Whole Slide Imaging for Analytical Anatomic Pathology and Telepathology: Practical Applications Today, Promises, and Perils

Farris, Alton B.; Cohen, Cynthia; Rogers, Thomas E.; Smith, Geoffrey H.

doi:10.5858/arpa.2016-0265-sa

Cited by 49 publications

(44 citation statements)

References 73 publications

(68 reference statements)

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“…Third, this software can be linked to a variety of microscope digital cameras; the analytic process requires real time viewing of microscope images. In addition, the validation process of the count images reviewed by pathologists was shown in this study to contribute to accurate Ki-67 quantitation by IA, in keeping with the concept proposed by Farris et al, 14 which was an effective complementation of the modified NIH criteria in predicting the prognosis of all GIST patients.…”

Section: Discussionsupporting

confidence: 81%

Image analysis is an excellent tool for quantifying Ki‐67 to predict the prognosis of gastrointestinal stromal tumor patients

Sugita

Honda

Hatanaka

et al. 2017

Pathology International

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We investigated the quantification of Ki-67 staining using digital image analysis (IA) as a complementary prognostic factor to the modified National Institutes of Health (NIH) classification in patients with gastrointestinal stromal tumor (GIST). We examined 92 patients, focusing on the correlation between age, sex, primary tumor site, tumor size, predominant histologic type, mitotic index, modified NIH classification (low/intermediate vs high), Ki-67 quantitation, and recurrence-free survival (RFS). We compared two IA processes for whole slide imaging (WSI) and manually captured image (MCI) methods. A Ki-67 quantitation cutoff was determined by receiver operator characteristics curve analysis. In the survival analysis, the high-risk group of a modified NIH classification, a mitotic count >5 per 20 high-powered fields, and Ki-67 cutoffs of ≥6% and ≥8% obtained by IA of the WSI and MCI methods, respectively, had an adverse impact on RFS. On multivariate analysis, each Ki-67 quantitation method strongly predicted prognosis, more strongly than the modified NIH classification. In addition, Ki-67 quantitation using IA of the MCI method could stratify low or intermediate risk and high risk GIST patients. Thus, IA is an excellent tool for quantifying Ki-67 to predict the prognosis of GIST patients, and this semiautomated approach may be preferable for patient care.

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Section: Discussionsupporting

confidence: 81%

Image analysis is an excellent tool for quantifying Ki‐67 to predict the prognosis of gastrointestinal stromal tumor patients

Sugita

Honda

Hatanaka

et al. 2017

Pathology International

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“…The diffusion of digital scanners, able to transform the physical histological slides into multi-resolution digital resources called Whole-Slide Images (WSIs), is rapidly changing the workflow of clinical laboratories [2]. Traditional histopathology, based on visual evaluation of the samples directly under the microscope, is being progressively abandoned in favor of Computer-Aided Diagnosis (CAD) systems, fostering a complete automatization of downstream image analysis.…”

Section: Introductionmentioning

confidence: 99%

Dealing with Lack of Training Data for Convolutional Neural Networks: The Case of Digital Pathology

et al. 2019

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Thanks to their capability to learn generalizable descriptors directly from images, deep Convolutional Neural Networks (CNNs) seem the ideal solution to most pattern recognition problems. On the other hand, to learn the image representation, CNNs need huge sets of annotated samples that are unfeasible in many every-day scenarios. This is the case, for example, of Computer-Aided Diagnosis (CAD) systems for digital pathology, where additional challenges are posed by the high variability of the cancerous tissue characteristics. In our experiments, state-of-the-art CNNs trained from scratch on histological images were less accurate and less robust to variability than a traditional machine learning framework, highlighting all the issues of fully training deep networks with limited data from real patients. To solve this problem, we designed and compared three transfer learning frameworks, leveraging CNNs pre-trained on non-medical images. This approach obtained very high accuracy, requiring much less computational resource for the training. Our findings demonstrate that transfer learning is a solution to the automated classification of histological samples and solves the problem of designing accurate and computationally-efficient CAD systems with limited training data.

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“…WSI offers a convenient platform for measuring histopathological features of the liver and other pathological specimens, but its implementation in the clinical practice is still in progress (16)(17)(18)(19)(20). Validation of WSI is crucial to ensure that diagnostic performance based on digitized slides is at least equivalent to that of glass slides analyzed under conventional light microscopy, as established by the College of American Pathologists-CAP (21).…”

Section: Wsi For Clinical Practice Of Liver Pathologymentioning

confidence: 99%

Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders

et al. 2020

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Histological analysis of hepatic tissue specimens is essential for evaluating the pathology of several liver disorders such as chronic liver diseases, hepatocellular carcinomas, liver steatosis, and infectious liver diseases. Manual examination of histological slides on the microscope is a classically used method to study these disorders. However, it is considered time-consuming, limited, and associated with intra-and inter-observer variability. Emerging technologies such as whole slide imaging (WSI), also termed virtual microscopy, have increasingly been used to improve the assessment of histological features with applications in both clinical and research laboratories. WSI enables the acquisition of the tissue morphology/pathology from glass slides and translates it into a digital form comparable to a conventional microscope, but with several advantages such as easy image accessibility and storage, portability, sharing, annotation, qualitative and quantitative image analysis, and use for educational purposes. WSI-generated images simultaneously provide high resolution and a wide field of observation that can cover the entire section, extending any single field of view. In this review, we summarize current knowledge on the application of WSI to histopathological analyses of liver disorders as well as to understand liver biology. We address how WSI may improve the assessment and quantification of multiple histological parameters in the liver, and help diagnose several hepatic conditions with important clinical implications. The WSI technical limitations are also discussed.

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Whole Slide Imaging for Analytical Anatomic Pathology and Telepathology: Practical Applications Today, Promises, and Perils

Cited by 49 publications

References 73 publications

Image analysis is an excellent tool for quantifying Ki‐67 to predict the prognosis of gastrointestinal stromal tumor patients

Image analysis is an excellent tool for quantifying Ki‐67 to predict the prognosis of gastrointestinal stromal tumor patients

Dealing with Lack of Training Data for Convolutional Neural Networks: The Case of Digital Pathology

Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders

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