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Predictions of Oncotype DX^® High-Risk Classification Using Magnetic Resonance Imaging-Based Intratumoral

Sung Joon Park^1,2, Won Hwa Kim^2,3, Jaeil Kim^1,2

¹School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea.

Bioengineering (Basel, Switzerland)

|June 26, 2026

Summary

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Intratumoral tumor ecological diversity (iTED) imaging may predict Oncotype DX high-risk breast cancer, reducing need for invasive tests. This non-invasive approach shows promise as an imaging biomarker.

Area of Science:

Oncology
Radiology
Medical Imaging

Background:

The Oncotype DX 21-gene recurrence score (RS) aids chemotherapy decisions for ER+/HER2- breast cancer.
Current methods require invasive tissue sampling and incur significant costs.

Purpose of the Study:

To evaluate intratumoral tumor ecological diversity (iTED) using MRI as a non-invasive predictor of high-risk Oncotype DX classification (RS > 25).

Main Methods:

A retrospective multi-center study of 312 patients with ER+/HER2- breast cancer.
iTED framework used superpixel-based habitat determination with Gaussian mixture models on dynamic contrast-enhanced MRI.
Compared clinical, conventional radiomics (C-radiomics), iTED, and combined models.

Main Results:

Keywords:

Oncotype DX®breast cancer dynamic contrast-enhanced magnetic resonance imaging habitat imaging iTED intratumoral heterogeneity radiomics

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The iTED model demonstrated superior discrimination over C-radiomics in both development (AUC: 0.868) and validation (AUC: 0.811) sets.
A combined Clinical + iTED model further enhanced performance (development AUC: 0.908; external AUC: 0.889).

Conclusions:

Habitat imaging-based iTED shows higher performance than conventional radiomics for predicting high-risk Oncotype DX classification.
iTED holds potential as a non-invasive imaging biomarker to complement molecular testing in clinical decision-making.