Jose M. Net

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.

Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P=0.04 for lesions ⩽2 cm; P=0.02 for lesions >2 to ⩽5 cm) as with the entire data set (P-value=0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine.

Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage

The objective of this study was to demonstrate that computer‐extracted image phenotypes (CEIPs) of biopsy‐proven breast cancer on magnetic resonance imaging (MRI) can accurately predict pathologic stage.

Contralateral Intramammary Silicone Lymphadenitis in a Patient with an Intact Standard Dual-Lumen Breast Implant in the Opposite Reconstructed Breast

Silicone lymphadenopathy is a recognized complication of silicone gel implant rupture; the ipsilateral axillary lymph nodes are most commonly involved. We report imaging findings on a range of different imaging modalities and biopsy results in a case of biopsy-proven silicone lymphadenitis involving contralateral intramammary and axillary lymph nodes in a patient with an intact standard dual-lumen breast implant in the opposite reconstructed breast. This case demonstrates that in a patient with disrupted lymph drainage due to prior mastectomy and axillary node dissection for breast cancer treatment, silicone particles can migrate in a retrograde fashion via the ipsilateral internal mammary lymph nodes and reach not only the contralateral axilla but also the outer quadrants of the contralateral breast, even in the presence of an intact breast implant.

Resident and Fellow Education Feature: US Evaluation of Axillary Lymph Nodes

1From the Breast Imaging Section, Department of Radiology, University of Miami Sylvester Comprehensive Cancer Center, 1475 NW 12th Ave, Miami, FL 33136. Received April 14, 2013; revision requested November 18; revision received March 13, 2014; accepted July 23. All authors have disclosed no relevant relationships. Address correspondence to J.M.N. (e-mail: jnet@med.miami.edu). The full digital presentation is available online.

Breast MRI radiomics: comparison of computer- and human-extracted imaging phenotypes

BackgroundIn 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.MethodsOur 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.ResultsInter-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 < 10−12. CEIP of shape, specifically sphericity and irregularity, replicated HEIP with both p values < 0.001. CEIP did not demonstrate agreement with HEIP of tumour margin or internal enhancement.ConclusionsQuantitative radiomics of breast cancer may replicate human-extracted tumour size and BI-RADS imaging phenotypes, thus enabling precision medicine.

Primary neuroendocrine carcinoma of the breast: report of 2 cases and literature review

Neuroendocrine tumors of the breast are very rare accounting for less than 0.1% of all breast cancers and less than 1% of all neuroendocrine tumors. Focal neuroendocrine differentiation can be found in different histologic types of breast carcinoma including in situ and invasive ductal or invasive lobular. However, primary neuroendocrine carcinoma of the breast requires the expression of neuroendocrine markers in more than 50% of the cell population, the presence of ductal carcinoma in situ, and the absence of clinical evidence of concurrent primary neuroendocrine carcinoma of any other organ. Reports discussing the imaging characteristics of this rare carcinoma in different breast imaging modalities are scarce. We present 2 cases of primary neuroendocrine carcinoma of the breast for which mammography, ultrasound, and magnetic resonance imaging findings and pathology findings are described. A review of the medical literature on this particular topic was performed, and the results are presented.

Utility of supplemental screening with breast ultrasound in asymptomatic women with dense breast tissue who are not at high risk for breast cancer

Objective: To assess the results of an initial round of supplemental screening with hand-held bilateral breast ultrasound following a negative screening mammogram in asymptomatic women with dense breast tissue who are not at high risk for breast cancer. Materials and Methods: A retrospective, Health Insurance Portability and Accountability Act compliant, Institutional Research Board approved study was performed at a single academic tertiary breast center. Informed consent was waived. A systematic review of the breast imaging center database was conducted to identify and retrieve data for all asymptomatic women, who were found to have heterogeneously dense or extremely dense breast tissue on screening bilateral mammograms performed from July 1, 2010 through June 30, 2012 and who received a mammographic final assessment American College of Radiologys (ACR) Breast Imaging Reporting and Data System (BI-RADS) category 1 or BI-RADS category 2. Hand-held screening ultrasound was performed initially by a technologist followed by a radiologist. Chi-square and t-test were used and statistical significance was considered at P < 0.05. Results: A total of 1210 women were identified. Of these, 394 underwent the offered supplemental screening ultrasound. BI-RADS category 1 or 2 was assigned to 323 women (81.9%). BI-RADS category 3 was assigned to 50 women (12.9%). A total of 26 biopsies/aspirations were recommended and performed in 26 women (6.6%). The most common finding for which biopsy was recommended was a solid mass (88.5%) with an average size of 0.9 cm (0.5–1.7 cm). Most frequent pathology result was fibroadenoma (60.8%). No carcinoma was found. Conclusion: Our data support the reported occurrence of a relatively high number of false positives at supplemental screening with breast ultrasound following a negative screening mammogram in asymptomatic women with dense breast tissue, who are not at a high risk of developing breast cancer, and suggests that caution is necessary in establishing wide implementation of this type of supplemental screening for all women with dense breast tissue without considering other risk factors for breast cancer.

Relationships Between Human-Extracted MRI Tumor Phenotypes of Breast Cancer and Clinical Prognostic Indicators Including Receptor Status and Molecular Subtype

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 (23.7mm vs 29.2mm, 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 (21.8mm vs 27.5mm, 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.