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Introduction: Breast cancer is the most common cause of cancer-related mortality among women in Nigeria. Physical proximity to a diagnostic center is an important component of access, which has direct implication on the cost and timeliness of diagnosis and treatment. Beyond diagnostic mammography, ultrasound (US)-guided biopsy is an essential tool in the diagnostic pathway for suspicious lesions of the breast and is recommended by the Whole Health Organization Breast Health Global Initiative for LMICs. Unfortunately, there is a dearth of data on the population level access to mammography and US-guided breast biopsy in Nigeria and little is known about the geospatial inequities in access that targeted programming or investment could potentially address. This study undertook a comprehensive evaluation of breast imaging and diagnostic services in Nigeria and using a previously validated geographic information system (GIS) model, evaluated geospatial access to diagnostic mammography and US-guided breast biopsy in Nigeria. Methods: A comprehensive list of public and private facilities offering diagnostic mammography and/or US-guided breast biopsy was compiled using publicly available facility data from the Nigerian Ministry of Health, a survey administered to members of the Breast Imaging Society of Nigeria (BISON) as well as key stakeholder interviews from each of the countries six geopolitical zones. A novel survey was delivered to BISON members to identify additional/new facilities not captured by the latest Nigerian Ministry of Health data. Facility location, administration (public vs. private) and duration of service delivery were elicited from respondents and paired with the Ministry of Health facility dataset. Data on provider training and volume were also captured in the survey. All facilities were geolocated using Google Earth™ (Google, Mountain View, CA). A previously described cost-distance model, that uses open-source population density data to 100m2 (GeoData Institute) and road network data (OpenStreetMap) was used to estimate population level travel time to the nearest diagnostic center. Any portion of a route that included travel over terrain without roads was assigned a walking speed of 5 kmh-1. Geospatial access was calculated for mammography and US-guided biopsy separately and as well as by geopolitical zone. This study was approved by the research ethics board at Obafemi Awolowo University. Results: In addition to publicly available data from the Ministry of Health, facility and practice data was obtained from 63 Nigerian Radiologists from across the country. In total, 124 centers were identified that offer diagnostic mammography, of which 78 (63%) are privately administered. Nine of the countries 36 states did not have a center offering this service. Across the country, 33 centers offer US-guided breast biopsy, of which the majority (72.7%) are public. At a population level, 83.1% of the population has access within 120 minutes of continuous one-way travel to a center with diagnostic mammogram or US. At 240 minutes of continuous one-way travel, which corresponds to a full day of travel round-trip, 80.8% of the population has access to US-guided breast biopsy. However, there are differences in access between geopolitical zones. Just 68.7% of the population in the North East geopolitical zone has access to US-guided biopsy within a day’s travel (i.e. 240 minutes one-way). The remaining five geopolitical zones have population level access to this service of ≥80%. Conclusions: This is the first comprehensive evaluation of breast cancer imaging and diagnostic services in Nigeria. Our results, demonstrate that the majority of the population in Nigeria has reasonable geospatial access to basic breast cancer imaging services. However, there are inequalities in access between states and geopolitical zones in the north and south of the country, which may have an impact on timely diagnosis and care. Citation Format: Adeleye Omisore, Elizabeth J. Sutton, Gavin Tansley, Rachael Adeyanju AKINOLA, Gregory Knapp. Population level access to diagnostic mammography and ultrasound guided breast biopsy in Nigeria: a geospatial analysis [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-04-02.

Summary Background Female breast cancer is the most commonly diagnosed cancer in the world, with wide variations in reported survival by country. Women in low-income and middle-income countries (LMICs) in particular face several barriers to breast cancer services, including diagnostics and treatment. We aimed to estimate the potential impact of scaling up the availability of treatment and imaging modalities on breast cancer survival globally, together with improvements in quality of care. Methods For this simulation-based analysis, we used a microsimulation model of global cancer survival, which accounts for the availability and stage-specific survival impact of specific treatment modalities (chemotherapy, radiotherapy, surgery, and targeted therapy), imaging modalities (ultrasound, x-ray, CT, MRI, PET, and single-photon emission computed tomography [SPECT]), and quality of cancer care, to simulate 5-year net survival for women with newly diagnosed breast cancer in 200 countries and territories in 2018. We calibrated the model to empirical data on 5-year net breast cancer survival in 2010–14 from CONCORD-3. We evaluated the potential impact of scaling up specific imaging and treatment modalities and quality of care to the mean level of high-income countries, individually and in combination. We ran 1000 simulations for each policy intervention and report the means and 95% uncertainty intervals (UIs) for all model outcomes. Findings We estimate that global 5-year net survival for women diagnosed with breast cancer in 2018 was 67·9% (95% UI 62·9–73·4) overall, with an almost 25-times difference between low-income (3·5% [0·4–10·0]) and high-income (87·0% [85·6–88·4]) countries. Among individual treatment modalities, scaling up access to surgery alone was estimated to yield the largest survival gains globally (2·7% [95% UI 0·4–8·3]), and scaling up CT alone would have the largest global impact among imaging modalities (0·5% [0·0–2·0]). Scaling up a package of traditional modalities (surgery, chemotherapy, radiotherapy, ultrasound, and x-ray) could improve global 5-year net survival to 75·6% (95% UI 70·6–79·4), with survival in low-income countries improving from 3·5% (0·4–10·0) to 28·6% (4·9–60·1). Adding concurrent improvements in quality of care could further improve global 5-year net survival to 78·2% (95% UI 74·9–80·4), with a substantial impact in low-income countries, improving net survival to 55·3% (42·2–67·8). Comprehensive scale-up of access to all modalities and improvements in quality of care could improve global 5-year net survival to 82·3% (95% UI 79·3–85·0). Interpretation Comprehensive scale-up of treatment and imaging modalities, and improvements in quality of care could improve global 5-year net breast cancer survival by nearly 15 percentage points. Scale-up of traditional modalities and quality-of-care improvements could achieve 70% of these total potential gains, with substantial impact in LMICs, providing a more feasible pathway to improving breast cancer survival in these settings even without the benefits of future investments in targeted therapy and advanced imaging. Funding Harvard T H Chan School of Public Health, National Cancer Institute P30 Cancer Center Support Grant to Memorial Sloan Kettering Cancer Center, and Breast Cancer Research Foundation.

As neoadjuvant chemotherapy (NAC) for breast cancer has become more widely used, so has nipple-sparing mastectomy. A common criterion for eligibility is a 1 cm tumor-to-nipple distance (TND), but its suitability after NAC is unclear. In this study, we examined factors predictive of negative nipple pathologic status (NS−) in women undergoing total mastectomy after NAC. Women with invasive breast cancer treated with NAC and total mastectomy from August 2014 to April 2018 at our institution were retrospectively identified. Following review of pre- and post-NAC magnetic resonance imaging (MRI) and mammograms, the association of clinicopathologic and imaging variables with NS− was examined and the accuracy of 1 cm TND on imaging for predicting NS− was determined. Among 175 women undergoing 179 mastectomies, 74% of tumors were cT1-T2 and 67% were cN+ on pre-NAC staging; 10% (18/179) had invasive or in situ carcinoma in the nipple on final pathology. On multivariable analysis, after adjusting for age, grade, and tumor stage, three factors, namely number of positive nodes, pre-NAC nipple-areolar complex retraction, and decreasing TND, were significant predictors of nipple involvement (p

Objective To investigate the feasibility of using deep learning to identify tumor-containing axial slices on breast MRI images. Methods This IRB–approved retrospective study included consecutive patients with operable invasive breast cancer undergoing pretreatment breast MRI between January 1, 2014, and December 31, 2017. Axial tumor-containing slices from the first postcontrast phase were extracted. Each axial image was subdivided into two subimages: one of the ipsilateral cancer-containing breast and one of the contralateral healthy breast. Cases were randomly divided into training, validation, and testing sets. A convolutional neural network was trained to classify subimages into “cancer” and “no cancer” categories. Accuracy, sensitivity, and specificity of the classification system were determined using pathology as the reference standard. A two-reader study was performed to measure the time savings of the deep learning algorithm using descriptive statistics. Results Two hundred and seventy-three patients with unilateral breast cancer met study criteria. On the held-out test set, accuracy of the deep learning system for tumor detection was 92.8% (648/706; 95% confidence interval: 89.7%–93.8%). Sensitivity and specificity were 89.5% and 94.3%, respectively. Readers spent 3 to 45 seconds to scroll to the tumor-containing slices without use of the deep learning algorithm. Conclusion In breast MR exams containing breast cancer, deep learning can be used to identify the tumor-containing slices. This technology may be integrated into the picture archiving and communication system to bypass scrolling when viewing stacked images, which can be helpful during nonsystematic image viewing, such as during interdisciplinary tumor board meetings.

Social scientists have advocated for the use of participatory research methods for Global Health project design and planning. However, community-engaged approaches can be time and resource-intensive. This article proposes a feasible framework for conducting a participatory needs assessment in time-limited settings using multiple, triangulated qualitative methods. This framework is outlined through a case study: a participatory needs assessment to inform the design of an ultrasound-guided biopsy training program in Nigeria. Breast cancer is the leading cause of death for Nigerian women and most cases in Nigeria are diagnosed at an advanced stage; timely diagnosis is impeded by fractious referral pathways, costly imaging equipment, and limited access outside urban centers. The project involved participant observation, surveys, and focus groups at the African Research Group for Oncology (ARGO) in Ile-Ife, Nigeria. Through this timely research and engagement, participants spoke about diagnostic challenges, institutional power dynamics, and infrastructure considerations for program implementation.

Background parenchymal enhancement (BPE) is assessed on breast MRI reports as mandated by the Breast Imaging Reporting and Data System (BI-RADS) but is prone to inter and intrareader variation. Semiautomated and fully automated BPE assessment tools have been developed but none has surpassed radiologist BPE designations.To develop a deep learning model for automated BPE classification and to compare its performance with current standard-of-care radiology report BPE designations.Retrospective.Consecutive high-risk patients (i.e.gt;20% lifetime risk of breast cancer) who underwent contrast-enhanced screening breast MRI from October 2013 to January 2019. The study included 5224 breast MRIs, divided into 3998 training, 444 validation, and 782 testing exams. On radiology reports, 1286 exams were categorized as high BPE (i.e., marked or moderate) and 3938 as low BPE (i.e., mild or minimal).A 1.5 T or 3 T system; one precontrast and three postcontrast phases of fat-saturated T1-weighted dynamic contrast-enhanced imaging.Breast MRIs were used to develop two deep learning models (Slab artificial intelligence (AI); maximum intensity projection [MIP] AI) for BPE categorization using radiology report BPE labels. Models were tested on a heldout test sets using radiology report BPE and three-reader averaged consensus as the reference standards.Model performance was assessed using receiver operating characteristic curve analysis. Associations between high BPE and BI-RADS assessments were evaluated using McNemar's chi-square test (α* = 0.025).The Slab AI model significantly outperformed the MIP AI model across the full test set (area under the curve of 0.84 vs. 0.79) using the radiology report reference standard. Using three-reader consensus BPE labels reference standard, our AI model significantly outperformed radiology report BPE labels. Finally, the AI model was significantly more likely than the radiologist to assign "high BPE" to suspicious breast MRIs and significantly less likely than the radiologist to assign "high BPE" to negative breast MRIs.Fully automated BPE assessments for breast MRIs could be more accurate than BPE assessments from radiology reports.4 TECHNICAL EFFICACY STAGE: 3.

Purpose: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI. Materials and Methods: In this retrospective study, 38229 examinations (composed of 64063 individual breast scans from 14475 patients) were performed in female patients (age range, 12-94 years; mean age, 52 years +/- 10 [standard deviation]) who presented between 2002 and 2014 at a single clinical site. A total of 2555 breast cancers were selected that had been segmented on two-dimensional (2D) images by radiologists, as well as 60108 benign breasts that served as examples of noncancerous tissue; all these were used for model training. For testing, an additional 250 breast cancers were segmented independently on 2D images by four radiologists. Authors selected among several three-dimensional (3D) deep convolutional neural network architectures, input modalities, and harmonization methods. The outcome measure was the Dice score for 2D segmentation, which was compared between the network and radiologists by using the Wilcoxon signed rank test and the two one-sided test procedure. Results: The highest-performing network on the training set was a 3D U-Net with dynamic contrast-enhanced MRI as input and with intensity normalized for each examination. In the test set, the median Dice score of this network was 0.77 (interquartile range, 0.26). The performance of the network was equivalent to that of the radiologists (two one-sided test procedures with radiologist performance of 0.69-0.84 as equivalence bounds, P

To assess whether no enhancement on pre-treatment MRI can rule out malignancy of additional US mass(es) initially assessed as BI-RADS 3 or 4 in women with newly diagnosed breast cancer. This retrospective study included consecutive women from 2010–2018 with newly diagnosed breast cancer; at least one additional breast mass (distinct from index cancer) assigned a BI-RADS 3 or 4 on US; and a bilateral contrast-enhanced breast MRI performed within 90 days of US. All malignant masses were pathologically proven; benign masses were pathologically proven or defined as showing at least 2 years of imaging stability. Incidence of malignant masses and NPV were calculated on a per-patient level using proportions and exact 95% CIs. In 230 patients with 309 additional masses, 140/309 (45%) masses did not enhance while 169/309 (55%) enhanced on MRI. Of the 140 masses seen in 105 women (mean age, 54 years; range 28–82) with no enhancement on MRI, all had adequate follow-up and 140/140 (100%) were benign, of which 89/140 (63.6%) were pathologically proven and 51/140 (36.4%) demonstrated at least 2 years of imaging stability. Pre-treatment MRI demonstrating no enhancement of US mass correlate(s) had an NPV of 100% (95% CI 96.7–100.0). All BI-RADS 3 and 4 US masses with a non-enhancing correlate on pre-treatment MRI were benign. The incorporation of MRI, when ordered by the referring physician, may decrease unnecessary follow-up imaging and/or biopsy if the initial US BI-RADS assessment and management recommendation were to be retrospectively updated. • Of 309 BI-RADS 3 or 4 US masses with a corresponding mass on MRI, 140/309 (45%) demonstrated no enhancement whereas 169/309 (55%) demonstrated enhancement • All masses classified as BI-RADS 3 or 4 on US without enhancement on MRI were benign • MRI can rule out malignancy in non-enhancing US masses with an NPV of 100%

Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) is a rare but increasingly important diagnosis as the incidence of breast implant placement, both elective and reconstructive, continues to rise. When detected and treated early, this indolent disease carries an excellent prognosis. However, because the clinical presentation is often nonspecific, it is crucial for radiologists to accurately identify the imaging findings associated with BIA-ALCL to facilitate a timely diagnosis. This article will provide radiologists with an overview of the diagnosis, imaging findings, and management of BIA-ALCL.

Background For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be assessed non-invasively so all patients undergo surgery. The aim of our study was to develop and validate a radiomics classifier that classifies breast cancer pCR post-NAC on MRI prior to surgery. Methods This retrospective study included women treated with NAC for breast cancer from 2014 to 2016 with (1) pre- and post-NAC breast MRI and (2) post-NAC surgical pathology report assessing response. Automated radiomics analysis of pre- and post-NAC breast MRI involved image segmentation, radiomics feature extraction, feature pre-filtering, and classifier building through recursive feature elimination random forest (RFE-RF) machine learning. The RFE-RF classifier was trained with nested five-fold cross-validation using (a) radiomics only (model 1) and (b) radiomics and molecular subtype (model 2). Class imbalance was addressed using the synthetic minority oversampling technique. Results Two hundred seventy-three women with 278 invasive breast cancers were included; the training set consisted of 222 cancers (61 pCR, 161 no-pCR; mean age 51.8 years, SD 11.8), and the independent test set consisted of 56 cancers (13 pCR, 43 no-pCR; mean age 51.3 years, SD 11.8). There was no significant difference in pCR or molecular subtype between the training and test sets. Model 1 achieved a cross-validation AUROC of 0.72 (95% CI 0.64, 0.79) and a similarly accurate (P = 0.1) AUROC of 0.83 (95% CI 0.71, 0.94) in both the training and test sets. Model 2 achieved a cross-validation AUROC of 0.80 (95% CI 0.72, 0.87) and a similar (P = 0.9) AUROC of 0.78 (95% CI 0.62, 0.94) in both the training and test sets. Conclusions This study validated a radiomics classifier combining radiomics with molecular subtypes that accurately classifies pCR on MRI post-NAC.

Objective To determine survival outcomes in women with breast cancer detected at combined screening with breast MRI and mammography versus screening mammography alone. Methods This is an institutional review board-approved retrospective study, and the need for informed consent was waived. A total of 3002 women with an increased risk of breast cancer were screened between 2001 and 2004. Of the 3002 women, 1534 (51.1%) had 2780 combined screenings (MRI and mammography) and 1468 (48.9%) had 4811 mammography-only screenings. The Χ2 test and the Kaplan-Meier method were used to compare cancer detection rates and survival rates. Results The overall cancer detection rate was significantly higher in the MRI plus mammography group compared with the mammography-only group (1.4% [40 of 2780] vs 0.5% [23 of 4811]; P < 0.001). No interval cancers occurred in the MRI plus mammography group, whereas 9 interval cancers were found in the mammography-only group. During a median follow-up of 10.9 years (range: 0.7 to 15.2), a total of 11 recurrences and 5 deaths occurred. Of the 11 recurrences, 6 were in the MRI plus mammography group and 5 were in the mammography-only group. All five deaths occurred in the mammography-only group. Disease-free survival showed no statistically significant difference between the two groups (P = 0.32). However, overall survival was significantly improved in the MRI plus mammography group (P = 0.002). Conclusion Combined screening with MRI and mammography in women at elevated risk of breast cancer improves cancer detection and overall survival.

Background: Differences in imaging parameters influence computer-extracted parenchymal enhancement measures from breast MRI. Purpose: To investigate the effect of differences in dynamic contrast-enhanced MRI acquisition parameter settings on quantitative parenchymal enhancement of the breast, and to evaluate harmonization of contrast-enhancement values with respect to flip angle and repetition time. Study Type: Retrospective. Phantom/Populations: We modeled parenchymal enhancement using simulations, a phantom, and two cohorts (N = 398 and N = 302) from independent cancer centers. Sequence Field/Strength: 1.5T dynamic contrast-enhanced T1-weighted spoiled gradient echo MRI. Vendors: Philips, Siemens, General Electric Medical Systems. Assessment: We assessed harmonization of parenchymal enhancement in simulations and phantom by varying the MR parameters that influence the amount of T1-weighting: flip angle (8°–25°) and repetition time (4–12 msec). We calculated the median and interquartile range (IQR) of the enhancement values before and after harmonization. In vivo, we assessed overlap of quantitative parenchymal enhancement in the cohorts before and after harmonization using kernel density estimations. Cohort 1 was scanned with flip angle 20° and repetition time 8 msec; cohort 2 with flip angle 10° and repetition time 6 msec. Statistical Tests: Paired Wilcoxon signed-rank-test of bootstrapped kernel density estimations. Results: Before harmonization, simulated enhancement values had a median (IQR) of 0.46 (0.34–0.49). After harmonization, the IQR was reduced: median (IQR): 0.44 (0.44–0.45). In the phantom, the IQR also decreased, median (IQR): 0.96 (0.59–1.22) before harmonization, 0.96 (0.91–1.02) after harmonization. Harmonization yielded significantly (P < 0.001) better overlap in parenchymal enhancement between the cohorts: median (IQR) was 0.46 (0.37–0.58) for cohort 1 vs. 0.37 (0.30–0.44) for cohort 2 before harmonization (57% overlap); and 0.35 (0.28–0.43) vs.0.37 (0.30–0.44) after harmonization (85% overlap). Data Conclusion: The proposed practical harmonization method enables an accurate comparison between patients scanned with differences in imaging parameters. Level of Evidence: 3. Technical Efficacy Stage: 4.

581 Background: Tumour budding is an independent prognostic factor in colorectal cancer and has recently been defined by the International Consensus Conference on Tumour Budding. The aim was to use the ITBCC budding evaluation method to examine relationships between tumour budding, tumour factors, tumour microenvironment, gene expression profiles and survival in patients with primary operable CRC. Methods: Hematoxylin and Eosin (H&E) stained slides of 953 CRC patients, diagnosed between 1997 and 2007 were evaluated for tumour budding according to the ITBCC-criteria. The tumour microenvironment was evaluated using tumour stroma percentage (TSP) and Klintrup–Makinen (KM) grade to assess the tumour inflammatory cell infiltrate. Differential gene expression was assessed using TempO-Seq gene expression profiling (BioSpyder Technologies Inc., CA, USA) using the Surrogate+Tox targeted panel (2,733 genes selected for biological diversity, maximal information content, and widespread pathway coverage). Results: High budding (n = 269/ 28%) was significantly associated with TNM stage (P < 0.001), venous invasion (P < 0.001), weak KM grade (P < 0.001), high TSP (P < 0.001) and reduced cancer specific survival (CSS) (HR = 5.04; 95% confidence interval [CI], 3.50-9.51; P < 0.001) and was independent of venous invasion, KM grade, and Ki67 proliferation index. RNA expression analysis was employed using TempO-Seq to determine differential gene expression between tumours with (n = 8) and without budding (n = 18). Three genes were identified as significantly differentially expressed: S100A2 (S100 calcium binding protein A2) was upregulated by 2.9 fold (padj < 0.00001); REG1A (regenerating family member 1 alpha) was downregulated by 4.7 fold (padj < 0.01) and LCN2 (lipocalin 2) was downregulated by 2.2 fold (padj < 0.01). Conclusions: Tumour budding stratifies patients’ survival in primary operable colorectal cancer and associates with differing gene expression profiles and factors of the tumour. Therefore, the ITBCC budding evaluation method should be used to assess tumour budding as supplement the TNM staging system and can help to further subdivide colorectal cancer into new prognostic groups.

ORIGINAL RESEARCH ABSTRACT PURPOSE The purpose of this study was to investigate if human-extracted MRI tumor phenotypes of breast cancer could predict receptor status and tumor molecular subtype using MRIs from The Cancer Genome Atlas project. MATERIALS AND METHODS Our retrospective interpretation study utilized the analysis of HIPAA-compliant breast MRI data from The Cancer Imaging Archive. One hundred and seven preoperative breast MRIs of biopsy proven invasive breast cancers were analyzed by 3 fellowship-trained breast-imaging radiologists. Each study was scored according to the Breast Imaging Reporting and Data System lexicon for mass and nonmass features. The Spearman rank correlation was used for association analysis of continuous variables; the Kruskal-Wallis test was used for associating continuous outcomes with categorical variables. The Fisher-exact test was used to assess correlations between categorical image-derived features and receptor status. Prediction of estrogen receptor (ER), progesterone receptor, human epidermal growth factor receptor, and molecular subtype were performed using random forest classifiers. RESULTS ER+ tumors were associated with the absence of rim enhancement (P = 0.019, odds ratio [OR] 5.5), heterogeneous internal enhancement (P = 0.02, OR 6.5), peritumoral edema (P = 0.0001, OR 10.0), and axillary adenopathy (P = 0.04, OR 4.4). ER+ tumors were smaller than ER− tumors (23.7 mm vs 29.2 mm, P = 0.02, OR 8.2). All of these variables except the lack of axillary adenopathy were also associated with progesterone receptor+ status. Luminal A tumors (n = 57) were smaller compared to nonLuminal A (21.8 mm vs 27.5 mm, P = 0.035, OR 7.3) and lacked peritumoral edema ( P = 0.001, OR 6.8). Basal like tumors were associated with heterogeneous internal enhancement (P = 0.05, OR 10.1), rim enhancement (P = 0.05, OR6.9), and perituomral edema (P = 0.0001, OR 13.8). CONCLUSIONS Human extracted MRI tumor phenotypes may be able to differentiate those tumors with a more favorable clinical prognosis from their more aggressive counterparts.

This study aims to evaluate ultrafast DCE-MRI-derived kinetic parameters that reflect contrast agent inflow effects in differentiating between subcentimeter BI-RADS 4–5 breast carcinomas and benign lesions. We retrospectively reviewed consecutive 3-T MRI performed from February to October 2017, during which ultrafast DCE-MRI was performed as part of a hybrid clinical protocol with conventional DCE-MRI. In total, 301 female patients with 369 biopsy-proven breast lesions were included. Ultrafast DCE-MRI was acquired continuously over approximately 60 s (temporal resolution, 2.7–7.1 s/phase) starting simultaneously with the start of contrast injection. Four ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS], contrast enhancement ratio [CER], bolus arrival time [BAT], and initial area under gadolinium contrast agent concentration [IAUGC]) and one conventional DCE-MRI-derived kinetic parameter (signal enhancement ratio [SER]) were calculated for each lesion. Wilcoxon rank sum test or Fisher’s exact test was performed to compare kinetic parameters, volume, diameter, age, and BI-RADS morphological descriptors between subcentimeter carcinomas and benign lesions. Univariate/multivariate logistic regression analyses were performed to determine predictive parameters for subcentimeter carcinomas. In total, 125 lesions (26 carcinomas and 99 benign lesions) were identified as BI-RADS 4–5 subcentimeter lesions. Subcentimeter carcinomas demonstrated significantly larger MS and SER and shorter BAT than benign lesions (p = 0.0117, 0.0046, and 0.0102, respectively). MS, BAT, and age were determined as significantly predictive for subcentimeter carcinoma (p = 0.0208, 0.0023, and

Purpose To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping. Procedures In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification with leave-one-out cross-validation was applied for pairwise differentiation of receptor status and molecular subtyping. Histopathologic results were considered the gold standard. Results For lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM). Conclusions Radiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.

Key Points Question Is the accuracy of magnetic resonance imaging (MRI)–guided biopsy comparable with reference-standard surgical resection for diagnosing pathologic complete response after neoadjuvant chemotherapy in patients with breast cancer? Findings In this pilot nonrandomized controlled trial of 20 patients with evaluable data, the accuracy of MRI-guided biopsy for diagnosing pathologic complete response after neoadjuvant chemotherapy was 95% and the negative predictive value was 92.8%. Meaning The results from this pilot study suggest greater accuracy with this method than do the majority of published data and support the need for a larger study comparing MRI-guided biopsy with reference-standard surgical resection in diagnosing a pathologic complete response after neoadjuvant chemotherapy., This nonrandomized controlled trial uses data from a single tertiary care center in the US to investigate the accuracy of magnetic resonance imaging (MRI)–guided biopsy compared with surgical resection for assessing pathologic complete response after neoadjuvant chemotherapy in patients with breast cancer., Importance After neoadjuvant chemotherapy (NAC), pathologic complete response (pCR) is an optimal outcome and a surrogate end point for improved disease-free and overall survival. To date, surgical resection remains the only reliable method for diagnosing pCR. Objective To evaluate the accuracy of magnetic resonance imaging (MRI)–guided biopsy for diagnosing a pCR after NAC compared with reference-standard surgical resection. Design, Setting, and Participants Single-arm, phase 1, nonrandomized controlled trial in a single tertiary care cancer center from September 26, 2017, to July 29, 2019. The median follow-up was 1.26 years (interquartile range, 0.85-1.59 years). Data analysis was performed in November 2019. Eligible patients had (1) stage IA to IIIC biopsy-proven operable invasive breast cancer; (2) standard-of-care NAC; (3) MRI before and after NAC, with imaging complete response defined as no residual enhancement on post-NAC MRI; and (4) definitive surgery. Patients were excluded if they were younger than 18 years, had a medical reason precluding study participation, or had a prior history of breast cancer. Interventions Post-NAC MRI-guided biopsy without the use of intravenous contrast of the tumor bed before definitive surgery. Main Outcomes and Measures The primary end point was the negative predictive value of MRI-guided biopsy, with true-negative defined as negative results of the biopsy (ie, no residual cancer) corresponding to a surgical pCR. Accuracy, sensitivity, positive predictive value, and specificity were also calculated. Two clinical definitions of pCR were independently evaluated: definition 1 was no residual invasive cancer; definition 2, no residual invasive or in situ cancer. Results Twenty of 23 patients (87%) had evaluable data (median [interquartile range] age, 51.5 [39.0-57.5] years; 20 women [100%]; 13 White patients [65%]). Of the 20 patients, pre-NAC median tumor size on MRI was 3.0 cm (interquartile range, 2.0-5.0 cm). Nineteen of 20 patients (95%) had invasive ductal carcinoma; 15 of 20 (75%) had stage II cancer; 11 of 20 (55%) had ERBB2 (formerly HER2 or HER2/neu)–positive cancer; and 6 of 20 (30%) had triple-negative cancer. Surgical pathology demonstrated a pCR in 13 of 20 (65%) patients and no pCR in 7 of 20 patients (35%) when pCR definition 1 was used. Results of MRI-guided biopsy had a negative predictive value of 92.8% (95% CI, 66.2%-99.8%), with accuracy of 95% (95% CI, 75.1%-99.9%), sensitivity of 85.8% (95% CI, 42.0%-99.6%), positive predictive value of 100%, and specificity of 100% for pCR definition 1. Only 1 patient had a false-negative MRI-guided biopsy result (surgical pathology showed

Key Points Question Following resection of a localized breast cancer, why does adjuvant regional nodal irradiation (RNI) reduce distant recurrences to a greater extent than regional recurrences, despite only targeting the regional lymph nodes? Findings In this cohort study of 597 patients with de novo metastatic breast cancer, 85.8% harbored regional disease at the time of metastatic presentation. The consistent detection of concurrent regional and metastatic disease, along with the reduction in distant recurrences observed following RNI, is consistent with the hypothesis that regional nodal disease may precede metastatic dissemination. Meaning These findings suggest that RNI may reduce distant recurrences by sterilizing occult regional nodal disease prior to systemic seeding., This cohort study examines whether and why regional node irradiation in patients with de novo metastatic breast cancer has different levels of effectiveness for preventing recurrences of cancer in regional nodes and distant mestastases., Importance Regional nodal irradiation (RNI) for node-positive breast cancer reduces distant metastases and improves survival, albeit with limited reduction in regional nodal recurrences. The mechanism by which RNI robustly reduces distant metastases while modestly influencing nodal recurrences (ie, the presumed target of RNI) remains unclear. Objective To determine whether some distant metastases putatively arise from occult regional nodal disease and whether regional recurrences otherwise remain largely undetected until an advanced cancer presentation. Design, Setting, and Participants This cohort study examined patients presenting with de novo stage IV breast cancer to the Memorial Sloan Kettering Cancer Center in New York, New York, from 2006 to 2018. Medical records were reviewed to ascertain clinicopathological parameters, including estrogen receptor status and survival. Pretreatment positron emission tomography–computed tomography (PET-CT) imaging was reviewed to ascertain the extent of regional nodal involvement at metastatic diagnosis using standard nodal assessment criteria. A subset underwent regional lymph node biopsy for diagnostic confirmation and served to validate the radiographic nodal assessment. Data analysis was performed from October 2019 to February 2020. Exposures Untreated metastatic breast cancer. Main Outcome and Measures The primary outcome was the likelihood of regional nodal involvement at the time of metastatic breast cancer presentation and was determined by reviewing pretreatment PET-CT imaging and lymph node biopsy findings. Results Among 597 women (median [interquartile range] age, 53 [44–65] years) with untreated metastatic breast cancer, 512 (85.8%) exhibited regional lymph node involvement by PET-CT or nodal biopsy, 509 (85%) had involvement of axillary level I, 328 (55%) had involvement in axillary level II, 136 (23%) had involvement in axillary level III, 101 (17%) had involvement in the supraclavicular fossa, and 96 (16%) had involvement in the internal mammary chain. Lymph node involvement was more prevalent among estrogen receptor–negative tumors (92.4%) than estrogen receptor–positive tumors (83.6%). Nodal involvement at the time of metastatic diagnosis was not associated with overall survival. Conclusions and Relevance These findings suggest that a majority of patients with de novo metastatic breast cancer harbor regional lymph node disease at presentation, consistent with the hypothesis that regional involvement may precede metastatic dissemination. This is in alignment with the findings of landmark trials suggesting that RNI reduces distant recurrences. It is possible that this distant effect of RNI may act via eradication of occult regional disease prior to systemic seeding. The challenges inherent in detecting isolated nodal disease (which is typically asymptomatic) may account for the more modest observed benefit of RNI on regional recurrences. Alternative explanations of nodal involvement that arises concurrently or after metastatic dissemination remain possible, but do not otherwise explain the association of RNI with distant recurrence.

Background Ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived kinetic parameters have demonstrated at least equivalent accuracy to standard DCE-MRI in differentiating malignant from benign breast lesions. However, it is unclear if they have any efficacy as prognostic imaging markers. The aim of this study was to investigate the relationship between ultrafast DCE-MRI-derived kinetic parameters and breast cancer characteristics. Methods Consecutive breast MRI examinations between February 2017 and January 2018 were retrospectively reviewed to determine those examinations that meet the following inclusion criteria: (1) BI-RADS 4–6 MRI performed on a 3T scanner with a 16-channel breast coil and (2) a hybrid clinical protocol with 15 phases of ultrafast DCE-MRI (temporal resolution of 2.7–4.6 s) followed by early and delayed phases of standard DCE-MRI. The study included 125 examinations with 142 biopsy-proven breast cancer lesions. Ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS] and bolus arrival time [BAT]) were calculated for the entire volume of each lesion. Comparisons of these parameters between different cancer characteristics were made using generalized estimating equations, accounting for the presence of multiple lesions per patient. All comparisons were exploratory and adjustment for multiple comparisons was not performed; P values Results Significantly larger MS and shorter BAT were observed for invasive carcinoma than ductal carcinoma in situ (DCIS) (P P = 0.008, respectively). Significantly shorter BAT was observed for invasive carcinomas with more aggressive characteristics than those with less aggressive characteristics: grade 3 vs. grades 1–2 (P = 0.025), invasive ductal carcinoma vs. invasive lobular carcinoma (P = 0.002), and triple negative or HER2 type vs. luminal type (P Conclusions Ultrafast DCE-MRI-derived parameters showed a strong relationship with some breast cancer characteristics, especially histopathology and molecular subtype.

Background Differences in imaging parameters influence computer‐extracted parenchymal enhancement measures from breast MRI. Purpose To investigate the effect of differences in dynamic contrast‐enhanced MRI acquisition parameter settings on quantitative parenchymal enhancement of the breast, and to evaluate harmonization of contrast‐enhancement values with respect to flip angle and repetition time. Study Type Retrospective. Phantom/Populations We modeled parenchymal enhancement using simulations, a phantom, and two cohorts (N = 398 and N = 302) from independent cancer centers. Sequence Field/Strength 1.5T dynamic contrast‐enhanced T1‐weighted spoiled gradient echo MRI. Vendors: Philips, Siemens, General Electric Medical Systems. Assessment We assessed harmonization of parenchymal enhancement in simulations and phantom by varying the MR parameters that influence the amount of T1‐weighting: flip angle (8°–25°) and repetition time (4–12 msec). We calculated the median and interquartile range (IQR) of the enhancement values before and after harmonization. In vivo, we assessed overlap of quantitative parenchymal enhancement in the cohorts before and after harmonization using kernel density estimations. Cohort 1 was scanned with flip angle 20° and repetition time 8 msec; cohort 2 with flip angle 10° and repetition time 6 msec. Statistical Tests Paired Wilcoxon signed‐rank‐test of bootstrapped kernel density estimations. Results Before harmonization, simulated enhancement values had a median (IQR) of 0.46 (0.34–0.49). After harmonization, the IQR was reduced: median (IQR): 0.44 (0.44–0.45). In the phantom, the IQR also decreased, median (IQR): 0.96 (0.59–1.22) before harmonization, 0.96 (0.91–1.02) after harmonization. Harmonization yielded significantly (P

To assess whether contralateral parenchymal enhancement reproduces as an independent biomarker for patient survival in an independent patient cohort from a different cancer institution. This is a HIPAA-compliant IRB approved retrospective study. Patients with ER-positive/HER2-negative operable invasive ductal carcinoma and preoperative dynamic contrast-enhanced MRI were consecutively included between 2005 and 2009. The parenchyma of the breast contralateral to known cancer was segmented automatically on MRI and contralateral parenchymal enhancement (CPE) was calculated. CPE was split into tertiles and tested for association with invasive disease-free survival (IDFS) and overall survival (OS). Propensity score analysis with inverse probability weighting (IPW) was used to adjust CPE for patient and tumour characteristics as well as systemic therapy. Three hundred and two patients were included. The median age at diagnosis was 48 years (interquartile range, 42-57). Median follow-up was 88 months (interquartile range, 76-102); 15/302 (5%) patients died and 37/302 (13%) had a recurrence or died. In context of multivariable analysis, IPW-adjusted CPE was associated with IDFS [hazard ratio (HR) = 0.27, 95% confidence interval (CI) = 0.05-0.68, p = 0.004] and OS (HR = 0.22, 95% CI = 0.00-0.83, p = 0.032). Contralateral parenchymal enhancement on pre-treatment dynamic contrast-enhanced MRI as an independent biomarker of survival in patients with ER-positive/HER2-negative breast cancer has been upheld in this study. These findings are a promising next step towards a practical and inexpensive test for risk stratification of ER-positive/HER2-negative breast cancer. • High parenchymal-enhancement in the disease-free contralateral breast reproduces as biomarker for survival. • This is in patients with ER-positive/HER2-negative breast cancer from an independent cancer centre. • This is independent of patient and pathology parameters and systemic therapy.

With the genomic revolution in the early 1990s, medical research has been driven to study the basis of human disease on a genomic level and to devise precise cancer therapies tailored to the specific genetic makeup of a tumor. To match novel therapeutic concepts conceived in the era of precision medicine, diagnostic tests must be equally sufficient, multilayered, and complex to identify the relevant genetic alterations that render cancers susceptible to treatment. With significant advances in training and medical imaging techniques, image analysis and the development of high-throughput methods to extract and correlate multiple imaging parameters with genomic data, a new direction in medical research has emerged. This novel approach has been termed radiogenomics. Radiogenomics aims to correlate imaging characteristics (ie, the imaging phenotype) with gene expression patterns, gene mutations, and other genome-related characteristics and is designed to facilitate a deeper understanding of tumor biology and capture the intrinsic tumor heterogeneity. Ultimately, the goal of radiogenomics is to develop imaging biomarkers for outcome that incorporate both phenotypic and genotypic metrics. Due to the noninvasive nature of medical imaging and its ubiquitous use in clinical practice, the field of radiogenomics is rapidly evolving and initial results are encouraging. In this article, we briefly discuss the background and then summarize the current role and the potential of radiogenomics in brain, liver, prostate, gynecological, and breast tumors. Level of evidence 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;47:604-620.

Purpose To measure the apparent diffusion coefficient (ADC) values in estrogen receptor-positive (ER+) and axillary lymph node-negative (LN–) invasive breast cancer and investigate the correlation of ADC with Oncotype Dx test recurrence scores (ODxRS). Materials and Methods This was a Health Insurance Portability and Accountability Act (HIPAA)-compliant single-site retrospective study. Patients underwent preoperative 3.0T MRI scans with additional diffusion-weighted imaging sequential scans (b = 0, 600 and b = 0, 1000 s/mm2) from January 2011 to 2013. The study population included 31 ER+/LN– invasive breast cancers, which underwent ODxRS genomic testing. ADC600 and ADC1000 parametric maps were generated, and ADC values were calculated from a user-drawn region of interest. ODxRS predicts 10-year recurrence risk in individual patients: low (RS 30). All breast lesions, including subgroups of invasive ductal carcinoma (IDC) lesions and mass-only lesions were dichotomized by RS scores, low-risk versus intermediate/high-risk, and statistical analysis was performed using Mann–Whitney's test (statistical significance at P < 0.05) and receiver operating characteristic (ROC) curves. Multivariate analysis was also performed. Results Invasive breast cancers, when scored as low-risk by ODxRS, had significantly higher ADC values compared with intermediate/high-risk lesions for both ADC600 (P = 0.007) and ADC1000 (P = 0.008) mean values. This was true both when analyzing only mass-lesions (P = 0.03 and 0.01) or only IDCs (P = 0.001 and 0.009). Conclusion Preliminary findings suggest that lesion ADC values correlate with recurrence risk likelihood stratified using ODxRS. Hence, ADC is a potential surrogate biomarker for tumor aggressiveness. Level of Evidence: 3 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2017.

BACKGROUND: Small breast lesions are difficult to visually categorize due to the inherent lack of morphological and kinetic detail. PURPOSE: To assess the efficacy of radiomics analysis in discriminating small benign and malignant lesions utilizing model free parameter maps. STUDY TYPE: Retrospective, single center. POPULATION: In all, 149 patients, with a total of 165 lesions scored as BI-RADS 4 or 5 on MRI, with an enhancing volume of 89%) and positive predictive values (>83%) were found for all models. DATA CONCLUSION: Radiomics analysis of small contrast-enhancing breast lesions is of value. Texture features calculated from later timepoints on the enhancement curve appear to offer limited additional value when compared with features determined from initial enhancement for this patient cohort. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2

Magnetic resonance imaging (MRI) has changed the landscape of breast disease diagnosis and management, and it has been incorporated into treatment algorithms according to evidence-based consensus guidelines [1–3]. In oncology, the ability to biopsy a finding seen only on MRI has been a significant advancement in the field [4–7]. Preoperatively, MRI has the ability to detect breast disease occult on other imaging modalities as well as additional sites of disease within the ipsilateral or contralateral breast, assess treatment response to neoadjuvant chemotherapy, and guide preoperative needle localization. Breast MRI also has high sensitivity for the evaluation of residual disease post-lumpectomy with positive surgical margins and the evaluation of recurrent disease [8–12]. Other more controversial and emerging uses for MRI in the preoperative setting include axillary staging and aiding in the planning of reconstructive procedures.

PURPOSE: To assess the associations between inter-site texture heterogeneity parameters derived from computed tomography (CT), survival, and BRCA mutation status in women with high-grade serous ovarian cancer (HGSOC). MATERIAL AND METHODS: retrospective study of 88 HGSOC patients undergoing CT and BRCA mutation status testing prior to primary cytoreductive surgery. Associations between texture metrics – namely inter-site cluster variance (SCV), inter-site cluster prominence (SCP), inter-site cluster entropy (SE) – and overall survival (OS), progression-free survival (PFS) as well as BRCA mutation status were assessed. RESULTS: Higher inter-site cluster variance (SCV) was associated with lower PFS (p=0.006) and OS (p=0.003). Higher inter-site cluster prominence (SCP) was associated with lower PFS (p=0.02) and higher inter-site cluster entropy (SE) correlated with lower OS (p=0.01). Higher values of all three metrics were significantly associated with lower complete surgical resection status in BRCA negative patients (SE p=0.039, SCV p=0.006, SCP p=0.02), but not in BRCA positive patients (SE p=0.7, SCV p=0.91, SCP p=0.67). None of the metrics were able to distinguish between BRCA mutation carrier versus non-mutation carrier. CONCLUSION: The assessment of tumoral heterogeneity in the era of personalized medicine is important, as increased heterogeneity has been associated with distinct genomic abnormalities and worse patient outcomes. A radiomics approach using standard-of-care CT scans might have a clinical impact by offering a noninvasive tool to predict outcome and therefore improving treatment effectiveness. However, it was not able to assess BRCA mutation status in women with HGSOC.

To assess the association between apparent diffusion coefficient (ADC), and histological prognostic parameters in malignant breast lesions. The ability of ADC to identify lesions with the presence of Lymphovascular invasion (LVI) in breast carcinoma was also examined.This HIPAA-compliant retrospective study consisted of 212 consecutive patients with known cancers who underwent 3.0T MRI between January 2011 and 2013. In this study, a total of 126 malignant lesions in 114 women, who had undergone DWI (b-values of 0 and 1000s/mm(2)) in addition to diagnostic MRI, were included. Patients with less than 0.8cm lesions, or those who underwent neoadjuvant chemotherapy or suboptimal DW images were excluded. Classical prognostic factors [lesion size, histopathological type and grade, lymph node (LN) status and lymphovascular invasion (LVI)], molecular prognostic markers [estrogen receptor (ER), progesterone receptor (PR) and human epidermal grow factor receptor 2 (HER2)] were reviewed and recorded. A region of interest (ROI) was drawn within the lesions to measure ADC values. Statistical analyses were performed by the Wilcoxon rank sum test (statistical significance at P0.05). Adjusted p values from multiple comparison analysis were also calculated.This study demonstrates an inverse correlation between ADC and LVI in malignant lesions and the ability of ADC to identify aggressiveness in lesions with positive LVI. Tumor size, grade, ER, PR, HER2 and lymph node status did not impact tumor ADC value. However, tumors with LVI showed significantly lower ADC values when compared to tumors without LVI, regardless of the enhancement type, histological grade, histological type, and LN status.Our study shows that ADC could be a potential clinical adjunct in the evaluation of prognostic factors related to malignant lesion aggressiveness such as LVI.

Purpose To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes. Materials and Methods This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006–2011 with: 1) ERPR + (n = 95, 53.4%), ERPR–/HER2 + (n = 35, 19.6%), or triple negative (TN, n = 48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram-based features were extracted from each tumor contoured on pre- and three postcontrast MR images using in-house software. Clinical and pathologic features were also collected. Machine-learning-based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal–Wallis test. Results Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P < 0.05. When the top nine pathologic and imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 83.4%. The combined pathologic and imaging model's accuracy for each subtype was 89.2% (ERPR+), 63.6% (ERPR–/HER2+), and 82.5% (TN). When only the top nine imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 71.2%. The combined pathologic and imaging model's accuracy for each subtype was 69.9% (ERPR+), 62.9% (ERPR–/HER2+), and 81.0% (TN). Conclusion We developed a machine-learning-based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power. J. Magn. Reson. Imaging 2016;44:122–129.

To assess the incidence of benign and malignant internal mammary lymph nodes (IMLNs) at magnetic resonance (MR) imaging among women with a history of treated breast cancer and silicone implant reconstruction.The institutional review board approved this HIPAA-compliant retrospective study and waived informed consent. Women were identified who (a) had breast cancer, (b) underwent silicone implant oncoplastic surgery, and (c) underwent postoperative implant-protocol MR imaging with or without positron emission tomography (PET)/computed tomography (CT) between 2000 and 2013. The largest IMLNs were measured. A benign IMLN was pathologically proven or defined as showing 1 year of imaging stability and/or no clinical evidence of disease. Malignant IMLNs were pathologically proven. Incidence of IMLN and positive predictive value (PPV) were calculated on a per-patient level by using proportions and exact 95% confidence intervals (CIs). The Wilcoxon rank sum test was used to assess the difference in axis size.In total, 923 women with breast cancer and silicone implants were included (median age, 46 years; range, 22-89 years). The median time between reconstructive surgery and first MR imaging examination was 49 months (range, 5-513 months). Of the 923 women, 347 (37.6%) had IMLNs at MR imaging. Median short- and long-axis measurements were 0.40 cm (range, 0.20-1.70 cm) and 0.70 cm (range, 0.30-1.90 cm), respectively. Two hundred seven of 923 patients (22.4%) had adequate follow-up; only one of the 207 IMLNs was malignant, with a PPV of 0.005 (95% CI: 0.000, 0.027). Fifty-eight of 923 patients (6.3%) had undergone PET/CT; of these, 39 (67.2%) had IMLN at MR imaging. Twelve of the 58 patients (20.7%) with adequate follow-up had fluorine 18 fluorodeoxyglucose-avid IMLN, with a median standardized uptake value of 2.30 (range, 1.20-6.10). Only one of the 12 of the fluorodeoxyglucose-avid IMLNs was malignant, with a PPV of 0.083 (95% CI: 0.002, 0.385).IMLNs identified at implant-protocol breast MR imaging after oncoplastic surgery for breast cancer are overwhelmingly more likely to be benign than malignant. Imaging follow-up instead of immediate metastatic work-up may be warranted.

e20028 Background: Breast Implant Associated Anaplastic Large Cell Lymphoma (BIA-ALCL), a subtype of ALCL, arises as a seroma in the space between the implant and the capsule, or as an adjacent mass. BIA-ALCL appears to be related to textured-surface implants, after 7-10 years of exposure. We conducted two large cohort studies. The 1st, a retrospective series (Sutton, 2019) assessed the incidental findings of masses or seromas in 1070 women with breast implants undergoing MR for FDA recommended screening for silent ruptures of silicone implants. Incidental finding of seromas or breast masses on MRI were found in 18/1070 (1.7%) women, of which 1/15 had BIA-ALCL, and was symptomatic at the time of MRI. The 2nd, a prospective study (Cordeiro, 2020) defined the incidence of BIA-ALCL (1/355) in a cohort of 3546 women with textured implants followed long term. Within this cohort, there were 28 clinically relevant delayed seromas (0.79%), 8 of which were BIA-ALCL (28.5%). We hypothesize that combining these databases will inform whether asymptomatic women with textured implants may benefit from MR to r/o BIA-ALCL. Methods: The two IRB approved databases were merged. Patients with incidental findings of seroma on MRI were identified. A majority of the MRIs in this merged cohort were performed to follow the FDA recommended screening for silent ruptures. We identified all clinically relevant late seromas sent to hematopathology to r/o BIA-ALCL by cross checking pathology reports containing the words “lymphoma” or “anaplastic” or “ALCL”. Results: 572 women were included in both studies: followed long term and received an MRI after a median time of 7.4 years after breast reconstruction. 8 of 572 women had an incidental finding of seroma on random MRI, and 2 had capsular masses. None of these 10 asymptomatic women have developed BIA-ALCL to date (median follow-up 9 years). 11 of 572 women had a symptomatic seroma or mass, 4 of which later developed BIA-ALCL, a fifth patient was found to have BIA-ALCL on a PET/CT + lymph node, despite being asymptomatic. Median time from last MR to lymphoma was 5 years (3-8 years). Conclusions: In this merged cohort of patients with textured breast implants, incidence of BIA-ALCL in patients with symptomatic late seromas is around 30%, while seromas found incidentally on MR of asymptomatic patients were negative for BIA-ALCL.

We present a segmentation approach that combines GrowCut (GC) with cancer-specific multi-parametric Gaussian Mixture Model (GCGMM) to produce accurate and reproducible segmentations. We evaluated GCGMM using a retrospectively collected 75 invasive ductal carcinoma with ERPR+ HER2− (n = 15), triple negative (TN) (n = 9), and ER-HER2+ (n = 57) cancers with variable presentation (mass and non-mass enhancement) and background parenchymal enhancement (mild and marked). Expert delineated manual contours were used to assess the segmentation performance using Dice coefficient (DSC), mean surface distance (mSD), Hausdorff distance, and volume ratio (VR). GCGMM segmentations were significantly more accurate than GrowCut (GC) and fuzzy c-means clustering (FCM). GCGMM’s segmentations and the texture features computed from those segmentations were the most reproducible compared with manual delineations and other analyzed segmentation methods. Finally, random forest (RF) classifier trained with leave-one-out cross-validation using features extracted from GCGMM segmentation resulted in the best accuracy for ER-HER2+ vs. ERPR+/TN (GCGMM 0.95, expert 0.95, GC 0.90, FCM 0.92) and for ERPR + HER2− vs. TN (GCGMM 0.92, expert 0.91, GC 0.77, FCM 0.83).

The aims of this study were to compare dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) with apparent diffusion coefficient mapping as a stand-alone parameter without any other supportive sequence for breast cancer detection and to assess its combination as multiparametric MRI (mpMRI) of the breast.In this institutional review board-approved single-center study, prospectively acquired data of 106 patients who underwent breast MRI from 12/2010 to 09/2014 for an imaging abnormality (Breast Imaging Reporting and Data System 0, 4/5) were retrospectively analyzed. Four readers independently assessed DWI and DCE as well as combined as mpMRI. Breast Imaging Reporting and Data System categories, lesion size, and mean apparent diffusion coefficient values were recorded. Histopathology was used as the gold standard. Appropriate statistical tests were used to compare diagnostic values.There were 69 malignant and 41 benign tumors in 106 patients. Four patients presented with bilateral lesions. Dynamic contrast-enhanced MRI was the most sensitive test for breast cancer detection, with an average sensitivity of 100%. Diffusion-weighted imaging alone was less sensitive (82%; P0.001) but more specific than DCE-MRI (86.8% vs 76.6%; P = 0.002). Diagnostic accuracy was 83.7% for DWI and 90.6% for DCE-MRI. Multiparametric MRI achieved a sensitivity of 96.8%, not statistically different from DCE-MRI (P = 0.12) and with a similar specificity as DWI (83.8%; P = 0.195), maximizing diagnostic accuracy to 91.9%. There was almost perfect interreader agreement for DWI (κ = 0.864) and DCE-MRI (κ = 0.875) for differentiation of benign and malignant lesions.Dynamic contrast-enhanced MRI is most sensitive for breast cancer detection and thus still indispensable. Multiparametric MRI using DCE-MRI and DWI maintains a high sensitivity, increases specificity, and maximizes diagnostic accuracy, often preventing unnecessary breast biopsies. Diffusion-weighted imaging should not be used as a stand-alone parameter because it detects significantly fewer cancers in comparison with DCE-MRI and mpMRI.

This study investigates whether quantitative image analysis of pretreatment CT scans can predict volumetric response to chemotherapy for patients with colorectal liver metastases (CRLM). Patients treated with chemotherapy for CRLM (hepatic artery infusion (HAI) combined with systemic or systemic alone) were included in the study. Patients were imaged at baseline and approximately 8 weeks after treatment. Response was measured as the percentage change in tumour volume from baseline. Quantitative imaging features were derived from the index hepatic tumour on pretreatment CT, and features statistically significant on univariate analysis were included in a linear regression model to predict volumetric response. The regression model was constructed from 70% of data, while 30% were reserved for testing. Test data were input into the trained model. Model performance was evaluated with mean absolute prediction error (MAPE) and R2. Clinicopatholologic factors were assessed for correlation with response. 157 patients were included, split into training (n = 110) and validation (n = 47) sets. MAPE from the multivariate linear regression model was 16.5% (R2 = 0.774) and 21.5% in the training and validation sets, respectively. Stratified by HAI utilisation, MAPE in the validation set was 19.6% for HAI and 25.1% for systemic chemotherapy alone. Clinical factors associated with differences in median tumour response were treatment strategy, systemic chemotherapy regimen, age and KRAS mutation status (p < 0.05). Quantitative imaging features extracted from pretreatment CT are promising predictors of volumetric response to chemotherapy in patients with CRLM. Pretreatment predictors of response have the potential to better select patients for specific therapies. • Colorectal liver metastases (CRLM) are downsized with chemotherapy but predicting the patients that will respond to chemotherapy is currently not possible. • Heterogeneity and enhancement patterns of CRLM can be measured with quantitative imaging. • Prediction model constructed that predicts volumetric response with 20% error suggesting that quantitative imaging holds promise to better select patients for specific treatments.

Background In this study, we sought to investigate if computer-extracted magnetic resonance imaging (MRI) phenotypes of breast cancer could replicate human-extracted size and Breast Imaging-Reporting and Data System (BI-RADS) imaging phenotypes using MRI data from The Cancer Genome Atlas (TCGA) project of the National Cancer Institute. Methods Our retrospective interpretation study involved analysis of Health Insurance Portability and Accountability Act-compliant breast MRI data from The Cancer Imaging Archive, an open-source database from the TCGA project. This study was exempt from institutional review board approval at Memorial Sloan Kettering Cancer Center and the need for informed consent was waived. Ninety-one pre-operative breast MRIs with verified invasive breast cancers were analysed. Three fellowship-trained breast radiologists evaluated the index cancer in each case according to size and the BI-RADS lexicon for shape, margin, and enhancement (human-extracted image phenotypes [HEIP]). Human inter-observer agreement was analysed by the intra-class correlation coefficient (ICC) for size and Krippendorff’s α for other measurements. Quantitative MRI radiomics of computerised three-dimensional segmentations of each cancer generated computer-extracted image phenotypes (CEIP). Spearman’s rank correlation coefficients were used to compare HEIP and CEIP. Results Inter-observer agreement for HEIP varied, with the highest agreement seen for size (ICC 0.679) and shape (ICC 0.527). The computer-extracted maximum linear size replicated the human measurement with p

Background Imaging features derived from MRI scans can be used for not only breast cancer detection and measuring disease extent, but can also determine gene expression and patient outcomes. The relationships between imaging features, gene/protein expression, and response to therapy hold potential to guide personalized medicine. We aim to characterize the relationship between radiologist-annotated tumor phenotypic features (based on MRI) and the underlying biological processes (based on proteomic profiling) in the tumor. Methods Multiple-response regression of the image-derived, radiologist-scored features with reverse-phase protein array expression levels generated association coefficients for each combination of image-feature and protein in the RPPA dataset. Significantly-associated proteins for features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis determined which features were most strongly correlated with pathway activity and cellular functions. Results Each of the twenty-nine imaging features was found to have a set of significantly correlated molecules, associated biological functions, and pathways. Conclusions We interrogated the pathway alterations represented by the protein expression associated with each imaging feature. Our study demonstrates the relationships between biological processes (via proteomic measurements) and MRI features within breast tumors.

PURPOSE Evaluate the clinical presentation and imaging findings of breast implant-associated anaplastic large cell lymphoma (BIA ALCL) at a large US cancer center. MATERIALS AND METHODS HIPAA-compliant IRB approved retrospective study, for which informed consent was waived. The Hospital Information System was screened for women who underwent implant reconstruction and were diagnosed with BIA ALCL between 2010 and 2016. Two radiologists reviewed images in consensus. Clinical and imaging characteristics were summarized using means and ranges for continuous variables and percentages for categorical variables. RESULTS Patient cohort included 11 women with BIA ALCL (mean age at diagnosis = 54 years, range: 35-77), including women with (9/11) and without (2/11) history of breast cancer. Mean time from breast implant placement to diagnosis was 10 years (range: 6-14). BIA ALCL was identified in patients with saline (4/11) and silicone (5/11) implants. Implants were textured in 7/11 (63%) and unknown in 4/11 (36%) cases. All patients presented with a peri-implant seroma, (9/11 documented on imaging). Two of 11 patients had a mass within this seroma. Ten of 11 patients (91%) presented with symptoms. CONCLUSIONS Saline and silicone breast implants may predispose patients to a rare lymphoma subtype, BIA ALCL, which presents on imaging as a peri-implant fluid collection ± mass.

Breast cancer subtypes are increasingly classified based on tumor genotype, which is often predictive of outcome, and molecular characterization is now often used to guide targeted therapy. Luminal A subtype is defined by immunohistochemistry surrogates, including positive estrogen receptor (ER+) and/or positive progesterone receptor (PR+), negative HER2 receptor (HER2−), and a low Ki67 level. This subtype of ER-positive breast cancer is the most common subtype and correlates with the highest probability of long-term disease-free and overall survival compared to luminal B, HER2-overexpressing, and basal-like subtypes.1 However, there is significant intertumoral and intratumoral variability in biological aggressiveness within the luminal A subtype, which has motivated a search for biomarkers and predictive models to more effectively personalize treatment regimes.2–4 The Oncotype Dx (Genomic Health, Redwood City, CA) breast cancer assay incorporates the mRNA expression of 21 genes, resulting in the so-called Recurrence Score (RS), that has been shown to be predictive and prognostic in early-stage ER-positive/HER2-negative invasive breast cancer.5–12 The American Society of Clinical Oncology (ASCO) recommended its clinical use based on data showing that RS correlates with the magnitude of chemotherapy benefit as well as the 10-year risk of distant recurrence, both increasing with higher RS scores.13 Oncotype Dx RS (hereafter, ODxRS) is used clinically to influence decision-making, sometimes preventing unnecessary chemotherapy.5 Correlating imaging phenotype with genomic information (often referred to as “radiogenomics”), in order to better understand genetic variability and the ability to predict prognosis or response to therapy, is a new field of research.14 The overlap between image-based breast cancer phenotype features and genomic characteristics is not currently well established. Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for tumor characterization and accurate size measurement. Clinical indication includes preoperative evaluation to define extent of disease in newly diagnosed breast cancer.15,16 Tumors are characterized clinically using the Breast Imaging-Reporting and Data System (BI-RADS) Lexicon. However, image features extracted through computer-based automated image analysis are quantitative and may include characteristics that are imperceptible to the eye. Such efforts to discover and use quantitative features are termed “radiomics.” Types of radiomics features that are often extracted include texture-based features (ie, characteristics associated with “roughness” or “smoothness” and the like). Previous applications of texture analysis for breast cancer have predominantly been for differentiating between benign and malignant lesions as well as differentiating between ductal and lobular breast cancers.17–20 Sophisticated methods using gray-scale correlation matrix (GLCM) textures or variations of the same textures computed from Gabor edge images and temporal kinetics,21 as well as fractal-based textures,22 have been employed for classifying malignant from benign breast tumors. The purpose of this study was to investigate the association between ODxRS and image analysis-based features extracted from MRI scans.

To evaluate the impact of whole-body 18 F-FDG PET/CT on initial staging of breast cancer in comparison to conventional staging modalities. This study included 102 breast cancer patients, 101 patients were eligible for evaluation. Preoperative whole-body staging with PET/CT was performed in patients with clinical stage ≥ T2 tumours or positive local lymph nodes (n = 91). Postoperative PET/CT was performed in patients without these criteria but positive sentinel lymph node biopsy (n = 10). All patients underwent PET/CT and a conventional staging algorithm, which included bone scan, chest X-ray and abdominal ultrasound. PET/CT findings were compared to conventional staging and the impact on therapeutic management was evaluated. PET/CT led to an upgrade of the N or M stage in overall 19 patients (19 %) and newly identified manifestation of breast cancer in two patients (2 %). PET/CT findings caused a change in treatment of 11 patients (11 %). This is within the range of recent studies, all applying conventional inclusion criteria based on the initial T and N status. PET/CT has a relevant impact on initial staging and treatment of breast cancer when compared to conventional modalities. Further studies should assess inclusion criteria beyond the conventional T and N status, e.g. tumour grading and receptor status. • PET/CT may be relevant in staging breast cancer patients at higher risk for metastases • PET/CT may modify the N and M stage in multiple patients • PET/CT may impact treatment planning in breast cancer patients