Masson's tumor of the reconstructed breast

Intravascular papillary endothelial hyperplasia (Masson's Tumor) is a rare benign endothelial vascular lesion that can mimic angiosarcoma if not properly recognized. It represents less than 2% of all vascular tumors, but has been seen in the postradiation setting, which also makes differentiating it from angiosarcoma crucial. It is classically characterized as a circumscribed, intravascular mass that is hypoechoic on ultrasound, and T1 isointense and T2 heterogenous on MRI with variable enhancement. Histologically, it demonstrates papillary architecture without significant atypia, and associated vascular thrombus. Although it typically occurs in the soft tissues of the trunk and neck, a very small percentage of cases have been found in the breast. The following case will involve a 64-year-old female with a Masson's tumor involving the capsule of her left breast implant, in the setting of previously treated ductal carcinoma in situ, which was surgically excised and irradiated over 20 years prior

BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images.

Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by using ODX as a guide for personalized therapy. However, ODX and similar gene assays are expensive, time-consuming, and tissue destructive. Therefore, developing an AI-based ODX prediction model that identifies patients who will benefit from chemotherapy in the same way that ODX does would give a low-cost alternative to the genomic test. To overcome this problem, we developed a deep learning framework, Breast Cancer Recurrence Network (BCR-Net), which automatically predicts ODX recurrence risk from histopathology slides. Our proposed framework has two steps. First, it intelligently samples discriminative features from whole-slide histopathology images of breast cancer patients. Then, it automatically weights all features through a multiple instance learning model to predict the recurrence score at the slide level. On a dataset of H&E and Ki67 breast cancer resection whole slides images (WSIs) from 99 anonymized patients, the proposed framework achieved an overall AUC of 0.775 (68.9% and 71.1% accuracies for low and high risk) on H&E WSIs and overall AUC of 0.811 (80.8% and 79.2% accuracies for low and high risk) on Ki67 WSIs of breast cancer patients. Our findings provide strong evidence for automatically risk-stratify patients with a high degree of confidence. Our experiments reveal that the BCR-Net outperforms the state-of-the-art WSI classification models. Moreover, BCR-Net is highly efficient with low computational needs, making it practical to deploy in limited computational settings

Optimized generation of high-resolution phantom images using cGAN: Application to quantification of Ki67 breast cancer images - Fig 5

<p>Example images (a) original image used for annotation (b) a dot based annotation, (c) cGAN generated synthetic image from (b). (d) Original image used for segmentation (e) segmentation result using [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196846#pone.0196846.ref023" target="_blank">23</a>], (f) cGAN generated image from (e).</p

Experts’ discrimination performance on synthetic/real images.

<p>TP represents the number of correctly identified synthetic images and TN represents the number of correctly identified real images.</p

Example (a) real image, (b) segmentation result based on [5], (c) synthetic image used for evaluation of computerized quantitative method, (d) visual ImmunoRatio output for the real image, visual ImmunoRatio output for synthetic image.

<p>Example (a) real image, (b) segmentation result based on [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196846#pone.0196846.ref005" target="_blank">5</a>], (c) synthetic image used for evaluation of computerized quantitative method, (d) visual ImmunoRatio output for the real image, visual ImmunoRatio output for synthetic image.</p

An example case where the immunoRatio values are different in the real and synthetic images.

<p>The upper left nucleus in the real image (a) was missed by the segmentation result (b) based on [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0196846#pone.0196846.ref005" target="_blank">5</a>]. Therefore the synthetic image (c) was not including that nucleus.</p

Bland-Altman plot comparing ImmunoRatio values of real and artificial images.

<p>Shaded region corresponds to the limits of agreement.</p

Mesothelin expression in triple negative breast carcinomas correlates significantly with basal-like phenotype, distant metastases and decreased survival.

Mesothelin is a cell surface associated antigen expressed on mesothelial cells and in some malignant neoplasms. Mesothelin-targeted therapies are in phase I/II clinical trials. The clinicopathologic and prognostic significance of mesothelin expression in triple negative breast carcinomas (TNBC) has not been fully assessed. We evaluated the expression of mesothelin and of basal markers in tissue microarrays of 226 TNBC and 88 non-TNBC and assessed the clinicopathologic features of mesothelin-expressing breast carcinomas. Furthermore, we investigated the impact of mesothelin expression on the disease-free and overall survival of patients with TNBC. We found that mesothelin expression is significantly more frequent in TNBC than in non-TNBC (36% vs 16%, respectively; p = 0.0006), and is significantly correlated with immunoreactivity for basal keratins, but not for EGFR. Mesothelin-positive and mesothelin-negative TNBC were not significantly different by patients' race, tumor size, histologic grade, tumor subtype, lymphovascular invasion and lymph node metastases. Patients with mesothelin-positive TNBC were older than patients with mesothelin-negative TNBC, developed more distant metastases with a shorter interval, and had significantly lower overall and disease-free survival. Based on our results, patients with mesothelin-positive TNBC could benefit from mesothelin-targeted therapies

Optimized generation of high-resolution phantom images using cGAN: Application to quantification of Ki67 breast cancer images - Fig 3

<p>Fully synthetic images (e-g). We created several toy data to generate synthetic images with different characteristics by using annotation based input (a) and segmentation based input (b and c).</p

MSLN expression in TNBC correlates with significantly decreased overall survival (p<0.0001, n = 198).

<p>MSLN expression in TNBC correlates with significantly decreased overall survival (p<0.0001, n = 198).</p