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Session: Machine Learning for Toxicity Prediction [Return to Session]

Patient-Specific Nuclei Size and Cell Spacing Distribution Extraction From Histopathology Whole Slide Images for Treatment Outcome Prediction Modelling

Y Zou1*, M Lecavalier-barsoum2, M Pelmus3, S Abbasinejad Enger1,2,4, (1) Medical Physics Unit, Department of Oncology, Faculty of Medicine, McGill University, Montreal, QC, CA, (2) Department of Radiation Oncology, Jewish General Hospital, Montreal, QC, Canada, (3) Department of Pathology, Faculty of Medicine, McGill University, Montreal, QC, CA, (4) Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, CA


MO-C930-IePD-F5-1 (Monday, 7/11/2022) 9:30 AM - 10:00 AM [Eastern Time (GMT-4)]

Exhibit Hall | Forum 5

Purpose: To deliver a fully automated and generalizable approach extracting patient-specific nuclei size (ns) and cell spacing (cs) distributions from cancerous and non-tumoral regions of hematoxylin and eosin (H&E) stained digital histopathology Whole-Slide-Images (WSI) for gynecological cancer multiscale treatment outcome modelling.

Methods: Each pre-treatment gigapixel H&E WSI digitized at 40 x magnification (0.2482 microns/pixel) were divided into 5000 x 5000-pixel patches. Within each patch, the nucleus centers were identified by a difference of gaussians blob detection algorithm obtaining Delaunay triangulations and Voronoi diagrams providing cs radius. The ns radius was computed from stained pixels dominated by hematoxylin content with an automatic thresholding algorithm. With multiprocessing CPUs on a PC for each WSI, eight feature types were calculated preserving biopsies tissue heterogeneity: the mean and standard deviation of cs and ns distributions concatenated from all patches for cancerous and non-tumoral regions. This method was applied to 40 patients (1 WSI per patient) with treatment outcomes of post radiation-therapy (RT) recurrence (n = 9),and death (n = 8)).

Results: The WSI cancerous region cs distribution mean among patients without post-RT recurrence has a median of 6.64 microns, and those with post-RT recurrence with a median of 7.11 microns. This indicates the potential of utilizing such distribution features in treatment outcome prognosis modelling. Furthermore, at the third quartile, the WSI non-tumoral region ns distribution standard deviation among patients without post-RT recurrence has a value of 2.16 microns, and 1.46 microns for those with post-RT recurrence.

Conclusion: Our approach derives patient-specific microscopic data distributions from histopathology WSI that can be directly associated with retrospective patient outcomes. They are complementary and spatially orthogonal to information served by other medical imaging modalities such as CT, MR, and Ultrasound. Therefore, it has the unique potential to augment treatment outcome model inference when properly fused with radiological scans.

Funding Support, Disclosures, and Conflict of Interest: CIHR grant number 103548 and Canada Research Chairs Program (grant #252135)


Image Analysis, Feature Extraction, Radiation Therapy


IM/TH- Image Analysis (Single Modality or Multi-Modality): Imaging biomarkers and radiomics

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