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<p>Accurate differentiation between benign and malignant thyroid nodules on ultrasound remains clinically important, yet interpretation is operator-dependent and subject to inter-observer variability. Recent deep learning studies report strong performance for thyroid ultrasound classification, but many prior approaches remain centred on image-level prediction, with limited emphasis on patient-level baselines, calibration-aware evaluation, and mask-related shortcut analysis. To address these gaps, we present a patient-level thyroid ultrasound malignancy prediction framework centred on a Region-Aware Context-Aware Fusion (RCAF) model evaluated under a strict leakage-free protocol. RCAF combines lesion-focused and context-preserving frame representations through a dual-branch design with gated fusion, followed by attention-based multiple instance learning (AttnMIL) for patient-level aggregation. The framework incorporates development-only probability calibration and threshold selection before single-shot evaluation on an untouched independent test cohort. Experiments on the public ThyroidXL benchmark show that RCAF outperforms stronger fair patient-level baselines, including image-only, transformer-based, and lesion-only comparators. Calibration analysis improves probability reliability, threshold analysis demonstrates stable behaviour under clinically relevant operating conditions, and shortcut sensitivity experiments show that naive mask concatenation produces shortcut-prone gains, whereas RCAF degrades by only 0.001 ROC-AUC under within-patient mask permutation, supporting principled region-aware reasoning. For cross-domain assessment, RCAF was evaluated on TN5000, a Chinese thyroid ultrasound dataset acquired under different imaging conditions; following domain adaptation and 8-view test-time augmentation, the model achieved AUC&#xa0;=&#xa0;0.914 [0.879–0.948] on a class-balanced validation subset. Overall, RCAF constitutes a strong patient-level thyroid ultrasound classification framework, with encouraging cross-domain adaptability. Broader prospective multi-centre validation remains necessary before clinical deployment.</p>

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RCAF for patient-level thyroid ultrasound malignancy prediction under leakage-free evaluation and calibration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月16日

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  • Mennatallah Sherif,
  • Eman K. Elsayed,
  • Mohanad A. Deif

摘要

Accurate differentiation between benign and malignant thyroid nodules on ultrasound remains clinically important, yet interpretation is operator-dependent and subject to inter-observer variability. Recent deep learning studies report strong performance for thyroid ultrasound classification, but many prior approaches remain centred on image-level prediction, with limited emphasis on patient-level baselines, calibration-aware evaluation, and mask-related shortcut analysis. To address these gaps, we present a patient-level thyroid ultrasound malignancy prediction framework centred on a Region-Aware Context-Aware Fusion (RCAF) model evaluated under a strict leakage-free protocol. RCAF combines lesion-focused and context-preserving frame representations through a dual-branch design with gated fusion, followed by attention-based multiple instance learning (AttnMIL) for patient-level aggregation. The framework incorporates development-only probability calibration and threshold selection before single-shot evaluation on an untouched independent test cohort. Experiments on the public ThyroidXL benchmark show that RCAF outperforms stronger fair patient-level baselines, including image-only, transformer-based, and lesion-only comparators. Calibration analysis improves probability reliability, threshold analysis demonstrates stable behaviour under clinically relevant operating conditions, and shortcut sensitivity experiments show that naive mask concatenation produces shortcut-prone gains, whereas RCAF degrades by only 0.001 ROC-AUC under within-patient mask permutation, supporting principled region-aware reasoning. For cross-domain assessment, RCAF was evaluated on TN5000, a Chinese thyroid ultrasound dataset acquired under different imaging conditions; following domain adaptation and 8-view test-time augmentation, the model achieved AUC = 0.914 [0.879–0.948] on a class-balanced validation subset. Overall, RCAF constitutes a strong patient-level thyroid ultrasound classification framework, with encouraging cross-domain adaptability. Broader prospective multi-centre validation remains necessary before clinical deployment.