scholarly journals Radiation-Induced Hypothyroidism in Patients with Oropharyngeal Cancer Treated with IMRT: Independent and External Validation of Five Normal Tissue Complication Probability Models

Cancers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2716
Author(s):  
Zuzanna Nowicka ◽  
Bartłomiej Tomasik ◽  
Anna Papis-Ubych ◽  
Robert Bibik ◽  
Łukasz Graczyk ◽  
...  
Keyword(s):  
Oropharyngeal Cancer ◽  
Normal Tissue ◽  
External Validation ◽  
Intensity Modulated Radiation Therapy ◽  
Thyroid Volume ◽  
Normal Tissue Complication Probability ◽  
Probability Models ◽  
Short Term ◽  
Radiation Induced

We aimed to externally validate five normal tissue complication probability (NTCP) models for radiation-induced hypothyroidism (RIHT) in a prospectively recruited cohort of 108 patients with oropharyngeal cancer (OPC). NTCP scores were calculated using original published formulas. Plasma thyrotropin (TSH) level was additionally assessed in the short-term after RT. After a median of 28 months of follow-up, thirty one (28.7%) patients developed RIHT. Thyroid mean dose and thyroid volume were significant predictors of RIHT: odds ratio equal to 1.11 (95% CI 1.03–1.19) for mean thyroid dose and 0.87 (95%CI 0.81–0.93) for thyroid volume in univariate analyses. Two of the evaluated NTCP models, published by Rønjom et al. and by Boomsma et al., had satisfactory performance with accuracies of 0.87 (95%CI 0.79–0.93) and 0.84 (95%CI: 0.76–0.91), respectively. Three remaining models, by Cella et al., Bakhshandeh et al. and Vogelius et al., performed significantly worse, overestimating the risk of RIHT in this patient cohort. A short-term TSH level change relative to baseline was not indicative of RIHT development in the follow-up (OR 0.96, 95%CI: 0.65–1.42, p = 0.825). In conclusion, the models by Rønjom et al. and by Boomsma et al. demonstrated external validity and feasibility for long-term prediction of RIHT in survivors of OPC treated with Intensity-Modulated Radiation Therapy (IMRT).

scholarly journals Prediction of radiation-induced hypothyroidism using radiomic data analysis does not show superiority over standard normal tissue complication models

2021 ◽  
Author(s):  
Urszula Smyczynska ◽  
Szymon Grabia ◽  
Zuzanna Nowicka ◽  
Anna Papis-Ubych ◽  
Robert Bibik ◽  
...  
Keyword(s):  
Normal Tissue ◽  
Performance Metrics ◽  
Intensity Modulated Radiation Therapy ◽  
Anatomical Variations ◽  
Normal Tissue Complication Probability ◽  
Imaging Biomarkers ◽  
Standard Normal ◽  
Radiation Induced ◽  
Sensitivity Specificity

State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33-35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). Our models reach accuracy comparable with or better than previously presented non-radiomic NTCP models. The benefit of our approach is obtaining the RIHT predictions before treatment planning to adjust IMRT plan to avoid the thyroid region in most susceptible patients.

scholarly journals External validation of a normal tissue complication probability model for radiation-induced hypothyroidism in an independent cohort

2015 ◽  
Vol 54 (9) ◽  
pp. 1301-1309 ◽  
Author(s):  
Marianne F. Rønjom ◽  
Carsten Brink ◽  
Søren M. Bentzen ◽  
Laszlo Hegedüs ◽  
Jens Overgaard ◽  
...  
Keyword(s):  
Normal Tissue ◽  
Probability Model ◽  
External Validation ◽  
Normal Tissue Complication Probability ◽  
Radiation Induced ◽  
Independent Cohort

scholarly journals Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models

Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5584
Author(s):  
Urszula Smyczynska ◽  
Szymon Grabia ◽  
Zuzanna Nowicka ◽  
Anna Papis-Ubych ◽  
Robert Bibik ◽  
...  
Keyword(s):  
Normal Tissue ◽  
Performance Metrics ◽  
Intensity Modulated Radiation Therapy ◽  
Anatomical Variations ◽  
Normal Tissue Complication Probability ◽  
Imaging Biomarkers ◽  
Susceptible Population ◽  
Radiation Induced ◽  
Sensitivity Specificity ◽  
State Of Art

State-of-art normal tissue complication probability (NTCP) models do not take into account more complex individual anatomical variations, which can be objectively quantitated and compared in radiomic analysis. The goal of this project was development of radiomic NTCP model for radiation-induced hypothyroidism (RIHT) using imaging biomarkers (radiomics). We gathered CT images and clinical data from 98 patients, who underwent intensity-modulated radiation therapy (IMRT) for head and neck cancers with a planned total dose of 70.0 Gy (33–35 fractions). During the 28-month (median) follow-up 27 patients (28%) developed RIHT. For each patient, we extracted 1316 radiomic features from original and transformed images using manually contoured thyroid masks. Creating models based on clinical, radiomic features or a combination thereof, we considered 3 variants of data preprocessing. Based on their performance metrics (sensitivity, specificity), we picked best models for each variant ((0.8, 0.96), (0.9, 0.93), (0.9, 0.89) variant-wise) and compared them with external NTCP models ((0.82, 0.88), (0.82, 0.88), (0.76, 0.91)). We showed that radiomic-based models did not outperform state-of-art NTCP models (p > 0.05). The potential benefit of radiomic-based approach is that it is dose-independent, and models can be used prior to treatment planning allowing faster selection of susceptible population.

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