Radiation-induced hypothyroidism in head and neck cancer patients: A systematic review
ABSTRACT To review literature on the relationship between the dose distribution in the thyroid gland and the incidence of radiation-induced hypothyroidism in adults.
Articles were identified through a search in MEDLINE, EMBASE and the Cochrane Library. Approximately 2449 articles were screened and selected by inclusion- and exclusion criteria. Eventually, there were five papers that fulfilled the eligibility criteria to be included in this review.
The sample sizes of the reviewed studies vary from 57 to 390 patients. The incidence of hypothyroidism was much higher (23-53%) than would be expected in a non-irradiated cohort. There was a large heterogeneity between the studies regarding study design, estimation of the dose to the thyroid gland and definition of endpoints. In general, the relationship between thyroid gland volume absorbing 10-70Gy (V10-V70), mean dose (Dmean), minimal dose (Dmin), maximum dose (Dmax) and point doses with hypothyroidism were analysed. An association between dose-volume parameters and hypothyroidism was found in two studies.
Hypothyroidism is frequently observed after radiation. Although the results suggest that higher radiation doses to the thyroid gland are associated with hypothyroidism, it was not possible to define a clear threshold radiation dose for the thyroid gland.
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- "It is also necessary to consider the influence on thyroid function because irradiation fields often include a part of the thyroid gland. Although it is well known that radiation exposure of the thyroid gland can induce hypothyroidism, its potential influence can be ignored in this study because radiation-induced hypothyroidism would be detected several years after completion of irradiation 48. Furthermore, possible influence of CDDP-based chemotherapy on thyroid function has been reported in children 49. "
ABSTRACT: Objective: Chemotherapy-related toxicities are difficult to predict before treatment. In this study, we investigated whether thyroid hormone receptor beta (THRB) genetic polymorphisms can serve as a potential biomarker in patients with esophageal squamous cell carcinoma (ESCC). Methods: Forty-nine Japanese patients with ESCC who received a definitive chemoradiotherapy (CRT) with 5-fluorouracil and cisplatin in conjunction with concurrent irradiation were retrospectively analyzed. Severe acute toxicities, including leukopenia, stomatitis, and cheilitis, were evaluated according to 6 single nucleotide polymorphisms (SNPs) in the gene; the intronic SNPs of rs7635707 G/T, rs6787255 A/C, rs9812034 G/T, and rs9310738 C/T and the SNPs in the 3′-untranslated region (3′-UTR) of rs844107 C/T and rs1349265 G/A. Results: Distribution of the 4 intronic SNPs, but not the 2 SNPs in the 3′-UTR, showed a significant difference between patients with and without severe acute leukopenia. Stomatitis and cheilitis were not associated with any of the 6 analyzed SNPs. Frequency of haplotype of the 4 intronic SNPs reached approximately 97% with the 2 major haplotypes G-A-G-C (73.4%) and T-C-T-T (23.5%). Conclusions: THRB intronic SNPs can provide useful information on CRT-related severe myelotoxicity in patients with ESCC. Future studies will be needed to confirm these findings.International journal of medical sciences 10/2012; 9(9):748-56. DOI:10.7150/ijms.5081 · 1.55 Impact Factor
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- "Therefore, 200 samples (with at least 32 events, to avoid severe overfitting) appear to be a robust number to set as an efficient minimum data set size to obtain a model with high predictive power. This lower bound is within the relevant range of current practice, as, for example, in [12–14,17,18], and    the range of data set sizes is 94–529 patients (median 214) with a range of 11–122 events (median 50). Although the increase of predictive power is limited for data sets larger than 200 samples, the benefit is that more variables can be included in the model (see Fig. 4), which widens the potential applicability, as, for example , in . "
ABSTRACT: PURPOSE: Multivariate modeling of complications after radiotherapy is frequently used in conjunction with data driven variable selection. This study quantifies the risk of overfitting in a data driven modeling method using bootstrapping for data with typical clinical characteristics, and estimates the minimum amount of data needed to obtain models with relatively high predictive power. MATERIALS AND METHODS: To facilitate repeated modeling and cross-validation with independent datasets for the assessment of true predictive power, a method was developed to generate simulated data with statistical properties similar to real clinical data sets. Characteristics of three clinical data sets from radiotherapy treatment of head and neck cancer patients were used to simulate data with set sizes between 50 and 1000 patients. A logistic regression method using bootstrapping and forward variable selection was used for complication modeling, resulting for each simulated data set in a selected number of variables and an estimated predictive power. The true optimal number of variables and true predictive power were calculated using cross-validation with very large independent data sets. RESULTS: For all simulated data set sizes the number of variables selected by the bootstrapping method was on average close to the true optimal number of variables, but showed considerable spread. Bootstrapping is more accurate in selecting the optimal number of variables than the AIC and BIC alternatives, but this did not translate into a significant difference of the true predictive power. The true predictive power asymptotically converged toward a maximum predictive power for large data sets, and the estimated predictive power converged toward the true predictive power. More than half of the potential predictive power is gained after approximately 200 samples. Our simulations demonstrated severe overfitting (a predicative power lower than that of predicting 50% probability) in a number of small data sets, in particular in data sets with a low number of events (median: 7, 95th percentile: 32). Recognizing overfitting from an inverted sign of the estimated model coefficients has a limited discriminative value. CONCLUSIONS: Despite considerable spread around the optimal number of selected variables, the bootstrapping method is efficient and accurate for sufficiently large data sets, and guards against overfitting for all simulated cases with the exception of some data sets with a particularly low number of events. An appropriate minimum data set size to obtain a model with high predictive power is approximately 200 patients and more than 32 events. With fewer data samples the true predictive power decreases rapidly, and for larger data set sizes the benefit levels off toward an asymptotic maximum predictive power.Radiotherapy and Oncology 01/2012; 105(1). DOI:10.1016/j.radonc.2011.12.006 · 4.86 Impact Factor
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ABSTRACT: To establish a multivariate normal tissue complication probability (NTCP) model for radiation-induced hypothyroidism. The thyroid-stimulating hormone (TSH) level of 105 patients treated with (chemo-) radiation therapy for head-and-neck cancer was prospectively measured during a median follow-up of 2.5 years. Hypothyroidism was defined as elevated serum TSH with decreased or normal free thyroxin (T4). A multivariate logistic regression model with bootstrapping was used to determine the most important prognostic variables for radiation-induced hypothyroidism. Thirty-five patients (33%) developed primary hypothyroidism within 2 years after radiation therapy. An NTCP model based on 2 variables, including the mean thyroid gland dose and the thyroid gland volume, was most predictive for radiation-induced hypothyroidism. NTCP values increased with higher mean thyroid gland dose (odds ratio [OR]: 1.064/Gy) and decreased with higher thyroid gland volume (OR: 0.826/cm(3)). Model performance was good with an area under the curve (AUC) of 0.85. This is the first prospective study resulting in an NTCP model for radiation-induced hypothyroidism. The probability of hypothyroidism rises with increasing dose to the thyroid gland, whereas it reduces with increasing thyroid gland volume.International journal of radiation oncology, biology, physics 06/2012; 84(3):e351-6. DOI:10.1016/j.ijrobp.2012.05.020 · 4.18 Impact Factor