Imaging Modalities in Inflammatory Breast Cancer (IBC) Diagnosis: A Computer-Aided Diagnosis System Using Bilateral Mammography Images.

Inflammatory breast cancer (IBC) is an aggressive type of breast cancer. It leads to a significantly shorter survival than other types of breast cancer in the U.S. The American Joint Committee on Cancer (AJCC) defines the diagnosis based on specific criteria. However, the clinical presentation of IBC in North Africa (Egypt, Morocco, and Tunisia) does not agree, in many cases, with the AJCC criteria. Healthcare providers with expertise in IBC diagnosis are limited because of the rare nature of the disease. This paper reviewed current imaging modalities for IBC diagnosis and proposed a computer-aided diagnosis system using bilateral mammograms for early and improved diagnosis. The National Institute of Cancer in Egypt provided the image dataset consisting of IBC and non-IBC cancer cases. Type 1 and Type 2 fuzzy logic classifiers use the IBC markers that the expert team identified and extracted carefully. As this research is a pioneering work in its field, we focused on breast skin thickening, its percentage, the level of nipple retraction, bilateral breast density asymmetry, and the ratio of the breast density of both breasts in bilateral digital mammogram images. Granulomatous mastitis cases are not included in the dataset. The system's performance is evaluated according to the accuracy, recall, precision, F1 score, and area under the curve. The system achieved accuracy in the range of 92.3-100%.

The role of automated breast ultrasound in the assessment of the local extent of breast cancer.

Automated breast ultrasound (ABUS) is a non-invasive advanced ultrasound modality. The degree of extension of the cancer within the breast is very important to choose the appropriate kind of surgery/therapy. In the current work, the aim was to evaluate the role of the ABUS in the assessment of the local extent of the breast cancer before management. This is a prospective analysis that studied 562 female patients with proved breast cancers. Evaluation was in regard of the size, multiplicity, and the stromal invasion (ie, the presence of tumor emboli or tumor masses within the stroma of the breast tissue) around the tumor. Cases were subjected to automated breast ultrasound performed in the axial and coronal planes. ABUS showed high accuracy of assessment of the tumor multiplicity (82.2%) and the stromal involvement (93.5%). There was a statistical significance (P < .001) between the ABUS and the pathology regarding the measurement of the size of the index cancer. In conclusion, ABUS could be used for determination of the intramammary extend of the breast cancer. ABUS provided accurate assessment of the peritumor stromal involvement and multiplicity of the cancer which is required to choose the proper choice of surgery.

Validation of artificial intelligence contrast mammography in diagnosis of breast cancer: Relationship to histopathological results.

INTRODUCTION: Contrast-enhanced mammography (CEM) is used for characterization of breast lesions with increased diagnostic accuracy compared to digital mammography (DM). Artificial intelligence (AI) approaches are emerging with accuracies equal to an average radiologist. However, most studies trained deep learning (DL) models on DM images and there is a paucity in literature for discovering the application of AI using CEM. OBJECTIVES: To develop and test a DL model that classifies CEM images and produces corresponding highlights of lesions detected. METHODS: Fully annotated 2006 images of 326 females available from the previously published Categorized Digital Database for Contrast Enhanced Mammography images (CDD-CESM) were used for training. We developed a DL multiview contrast mammography model (MVCM) for classification of CEM low energy and recombined images. An external test set of 288 images of 37 females not included in the training was used for validation. Correlation with histopathological results and follow-up was considered the standard reference. The study protocol was approved by the Institutional Review Board and patient informed consent was obtained. RESULTS: Assessment was done on an external test set of 37 females (mean age, 51.31 years ± 11.07 [SD]) with AUC-ROC for AI performance 0.936; (95 % CI: 0.898, 0.973; p < 0.001) and the best cut off value for prediction of malignancy using AI score = 0.28. Findings were then correlated with histopathological results and follow up which revealed a sensitivity of 75 %, specificity 96.3 %, total accuracy of 90.1 %, positive predictive value (PPV) 87.1 %, and negative predictive value (NPV) 92 %, p-value (<0.001). Diagnostic indices of radiologists were sensitivity 88.9 %, specificity 92.6 %, total accuracy 91.7 %, PPV 80 %, and NPV 96.2 %, p-value (<0.001). CONCLUSION: A deep learning multiview CEM model was developed and evaluated in a cohort of female participants and showed promising results in detecting breast cancer. This warrants further studies, external training, and validation.

Categorized contrast enhanced mammography dataset for diagnostic and artificial intelligence research.

Contrast-enhanced spectral mammography (CESM) is a relatively recent imaging modality with increased diagnostic accuracy compared to digital mammography (DM). New deep learning (DL) models were developed that have accuracies equal to that of an average radiologist. However, most studies trained the DL models on DM images as no datasets exist for CESM images. We aim to resolve this limitation by releasing a Categorized Digital Database for Low energy and Subtracted Contrast Enhanced Spectral Mammography images (CDD-CESM) to evaluate decision support systems. The dataset includes 2006 images, with an average resolution of 2355 × 1315, consisting of 310 mass images, 48 architectural distortion images, 222 asymmetry images, 238 calcifications images, 334 mass enhancement images, 184 non-mass enhancement images, 159 postoperative images, 8 post neoadjuvant chemotherapy images, and 751 normal images, with 248 images having more than one finding. This is the first dataset to incorporate data selection, segmentation annotation, medical reports, and pathological diagnosis for all cases. Moreover, we propose and evaluate a DL-based technique to automatically segment abnormal findings in images.

Benign metastasizing leiomyoma: a rare type of lung metastases-two case reports and review of the literature.

Benign metastasizing leiomyoma (BML) is a rare disease that usually occurs in women of reproductive age. They typically have history of uterine leiomyoma treated with hysterectomy. BML can metastasize to distant organs, with the lung being the most common organ. We report two patients who presented with benign metastasizing leiomyoma to the lung. Our first case was a fifty-two-year-old female who presented with multiple lung masses, with a past medical history of uterine leiomyoma who underwent hysterectomy 17 years ago. A CT-guided biopsy showed benign appearing spindle cells and pathology confirmed her diagnosis with additional positive estrogen/progesterone receptor stains. Our second case was a fifty-six-year-old female who presented with multiple cavitary pulmonary nodules. She subsequently underwent a video-assisted thoracoscopic surgery (VATS) with wedge resection of one of the nodules. Pathology confirmed the diagnosis based on morphology and immunohistochemical staining strongly positive for estrogen/progesterone receptors. Benign metastasizing leiomyoma is a rare condition which may affect women of reproductive age. This should be considered in the differential in patients who present with multiple pulmonary nodules, especially with a history of uterine leiomyoma. Additional stains, such as estrogen/progesterone receptors, may need to be done to confirm the diagnosis.