The Cost of Binarizing Survival Outcomes in Clinical Prognostic Modeling

• URL: http://arxiv.org/abs/2608.04046v1
• Date: Tue, 04 Aug 2026 06:56:24 GMT
• Title: The Cost of Binarizing Survival Outcomes in Clinical Prognostic Modeling
• Abstract summary: We replace the binary scoring function with the Cox partial log-likelihood for feature-to-outcome edges, and recover prognostic features that binarization misses.<n>Our ablation experiment confirms that the improvement is driven by the time-to-event scoring formulation rather than by retaining more patients.
• Score: 2.085853639053605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Survival analysis is an established framework for analyzing time-to-event data, yet many clinical machine learning studies still binarize the outcome before model training. This practice excludes censored patients, collapses temporal information into a single threshold, and can affect which features are selected as prognostically relevant. We examine the cost of this binarization in the context of Bayesian network (BN) feature selection, using two recent publications as case studies: one that applies BN-based feature selection to a head-and-neck cancer cohort and a second surgical cohort study that, while not BN-based, likewise binarizes its survival endpoint. We replace the binary scoring function with the Cox partial log-likelihood for feature-to-outcome edges, a modification we call the Survival-Aware Bayesian network, and recover prognostic features that binarization misses. Our ablation experiment confirms that the improvement is driven by the time-to-event scoring formulation rather than by retaining more patients. The results generalize across five endpoint-cohort combinations in head-and-neck cancer and extend to three further cancer types (breast, colorectal, and kidney). We propose that clinical studies with survival outcomes should use time-to-event methods by default, as binarization discards the prognostic signal retained by survival analysis.

Related papers

• An Automated Radiomics Framework for Postoperative Survival Prediction in Colorectal Liver Metastases using Preoperative MRI [0.8571977422745714]
  We present an automated AI-based framework for postoperative colorectal liver metastasis survival prediction.<n>We developed a survival prediction framework comprising an anatomy-aware segmentation pipeline followed by a radiomics pipeline.<n>Our results show the potential of integrating segmentation algorithms with radiomics-based survival analysis.
  arXiv  Detail & Related papers  (2026-03-10T20:34:24Z)
• 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)
• Assessing the robustness of heterogeneous treatment effects in survival analysis under informative censoring [50.164756034797136]
  Dropout is common in clinical studies, with up to half of patients leaving early due to side effects or other reasons.<n>When dropout is informative, it introduces censoring bias, because of which treatment effect estimates are also biased.<n>We propose an assumption-lean framework to assess the robustness of conditional average treatment effect estimates in survival analysis when facing censoring bias.
  arXiv  Detail & Related papers  (2025-10-15T10:51:17Z)
• Live(r) Die: Predicting Survival in Colorectal Liver Metastasis [0.01268579273097071]
  Colorectal cancer frequently metastasizes to the liver, significantly reducing long-term survival.<n>Current prognostic models, often based on limited clinical or molecular features, lack sufficient predictive power.<n>We present a fully automated framework for surgical outcome prediction from pre- and post-contrast MRI.
  arXiv  Detail & Related papers  (2025-09-10T19:02:59Z)
• Deep Survival Analysis in Multimodal Medical Data: A Parametric and Probabilistic Approach with Competing Risks [47.19194118883552]
  We introduce a multimodal deep learning framework for survival analysis capable of modeling both single and competing risks scenarios.<n>We propose SAMVAE (Survival Analysis Multimodal Variational Autoencoder), a novel deep learning architecture designed for survival prediction.
  arXiv  Detail & Related papers  (2025-07-10T14:29:48Z)
• Interpretable Survival Analysis for Heart Failure Risk Prediction [50.64739292687567]
  We propose a novel survival analysis pipeline that is both interpretable and competitive with state-of-the-art survival models. Our pipeline achieves state-of-the-art performance and provides interesting and novel insights about risk factors for heart failure.
  arXiv  Detail & Related papers  (2023-10-24T02:56:05Z)
• Contrastive Learning of Temporal Distinctiveness for Survival Analysis in Electronic Health Records [10.192973297290136]
  We propose a novel Ontology-aware Temporality-based Contrastive Survival (OTCSurv) analysis framework. OTCSurv uses survival durations from both censored and observed data to define temporal distinctiveness. We conduct experiments using a large EHR dataset to forecast the risk of hospitalized patients who are in danger of developing acute kidney injury (AKI)
  arXiv  Detail & Related papers  (2023-08-24T22:36:22Z)
• 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)
• A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images [58.17507437526425]
  Collateral circulation results from specialized anastomotic channels which provide oxygenated blood to regions with compromised blood flow. The actual grading is mostly done through manual inspection of the acquired images. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data.
  arXiv  Detail & Related papers  (2021-10-24T18:58:40Z)
• 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)
• An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
  We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks. We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem. We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
  arXiv  Detail & Related papers  (2020-12-06T18:55:07Z)

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.