Histopathologic Misdiagnoses and Their Clinical Consequences

“…Then, an expert histopathologist examines the glass sides under a light microscope to provide the diagnosis for each sample, as shown in Figure 1 . Accurate interpretation of glass slides is crucial to avoid misdiagnoses,[ 4 ] which requires extensive time and effort by the pathologist. Each woman could have up to a dozen biopsy samples that require analysis.…”

Section: Introductionmentioning

confidence: 99%

Automated Cervical Digitized Histology Whole-Slide Image Analysis Toolbox

Sornapudi

Addanki

Stanley

et al. 2021

Journal of Pathology Informatics

View full text Add to dashboard Cite

Background: Cervical intraepithelial neoplasia (CIN) is regarded as a potential precancerous state of the uterine cervix. Timely and appropriate early treatment of CIN can help reduce cervical cancer mortality. Accurate estimation of CIN grade correlated with human papillomavirus type, which is the primary cause of the disease, helps determine the patient's risk for developing the disease. Colposcopy is used to select women for biopsy. Expert pathologists examine the biopsied cervical epithelial tissue under a microscope. The examination can take a long time and is prone to error and often results in high inter-and intra-observer variability in outcomes. Methodology: We propose a novel image analysis toolbox that can automate CIN diagnosis using whole slide image (digitized biopsies) of cervical tissue samples. The toolbox is built as a four-step deep learning model that detects the epithelium regions, segments the detected epithelial portions, analyzes local vertical segment regions, and finally classifies each epithelium block with localized attention. We propose an epithelium detection network in this study and make use of our earlier research on epithelium segmentation and CIN classification to complete the design of the end-to-end CIN diagnosis toolbox. Results: The results show that automated epithelium detection and segmentation for CIN classification yields comparable results to manually segmented epithelium CIN classification. Conclusion: This highlights the potential as a tool for automated digitized histology slide image analysis to assist expert pathologists.

show abstract

“…2 After a skin lesion is biopsied, the tissue is fixed, embedded, sectioned, and stained with hematoxylin and eosin (H&E) on glass slides, ultimately to be examined under microscope by a dermatologist, general pathologist or dermatopathologist who provides a diagnosis for each tissue specimen. Owing to the large variety of over 500 distinct skin pathologies 3 and the severe consequences of a critical misdiagnosis, 4 diagnosis in dermatopathology demands specialized training and education. Although the inter-observer concordance rate in dermatopathology is estimated to be between 90 and 95%, 5,6 there are some distinctions which present frequent disagreement among pathologists, such as in the case of melanoma vs. melanocytic nevi.…”

Section: Introductionmentioning

confidence: 99%

Augmenting the Pathology Lab: An Intelligent Whole Slide Image Classification System for the Real World

Ianni,

Soans,

Sankarapandian

et al. 2019

Preprint

View full text Add to dashboard Cite

Standard of care diagnostic procedure for suspected skin cancer is microscopic examination of hematoxylin & eosin stained tissue by a pathologist. Areas of high inter-pathologist discordance and rising biopsy rates necessitate higher efficiency and diagnostic reproducibility. We present and validate a deep learning system which classifies digitized dermatopathology slides into 4 categories.The system is developed using 5,070 images from a single lab, and tested on an uncurated set of 13,537 images from 3 test labs, using whole slide scanners manufactured by 3 different vendors.The system's use of deep-learning-based confidence scoring as a criterion to consider the result as accurate yields an accuracy of up to 98%, and makes it adoptable in a real-world setting. Without confidence scoring, the system achieved an accuracy of 78%. We anticipate that our deep learning system will serve as a foundation enabling faster diagnosis of skin cancer, identification of cases for specialist review, and targeted diagnostic classifications.

show abstract

Histopathologic Misdiagnoses and Their Clinical Consequences

Cited by 10 publications

References 0 publications

Diagnosis of Nonmelanoma Skin Cancer/Keratinocyte Carcinoma: A Review of Diagnostic Accuracy of Nonmelanoma Skin Cancer Diagnostic Tests and Technologies

Diagnosis of Nonmelanoma Skin Cancer/Keratinocyte Carcinoma: A Review of Diagnostic Accuracy of Nonmelanoma Skin Cancer Diagnostic Tests and Technologies

Automated Cervical Digitized Histology Whole-Slide Image Analysis Toolbox

Augmenting the Pathology Lab: An Intelligent Whole Slide Image Classification System for the Real World

Contact Info

Product

Resources

About