Multivariate Normal Tissue Complication Probability Research Articles

A multivariable normal tissue complication probability model for predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma patients in the modern radiotherapy era.

Radiation-induced hypothyroidism (RHT) is a common long-term complication for nasopharyngeal carcinoma (NPC) survivors. A model using clinical and dosimetric factors for predicting risk of RHT could suggest a proper dose-volume parameters for the treatment planning in an individual level. We aim to develop a multivariable normal tissue complication probability (NTCP) model for RHT in NPC patients after intensity-modulated radiotherapy or volumetric modulated arc therapy. The model was developed using retrospective clinical data and dose-volume data of the thyroid and pituitary gland based on a standard backward stepwise multivariable logistic regression analysis and was then internally validated using 10-fold cross-validation. The final NTCP model consisted of age, pretreatment thyroid-stimulating hormone and mean thyroid dose. The model performance was good with an area under the receiver operating characteristic curve of 0.749 on an internal (200 patients) and 0.812 on an external (25 patients) validation. The mean thyroid dose at ≤45Gy was suggested for treatment plan, owing to an RHT incidence of 2% versus 61% in the >45Gy group.

Evidence-Based Multivariate Normal Tissue Complication Probability (NTCP) Study of Domain-Specific Cognitive Decline after Partial Brain RT

Beyond the hippocampus, there are no evidence-based dose constraints for eloquent brain structures which subserve memory and attention/processing speed. We performed a multivariate normal tissue complication probability analysis of post-RT neurocognitive decline, examining dosimetric predictors of eloquent brain regions. Data were analyzed from a prospective longitudinal clinical trial. Patients (n = 78) with primary brain tumors receiving fractionated RT complete a comprehensive neurocognitive evaluation and high-resolution volumetric and diffusion MRI at baseline and 6 months post-RT. Image processing using robust, validated automated segmentation parcellated individual WM tracts, cortical regions, and hippocampi. Well-validated neurocognitive tests including Delis-Kaplan Executive Function System and Wechsler Adult Intelligence Scale-IV coding (attention/processing), Boston Naming Test (language) and Hopkins Verbal Learning Test and Brief Visuospatial Memory Test (verbal/visuospatial memory) were assessed. Reliable change indices adjusted for practice effects (RCI-PE) were calculated for each patient between baseline and 6 months; a negative RCI-PE was scored as decline. Univariate logistic regression was performed with mean and max dose to structures of interest as well as clinical variables. Multivariate model building was performed using automated bootstrapped logistic regression, LASSO and random forest modeling. On univariate analysis mean and max dose to multiple regions of the corpus callosum (CC) were correlated with attention/processing speed decline; most significantly in WAIS coding, including Dmax to the anterior CC (p = 0.011) and central CC (p = 0.010), and Dmax and Dmean to the mid anterior CC (p = 0.006 and 0.010). Mean dose to the left fornix was associated with decline in memory (p = 0.023, cutoff 12.9 Gy), as were increasing age and both concurrent and adjuvant chemotherapy. On multivariate analysis for attention, automated bootstrapped logistic regression showed the most frequently selected variable was mean dose to the mid anterior CC. Performance at nested cross-validation by AUC was 0.80 (0.75-0.84); LASSO model performance by AUC was 0.76 (0.72-0.81) with Dmean to the mid anterior CC being the most frequent variable. The top five most important variables in the Random Forest as ranked by mean decrease in Gini coefficient were mean dose to mid anterior CC, all white matter, combined CC and max dose to CC and posterior CC. Model performance by AUC was 0.66 (0.60-0.71). Here, we present the first, to our knowledge, NTCP model for decline in attention/processing speed, along with dosimetric predictors of memory decline beyond the hippocampus. We found that after partial brain RT, dose to several ROIs significantly correlated with post-RT impairment. These data can guide future cognitive-sparing strategies for brain RT.

LMAP-20 EVIDENCE-BASED MULTIVARIATE NORMAL TISSUE COMPLICATION PROBABILITY (NTCP) STUDY OF DOMAIN-SPECIFIC COGNITIVE DECLINE AFTER PARTIAL BRAIN RT

Abstract Beyond the hippocampus, there are no evidence-based dose constraints for eloquent structures which subserve memory and attention/processing speed. We performed a multivariate normal tissue complication probability analysis of post-RT neurocognitive decline, examining dosimetric predictors of eloquent brain regions in a prospective longitudinal clinical trial. 78 patients with primary brain tumors receiving fractionated RT completed a comprehensive neurocognitive evaluation and high-resolution volumetric and diffusion MRI at baseline and 6 months post-RT. Image processing using validated automated segmentation parcellated individual white matter (WM) tracts, cortical regions, and hippocampi. Neurocognitive test performance was scored using reliable change indices adjusted for practice effects: negative scores indicate decline. Univariate logistic regression and multivariate model building were performed. On univariate analysis mean and max dose to multiple regions of the corpus callosum (CC) correlated with attention/processing speed decline including Dmax to anterior CC (p=0.011) and central CC (p=0.010), and Dmax/Dmean to mid-anterior CC (p=0.006 and 0.010). Dmean to left fornix was associated with memory decline (p=0.023), as were increasing age and chemotherapy. On multivariate analysis for attention, automated bootstrapped logistic regression showed the most frequently selected variable was Dmean to mid-anterior CC. Performance at nested cross-validation by AUC was 0.80 (0.75-0.84); LASSO model performance by AUC was 0.76 (0.72-0.81) with Dmean to the mid-anterior CC being the most frequent variable. The top five most important variables in Random Forest were Dmean to mid-anterior CC, all WM, combined CC and Dmax to CC and posterior CC. Model performance by AUC was 0.66 (0.60-0.71). We present the first, to our knowledge, multivariate NTCP model for decline in attention/processing speed, and dosimetric predictors of memory decline beyond the hippocampus. Dose to several eloquent ROIs significantly correlated with post-RT impairment. These data can guide future cognitive-sparing strategies for brain RT.

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.

Comparison of Estimated Late Toxicities between IMPT and IMRT Based on Multivariable NTCP Models for High-Risk Prostate Cancers Treated with Pelvic Nodal Radiation.

PurposeTo compare the late gastrointestinal (GI) and genitourinary toxicities (GU) estimated using multivariable normal tissue complication probability (NTCP) models, between pencil-beam scanning proton beam therapy (PBT) and helical tomotherapy (HT) in patients of high-risk prostate cancers requiring pelvic nodal irradiation (PNI) using moderately hypofractionated regimen.Materials and MethodsTwelve consecutive patients treated with PBT at our center were replanned with HT using the same planning goals. Six late GI and GU toxicity domains (stool frequency, rectal bleeding, fecal incontinence, dysuria, urinary incontinence, and hematuria) were estimated based on the published multivariable NTCP models. The ΔNTCP (difference in absolute NTCP between HT and PBT plans) for each of the toxicity domains was calculated. A one-sample Kolmogorov-Smirnov test was used to analyze distribution of data, and either a paired t test or a Wilcoxon matched-pair signed rank test was used to test statistical significance.ResultsProton beam therapy and HT plans achieved adequate target coverage. Proton beam therapy plans led to significantly better sparing of bladder, rectum, and bowel bag especially in the intermediate range of 15 to 40 Gy, whereas doses to penile bulb and femoral heads were higher with PBT plans. The average ΔNTCP for grade (G)2 rectal bleeding, fecal incontinence, stool frequency, dysuria, urinary incontinence, and G1 hematuria was 12.17%, 1.67%, 2%, 5.83%, 2.42%, and 3.91%, respectively, favoring PBT plans. The average cumulative ΔNTCP for GI and GU toxicities (ΣΔNTCP) was 16.58% and 11.41%, respectively, favoring PBT. Using a model-based selection threshold of any G2 ΔNTCP >10%, 67% (8 patients) would be eligible for PBT.ConclusionProton beam therapy plans led to superior sparing of organs at risk compared with HT, which translated to lower NTCP for late moderate GI and GU toxicities in patients of prostate cancer treated with PNI. For two-thirds of our patients, the difference in estimated absolute NTCP values between PBT and HT crossed the accepted threshold for minimal clinically important difference.

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.

Development and validation of a normal tissue complication probability model for acquired nasal cavity stenosis and atresia after radical radiotherapy for nasopharyngeal carcinoma

PurposeCurative radiotherapy for nasopharyngeal carcinoma (NPC) can lead to acquired nasal cavity stenosis and atresia (ANCSA). As the first study to investigate risk factors of ANCSA in a large cohort of NPC patients, this article aims to develop and validate a multivariate normal tissue complication probability (NTCP) model to predict the development of ANCSA and to establish a nomogram for clinical use. Methods and materialsThe retrospective cohort was comprised of 548 NPC patients treated with radical radiotherapy. The cohort was randomly divided into training and validation groups. Least absolute shrinkage and selection operator regression was performed for variable selection from the clinical and dosimetric characteristics in the training group. A multivariate NTCP model and a nomogram were established for the prediction of ANCSA development. Discrimination and calibration were tested using receiver operating characteristic (ROC) curves and calibration tests, respectively, for both groups. ResultsANCSA was observed in 132 (24.1%) of 548 patients with NPC who underwent radical radiotherapy. The median time to ANCSA detection after treatment was 2.8 months (range, 0.0–57.7 months). Five potential predictors, including choanal invasion, low white blood cell count, high C-reactive protein level, high serum amyloid A level, and high V70Gy of the nasal cavity, were selected to develop the NTCP model based on 365 patients in the training group. The model had a fairly good discriminative power according to the ROC analysis in both the training (area under ROC curve = 0.79, 95%CI: 0.73–0.84) and validation (0.73, 0.64–0.82) groups. The calibration power was tested using the calibration test in the training (E-max = 0.069, E-avg = 0.015, p = 0.977) and validation (E-max = 0.057, E-avg = 0.032, p = 0.747) groups. ConclusionsWe developed and successfully validated an NTCP model for early prediction of ANCSA in patients with NPC after radical radiotherapy. This could help clinicians assess the risk of ANCSA before the initiation of follow-ups and ensure appropriate and timely management of this complication.

A Risk Prediction Model by LASSO for Radiation-Induced Xerostomia in Patients With Nasopharyngeal Carcinoma Treated With Comprehensive Salivary Gland-Sparing Helical Tomotherapy Technique.

ObjectiveThis study aimed to develop a least absolute shrinkage and selection operator (LASSO)-based multivariable normal tissue complication probability (NTCP) model to predict radiation-induced xerostomia in patients with nasopharyngeal carcinoma (NPC) treated with comprehensive salivary gland–sparing helical tomotherapy technique.Methods and MaterialsLASSO with the extended bootstrapping technique was used to build multivariable NTCP models to predict factors of patient-reported xerostomia relieved by 50% and 80% compared with the level at the end of radiation therapy within 1 year and 2 years, R50-1year and R80-2years, in 203 patients with NPC. The model assessment was based on 10-fold cross-validation and the area under the receiver operating characteristic curve (AUC).ResultsThe prediction model by LASSO with 10-fold cross-validation showed that radiation-induced xerostomia recovery could be predicted by prognostic factors of R50-1year (age, gender, T stage, UICC/AJCC stage, parotid Dmean, oral cavity Dmean, and treatment options) and R80-2years (age, gender, T stage, UICC/AJCC stage, oral cavity Dmean, N stage, and treatment options). These prediction models also demonstrated a good performance by the AUC.ConclusionThe prediction models of R50-1year and R80-2years by LASSO with 10-fold cross-validation were recommended to validate the NTCP model before comprehensive salivary gland–sparing radiation therapy in patients with NPC.

Multivariate normal tissue complication probability models for rectal and bladder morbidity in prostate cancer patients treated with proton therapy

Background and purposeNormal tissue complication probability (NTCP) models applied for model-based patient selection to proton therapy (PT) have usually been derived using dose/volume histogram (DVH) parameters from photon-based radiotherapy. This study aimed to derive PT-specific multivariate NTCP models that also accounted for the spatial dose distribution (rectum only) as well as non-dose/volume related factors. Materials and methodsThe study included rectum and bladder DVHs, 2D rectal dose maps and relevant patient/treatment characteristics from 1151 prostate cancer cases treated with PT. Prospectively scored Grade 2 late rectal bleeding (CTCAE v3.0, also procedural interventions separately) (n = 156 (15%)) and Grade 3+ GU morbidity (n = 51 (4%)) were entered into a multivariate logistic regression analysis. Model evaluation included assessment of the area under the receiver operating characteristic curve (AUC). ResultsAnticoagulant use was a dominant predictor, chosen in four of the six rectum models and in the bladder model. Age was a dominant predictor in all procedural only rectum models while prostate volume, bladder D5% and V75Gy were predictors in the bladder model. The selection frequency of the dose/volume predictors varied widely, where the percentage of the anterior rectum surface receiving >=75 Gy was the most robust. AUC values ranged from 0.58 to 0.70 across all models, with no clear difference between the DVH- and spatial-based models for the rectum. ConclusionAnticoagulant use and age were the most prominent predictors in the NTCP models. V75Gy of the rectal wall and the bladder was a predictor in the DVH-based models of the rectum and bladder respectively.

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.

External validation of an NTCP model for acute esophageal toxicity in locally advanced NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

Background and purposeWe externally validated a previously established multivariable normal-tissue complication probability (NTCP) model for Grade ≥2 acute esophageal toxicity (AET) after intensity-modulated (chemo-)radiotherapy or volumetric-modulated arc therapy for locally advanced non-small cell lung cancer. Materials and methodsA total of 603 patients from five cohorts (A–E) within four different Dutch institutes were included. Using the NTCP model, containing predictors concurrent chemoradiotherapy, mean esophageal dose, gender and clinical tumor stage, the risk of Grade ≥2 AET was estimated per patient and model discrimination and (re)calibration performance were evaluated. ResultsFour validation cohorts (A, B, D, E) experienced higher incidence of Grade ≥2 AET compared to the training cohort (49.3–70.2% vs 35.6%; borderline significant for one cohort, highly significant for three cohorts). Cohort C experienced lower Grade ≥2 AET incidence (21.7%, p < 0.001). For three cohorts (A–C), discriminative performance was similar to the training cohort (area under the curve (AUC) 0.81–0.89 vs 0.84). In the two remaining cohorts (D–E) the model showed poor discriminative power (AUC 0.64 and 0.63). Reasonable calibration performance was observed in two cohorts (A–B), and recalibration further improved performance in all three cohorts with good discrimination (A–C). Recalibration for the two poorly discriminating cohorts (D–E) did not improve performance. ConclusionsThe NTCP model for AET prediction was successfully validated in three out of five patient cohorts (AUC ≥0.80). The model did not perform well in two cohorts, which included patients receiving substantially different treatment. Before applying the model in clinical practice, validation of discrimination and (re)calibration performance in a local cohort is recommended.

Multivariate normal tissue complication probability modeling of vaginal late toxicity after brachytherapy for cervical cancer

PurposeTo explore the best variables combination for a predictive model of vaginal toxicity in cervical cancer patients undergoing brachytherapy (BT). Methods and MaterialsClinical and 3-dimensional dosimetric parameters were retrospectively extracted from an institutional database of consecutive patients undergoing intracavitary BT after external beam radiation therapy from 2006 to 2013 for a cervical cancer. A least absolute shrinkage and selection operator selection procedure in Cox's proportional hazards regression model was performed to select a set of relevant predictors for a multivariate normal tissue complication probability model of Grade ≥2 vaginal late toxicity. Outcomes reliability was internally assessed by bootstrap resampling method. ResultsOne hundred sixty-nine women were included in the present study with a median followup time of 3.8 years (interquartile range [IQR], 1.9–5.6 years). The 2 years and 5 years cumulative incidence rates of Grade ≥2 late vaginal toxicity were 19.9% and 27.5%, respectively. Among 31 metrics and six clinical factors extracted, the optimal model included two dosimetric variables: V70Gy and D5% (the percentage volume that received a dose greater or equal to 70 Gy and the minimum dose given to the hottest 5% volume, respectively). Area under the ROC curve at 2 and 5 years of followup were 0.85 and 0.91, respectively. Regarding internal validation, median area under the ROC curve of bootstrap predictions was 0.83 (IQR, 0.78–0.88) and 0.89 (IQR, 0.85–0.93) at 2 and 5 years of followup, respectively. ConclusionsA multivariate normal tissue complication probability model for severe vaginal toxicity based on two dosimetric variables (V70Gy and D5%) provides reliable discrimination capability in a cohort of cervical cancer treated with external beam radiation therapy and BT.

A prospective cohort study of hepatic toxicity after stereotactic body radiation therapy for hepatocellular carcinoma

PurposeTo build and validate multivariate normal tissue complication probability (NTCP) models for radiation-induced hepatic toxicity (RIHT) after stereotactic body radiation therapy (SBRT). MethodsEighty-five patients with hepatocellular carcinoma (HCC) in a phase II clinical trial were enroled. A progression of at least 1 or 2 points in the Child–Pugh (CP) score post-SBRT was classified as RIHT (≥1 or ≥2). NTCP models for RIHT (≥1 or ≥2) were developed using logistic regression. Nomograms for each model were formulated. The cut-off point of each independent dosimetric risk factor was obtained using receiver-operating characteristic (ROC) analysis. We used an independent cohort (101 patients) for model validation. ResultsTwenty (23.5%) and 12 (14.2%) patients experienced RIHT (≥1) and RIHT (≥2), respectively. V15, VS10, and pretreatment CP (pre-CP) were the optimal predictors for RIHT (≥1 and ≥2) modelling. V15 ≤33.1% and VS10 ≥416.2 mL for RIHT (≥1), and V15 ≤21.5% and VS10 ≥621.8 mL for RIHT (≥2), were the cut-off points. Four NTCP models and their nomograms were generated. These models and nomograms showed good prediction performance (area under the curve (AUC), 0.83–0.89). Our NTCP model (RIHT ≥2) based on V15 plus pre-CP performed well (AUC = 0.78) in a validation cohort. ConclusionV15, VS10, and pre-CP are crucial predictors for RIHT (≥1 and ≥2). Our NTCP models and nomograms were conducive to obtain individual constraints for patients with HCC. Registration NumberChiCTR-IIC-16008233.

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.

Validation and Modification of a Prediction Model for Acute Cardiac Events in Patients With Breast Cancer Treated With Radiotherapy Based on Three-Dimensional Dose Distributions to Cardiac Substructures.

PurposeA relationship between mean heart dose (MHD) and acute coronary event (ACE) rate was reported in a study of patients with breast cancer (BC). The main objective of our cohort study was to validate this relationship and investigate if other dose-distribution parameters are better predictors for ACEs than MHD.Patients and MethodsThe cohort consisted of 910 consecutive female patients with BC treated with radiotherapy (RT) after breast-conserving surgery. The primary end point was cumulative incidence of ACEs within 9 years of follow-up. Both MHD and various dose-distribution parameters of the cardiac substructures were collected from three-dimensional computed tomography planning data.ResultsThe median MHD was 2.37 Gy (range, 0.51 to 15.25 Gy). The median follow-up time was 7.6 years (range, 0.1 to 10.1 years), during which 30 patients experienced an ACE. The cumulative incidence of ACE increased by 16.5% per Gy (95% CI, 0.6 to 35.0; P = .042). Analysis showed that the volume of the left ventricle receiving 5 Gy (LV-V5) was the most important prognostic dose-volume parameter. The most optimal multivariable normal tissue complication probability model for ACEs consisted of LV-V5, age, and weighted ACE risk score per patient (c-statistic, 0.83; 95% CI, 0.75 to 0.91).ConclusionA significant dose-effect relationship was found for ACEs within 9 years after RT. Using MHD, the relative increase per Gy was similar to that reported in the previous study. In addition, LV-V5 seemed to be a better predictor for ACEs than MHD. This study confirms the importance of reducing exposure of the heart to radiation to avoid excess risk of ACEs after radiotherapy for BC.

Multivariable normal tissue complication probability model-based treatment plan optimization for grade 2–4 dysphagia and tube feeding dependence in head and neck radiotherapy

Background and purposeRadiotherapy of the head and neck is challenged by the relatively large number of organs-at-risk close to the tumor. Biologically-oriented objective functions (OF) could optimally distribute the dose among the organs-at-risk. We aimed to explore OFs based on multivariable normal tissue complication probability (NTCP) models for grade 2–4 dysphagia (DYS) and tube feeding dependence (TFD). Materials and methodsOne hundred head and neck cancer patients were studied. Additional to the clinical plan, two more plans (an OFDYS and OFTFD-plan) were optimized per patient. The NTCP models included up to four dose–volume parameters and other non-dosimetric factors. A fully automatic plan optimization framework was used to optimize the OFNTCP-based plans. ResultsAll OFNTCP-based plans were reviewed and classified as clinically acceptable. On average, the Δdose and ΔNTCP were small comparing the OFDYS-plan, OFTFD-plan, and clinical plan. For 5% of patients NTCPTFD reduced >5% using OFTFD-based planning compared to the OFDYS-plans. ConclusionsPlan optimization using NTCPDYS- and NTCPTFD-based objective functions resulted in clinically acceptable plans. For patients with considerable risk factors of TFD, the OFTFD steered the optimizer to dose distributions which directly led to slightly lower predicted NTCPTFD values as compared to the other studied plans.

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.