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* Context.--Despite several studies focusing on the validation of whole slide imaging (WSI) across organ systems or subspecialties, the use of WSI for specific primary diagnosis tasks has been underexamined. Objective.--To assess pathologist performance for the histologic subtyping of individual sections of ovarian carcinomas using a light microscope and WSI. Design.--A panel of 3 experienced gynecologic pathologists provided reference subtype diagnoses for 212 histologic sections from 109 ovarian carcinomas based on optical microscopy review. Two additional attending pathologists provided diagnoses and also identified the presence of a set of 8 histologic features important for ovarian tumor subtyping. Two experienced gynecologic pathologists and 2 fellows reviewed the corresponding WSI images for subtype classification and feature identification. Results.--Across pathologists specialized in gynecologic pathology, concordance with the reference diagnosis for the 5 major ovarian carcinoma subtypes was significantly higher for a pathologist reading on a microscope than each of 2 pathologists reading on WSI. Differences were primarily due to more frequent classification of mucinous carcinomas as endometrioid with WSI. Pathologists had generally low agreement in identifying histologic features important to ovarian tumor subtype classification with either an optical microscopy or WSI. This result suggests the need for refined histologic criteria for identifying such features. Interobserver agreement was particularly low for identifying intracytoplasmic mucin with WSI. Inconsistencies in evaluating nuclear atypia and mitoses with WSI were also observed. Conclusions.--Further research is needed to specify the reasons for these diagnostic challenges and to inform users and manufacturers of WSI technology., More than 90% of ovarian malignancies consist of epithelial cancers, (1) which are further classified into 5 major subtypes, namely high-grade serous carcinoma (HGSC), low-grade serous carcinoma (LGSC), clear cell [...]

* Context.--Clinical decision support (CDS) systems could assist less experienced pathologists with certain diagnostic tasks for which subspecialty training or extensive experience is typically needed. The effect of decision support on pathologist performance for such diagnostic tasks has not been examined. Objective.--To examine the impact of a CDS tool for the classification of ovarian carcinoma subtypes by pathology trainees in a pilot observer study using digital pathology. Design.--Histologic review on 90 whole slide images from 75 ovarian cancer patients was conducted by 6 pathology residents using: (1) unaided review of whole slide images, and (2) aided review, where in addition to whole slide images observers used a CDS tool that provided information about the presence of 8 histologic features important for subtype classification that were identified previously by an expert in gynecologic pathology. The reference standard of ovarian subtype consisted of majority consensus from a panel of 3 gynecologic pathology experts. Results.--Aided review improved pairwise concordance with the reference standard for 5 of 6 observers by 3.3% to 17.8% (for 2 observers, increase was statistically significant) and mean interobserver agreement by 9.2% (not statistically significant). Observers benefited the most when the CDS tool prompted them to look for missed histologic features that were definitive for a certain subtype. Observer performance varied widely across cases with unanimous and nonunanimous reference classification, supporting the need for balancing data sets in terms of case difficulty. Conclusions.--Findings showed the potential of CDS systems to close the knowledge gap between pathologists for complex diagnostic tasks. (Arch Pathol Lab Med. 2020;144:869-877; doi: 10.5858/arpa.2019-0390-OA), Advances in whole slide imaging (1) and the recent US Food and Drug Administration clearances of the first 2 whole slide imaging systems for primary diagnosis have accelerated efforts to [...]

Rationale and objectivesTumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile.Materials and methodsIndustry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers.ResultsIntra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail.ConclusionsNine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm.

Rationale and objectivesTo estimate and statistically compare the bias and variance of radiologists measuring the size of spherical and complex synthetic nodules.Materials and methodsThis study did not require the institutional review board approval. Six radiologists estimated the size of 10 synthetic nodules embedded within an anthropomorphic thorax phantom from computed tomography scans at 0.8- and 5-mm slice thicknesses. The readers measured the nodule size using unidimensional (1D) longest in-slice dimension, bidimensional (2D) area from longest in-slice and longest perpendicular dimension, and three-dimensional (3D) semiautomated volume. Intercomparisons of bias (difference between average and true size) and variance among methods were performed after converting the 2D and 3D estimates to a compatible 1D scale.ResultsThe relative biases of radiologists with the 3D tool were -1.8%, -0.4%, -0.7%, -0.4%, and -1.6% for 10-mm spherical, 20-mm spherical, 20-mm elliptical, 10-mm lobulated, and 10-mm spiculated nodules compared to 1.4%, -0.1%, -26.5%, -7.8%, and -39.8% for 1D. The three-dimensional measurements were significantly less biased than 1D for elliptical, lobulated, and spiculated nodules. The relative standard deviations for 3D were 7.5%, 3.9%, 3.6%, 9.7%, and 8.3% compared to 5.7%, 2.6%, 20.3%, 5.3%, and 16.4% for 1D. Unidimensional sizing was significantly less variable than 3D for the lobulated nodule and significantly more variable for the ellipsoid and spiculated nodules. Three-dimensional bias and variability were smaller for thin 0.8-mm slice data compared to thick 5.0-mm data.ConclusionsThe study shows that radiologist-controlled 3D volumetric lesion sizing can not only achieve smaller bias but also achieve similar or smaller variability compared to 1D sizing, especially for complex lesion shapes.

The expression of the HER-2/neu (HER2) gene, a member of the epidermal growth factor receptor family, has been shown to be a valuable prognostic indicator for breast cancer. However, interobserver variability has been reported in the evaluation of HER2 with immunohistochemistry. It has been suggested that automated computer-based evaluation can provide a consistent and objective evaluation of HER2 expression. In this manuscript, we present an automated method for the quantitative assessment of HER2 using digital microscopy. The method processes microscopy images from tissue slides with a multistage algorithm, including steps of color pixel classification, nuclei segmentation, and cell membrane modeling, and extracts quantitative, continuous measures of cell membrane staining intensity and completeness. A minimum cluster distance classifier merges the features to classify the slides into HER2 categories. An evaluation based on agreement analysis with pathologist-derived HER2 scores, showed good agreement with the provided truth. Agreement varied within the different classes with highest agreement (up to 90%) for positive (3+) slides, and lowest agreement (72%-78%) for equivocal (2+) slides which contained ambiguous scoring. The developed automated method has the potential to be used as a computer aid for the immunohistochemical evaluation of HER2 expression with the objective of increasing observer reproducibility. Index Terms--Biomarker, breast cancer, computer-aided immunohistochemistry, digital microscopy, HER2/neu, pathology.

A multistage algorithm for the automated classification of fluorescent in situ hybridization (FISH) images from breast carcinomas is presented. The algorithm focuses on the detection of FISH dots per image and also on the combining results from multiple images taken from a slice for overall case classification.

* Context.--The use of computer aids has been suggested as a way to reduce interobserver variability that is known to exist in the interpretation of immunohistochemical staining in pathology. Such computer aids should be automated in their usage but also they should be trained in an automated and reproducible fashion. Objective.--To present a computer aid for the quantitative analysis of tissue-based biomarkers, based on color content analysis. Design.--The developed system incorporates an automated algorithm to allow retraining based on the color properties of different training sets. The algorithm first generates a color palette containing the colors present in a training subset. Based on the palette, color histograms are derived and are used as feature vectors to a pattern recognition system, which returns an output proportional to biomarker continuous expression or a categorical classification. The method was evaluated on a database of HER2/neu digital breast cancer slides, for which expression scores from a pathologist panel were available. The system was retrained and evaluated on different transformations of the database, including compression, blurring, and changes in illumination, to examine its robustness to different imaging conditions frequently met in digital pathology. Results.--Results showed high agreement between the results of the algorithm and the truth from the pathologist panel as well as robustness to image transformations. Conclusions.--The results of the study are encouraging for the potential of this method as a computer aid to assess biomarker expression in a consistent and reproducible manner. (Arch Pathol Lab Med. 2012; 136:539-550; doi:10.5858/arpa.2011-0195-OA), Tissue-based molecular biomarkers have been increasingly used in clinical practice to aid in disease diagnosis, in assessing patient prognosis, and in treatment planning by predicting individual patient response to therapy. [...]

* Context.--Observer variability in digital microscopy and the effect of computer-aided digital microscopy are underexamined areas in need of further research, considering the increasing use and future role of digital imaging in pathology. A reduction in observer variability using computer aids could enhance the statistical power of studies designed to determine the utility of new biomarkers and accelerate their incorporation in clinical practice. Objectives.--To quantify interobserver and intraobserver variability in immunohistochemical analysis of HER2/ neu with digital microscopy and computer-aided digital microscopy, and to test the hypothesis that observer agreement in the quantitative assessment of HER2/neu immunohistochemical expression is increased with the use of computer-aided microscopy. Design.--A set of 335 digital microscopy images extracted from 64 breast cancer tissue slides stained with a HER2 antibody, were read by 14 observers in 2 reading modes: the unaided mode and the computer-aided mode. In the unaided mode, HER2 images were displayed on a calibrated color monitor with no other information, whereas in the computer-aided mode, observers were shown a HER2 image along with a corresponding feature plot showing computer-extracted values of membrane staining intensity and membrane completeness for the particular image under examination and, at the same time, mean feature values of the different HER2 categories. In both modes, observers were asked to provide a continuous score of HER2 expression. Results.--Agreement analysis performed on the output of the study showed significant improvement in both interobserver and intraobserver agreement when the computer-aided reading mode was used to evaluate preselected image fields. Conclusion.--The role of computer-aided digital microscopy in reducing observer variability in immunohistochemistry is promising., Immunohistochemistry (IHC) has evolved during the past 40 years to become central to both diagnostic pathology and clinical research. (1) Advances in molecular biology leading to the discovery of numerous [...]