Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data

• URL: http://arxiv.org/abs/2203.11391v1
• Date: Mon, 21 Mar 2022 23:48:47 GMT
• Title: Survival Analysis for Idiopathic Pulmonary Fibrosis using CT Images and Incomplete Clinical Data
• Authors: Ahmed H. Shahin, Joseph Jacob, Daniel C. Alexander, David Barber
• Abstract summary: Idiopathic Pulmonary Fibrosis (IPF) is an inexorably progressive fibrotic lung disease with a variable and unpredictable rate of progression. CT scans of the lungs inform clinical assessment of IPF patients and contain pertinent information related to disease progression. We propose a multi-modal method that uses neural networks and memory banks to predict the survival of IPF patients using clinical and imaging data.
• Score: 17.162038700963418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Idiopathic Pulmonary Fibrosis (IPF) is an inexorably progressive fibrotic lung disease with a variable and unpredictable rate of progression. CT scans of the lungs inform clinical assessment of IPF patients and contain pertinent information related to disease progression. In this work, we propose a multi-modal method that uses neural networks and memory banks to predict the survival of IPF patients using clinical and imaging data. The majority of clinical IPF patient records have missing data (e.g. missing lung function tests). To this end, we propose a probabilistic model that captures the dependencies between the observed clinical variables and imputes missing ones. This principled approach to missing data imputation can be naturally combined with a deep survival analysis model. We show that the proposed framework yields significantly better survival analysis results than baselines in terms of concordance index and integrated Brier score. Our work also provides insights into novel image-based biomarkers that are linked to mortality.

Related papers

• MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion [18.395418853966266]
  We propose a novel deep learning approach for breast cancer survival risk stratification. We employ vision transformers, specifically the MaxViT model, for image feature extraction, and self-attention to capture intricate image relationships at the patient level. A dual cross-attention mechanism fuses these features with genetic data, while clinical data is incorporated at the final layer to enhance predictive accuracy.
  arXiv  Detail & Related papers  (2024-02-19T02:31:36Z)
• Graph-based multimodal multi-lesion DLBCL treatment response prediction from PET images [0.0]
  After diagnosis, the number of nonresponding patients to standard front-line therapy remains significant (30-40%) This work aims to develop a computer-aided approach to identify high-risk patients requiring adapted treatment. We propose a method based on recent graph neural networks that combine imaging information from multiple lesions.
  arXiv  Detail & Related papers  (2023-10-25T08:16:45Z)
• Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Data [3.790959613880792]
  Renal cell carcinoma represents a significant global health challenge with a low survival rate. This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma. The proposed framework comprises three modules: a 3D image feature extractor, clinical variable selection, and survival prediction.
  arXiv  Detail & Related papers  (2023-07-07T13:09:07Z)
• DeepMMSA: A Novel Multimodal Deep Learning Method for Non-small Cell Lung Cancer Survival Analysis [8.78724404464036]
  We propose a multimodal deep learning method for non-small cell lung cancer (NSCLC) survival analysis, named DeepMMSA. This method leverages CT images in combination with clinical data, enabling the abundant information hold within medical images to be associate with lung cancer survival information.
  arXiv  Detail & Related papers  (2021-06-12T11:02:14Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• Fibrosis-Net: A Tailored Deep Convolutional Neural Network Design for Prediction of Pulmonary Fibrosis Progression from Chest CT Images [59.622239796473885]
  Pulmonary fibrosis is a chronic lung disease that causes irreparable lung tissue scarring and damage, resulting in progressive loss in lung capacity and no known cure. We introduce Fibrosis-Net, a deep convolutional neural network design tailored for the prediction of pulmonary fibrosis progression from chest CT images.
  arXiv  Detail & Related papers  (2021-03-06T02:16:41Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging [85.00066186644466]
  We propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions.
  arXiv  Detail & Related papers  (2020-10-07T06:22:24Z)
• Potential Features of ICU Admission in X-ray Images of COVID-19 Patients [8.83608410540057]
  This paper presents an original methodology for extracting semantic features that correlate to severity from a data set with patient ICU admission labels. The methodology employs a neural network trained to recognise lung pathologies to extract the semantic features. The method has shown to be capable of selecting images for the learned features, which could translate some information about their common locations in the lung.
  arXiv  Detail & Related papers  (2020-09-26T13:48:39Z)
• Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
  We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
  arXiv  Detail & Related papers  (2020-07-20T19:08:50Z)
• Trajectories, bifurcations and pseudotime in large clinical datasets: applications to myocardial infarction and diabetes data [94.37521840642141]
  We suggest a semi-supervised methodology for the analysis of large clinical datasets, characterized by mixed data types and missing values. The methodology is based on application of elastic principal graphs which can address simultaneously the tasks of dimensionality reduction, data visualization, clustering, feature selection and quantifying the geodesic distances (pseudotime) in partially ordered sequences of observations.
  arXiv  Detail & Related papers  (2020-07-07T21:04:55Z)

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.