Multivariate Normal Tissue Complication Probability Models Research Articles

Fine Motor Skill Decline After Brain Radiation Therapy—A Multivariate Normal Tissue Complication Probability Study of a Prospective Trial

Brain radiation therapy can impair fine motor skills (FMS). Fine motor skills are essential for activities of daily living, enabling hand-eye coordination for manipulative movements. We developed normal tissue complication probability (NTCP) models for the decline in FMS after fractionated brain radiation therapy (RT). On a prospective trial, 44 patients with primary brain tumors received fractioned RT; underwent high-resolution volumetric magnetic resonance imaging, diffusion tensor imaging, and comprehensive FMS assessments (Delis-Kaplan Executive Function System Trail Making Test Motor Speed [DKEFS-MS]; and Grooved Pegboard dominant/nondominant hands) at baseline and 6 months postRT. Regions of interest subserving motor function (including cortex, superficial white matter, thalamus, basal ganglia, cerebellum, and white matter tracts) were autosegmented using validated methods and manually verified. Dosimetric and clinical variables were included in multivariate NTCP models using automated bootstrapped logistic regression, least absolute shrinkage and selection operator logistic regression, and random forests with nested cross-validation. Half of the patients showed a decline on grooved pegboard test of nondominant hands, 17 of 42 (40.4%) on grooved pegboard test of -dominant hands, and 11 of 44 (25%) on DKEFS-MS. Automated bootstrapped logistic regression selected a 1-term model including maximum dose to dominant postcentral white matter. The least absolute shrinkage and selection operator logistic regression selected this term and steroid use. The top 5 variables in the random forest were all dosimetric: maximum dose to dominant thalamus, mean dose to dominant caudate, mean and maximum dose to the dominant corticospinal tract, and maximum dose to dominant postcentral white matter. This technique performed best with an area under the curve of 0.69 (95% CI, 0.68-0.70) on nested cross-validation. We present the first NTCP models for FMS impairment after brain RT. Dose to several supratentorial motor-associated regions of interest correlated with a decline in dominant-hand fine motor dexterity in patients with primary brain tumors in multivariate models, outperforming clinical variables. These data can guide prospective fine motor-sparing strategies for brain RT.

Development and Validation of a Normal Tissue Complication Probability Model for Lymphedema After Radiation Therapy in Breast Cancer

To develop and test a multivariable normal tissue complication probability (NTCP) model predicting lymphedema in patients with breast cancer receiving radiotherapy. We retrospectively reviewed 1345 patients with breast cancer who received radiotherapy from two independent institutions. The patients were divided into a training cohort (Institution A, n=368, all treated with 3D conformal external beam RT [3DCRT] with 2Gy/fraction) and an external validation cohort (Institution B, n=977, treated either with 3DCRT or with volumetric modulated RT and either with 1.8-2.0Gy/fraction or with 2.67Gy/fraction). Axillary-lateral thoracic vessel juncture (ALTJ) was delineated. The multivariable model was generated using dosimetric and clinical parameters. The performance of the model was comprehensively validated internally and externally. During a median follow-up of 78.7 months for the entire cohort, 97 patients (7.2%) developed lymphedema. The multivariable model that took into account the number of lymph nodes dissected (LNDno), as well as the volume of the ALTJ receiving a dose ≥ 35Gy EQD2 (ALTJ V35), showed good agreement between predicted and observed results for both internal and external validation (Hosmer-Lemeshow p-value > 0.05). The area under the curve (AUC) and negative log-likelihood (NLL) values for the multivariable NTCP model were 0.89 and 0.19 in internal validation and 0.83 and 0.19 in external validation. In addition, the multivariable model performance was acceptable for hypofractionated regimens (AUC 0.70) and VMAT (AUC 0.69). LNDno and ALTJ V35 were found to be the most important factors influencing lymphedema after radiotherapy. We first developed and validated the multivariable NTCP model for the lymphedema incidence in patients with breast cancer after radiotherapy. The multivariable NTCP model showed excellent performance and robustness in predicting lymphedema in both internal and completely independent external validations. The multivariable model for lymphedema prediction was robust and reliable for different treatment modalities and fractionation regimens.

Longitudinal study of irradiation-induced brain functional network alterations in patients with nasopharyngeal carcinoma

BackgroundTo investigate radiotherapy (RT)-related brain network changes in patients with nasopharyngeal carcinoma (NPC) over time and develop least absolute shrinkage and selection operator (LASSO)-based multivariable normal tissue complication probability (NTCP) models to predict RT-related brain network changes. Methods36 NPC patients were followed up at four timepoints: baseline, within 3 months (acute), 6 months (subacute), and 12 months (delayed) post-RT. 15 comparable healthy controls (HCs) were finally included and followed up in parallel. Functional neuroimaging data, dose–volume parameters of bilateral temporal lobes and Montreal Cognitive Assessment (MoCA) were acquired. Graph theoretical analysis and mixed-design analysis of variance were performed to investigate how the brain global and nodal changes were affected by RT. Multivariate logistic regression NTCP models were developed. LASSO with nested cross-validation strategy was used to select features. The relationships between network changes and MoCA changes were also examined. ResultsSignificant changes were detected in nodal efficiency (NE) in NPC patients but not in HCs over time. Altered NE was distributed in the bilateral frontal, temporal lobes and the right insula, which showed a “decrease-increase/recovery” pattern over time. Among all models, the model for predicting NE changes of STG.R showed a relatively good performance (area under the receiver operating curve: 0.68), and D20cc and V20 to right temporal lobe outperformed in this model. ConclusionOur findings indicate that RT-induced brain injury begin at the acute period and follow a recovery over time. Furthermore, our study presents prediction models for brain dysfunction based on the dosimetric and clinical parameters.

Normal Tissue Complication Probability Model for Acute Radiation Dermatitis in Patients With Head and Neck Cancer Treated With Carbon Ion Radiation Therapy

This study aimed to explore the prognostic factors associated with acute radiation dermatitis (ARD). A normal tissue complication probability (NTCP) model for ARD in patients with head and neck cancer treated with carbon ion radiation therapy was developed. A total of 187 patients were included in the analysis, and the endpoint was ≥grade 2 ARD. The biological and physical dose-surface parameters associated with ARD were used in the logistic regression model. The mean areas under the receiver operating characteristic curve in the internal cross-validation and Akaike's corrected Information Criterion were examined for model evaluation and selection. The multivariate logistic regression NTCP models were established based on factors with weak correlation. Tumor volume, planning target volume to the skin, radiation technique and all dose-surface parameters were significantly associated with ARD (P < .05). Models with high performance for grade 2 to 3 ARD were constructed. The most significant prognostic predictors were S40Gy(relative biological effectiveness,RBE) and S20Gy (absolute surface area receiving RBE-weighted dose of 40 Gy (RBE) or physical dose of 20 Gy). The internal cross-validation-based areas under the receiver operating characteristic curve for models with S40Gy(RBE) and S20Gy were 0.78 and 0.77, respectively. The biological and physical dose-surface parameters had similar performance at various dose levels. However, the performance of the multivariate NTCP models based on 2 factors was not better than that of the univariate models. NTCP models for ARD may provide a basis for the development of individualized treatment strategies and reduce the incidence of severe ARD in patients with head and neck cancer receiving carbon ion radiation therapy. Furthermore, biological and physical dose-surface parameter-based models are comparable. However, further validation with more evaluation parameters is warranted.

Assessment of the confidence interval in the multivariable normal tissue complication probability model for predicting radiation-induced liver disease in primary liver cancer.

We developed a confidence interval-(CI) assessing model in multivariable normal tissue complication probability (NTCP) modeling for predicting radiation-induced liver disease (RILD) in primary liver cancer patients using clinical and dosimetric data. Both the mean NTCP and difference in the mean NTCP (ΔNTCP) between two treatment plans of different radiotherapy modalities were further evaluated and their CIs were assessed. Clinical data were retrospectively reviewed in 322 patients with hepatocellular carcinoma (n = 215) and intrahepatic cholangiocarcinoma (n = 107) treated with photon therapy. Dose–volume histograms of normal liver were reduced to mean liver dose (MLD) based on the fraction size-adjusted equivalent uniform dose. The most predictive variables were used to build the model based on multivariable logistic regression analysis with bootstrapping. Internal validation was performed using the cross-validation leave-one-out method. Both the mean NTCP and the mean ΔNTCP with 95% CIs were calculated from computationally generated multivariate random sets of NTCP model parameters using variance–covariance matrix information. RILD occurred in 108/322 patients (33.5%). The NTCP model with three clinical and one dosimetric parameter (tumor type, Child–Pugh class, hepatitis infection status and MLD) was most predictive, with an area under the receiver operative characteristics curve (AUC) of 0.79 (95% CI 0.74–0.84). In eight clinical subgroups based on the three clinical parameters, both the mean NTCP and the mean ΔNTCP with 95% CIs were able to be estimated computationally. The multivariable NTCP model with the assessment of 95% CIs has potential to improve the reliability of the NTCP model-based approach to select the appropriate radiotherapy modality for each patient.

Normal tissue complication probability (NTCP) models of acute urinary toxicity (AUT) following carbon ion radiotherapy (CIRT) for prostate cancer

PurposeTo estimate the Lyman Kutcher Burman (LKB) and multivariate NTCP models predicting the AUT of prostate cancer treated with CIRT. Materials and methodsA cohort of 154 prostate adenocarcinoma patients were retrospectively analyzed. The AUT levels were graded according to CTCAE 4.03. Based on dosimetric parameters and/or clinical factors, a set of variables with best-fit values determined in the two models was validated by the area under the receiver operating characteristic curve (AUC) and used to correlate the predicted and observed NTCP rates for both levels and related endpoints. Result59 (38.3%) patients experienced AUT. For LKB model, the equivalent uniform doses (EUDs) were calculated to be 62.0 GyE (following V61.5 > 1.7%) and 61.2 GyE (following maximum dose > 63.0 GyE) with predicted NTCP rates of 37.0% (AUC: 0.71) and 15.6% (AUC: 0.65) for AUT G1&2 and G2 of bladder. While for the multivariate model, the predicted NTCP rates was 37.1% (AUC: 0.70) and 20.2% (AUC: 0.64) for AUT G1&2 and G2, associated with V61 and V65, respectively. Nocturia was associated with bladder volume and maximum dose for G1&2, with patient’s age and maximum bladder dose for G2. Other predictable endpoints were associated with V≥61. The predicted NTCPs agree with the observed complication rates for bladder and its wall. ConclusionsThe LKB model successfully predicted the NTCP rates of both AUT levels and urgency urination. The multivariate model predicted well on both levels and nocturia. Decreasing high bladder dose volume may reduce the incidence of AUT.

Modelling the risk of radiation induced alopecia in brain tumor patients treated with scanned proton beams

PurposeTo develop normal tissue complication probability (NTCP) models for radiation-induced alopecia (RIA) in brain tumor patients treated with proton therapy (PT). Methods and materialsWe analyzed 116 brain tumor adult patients undergoing scanning beam PT (median dose 54 GyRBE; range 36–72) for CTCAE v.4 grade 2 (G2) acute (≤90 days), late (>90 days) and permanent (>12 months) RIA.The relative dose-surface histogram (DSH) of the scalp was extracted and used for Lyman-Kutcher-Burman (LKB) modelling. Moreover, DSH metrics (Sx: the surface receiving ≥ X Gy, D2%: near maximum dose, Dmean: mean dose) and non-dosimetric variables were included in a multivariable logistic regression NTCP model. Model performances were evaluated by the cross-validated area under the receiver operator curve (ROC-AUC). ResultsAcute, late and permanent G2-RIA was observed in 52%, 35% and 19% of the patients, respectively. The LKB models showed a weak dose-surface effect (0.09 ≤ n ≤ 0.19) with relative steepness 0.29 ≤ m ≤ 0.56, and increasing tolerance dose values when moving from acute and late (22 and 24 GyRBE) to permanent RIA (44 GyRBE).Multivariable modelling selected S21Gy for acute and S25Gy, for late G2-RIA as the most predictive DSH factors. Younger age was selected as risk factor for acute G2-RIA while surgery as risk factor for late G2-RIA. D2% was the only variable selected for permanent G2-RIA. Both LKB and logistic models exhibited high predictive performances (ROC-AUCs range 0.86–0.90). ConclusionWe derived NTCP models to predict G2-RIA after PT, providing a comprehensive modelling framework for acute, late and permanent occurrences that, once externally validated, could be exploited for individualized scalp sparing treatment planning strategies in brain tumor patients.

Development of Normal Tissue Complication Probability Model for Trismus in Head and Neck Cancer Patients Treated With Radiotherapy: The Role of Dosimetric and Clinical Factors.

The aim of this study was to develop a normal tissue complication probability (NTCP) model for trismus in head and neck cancer (HNC) patients treated with radiotherapy (RT). Prospective measurements of maximum inter-incisal opening (MIO) were performed at baseline and 6 months after definitive RT in 132 HNC patients. The primary endpoint of this study was defined when a patient fulfilled both of the following criteria: 1) MIO at 6 months after RT ≤35 mm and 2) MIO at 6 months after RT ≤80% of baseline MIO. Eleven clinical factors and a wide range of dosimetric factors (mean dose, maximum dose, V5, V10, V20, and V40) in twelve organs at risk (OARs) were chosen as candidate prognostic variables. Thirty out of 132 patients (23%) developed the primary endpoint. Multivariate logistic regression analysis revealed that the mean dose to the contralateral mandible joint (p=0.001) and baseline MIO (p=0.027) were independent prognostic factors. A multivariable NTCP model for trismus in HNC patients treated with RT was established including the mean dose to contralateral mandible joint and baseline MIO.

Developing a multivariable normal tissue complication probability model to predict late rectal bleeding following intensity-modulated radiation therapy.

Purpose: To develop a multivariable normal tissue complication probability (NTCP) model to predict moderate to severe late rectal bleeding following intensity-modulated radiation therapy (IMRT).Methods and materials: Sixty-eight patients with localized prostate cancer treated by IMRT from 2008 to 2011 were enrolled. The median follow-up time was 56 months. According to the criteria of D'Amico risk classifications, there were 9, 20 and 39 patients in low, intermediate and high-risk groups, respectively. Forty-two patients were combined with androgen deprivation therapy. Fifteen patients had suffered from grade 2 or more (grade 2+) late rectal bleeding. The numbers of predictors for a multivariable logistic regression NTCP model were determined by the least absolute shrinkage and selection operator (LASSO).Results: The most important predictors for late rectal bleeding ranked by LASSO were platelet count, risk group and the relative volume of rectum receiving at least 65 Gy (V65). The NTCP model of grade 2+ rectal bleeding was as follows: S = -17.49 + Platelets (1000/μL) * (-0.025) + Risk group * Corresponding coefficient (low-risk group = 0; intermediate-risk group = 19.07; high-risk group = 20.41) + V65 * 0.045.Conclusions: A LASSO-based multivariable NTCP model comprising three important predictors (platelet count, risk group and V65) was established to predict the incidence of grade 2+ late rectal bleeding after IMRT.

Multivariable Normal Tissue Complication Modeling of Acute Esophageal Toxicity in Esophageal Squamous Cancer Patients Treated with Radio(Chemo)Therapy

To establish a multivariable normal-tissue complication probability (NTCP) model for the Grade ≧2 acute esophageal toxicity (AET) after curative radio(chemo)therapy in patients with esophageal squamous cell carcinoma (ESCC). A cohort of 184 ESCC patients who were treated with definitive radio(chemo)therapy between April 2008 and December 2011 was enrolled in this study. The analysis included clinical and esophageal dose–volume histogram parameters. Clinical parameters included age, sex, weight loss during treatment, alcohol history, tumor site, stage, the length of gross tumor volume, the length of planning target volume (LPTV), the LPTV of the primary tumor (LPTV-P). Dose–volume histogram parameters included V5 to V65 with step of 5Gy, Dmean, and Dmax of the esophagus. Univariate logistic analysis was performed for each available variable.The multivariable logistic regression model was achieved by using of least absolute shrinkage and selection operator (LASSO) with 10-folder cross-validation. Model performance was assessed by the area under the receiver operating characteristic curve. A total of 42 patients (22.83%) developed Grade ≧2 AET. In the univariate analysis, age (>65, OR=0.323, 95%CI=0.144-0.725, p=0.006) and LPTV-P (OR=1.12, 95%CI=1.007-1.246,p=0.036) were associated with Grade≧2 AET. The final LASSO model had enrolled age (>65, OR=0.65), LPTVP (OR=1.02), and tumor site (lower, OR=0.63), The multivariable NTCP model for Grade ≧2 AET was expressed by the multivariable logistic regression formula: NTCP=(1+exp(-S))-1, and S=0.0209×LPTV-P-0.4260×age-0.4586×site-1.2560.The area under the curve was 0.68 (CI=0.5925-0.7688). A multivariable NTCP model including age, LPTVP, tumor site could predict Grade≧2 AET after radio(chemo)therapy for ESCC.

Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia.

The purpose of this study is to investigate whether machine learning with dosiomic, radiomic, and demographic features allows for xerostomia risk assessment more precise than normal tissue complication probability (NTCP) models based on the mean radiation dose to parotid glands. A cohort of 153 head-and-neck cancer patients was used to model xerostomia at 0-6 months (early), 6-15 months (late), 15-24 months (long-term), and at any time (a longitudinal model) after radiotherapy. Predictive power of the features was evaluated by the area under the receiver operating characteristic curve (AUC) of univariate logistic regression models. The multivariate NTCP models were tuned and tested with single and nested cross-validation, respectively. We compared predictive performance of seven classification algorithms, six feature selection methods, and ten data cleaning/class balancing techniques using the Friedman test and the Nemenyi post hoc analysis. NTCP models based on the parotid mean dose failed to predict xerostomia (AUCs < 0.60). The most informative predictors were found for late and long-term xerostomia. Late xerostomia correlated with the contralateral dose gradient in the anterior-posterior (AUC = 0.72) and the right-left (AUC = 0.68) direction, whereas long-term xerostomia was associated with parotid volumes (AUCs > 0.85), dose gradients in the right-left (AUCs > 0.78), and the anterior-posterior (AUCs > 0.72) direction. Multivariate models of long-term xerostomia were typically based on the parotid volume, the parotid eccentricity, and the dose-volume histogram (DVH) spread with the generalization AUCs ranging from 0.74 to 0.88. On average, support vector machines and extra-trees were the top performing classifiers, whereas the algorithms based on logistic regression were the best choice for feature selection. We found no advantage in using data cleaning or class balancing methods. We demonstrated that incorporation of organ- and dose-shape descriptors is beneficial for xerostomia prediction in highly conformal radiotherapy treatments. Due to strong reliance on patient-specific, dose-independent factors, our results underscore the need for development of personalized data-driven risk profiles for NTCP models of xerostomia. The facilitated machine learning pipeline is described in detail and can serve as a valuable reference for future work in radiomic and dosiomic NTCP modeling.

Estimated Benefit of Dose Reduction to Highly Ventilated Lung Regions for Stage III Non–small Cell Lung Cancer Patients Using Normal Tissue Complication Probability Models

We hypothesize that preferential and intentional dose reduction to regions of lung that are highly ventilated at baseline in radiation treatment plan optimization for stage III non-small cell lung cancer (NSCLC) patients will preserve lung ventilation and ultimately limit radiation-induced toxicities. To test this hypothesis, this study develops a novel treatment planning formulism combining anatomical and functional imaging and estimates the normal tissue complication probability (NTCP) improvement with this formulism. Fifty-eight stage III NSCLC patients from 2 institutions were retrospectively studied. In 21 patients from institution A, in-silico planning comparisons (ISPC) were conducted between PlanDV, using dose-volume (DV) constraints of organs-at-risk (OAR), and PlanDFV, using dose-function-volume (DFV) constraints derived from fractional regional ventilation (FRV) images. A multivariable logistic regression NTCP model was then developed using a cohort of 37 patients from institution B from whom clinical data on radiation fibrosis (RF) was available. Two hallmark characteristics of RF were recorded: volume loss and airway dilation. The NTCP model specifically incorporated the volume of functional lung exposed to irradiation. Other candidate variables included in the model were demographics (age, sex, etc.) and clinical treatment parameters (TNM stage, histology, etc.). We estimated the potential benefit of PlanDFV over PlanDV in reducing RF by integrating the results of the ISPC into the NTCP models. ISPC showed that with same target coverage, PlanDFV met OAR constraints such that all conventional DV metrics were indistinguishable (P>0.05). When DFV metrics were compared, 5-10% reductions of function-weighted V20 (P = 0.04) and function-weighted mean lung dose (P<0.01) were achieved with PlanDFV. The most predictive variables for volume loss were sV20 (lung voxels with relative function values above 60% receiving 20 Gy), smoking history and age (AUC = 0.95). NTCP modeling showed that lowering sV20 by 5% using PlanDFV, a 21% toxicity reduction could be achieved for 55 year-old non-smoker patients. Similarly, the most predictive variables for airway dilation were sV20, T stage and histology (AUC = 0.93). For squamous cell carcinoma, lowering sV20 by 5% using PlanDFV, toxicity could be reduced by 22% and 11% for T2 and T4 patients, respectively. This study showed that the inclusion of lung ventilation (in addition to lung volume), when optimizing radiation therapy plans for NSCLC patients, may allow for a clinically meaningful benefit for individual patients. A prospective trial is planned to validate these findings to determine if treatment-related morbidity and mortality can be reduced that might guide potential future dose escalation trials in NSCLC.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79−0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC = 0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Swallowing sparing intensity modulated radiotherapy (SW-IMRT) in head and neck cancer: Clinical validation according to the model-based approach

PurposeThe aim of this study was to clinically validate a multivariable normal tissue complication probability (NTCP) model for grade 2–4 swallowing dysfunction at 6months after radiotherapy or chemoradiation (SWALM6) in head and neck cancer patients treated with swallowing sparing intensity modulated radiotherapy (SW-IMRT) and to test if SW-IMRT resulted in a reduction of the prevalence of SWALM6. Materials and methodsThe primary endpoint was SWALM6. For all 186 patients, a standard IMRT (parotid sparing) and a SW-IMRT plan (additional constraints for swallowing organs at risk) was created. The difference in NTCP for SWALM6 (ΔNTCPSWALM6=NTCPstandard−NTCPSW-IMRT) was calculated. Patients were treated with SW-IMRT. The external validation of the NTCP model was analyzed by comparing performance measures. ResultsThe mean ΔNTCPSWALM6 was 4.9% (range 0.01–17.3%), with a significant lower mean predicted NTCPSW-IMRT of 22.6% (95% CI 20.2–24.9%), compared to NTCPstandard of 27.5% (95% CI 24.9–29.9%) (p<0.001). There was a perfect correspondence of NTCPSW-IMRT with the observed prevalence of SWALM6 (22.6%). The overall model performance, discrimination and ‘goodness of fit’ were good. ConclusionWe externally validated the multivariable NTCP model for SWALM6 in SW-IMRT treated patients, showing reduced swallowing dysfunction by reducing the dose parameters included in this NTCP model.

Multivariable normal-tissue complication modeling of acute esophageal toxicity in advanced stage non-small cell lung cancer patients treated with intensity-modulated (chemo-)radiotherapy

Background and purposeThe majority of normal-tissue complication probability (NTCP) models for acute esophageal toxicity (AET) in advanced stage non-small cell lung cancer (AS-NSCLC) patients treated with (chemo-)radiotherapy are based on three-dimensional conformal radiotherapy (3D-CRT). Due to distinct dosimetric characteristics of intensity-modulated radiation therapy (IMRT), 3D-CRT based models need revision. We established a multivariable NTCP model for AET in 149 AS-NSCLC patients undergoing IMRT. Materials and methodsAn established model selection procedure was used to develop an NTCP model for Grade ⩾2 AET (53 patients) including clinical and esophageal dose–volume histogram parameters. ResultsThe NTCP model predicted an increased risk of Grade ⩾2 AET in case of: concurrent chemoradiotherapy (CCR) [adjusted odds ratio (OR) 14.08, 95% confidence interval (CI) 4.70–42.19; p<0.001], increasing mean esophageal dose [Dmean; OR 1.12 per Gy increase, 95% CI 1.06–1.19; p<0.001], female patients (OR 3.33, 95% CI 1.36–8.17; p=0.008), and ⩾cT3 (OR 2.7, 95% CI 1.12–6.50; p=0.026). The AUC was 0.82 and the model showed good calibration. ConclusionsA multivariable NTCP model including CCR, Dmean, clinical tumor stage and gender predicts Grade ⩾2 AET after IMRT for AS-NSCLC. Prior to clinical introduction, the model needs validation in an independent patient cohort.

Development of a multivariable normal tissue complication probability (NTCP) model for tube feeding dependence after curative radiotherapy/chemo-radiotherapy in head and neck cancer

Background and purposeCurative radiotherapy/chemo-radiotherapy for head and neck cancer (HNC) may result in severe acute and late side effects, including tube feeding dependence. The purpose of this prospective cohort study was to develop a multivariable normal tissue complication probability (NTCP) model for tube feeding dependence 6months (TUBEM6) after definitive radiotherapy, radiotherapy plus cetuximab or concurrent chemoradiation based on pre-treatment and treatment characteristics. Materials and methodsThe study included 355 patients with HNC. TUBEM6 was scored prospectively in a standard follow-up program. To design the prediction model, the penalized learning method LASSO was used, with TUBEM6 as the endpoint. ResultsThe prevalence of TUBEM6 was 10.7%. The multivariable model with the best performance consisted of the variables: advanced T-stage, moderate to severe weight loss at baseline, accelerated radiotherapy, chemoradiation, radiotherapy plus cetuximab, the mean dose to the superior and inferior pharyngeal constrictor muscle, to the contralateral parotid gland and to the cricopharyngeal muscle. ConclusionsWe developed a multivariable NTCP model for TUBEM6 to identify patients at risk for tube feeding dependence. The dosimetric variables can be used to optimize radiotherapy treatment planning aiming at prevention of tube feeding dependence and to estimate the benefit of new radiation technologies.

Development of multivariate NTCP models for radiation-induced hypothyroidism: a comparative analysis

BackgroundHypothyroidism is a frequent late side effect of radiation therapy of the cervical region. Purpose of this work is to develop multivariate normal tissue complication probability (NTCP) models for radiation-induced hypothyroidism (RHT) and to compare them with already existing NTCP models for RHT.MethodsFifty-three patients treated with sequential chemo-radiotherapy for Hodgkin’s lymphoma (HL) were retrospectively reviewed for RHT events. Clinical information along with thyroid gland dose distribution parameters were collected and their correlation to RHT was analyzed by Spearman’s rank correlation coefficient (Rs). Multivariate logistic regression method using resampling methods (bootstrapping) was applied to select model order and parameters for NTCP modeling. Model performance was evaluated through the area under the receiver operating characteristic curve (AUC). Models were tested against external published data on RHT and compared with other published NTCP models.ResultsIf we express the thyroid volume exceeding X Gy as a percentage (Vx(%)), a two-variable NTCP model including V30(%) and gender resulted to be the optimal predictive model for RHT (Rs = 0.615, p < 0.001. AUC = 0.87). Conversely, if absolute thyroid volume exceeding X Gy (Vx(cc)) was analyzed, an NTCP model based on 3 variables including V30(cc), thyroid gland volume and gender was selected as the most predictive model (Rs = 0.630, p < 0.001. AUC = 0.85). The three-variable model performs better when tested on an external cohort characterized by large inter-individuals variation in thyroid volumes (AUC = 0.914, 95% CI 0.760–0.984). A comparable performance was found between our model and that proposed in the literature based on thyroid gland mean dose and volume (p = 0.264).ConclusionsThe absolute volume of thyroid gland exceeding 30 Gy in combination with thyroid gland volume and gender provide an NTCP model for RHT with improved prediction capability not only within our patient population but also in an external cohort.