Analysis of acute-phase toxicities of intensity-modulated proton therapy using a model-based approach in pharyngeal cancer patients

Yasuda, Kôichi; Minatogawa, Hideki; Dekura, Yasuhiro; Sugiyama, Takao; Tamura, Masaya; Tsushima, Nobuko; Suzuki, Takayoshi; Kano, Satoshi; Mizumachi, Takatsugu; Mori, Takashi; Nishioka, Kentaro; Shido, Motoyasu; Katoh, Norio; Taguchi, Hiroshi; Fujima, Noriyuki; Onimaru, Rikiya; Yokota, Isao; Kobashi, Keiji; Shimizu, Satoru; Homma, Akihiro; Shirato, Hiroki; Aoyama, Hidefumi

doi:10.1093/jrr/rraa130

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…NTCP values computed from the clinically planned doses can be found (Table A.2). These results are in line with the clinical study 25 and are due to the location of the target in the oropharynx. In that context, AI‐PROTIPP yielded a weighted accuracy of 87.4% with 41 TP, seven TN, one FP, and six FN (Figure 6).…”

Section: Resultssupporting

confidence: 91%

Patient selection for proton therapy using Normal Tissue Complication Probability with deep learning dose prediction for oropharyngeal cancer

Huet‐Dastarac¹,

Michiels²,

Rivas³

et al. 2023

Medical Physics

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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 benefits of each therapeutic option. Our method uses deep learning (DL) models to directly predict the dose distributions for a given patient for both XT and PT. By using models that estimate the Normal Tissue Complication Probability (NTCP), namely the likelihood of side effects to occur for a specific patient, AI‐PROTIPP can propose a treatment selection quickly and automatically.MethodsA database of 60 patients presenting oropharyngeal cancer, obtained from the Cliniques Universitaires Saint Luc in Belgium, was used in this study. For every patient, a PT plan and an XT plan were generated. The dose distributions were used to train the two dose DL prediction models (one for each modality). The model is based on U‐Net architecture, a type of convolutional neural network currently considered as the state of the art for dose prediction models. A NTCP protocol used in the Dutch model‐based approach, including grades II and III xerostomia and grades II and III dysphagia, was later applied in order to perform automatic treatment selection for each patient. The networks were trained using a nested cross‐validation approach with 11‐folds. We set aside three patients in an outer set and each fold consists of 47 patients in training, five in validation and five for testing. This method allowed us to assess our method on 55 patients (five patients per test times the number of folds).ResultsThe treatment selection based on the DL‐predicted doses reached an accuracy of 87.4% for the threshold parameters set by the Health Council of the Netherlands. The selected treatment is directly linked with these threshold parameters as they express the minimal gain brought by the PT treatment for a patient to be indicated to PT. To validate the performance of AI‐PROTIPP in other conditions, we modulated these thresholds, and the accuracy was above 81% for all the considered cases. The difference in average cumulative NTCP per patient of predicted and clinical dose distributions is very similar (less than 1% difference).ConclusionsAI‐PROTIPP shows that using DL dose prediction in combination with NTCP models to select PT for patients is feasible and can help to save time by avoiding the generation of treatment plans only used for the comparison. Moreover, DL models are transferable, allowing, in the future, experience to be shared with centers that would not have PT planning expertise.

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Section: Resultssupporting

confidence: 91%

Patient selection for proton therapy using Normal Tissue Complication Probability with deep learning dose prediction for oropharyngeal cancer

Huet‐Dastarac¹,

Michiels²,

Rivas³

et al. 2023

Medical Physics

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“…In the treatment of patients with early glottic cancer, PBT achieved superior outcomes for all organs at risk, including the contralateral carotid artery [ 41 ]. PBT also induced less toxicity in patients with ipsilateral salivary gland, oral–pharyngeal, or pharyngeal cancer [ 42 , 43 , 44 ]. Nevertheless, the effect of PBT on each individual carotid artery remains uncertain.…”

Section: Discussionmentioning

confidence: 99%

Head and Neck Cancer Types and Risks of Cervical–Cranial Vascular Complications within 5 Years after Radiation Therapy

Liu

Huang

Lin

et al. 2022

JPM

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Background and purpose: to investigate the frequency of cervical–cranial vascular complications soon after radiation therapy (RT) and identify differences among patients with various types of head and neck cancer (HNC). Methods: We enrolled 496 patients with HNC who had received their final RT dose in our hospital. These patients underwent carotid duplex ultrasound (CDU) for monitoring significant carotid artery stenosis (CAS). Brain imaging were reviewed to detect vertebral, intracranial artery stenosis, or preexisted CAS before RT. Primary outcome was significant CAS at the internal or common carotid artery within first 5 years after RT. We categorized the patients into nasopharyngeal carcinoma (NPC) and non-NPC groups and compared the cumulative occurrence of significant CAS between the groups using Kaplan–Meier and Cox-regression analyses. Results: Compared to the NPC group, the non-NPC group had a higher frequency of significant CAS (12.7% vs. 2.0%) and were more commonly associated with significant CAS after adjusting the covariates (Adjusted hazard ratio: 0.17, 95% confident interval: 0.05–0.57) during the follow-up period. All the non-NPC subtypes (oral cancer/oropharyngeal, hypopharyngeal, and laryngeal cancers) were associated with higher risks of significant CAS than the NPC group (p < 0.001 respectively). Conclusion: Significant CAS was more frequently noted within 5 years of RT among the patients with non-NPC HNC than among the patients with NPC. Scheduled carotid artery surveillance and vascular risk monitoring should be commenced earlier for patients with non-NPC HNC. By contrast, vascular surveillance could be deferred to 5 years after RT completion in NPC patients.

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“…La terapia de protones de intensidad modulada (IMPT, por sus siglas en inglés) proporciona una mejor distribución de la dosis que la radioterapia basada en fotones, gracias a que los protones tienen mejores propiedades físicas y radiobiológicas, depositando alta energía en el tumor y casi ninguna en los tejidos normales 43 . Un estudio realizado en pacientes con cáncer de orofaringe determinó que, 18 a 36 meses después del tratamiento, la xerostomía moderada-grave fue menos frecuente en el grupo que recibió IMPT que en el que recibió IMRT 44 .…”

Section: Manejo Preventivo Para La Xerostomíaunclassified

Manejo odontológico de las manifestaciones orales inducidas por radioterapia de cabeza y cuello

Mejía-Chuquispuma

Flores-Jiménez

Castro-Auqui

et al. 2023

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La radioterapia es una de las primeras opciones de tratamiento para el cáncer de cabeza y cuello; sin embargo, puede ocasionar diversas manifestaciones secundarias en la cavidad oral tales como mucositis oral, xerostomía, infecciones orales oportunistas, osteorradionecrosis y trismo. El presente artículo tiene como objetivo describir el manejo preventivo y terapéutico de las complicaciones orales inducidas por radioterapia de cabeza y cuello en la práctica odontológica propuestos hasta la actualidad. Se encontraron diversas medidas que han logrado reducir la incidencia y gravedad de dichas manifestaciones orales; sin embargo, aún existen algunas que requieren de más estudios para confirmar su efectividad. Palabras clave: cáncer de cabeza y cuello; manifestaciones bucales; radioterapia; terapéutica.

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Analysis of acute-phase toxicities of intensity-modulated proton therapy using a model-based approach in pharyngeal cancer patients

Cited by 7 publications

References 21 publications

Patient selection for proton therapy using Normal Tissue Complication Probability with deep learning dose prediction for oropharyngeal cancer

Patient selection for proton therapy using Normal Tissue Complication Probability with deep learning dose prediction for oropharyngeal cancer

Head and Neck Cancer Types and Risks of Cervical–Cranial Vascular Complications within 5 Years after Radiation Therapy

Manejo odontológico de las manifestaciones orales inducidas por radioterapia de cabeza y cuello

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