Colorectal cancer survival prediction using deep distribution based multiple-instance learning

• URL: http://arxiv.org/abs/2204.11294v1
• Date: Sun, 24 Apr 2022 14:55:57 GMT
• Title: Colorectal cancer survival prediction using deep distribution based multiple-instance learning
• Authors: Xingyu Li, Jitendra Jonnagaddala, Min Cen, Hong Zhang, Xu Steven Xu
• Abstract summary: We develop a distribution based multiple-instance survival learning algorithm (DeepDisMISL) Our results suggest that the more information about the distribution of the patch scores for a WSI, the better is the prediction performance. DeepDisMISL demonstrated superior predictive ability compared to other recently published, state-of-the-art algorithms.
• Score: 5.231498575799198
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Several deep learning algorithms have been developed to predict survival of cancer patients using whole slide images (WSIs).However, identification of image phenotypes within the WSIs that are relevant to patient survival and disease progression is difficult for both clinicians, and deep learning algorithms. Most deep learning based Multiple Instance Learning (MIL) algorithms for survival prediction use either top instances (e.g., maxpooling) or top/bottom instances (e.g., MesoNet) to identify image phenotypes. In this study, we hypothesize that wholistic information of the distribution of the patch scores within a WSI can predict the cancer survival better. We developed a distribution based multiple-instance survival learning algorithm (DeepDisMISL) to validate this hypothesis. We designed and executed experiments using two large international colorectal cancer WSIs datasets - MCO CRC and TCGA COAD-READ. Our results suggest that the more information about the distribution of the patch scores for a WSI, the better is the prediction performance. Including multiple neighborhood instances around each selected distribution location (e.g., percentiles) could further improve the prediction. DeepDisMISL demonstrated superior predictive ability compared to other recently published, state-of-the-art algorithms. Furthermore, our algorithm is interpretable and could assist in understanding the relationship between cancer morphological phenotypes and patients cancer survival risk.

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