Generalizability assessment of head and neck cancer NTCP models based on the TRIPOD criteria

Sharabiani, M.; Clementel, E.; Andratschke, Nicolaus; Hurkmans, Coen

doi:10.1016/j.radonc.2020.02.013

Cited by 26 publications

(19 citation statements)

References 55 publications

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“…Secondly, in view of the frequent single-center retrospective design and the generally low number of enrolled patients and of clinical endpoints (i.e., side effects), the robustness of data is questionable for most studies (122). In this perspective, the lack of or very limited external validation in independent datasets is a point of weakness for both conventional NTCP models (123) and radiomics applications (119). Thirdly, progress in the field of radio-genomics is eagerly awaited (124), in order to improve the understanding of underlying biological processes, such as intrinsic radio-sensitivity.…”

Section: Resultsmentioning

confidence: 99%

Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art

et al. 2020

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Normal tissue complication probability (NTCP) models that were formulated in the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) are one of the pillars in support of everyday's clinical radiation oncology. Because of steady therapeutic refinements and the availability of cutting-edge technical solutions, the ceiling of organsat-risk-sparing has been reached for photon-based intensity modulated radiotherapy (IMRT). The possibility to capture heterogeneity of patients and tissues in the prediction of toxicity is still an unmet need in modern radiation therapy. Potentially, a major step towards a wider therapeutic index could be obtained from refined assessment of radiation-induced morbidity at an individual level. The rising integration of quantitative imaging and machine learning applications into radiation oncology workflow offers an unprecedented opportunity to further explore the biologic interplay underlying the normal tissue response to radiation. Based on these premises, in this review we focused on the current-state-of-the-art on the use of radiomics for the prediction of toxicity in the field of head and neck, lung, breast and prostate radiotherapy.

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Section: Resultsmentioning

confidence: 99%

Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art

et al. 2020

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“…Instead of evaluating the target coverage and the risk of toxicity through a single dosimetric endpoint, the tumour control probabil-ity (TCP) and normal tissue complication probability (NTCP) can be calculated from biological models [13,14], which are usually based on the DVH information. Most of these tolerances and NTCP models were generated using data from one or few centres and should, therefore, be used with caution.…”

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

134

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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“…In addition, most models that are available did not use the OAR definitions as defined in the international consensus guidelines [22], nor have they been externally validated [23]. Consequently, their generalisability is limited, impeding their clinical use [24,25].…”

mentioning

confidence: 99%

Comprehensive toxicity risk profiling in radiation therapy for head and neck cancer: A new concept for individually optimised treatment

Bosch

Schaaf

Laan

et al. 2021

Radiotherapy and Oncology

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Background and purpose: A comprehensive individual toxicity risk profile is needed to improve radiation treatment optimisation, minimising toxicity burden, in head and neck cancer (HNC) patients. We aimed to develop and externally validate NTCP models for various toxicities at multiple time points. Materials and methods: Using logistic regression, we determined the relationship between normal tissue irradiation and the risk of 22 toxicities at ten time points during and after treatment in 750 HNC patients. The toxicities involved swallowing, salivary, mucosal, speech, pain and general complaints. Studied predictors included patient, tumour and treatment characteristics and dose parameters of 28 organs. The resulting NTCP models were externally validated in 395 HNC patients. Results: The NTCP models involved 14 organs that were associated with at least one toxicity. The oral cavity was the predominant organ, associated with 12 toxicities. Other important organs included the parotid and submandibular glands, buccal mucosa and swallowing muscles. In addition, baseline toxicity, treatment modality, and tumour site were common predictors of toxicity. The median discrimination performance (AUC) of the models was 0.71 (interquartile range: 0.68-0.75) at internal validation and 0.67 (interquartile range: 0.62-0.71) at external validation. Conclusion: We established a comprehensive individual toxicity risk profile that provides essential insight into how radiation exposure of various organs translates into multiple acute and late toxicities. This comprehensive understanding of radiation-induced toxicities enables a new radiation treatment optimisation concept that balances multiple toxicity risks simultaneously and minimises the overall toxicity burden for an individual HNC patient who needs to undergo radiation treatment.

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Generalizability assessment of head and neck cancer NTCP models based on the TRIPOD criteria

Cited by 26 publications

References 55 publications

Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art

Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art

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

Comprehensive toxicity risk profiling in radiation therapy for head and neck cancer: A new concept for individually optimised treatment

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