2023
DOI: 10.1002/mp.16431
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Patient selection for proton therapy using Normal Tissue Complication Probability with deep learning dose prediction for oropharyngeal cancer

Abstract: BackgroundIn cancer care, determining the most beneficial treatment technique is a key decision affecting the patient's survival and quality of life. Patient selection for proton therapy (PT) over conventional radiotherapy (XT) currently entails comparing manually generated treatment plans, which requires time and expertise.PurposeWe developed an automatic and fast tool, AI‐PROTIPP (Artificial Intelligence Predictive Radiation Oncology Treatment Indication to Photons/Protons), that assesses quantitatively the … Show more

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Cited by 7 publications
(2 citation statements)
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“…The in-house implementation of the architecture is publicly available in https://gitlab.com/ai4miro/ntcp_predicted_dose [21]. Dose prediction with U-net architectures has already been studied over many treatment locations [22][23][24][25][26] and demonstrated good performances across several architecture variations including the HD-UNET version [27][28][29].…”
Section: Dose Prediction and Dose Mimicking Planning (Dp+dm Plans)mentioning
confidence: 99%
“…The in-house implementation of the architecture is publicly available in https://gitlab.com/ai4miro/ntcp_predicted_dose [21]. Dose prediction with U-net architectures has already been studied over many treatment locations [22][23][24][25][26] and demonstrated good performances across several architecture variations including the HD-UNET version [27][28][29].…”
Section: Dose Prediction and Dose Mimicking Planning (Dp+dm Plans)mentioning
confidence: 99%
“…ML models may also aid in radiation dosage and modality planning to prevent swallowing impairment. Huet-Dastarac et al [43 ▪▪ ] utilized DL dose prediction in combination with NTCP models for dysphagia and xerostomia to select patients who would benefit receiving proton therapy over conventional radiotherapy. Mayo et al [44] combined big data analytics with ML to identify structure-dose-volume histogram metrics and clinically actionable dose thresholds most strongly associated with worsening dysphagia in HNC patients.…”
Section: Machine Learning Applications To Predict and Prevent Voice A...mentioning
confidence: 99%