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Evidential reasoning-enabled deep learning for reliable treatment outcome prediction in cancer therapy
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DOI：10.1016/j.artmed.2026.103445.png)
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Abstract
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• We present a unified ER2-DNN framework for predicting treatment outcomes: pCR in TNBC and lesion progression in HNC, using only pre-treatment imaging data. • We incorporate the Evidential Reasoning Rule (ER2) to perform structured, reliability-weighted prediction fusion, enabling better uncertainty estimation and model trustworthiness. • We systematically evaluate prediction calibration using Expected Calibration Error (ECE) and Maximum Calibration Error (MCE), also compare ER2 to other alternative fusion strategies.
Keywords:
Evidential Reasoning
Deep Learning
Treatment Outcome Prediction
Uncertainty Estimation
Cancer Therapy
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