Digitized Whole Slides for Breast Pathology Interpretation: Current Practices and Perceptions

Onega, Tracy; Weaver, Donald L.; Geller, Berta M.; Oster, Natalia V.; Carney, Patricia A.; Nelson, Heidi D; Allison, Kimberly H.; O’Malley, Frances P.; Schnitt, Stuart J.; Elmore, Joann G.

doi:10.1007/s10278-014-9683-2

Cited by 21 publications

(17 citation statements)

References 25 publications

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“…Only a fraction of pathology practices digitize their slides (Onega et al, 2014). Even if digital images were more widely available, there are two key challenges to computerized analysis of whole-slide pathology images (WSI).…”

Section: Introductionmentioning

confidence: 99%

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Barker

Hoogi

Depeursinge

et al. 2016

Medical Image Analysis

193

110

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a b s t r a c tComputerized analysis of digital pathology images offers the potential of improving clinical care (e.g. automated diagnosis) and catalyzing research (e.g. discovering disease subtypes). There are two key challenges thwarting computerized analysis of digital pathology images: first, whole slide pathology images are massive, making computerized analysis inefficient, and second, diverse tissue regions in whole slide images that are not directly relevant to the disease may mislead computerized diagnosis algorithms. We propose a method to overcome both of these challenges that utilizes a coarse-to-fine analysis of the localized characteristics in pathology images. An initial surveying stage analyzes the diversity of coarse regions in the whole slide image. This includes extraction of spatially localized features of shape, color and texture from tiled regions covering the slide. Dimensionality reduction of the features assesses the image diversity in the tiled regions and clustering creates representative groups. A second stage provides a detailed analysis of a single representative tile from each group. An Elastic Net classifier produces a diagnostic decision value for each representative tile. A weighted voting scheme aggregates the decision values from these tiles to obtain a diagnosis at the whole slide level. We evaluated our method by automatically classifying 302 brain cancer cases into two possible diagnoses (glioblastoma multiforme (N = 182) versus lower grade glioma (N = 120)) with an accuracy of 93.1 % (p << 0.001). We also evaluated our method in the dataset provided for the 2014 MICCAI Pathology Classification Challenge, in which our method, trained and tested using 5-fold cross validation, produced a classification accuracy of 100% (p << 0.001). Our method showed high stability and robustness to parameter variation, with accuracy varying between 95.5% and 100% when evaluated for a wide range of parameters. Our approach may be useful to automatically differentiate between the two cancer subtypes.

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Section: Introductionmentioning

confidence: 99%

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Barker

Hoogi

Depeursinge

et al. 2016

Medical Image Analysis

193

110

View full text Add to dashboard Cite

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“…Our previous studies found that accuracy was slightly higher when pathologists used glass slides, as the current study did, compared to digital whole slide imaging (WSI), an emerging technology for pathology interpretation. 18,23 Although currently understudied, intra-observer variability also has the potential to be greater using WSI.…”

Section: Discussionmentioning

confidence: 99%

“…Other aspects of identification and recruitment have been previously reported. 18 Demographic data, practice characteristics, and interpretive experience of the pathologists were queried using a web-based survey. 13,18 …”

Section: Methodsmentioning

confidence: 99%

Diagnostic Reproducibility: What Happens When the Same Pathologist Interprets the Same Breast Biopsy Specimen at Two Points in Time?

et al. 2016

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Background Surgeons may receive a different diagnosis when a breast biopsy is interpreted by a second pathologist. The extent to which diagnostic agreement by the same pathologist varies at two time points is unknown. Participants and Methods Pathologists from 8 U.S. states independently interpreted 60 breast specimens, one glass slide per case, on 2 occasions separated by ≥9 months. Reproducibility was assessed by comparing interpretations between the two time points; associations between reproducibility (intra-observer agreement rates) and characteristics of pathologists and cases were determined and also compared with inter-observer agreement of baseline interpretations. Results Sixty-five percent of invited, responding pathologists were eligible and consented; 49 interpreted glass slides in both study phases resulting in 2,940 interpretations. Intra-observer agreement rates between the two phases were 92% (95% CI 88%-95%) for invasive breast cancer, 84% (95% CI 81%-87%) for ductal carcinoma in situ (DCIS), 53% (95% CI 47%-59%) for atypia, and 84% (95% CI 81%-86%) for benign without atypia. When comparing all study participants' case interpretations at baseline, inter-observer agreement rates were 89% (95% CI 84%-92%) for invasive cancer, 79% (95% CI 76%-81%) for DCIS, 43% (95% CI 41%-45%) for atypia, and 77% (95% CI 74%-79%) for benign without atypia. Conclusions Interpretive agreement between two time points by the same individual pathologists was low for atypia, and similar to observed rates of agreement for atypia between different pathologists. Physicians and patients should be aware of the diagnostic challenges associated with a breast biopsy diagnosis of atypia when considering treatment and surveillance decisions.

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“…Details of their identification and recruitment have been described elsewhere 10 14. Participating pathologists completed a web-based survey followed by interpreting their assigned test set of 60 cases.…”

Section: Methodsmentioning

confidence: 99%

Characteristics associated with requests by pathologists for second opinions on breast biopsies

et al. 2017

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Aims Second opinions in pathology improve patient safety by reducing diagnostic errors, leading to more appropriate clinical treatment decisions. Little objective data are available regarding the factors triggering a request for second opinion despite second opinion consultations being part of the diagnostic system of pathology. Therefore we sought to assess breast biopsy cases and interpreting pathologists characteristics associated with second opinion requests. Methods Collected pathologist surveys and their interpretations of 60 test set cases were used to explore the relationships between case characteristics, pathologist characteristics and case perceptions, and requests for second opinions. Data were evaluated by logistic regression and generalised estimating equations. Results 115 pathologists provided 6900 assessments; pathologists requested second opinions on 70% (4827/6900) of their assessments 36% (1731/4827) of these would not have been required by policy. All associations between case characteristics and requesting second opinions were statistically significant, including diagnostic category, breast density, biopsy type, and number of diagnoses noted per case. Exclusive of institutional policies, pathologists wanted second opinions most frequently for atypia (66%) and least frequently for invasive cancer (20%). Second opinion rates were higher when the pathologist had lower assessment confidence, in cases with higher perceived difficulty, and cases with borderline diagnoses. Conclusions Pathologists request second opinions for challenging cases, particularly those with atypia, high breast density, core needle biopsies, or many co-existing diagnoses. Further studies should evaluate whether the case characteristics identified in this study could be used as clinical criteria to prompt system-level strategies for mandating second opinions.

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Digitized Whole Slides for Breast Pathology Interpretation: Current Practices and Perceptions

Cited by 21 publications

References 25 publications

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles

Diagnostic Reproducibility: What Happens When the Same Pathologist Interprets the Same Breast Biopsy Specimen at Two Points in Time?

Characteristics associated with requests by pathologists for second opinions on breast biopsies

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