Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Lacrimal gland tumors in Turkey: types, frequency, and outcomes.

AIM: To evaluate the clinical, radiological, and treatment features of lacrimal gland tumors. METHODS: Retrospective review of 99 eyes of 92 patients with lacrimal gland tumors diagnosed and managed in a single institution between January 1999 and March 2017. Clinical and radiological features, histopathology, treatment methods, and prognosis were evaluated. RESULTS: The mean patient age was 40.3 (range: 7-80)y. The diagnosis was made histopathologically in 91 (91.9%) tumors and on a clinical and radiological basis in 8 (8.1%) tumors. Final diagnoses included idiopathic orbital inflammation (pseudotumor) in 46 (46.5%) lesions, pleomorphic adenoma in 14 (14.1%), adenoid cystic carcinoma in 12 (12.1%), granulomatous inflammation in 10 (10.1%), lymphoma in 5 (5.0%), benign reactive lymphoid hyperplasia in 3 (3.0%), dacryops in 3 (3.0%), carcinoma ex pleomorphic adenoma in 2 (2.0%), adenocarcinoma in 1 (1.0%), dermoid cyst in 1 (1.0%), cavernous hemangioma in 1 (1.0%), and leukemic infiltration in 1 (1.0%). Non-epithelial tumors comprised 64.6% ( CONCLUSION: Overall, 65% of lacrimal gland tumors were of non-epithelial origin and 32% of epithelial origin. By histopathology and clinical evaluation, 79% of lacrimal gland tumors were benign. The most common lacrimal gland tumors include idiopathic orbital inflammation (46.5%), epithelial (32.3%), and lymphoproliferative (8.1%) lesions

Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.Comment: See the final version published in Frontiers in Oncology here: https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful

Diagnostic accuracy of multidetector computed tomography scan in mediastinal masses assuming histopathological findings as gold standard

Purpose: Aim of the study was to: 1) present MDCT characteristics of different mediastinal mass lesions, 2) estimate proportion of benign and malignant mediastinal mass lesions based on MDCT findings, and 3) find out the diagnostic accuracy with sensitivity, specificity, positive predictive value, and negative predictive value of MDCT in mediastinal mass lesions assuming histopathology as gold standard. Material and methods: This study was an analysis of 60 patients who underwent MDCT scan for characterisation of mediastinal mass lesion, and subsequently imaging findings were verified with pathological diagnosis. Results: Out of 60 patients 65% were malignant and 35% were benign. Metastatic carcinoma was the leading diagnosis. Sensitivity of MDCT in this study came out to be 94%, specificity is 90%, with a positive predictive value of 94% and negative predictive value of 90% with diagnostic accuracy of 93%. Conclusions: Mediastinal mass lesion can be accurately diagnosed with MDCT which is a non-invasive and easily available modality requiring clinical data for accurate diagnosis and management. Co-relation of MDCT findings with other imaging findings is complex and requires adequate clinical data for optimum diagnostic confidence

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

© 2017 The Author(s). This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic