A1 Refereed original research article in a scientific journal
PATHOS: Pathology attention framework for treatment response stratification in ovarian high-grade serous carcinomas following neoadjuvant chemotherapy on H&E images
Authors: Miccolis, Francesca; Lovino, Marta; Lehtonen, Oskari; Hynninen, Johanna; Hautaniemi, Sampsa; Virtanen, Anni; Ficarra, Elisa
Publication year: 2026
Journal: Journal of Pathology Informatics
Article number: 100545
Volume: 21
ISSN: 2153-3539
DOI: https://doi.org/10.1016/j.jpi.2026.100545
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Open Access publication channel
Web address : https://doi.org/10.1016/j.jpi.2026.100545
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/523077504
Self-archived copy's licence: CC BY
Self-archived copy's version: Publisher`s PDF
Ovarian high-grade serous carcinoma (ovarian HGSC) is a clinically challenging disease with a poor prognosis, particularly for patients receiving neoadjuvant chemotherapy (NACT) before debulking surgery. In this study, we evaluate the progression-free interval (PFI) after NACT based on hematoxylin and eosin-stained whole-slide images (WSIs) of omental tumor tissue. Digital pathology tools are emerging, aiming at assisting pathologists in diagnosis and analysis; however, distinguishing features associated with response to NACT remain elusive. Multiple instance learning (MIL) coupled with attention mechanisms has shown promise in predicting treatment response from WSIs. Additionally, segmentation tools can identify and delineate regions in WSIs. Whereas some efforts have been made to develop explainable models for clinical outcome, there remains a need for genuinely interpretable models for pathologists. This article introduces the PATHOS framework, a novel approach to explaining crucial features of treatment response based on the PFI time in NACT treated patients from WSIs. PATHOS is composed of three blocks: (1) MIL block to identify informative regions, (2) panoptic segmentation and downstream analysis block for feature computation, and (3) classification block to predict the PFI. The results demonstrate that PATHOS enhances the interpretability of response to NACT in ovarian HGSC patients by highlighting pathologically significant features relevant to PFI prediction, such as tumor cell morphology, stromal abundance, and the spatial distribution of stromal regions. Furthermore, PATHOS identifies approximately 10% of the total WSI area as an informative region for clinical outcome.
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Funding information in the publication:
The Funding section appears to have been inadvertently omitted during submission; we would like to acknowledge support from the European Union’s Horizon 2020 research and innovation programme DECIDER (Grant Agreement No. 965193), the Italian funding PRIN 2022 project AIDA – explAinable multImodal Deep learning for personAlized oncology (MUR project code 20228MZFAA), and the FARD-2024 grant program of the Enzo Ferrari Engineering Department, University of Modena and Reggio Emilia.
