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Microarray-based class discovery for molecular classification of breast cancer: analysis of interobserver agreement.

ABSTRACT: BACKGROUND:Breast cancers can be classified by hierarchical clustering using an "intrinsic" gene list into one of at least five molecular subtypes: basal-like, HER2, luminal A, luminal B, and normal breast-like. Five different intrinsic gene lists composed of varying numbers of genes have been used for molecular subtype identification and classification of breast cancers. The aim of this study was to determine the objectivity and interobserver reproducibility of the assignment of molecular subtype classes by hierarchical cluster analysis. METHODS:Three publicly available breast cancer datasets (n = 779) were subjected to two-way average-linkage hierarchical cluster analysis using five distinct intrinsic gene lists. We used free-marginal Kappa statistics to analyze interobserver agreement among five breast cancer researchers for the whole classification and for each molecular subtype separately according to each intrinsic gene list for each breast cancer dataset. RESULTS:None of the classification systems tested produced almost perfect agreement (Kappa ? 0.81) among observers. However, substantial interobserver agreement (70.8% to 76.1% of the samples and free-marginal Kappa scores from 0.635 to 0.701) was consistently observed in all datasets for four molecular subtypes (luminal, basal-like, HER2, and normal breast-like). When luminal cancers were subdivided (luminal A, B, and C), none of the classification systems produced substantial agreement (Kappa ? 0.61) in all the datasets analyzed. Analysis of each subtype separately revealed that only two (basal-like and HER2) could be reproducibly identified by independent observers (Kappa ? 0.81). CONCLUSIONS:Assignment of molecular subtype classes of breast cancer based on the analysis of dendrograms obtained with hierarchical cluster analysis is subjective and shows modest interobserver reproducibility. For the development of a molecular taxonomy, objective definitions for each molecular subtype and standardized methods for their identification are required.

SUBMITTER: Mackay A

PROVIDER: S-EPMC3079850 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Microarray-based class discovery for molecular classification of breast cancer: analysis of interobserver agreement.

Mackay Alan A Weigelt Britta B Grigoriadis Anita A Kreike Bas B Natrajan Rachael R A'Hern Roger R Tan David S P DS Dowsett Mitch M Ashworth Alan A Reis-Filho Jorge S JS

Journal of the National Cancer Institute 20110318 8

<h4>Background</h4>Breast cancers can be classified by hierarchical clustering using an "intrinsic" gene list into one of at least five molecular subtypes: basal-like, HER2, luminal A, luminal B, and normal breast-like. Five different intrinsic gene lists composed of varying numbers of genes have been used for molecular subtype identification and classification of breast cancers. The aim of this study was to determine the objectivity and interobserver reproducibility of the assignment of molecul ...[more]

PMID: 21421860

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Project description:BackgroundTumor proliferation speed, most commonly assessed by counting of mitotic figures in histological slide preparations, is an important biomarker for breast cancer. Although mitosis counting is routinely performed by pathologists, it is a tedious and subjective task with poor reproducibility, particularly among non-experts. Inter- and intraobserver reproducibility of mitosis counting can be improved when a strict protocol is defined and followed. Previous studies have examined only the agreement in terms of the mitotic count or the mitotic activity score. Studies of the observer agreement at the level of individual objects, which can provide more insight into the procedure, have not been performed thus far.MethodsThe development of automatic mitosis detection methods has received large interest in recent years. Automatic image analysis is viewed as a solution for the problem of subjectivity of mitosis counting by pathologists. In this paper we describe the results from an interobserver agreement study between three human observers and an automatic method, and make two unique contributions. For the first time, we present an analysis of the object-level interobserver agreement on mitosis counting. Furthermore, we train an automatic mitosis detection method that is robust with respect to staining appearance variability and compare it with the performance of expert observers on an "external" dataset, i.e. on histopathology images that originate from pathology labs other than the pathology lab that provided the training data for the automatic method.ResultsThe object-level interobserver study revealed that pathologists often do not agree on individual objects, even if this is not reflected in the mitotic count. The disagreement is larger for objects from smaller size, which suggests that adding a size constraint in the mitosis counting protocol can improve reproducibility. The automatic mitosis detection method can perform mitosis counting in an unbiased way, with substantial agreement with human experts.

Project description:To treat lupus nephritis effectively, proper identification of the histologic class is essential. Although the classification system for lupus nephritis is nearly 40 years old, remarkably few studies have investigated interobserver agreement. Interobserver agreement among nephropathologists was studied, particularly with respect to the recognition of class III/IV lupus nephritis lesions, and possible causes of disagreement were determined.A link to a survey containing pictures of 30 glomeruli was provided to all 360 members of the Renal Pathology Society; 34 responses were received from 12 countries (a response rate of 9.4%). The nephropathologist was asked whether glomerular lesions were present that would categorize the biopsy as class III/IV. If so, additional parameters were scored. To determine the interobserver agreement among the participants, ? or intraclass correlation values were calculated. The intraclass correlation or ?-value was also calculated for two separate levels of experience (specifically, nephropathologists who were new to the field or moderately experienced [less experienced] and nephropathologists who were highly experienced).Intraclass correlation for the presence of a class III/IV lesion was 0.39 (poor). The ?/intraclass correlation values for the additional parameters were as follows: active, chronic, or both: 0.36; segmental versus global: 0.39; endocapillary proliferation: 0.46; influx of inflammatory cells: 0.32; swelling of endothelial cells: 0.46; extracapillary proliferation: 0.57; type of crescent: 0.46; and wire loops: 0.35. The highly experienced nephropathologists had significantly less interobserver variability compared with the less experienced nephropathologists (P=0.004).There is generally poor agreement in terms of recognizing class III/IV lesions. Because experience clearly increases interobserver agreement, this agreement may be improved by training nephropathologists. These results also underscore the importance of a central review by experienced nephropathologists in clinical trials.

Project description:Background Classification of lung cancer screening CT scans depends on measurement of lung nodule size. Information about interobserver agreement is limited. Purpose To assess interobserver agreement in the measurements and American College of Radiology Lung CT Screening Reporting and Data System (Lung-RADS) classifications of solid lung nodules detected at lung cancer screening using manual measurements of average diameter and computer-aided semiautomated measurements of average diameter and volume (CT volumetry). Materials and Methods Two radiologists and one radiology resident retrospectively measured lung nodules from screening CT scans obtained between September 2016 and June 2018 with a Lung-RADS (version 1.0) classification of 2, 3, 4A, or 4B in the clinical setting. Average manual diameter and semiautomated computer-aided diameter and volume measurements were converted to the corresponding Lung-RADS categories. Interobserver agreement in raw measurements was assessed using intraclass correlation and Bland-Altman indexes, and interobserver agreement in Lung-RADS classification was assessed using bi-rater κ. Results One hundred twenty patients (mean age, 63 years ± 6 [standard deviation]; 67 women) were evaluated. All manual, semiautomated diameter, and semiautomated volume measurements were obtained by all three readers in 120 of 147 nodules (82%). Intraclass correlation coefficients were greater than or equal to 0.95 for all reader pairs using all measurement methods and were highest using volumetry. Bias and 95% limits of agreement for average diameter were smaller with semiautomated measurements than with manual measurements. κ values across all Lung-RADS classifications were greater than or equal to 0.81, with the lowest being for manual measurements and the highest being for volumetric measurements. Forty-three of 120 (36%) of the nodules were classified into a lower Lung-RADS category on the basis of volumetry compared with using manual diameter measurements by at least one reader, whereas the reverse occurred for four of 120 (3%) of the nodules. Conclusion Interobserver agreement was high with manual diameter measurements and increased with semiautomated CT volumetric measurements. Semiautomated CT volumetry enabled classification of more nodules into lower Lung CT Screening Reporting and Data System categories than manual or semiautomated diameter measurements. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Nishino in this issue.

Dataset Information

Microarray-based class discovery for molecular classification of breast cancer: analysis of interobserver agreement.

Publications

Microarray-based class discovery for molecular classification of breast cancer: analysis of interobserver agreement.

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