Multimodality Imaging of Benign and Malignant Diseases of the Nipple-Areolar Complex

The nipple-areolar complex (NAC), a unique anatomic structure of the breast, encompasses the terminal intramammary ducts and skin appendages. Several benign and malignant diseases can arise within the NAC. As several conditions have overlapping symptoms and imaging findings, understanding the distinctive nipple anatomy, as well as the clinical and imaging features of each NAC disease process, is essential. A multimodality imaging approach is optimal in the presence or absence of clinical symptoms. The authors review the ductal anatomy and anomalies, including congenital abnormalities and nipple retraction. They then discuss the causes of nipple discharge and highlight best practices for the imaging workup of pathologic nipple discharge, a common condition that can pose a diagnostic challenge and may be the presenting symptom of breast cancer. The imaging modalities used to evaluate and differentiate benign conditions (eg, dermatologic conditions, epidermal inclusion cyst, mammary ductal ectasia, periductal mastitis, and nonpuerperal abscess), benign tumors (eg, papilloma, nipple adenoma, and syringomatous tumor of the nipple), and malignant conditions (eg, breast cancer and Paget disease of the breast) are reviewed. Breast MRI is the current preferred imaging modality used to evaluate for NAC involvement by breast cancer and select suitable candidates for nipple-sparing mastectomy. Different biopsy techniques (US -guided biopsy and stereotactic biopsy) for sampling NAC masses and calcifications are described. This multimodality imaging approach ensures an accurate diagnosis, enabling optimal clinical management and patient outcomes. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Durvalumab and tremelimumab before surgery in patients with hormone receptor positive, HER2-negative stage II-III breast cancer

A clinical trial was conducted to assess the feasibility of enrolling patients with Stage II or III hormone receptor positive (HR+)/HER2-negative breast cancer to pre-operative dual PD-L1/CTLA-4 checkpoint inhibition administered prior to neoadjuvant chemotherapy (NACT). Eight eligible patients were treated with upfront durvalumab and tremelimumab for two cycles. Patients then received NACT prior to breast surgery. Seven patients had baseline and interval breast ultrasounds after combination immunotherapy and the responses were mixed: 3/7 patients experienced a ≥30% decrease in tumor volume, 3/7 a ≥30% increase, and 1 patient had stable disease. At the time of breast surgery, 1/8 patients had a pathologic complete response (pCR). The trial was stopped early after 3 of 8 patients experienced immunotherapy-related toxicity or suspected disease progression that prompted discontinuation or a delay in the administration of NACT. Two patients experienced grade 3 immune-related adverse events (1 with colitis, 1 with endocrinopathy). Analysis of the tumor microenvironment after combination immunotherapy did not show a significant change in immune cell subsets from baseline. There was limited benefit for dual checkpoint blockade administered prior to NACT in our study of 8 patients with HR+/HER2-negative breast cancer.

Comprehensive Analysis Identifies Variability in PI3K Pathway Alterations in Triple-Negative Breast Cancer Subtypes

PURPOSE

The PI3K pathway is frequently altered in triple-negative breast cancer (TNBC). Limited cell line and human data suggest that TNBC tumors characterized as mesenchymal (M) and luminal androgen receptor (LAR) subtypes have increased incidence of alterations in the PI3K pathway. The impact of PI3K pathway alterations across TNBC subtypes is poorly understood.

METHODS

Pretreatment tumor was evaluated from operable TNBC patients enrolled on a clinical trial of neoadjuvant therapy (NAT; A Robust TNBC Evaluation fraMework to Improve Survival [ClinicalTrials.gov identifier: NCT02276443]). Tumors were characterized into seven TNBC subtypes per Pietenpol criteria (basal-like 1, basal-like 2, immunomodulatory, M, mesenchymal stem-like, LAR, and unstable). Using whole-exome sequencing, RNA sequencing, and immunohistochemistry for PTEN, alterations were identified in 32 genes known to activate the PI3K pathway. Alterations in each subtype were associated with pathologic response to NAT.

RESULTS

In evaluated patients (N = 177), there was a significant difference in the incidence of PI3K pathway alterations across TNBC subtypes (P < .01). The highest incidence of alterations was seen in LAR (81%), BL2 (79%), and M (62%) subtypes. The odds ratio for pathologic complete response (pCR) in the presence of PIK3CA mutation, PTEN mutation, and/or PTEN loss was highest in the LAR subtype and lowest in the M subtype, but these findings did not reach statistical significance. Presence of PIK3CA mutation was associated with pCR in the LAR subtype (P = .02).

CONCLUSION

PI3K pathway alteration can affect response to NAT in TNBC, and targeted agents may improve outcomes, particularly in patients with M and LAR TNBC.

Diffusion Tensor Imaging for Characterizing Changes in Triple-Negative Breast Cancer During Neoadjuvant Systemic Therapy

BACKGROUND

Assessment of treatment response in triple-negative breast cancer (TNBC) may guide individualized care for improved patient outcomes. Diffusion tensor imaging (DTI) measures tissue anisotropy and could be useful for characterizing changes in the tumors and adjacent fibroglandular tissue (FGT) of TNBC patients undergoing neoadjuvant systemic treatment (NAST).

PURPOSE

To evaluate the potential of DTI parameters for prediction of treatment response in TNBC patients undergoing NAST.

STUDY TYPE

Prospective.

POPULATION

Eighty-six women (average age: 51 ± 11 years) with biopsy-proven clinical stage I-III TNBC who underwent NAST followed by definitive surgery. 47% of patients (40/86) had pathologic complete response (pCR).

FIELD STRENGTH/SEQUENCE

3.0 T/reduced field of view single-shot echo-planar DTI sequence.

ASSESSMENT

Three MRI scans were acquired longitudinally (pre-treatment, after 2 cycles of NAST, and after 4 cycles of NAST). Eleven histogram features were extracted from DTI parameter maps of tumors, a peritumoral region (PTR), and FGT in the ipsilateral breast. DTI parameters included apparent diffusion coefficients and relative diffusion anisotropies. pCR status was determined at surgery.

STATISTICAL TESTS

Longitudinal changes of DTI features were tested for discrimination of pCR using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC). A P value <0.05 was considered statistically significant.

RESULTS

47% of patients (40/86) had pCR. DTI parameters assessed after 2 and 4 cycles of NAST were significantly different between pCR and non-pCR patients when compared between tumors, PTRs, and FGTs. The median surface/average anisotropy of the PTR, measured after 2 and 4 cycles of NAST, increased in pCR patients and decreased in non-pCR patients (AUC: 0.78; 0.027 ± 0.043 vs. -0.017 ± 0.042 mm2 /s).

DATA CONCLUSION

Quantitative DTI features from breast tumors and the peritumoral tissue may be useful for predicting the response to NAST in TNBC.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 4.

Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer

Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST.

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer

Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients' treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.

Triple-Negative Breast Cancer: Histopathologic Features, Genomics, and Treatment

Triple-negative breast cancer (TNBC) is a heterogeneous and aggressive group of tumors that are defined by the absence of estrogen and progesterone receptors and lack of ERBB2 (formerly HER2 or HER2/neu) overexpression. TNBC accounts for 8%-13% of breast cancers. In addition, it accounts for a higher proportion of breast cancers in younger women compared with those in older women, and it disproportionately affects non-Hispanic Black women. TNBC has high metastatic potential, and the risk of recurrence is highest during the 5 years after it is diagnosed. TNBC exhibits benign morphologic imaging features more frequently than do other breast cancer subtypes. Mammography can be suboptimal for early detection of TNBC owing to factors that include the fast growth of this cancer, increased mammographic density in young women, and lack of the typical features of malignancy at imaging. US is superior to mammography for TNBC detection, but benign-appearing features can lead to misdiagnosis. Breast MRI is the most sensitive modality for TNBC detection. Most cases of TNBC are treated with neoadjuvant chemotherapy, followed by surgery and radiation. MRI is the modality of choice for evaluating the response to neoadjuvant chemotherapy. Survival rates for individuals with TNBC are lower than those for persons with hormone receptor-positive and human epidermal growth factor receptor 2-positive cancers. The 5-year survival rates for patients with localized, regional, and distant disease at diagnosis are 91.3%, 65.8%, and 12.0%, respectively. The early success of immunotherapy has raised hope regarding the development of personalized strategies to treat TNBC. Imaging and tumor biomarkers are likely to play a crucial role in the prediction of TNBC treatment response and TNBC patient survival in the future. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs

We trained and validated a deep learning model that can predict the treatment response to neoadjuvant systemic therapy (NAST) for patients with triple negative breast cancer (TNBC). Dynamic contrast enhanced (DCE) MRI and diffusion-weighted imaging (DWI) of the pre-treatment (baseline) and after four cycles (C4) of doxorubicin/cyclophosphamide treatment were used as inputs to the model for prediction of pathologic complete response (pCR). Based on the standard pCR definition that includes disease status in either breast or axilla, the model achieved areas under the receiver operating characteristic curves (AUCs) of 0.96 ± 0.05, 0.78 ± 0.09, 0.88 ± 0.02, and 0.76 ± 0.03, for the training, validation, testing, and prospective testing groups, respectively. For the pCR status of breast only, the retrained model achieved prediction AUCs of 0.97 ± 0.04, 0.82 ± 0.10, 0.86 ± 0.03, and 0.83 ± 0.02, for the training, validation, testing, and prospective testing groups, respectively. Thus, the developed deep learning model is highly promising for predicting the treatment response to NAST of TNBC.Clinical Relevance- Deep learning based on serial and multiparametric MRIs can potentially distinguish TNBC patients with pCR from non-pCR at the early stage of neoadjuvant systemic therapy, potentially enabling more personalized treatment of TNBC patients.

A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer

Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.

Predictive Roles of Baseline Stromal Tumor-Infiltrating Lymphocytes and Ki-67 in Pathologic Complete Response in an Early-Stage Triple-Negative Breast Cancer Prospective Trial

High stromal tumor-infiltrating lymphocytes (sTILs) are associated with improved pathologic complete response (pCR) in triple-negative breast cancer (TNBC). We hypothesize that integrating high sTILs and additional clinicopathologic features associated with pCR could enhance our ability to predict the group of patients on whom treatment de-escalation strategies could be tested. In this prospective early-stage TNBC neoadjuvant chemotherapy study, pretreatment biopsies from 408 patients were evaluated for their clinical and demographic features, as well as biomarkers including sTILs, Ki-67, PD-L1 and androgen receptor. Multivariate logistic regression models were developed to generate a computed response score to predict pCR. The pCR rate for the entire cohort was 41%. Recursive partitioning analysis identified ≥20% as the optimal cutoff for sTILs to denote 35% (143/408) of patients as having high sTILs, with a pCR rate of 59%, and 65% (265/408) of patients as having low sTILs, with a pCR rate of 31%. High Ki-67 (cutoff > 35%) was identified as the only predictor of pCR in addition to sTILs in the training set. This finding was verified in the testing set, where the highest computed response score encompassing both high sTILa and high Ki-67 predicted a pCR rate of 65%. Integrating Ki67 and sTIL may refine the selection of early stage TNBC patients for neoadjuvant clinical trials evaluating de-escalation strategies.

Review of the Sonographic Features of Interpectoral (Rotter) Lymph Nodes in Breast Cancer Staging

ABSTRACT

This article reviews the ultrasound evaluation and staging of breast cancer with respect to the involvement of interpectoral (Rotter) lymph nodes. The primary objective is to demonstrate and assess the characteristic sonographic findings of interpectoral (Rotter) lymph nodes to help provide accurate nodal staging information. We aim to provide a comprehensive review and serve as an imaging guide for the identification and evaluation of Rotter lymph nodes. The detection of abnormalities and pathologic features of metastatic axillary nodal disease in the interpectoral region is reviewed, and the impact on clinical management and treatment is discussed. In the radiology literature, there is no comprehensive review of the sonographic appearance and evaluation of Rotter lymph nodes.

A phase II study of neoadjuvant atezolizumab and nab-paclitaxel in patients with anthracycline-resistant early-stage triple-negative breast cancer

PURPOSE

Neoadjuvant anti-PD-(L)1 therapy improves the pathological complete response (pCR) rate in unselected triple-negative breast cancer (TNBC). Given the potential for long-term morbidity from immune-related adverse events (irAEs), optimizing the risk-benefit ratio for these agents in the curative neoadjuvant setting is important. Suboptimal clinical response to initial neoadjuvant therapy (NAT) is associated with low rates of pCR (2-5%) and may define a patient selection strategy for neoadjuvant immune checkpoint blockade. We conducted a single-arm phase II study of atezolizumab and nab-paclitaxel as the second phase of NAT in patients with doxorubicin and cyclophosphamide (AC)-resistant TNBC (NCT02530489).

METHODS

Patients with stage I-III, AC-resistant TNBC, defined as disease progression or a < 80% reduction in tumor volume after 4 cycles of AC, were eligible. Patients received atezolizumab (1200 mg IV, Q3weeks × 4) and nab-paclitaxel (100 mg/m² IV,Q1 week × 12) as the second phase of NAT before undergoing surgery followed by adjuvant atezolizumab (1200 mg IV, Q3 weeks, × 4). A two-stage Gehan-type design was employed to detect an improvement in pCR/residual cancer burden class I (RCB-I) rate from 5 to 20%.

RESULTS

From 2/15/2016 through 1/29/2021, 37 patients with AC-resistant TNBC were enrolled. The pCR/RCB-I rate was 46%. No new safety signals were observed. Seven patients (19%) discontinued atezolizumab due to irAEs.

CONCLUSION

This study met its primary endpoint, demonstrating a promising signal of activity in this high-risk population (pCR/RCB-I = 46% vs 5% in historical controls), suggesting that a response-adapted approach to the utilization of neoadjuvant immunotherapy should be considered for further evaluation in a randomized clinical trial.

Challenging Contrast-Enhanced Mammography-Guided Biopsies: Practical Approach Using Real-Time Multimodality Imaging and a Proposed Procedural Algorithm

Contrast-enhanced mammography (CEM) is an emerging functional breast imaging technique that entails the acquisition of dual-energy digital mammographic images after IV administration of iodine-based contrast material. CEM-guided biopsy technology was introduced in 2019 and approved by the U.S. FDA in 2020. This technology's availability enables direct sampling of suspicious enhancement seen only on or predominantly on recombined CEM images and addresses a major obstacle to the clinical implementation of CEM technology. The literature describing clinical indications and procedural techniques of CEM-guided biopsy is scarce. This article describes our initial experience in performing challenging CEM-guided biopsies and proposes a step-by-step procedural algorithm designed to proactively address anticipated technical difficulties and thereby increase the likelihood of achieving successful targeting.

Predictors of success in establishing orthotopic patient-derived xenograft models of triple negative breast cancer

Patient-derived xenograft (PDX) models of breast cancer are an effective discovery platform and tool for preclinical pharmacologic testing and biomarker identification. We established orthotopic PDX models of triple negative breast cancer (TNBC) from the primary breast tumors of patients prior to and following neoadjuvant chemotherapy (NACT) while they were enrolled in the ARTEMIS trial (NCT02276443). Serial biopsies were obtained from patients prior to treatment (pre-NACT), from poorly responsive disease after four cycles of Adriamycin and cyclophosphamide (AC, mid-NACT), and in cases of AC-resistance, after a 3-month course of different experimental therapies and/or additional chemotherapy (post-NACT). Our study cohort includes a total of 269 fine needle aspirates (FNAs) from 217 women, generating a total of 62 PDX models (overall success-rate = 23%). Success of PDX engraftment was generally higher from those cancers that proved to be treatment-resistant, whether poorly responsive to AC as determined by ultrasound measurements mid-NACT (p = 0.063), RCB II/III status after NACT (p = 0.046), or metastatic relapse within 2 years of surgery (p = 0.008). TNBC molecular subtype determined from gene expression microarrays of pre-NACT tumors revealed no significant association with PDX engraftment rate (p = 0.877). Finally, we developed a statistical model predictive of PDX engraftment using percent Ki67 positive cells in the patient's diagnostic biopsy, positive lymph node status at diagnosis, and low volumetric reduction of the patient's tumor following AC treatment. This novel bank of 62 PDX models of TNBC provides a valuable resource for biomarker discovery and preclinical therapeutic trials aimed at improving neoadjuvant response rates for patients with TNBC.

Quantitative Apparent Diffusion Coefficients From Peritumoral Regions as Early Predictors of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer

BACKGROUND

Pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in triple-negative breast cancer (TNBC) is a strong predictor of patient survival. Edema in the peritumoral region (PTR) has been reported to be a negative prognostic factor in TNBC.

PURPOSE

To determine whether quantitative apparent diffusion coefficient (ADC) features from PTRs on reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) predict the response to NAST in TNBC.

STUDY TYPE

Prospective.

POPULATION/SUBJECTS

A total of 108 patients with biopsy-proven TNBC who underwent NAST and definitive surgery during 2015-2020.

FIELD STRENGTH/SEQUENCE

A 3.0 T/rFOV single-shot diffusion-weighted echo-planar imaging sequence (DWI).

ASSESSMENT

Three scans were acquired longitudinally (pretreatment, after two cycles of NAST, and after four cycles of NAST). For each scan, 11 ADC histogram features (minimum, maximum, mean, median, standard deviation, kurtosis, skewness and 10th, 25th, 75th, and 90th percentiles) were extracted from tumors and from PTRs of 5 mm, 10 mm, 15 mm, and 20 mm in thickness with inclusion and exclusion of fat-dominant pixels.

STATISTICAL TESTS

ADC features were tested for prediction of pCR, both individually using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC), and in combination in multivariable models with k-fold cross-validation. A P value < 0.05 was considered statistically significant.

RESULTS

Fifty-one patients (47%) had pCR. Maximum ADC from PTR, measured after two and four cycles of NAST, was significantly higher in pCR patients (2.8 ± 0.69 vs 3.5 ± 0.94 mm2 /sec). The top-performing feature for prediction of pCR was the maximum ADC from the 5-mm fat-inclusive PTR after cycle 4 of NAST (AUC: 0.74; 95% confidence interval: 0.64, 0.84). Multivariable models of ADC features performed similarly for fat-inclusive and fat-exclusive PTRs, with AUCs ranging from 0.68 to 0.72 for the cycle 2 and cycle 4 scans.

DATA CONCLUSION

Quantitative ADC features from PTRs may serve as early predictors of the response to NAST in TNBC.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 4.

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

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 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 patients with TNBC 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: (i) images acquired before and after two cycles of A/C for calibration and predicting tumor status after A/C, and (ii) 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 &amp;lt; 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.

SIGNIFICANCE

Integrating MRI data with biologically based mathematical modeling successfully predicts breast cancer response to chemotherapy, suggesting digital twins could facilitate a paradigm shift from simply assessing response to predicting and optimizing therapeutic efficacy.

Tumor Immune Microenvironment Changes by Multiplex Immunofluorescence Staining in a Pilot Study of Neoadjuvant Talazoparib for Early-Stage Breast Cancer Patients with a Hereditary BRCA Mutation

PURPOSE

The immunological profile of early-stage breast cancer treated with neoadjuvant PARP inhibitors has not been described. The aim of this study was to delineate the changes in the tumor immune microenvironment (TiME) induced by talazoparib.

PATIENTS AND METHODS

Patients with operable germline BRCA1/2 pathogenic variant (gBRCA1/2+) breast cancer were enrolled in a feasibility study of neoadjuvant talazoparib. Thirteen patients who received 8 weeks of neoadjuvant talazoparib were available for analysis, including 11 paired pre- and post-talazoparib core biopsies. Treatment-related changes in tumor-infiltrating lymphocytes were examined and immune cell phenotypes and their spatial distribution in the TiME were identified and quantified by multiplex immunofluorescence using a panel of 6 biomarkers (CD3, CD8, CD68, PD-1, PD-L1, and CK).

RESULTS

Neoadjuvant talazoparib significantly increased infiltrating intratumoral and stromal T-cell and cytotoxic T-cell density. There was no difference in PD-1 or PD-L1 immune cell phenotypes in the pre- and post-talazoparib specimens and PD-L1 expression in tumor cells was rare in this cohort. Spatial analysis demonstrated that pre-talazoparib interactions between macrophages and T cells may correlate with pathologic complete response.

CONCLUSIONS

This is the first study with phenotyping to characterize the immune response to neoadjuvant talazoparib in patients with gBRCA1/2+ breast cancer. These findings support an emerging role for PARP inhibitors in enhancing tumor immunogenicity. Further investigation of combinatorial strategies is warranted with agents that exploit the immunomodulatory effects of PARP inhibitors on the TiME.

Molecular Characterization and Prospective Evaluation of Pathologic Response and Outcomes with Neoadjuvant Therapy in Metaplastic Triple-Negative Breast Cancer

PURPOSE

Metaplastic breast cancer (MpBC) is a rare subtype of breast cancer that is commonly triple-negative and poorly responsive to neoadjuvant therapy in retrospective studies.

EXPERIMENTAL DESIGN

To better define clinical outcomes and correlates of response, we analyzed the rate of pathologic complete response (pCR) to neoadjuvant therapy, survival outcomes, and genomic and transcriptomic profiles of the pretreatment tumors in a prospective clinical trial (NCT02276443). A total of 211 patients with triple-negative breast cancer (TNBC), including 39 with MpBC, received doxorubicin-cyclophosphamide-based neoadjuvant therapy.

RESULTS

Although not meeting the threshold for statistical significance, patients with MpBCs were less likely to experience a pCR (23% vs. 40%; P = 0.07), had shorter event-free survival (29.4 vs. 32.2 months, P = 0.15), metastasis-free survival (30.3 vs. 32.4 months, P = 0.22); and overall survival (32.6 vs. 34.3 months, P = 0.21). This heterogeneity is mirrored in the molecular profiling. Mutations in PI3KCA (23% vs. 9%, P = 0.07) and its pathway (41% vs. 18%, P = 0.02) were frequently observed and enriched in MpBCs. The gene expression profiles of each histologically defined subtype were distinguishable and characterized by distinctive gene signatures. Among nonmetaplastic (non-Mp) TNBCs, 10% possessed a metaplastic-like gene expression signature and had pCR rates and survival outcomes similar to MpBC.

CONCLUSIONS

Further investigations will determine if metaplastic-like tumors should be treated more similarly to MpBC in the clinic. The 23% pCR rate in this study suggests that patients with MpBC should be considered for NAT. To improve this rate, a pathway analysis predicted enrichment of histone deacetylase (HDAC) and RTK/MAPK pathways in MpBC, which may serve as new targetable vulnerabilities.

MRI for Evaluation of Complications of Breast Augmentation

Breast augmentation is one of the most common aesthetic procedures performed in the United States. Several techniques of breast augmentation have been developed, including the implantation of breast prostheses and the injection of autologous fat and other materials. The most common method of breast augmentation is to implant a prosthesis. There are different types of breast implants that vary in shape, composition, and the number of lumina. The rupture of breast implants is the leading cause of implant removal. The rupture rate increases substantially with the increasing age of the implant. Most implant ruptures are asymptomatic. Implant complications can be grouped into two categories: local complications in the breast and adjacent soft tissue, and systemic complications associated with rheumatologic or neurologic symptoms. The onset of local complications may be early (infection and periprosthetic collections including seromas, hematomas, or abscesses) or late (capsular contraction, implant rupture, gel bleed, or breast implant-associated anaplastic large cell lymphoma). Although mammography is the imaging modality for breast cancer screening, noncontrast breast MRI is the imaging modality of choice for evaluation of the integrity of breast implants and the complications of breast augmentation, for equivocal findings at conventional imaging, and as a supplement to mammography in patients with free injectable materials. The fifth edition of the Breast Imaging Reporting and Data System (BI-RADS) provides a systematic outline for MRI evaluation of patients with breast implants. Silicone- and water-selective sequences provide useful supplemental information to confirm intracapsular and extracapsular rupture. Breast MRI for evaluation of implant integrity does not require intravenous contrast material. The use of MRI contrast material in patients with breast augmentation is indicated when infection or malignancy is suspected. Radiologists should have a thorough understanding of the different techniques for breast augmentation, normal imaging features, and complications specific to breast augmentation. An invited commentary by Ojeda-Fournier is available online. ^©RSNA, 2022.

Prospective Comparison of Synthesized Mammography with DBT and Full-Field Digital Mammography with DBT Uncovers Recall Disagreements That may Impact Cancer Detection

RATIONALE AND OBJECTIVES

Synthesized mammography with digital breast tomosynthesis (SM+DBT) and full-field digital mammography with DBT were prospectively evaluated for recall rate (RR), cancer detection rate (CDR), positive predictive value 1 (PPV1), lesion recall differences, and disagreements in recall for additional imaging.

MATERIALS AND METHODS

From December 15, 2015 to January 15, 2017, after informed consent was obtained for this Health Insurance Portability and Accountability Act compliant study, each enrolled patient's SM+DBT and FFDM+DBT were interpreted sequentially by one of eight radiologists. RR, CDR, PPV1, and imaging findings (asymmetry, focal asymmetry, mass, architectural distortion, and calcifications) recalled were reviewed.

RESULTS

For SM+DBT and FFDM+DBT in 1022 patients, RR was 7.3% and 7.9% (SM+DBT vs. FFDM+DBT: diff= -0.6%; 90% CI= -1.4%, 0.1%); CDR was 6.8 and 7.8 per 1000 (SM+DBT vs. FFDM+DBT: diff= -1.0, 95% CI= -5.5, 2.8, p = 0.317); PPV1 was 9.3% and 9.9% (relative positive predictive value for SM+DBT vs. FFDM+DBT: 0.95, 95% CI: 0.73-1.22, p = 0.669). FFDM+DBT detected eight cancers; SM+DBT detected seven (missed 1 cancer with calcifications). SM+DBT and FFDM+DBT disagreed on patient recall for additional imaging in 19 patients, with majority (68%, 13/19 patients) in the recall of patients for calcifications. For calcifications, SM+DBT recalled six patients that FFDM+DBT did not recall, and FFDM+DBT recalled seven patients that SM+DBT did not recall, even though the total number of calcifications finding recalled was similar overall for both SM+DBT and FFDM+DBT.

CONCLUSION

Disagreement in recall of patients for calcifications may impact cancer detection by SM+DBT, warranting further investigation.

Abstract 2736: Forecasting treatment response to neoadjuvant therapy in triple-negative breast cancer via an image-guided digital twin

Introduction: Patients with locally advanced, triple-negative breast cancer (TNBC) typically receive neoadjuvant therapy (NAT) to downstage the tumor and to improve the outcome of subsequent breast conservation surgery. There are currently no methods to accurately predict how a TNBC patient will respond to NAT before surgery. In this work, we applied a digital twin framework to address this unmet clinical need, by integrating quantitative magnetic resonance imaging (MRI) data with mechanism-based mathematical modeling.

Methods: Multiparametric MRI was acquired in patients (N = 50) before, after 2 and 4 cycles of Adriamycin/Cyclophosphamide (A/C), and again after 12 cycles of Paclitaxel as part of the ARTEMIS (NCT02276433) trial. Within each imaging session, dynamic contrast-enhanced (DCE) MRI, diffusion-weighted imaging (DWI), and a pre-contrast T1-map were acquired. The images were processed by a pipeline consisting of motion correction, multiparametric image alignment, inter-visit image registration to align the tumor and surrounding breast tissue, tissue segmentation, and estimation of tumor cellularity from DWI. A mechanism-based mathematical model, a reaction-diffusion equation, is used to characterize the mobility of tumor cells via diffusion damped by mechanical tissue properties, tumor proliferation via logistic growth, and treatment-induced cell death via the delivery and decay of therapies. For each patient, pre-treatment images were used for model initialization. The model calibration and prediction were implemented with two strategies: 1) using images acquired during the A/C for calibration and predicting up to the end of A/C, and 2) using images acquired during and after the A/C for calibration and predicting up to the end of NAT. For strategy 1), we evaluated the model by comparing its predicted tumor volume and total tumor cellularity to the imaging measurements at the end of A/C. For strategy 2), we evaluated the model by comparing its predicted final response to the post-surgical pathological findings.

Results: For strategy 1), our framework predicted the change of tumor volume and total tumor cellularity with Pearson correlation coefficients of 0.91 and 0.89, respectively. Regarding strategy 2), our framework achieved an area under the receiver operator characteristic curve of 0.88 for distinguishing pCR from non-pCR. As a comparison, imaging measurement of tumor volume at the end of A/C achieved an AUC of 0.79.

Conclusion: Our approach successfully captures the patient-specific dynamics of TNBC response to NAT and provides an improved prediction of final response, which demonstrates the potential of a digital twin framework to be a powerful tool for predicting response to NAT. Once validated, the method will provide a unique opportunity for optimizing treatment plans on a patient-specific basis.

Citation Format: Chengyue Wu, Angela M. Jarrett, Zijian Zhou, Nabil Elshafeey, Beatriz E. Adrada, Rosalind P. Candelaria, Rania Mohamed, Medine Boge, Lei Huo, Jason White, Debu Tripathy, Vicente Valero, Jennifer Litton, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov. Forecasting treatment response to neoadjuvant therapy in triple-negative breast cancer via an image-guided digital twin [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2736.

Mid-treatment Ultrasound Descriptors as Qualitative Imaging Biomarkers of Pathologic Complete Response in Patients with Triple-Negative Breast Cancer

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 (p < 0.0001 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.

Abstract P3-02-03: Quantitative molecular breast imaging for early prediction of neoadjuvant systemic therapy response in locally advanced breast cancer patients

BACKGROUND: Increasing use of neoadjuvant systemic therapy (NAT) for early and locally advanced breast cancer led to critical need for development of tools capable of early treatment response assessment after NAT. Tc-99m sestamibi Molecular breast Imaging (MBI) as a functional imaging modality has a promise to detect changes in the tumor prior to anatomical changes detected by mammogram or ultrasound. PURPOSE: To evaluate the ability of quantitative MBI parameters to predict pathologic complete response (pCR) after completion of NAT in breast cancer patients. MATERIALS AND METHODS: Patients with invasive breast cancer (T1-T4, N0-N3, M0) planned for NAT followed by surgery were enrolled in a prospective IRB approved trial. MBI was performed at baseline and after two cycles of NAT. Patient demographic and tumor biology information (Ki-67, HER2, ER/PR) was collected. MBI images were quantified using a novel approach with corrections for scatter and attenuation and regions of interest (ROI) were drawn over tumors to compute three quantitative MBI uptake metrics for correlation with pathologic response: MBI-specific standardized uptake value (SUV), tumor to background ratio (TBR), and tumor volume. Pathologic complete response was determined based on final histopathology report at the time of surgery as absence of the invasive disease in the breast and axillary lymph nodes. MBI metrics at baseline, after 2 cycles of NAT and interval change were correlated with pCR and tumor biology using the Wilcoxon Rank Sum test, Kruskal-Wallis test or Fisher’s exact test. Statistical analysis was carried out using R (version 3.6.3, R Development Core Team). RESULTS: A total of 70 patients with median age 47.5 years (range 30-77) were included in the analysis. Breast cancer subtypes were: HER2 negative (ER/PR+) 35.7% (25/70), HER2 positive (ER/PR +/-) 35.7% (25/70), and triple negative (HER2-, ER/PR-) 28.6% (20/70). Change in SUV after 2 cycles of NAT was higher in patients with pCR compared to those who did not achieve pCR (mean decrease in SUV of 15.57 and 4.83 respectively, p&lt;0.001). Additionally, change in TBR in patients with pCR was also higher compared to patients who did not achieve pCR (mean decreases of 1.14 and 0.56, respectively, p&lt;0.001). No correlation was found between baseline SUV, baseline TBR, change in volume, and pCR. CONCLUSION: MBI-specific SUV and TBR changes after two cycles of NAT correlate and may predict pCR in patients with locally advanced breast cancer. Quantitative MBI parameters are novel promising imaging tools that may help to detect early clinical benefit and optimize management in patients receiving NAT.

Citation Format: Miral M Patel, Beatriz E Adrada, Benjamin Lopez, Rosalind P Candelaria, Jia Sun, Medine Boge, Rania M Mohamed, Nabil Elshafeey, Gary Whitman, MD, Huong T Le-Petross, Lumarie Santiago, Marion E Scoggins, Deanna Lane, Tanya Moseley, Galit Zylberman, Jerica Saddler, Jessica WT Leung, Wei T Yang, Vincente Valero, S Cheenu Kappadath, Gaiane M Rauch. Quantitative molecular breast imaging for early prediction of neoadjuvant systemic therapy response in locally advanced breast cancer patients [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 P3-02-03.

Abstract PD11-07: Integrated model for early prediction of neoadjuvant systemic therapy response in triple negative breast cancer

Background: TNBC constitutes an aggressive and heterogeneous group of tumors with variable response to neoadjuvant therapy (NAT) that currently lacks clinically available profiling strategies for prediction. We aimed to develop an integrated model based on imaging, pathological and clinical data capable to predict NAT response in TNBC early during therapy. METHOD AND MATERIALS:125 Stage I-III TNBC patients enrolled in an IRB approved prospective clinical trial (NCT02276433) who had DCE-MRI at baseline (BL) and post 2 cycles (C2) of NAT, and had surgery were included in this analysis. Tumor volume was calculated using 3D measurements at BL and C2 time points DCE-MRI. Percent tumor volume reduction (TVR) between BL and C2 was calculated. Demographic, clinical, and pathological data (age, T and N stage, histology, androgen receptor expression, Ki-67, stromal tumor infiltrating lymphocytes level (sTIL), and PD-L1 expression), and treatment response at surgery (pCR vs non-pCR) were documented. Recursive partitioning was used to identify TVR cutoff value. Multivariate logistic regression and ROC analysis were used to assess associations and build and evaluate predictive models. RESULTS: 61 (49%) TNBC pts showed pCR at surgery, and 64 (51%) non-pCR. Recursive partitioning analysis identified ≥ 55% TVR as the optimal cutoff values for pCR prediction at C2. TVR, N stage and sTIL were significantly associated with pCR in the multivariate analyses (p&lt;0.002, p&lt;0.01, p&lt;0.001, respectively). Integrated model containing TVR (≥55% vs &lt;55%), N stage (N0 vs N+) and sTIL (≥20% vs &lt;20%) was predictive of pCR with AUC 0.84 (95% CI:0.77-0.91). Integrated model performance was significantly better than TVR only or clinical only (sTIL, and N stage) models (p&lt;0.001). CONCLUSION: Integrated model that included imaging (DCE-MRI TVR), clinical (N stage) and pathological (sTIL) data showed high accuracy for prediction of NAT response in TNBC patients early during treatment. Validation of these results in a large prospective study is ongoing.

Citation Format: Gaiane Margishvili Rauch, Rosalind P. Candelaria, Mary Saber Guirguis, Medine Boge, Rania M. M. Mohamed, Nabil Elshafeey, Jia Sun, Gary J Whitman, Jessica Leung, Huong C Le-Petross, Lumarie Santiago, Deanna Lane, Marion Scoggins, David Spak, Miral M Patel, Frances Perez, Jason B. White, Elizabeth Ravenberg, Wei Peng, Debu Tripathy, Vicente Valero, Jennifer Litton, Lei Huo, Clinton Yam, Alastair Thompson, Jingfei Ma, Stacy L. Moulder, Wei Yang, Beatriz E. Adrada. Integrated model for early prediction of neoadjuvant systemic therapy response 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 PD11-07.

Abstract P1-08-03: Deep learning for early prediction of neoadjuvant chemotherapy response in triple negative breast cancers

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.

Abstract P2-14-14: Durvalumab and tremelimumab before surgery in patients with hormone receptor positive, HER2 negative stage II-III breast cancer

Background: The checkpoint inhibitors atezolizumab (anti-PD-L1) and pembrolizumab (anti-PD-1) are FDA approved for the treatment of patients with PD-L1 positive, metastatic triple negative breast cancer in combination with chemotherapy. The response rates to checkpoint blockade in hormone receptor positive, HER2 negative metastatic breast cancer have been less encouraging (RR 12% for pembrolizumab in KEYNOTE-028 and RR 2.8% for avelumab in the phase 1b trial JAVELIN). We conducted a feasibility trial and enrolled patients with Stage II-III hormone receptor positive, HER2 negative breast cancer to treatment with 2 cycles of durvalumab (anti-PD-L1) plus tremelimumab (anti-CTLA-4) prior to standard neoadjuvant chemotherapy and breast surgery.Methods: Eligible patients were treated with durvalumab at a dose of 1500 mg and tremelimumab at a dose of 75 mg, both administered intravenously, on days 1 and 28. Pre- and post-treatment tumor biopsies and blood samples were available for 5 out of 8 patients and were analyzed by CyTOF, IHC, and NanoString. Patients then received standard neoadjuvant chemotherapy prior to breast surgery. The target enrollment was 20 patients. Results: After 8 patients were enrolled and treated on protocol, the trial was stopped early due to toxicity, slow accrual, and delays in patients receiving standard neoadjuvant chemotherapy. The median age of the patients was 55 (range 39-66) and all 8 patients were women. All patients had ER/PR positive, HER2 negative invasive ductal carcinoma (2 with lobular features, 1 with focal mucinous features) and all patients had clinical Stage II disease. Five patients received both cycles of durvalumab/tremelimumab and the remaining 3 patients only received the first cycle. One patient discontinued therapy out of concern for progression, though repeat breast and lymph node biopsies were benign and she went on to have a pathologic complete response after standard neoadjuvant chemotherapy. The remaining 2 patients discontinued treatment after the first cycle due to Grade 3 colitis (Patient #1) and Grade 3 thyroiditis/adrenal insufficiency (Patient #8). Both patients required treatment with steroids and experienced delays in receiving standard therapy due to these adverse events (AEs). The other AEs reported were all Grade 1 or 2. A post-durvalumab/tremelimumab breast ultrasound was performed in 7 of 8 patients and the percentage change in volume of each patient’s primary breast mass was: -55%, +104% (biopsy benign), +65%, -30%, +90%, -8%, and -53%. Patient #1 (colitis) had chemotherapy administered adjuvantly and Patient #8 (thyroiditis, adrenal insufficiency) declined chemotherapy. Only 1 of 8 patients had a pathologic complete response at the time of surgery. IHC, CYTOF, and gene expression analyses showed an increase in immune cell subsets in tumor stroma post neoadjuvant immunotherapy. Conclusions: We conducted a feasibility trial of neoadjuvant durvalumab plus tremelimumab administered prior to standard neoadjuvant chemotherapy in patients with hormone receptor positive/HER2 negative Stage II or III breast cancer. The trial was stopped early after 2 of 8 patients experienced Grade 3 immune-related adverse events (colitis in 1 patient and thyroiditis/adrenal insufficiency in another patient).

Citation Format: Haven R. Garber, Sreyashi Basu, Sonali Jindal, Akshara Singareeka Raghavendra, Lumarie Santiago, Beatriz E. Adrada, Padmanee Sharma, James P. Allison, Jennifer Litton. Durvalumab and tremelimumab before surgery in patients with hormone receptor positive, HER2 negative stage II-III 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 P2-14-14.

Abstract P3-03-06: Prediction of response to neoadjuvant systemic therapy in triple negative breast cancer using baseline tumor MRI characteristics and imaging patterns of response

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&lt;0.001), while types 4 and 5 were associated with non-pCR at C2, (p&lt;0.001). The four patterns: types 2, 3, 4, 5, were associated with non-pCR at C4 (p&lt;0.001). Conclusion: Baseline MRI tumor morphological characteristics and MRI imaging response patterns during NST may be valuable markers for pCR prediction in TNBC. Qualitative breast MRI assessment may act as an accessible tool to identify TNBC patients who are unlikely to achieve pCR and may benefit from targeted therapies.

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.

Abstract P1-08-08: Forecasting treatment response to neoadjuvant systemic therapy in triple negative breast cancer viamathematical modeling and quantitative MRI

Introduction:. Patients with locally advanced triple-negative breast cancer (TNBC) typically receive neoadjuvant therapy (NAT) to downstage the tumor and to improve the outcome of the subsequent breast conservation surgery. A critical unmet need is the lack of a method to accurately predict how a patient with TNBC will respond to NAT before surgery. In this work, we applied a clinical-computational framework to predict response of TNBC early in the course of NAT, by integrating quantitative MRI with mechanism-based mathematical modeling. Methods:. Patients and Data. Multiparametric quantitative MRI was acquired in patients (n = 46) before, and after 2 and 4 cycles of Adriamycin/Cyclophosphamide (A/C) regimen as part of the MD Anderson Cancer Center TNBC Moonshot Program. Within each imaging session, dynamic contrast-enhanced (DCE-), diffusion-weighted imaging (DWI), and a pre-contrast T1-map were acquired. Image processing. The processing pipeline consisted of three components. First, the images within each visit were registered to account for patient motion, and the parametric maps from the DCE and DWI images were computed. Second, inter-visit image registration was achieved by a non-rigid registration applied on breast, with a rigid penalty applied on the tumor region to preserve its size and shape. Third, post-processing was performed for preparation of modeling, including segmentation of the breast contour and tissues, and calculation of voxel-wise cellularity within tumors. Mathematical modeling. A predictive model was developed based on a reaction-diffusion equation (Eq. 1). The mobility of tumor cells is represented by diffusion coupled to mechanical properties of the tissue (Eq. 2), and the proliferation of the tumor is described with logistic growth. The injection and decay of administered therapies, inducing tumor cell death, is also represented in the model (Eq. 3). The variables and parameters used are listed in Table 1. Eq. 1: ∂N(x,t)/∂t = ∇⋅(D(x,t) ∇N(x,t)) + k(x) (1 - N(x,t)/θ)N(x,t) - (λ1(x,t) + λ2(x,t))N(x,t). Eq. 2: D(x,t) = D0 e-γσ(x,t). Eq. 3: λn(x,t) = αne-βn t C(x,t), n = 1, 2. For each patient, the domain and initial condition were generated from the pre-treatment images, and the images acquired during NAT were used for patient-specific calibration of parameters. The calibrated model was then used to predict the response to be observed at the end of NAT. We evaluated the model by comparing its predictions of tumor volume, longest axis, voxel-wise cellularity, and total tumor cellularity to the imaging measurements at the end of A/C. Results:. Our model predicted the tumor volume, total cellularity, and longest axis with a Pearson correlation coefficient (PCC) of 0.85, 0.80, and 0.60, respectively. The accuracy of voxel-wise cellularity achieved a PCC with the median (range) of 0.89 (0.77 - 0.93) between the prediction and the actual measurement. Moreover, we set criteria of 70% shrinkage of tumor volume to define response versus non-response cases, with which our model achieved a differentiation sensitivity/specificity of 0.90/0.73. Discussion:. Preliminary results of our study demonstrate the potential of the clinical-computational framework as a powerful tool for predicting response to NAT. Once validated, the method could also assist in optimizing treatment plans on a patient specific basis, or guiding patient selection in trials for novel NAT regimens.

Table 1. Summary of the variables and parameters in the modelQuantitiesDefinition AssignmentDomainsΩbreast tissue domainGenerated from pre-treatment MRITEnd time point of NAT procedureDetermined from NAT schedulexCoordinate in breast tissueAssociated with spatial domain, ΩttimeAssociated with temporal domain, [0, T]VariablesN(x,t)Tumor cell numberInitialized from pre-treatment ADC, computed via Eq. 1D(x,t)Diffusive mobility of tumor cellsComputed via Eq. 2λn(x,t)Death rate induced by nth type of drugComputed via Eq. 3, n = 1 and 2 for A/Cσ(x,t)Von Mises stressComputed from gradient of N(x,t), based on Hormuth et al., 2018C(x,t)Spatiotemporal distribution of drugsAssigned based on NAT schedule and DCE imagesParametersk(x)Proliferation rate of tumor cellsLocally calibratedθTumor cells carry capacityGlobally calibratedαnEfficacy rate of nth type of drugGlobally calibratedβnDecay rate of of nth type of drugGlobally calibratedD0Diffusion coefficient of tumor cells in the absence of mechanical restrictionsGlobally calibratedγStress-tumor cell diffusion coupling constantAssigned based on Hormuth et al., 2018

Citation Format: Chengyue Wu, Angela M. Jarrett, Zijian Zhou, Nabil Elshafeey, Beatriz E. Adrada, Rosalind P. Candelaria, Rania M. Mohamed, Medine Boge, Lei Huo, Jason White, Debu Tripathy, Vicente Valero, Jennifer Litton, Stacy Moulder, Clinton Yam, Jong Bum Son, Jingfei Ma, Gaiane M. Rauch, Thomas E. Yankeelov. Forecasting treatment response to neoadjuvant systemic therapy in triple negative breast cancer viamathematical modeling and quantitative MRI [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 P1-08-08.

MRI-guided Breast Biopsy Case-based Review: Essential Techniques and Approaches to Challenging Cases

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.

Bracketing with Multiple Radioactive Seeds to Achieve Negative Margins in Breast Conservation Surgery: Multiple Seeds in Breast Surgery

INTRODUCTION

Breast conservation surgery (BCS) is the treatment of choice for unifocal, early-stage breast cancer. The ability to offer BCS to a wider subset of patients, including those with multifocal/multicentric cancer as well as extensive ductal carcinoma in situ, has emerged over time, especially in those undergoing joint oncoplastic reconstruction and those treated with neoadjuvant therapy. However, localization techniques using multiple radioactive seeds for bracketing in this patient subset have not been validated.

MATERIALS AND METHODS

A single-institution retrospective review was conducted of all patients with breast cancer who underwent BCS, guided by multiple bracketed iodine I 125 radioactive seeds between January 2014 and April 2017.

RESULTS

Bracketing of breast cancer using 2 or more radioactive seeds was performed in 157 breasts in 156 patients. Negative margins were achieved in 124 of 157 (79%) breasts, including 33 cases (21%) that underwent targeted margin reexcision at the time of surgery after intraoperative, multidisciplinary margin assessment. Thirty-three cases (21%) resulted in close or positive margins, of which 11 (7%) and 10 (6.4%) underwent completion mastectomy or repeat lumpectomy, respectively. Twelve patients (7.6%) did not undergo reexcision. En bloc resection was successful in 134 of 157 (85.4%) lumpectomies. Eighty-nine percent of the procedures were coupled with oncoplastic reconstruction.

CONCLUSION

Bracketing techniques using multiple radioactive seeds expands the indications for breast conservation therapy in patients who would have traditionally required mastectomy. Intraoperative margin assessment improves surgical and pathologic success. Larger defects created by multifocal resection are optimally managed in concert with oncoplastic reconstruction to minimize asymmetries and aesthetic defects.

BI-RADS Ultrasound Lexicon Descriptors and Stromal Tumor-Infiltrating Lymphocytes in Triple-Negative Breast Cancer

PURPOSE

Increased levels of stromal tumor-infiltrating lymphocytes (sTILs) have recently been considered a favorable independent prognostic and predictive biomarker in triple-negative breast cancer (TNBC). The purpose of this study was to determine the relationship between BI-RADS (Breast Imaging Reporting and Data System) ultrasound lexicon descriptors and sTILs in TNBC.

MATERIALS AND METHODS

Patients with stage I-III TNBC were evaluated within a single-institution neoadjuvant clinical trial. Two fellowship-trained breast radiologists used the BI-RADS ultrasound lexicon to assess pretreatment tumor shape, margin, echo pattern, orientation, posterior features, and vascularity. sTILs were defined as low <20 or high ≥20 on the pretreatment biopsy. Fisher's exact tests were used to assess the association between lexicon descriptors and sTIL levels.

RESULTS

The 284 patients (mean age 52 years, range 24-79 years) were comprised of 68% (193/284) with low-sTIL tumors and 32% (91/284) with high-sTIL tumors. TNBC tumors with high sTILs were more likely to have the following features: (1) oval/round shape than irregular shape (p = 0.003), (2) circumscribed or microlobulated margins than spiculated, indistinct, or angular margins (p = 0.0005); (3) complex cystic and solid pattern than heterogeneous pattern (p = 0.006); and (4) posterior enhancement than shadowing (p = 0.002). There was no significant association between sTILs and descriptors for orientation and vascularity (p = 0.06 and p = 0.49, respectively).

CONCLUSION

BI-RADS ultrasound descriptors of the pretreatment appearance of a TNBC tumor can be useful in discriminating between tumors with low and high sTIL levels. Therefore, there is a potential use of ultrasound tumor characteristics to complement sTILs when used as stratification factors in treatment algorithms for TNBC.

Assessment of Early Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer Using Amide Proton Transfer-weighted Chemical Exchange Saturation Transfer MRI: A Pilot Study

Purpose To determine if amide proton transfer-weighted chemical exchange saturation transfer (APT_W CEST) MRI is useful in the early assessment of treatment response in persons with triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, a total of 51 participants (mean age, 51 years [range, 26-79 years]) with TNBC were included who underwent APT_W CEST MRI with 0.9- and 2.0-µT saturation power performed at baseline, after two cycles (C2), and after four cycles (C4) of neoadjuvant systemic therapy (NAST). Imaging was performed between January 31, 2019, and November 11, 2019, and was a part of a clinical trial (registry number NCT02744053). CEST MR images were analyzed using two methods-magnetic transfer ratio asymmetry (MTR_asym) and Lorentzian line shape fitting. The APT_W CEST signals at baseline, C2, and C4 were compared for 51 participants to evaluate the saturation power levels and analysis methods. The APT_W CEST signals and their changes during NAST were then compared for the 26 participants with pathology reports for treatment response assessment. Results A significant APT_W CEST signal decrease was observed during NAST when acquisition at 0.9-µT saturation power was paired with Lorentzian line shape fitting analysis and when the acquisition at 2.0 µT was paired with MTR_asym analysis. Using 0.9-µT saturation power and Lorentzian line shape fitting, the APT_W CEST signal at C2 was significantly different from baseline in participants with pathologic complete response (pCR) (3.19% vs 2.43%; P = .03) but not with non-pCR (2.76% vs 2.50%; P > .05). The APT_W CEST signal change was not significant between pCR and non-pCR at all time points. Conclusion Quantitative APT_W CEST MRI depended on optimizing acquisition saturation powers and analysis methods. APT_W CEST MRI monitored treatment effects but did not differentiate participants with TNBC who had pCR from those with non-pCR. © RSNA, 2021 Clinical trial registration no. NCT02744053 Supplemental material is available for this article.Keywords Molecular Imaging-Cancer, Molecular Imaging-Clinical Translation, MR-Imaging, Breast, Technical Aspects, Tumor Response, Technology Assessment.

Neoadjuvant atezolizumab (atezo) and nab-paclitaxel (nab-p) in patients (pts) with triple-negative breast cancer (TNBC) with suboptimal clinical response to doxorubicin and cyclophosphamide (AC)

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Background: Neoadjuvant anti-PD-(L)1 therapy confers an improvement in pathological complete response (pCR) rate in unselected TNBC. However, given the potential for long-term morbidity from immune related adverse events (irAE), it is important to optimize the risk-benefit ratio for the use of these novel agents in the curative neoadjuvant setting. Suboptimal clinical response to neoadjuvant therapy (NAT) by sonography is associated with low rates of pCR rate (2-5%, GeparTrio and Aberdeen trials). Here, we report the results of a single arm phase II study of atezo and nab-p as the second phase of NAT in pts with TNBC with suboptimal clinical response to AC (NCT02530489). Methods: Pts with stage I-III TNBC showing suboptimal response to 4 cycles of doxorubicin and cyclophosphamide (AC), defined as disease progression or a <80% reduction in tumor volume by sonography, were eligible. Pts received atezo (1200mg IV, Q3 weeks x 4), and nab-p (100mg/m2 IV, Q1 week, x 12) as the second phase of NAT before undergoing surgery followed by adjuvant atezo (1200mg IV, Q3 weeks, x 4 cycles). This single arm, two-stage Gehan-type study was designed to detect an improvement in pCR from 5% to 20% in order to deem the regimen worthy of further study in a large, randomized, phase II/III trial; success was defined as pCR in 8 out of 37 pts enrolled. In a subset of pts, sufficient baseline tumor tissue was available for stromal TIL assessment (n=29). Results: 34 pts were enrolled from 2/2016-12/2020. Among the 33 pts who have completed NAT, the pCR rate was 30% (10/33, 95% CI: 16-49%) and the pCR/RCB-I rate was 42% (14/33, 95% CI: 25-61%). Clinicopathological characteristics are described in the table below. Treatment-related adverse events (all grades) occurring in ≥ 20% of pts include fatigue (73%), anemia (55%), peripheral sensory neuropathy (55%), neutropenia (48%), rash (42%), ALT elevation (39%), AST elevation (33%), nausea (30%), anorexia (24%), diarrhea (21%), myalgia (21%). Discontinuation of atezo due to irAEs occurred in 4 pts (12%, nephritis [n=2]; adrenal insufficiency [n=1]; hepatitis [n=1]); 2 of these pts had pCR. Conclusions: This study met its primary endpoint, demonstrating a promising signal of activity in this high risk pt population (pCR=30% vs 5% in historical controls). The 12% discontinuation rate due to irAEs confirms that further evaluation of a strategy administering immunotherapy only to pts with high risk disease not responding to AC warrants further investigation. Exploratory genomic and immunological correlative studies are ongoing. Clinical trial information: NCT02530489. [Table: see text]

Early ultrasound evaluation identifies excellent responders to neoadjuvant systemic therapy among patients with triple-negative breast cancer

BACKGROUND

Heterogeneity exists in the response of triple-negative breast cancer (TNBC) to standard anthracycline (AC)/taxane-based neoadjuvant systemic therapy (NAST), with 40% to 50% of patients having a pathologic complete response (pCR) to therapy. Early assessment of the imaging response during NAST may identify a subset of TNBCs that are likely to have a pCR upon completion of treatment. The authors aimed to evaluate the performance of early ultrasound (US) after 2 cycles of neoadjuvant NAST in identifying excellent responders to NAST among patients with TNBC.

METHODS

Two hundred fifteen patients with TNBC were enrolled in the ongoing ARTEMIS (A Robust TNBC Evaluation Framework to Improve Survival) clinical trial. The patients were divided into a discovery cohort (n = 107) and a validation cohort (n = 108). A receiver operating characteristic analysis with 95% confidence intervals (CIs) and a multivariate logistic regression analysis were performed to model the probability of a pCR on the basis of the tumor volume reduction (TVR) percentage by US from the baseline to after 2 cycles of AC.

RESULTS

Overall, 39.3% of the patients (42 of 107) achieved a pCR. A positive predictive value (PPV) analysis identified a cutoff point of 80% TVR after 2 cycles; the pCR rate was 77% (17 of 22) in patients with a TVR ≥ 80%, and the area under the curve (AUC) was 0.84 (95% CI, 0.77-0.92; P < .0001). In the validation cohort, the pCR rate was 44%. The PPV for pCR with a TVR ≥ 80% after 2 cycles was 76% (95% CI, 55%-91%), and the AUC was 0.79 (95% CI, 0.70-0.87; P < .0001).

CONCLUSIONS

The TVR percentage by US evaluation after 2 cycles of NAST may be a cost-effective early imaging biomarker for a pCR to AC/taxane-based NAST.

68Ga-DOTATATE Uptake in Primary Breast Cancer

ABSTRACT

68Ga-DOTA peptides have revolutionized the imaging of neuroendocrine tumors because the agents are specific to somatostatin receptors. However, other tumors, including breast cancer, have been shown to express somatostatin receptors. We present the case of a 74-year-old woman with primary cardiac paraganglioma, who was found to have 68Ga-DOTATATE activity in the breast on staging PET/CT. Subsequent breast imaging workup and biopsy confirmed primary invasive lobular breast cancer, which was not 18F-FDG-avid on prior FDG PET/CT. Our case is in alignment with prior studies that suggest that 68Ga-DOTA peptides may play a future role in imaging breast cancer patients.

Abstract PD6-06: Radiomic phenotypes from dynamic contrast-enhanced MRI (DCE-MRI) parametric maps for early prediction of response to neoadjuvant systemic therapy (NAST) in triple negative breast cancer (TNBC) patients

Background and Purpose:Early and accurate assessment ofbreast cancer response to NAST is important for patient management. In this study, we investigated the value of radiomic phenotypes derived from semi-quantitative and quantitative DCE-MRI parametric maps for early prediction of NASTresponse in TNBC patients. MATERIALS AND METHODS:This IRB approved study included 74 patients with stage I-III TNBC who were enrolled in the prospective ARTEMIS trial (NCT02276443). Pathologic complete response (pCR) and non-pCR were assessed by surgical histopathology after NAST (pCR=34; non-pCR=40).MRI scans were obtained at 3 time points during the NAST treatment with every 2-week anthracycline-based chemotherapy (AC): at baseline (BSL=74), post-2 cycles of AC (C2= 27) and post-4 cycles of AC (C4= 27). Patients went on to receive taxane-based chemotherapy prior to surgery. Tumor regions of interest (ROIs) were segmented by a breast radiologist at the early-phase subtractions of DCE-MRI scans using in-house developed software, followed by co-registration of the ROIs with quantitative (Ktrans, Veand Kep), and semi-quantitative DCE parametric maps (Maximum Slope Increase (MSI), Positive Enhancement Integral (PEI) and Peak Signal Enhancement Ratio (SER)).A total of 93 first order radiomic features were extracted from the tumor ROIs of each time point semi-quantitative DCE parametric map, while a total of 390 extracted radiomic features (first order-histogram features and second order grey-level-co-occurrence matrix) were extracted from each quantitative DCE parametric map using an in-house developed Matlab software.Radiomic features at each time point and changes between the 3 time points were compared between pCR and non-pCR using Wilcoxon Rank Sum test and Fisher’s exact test. Area under the receiver operating characteristics curve (AUC) was used to determine which features predicted pCR.Logistic regression was performed for feature selection, and used to build the radiomic phenotype model. The model performance was assessed by leave-one-out cross validation and 3-fold cross validation. RESULTS:Thirty-three radiomic features from PEI map were significantly different between pCR and non-pCR. The PEI most significant features were changesbetween BSL and C4 in skewness, mean and median (AUC=0.87, 0.85 and 0.87, p=&lt;0.001, 0.001 and 0.002 respectively). Additionally, 31 MSI features were significantly different between pCR and non-pCR. The top 2 features were the interscan-change in skewness between BSL and C2 (AUC=0.80, P=0.007) and C4 standard deviation (AUC=0.80, P=0.006). Four BSL Veradiomic features were statistically significant between pCR and non-pCR with the best being range of difference variance (AUC=0.64, P=0.03). One BSL Kepfeature (Angular-Variance of Information measure of correlation-2) was able to differentiate pCR from non-pCR (AUC=0.64, P=0.04). Five C4-Ktrans features were able to differentiate pCR and non-pCR, with the most significant being mean value (AUC=0.86, P=0.001). BSL-Kepradiomic model built from 24 features (AUC=0.80, p=0.003) and combined (Ktrans, Veand Kep)C2-radiomic model consisting of 20 features (AUC=0.97, p=0.01) showed the best performance for prediction of pCR. CONCLUSIONS:Radiomic phenotypes form DCE-MRI parametric maps were useful for differentiation between pCR and non-pCR and showed promise as noninvasive imaging biomarkers for early prediction of NAST response in TNBC. Potentially, DCE-MRI radiomic features may be used for development of diagnostic predictive model for early noninvasive assessment of NAST treatment response in TNBC patients.

Citation Format: Nabil Elshafeey, Beatriz E Adrada, Rosalind P Candelaria, Abeer H Abdelhafez, Benjamin C Musall, Jia Sun, Medine Boge, Rania M.M Mohamed, Hagar S Mahmoud, Jong Bum Son, Aikaterini Kotrosou, Shu Zhang, Jessica Leung, Deanna Lane, Marion Scoggins, David Spak, Elsa Arribas, Lumarie Santiago, Gary J. Whitman, Huong T Le-Petross, Tanya W Moseley, Jason B White, Elizabeth Ravenberg, Ken-Pin Hwang, Peng Wei, Jennifer K Litton, Lei Huo, Debu Tripathy, Vicente Valero, Alastair M Thompson, Stacy Moulder, Wei T Yang, Mark D Pagel, Jingfei Ma, Gaiane M Rauch. Radiomic phenotypes from dynamic contrast-enhanced MRI (DCE-MRI) parametric maps for early prediction of response to neoadjuvant systemic therapy (NAST) in triple negative breast cancer (TNBC) patients [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PD6-06.

Abstract PS3-08: Assessment of early response to neoadjuvant systemic therapy (NAST) of triple-negative breast cancer (TNBC) using chemical exchange saturation transfer (CEST) MRI: A pilot study

Introduction CEST MRI permits quantitation of macromolecules such as amide proteins that are of interest in cancer metabolism. However, optimal CEST acquisition and analysis methods remain undetermined. In this study, we investigated CEST MRI as an imaging biomarker for early treatment response in 51 TNBC patients receiving NAST and compared the performance with two different CEST saturation power levels and two analysis methods.

Methods A total of 51 stage I-III TNBC patients enrolled in the prospective ARTEMIS trial (NCT02276443) had CEST imaging performed on a 3T MRI scanner at baseline before NAST (BL, N = 51), after 2 cycles (C2, N = 37), and 4 cycles (C4, N = 44) of NAST. 33 of the 51 patients had imaging at all 3 time points. 29 of the 33 patients had pathological findings, with N = 16 with pathological complete response (pCR) and N = 13 with non-pCR. Two sets of CEST images using 0.9 and 2.0 µT saturation power levels were acquired and analyzed using the magnetization transfer ratio asymmetry (MTRasym) and the Lorentzian line fitting (Mag3.5) methods, for a total of 4 acquisition/analysis combinations. The group averaged CEST signals, MTRasym at 0.9 and 2.0 µT and Mag3.5 at 0.9 and 2.0 µT, at BL, C2 and C4 were determined and evaluated using unpaired (51 patients) and paired (33 patients) Kruskal-Wallis tests. The Mag3.5 at 0.9 µT and the MTRasym at 2.0 µT were further compared between pCR and non-pCR. The group averaged CEST signals at BL, C2, and C4 were evaluated using the Friedman test for the pCR and the non-PCR groups. Separately, the change in the CEST signal from BL to C2 and C4 was determined for each patient and evaluated using the Mann-Whitney test for both groups. P &lt; 0.05 was considered statistically significant.

Results The MTRasym at BL was higher at 2.0 µT than at 0.9 µT. In contrast, the Mag3.5 at BL was higher at 0.9 µT than at 2.0 µT. The MTRasym at 2.0 µT and the Mag3.5 at 0.9 µT decreased during treatment while the MTRasym at 0.9 µT and the Mag3.5 at 2.0 µT were similar. Both the unpaired and the paired Mag3.5 at 0.9 µT showed a significant decrease at C2 and C4 vs. BL (p &lt; 0.01). The unpaired and paired MTRasym at 2.0 µT showed a decrease, although the change was not significant except for the unpaired data at C4. The decrease in the group averaged Mag3.5 at 0.9 µT was significant at C2 vs. BL for the pCR group (p = 0.04), while it was not significant for the pCR group at C4 vs. BL and for the non-pCR group at either C2 or C4 vs. BL. The group averaged MTRasym at 2.0 µT changes were not significant for either the pCR or the non-pCR groups. None of the CEST signal changes on a per patient basis at C2-BL, C4-BL and C4-C2 were significantly different between the pCR and the non-pCR groups. Further, none of the group averaged CEST signals at BL, C2 and C4 were significantly different between the pCR and the non-pCR groups.

Conclusion Our study demonstrates that the CEST quantitation in TNBC patients undergoing NAST depends on acquisition and analysis. For a maximum change in the CEST effect, Lorentzian line fitting is better paired with acquisition at a low saturation power (0.9 µT) and MTRasym is better paired with acquisition at a high saturation power (2.0 µT). Further, a significant CEST signal decrease was observed in TNBC patients with pCR after NAST when a 0.9 µT saturation power and the Lorentzian line fitting were used. In comparison, the decrease was not significant in non-pCR patients using the same saturation power and analysis method. The results suggest that the CEST signal acquired at 0.9 µT saturation power and analyzed using Lorentzian line fitting may be able to differentiate between pCR and non-pCR among TNBC patients undergoing NAST. Additional studies with a larger patient population are ongoing to further validate our findings and their potential for determining pCR.

Citation Format: Shu Zhang, Gaiane M Rauch, Beatriz E Adrada, Medine Boge, Rania MM Mohamed, Abeer H Abdelhafez, Jong Bum Son, Jia Sun, Nabil A Elshafeey, Jason B White, Deanna L Lane, Jessica WT Leung, Marion E Scoggins, David A Spak, Elsa Arribas, Elizabeth Ravenberg, Lumarie Santiago, Tanya W Moseley, Gary J Whitman, Huong Le-Petross, Benjamin C Musall, Mitsuharu Miyoshi, Xinzeng Wang, Brandy Willis, Stacy Hash, Aikaterini Kotrotsou, Peng Wei, Ken-Pin Hwang, Alastair Thompson, Stacy L Moulder, Rosalind P Candelaria, Wei Yang, Jingfei Ma, Mark D Pagel. Assessment of early response to neoadjuvant systemic therapy (NAST) of triple-negative breast cancer (TNBC) using chemical exchange saturation transfer (CEST) MRI: A pilot study [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-08.

Abstract PS3-01: Quantitative dynamic contrast-enhanced (DCE) MRI radiomic phenotypes for prediction of nodal and distal metastasis in breast cancer patients

Background and Purpose:Image-based tumor phenotypes by using computer extraction techniques have been studied for evaluation of breast cancer invasiveness, stage, lymph node involvement, molecular subtypes and genomics. In this project we aimed to investigate ability of computer-extracted breast MR imaging radiomic phenotypes to predict nodal and distant metastasis in breast cancer patients.

MATERIALS AND METHODS:This retrospective IRB approved study included 416 biopsy proven breast cancer patients who had pretreatment DCE MRI in a single institution between 2014 and 2018. Patient’s demographic, clinical data, pathology at diagnosis and surgery, nodal and distant metastasis (M1) at follow up were documented. Using QuantX imaging software, the tumor volume of interest was automatically-segmented using the multiple dynamic phases of DCE MRI. A total of 33 radiomic features describing tumor phenotype were extracted from each tumor site. A linear discriminant analysis (LDA) as a classifier with nested feature selection 10-fold cross validation was used to build the radiomic signature for prediction of nodal and distant metastasis occurrence. Receiver operating characteristic (ROC) and precision-recall analyses were used to evaluate performance, with 95% confidence intervals from 1000 bootstraps, and Kaplan-Meier was used to calculate the progression-free survival estimates and associated hazard ratio at the median cutpoint of the probability of metastasis calculated by the LDA in the 10-fold cross-validation.

RESULTS:The quantitative DCE MRI radiomic model was able to differentiate between breast cancer patients with and without distant metastatic disease at follow up with area under the ROC of 0.75 (95% CI 0.65; 0.82) and precision-recall curves 0.46 (0.33;0.69), hazard ratio at median cut point is 3.76 (2.27; 6.24), p&lt;0.001. Volume, surface area, sphericity, margin, maximum uptake, and washout rate variation features played the most important role in differentiating between breast cancer patients with and without distant metastasis.

The DCE radiomic model was able predict presence of ipsilateral nodal disease (≥1 positive lymph nodes) at surgery with AUC 0.66 (95% CI: 0.60; 0.71), ≥4 positive lymph nodes at surgery with AUC 0.67 (95% CI: 0.60; 0.74), and N2/N3 disease with AUC 0.64 (95% CI: 0.56; 0.72). Effective radius was most important feature for nodal disease prediction.

CONCLUSIONS:Our results show that DCE MRI based radiomic phenotypes were able to predict nodal involvement and distant metastasis in breast cancer patients. Quantitative breast DCE MRI radiomics shows promise for noninvasive image based phenotyping for prediction of nodal and distant metastatic disease in breast cancer patients.

Citation Format: Gaiane Margishvili Rauch, Karen Drukker, Nabil Elshafeey, Rania M.m. Mohamed, Medina Boge, Beatriz E. Adrada, Rosalind P Candelaria, Mo Salama, Irene Shkatova, Maryellen Giger, Wei T Yang. Quantitative dynamic contrast-enhanced (DCE) MRI radiomic phenotypes for prediction of nodal and distal metastasis in breast cancer patients [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-01.

Functional Tumor Volume by Fast Dynamic Contrast-Enhanced MRI for Predicting Neoadjuvant Systemic Therapy Response in Triple-Negative Breast Cancer

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 triple-negative 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).

DATA CONCLUSION

FTV and TV measured at 1 minute after injection can predict response to NAST in TNBC.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: 4.

MRI features of pseudoangiomatous stromal hyperplasia with histopathological correlation

Pseudoangiomatous stromal hyperplasia (PASH), a rare, noncancerous lesion, is often an incidental finding on magnetic resonance imaging (MRI)-guided biopsy analysis of other breast lesions. We sought to describe the characteristics of PASH on MRI and identify the extent to which these characteristics are correlated with the amount of PASH in the pathology specimens. We identified 69 patients who underwent MRI-guided biopsies yielding a final pathological diagnosis of PASH between 2008 and 2015. We analyzed pre-biopsy MRI scans to document the appearance of the lesions of interest. All biopsy samples were classified as having ≤50% PASH or ≥51% PASH present on the pathological specimen. On MRI, 9 lesions (13%) appeared as foci, 19 (28%) appeared as masses with either washout or persistent kinetics, and 41 (59%) appeared as regions of nonmass enhancement. Of this latter group, 33 lesions (80%) showed persistent kinetic features. Masses, foci, and regions of nonmass enhancement did not significantly correlate with the percentage of PASH present in the biopsy specimens (P ≥ .05). Our findings suggest that PASH has a wide-ranging appearance on MRI but most commonly appears as a region of nonmass enhancement with persistent kinetic features. Our finding that most specimens had ≤50% PASH supports the notion that PASH is usually an incidental finding. We did not identify a definitive imaging characteristic that reliably identifies PASH.

Tumor necrosis by pretreatment breast MRI: association with neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC)

PURPOSE

To determine if tumor necrosis by pretreatment breast MRI and its quantitative imaging characteristics are associated with response to NAST in TNBC.

METHODS

This retrospective study included 85 TNBC patients (mean age 51.8 ± 13 years) with MRI before NAST and definitive surgery during 2010-2018. Each MRI included T2-weighted, diffusion-weighted (DWI), and dynamic contrast-enhanced (DCE) imaging. For each index carcinoma, total tumor volume including necrosis (TTV), excluding necrosis (TV), and the necrosis-only volume (NV) were segmented on early-phase DCE subtractions and DWI images. NV and %NV were calculated. Percent enhancement on early and late phases of DCE and apparent diffusion coefficient were extracted from TTV, TV, and NV. Association between necrosis with pathological complete response (pCR) was assessed using odds ratio (OR). Multivariable analysis was used to evaluate the prognostic value of necrosis with T stage and nodal status at staging. Mann-Whitney U tests and area under the curve (AUC) were used to assess performance of imaging metrics for discriminating pCR vs non-pCR.

RESULTS

Of 39 patients (46%) with necrosis, 17 had pCR and 22 did not. Necrosis was not associated with pCR (OR, 0.995; 95% confidence interval [CI] 0.4-2.3) and was not an independent prognostic factor when combined with T stage and nodal status at staging (P = 0.46). None of the imaging metrics differed significantly between pCR and non-pCR in patients with necrosis (AUC < 0.6 and P > 0.40).

CONCLUSION

No significant association was found between necrosis by pretreatment MRI or the quantitative imaging characteristics of tumor necrosis and response to NAST in TNBC.

Molecular Breast Imaging-guided Percutaneous Biopsy of Breast Lesions: A New Frontier on Breast Intervention

Molecular breast imaging (MBI) is an increasingly recognized nuclear medicine imaging modality to detect breast lesions suspicious for malignancy. Recent advances have allowed the development of tissue sampling of MBI-detected lesions using a single-headed camera (breast-specific gamma imaging system) or a dual-headed camera system (MBI system). In this article, we will review current indications of MBI, differences of the two single- and dual-headed camera systems, the appropriate selection of biopsy equipment, billing considerations, and radiation safety. It will also include practical considerations and guidance on how to integrate MBI and MBI-guided biopsy in the current breast imaging workflow.

Axillary ultrasound during neoadjuvant systemic therapy in triple-negative breast cancer patients

PURPOSE

To investigate the value of performing mid-treatment axillary ultrasound (AUS) in triple-negative breast cancer (TNBC) patients who are undergoing neoadjuvant systemic therapy (NAST) by determining the optimal cutoff number of abnormal nodes associated with residual nodal disease on surgical pathology.

MATERIALS AND METHODS

This sub-study, an interim analysis of an ongoing single-institution clinical trial enrolling patients with stage I-III TNBC, included 106 patients. Number of abnormal nodes at mid-treatment was assessed and recorded by experienced breast radiologists, who empirically categorized lymph nodes using a binary approach of sonographically-normal versus abnormal. Pathologic lymph node positivity was defined as presence of macrometastasis or micrometastasis in ≥1 axillary node from sentinel lymph node biopsy and/or axillary lymph node dissection.

RESULTS

Of 106 patients, 26 (25 %) had residual nodal disease and 80 (75 %) had no nodal disease at surgery. Median number of abnormal nodes at mid-treatment was 5 (standard deviation [SD], 5) for patients with residual nodal disease and 0 (SD, 2) for patients with no nodal disease at surgery (p < 0.0001). TNBC patients with >4 abnormal nodes at mid-treatment had a significantly higher chance of being node-positive at surgery (AUC = 0.908, p < 0.0001; PPV = 90 %).

CONCLUSION

Our data suggest that a cutoff of >4 abnormal nodes on mid-treatment AUS is associated with residual disease post-NAST. If our findings are substantiated by subsequent analyses, then mid-treatment AUS could be used to identify patients unlikely to achieve nodal pathologic complete response and who should be offered alternative therapy.

Prognostic impact of high stromal tumor-infiltrating lymphocytes (sTIL) in the absence of pathologic complete response (pCR) to neoadjuvant therapy (NAT) in early stage triple negative breast cancer (TNBC)

583

Background: Pathologic complete response is an excellent surrogate for disease-free survival (DFS) and overall survival (OS) in TNBC. High sTIL is associated with improved pCR rates in TNBC. Recent data suggest that high sTIL is also associated with improved outcomes in patients who received no chemotherapy for early stage TNBC (Park, Annals of Oncology, 2019). Thus, we hypothesized that high sTIL may have prognostic impact in patients who do not achieve pCR to NAT. Methods: Pretreatment core biopsies from 182 patients with early-stage TNBC enrolled on the ARTEMIS trial (NCT02276443) were evaluated for sTIL by H&E. Patients were stratified according to sTIL (low < 30%, and high > 30%) and pCR (patients with pCR vs. no pCR). The primary outcome measure was DFS, defined from the date of diagnosis to the first local recurrence, distant metastases or death. Cox proportional hazards regression model was used. During follow-up 33 events for DFS were observed. Results: Among subjects who achieve pCR, DFS was excellent regardless of sTIL status and significantly better than those without pCR (p < 0.05). However, patients with high sTIL and no pCR demonstrated significantly worse DFS compared to all subjects having pCR (HR 0.18, 95% CI 0.04-0.76, p = 0.02). Additionally, we did not find a significant difference between high and low sTIL patients who did not achieve pCR. Conclusions: In early TNBC receiving NAT, for patients failing to achieve pCR, high sTIL was not associated with improved DFS; outcomes were comparable to those with low sTIL without pCR. Thus, high sTIL at baseline does not appear to confer an intrinsic prognostic benefit in the absence of pCR.

Statistical modeling of a novel clinical trial design using neoadjuvant therapy (NAT) to personalize therapy in patients (pts) with triple-negative breast cancer (TNBC)

595

Background: 40-50% of pts with TNBC develop pathologic complete response (pCR) with adriamycin/cyclophosphamide (AC)àtaxane (T) NAT; thus, most pts treated in randomized trials (RCTs) adding experimental drugs (ED) to standard NAT do not benefit from trial participation. A personalized trial design that enriches for non-pCR to standard NAT would diminish toxicity from ED in pts who do not need them and enrich ED in high-risk pts that are most likely to benefit. Methods: ARTEMIS (NCT02276443) is a non-randomized trial to study personalization of NAT in TNBC. Tumor biopsies were performed pre-NAT and volumetric change by ultrasound (VCU) after 4 cycles of AC (or upon clinical progression) assessed response. Pts with sensitive TNBC (VCU >=70% after AC) had T as the second phase of NAT. Pts with <70% VCU were offered phase II trials. pCR was assessed at surgical resection. 273 pts had available pCR status and 222 had complete data to generate a model predictive of response using multivariate logistic regression with common clinical factors. Data was randomly divided into training (n=111) and validation (n=111) sets. Results: 85 pts (38%) had pCR and VCU after AC x 4 was the strongest predictor of pCR. Other factors significant on multivariate analysis and included in the model were T stage (T1-4), stromal TIL, Ki67 and PD-L1. When applied to the validation data set, the accuracy of this model for predicting pCR was 76.6%, sensitivity 78.6% and specificity 75.4%. The PPV was 66.0% and the NPV was 85.2% with a ROC curve AUC of 82.4%. Using these data, ED exposure (table) was estimated for the ARTEMIS study design vs a 1:1 or a 2:1 RCT design (with an estimated pCR in control arm=40%), with a demonstrated benefit for personalization. Conclusions: This modeling indicates that personalization of NAT trials has the potential to enrich ED exposure for non-responsive disease as well as diminish ED exposure in pts likely to achieve pCR with standard NAT. Improved prediction of pCR would further enhance personalized trial design. Clinical trial information: NCT02276443 . [Table: see text]