Imaging-Based Mortality Prediction in Patients with Systemic Sclerosis

• URL: http://arxiv.org/abs/2509.23530v1
• Date: Sat, 27 Sep 2025 23:46:57 GMT
• Title: Imaging-Based Mortality Prediction in Patients with Systemic Sclerosis
• Authors: Alec K. Peltekian, Karolina Senkow, Gorkem Durak, Kevin M. Grudzinski, Bradford C. Bemiss, Jane E. Dematte, Carrie Richardson, Nikolay S. Markov, Mary Carns, Kathleen Aren, Alexandra Soriano, Matthew Dapas, Harris Perlman, Aaron Gundersheimer, Kavitha C. Selvan, John Varga, Monique Hinchcliff, Krishnan Warrior, Catherine A. Gao, Richard G. Wunderink, GR Scott Budinger, Alok N. Choudhary, Anthony J. Esposito, Alexander V. Misharin, Ankit Agrawal, Ulas Bagci,
• Abstract summary: Interstitial lung disease (ILD) is a leading cause of morbidity and mortality in systemic sclerosis (SSc)<n>This study introduces a novel, large-scale longitudinal chest CT analysis framework that utilizes radiomics and deep learning to predict mortality.
• Score: 50.46938276133494
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interstitial lung disease (ILD) is a leading cause of morbidity and mortality in systemic sclerosis (SSc). Chest computed tomography (CT) is the primary imaging modality for diagnosing and monitoring lung complications in SSc patients. However, its role in disease progression and mortality prediction has not yet been fully clarified. This study introduces a novel, large-scale longitudinal chest CT analysis framework that utilizes radiomics and deep learning to predict mortality associated with lung complications of SSc. We collected and analyzed 2,125 CT scans from SSc patients enrolled in the Northwestern Scleroderma Registry, conducting mortality analyses at one, three, and five years using advanced imaging analysis techniques. Death labels were assigned based on recorded deaths over the one-, three-, and five-year intervals, confirmed by expert physicians. In our dataset, 181, 326, and 428 of the 2,125 CT scans were from patients who died within one, three, and five years, respectively. Using ResNet-18, DenseNet-121, and Swin Transformer we use pre-trained models, and fine-tuned on 2,125 images of SSc patients. Models achieved an AUC of 0.769, 0.801, 0.709 for predicting mortality within one-, three-, and five-years, respectively. Our findings highlight the potential of both radiomics and deep learning computational methods to improve early detection and risk assessment of SSc-related interstitial lung disease, marking a significant advancement in the literature.

Related papers

• A Transformer-based survival model for prediction of all-cause mortality in heart failure patients: a multi-cohort study [5.831730826863567]
  We developed and validated TRisk, a Transformer-based AI model predicting 36-month mortality in heart failure patients.<n>Our study included 403,534 heart failure patients (ages 40-90) from 1,418 English general practices.
  arXiv  Detail & Related papers  (2025-03-16T01:53:50Z)
• 4D VQ-GAN: Synthesising Medical Scans at Any Time Point for Personalised Disease Progression Modelling of Idiopathic Pulmonary Fibrosis [5.926086195644801]
  We propose 4D Vector Quantised Generative Adversarial Networks (4D-VQ-GAN), a model capable of generating realistic CT volumes of IPF patients.<n>We evaluate different configurations of our model for generating longitudinal CT scans and compare the results against ground truth data.
  arXiv  Detail & Related papers  (2025-02-08T22:25:53Z)
• Enhanced Mortality Prediction In Patients With Subarachnoid Haemorrhage Using A Deep Learning Model Based On The Initial CT Scan [34.86503928854081]
  Convolutional neural networks (CNN) are capable of generating highly accurate predictions from imaging data. Our objective was to predict mortality in Subarachnoid hemorrhage patients by processing the initial CT scan on a CNN based algorithm.
  arXiv  Detail & Related papers  (2023-08-25T13:33:56Z)
• A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study [17.26942882598847]
  We present a hybrid CNN-RNN approach to investigate long-term survival of subjects in a lung cancer screening study. The models were trained on subjects who underwent cardiovascular and respiratory deaths. The Cox neural network can achieve an IPCW C-index of 0.75 on the internal dataset and 0.69 on an external dataset.
  arXiv  Detail & Related papers  (2023-03-19T23:00:41Z)
• Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients [42.09584755334577]
  Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis to quantify the mortality risks of lung cancer patients. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model.
  arXiv  Detail & Related papers  (2023-03-09T15:38:16Z)
• 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)
• AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study [7.456548336226919]
  We investigate whether chest X-ray (CXR) can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. CXR is a radiological technique that compared to computed tomography (CT) it is simpler, faster, more widespread and it induces lower radiation dose. We present a dataset including data collected from 820 patients by six Italian hospitals in spring 2020 during the first COVID-19 emergency.
  arXiv  Detail & Related papers  (2020-12-11T18:03:08Z)
• 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)
• 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)
• COVID-Net S: Towards computer-aided severity assessment via training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity [58.23203766439791]
  Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity. In this study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system.
  arXiv  Detail & Related papers  (2020-05-26T16:33:52Z)
• Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT [48.785596536318884]
  The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
  arXiv  Detail & Related papers  (2020-04-02T21:49:14Z)

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.