Time-driven Survival Analysis from FDG-PET/CT in Non-Small Cell Lung Cancer

• URL: http://arxiv.org/abs/2604.06885v1
• Date: Wed, 08 Apr 2026 09:43:30 GMT
• Title: Time-driven Survival Analysis from FDG-PET/CT in Non-Small Cell Lung Cancer
• Abstract summary: We developed a framework to predict overall survival (OS) in patients with Non-Small Cell Lung Cancer (NSCLC)<n>The proposed framework employed a ResNet-50 backbone to process input images and generate corresponding image embeddings.<n>The embeddings were then combined with temporal data to produce OS probabilities as a function of time, effectively parameterizing the predictions based on time.
• Score: 0.4850337362214834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Purpose: Automated medical image-based prediction of clinical outcomes, such as overall survival (OS), has great potential in improving patient prognostics and personalized treatment planning. We developed a deep regression framework using tissue-wise FDG-PET/CT projections as input, along with a temporal input representing a scalar time horizon (in days) to predict OS in patients with Non-Small Cell Lung Cancer (NSCLC). Methods: The proposed framework employed a ResNet-50 backbone to process input images and generate corresponding image embeddings. The embeddings were then combined with temporal data to produce OS probabilities as a function of time, effectively parameterizing the predictions based on time. The overall framework was developed using the U-CAN cohort (n = 556) and evaluated by comparing with a baseline method on the test set (n = 292). The baseline utilized the ResNet-50 architecture, processing only the images as input and providing OS predictions at pre-specified intervals, such as 2- or 5-year. Results: The incorporation of temporal data with image embeddings demonstrated an advantage in predicting OS, outperforming the baseline method with an improvement in AUC of 4.3%. The proposed model using clinical + IDP features achieved strong performance, and an ensemble of imaging and clinical + IDP models achieved the best overall performance (0.788), highlighting the complementary value of multimodal inputs. The proposed method also enabled risk stratification of patients into distinct categories (high vs low risk). Heat maps from the saliency analysis highlighted tumor regions as key structures for the prediction. Conclusion: Our method provided an automated framework for predicting OS as a function of time and demonstrates the potential of combining imaging and tabular data for improved survival prediction.

Related papers

• PRIME: Prototype-Driven Multimodal Pretraining for Cancer Prognosis with Missing Modalities [86.63247982275396]
  PRIME is a missing-aware multimodal self-supervised pretraining framework.<n>It learns robust and transferable representations from partially observed cohorts.<n>We evaluate PRIME on The Cancer Genome Atlas with label-free pretraining on 32 cancer types.
  arXiv  Detail & Related papers  (2026-04-05T21:14:27Z)
• Methodology for Comparing Machine Learning Algorithms for Survival Analysis [55.65997641180011]
  Six machine learning models for survival analysis were evaluated.<n>XGB-AFT achieved the best performance (C-Index = 0.7618; IPCW = 0.7532, followed by GBSA and RSF)
  arXiv  Detail & Related papers  (2025-10-28T14:42:28Z)
• Multitask LSTM for Arboviral Outbreak Prediction Using Public Health Data [0.0]
  This paper presents a multitask learning approach for the joint prediction of arboviral outbreaks and case counts in Recife, Brazil.<n>The proposed model concurrently performs binary classification (outbreak detection) and regression (case forecasting) tasks.<n>The architecture delivers competitive performance across diseases and tasks, demonstrating the feasibility and advantages of unified modeling strategies for scalable epidemic forecasting in data-limited public health scenarios.
  arXiv  Detail & Related papers  (2025-05-07T16:58:18Z)
• Time-to-Event Pretraining for 3D Medical Imaging [44.46415168541444]
  We introduce time-to-event pretraining, a pretraining framework for 3D medical imaging models.<n>We use a dataset of 18,945 CT scans (4.2 million 2D images) and time-to-event distributions across thousands of EHR-derived tasks.<n>Our method improves outcome prediction, achieving an average AUROC increase of 23.7% and a 29.4% gain in Harrell's C-index across 8 benchmark tasks.
  arXiv  Detail & Related papers  (2024-11-14T11:08:54Z)
• A Framework for Evaluating Predictive Models Using Synthetic Image Covariates and Longitudinal Data [0.0]
  We present a novel framework for synthesizing patient data with longitudinal observations. Our approach introduces controlled association in latent spaces generating each data modality. We demonstrate our framework using optical coherence tomography ( OCT) scans.
  arXiv  Detail & Related papers  (2024-10-21T16:43:16Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time Adaptation [43.842694540544194]
  Applying pretrained medical deep learning segmentation models on out-of-domain images often yields predictions of insufficient quality.<n>In this study, we propose to use a powerful generalizing descriptor along with augmentation to enable domain-generalized pretraining and test-time adaptation.
  arXiv  Detail & Related papers  (2023-12-11T10:26:21Z)
• Histopathologic Cancer Detection [0.0]
  This work uses the PatchCamelyon benchmark datasets and trains them in a multi-layer perceptron and convolution model to observe the model's performance in terms of precision Recall, F1 Score, Accuracy, and AUC Score. Also, this paper introduced ResNet50 and InceptionNet models with data augmentation, where ResNet50 is able to beat the state-of-the-art model.
  arXiv  Detail & Related papers  (2023-11-13T19:51:46Z)
• tdCoxSNN: Time-Dependent Cox Survival Neural Network for Continuous-time Dynamic Prediction [19.38247205641199]
  We propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using longitudinal fundus images. We evaluate and compare our proposed method with joint modeling and landmarking approaches through extensive simulations.
  arXiv  Detail & Related papers  (2023-07-12T03:03:40Z)
• Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data and Symptomatic Assessments [1.1549572298362785]
  This study included subjects (1832 subjects, 3276 knees) from the baseline of the MOST study. PF joint regions-of-interest were identified using an automated landmark detection tool (BoneFinder) on lateral knee X-rays. Risk factors included age, sex, BMI and WOMAC score, and the radiographic osteoarthritis stage of the tibiofemoral joint (KL score)
  arXiv  Detail & Related papers  (2023-05-10T06:43:33Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)

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.