EchoRisk: A Multicentre Echocardiography Dataset and Benchmark for Cardio-Oncology

2026-07-01

AI Summary

This study addresses the lack of early, automated risk stratification for cardiotoxicity in breast cancer therapy by constructing the first multicenter, longitudinal echocardiography dataset tailored to cardio-oncology, featuring baseline imaging and well-defined cardiotoxicity labels. The authors establish a standardized evaluation benchmark encompassing three clinical tasks: left ventricular ejection fraction estimation, left ventricular dysfunction classification, and pretreatment prediction of cardiotoxicity. Methodologically, they employ an R(2+1)D video backbone pretrained on Kinetics-400, augmented with an LSTM for temporal modeling. Experimental results demonstrate strong performance in left ventricular function assessment and dysfunction classification, while highlighting the significant challenge of predicting cardiotoxicity from a single pretreatment echocardiogram.

Abstract

Therapy-induced cardiotoxicity is the leading non-oncological cause of treatment interruption in breast cancer patients, yet early, automated risk stratification from routine cardiac imaging remains an unsolved problem. We present EchoRisk, the first curated, multicentre, longitudinal echocardiography dataset with explicit cardiotoxicity labels, released as the primary technical reference for the EchoRisk-MICCAI 2026 challenge. The dataset comprises 422 patients enrolled in the EU-funded CARDIOCARE prospective study across five European sites, yielding 2,159 echocardiography videos across 1,123 clinical exams acquired at up to five longitudinal timepoints, alongside a dedicated cohort of 280 patients with baseline imaging for early cardiotoxicity prediction. Three clinically grounded tasks are defined: automated estimation of left ventricular ejection fraction from cine video (Task 1), classification of LV dysfunction from longitudinal imaging (Task 2), and early prediction of therapy-induced cardiotoxicity from pre-therapy baseline echocardiography alone (Task 3). For each task we specify the evaluation protocol, primary and secondary metrics, and ranking procedure. We establish baseline performance using an R(2+1)D video backbone with LSTM aggregation trained from Kinetics-400 pretrained weights, demonstrating strong discriminative performance for cardiac functional assessment and LV dysfunction classification, while early cardiotoxicity prediction from a single pre-therapy video remains a significant open problem for the community. The dataset, evaluation code, and baseline implementations are publicly available to serve as a benchmark for further collaboration, comparison, and the creation of task-specific architectures in cardio-oncology.

Problem

• cardiotoxicity
• echocardiography
• risk stratification
• cardio-oncology
• early prediction

Innovation

• multicentre echocardiography dataset
• cardio-oncology
• therapy-induced cardiotoxicity
• longitudinal video benchmark
• automated risk stratification