Survival time prediction from unannotated lung cancer histopathology images

Nikolai Fetisov; Lawrence O. Hall; Dmitry B. Goldgof; Matthew B. Schabath

doi:10.1117/12.2611232

4 April 2022 Survival time prediction from unannotated lung cancer histopathology images

Nikolai Fetisov, Lawrence O. Hall, Dmitry B. Goldgof, Matthew B. Schabath

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120391C (2022) https://doi.org/10.1117/12.2611232
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Lung cancer is the leading cause of cancer-related deaths responsible for over 130,000 deaths each year in the US. Early diagnosis and prompt treatment is crucial for prolonging survival. Many studies have been performed with regard to detecting lung cancer using computed tomography images, or classifying the type of a cancer from pathology images. Some research focuses on survival time prediction instead, which consists of ranking patients according to their expected survival time. This has proven to be a difficult task, and most approaches offer only a slight improvement over random guessing. Instead of ranking patients according to survival time, we propose to predict whether a patient falls into a long- or short-term survival group. We show that this approach outperforms regression-based approaches when predicting precise survival time is not necessary. In addition to that, we show that it is possible to predict short-term and long-term survival from lung cancer histopathology images without ROI annotations. We have obtained an 0.81 AUC when predicting whether a patient would fall into the short-term survival group (less than 12 months) or long-term survival group (greater than 60 months). Furthermore, we show that our model is capable of classifying patients into long and short-term survival even when their survival time falls outside of our chosen ranges.

Conference Presentation

Citation Download Citation

Nikolai Fetisov, Lawrence O. Hall, Dmitry B. Goldgof, and Matthew B. Schabath "Survival time prediction from unannotated lung cancer histopathology images", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120391C (4 April 2022); https://doi.org/10.1117/12.2611232

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