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

Smyczyńska, Urszula; Grabia, Szymon; Nowicka, Zuzanna; Papis-Ubych, Anna; Bibik, Robert; Latusek, Tomasz; Rutkowski, T.; Fijuth, Jacek; Fendler, Wojciech; Tomasik, Bartłomiej

doi:10.3390/cancers13215584

Cited by 4 publications

(3 citation statements)

References 47 publications

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“…Moreover, thyroid volume over 20 cm 3 was associated with a lower 2-year incidence of HT in some study of nasopharyngeal cancer [ 34 ]. Another report originated from Poland in oropharyngeal cancer treated by IMRT also validated the NTCP model for thyroid volume as a predictor of HT [ 31 , 35 ]. However, the analysis from this study did not reveal any correlation between thyroid volume and HT.…”

Section: Discussionmentioning

confidence: 99%

Incidence of radiation-induced hypothyroidism following head and neck irradiation: a single-center analysis

Jia-Mahasap

Assavanopakun

Chitapanarux

et al. 2022

Rep Pract Oncol Radiother.

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Background:The purpose of this study was to investigate the incidence of primary hypothyroidism (HT), as well as any correlation between dosimetric parameters and thyroid dysfunctions after neck radiotherapy (RT) in head and neck cancer (HNC) patients. Materials and methods:This study retrospectively reviewed HNC patients who finished neck RT for at least 12 months and who had available back-up treatment information. Eligible patients further received a single thyroid function test (TFT). Dosimetric parameters of the thyroid glands were retrospectively evaluated in order to detect any correlation between dosevolume parameters and primary HT. Results:We reviewed 1,102 HNC patients. Accordingly, 64 patients were deemed eligible and were included in this study. The median time interval between RT completion and TFT was 21 months (interquartile range, 14-34 months), while 26 patients (40.6%) were diagnosed with HT. The thyroid volume spared from a dose of 50 Gy (VS50 Gy ) was found to be statistically significant and considered an associated factor for developing HT (p=0.047). 1 Furthermore, there was an observable trend indicating a reduction in the risk of HT when VS50 Gy was more than 5 cm 3 (p = 0.052). Conclusion:In our study, VS50 Gy was determined to be a significant predictive parameter of radiation-induced HT.

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Section: Discussionmentioning

confidence: 99%

Incidence of radiation-induced hypothyroidism following head and neck irradiation: a single-center analysis

Jia-Mahasap

Assavanopakun

Chitapanarux

et al. 2022

Rep Pract Oncol Radiother.

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“…Medical imaging, as a clinical approach, plays a crucial role in assessing the side effects of the pathophysiological and functional processes associated with radiotherapy [15,35,36]. Therefore, identifying suitable predictive biomarkers for these complications is of great interest to clinicians and researchers.…”

Section: Discussionmentioning

confidence: 99%

Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study

Abbaspour,

Barahman,

Abdollahi

et al. 2023

Biomed. Phys. Eng. Express

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Purpose. This study aims to predict radiotherapy-induced rectal and bladder toxicity using computed tomography (CT) and magnetic resonance imaging (MRI) radiomics features in combination with clinical and dosimetric features in rectal cancer patients. Methods. A total of sixty-three patients with locally advanced rectal cancer who underwent three-dimensional conformal radiation therapy (3D-CRT) were included in this study. Radiomics features were extracted from the rectum and bladder walls in pretreatment CT and MR-T2W-weighted images. Feature selection was performed using various methods, including Least Absolute Shrinkage and Selection Operator (Lasso), Minimum Redundancy Maximum Relevance (MRMR), Chi-square (Chi2), Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and SelectPercentile. Predictive modeling was carried out using machine learning algorithms, such as K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Gradient Boosting (XGB), and Linear Discriminant Analysis (LDA). The impact of the Laplacian of Gaussian (LoG) filter was investigated with sigma values ranging from 0.5 to 2. Model performance was evaluated in terms of the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity, and specificity. Results. A total of 479 radiomics features were extracted, and 59 features were selected. The pre-MRI T2W model exhibited the highest predictive performance with an AUC: 91.0/96.57%, accuracy: 90.38/96.92%, precision: 90.0/97.14%, sensitivity: 93.33/96.50%, and specificity: 88.09/97.14%. These results were achieved with both original image and LoG filter (sigma = 0.5–1.5) based on LDA/DT-RF classifiers for proctitis and cystitis, respectively. Furthermore, for the CT data, AUC: 90.71/96.0%, accuracy: 90.0/96.92%, precision: 88.14/97.14%, sensitivity: 93.0/96.0%, and specificity: 88.09/97.14% were acquired. The highest values were achieved using XGB/DT-XGB classifiers for proctitis and cystitis with LoG filter (sigma = 2)/LoG filter (sigma = 0.5–2), respectively. MRMR/RFE-Chi2 feature selection methods demonstrated the best performance for proctitis and cystitis in the pre-MRI T2W model. MRMR/MRMR-Lasso yielded the highest model performance for CT. Conclusion. Radiomics features extracted from pretreatment CT and MR images can effectively predict radiation-induced proctitis and cystitis. The study found that LDA, DT, RF, and XGB classifiers, combined with MRMR, RFE, Chi2, and Lasso feature selection algorithms, along with the LoG filter, offer strong predictive performance. With the inclusion of a larger training dataset, these models can be valuable tools for personalized radiotherapy decision-making.

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“…Although radiomics has been successfully applied to enhance the prediction of radiation-induced complications in various studies 6 , 7 , 15 , there are also reports that the incorporation of radiomics features did not significantly improve the prediction of RIH some cancer types, such as oropharyngeal cancer 16 . One possible reason for the contradictory findings could be due to the difference in etiologies, which would influence the association between radiomics features and the clinical status of the tumors.…”

Section: Discussionmentioning

confidence: 99%

Improved prediction of radiation-induced hypothyroidism in nasopharyngeal carcinoma using pre-treatment CT radiomics

Ritlumlert,

Wongwattananard,

Prayongrat

et al. 2023

Sci Rep

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When planning radiation therapy, late effects due to the treatment should be considered. One of the most common complications of head and neck radiation therapy is hypothyroidism. Although clinical and dosimetric data are routinely used to assess the risk of hypothyroidism after radiation, the outcome is still unsatisfactory. Medical imaging can provide additional information that improves the prediction of hypothyroidism. In this study, pre-treatment computed tomography (CT) radiomics features of the thyroid gland were combined with clinical and dosimetric data from 220 participants to predict the occurrence of hypothyroidism within 2 years after radiation therapy. The findings demonstrated that the addition of CT radiomics consistently and significantly improves upon conventional model, achieving the highest area under the receiver operating characteristic curve (AUCs) of 0.81 ± 0.06 with a random forest model. Hence, pre-treatment thyroid CT imaging provides useful information that have the potential to improve the ability to predict hypothyroidism after nasopharyngeal radiation therapy.

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

Cited by 4 publications

References 47 publications

Incidence of radiation-induced hypothyroidism following head and neck irradiation: a single-center analysis

Incidence of radiation-induced hypothyroidism following head and neck irradiation: a single-center analysis

Multimodality radiomics prediction of radiotherapy-induced the early proctitis and cystitis in rectal cancer patients: a machine learning study

Improved prediction of radiation-induced hypothyroidism in nasopharyngeal carcinoma using pre-treatment CT radiomics

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