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Survival Time Prediction for Adenocarcinoma Lung Cancer based on Pathological Image Analysis

폐암 선암 생존시간 예측을 위한 병리학적 영상분석

  • Vo, Vi Thi-Tuong (Dept. of AI Convergence, Chonnam National University) ;
  • Kim, Aera (Dept. of AI Convergence, Chonnam National University) ;
  • Lee, TaeBum (Dept. of Pathology, Chonnam National University Medical School, Hwasun Hospital) ;
  • Kim, Soo-Hyung (Dept. of AI Convergence, Chonnam National University)
  • 보티트엉비 (전남대학교 인공지능융합학과) ;
  • 김애라 (전남대학교 인공지능융합학과) ;
  • 이태범 (화순전남대학교병원 병리과) ;
  • 김수형 (전남대학교 인공지능융합학과)
  • Published : 2021.11.04

Abstract

Survival time analysis is one of the main methods used by the pathologist to prognosis for cancer patients. In this paper, we strive to estimate the individual survival time of Adenocarcinoma (ADC) lung cancer patients from pathological images by adopting the convolutional neural network called the SurvPatchV1 model. First, we extracted tissue patches from the whole-slide images (WSI) to deal with extremely large dimensions of WSI. Then the survival time of each patch is estimated through the SurvPatchV1 model. Finally, the individual survival time of each patient is computed. The proposed method is trained and tested on the subset of the NLST dataset for ADC lung cancer. The result demonstrates that our model can obtain all tissue information in lieu of only tumor information in a whole pathological image to estimate the individual survival time.

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Acknowledgement

This research was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) & funded by the Korean government (MSIT) (NRF-2019M3E5D1A02067961), the National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (NRF-2020R1A4A1019191) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1I1A3A04036408).