Colon Cancer Imaging and Analysis

The cases selected for this study were based on the E-learning module of the UNITED study.⁸ (link) These cases were chosen for training purposes by limiting the number of stroma-low cases (e.g., 10% or 20% stroma cases), in order to increase the number of cases that are difficult to score for pathologists. All patients underwent surgical treatment at the Leiden University Medical Center (LUMC), had stage II or stage III colon cancer, and no patients received neo-adjuvant treatment. The slides were anonymized and scanned with the Panoramic 250 scanner (3DHistech, Hungary) (tissue level pixel size ∼0.33 μm/pixel), or with the IntelliSite Digital pathology slide scanner (Philips, the Netherlands) (tissue level pixel size ∼0.25–0.26 μm/pixel).

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Smit M.A., Ciompi F., Bokhorst J.M., van Pelt G.W., Geessink O.G., Putter H., Tollenaar R.A., van Krieken J.H., Mesker W.E, & van der Laak J.A. (2023). Deep learning based tumor–stroma ratio scoring in colon cancer correlates with microscopic assessment. Journal of Pathology Informatics, 14, 100191.

Publication 2023

Panoramic 250 scanner IntelliSite Digital pathology slide scanner

Adjuvant Colon cancer Pathologists Patients Scanner Surgical treatment Tissue

Corresponding Organization : Linköping University

Other organizations : Leiden University Medical Center

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Variable analysis

independent variables

Stroma percentage (10% or 20%)

dependent variables

Difficulty of scoring for pathologists

control variables

Stage of colon cancer (II or III)
No neo-adjuvant treatment received by patients
Surgical treatment at Leiden University Medical Center (LUMC)
Slide anonymization
Slide scanning with Panoramic 250 scanner or IntelliSite Digital pathology slide scanner

controls

No positive or negative controls were explicitly mentioned in the provided information.

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