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.

Backlogs in formal interpretation of radiology examinations: a pilot global survey.

OBJECTIVE: Anecdotal reports from imaging facilities globally suggest growing radiology interpretation reporting delays. This pilot study's primary aim was to estimate the backlog of formal interpretation of imaging examinations. METHODS: An online survey was distributed to radiologists globally to gather practice-specific characteristics, imaging volumes, and reporting for 3 types of examinations (brain/head CT scans, chest CT scans, and chest radiographs) at 4 time points: 7, 30, 90 days, and 6 months. RESULTS: We received responses from 49 radiologists in 16 countries on six continents. Unreported examinations (backlog) were present in thirty of 44 (68%) facilities. Backlogs for brain/head CT, chest CT, and chest radiographs were present in, respectively, 48%, 50%, and 59% of facilities at 7 days and 20%, 23%, and 32% of facilities at 6 months. When present, the mean proportion of backlog (range) at 7 days was 17% (1 to 96) for brain/head CT, 18% (3 to 82) for chest CT, and 22% (1 to 99) for chest radiographs. CONCLUSIONS: Our findings from this pilot study show a widespread global backlog in reporting common imaging examinations, and further research is needed on the issue and contributing factors.

A Deep Learning Decision Support Tool to Improve Risk Stratification and Reduce Unnecessary Biopsies in BI-RADS 4 Mammograms.

Purpose: To evaluate the performance of a biopsy decision support algorithmic model, the intelligent-augmented breast cancer risk calculator (iBRISK), on a multicenter patient dataset. Materials and Methods: iBRISK was previously developed by applying deep learning to clinical risk factors and mammographic descriptors from 9700 patient records at the primary institution and validated using another 1078 patients. All patients were seen from March 2006 to December 2016. In this multicenter study, iBRISK was further assessed on an independent, retrospective dataset (January 2015-June 2019) from three major health care institutions in Texas, with Breast Imaging Reporting and Data System (BI-RADS) category 4 lesions. Data were dichotomized and trichotomized to measure precision in risk stratification and probability of malignancy (POM) estimation. iBRISK score was also evaluated as a continuous predictor of malignancy, and cost savings analysis was performed. Results: The iBRISK model's accuracy was 89.5%, area under the receiver operating characteristic curve (AUC) was 0.93 (95% CI: 0.92, 0.95), sensitivity was 100%, and specificity was 81%. A total of 4209 women (median age, 56 years [IQR, 45-65 years]) were included in the multicenter dataset. Only two of 1228 patients (0.16%) in the "low" POM group had malignant lesions, while in the "high" POM group, the malignancy rate was 85.9%. iBRISK score as a continuous predictor of malignancy yielded an AUC of 0.97 (95% CI: 0.97, 0.98). Estimated potential cost savings were more than $420 million. Conclusion: iBRISK demonstrated high sensitivity in the malignancy prediction of BI-RADS 4 lesions. iBRISK may safely obviate biopsies in up to 50% of patients in low or moderate POM groups and reduce biopsy-associated costs.Keywords: Mammography, Breast, Oncology, Biopsy/Needle Aspiration, Radiomics, Precision Mammography, AI-augmented Biopsy Decision Support Tool, Breast Cancer Risk Calculator, BI-RADS 4 Mammography Risk Stratification, Overbiopsy Reduction, Probability of Malignancy (POM) Assessment, Biopsy-based Positive Predictive Value (PPV3) Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by McDonald and Conant in this issue.

Patient-Reported Outcomes of Omission of Breast Surgery Following Neoadjuvant Systemic Therapy: A Nonrandomized Clinical Trial.

Importance: Patients should have an active role in decisions about pursuing or forgoing specific therapies in treatment de-escalation trials. Objective: To evaluate longitudinal patient-reported outcomes (PROs) encompassing decisional comfort and health-related quality of life (HRQOL) among patients who elected to enroll in a clinical trial evaluating radiotherapy alone, without breast surgery, for invasive breast cancers with exceptional response to neoadjuvant systemic therapy (NST). Design, Setting, and Participants: Prospective, single-group, phase 2 clinical trial at 7 US medical centers. Women aged 40 years or older with invasive cT1-2 N0-1 M0 triple-negative or human epidermal growth factor receptor 2 (ERBB2)-positive breast cancer with no pathologic evidence of residual disease following standard NST enrolled from March 6, 2017, to November 9, 2021. Validated PRO measures were administered at baseline and 6, 12, and 36 months post-radiotherapy. Data were analyzed from January to February 2023. Interventions: PRO measures included the Decision Regret Scale (DRS), Functional Assessment of Cancer Therapy-Lymphedema (FACT-B+4), and Breast Cancer Treatment Outcomes Scale (BCTOS). Main Outcomes and Measures: Changes in PRO measure scores and subscores over time. Results: Among 31 patients, the median (IQR) age was 61 (56-66) years, 26 (84%) were White, and 26 (84%) were non-Hispanic. A total of 15 (48%) had triple-negative disease and 16 (52%) had ERBB2-positive disease. Decisional comfort was high at baseline (median [IQR] DRS score 10 [0-25] on a 0-100 scale, with higher scores indicating higher decisional regret) and significantly increased over time (median [IQR] DRS score at 36 months, 0 [0-20]; P < .001). HRQOL was relatively high at baseline (median [IQR] FACT-B composite score 121 [111-134] on a 0-148 scale, with higher scores indicating higher HRQOL) and significantly increased over time (median [IQR] FACT-B score at 36 months, 128 [116-137]; P = .04). Perceived differences between the affected breast and contralateral breast were minimal at baseline (median [IQR] BCTOS score 1.05 [1.00-1.23] on a 1-4 scale, with higher scores indicating greater differences) and increased significantly over time (median [IQR] BCTOS score at 36 months, 1.36 [1.18-1.64]; P < .001). At 36 months postradiotherapy, the cosmetic subscore was 0.45 points higher than baseline (95% CI, 0.16-0.74; P = .001), whereas function, pain, and edema subscores were not significantly different than baseline. Conclusions and Relevance: In this nonrandomized phase 2 clinical trial, analysis of PROs demonstrated an overall positive experience for trial participants, with longitudinal improvements in decisional comfort and overall HRQOL over time and minimal lasting adverse effects of therapy. Trial Registration: ClinicalTrials.gov Identifier: NCT02945579.

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.

Eliminating Breast Surgery for Invasive Cancer with Exceptional Response to Neoadjuvant Systemic Therapy: Prospective Multicenter Clinical Trial Planned Initial Feasibility Endpoint.

BACKGROUND: Response to neoadjuvant systemic therapy (NST) for breast cancer enables tailoring of subsequent therapy. Image-guided breast biopsy after NST can accurately predict a pathologic complete response (pCR). The feasibility phase of the clinical trial reported here assesses omission of breast surgery followed by radiotherapy in terms of local recurrence before trial expansion. STUDY DESIGN: Women with unicentric, cT1-2 N0-1 M0 triple-negative (TNBC) or human epidermal growth factor receptor 2-positive breast cancer (HER2+BC) cancer with <2 cm residual disease on post-NST imaging were eligible to enroll. If no residual invasive or in situ disease was identified by image-guided, vacuum-assisted core biopsy (VACB), breast surgery was omitted, and radiotherapy delivered. The primary endpoint for the feasibility phase was ipsilateral breast tumor recurrence at 6 months. If any recurrence occurred during the feasibility phase the trial would halt. RESULTS: Thirteen patients were enrolled from March 2017 to October 2018. The mean age was 60.8 years (range 51 to 75) and most patients were White (69.2%) and non-Hispanic/Latino (84.6%). All patients had invasive ductal carcinoma (6 TNBC, 7 HER2+BC). Mean tumor size was 2.4 cm (range 0.9 to 5.0) before NST and 0.7 cm (range 0 to 1.8) after NST. Seven patients (53.8%) had residual disease identified on VACB; the remaining 6 (46.2%) comprised the feasibility cohort. At a median follow-up of 44.3 months (range 41.3 to 51.3) there was no ipsilateral breast tumor recurrence in this cohort. CONCLUSIONS: These early data suggest that omission of breast surgery in patients with invasive TNBC and HER2+BC with no evidence of residual disease on standardized VACB after NST is potentially feasible. Results from the expansion phase of this clinical trial will be reported per protocol prespecified analyses.

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.

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.

Overview of incidental breast lesions.

Screening mammography identifies early-stage breast cancers and is associated with reduced breast cancer mortality. An unintended consequence of breast screening is the detection and diagnosis of multiple incidental benign and malignant breast lesions, including the heterogenous group of incidental benign (B3) lesions with uncertain biological outcomes and malignant potential, for which management remains controversial. Recent shifts toward more conservative management approaches, including the identification of patients who can undergo observation after vacuum-assisted excision, has gained interest. This commentary provides an overview of incidental B3 lesions and summarizes the current management paradigms for them.

Breast Angiosarcoma: Imaging Features With Histopathologic Correlation.

Breast angiosarcoma is a rare malignancy of endothelial origin that can be categorized as primary angiosarcoma (PAS) or secondary angiosarcoma (SAS) based on etiology. Primary angiosarcoma typically affects younger women with no known risk factors, whereas SAS of the breast typically develops in older women who have undergone breast cancer treatment. There are two types of SAS, one that develops in the setting of chronic lymphedema and one that develops as a radiation-associated neoplasm after breast-conserving therapy (BCT). Clinically, PAS often presents as a palpable mass that may be rapidly growing, whereas SAS presents with skin changes such as erythematous plaques or nodules or with areas of skin discoloration. Mammographically, the appearance of PAS can be nonspecific and may be obscured by the dense tissue that is characteristic of the young patient population it typically affects. Cases of mammographically occult PAS have been visible at US and MRI. Mammography and US have been found to be less sensitive than MRI for the diagnosis of secondary radiation-associated angiosarcoma. Angiosarcomas, both PAS and SAS, are graded, depending on degree of differentiation, as low, intermediate, or high grade. Endothelial markers such as ERG and CD31 immunohistochemical stains are used to support the diagnosis of angiosarcomas. In this article, we review the clinical presentation, imaging findings, associated histopathology, and treatment of primary and secondary breast angiosarcoma.

Deep Learning Models for Automated Assessment of Breast Density Using Multiple Mammographic Image Types.

Recently, convolutional neural network (CNN) models have been proposed to automate the assessment of breast density, breast cancer detection or risk stratification using single image modality. However, analysis of breast density using multiple mammographic types using clinical data has not been reported in the literature. In this study, we investigate pre-trained EfficientNetB0 deep learning (DL) models for automated assessment of breast density using multiple mammographic types with and without clinical information to improve reliability and versatility of reporting. 120,000 for-processing and for-presentation full-field digital mammograms (FFDM), digital breast tomosynthesis (DBT), and synthesized 2D images from 5032 women were retrospectively analyzed. Each participant underwent up to 3 screening examinations and completed a questionnaire at each screening encounter. Pre-trained EfficientNetB0 DL models with or without clinical history were optimized. The DL models were evaluated using BI-RADS (fatty, scattered fibroglandular densities, heterogeneously dense, or extremely dense) versus binary (non-dense or dense) density classification. Pre-trained EfficientNetB0 model performances were compared using inter-observer and commercial software (Volpara) variabilities. Results show that the average Fleiss' Kappa score between-observers ranged from 0.31-0.50 and 0.55-0.69 for the BI-RADS and binary classifications, respectively, showing higher uncertainty among experts. Volpara-observer agreement was 0.33 and 0.54 for BI-RADS and binary classifications, respectively, showing fair to moderate agreement. However, our proposed pre-trained EfficientNetB0 DL models-observer agreement was 0.61-0.66 and 0.70-0.75 for BI-RADS and binary classifications, respectively, showing moderate to substantial agreement. Overall results show that the best breast density estimation was achieved using for-presentation FFDM and DBT images without added clinical information. Pre-trained EfficientNetB0 model can automatically assess breast density from any images modality type, with the best results obtained from for-presentation FFDM and DBT, which are the most common image archived in clinical practice.

Eliminating breast surgery for invasive breast cancer in exceptional responders to neoadjuvant systemic therapy: a multicentre, single-arm, phase 2 trial.

BACKGROUND: Neoadjuvant systemic therapy (NST) for triple-negative breast cancer and HER2-positive breast cancer yields a pathological complete response in approximately 60% of patients. A pathological complete response to NST predicts an excellent prognosis and can be accurately determined by percutaneous image-guided vacuum-assisted core biopsy (VACB). We evaluated radiotherapy alone, without breast surgery, in patients with early-stage triple-negative breast cancer or HER2-positive breast cancer treated with NST who had an image-guided VACB-determined pathological complete response. METHODS: This multicentre, single-arm, phase 2 trial was done in seven centres in the USA. Women aged 40 years or older who were not pregnant with unicentric cT1-2N0-1M0 triple-negative breast cancer or HER2-positive breast cancer and a residual breast lesion less than 2 cm on imaging after clinically standard NST were eligible for inclusion. Patients had one biopsy (minimum of 12 cores) obtained by 9G image-guided VACB of the tumour bed. If no invasive or in-situ disease was identified, breast surgery was omitted, and patients underwent standard whole-breast radiotherapy (40 Gy in 15 fractions or 50 Gy in 25 fractions) plus a boost (14 Gy in seven fractions). The primary outcome was the biopsy-confirmed ipsilateral breast tumour recurrence rate determined using the Kaplan-Meier method assessed in the per-protocol population. Safety was assessed in all patients who received VACB. This study has completed accrual and is registered with ClinicalTrials.gov, NCT02945579. FINDINGS: Between March 6, 2017, and Nov 9, 2021, 58 patients consented to participate; however, four (7%) did not meet final inclusion criteria and four (7%) withdrew consent. 50 patients were enrolled and underwent VACB following NST. The median age of the enrolled patients was 62 years (IQR 55-77); 21 (42%) patients had triple-negative breast cancer and 29 (58%) had HER2-positive breast cancer. VACB identified a pathological complete response in 31 patients (62% [95% CI 47·2-75·4). At a median follow-up of 26·4 months (IQR 15·2-39·6), no ipsilateral breast tumour recurrences occurred in these 31 patients. No serious biopsy-related adverse events or treatment-related deaths occurred. INTERPRETATION: Eliminating breast surgery in highly selected patients with an image-guided VACB-determined pathological complete response following NST is feasible with promising early results; however, additional prospective clinical trials evaluating this approach are needed. FUNDING: US National Cancer Institute (National Institutes of Health).

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.

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.

A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer.

PURPOSE: We aimed to develop a predictive model based on pretreatment MRI radiomic features (MRIRF) and tumor-infiltrating lymphocyte (TIL) levels, an established prognostic marker, to improve the accuracy of predicting pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in triple-negative breast cancer (TNBC) patients. METHODS: This Institutional Review Board (IRB) approved retrospective study included a preliminary set of 80 women with biopsy-proven TNBC who underwent NAST, pretreatment dynamic contrast enhanced MRI, and biopsy-based pathologic assessment of TIL. A threshold of ≥ 20% was used to define high TIL. Patients were classified into pCR and non-pCR based on pathologic evaluation of post-NAST surgical specimens. pCR was defined as the absence of invasive carcinoma in the surgical specimen. Segmentation and MRIRF extraction were done using a Food and Drug Administration (FDA) approved software QuantX. The top five features were combined into a single MRIRF signature value. RESULTS: Of 145 extracted MRIRF, 38 were significantly correlated with pCR. Five nonredundant imaging features were identified: volume, uniformity, peak timepoint variance, homogeneity, and variance. The accuracy of the MRIRF model, P = .001, 72.7% positive predictive value (PPV), 72.0% negative predictive value (NPV), was similar to the TIL model (P = .038, 65.5% PPV, 72.6% NPV). When MRIRF and TIL models were combined, we observed improved prognostic accuracy (P < .001, 90.9% PPV, 81.4% NPV). The models area under the receiver operating characteristic curve (AUC) was 0.632 (TIL), 0.712 (MRIRF) and 0.752 (TIL + MRIRF). CONCLUSION: A predictive model combining pretreatment MRI radiomic features with TIL level on pretreatment core biopsy improved accuracy in predicting pCR to NAST in TNBC patients.

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.

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.

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.

Factors Affecting Image Quality and Lesion Evaluability in Breast Diffusion-weighted MRI: Observations from the ECOG-ACRIN Cancer Research Group Multisite Trial (A6702).

OBJECTIVE: The A6702 multisite trial confirmed that apparent diffusion coefficient (ADC) measures can improve breast MRI accuracy and reduce unnecessary biopsies, but also found that technical issues rendered many lesions non-evaluable on diffusion-weighted imaging (DWI). This secondary analysis investigated factors affecting lesion evaluability and impact on diagnostic performance. METHODS: The A6702 protocol was IRB-approved at 10 institutions; participants provided informed consent. In total, 103 women with 142 MRI-detected breast lesions (BI-RADS assessment category 3, 4, or 5) completed the study. DWI was acquired at 1.5T and 3T using a four b-value, echo-planar imaging sequence. Scans were reviewed for multiple quality factors (artifacts, signal-to-noise, misregistration, and fat suppression); lesions were considered non-evaluable if there was low confidence in ADC measurement. Associations of lesion evaluability with imaging and lesion characteristics were determined. Areas under the receiver operating characteristic curves (AUCs) were compared using bootstrapping. RESULTS: Thirty percent (42/142) of lesions were non-evaluable on DWI; 23% (32/142) with image quality issues, 7% (10/142) with conspicuity and/or localization issues. Misregistration was the only factor associated with non-evaluability (P = 0.001). Smaller (≤10 mm) lesions were more commonly non-evaluable than larger lesions (p <0.03), though not significant after multiplicity correction. The AUC for differentiating benign and malignant lesions increased after excluding non-evaluable lesions, from 0.61 (95% CI: 0.50-0.71) to 0.75 (95% CI: 0.65-0.84). CONCLUSION: Image quality remains a technical challenge in breast DWI, particularly for smaller lesions. Protocol optimization and advanced acquisition and post-processing techniques would help to improve clinical utility.