Multimodal Fusion of Imaging and Genomics for Lung Cancer Recurrence Prediction

Vaishnavi Subramanian, Minh N. Do, Tanveer Syeda-Mahmood

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Lung cancer has a high rate of recurrence in early-stage patients. Predicting the post-surgical recurrence in lung cancer patients has traditionally been approached using single modality information of genomics or radiology images. We investigate the potential of multimodal fusion for this task. By combining computed tomography (CT) images and genomics, we demonstrate improved prediction of recurrence using linear Cox proportional hazards models with elastic net regularization. We work on a recent non-small cell lung cancer (NSCLC) radiogenomics dataset of 130 patients and observe an increase in concordance-index values of up to 10%. Employing non-linear methods from the neural network literature, such as multi -layer perceptrons and visual-question answering fusion modules, did not improve performance consistently. This indicates the need for larger multimodal datasets and fusion techniques better adapted to this biological setting.

Original languageEnglish (US)
Title of host publicationISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages804-808
Number of pages5
ISBN (Electronic)9781538693308
DOIs
StatePublished - Apr 2020
Event17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States
Duration: Apr 3 2020Apr 7 2020

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2020-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
CountryUnited States
CityIowa City
Period4/3/204/7/20

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Subramanian, V., Do, M. N., & Syeda-Mahmood, T. (2020). Multimodal Fusion of Imaging and Genomics for Lung Cancer Recurrence Prediction. In ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging (pp. 804-808). [9098545] (Proceedings - International Symposium on Biomedical Imaging; Vol. 2020-April). IEEE Computer Society. https://doi.org/10.1109/ISBI45749.2020.9098545