OXA-MISS: A Robust Multimodal Architecture for Chemotherapy Response Prediction under Data Scarcity

Francesca Miccolis, Fabio Marinelli, Vittorio Pipoli, Daria Afenteva, Anni Virtanen, Marta Lovino, Elisa Ficarra
Proceedings of the MICCAI Workshop on Computational Pathology, PMLR 316:318-327, 2026.

Abstract

In clinical oncology, tumor heterogeneity, data scarcity, and missing modalities are pervasive issues that significantly hinder the effectiveness of predictive models. Although multimodal integration of Whole Slide Imaging (WSI) and molecular data has shown promise in predicting overall survival (OS), current approaches often struggle when dealing with scarce and incomplete multimodal datasets, a scenario that reflects the norm rather than the exception in real-world clinical practice, especially in tasks like chemotherapy resistance prediction, where data collection is substantially more challenging than for OS. Accurately identifying patients who will not respond to chemotherapy is a critical clinical need, enabling the timely redirection to alternative therapeutic strategies and avoiding unnecessary toxicity. Hence, this paper introduces OXA-MISS, a novel multimodal model for chemotherapy response prediction designed to handle missing modalities. In the task of chemotherapy response prediction in ovarian cancer, OXA-MISS achieves a 20% absolute improvement in AUC over state-of-the-art models when trained on scarce and incomplete WSI–transcriptomics datasets. To evaluate its generalizability, we benchmarked OXA-MISS on OS prediction across three TCGA cancer types under both complete and missing-modality conditions. In these settings, the results demonstrate that OXA-MISS achieves performance comparable to that of state-of-the-art models. In conclusion, the proposed OXA-MISS is shown to be effective in OS prediction tasks, while substantially improving predictive accuracy in realistic clinical settings, such as the proposed prediction of chemotherapy response. The code for OXA-MISS is publicly available at https://github.com/AI-BioInformatics/OXA-MISS.

Cite this Paper

BibTeX
@InProceedings{pmlr-v316-miccolis26a, title = {OXA-MISS: A Robust Multimodal Architecture for Chemotherapy Response Prediction under Data Scarcity}, author = {Miccolis, Francesca and Marinelli, Fabio and Pipoli, Vittorio and Afenteva, Daria and Virtanen, Anni and Lovino, Marta and Ficarra, Elisa}, booktitle = {Proceedings of the MICCAI Workshop on Computational Pathology}, pages = {318--327}, year = {2026}, editor = {Studer, Linda and Ciompi, Francesco and Khalili, Nadieh and Faryna, Khrystyna and Faryna, Khrystyna and Yeong, Joe and Lau, Mai Chan and Chen, Hao and Liu, Ziyi and Brattoli, Biagio}, volume = {316}, series = {Proceedings of Machine Learning Research}, month = {27 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v316/main/assets/miccolis26a/miccolis26a.pdf}, url = {https://proceedings.mlr.press/v316/miccolis26a.html}, abstract = {In clinical oncology, tumor heterogeneity, data scarcity, and missing modalities are pervasive issues that significantly hinder the effectiveness of predictive models. Although multimodal integration of Whole Slide Imaging (WSI) and molecular data has shown promise in predicting overall survival (OS), current approaches often struggle when dealing with scarce and incomplete multimodal datasets, a scenario that reflects the norm rather than the exception in real-world clinical practice, especially in tasks like chemotherapy resistance prediction, where data collection is substantially more challenging than for OS. Accurately identifying patients who will not respond to chemotherapy is a critical clinical need, enabling the timely redirection to alternative therapeutic strategies and avoiding unnecessary toxicity. Hence, this paper introduces OXA-MISS, a novel multimodal model for chemotherapy response prediction designed to handle missing modalities. In the task of chemotherapy response prediction in ovarian cancer, OXA-MISS achieves a 20% absolute improvement in AUC over state-of-the-art models when trained on scarce and incomplete WSI–transcriptomics datasets. To evaluate its generalizability, we benchmarked OXA-MISS on OS prediction across three TCGA cancer types under both complete and missing-modality conditions. In these settings, the results demonstrate that OXA-MISS achieves performance comparable to that of state-of-the-art models. In conclusion, the proposed OXA-MISS is shown to be effective in OS prediction tasks, while substantially improving predictive accuracy in realistic clinical settings, such as the proposed prediction of chemotherapy response. The code for OXA-MISS is publicly available at https://github.com/AI-BioInformatics/OXA-MISS.} }
Endnote
%0 Conference Paper %T OXA-MISS: A Robust Multimodal Architecture for Chemotherapy Response Prediction under Data Scarcity %A Francesca Miccolis %A Fabio Marinelli %A Vittorio Pipoli %A Daria Afenteva %A Anni Virtanen %A Marta Lovino %A Elisa Ficarra %B Proceedings of the MICCAI Workshop on Computational Pathology %C Proceedings of Machine Learning Research %D 2026 %E Linda Studer %E Francesco Ciompi %E Nadieh Khalili %E Khrystyna Faryna %E Khrystyna Faryna %E Joe Yeong %E Mai Chan Lau %E Hao Chen %E Ziyi Liu %E Biagio Brattoli %F pmlr-v316-miccolis26a %I PMLR %P 318--327 %U https://proceedings.mlr.press/v316/miccolis26a.html %V 316 %X In clinical oncology, tumor heterogeneity, data scarcity, and missing modalities are pervasive issues that significantly hinder the effectiveness of predictive models. Although multimodal integration of Whole Slide Imaging (WSI) and molecular data has shown promise in predicting overall survival (OS), current approaches often struggle when dealing with scarce and incomplete multimodal datasets, a scenario that reflects the norm rather than the exception in real-world clinical practice, especially in tasks like chemotherapy resistance prediction, where data collection is substantially more challenging than for OS. Accurately identifying patients who will not respond to chemotherapy is a critical clinical need, enabling the timely redirection to alternative therapeutic strategies and avoiding unnecessary toxicity. Hence, this paper introduces OXA-MISS, a novel multimodal model for chemotherapy response prediction designed to handle missing modalities. In the task of chemotherapy response prediction in ovarian cancer, OXA-MISS achieves a 20% absolute improvement in AUC over state-of-the-art models when trained on scarce and incomplete WSI–transcriptomics datasets. To evaluate its generalizability, we benchmarked OXA-MISS on OS prediction across three TCGA cancer types under both complete and missing-modality conditions. In these settings, the results demonstrate that OXA-MISS achieves performance comparable to that of state-of-the-art models. In conclusion, the proposed OXA-MISS is shown to be effective in OS prediction tasks, while substantially improving predictive accuracy in realistic clinical settings, such as the proposed prediction of chemotherapy response. The code for OXA-MISS is publicly available at https://github.com/AI-BioInformatics/OXA-MISS.
APA
Miccolis, F., Marinelli, F., Pipoli, V., Afenteva, D., Virtanen, A., Lovino, M. & Ficarra, E.. (2026). OXA-MISS: A Robust Multimodal Architecture for Chemotherapy Response Prediction under Data Scarcity. Proceedings of the MICCAI Workshop on Computational Pathology, in Proceedings of Machine Learning Research 316:318-327 Available from https://proceedings.mlr.press/v316/miccolis26a.html.

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