NTCP model validation method for DAHANCA patient selection of protons versus photons in head and neck cancer radiotherapy

Hansen, Christian; Friborg, Jeppe; Jensen, Kenneth; Samsøe, E.; Johnsen, Lars; Zukauskaite, Ruta; Grau, Cai; Maare, C.; Johansen, Jørgen; Primdahl, Hanne; Bratland, Åse; Kristensen, Claus Andrup; Andersen, Maria; Eriksen, Jesper Grau; Overgaard, Jens

doi:10.1080/0284186x.2019.1654129

Cited by 31 publications

(17 citation statements)

References 25 publications

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“…Most of these tolerances and NTCP models were generated using data from one or few centres and should, therefore, be used with caution. If possible, a local validation of the NTCP model should be conducted but, as a minimum, an understanding of the underlying model cohort and the limitations of the model is required [15]. It is also important, when possible, to minimise OAR doses below model tolerances to facilitate potential reirradiations in case of tumour relapse [16].…”

Section: Dose Metricsmentioning

confidence: 99%

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

Hernández

Hansen

Widesott³

et al. 2020

Radiotherapy and Oncology

123

114

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Plan evaluation is a key step in the radiotherapy treatment workflow. Central to this step is the assessment of treatment plan quality. Hence, it is important to agree on what we mean by plan quality and to be fully aware of which parameters it depends on. We understand plan quality in radiotherapy as the clinical suitability of the delivered dose distribution that can be realistically expected from a treatment plan. Plan quality is commonly assessed by evaluating the dose distribution calculated by the treatment planning system (TPS). Evaluating the 3D dose distribution is not easy, however; it is hard to fully evaluate its spatial characteristics and we still lack the knowledge for personalising the prediction of the clinical outcome based on individual patient characteristics. This advocates for standardisation and systematic collection of clinical data and outcomes after radiotherapy. Additionally, the calculated dose distribution is not exactly the dose delivered to the patient due to uncertainties in the dose calculation and the treatment delivery, including variations in the patient set-up and anatomy. Consequently, plan quality also depends on the robustness and complexity of the treatment plan. We believe that future work and consensus on the best metrics for quality indices are required. Better tools are needed in TPSs for the evaluation of dose distributions, for the robust evaluation and optimisation of treatment plans, and for controlling and reporting plan complexity. Implementation of such tools and a better understanding of these concepts will facilitate the handling of these characteristics in clinical practice and be helpful to increase the overall quality of treatment plans in radiotherapy.

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Section: Dose Metricsmentioning

confidence: 99%

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

Hernández

Hansen

Widesott³

et al. 2020

Radiotherapy and Oncology

123

114

View full text Add to dashboard Cite

show abstract

“…This might produce uncertainties from systematic errors and hence the planned vs. delivered dose will introduce uncertainties to the dose/volume endpoints, which are not counterbalanced by data and variability from other institutions. Therefore, the NTCP models reported herein should be externally validated in other cohorts before use for internal treatment predictions or comparisons with other modalities [35].…”

Section: Discussionmentioning

confidence: 99%

Normal tissue complication probability models for prospectively scored late rectal and urinary morbidity after proton therapy of prostate cancer

Pedersen

Liang

Bryant

et al. 2021

Physics and Imaging in Radiation Oncology

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“…However, even though these models seem very stable within the current environment, they should be validated externally before use in other institutes. Such validation could potentially be performed as described in Hansen et al [28].…”

Section: Discussionmentioning

confidence: 99%

Prediction of radiation-induced mucositis of H&N cancer patients based on a large patient cohort

Hansen

Bertelsen

Zukauskaite

et al. 2020

Radiotherapy and Oncology

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Radiation-induced mucositis is a severe acute side effect, which can jeopardize treatment compliance and cause weight loss during treatment. The study aimed to develop robust models to predict the risk of severe mucositis. Materials/methods: Mucosal toxicity scores were prospectively recorded for 802 consecutive Head and Neck (H&N) cancer patients and dichotomised into non-severe event (grade 0-2) and severe event (grade 3+) groups. Two different model approaches were utilised to evaluate the robustness of the models. These used LASSO and Best Subset selection combined with 10-fold cross-validation performed on two-thirds of the patient cohort using principal component analysis of DVHs. The remaining one-third of the patients were used for validation. Model performance was tested through calibration plot and model performance metrics. Results: The main predicted risk factors were treatment acceleration and the first two principal dose components, which reflect the mean dose and the balance between high and low doses to the oral cavity. For the LASSO model, gender and current smoker status were also included in the model. The AUC values of the two models on the validation cohort were 0.797 (95%CI: 0.741-0.857) and 0.808 (95%CI: 0.749-0.859), respectively. The two models predicted very similar risk values with an internal Pearson coefficient of 0.954, indicating their robustness. Conclusions: Robust prediction models of the risk of severe mucositis have been developed based on information from the entire dose distribution for a large cohort of patients consisting of all patients treated H&N for within our institution over a five year period.

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NTCP model validation method for DAHANCA patient selection of protons versus photons in head and neck cancer radiotherapy

Abstract: Behalf of the DAHANCA Group (2019) NTCP model validation method for DAHANCA patient selection of protons versus photons in head and neck cancer radiotherapy,

Cited by 31 publications

References 25 publications

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans

Normal tissue complication probability models for prospectively scored late rectal and urinary morbidity after proton therapy of prostate cancer

Prediction of radiation-induced mucositis of H&N cancer patients based on a large patient cohort

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