Overview of established and emerging immunohistochemical biomarkers and their role in correlative studies in MRI.

Clinical practice in radiology and pathology requires professional expertise and many years of training to visually evaluate and interpret abnormal phenotypic features in medical images and tissue sections to generate diagnoses that guide patient management and treatment. Recent advances in digital image analysis methods and machine learning have led to significant interest in extracting additional information from medical and digital whole-slide images in radiology and pathology, respectively. This has led to significant interest and research in radiomics and pathomics to correlate phenotypic features of disease with image analytics in order to identify image-based biomarkers. The expanding role of big data in radiology and pathology parallels the development and role of immunohistochemistry (IHC) in the daily practice of pathology. IHC methods were initially developed to provide additional information to help classify tumors and then transformed into an indispensable tool to guide treatment in many types of cancer. IHC markers are used in daily practice to identify specific types of cells and highlight their distributions in tissues in order to distinguish benign from neoplastic cells, determine tumor origin, subclassify neoplasms, and support and confirm diagnoses. In this regard, radiomics, pathomics, and IHC methods are very similar since they enable the extraction of image-based features to characterize various properties of diseases. Due to the dramatic advancements in recent radiomics research, we provide a brief overview of the role of established and emerging IHC biomarkers in various tumor types that have been correlated with radiologic biomarkers to improve diagnostic accuracy, predict prognosis, guide patient management, and select treatment strategies. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:341-354.

Role of expression of cell cycle inhibitor p27 and MIB-1 in predicting lymph node metastasis in male breast carcinoma.

Tumor expression of the proliferation marker (MIB-1) and the cell cycle-related protein (p27) may predict the biologic behavior of various human tumors. The purpose of this study was to evaluate the role of p27 and MIB-1 expression in predicting lymph node metastasis in male breast carcinomas (MBCs). We studied 67 patients with invasive MBC who had undergone modified radical mastectomy. Pathologic lymph node status was correlated with the p27 protein and the MIB-1 proliferation index. These factors were studied immunohistologically by standard methods. Men in this study ranged from 36 to 92 years of age (mean, 63 years); 43 (64%) were T1 lesions, and 24 (36%) were T2 lesions. Twenty-nine patients (43%) had positive nodes. p27 was expressed in 43 tumors (64%) and MIB-1 in 13 tumors (19.4%). Tumors with positive p27 showed positive lymph nodes in 10 cases (23%). In contrast, p27-negative tumors had positive lymph nodes in 18 cases (75%). Tumors positive for MIB-1 show positive lymph nodes in 11 cases (85%). However, when MIB-1 was negative, only 16 patients (30%) had positive lymph nodes. Multivariate logistic regression analysis confirmed the utility of MIB-1 overexpression in predicting lymph node metastasis ( p < 0.0006). Also, decreased p27 protein expression strongly correlates with lymph node metastasis ( p < or = 0.0001). Furthermore, when p27 was negative and MIB-1 was positive, 100% of the patients had positive lymph nodes. In contrast, when p27 was positive and MIB-1 was negative, only 12% of patients had positive lymph nodes. The reduced expression of the p27 protein and the overexpression of the MIB-1 proliferation index in this study show a significant correlation in predicting lymph nodes metastasis in MBCs.