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Researcher profile

Kimmo Kartasalo

Kimmo Kartasalo contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visioneess.IVArtificial IntelligenceGraphicsMachine LearningQuantitative Methods

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Published work

4 published item(s)

preprint2026arXiv

Validation of an AI-based end-to-end model for prostate pathology using long-term archived routine samples

Artificial intelligence (AI) is becoming a clinical tool for prostate pathology, but generalization across variations in sample preparation and preservation over prolonged time periods remains poorly understood. We evaluated GleasonAI, an end-to-end attention-based multiple instance learning model, on an independent validation cohort comprising 10,366 biopsy cores from 1,028 patients across 14 Swedish regions, using archival diagnostic specimens from the ProMort cohorts collected between 1998-2015. The model achieved an overall quadratic-weighted kappa of 0.86 for core-level ISUP grading, comparable to several experienced pathologists and consistent across geographic regions. Notably, performance remained stable across the 17-year collection period, demonstrating robustness to time-related variation in archival material, a property not consistently observed with foundation model-based approaches, with exploratory analysis demonstrating a significant prognostic gradient across AI-assigned grade groups for prostate cancer-specific mortality. These findings support the generalizability of the AI grading model and demonstrate the potential of pathology archives as a large-scale resource for AI development, validation, and retrospective prognostic research.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Detection of Perineural Invasion in Prostate Needle Biopsies with Deep Neural Networks

Background: The detection of perineural invasion (PNI) by carcinoma in prostate biopsies has been shown to be associated with poor prognosis. The assessment and quantification of PNI is; however, labor intensive. In the study we aimed to develop an algorithm based on deep neural networks to aid pathologists in this task. Methods: We collected, digitized and pixel-wise annotated the PNI findings in each of the approximately 80,000 biopsy cores from the 7,406 men who underwent biopsy in the prospective and diagnostic STHLM3 trial between 2012 and 2014. In total, 485 biopsy cores showed PNI. We also digitized more than 10% (n=8,318) of the PNI negative biopsy cores. Digitized biopsies from a random selection of 80% of the men were used to build deep neural networks, and the remaining 20% were used to evaluate the performance of the algorithm. Results: For the detection of PNI in prostate biopsy cores the network had an estimated area under the receiver operating characteristics curve of 0.98 (95% CI 0.97-0.99) based on 106 PNI positive cores and 1,652 PNI negative cores in the independent test set. For the pre-specified operating point this translates to sensitivity of 0.87 and specificity of 0.97. The corresponding positive and negative predictive values were 0.67 and 0.99, respectively. For localizing the regions of PNI within a slide we estimated an average intersection over union of 0.50 (CI: 0.46-0.55). Conclusion: We have developed an algorithm based on deep neural networks for detecting PNI in prostate biopsies with apparently acceptable diagnostic properties. These algorithms have the potential to aid pathologists in the day-to-day work by drastically reducing the number of biopsy cores that need to be assessed for PNI and by highlighting regions of diagnostic interest.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer

Molecular phenotyping is central in cancer precision medicine, but remains costly and standard methods only provide a tumour average profile. Microscopic morphological patterns observable in histopathology sections from tumours are determined by the underlying molecular phenotype and associated with clinical factors. The relationship between morphology and molecular phenotype has a potential to be exploited for prediction of the molecular phenotype from the morphology visible in histopathology images. We report the first transcriptome-wide Expression-MOrphology (EMO) analysis in breast cancer, where gene-specific models were optimised and validated for prediction of mRNA expression both as a tumour average and in spatially resolved manner. Individual deep convolutional neural networks (CNNs) were optimised to predict the expression of 17,695 genes from hematoxylin and eosin (HE) stained whole slide images (WSIs). Predictions for 9,334 (52.75%) genes were significantly associated with RNA-sequencing estimates (FDR adjusted p-value < 0.05). 1,011 of the genes were brought forward for validation, with 876 (87%) and 908 (90%) successfully replicated in internal and external test data, respectively. Predicted spatial intra-tumour variabilities in expression were validated in 76 genes, out of which 59 (77.6%) had a significant association (FDR adjusted p-value < 0.05) with spatial transcriptomics estimates. These results suggest that the proposed methodology can be applied to predict both tumour average gene expression and intra-tumour spatial expression directly from morphology, thus providing a scalable approach to characterise intra-tumour heterogeneity.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Virtual reality for 3D histology: multi-scale visualization of organs with interactive feature exploration

Virtual reality (VR) enables data visualization in an immersive and engaging manner, and it can be used for creating ways to explore scientific data. Here, we use VR for visualization of 3D histology data, creating a novel interface for digital pathology. Our contribution includes 3D modeling of a whole organ and embedded objects of interest, fusing the models with associated quantitative features and full resolution serial section patches, and implementing the virtual reality application. Our VR application is multi-scale in nature, covering two object levels representing different ranges of detail, namely organ level and sub-organ level. In addition, the application includes several data layers, including the measured histology image layer and multiple representations of quantitative features computed from the histology. In this interactive VR application, the user can set visualization properties, select different samples and features, and interact with various objects. In this work, we used whole mouse prostates (organ level) with prostate cancer tumors (sub-organ objects of interest) as example cases, and included quantitative histological features relevant for tumor biology in the VR model. Due to automated processing of the histology data, our application can be easily adopted to visualize other organs and pathologies from various origins. Our application enables a novel way for exploration of high-resolution, multidimensional data for biomedical research purposes, and can also be used in teaching and researcher training.

0 citations0 reviewsNo code linkedOpen access