Article

Applying Explainable Machine Learning Algorithms to Predict the Risk of Local Recurrence After Kidney Cancer Surgery

2026-05-22

Abstract excerpt

<title>Abstract</title> <p>Background Accurate prediction of postoperative local recurrence in renal cell carcinoma remains challenging due to low event rates and substantial censoring during long-term follow-up. Traditional statistical models often rely on linear assumptions and predefined variables, which may limit their ability to capture complex risk patterns. This study aimed to develop and validate an enha...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 05deb2ab-f90f-59d5-8dfc-29f33567b4c4
DOI: 10.21203/rs.3.rs-9421628/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Applying Explainable Machine Learning Algorithms to Predict the Risk of Local Recurrence After Kidney Cancer SurgeryDOI 10.21203/rs.3.rs-9421628/v1Read

Machine Learning–Based Prediction of Recurrence After Curative Resection in Non–Small Cell Lung CancerSemantic proximity Read

Development and Validation of a Prognostic Nomogram Model for Recurrence of Non-Muscle Invasive Bladder CancerSemantic proximity Read

Adapting models with single time-to-event outcomes to include a competing outcome: an exemplar adjusting risk of recurrence after nephrectomy for clear cell renal cell carcinoma for death from other causesSemantic proximity Read

Machine Learning for Optimal Individual Survival Prediction in Resectable Upper Gastrointestinal CancerSemantic proximity Read

A molecule based nomogram optimized the prediction of relapse in stage I NSCLCSemantic proximity Read

UTUC-PNet: A Multimodal Deep Learning Model with Feature Attribution for Postoperative Recurrence Risk PredictionSemantic proximity Read

Machine Learning-Based Survival Time Prediction in Colorectal Cancer with Peritoneal Metastasis: A Multi-Institutional Registry-Based StudySemantic proximity Read

Machine learning to predict early recurrence after oesophageal cancer surgerySemantic proximity Read

Machine learning models predict the progression of long-term renal insufficiency in patients with renal cancer after radical nephrectomySemantic proximity Read

RADAR CARE Study: A Multicenter Deep Learning Model for Real-Time Prediction of Recurrence in Early-Stage Non–Small Cell Lung Cancer Using a Multimodal ApproachSemantic proximity Read

An Interpretable Machine Learning Host-Response Score for Postoperative Disease-Free Survival Risk Stratification in Colorectal Adenocarcinoma: A Retrospective Prognostic Modelling StudySemantic proximity Read

Machine Learning for Predicting Delayed Graft Function and Graft Survival After Kidney Transplantation: A Systematic Review and Meta-AnalysisSemantic proximity Read