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Ovarian cancer through a multi-modal lens

Nature Cancer volume 3, pages 662–664 (2022)Cite this article

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Prognostic information for patients with ovarian cancer is captured in clinico-genomic data, histopathology slides and computed tomography imaging; however, how to integrate these data is unclear. A study now presents a method for combining complementary data types to stratify risk and aid treatment selection in patients with ovarian cancer.

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Fig. 1: Fusion models for risk prediction and visualizing results in clinical application.

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Authors and Affiliations

  1. Biological Sciences Division, University of Chicago, Chicago, IL, USA

    Hanna M. Hieromnimon & Alexander T. Pearson

  2. University of Chicago Comprehensive Cancer Center, University of Chicago, Chicago, IL, USA

    Alexander T. Pearson

Authors
  1. Hanna M. Hieromnimon
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  2. Alexander T. Pearson
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Corresponding author

Correspondence to Alexander T. Pearson.

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Competing interests

A.T.P. serves on advisory boards for Ayala, Elevar and Prelude Therapeutics; has consulting roles for Abbvie; and reports research funding from Abbvie and Kura Oncology. A.T.P. reports effort support via grants from NIH/NCI U01-CA243075, NIH/NIDCR R56-DE030958, NIH/NCI R25-CA240134, EU Horizon 2021-SC1-BHC, SU2C (Stand Up to Cancer) Fanconi Anemia Research Fund – Farrah Fawcett Foundation Head and Neck Cancer Research Team Grant during the conduct of the study. This has been funded in whole or in part with Federal funding by the NCI-DOE Collaboration established by the US Department of Energy (DOE) and the National Cancer Institute (NCI) of the National Institutes of Health (NIH), Cancer Moonshot Task Order no. 75N91019F00134 and under Frederick National Laboratory for Cancer Research Contract 75N91019D00024. This work was performed under the auspices of the US DOE by Argonne National Laboratory under Contract DE-AC02-06-CH11357.

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Hieromnimon, H.M., Pearson, A.T. Ovarian cancer through a multi-modal lens. Nat Cancer 3, 662–664 (2022). https://doi.org/10.1038/s43018-022-00397-8

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