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.

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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.

Fat necrosis of the breast is a well-described benign entity that can result in unnecessary biopsy of breast lesions. The pathogenesis of fat necrosis is a non-suppurative inflammatory process of adipose tissue, which may be seen after trauma, surgery, biopsy, post-breast reconstruction, post-fat grafting, post-radiotherapy, infection, and duct ectasia, among other conditions. Clinically, these patients may be asymptomatic or may present with a palpable lump, skin tethering, induration, and occasionally axillary lymphadenopathy. Depending on the time at which diagnostic imaging is performed, fat necrosis can have highly variable appearances on different modalities as it evolves. This is directly related to whether inflammation or fibrosis is predominating within the lesion, and correlation with clinical history is paramount in evaluating these patients. This review aims to analyze benign and suspicious imaging features of fat necrosis confirmed by tissue sampling. Knowledge of both benign and malignant-appearing features of fat necrosis on conventional modalities such as mammography and ultrasound, as well as newer applications including digital breast tomosynthesis, PET/CT, and MRI, should help the radiologist minimize the number of unnecessary biopsies.

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.