# Temporal Representation Learning of Phenotype Trajectories for pCR Prediction in Breast Cancer

Source: [Temporal Representation Learning of Phenotype Trajectories for pCR Prediction in Breast Cancer](<https://www.sota2.com/research/paper/68fa7b621e209131cea88137>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T07:02:34.900Z

Authors: Ivana Jan\\'i\\v{c}kov\\'a, Yen Y. Tan, Thomas H. Helbich, Konstantin Miloserdov, Zsuzsanna Bago-Horvath, Ulrike Heber, Georg Langs

Published: 2025-09-18T11:37:27.000Z

## Abstract

Effective therapy decisions require models that predict the individual response to treatment. This is challenging since the progression of disease and response to treatment vary substantially across patients. Here, we propose to learn a representation of the early dynamics of treatment response from imaging data to predict pathological complete response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NACT). The longitudinal change in magnetic resonance imaging (MRI) data of the breast forms trajectories in the latent space, serving as basis for prediction of successful response. The multi-task model represents appearance, fosters temporal continuity and accounts for the comparably high heterogeneity in the non-responder cohort.In experiments on the publicly available ISPY-2 dataset, a linear classifier in the latent trajectory space achieves a balanced accuracy of 0.761 using only pre-treatment data (T0), 0.811 using early response (T0 + T1), and 0.861 using four imaging time points (T0 -\> T3). The code will be made available upon paper acceptance.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Pathologic Complete Response (pCR) prediction on ISPY-2 5-fold cross-validation (test)](<https://www.sota2.com/research/sota/pathologic-complete-response-pcr-prediction-on-ispy-2-5-fold-cross-validation-test.md>): pathologic complete response (pCR) prediction; ISPY-2 5-fold cross-validation (test); bACC: 65.2.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2509.14872>)

[PDF](<https://arxiv.org/pdf/2509.14872.pdf>)