PEI only achieved an average AUC of 0.65 ± 0.09. The second CNN trained with ADC only achieved an average AUC of 0.72 ± 0.07. The third CNN trained with both PEI and ADC achieved an average AUC of 0.73 ± 0.06. Conclusion and Discussion: Using baseline and mid-treatment MRIs, deep learning CNN showed promising performance to predict pCR in the early course of NACT. The prediction AUC for the independent testing groups was largely improved by using ADC to train the network, indicating that ADC can have more critical information than PEI in assisting pCR prediction during the early course of NACT. Future work includes curation of a larger patient data for network training and evaluation to improve the prediction performance and further validate generalization of the network. We will also explore more advanced network structures, through which the prediction performance can be improved. Four-fold cross-validation AUCs of the network using different data as inputs.PEIADCPEI+ADCFold 10.570.640.66Fold 20.760.800.77Fold 30.660.700.68Fold 40.590.740.79Average0.65 ± 0.090.72 ± 0.070.73 ± 0.06 Citation Format: Zijian Zhou, Nabil A Elshafeey, David E Rauch, Beatriz E Adrada, Rosalind P Candelaria, Mary S Guirguis, Wei Yang, Medine Boge, Rania M Mohamed, Gary J Whitman, Deanna L Lane, Huong C Le-Petross, Jessica WT Leung, Lumarie Santiago, Marion E Scoggins, David A Spak, Miral M Patel, Frances Perez, Debu Tripathy, Vicente Valero, Clinton Yam, Stacy Moulder, Jason B White, Jong Bum Son, Mark D Pagel, Jingfei Ma. Deep learning for early prediction of neoadjuvant chemotherapy response in triple negative breast cancers [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-08-03.

Background: Triple negative breast cancer (TNBC) has a poor prognosis. In particular, TNBC patients who have significant residual disease at the time of surgery following completion of neoadjuvant systemic therapy (NST) have an especially poor prognosis. In an effort to identify patients who are unlikely to achieve pathologic complete response (pCR), we investigated if pre-treatment breast MRI morphological characteristics and imaging response patterns during NST can predict pCR in TNBC patients. Materials and Methods: As part of a prospective IRB-approved clinical trial (ARTEMIS, NCT02276443), 199 patients with biopsy-proven stage I-III TNBC received NST and were classified as pCR or non-pCR based on histopathology at surgery. Patients underwent breast MRI at baseline (BL), after 2 cycles (C2), and 4 cycles (C4) of Adriamycin-based chemotherapy (AC). Subsequently, patients received either taxane-based NST or targeted therapy guided by mid-treatment imaging response. MRI studies were reviewed by two fellowship-trained breast radiologists who were blinded to the pathology results. ACR MRI BIRADS lexicon (5th Ed) was used to describe BL tumor morphology. Imaging response pattern at C2 and C4 MRI was classified as follows: type 0 (complete), type 1 (concentric shrinkage), type 2 (crumble), type 3 (diffuse enhancement), type 4 (stable), or type 5 (progression). Morphological baseline features and response patterns were summarized and compared to the pCR status on surgical pathology using Fisher’s exact test. P values less than 0.05 were considered statistically significant. Results: Median age was 53 years, range 24-79. Of 199 patients, 95 (48%) had pCR and 104 (52%) had non-pCR. At BL MRI, an irregularly-shaped mass and homogenous or clumped non-mass enhancement were associated with pCR (p=0.026 and p=0.013, respectively). Multifocality, peritumoral edema, and intratumoral necrosis were independent of pCR. Following NST, the most common MRI response pattern was type 1, seen with equal frequency in pCR and non-pCR at C2 (58% and 42%, respectively) and C4 (47% and 53%, respectively). The following response pattern associations were found: type 0 was associated with pCR at both C2 and C4 timepoints (p Citation Format: Mary S Guirguis, Beatriz E Adrada, Rosalind P Candelaria, Jia Sun, Gary J Whitman, Wei T Yang, Medine Boge, Rania M Mohamed, Nabil A Elshafeey, Deanna L Lane, Huong Le-Petross, Jessica WT Leung, Lumarie Santiago, Marion E Scoggins, David A Spak, Miral Patel, Frances Perez, Peng Wei, Debu Tripathy, Jason White, Elizabeth Ravenberg, Lei Huo, Jennifer Litton, Banu Arun, Vincente Valero, Alastair Thompson, Stacy Moulder, Clinton Yam, Gaiane M Rauch. Prediction of response to neoadjuvant systemic therapy in triple negative breast cancer using baseline tumor MRI characteristics and imaging patterns of response [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-03-06.