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PATHOS: Pathology attention framework for treatment response stratification in ovarian high-grade serous carcinomas following neoadjuvant chemotherapy on H&E images
Tekijät: Miccolis, Francesca; Lovino, Marta; Lehtonen, Oskari; Hynninen, Johanna; Hautaniemi, Sampsa; Virtanen, Anni; Ficarra, Elisa
Julkaisuvuosi: 2026
Lehti: Journal of Pathology Informatics
Artikkelin numero: 100545
Vuosikerta: 21
ISSN: 2153-3539
DOI: https://doi.org/10.1016/j.jpi.2026.100545
Julkaisun avoimuus kirjaamishetkellä: Avoimesti saatavilla
Julkaisukanavan avoimuus : Kokonaan avoin julkaisukanava
Verkko-osoite: https://doi.org/10.1016/j.jpi.2026.100545
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/523077504
Rinnakkaistallenteen lisenssi: CC BY
Rinnakkaistallennetun julkaisun versio: Kustantajan versio
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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
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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.
