ArticleJournal of medical imaging (Bellingham, Wash.)2016
Evaluating stability of histomorphometric features across scanner and staining variations: prostate cancer diagnosis from whole slide images.
Article in Journal of medical imaging (Bellingham, Wash.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
38 citing papers in PubMed.
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- Developing a Reproducible Radiomics Model for Diagnosis of Active Crohn's Disease on CT Enterography Across Annotation Variations and Acquisition Differences.Journal of imaging informatics in medicine · 2025Article
- Decoding pathology: the role of computational pathology in research and diagnostics.Pflugers Archiv : European journal of physiology · 2025Review
- A comprehensive AI model development framework for consistent Gleason grading.Communications medicine · 2024Article
- Algorithm-assisted diagnosis of Hirschsprung's disease - evaluation of robustness and comparative image analysis on data from various labs and slide scanners.Diagnostic pathology · 2024Article
- Synergies and Challenges in the Preclinical and Clinical Implementation of Pathology Artificial Intelligence Applications.Mayo Clinic proceedings. Digital health · 2023Review
- Standardized CycleGAN training for unsupervised stain adaptation in invasive carcinoma classification for breast histopathology.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Learning Individual Survival Models from PanCancer Whole Transcriptome Data.Clinical cancer research : an official journal of the American Association for Cancer Research · 2023Article
- Improved bias and reproducibility of coronary artery calcification features using deconvolution.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Stable and Discriminatory Radiomic Features from the Tumor and Its Habitat Associated with Progression-Free Survival in Glioblastoma: A Multi-Institutional Study.AJNR. American journal of neuroradiology · 2022Article
- Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.An international journal on information fusion · 2022Article
- Challenges in ensuring the generalizability of image quantitation methods for MRI.Medical physics · 2022Review
- Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges.Arthritis research & therapy · 2022Review
- An Expandable Informatics Framework for Enhancing Central Cancer Registries with Digital Pathology Specimens, Computational Imaging Tools, and Advanced Mining Capabilities.Journal of pathology informatics · 2022Article
- Quality control stress test for deep learning-based diagnostic model in digital pathology.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2021Article
- Computationally Derived Cribriform Area Index from Prostate Cancer Hematoxylin and Eosin Images Is Associated with Biochemical Recurrence Following Radical Prostatectomy and Is Most Prognostic in Gleason Grade Group 2.European urology focus · 2021Article
- Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.NPJ precision oncology · 2021Article
- Utility of TScientific reports · 2021Article
- Automatic cancer detection on digital histopathology images of mid-gland radical prostatectomy specimens.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Quantitative histomorphometry (QH) is the process of computerized feature extraction from digitized tissue slide images to predict disease presence, behavior, and outcome. Feature stability between sites may be compromised by laboratory-specific variables including dye batch, slice thickness, and the whole slide scanner used. We present two new measures, preparation-induced instability score and latent instability score, to quantify feature instability across and within datasets. In a use case involving prostate cancer, we examined QH features which may detect cancer on whole slide images. Using our method, we found that five feature families (graph, shape, co-occurring gland tensor, sub-graph, and texture) were different between datasets in 19.7% to 48.6% of comparisons while the values expected without site variation were 4.2% to 4.6%. Color normalizing all images to a template did not reduce instability. Scanning the same 34 slides on three scanners demonstrated that Haralick features were most substantively affected by scanner variation, being unstable in 62% of comparisons. We found that unstable feature families performed significantly worse in inter- than intrasite classification. Our results appear to suggest QH features should be evaluated across sites to assess robustness, and class discriminability alone should not represent the benchmark for digital pathology feature selection.
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