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Published on: July 11, 2025
Investigation into diagnostic agreement using automated computer-assisted histopathology pattern recognition image
Joshua D Webster1, Aleksandra M Michalowski, Jennifer E Dwyer
1Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.
Abstract:
The extent to which histopathology pattern recognition image analysis (PRIA) agrees with microscopic assessment has not been established. Thus, a commercial PRIA platform was evaluated in two applications using whole-slide images. Substantial agreement, lacking significant constant or proportional errors, between PRIA and manual morphometric image segmentation was obtained for pulmonary metastatic cancer areas (Passing/Bablok regression). Bland-Altman analysis indicated heteroscedastic measurements and tendency toward increasing variance with increasing tumor burden, but no significant trend in mean bias. The average between-methods percent tumor content difference was -0.64. Analysis of between-methods measurement differences relative to the percent tumor magnitude revealed that method disagreement had an impact primarily in the smallest measurements (tumor burden <3%). Regression-based 95% limits of agreement indicated substantial agreement for method interchangeability. Repeated measures revealed concordance correlation of >0.988, indicating high reproducibility for both methods, yet PRIA reproducibility was superior (C.V.: PRIA = 7.4, manual = 17.1). Evaluation of PRIA on morphologically complex teratomas led to diagnostic agreement with pathologist assessments of pluripotency on subsets of teratomas. Accommodation of the diversity of teratoma histologic features frequently resulted in detrimental trade-offs, increasing PRIA error elsewhere in images. PRIA error was nonrandom and influenced by variations in histomorphology. File-size limitations encountered while training algorithms and consequences of spectral image processing dominance contributed to diagnostic inaccuracies experienced for some teratomas. PRIA appeared better suited for tissues with limited phenotypic diversity. Technical improvements may enhance diagnostic agreement, and consistent pathologist input will benefit further development and application of PRIA.
Insights
Histopathology pattern recognition image analysis (PRIA) shows substantial agreement with manual assessment for pulmonary metastases but struggles with complex teratomas. PRIA offers superior reproducibility, though algorithm improvements are needed for diverse tissues.
Area of Science:
- Digital pathology
- Computational pathology
- Histopathology image analysis
Background:
- The diagnostic accuracy of histopathology pattern recognition image analysis (PRIA) compared to traditional microscopic assessment remains largely unquantified.
- Evaluating PRIA's performance is crucial for its integration into clinical diagnostic workflows.
Purpose of the Study:
- To assess the agreement and reproducibility of a commercial PRIA platform against manual morphometric image segmentation and pathologist assessments.
- To identify the strengths and limitations of PRIA in analyzing diverse histopathological samples, including pulmonary metastases and teratomas.
Main Methods:
- Whole-slide images were analyzed using a commercial PRIA platform and manual morphometric segmentation.
- Statistical analyses included Passing/Bablok regression and Bland-Altman analysis to evaluate agreement and bias.
- Reproducibility was assessed using repeated measures and concordance correlation coefficients.
Main Results:
- PRIA demonstrated substantial agreement with manual segmentation for pulmonary metastatic cancer areas, with high reproducibility (CV: PRIA=7.4, manual=17.1).
- PRIA exhibited limitations with morphologically complex teratomas, leading to diagnostic inaccuracies influenced by histomorphology and algorithmic constraints.
- Method disagreement primarily impacted measurements of smaller tumor burdens (<3%).
Conclusions:
- PRIA shows promise for analyzing tissues with limited phenotypic diversity, such as pulmonary metastases, offering superior reproducibility.
- Further technical advancements and consistent pathologist input are essential to enhance PRIA's accuracy and applicability for complex histopathological diagnoses.
- PRIA's current limitations in handling diverse histomorphology necessitate careful consideration for its deployment in complex cases.
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