Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026
Multi-centre generalisability of deep learning-based dose prediction for head and neck radiotherapy using DAHANCA real-world data.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Nielsen Camilla P, Huet-Dastarac Margerie, Jensen Kenneth, Brink Carsten, Smulders Bob, Holm Anne I S, Nielsen Martin S, Sibolt Patrik, Lee John A, Sterpin Edmond, Zukauskaite Ruta, Johansen Jørgen, Krogh Simon L, Konrad Maximilian L, Friborg Jeppe, Sommer Jeanette F A, Stougaard Sarah W, Overgaard Jens, Toustrup Kasper, Lonkvist Camilla K, Farhadi Mohammad, Kaplan Laura P, Kjeldsen Rasmus, Lorenzen Ebbe L, Barragán-Montero Ana M, Hansen Christian R
Аннотация
INTRODUCTION: Deep learning dose prediction shows promise for automated radiotherapy planning and quality assurance in head and neck cancer (HNC). Clinical adoption requires validation of model generalisability using clinically relevant metrics. The study evaluated generalisability of a dose prediction model trained on single-centre data in a large multi-centre cohort.
MATERIALS AND METHODS: A deep neural network was trained on 388 HNC treatment plans from one Danish centre and evaluated on an internal test dataset of 42 patients and on a national DAHANCA cohort of 560 plans from six institutions. Performance was evaluated using dose metrics and compared with a median model assigning each OAR the median Dmean from the training data. Expected toxicity was explored using NTCP and the normalised toxicity index (NTI).
RESULTS: Predictions closely matched clinically applied dose distributions with median Dmean differences of -0.1 Gy [interquartile range -0.5, 0.3] for PTVs and 1.1 Gy [-0.6, 3.7] for OARs across cohorts. The median-based model showed larger interquartile ranges, with Dmean differences of 0.7 Gy [0.0, 2.1] for PTVs and -4.6 Gy [-14.6, 3.9] for OARs. Comparison of expected toxicity between model-predicted and clinically applied dose distributions showed median NTI differences of 4.4% [ 0.4, 9.4] and median NTCP differences of about 1-3% for xerostomia and dysphagia grade 2+.
CONCLUSIONS: The dose prediction model outperformed the median-based model, with prediction-plan differences within interquartile ranges observed across cohorts, supporting generalisability. Discrepancies between model-predicted and clinically planned toxicity may indicate suboptimality in clinical plans and could inform quality assurance.
MATERIALS AND METHODS: A deep neural network was trained on 388 HNC treatment plans from one Danish centre and evaluated on an internal test dataset of 42 patients and on a national DAHANCA cohort of 560 plans from six institutions. Performance was evaluated using dose metrics and compared with a median model assigning each OAR the median Dmean from the training data. Expected toxicity was explored using NTCP and the normalised toxicity index (NTI).
RESULTS: Predictions closely matched clinically applied dose distributions with median Dmean differences of -0.1 Gy [interquartile range -0.5, 0.3] for PTVs and 1.1 Gy [-0.6, 3.7] for OARs across cohorts. The median-based model showed larger interquartile ranges, with Dmean differences of 0.7 Gy [0.0, 2.1] for PTVs and -4.6 Gy [-14.6, 3.9] for OARs. Comparison of expected toxicity between model-predicted and clinically applied dose distributions showed median NTI differences of 4.4% [ 0.4, 9.4] and median NTCP differences of about 1-3% for xerostomia and dysphagia grade 2+.
CONCLUSIONS: The dose prediction model outperformed the median-based model, with prediction-plan differences within interquartile ranges observed across cohorts, supporting generalisability. Discrepancies between model-predicted and clinically planned toxicity may indicate suboptimality in clinical plans and could inform quality assurance.