EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological Images

• URL: http://arxiv.org/abs/2210.05258v1
• Date: Tue, 11 Oct 2022 08:40:40 GMT
• Title: EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological Images
• Authors: Tianling Liu and Ran Su and Changming Sun and Xiuting Li and Leyi Wei
• Abstract summary: Ovarian cancer is one of the most serious cancers that threaten women around the world. In this study, we designed a deep framework named EOCSA which analyzes the prognosis of EOC patients based on pathological whole slide images (WSIs) The experimental results demonstrate that our proposed framework has achieved state-of-the-art performance of 0.980 C-index.
• Score: 22.227676868758195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ovarian cancer is one of the most serious cancers that threaten women around the world. Epithelial ovarian cancer (EOC), as the most commonly seen subtype of ovarian cancer, has rather high mortality rate and poor prognosis among various gynecological cancers. Survival analysis outcome is able to provide treatment advices to doctors. In recent years, with the development of medical imaging technology, survival prediction approaches based on pathological images have been proposed. In this study, we designed a deep framework named EOCSA which analyzes the prognosis of EOC patients based on pathological whole slide images (WSIs). Specifically, we first randomly extracted patches from WSIs and grouped them into multiple clusters. Next, we developed a survival prediction model, named DeepConvAttentionSurv (DCAS), which was able to extract patch-level features, removed less discriminative clusters and predicted the EOC survival precisely. Particularly, channel attention, spatial attention, and neuron attention mechanisms were used to improve the performance of feature extraction. Then patient-level features were generated from our weight calculation method and the survival time was finally estimated using LASSO-Cox model. The proposed EOCSA is efficient and effective in predicting prognosis of EOC and the DCAS ensures more informative and discriminative features can be extracted. As far as we know, our work is the first to analyze the survival of EOC based on WSIs and deep neural network technologies. The experimental results demonstrate that our proposed framework has achieved state-of-the-art performance of 0.980 C-index. The implementation of the approach can be found at https://github.com/RanSuLab/EOCprognosis.

Related papers

• Deep Neural Networks for Predicting Recurrence and Survival in Patients with Esophageal Cancer After Surgery [0.0]
  Esophageal cancer is a major cause of cancer-related mortality internationally. We assessed prognostic factor identification and discriminative performances of three models for Disease-Free Survival (DFS) and Overall Survival (OS) DeepSurv and DeepHit demonstrated comparable discriminative accuracy to CoxPH.
  arXiv  Detail & Related papers  (2024-08-30T16:20:47Z)
• Benchmarking Histopathology Foundation Models for Ovarian Cancer Bevacizumab Treatment Response Prediction from Whole Slide Images [1.4999444543328293]
  We leverage the latest histopathology foundation models trained on large-scale whole slide image (WSI) to extract ovarian tumor tissue features for predicting bevacizumab response. Our survival models are able to stratify high- and low-risk cases with statistical significance.
  arXiv  Detail & Related papers  (2024-07-30T07:15:39Z)
• Survival Prediction Across Diverse Cancer Types Using Neural Networks [40.392772795903795]
  Gastric cancer and Colon adenocarcinoma represent widespread and challenging malignancies. Medical community has embraced the 5-year survival rate as a vital metric for estimating patient outcomes. This study introduces a pioneering approach to enhance survival prediction models for gastric and Colon adenocarcinoma patients.
  arXiv  Detail & Related papers  (2024-04-11T21:47:13Z)
• Cancer-Net PCa-Gen: Synthesis of Realistic Prostate Diffusion Weighted Imaging Data via Anatomic-Conditional Controlled Latent Diffusion [68.45407109385306]
  In Canada, prostate cancer is the most common form of cancer in men and accounted for 20% of new cancer cases for this demographic in 2022. There has been significant interest in the development of deep neural networks for prostate cancer diagnosis, prognosis, and treatment planning using diffusion weighted imaging (DWI) data. In this study, we explore the efficacy of latent diffusion for generating realistic prostate DWI data through the introduction of an anatomic-conditional controlled latent diffusion strategy.
  arXiv  Detail & Related papers  (2023-11-30T15:11:03Z)
• Post-Hoc Explainability of BI-RADS Descriptors in a Multi-task Framework for Breast Cancer Detection and Segmentation [48.08423125835335]
  MT-BI-RADS is a novel explainable deep learning approach for tumor detection in Breast Ultrasound (BUS) images. It offers three levels of explanations to enable radiologists to comprehend the decision-making process in predicting tumor malignancy.
  arXiv  Detail & Related papers  (2023-08-27T22:07:42Z)
• Pathology-and-genomics Multimodal Transformer for Survival Outcome Prediction [43.1748594898772]
  We propose a multimodal transformer (PathOmics) integrating pathology and genomics insights into colon-related cancer survival prediction. We emphasize the unsupervised pretraining to capture the intrinsic interaction between tissue microenvironments in gigapixel whole slide images. We evaluate our approach on both TCGA colon and rectum cancer cohorts, showing that the proposed approach is competitive and outperforms state-of-the-art studies.
  arXiv  Detail & Related papers  (2023-07-22T00:59:26Z)
• Enhancing Clinical Support for Breast Cancer with Deep Learning Models using Synthetic Correlated Diffusion Imaging [66.63200823918429]
  We investigate enhancing clinical support for breast cancer with deep learning models. We leverage a volumetric convolutional neural network to learn deep radiomic features from a pre-treatment cohort. We find that the proposed approach can achieve better performance for both grade and post-treatment response prediction.
  arXiv  Detail & Related papers  (2022-11-10T03:02:12Z)
• Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment [50.89811515036067]
  Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance.
  arXiv  Detail & Related papers  (2022-02-17T08:33:52Z)
• Analysis of MRI Biomarkers for Brain Cancer Survival Prediction [0.3093890460224435]
  Prediction of Overall Survival (OS) of brain cancer patients from multi-modal MRI is a challenging field of research. Most of the existing literature on survival prediction is based on Radiomic features. Age was found to be the most important biological predictor.
  arXiv  Detail & Related papers  (2021-09-03T05:35:47Z)
• Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation [5.050648346154715]
  Current prognostic risk scores in cardiac surgery are based on statistics and do not yet benefit from machine learning. This research aims to create a machine learning model to predict one-year mortality of a patient after TAVI.
  arXiv  Detail & Related papers  (2020-01-08T10:04:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.