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- Dijkstra, Jeroen R., et al.
(författare)
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Implementation of Formalin-Fixed, Paraffin-Embedded Cell Line Pellets as High-Quality Process Controls in Quality Assessment Programs for KRAS Mutation Analysis
- 2012
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Ingår i: The Journal Of Molecular Diagnostics. - : Elsevier BV. - 1525-1578. ; 14:3, s. 187-191
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, the mutational status of the KRAS oncogene has become incorporated into standard medical care as a predictive marker for therapeutic decisions related to patients with metastasized colorectal cancer. This is necessary, because these patients benefit from epidermal growth factor receptor (EGFR)-targeted therapy with increased progression-free survival only if the tumor does not carry a mutation in KRAS. Many different analytical platforms, both those commercially available and those developed in house, have been used within pathology laboratories to assess KRAS mutational status. For a testing laboratory to become accredited to perform such tests, it is essential that they perform reliability testing, but it has not previously been possible to perform this kind of testing on the complete workflow on a large scale without compromising reproducibility or the mimicry of the control sample. We assessed a novel synthetic control for formalin-fixed, paraffin-embedded (FFPE) tumor samples in a blind study conducted within nine laboratories across Europe. We show that FFPE material can, at least in part, mimic clinical samples and we demonstrate this control to be a valuable tool in the assessment of platforms used In testing for KRAS mutational status. (J Mal Diagn 2012; 14:187-191; DOI: 10.1016/j.jmoldx.2012.01.002).
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- Smit, Marloes A., et al.
(författare)
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Deep learning based tumor–stroma ratio scoring in colon cancer correlates with microscopic assessment
- 2023
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Ingår i: Journal of Pathology Informatics. - : Elsevier B.V.. - 2229-5089 .- 2153-3539. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Background: The amount of stroma within the primary tumor is a prognostic parameter for colon cancer patients. This phenomenon can be assessed using the tumor–stroma ratio (TSR), which classifies tumors in stroma-low (≤50% stroma) and stroma-high (>50% stroma). Although the reproducibility for TSR determination is good, improvement might be expected from automation. The aim of this study was to investigate whether the scoring of the TSR in a semi- and fully automated method using deep learning algorithms is feasible. Methods: A series of 75 colon cancer slides were selected from a trial series of the UNITED study. For the standard determination of the TSR, 3 observers scored the histological slides. Next, the slides were digitized, color normalized, and the stroma percentages were scored using semi- and fully automated deep learning algorithms. Correlations were determined using intraclass correlation coefficients (ICCs) and Spearman rank correlations. Results: 37 (49%) cases were classified as stroma-low and 38 (51%) as stroma-high by visual estimation. A high level of concordance between the 3 observers was reached, with ICCs of 0.91, 0.89, and 0.94 (all P < .001). Between visual and semi-automated assessment the ICC was 0.78 (95% CI 0.23–0.91, P-value 0.005), with a Spearman correlation of 0.88 (P < .001). Spearman correlation coefficients above 0.70 (N=3) were observed for visual estimation versus the fully automated scoring procedures. Conclusion: Good correlations were observed between standard visual TSR determination and semi- and fully automated TSR scores. At this point, visual examination has the highest observer agreement, but semi-automated scoring could be helpful to support pathologists. © 2023 The Authors
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