ArticleJournal of pathology informatics2013
Inter-reader variability in follicular lymphoma grading: Conventional and digital reading.
Article in Journal of pathology informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
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- Role of artificial intelligence in haematolymphoid diagnostics.Histopathology · 2025Review
- PseudoCell: Hard Negative Mining as Pseudo Labeling for Deep Learning-Based Centroblast Cell Detection.IEEE open journal of engineering in medicine and biology · 2024Article
- Spatial mapping of the DNA adducts in cancer.DNA repair · 2023Review
- The impact of histological grade on outcomes in follicular lymphoma: An analysis of patients in the SEER database in the context of evolving disease classification and treatment.British journal of haematology · 2022Article
- Ensemble deep learning enhanced with self-attention for predicting immunotherapeutic responses to cancers.Frontiers in immunology · 2022Article
- Deep learning in cancer diagnosis, prognosis and treatment selection.Genome medicine · 2021Review
- Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology.Cancers · 2020Review
- An image dataset related to automated macrophage detection in immunostained lymphoma tissue samples.GigaScience · 2020Article
- Evaluating Novel PET-CT Functional Parameters TLG and TMTV in Differentiating Low-grade Versus Grade 3A Follicular Lymphoma.Clinical lymphoma, myeloma & leukemia · 2020Article
- A modular cGAN classification framework: Application to colorectal tumor detection.Scientific reports · 2019Article
- Informatics Approaches to Address New Challenges in the Classification of Lymphoid Malignancies.JCO clinical cancer informatics · 2018Review
- Assessment of pathological response to therapy using lipid mass spectrometry imaging.Scientific reports · 2016Article
- Classification of follicular lymphoma: the effect of computer aid on pathologists grading.BMC medical informatics and decision making · 2015Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
contextPathologists grade follicular lymphoma (FL) cases by selecting 10, random high power fields (HPFs), counting the number of centroblasts (CBs) in these HPFs under the microscope and then calculating the average CB count for the whole slide. Previous studies have demonstrated that there is high inter-reader variability among pathologists using this methodology in grading.
aimsThe objective of this study was to explore if newly available digital reading technologies can reduce inter-reader variability. SETTINGS AND
designIN THIS STUDY, WE CONSIDERED THREE DIFFERENT READING CONDITIONS (RCS) IN GRADING FL: (1) Conventional (glass-slide based) to establish the baseline, (2) digital whole slide viewing, (3) digital whole slide viewing with selected HPFs. Six board-certified pathologists from five different institutions read 17 FL slides in these three different RCs.
resultsAlthough there was relative poor consensus in conventional reading, with lack of consensus in 41.2% of cases, which was similar to previously reported studies; we found that digital reading with pre-selected fields improved the inter-reader agreement, with only 5.9% lacking consensus among pathologists.
conclusionsDigital whole slide RC resulted in the worst concordance among pathologists while digital whole slide reading selected HPFs improved the concordance. Further studies are underway to determine if this performance can be sustained with a larger dataset and our automated HPF and CB detection algorithms can be employed to further improve the concordance.
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