Normal Tissue Complication Probability Models Research Articles

Radiomics Analysis of 3D Dose Distributions to Predict Toxicity of Radiotherapy for Cervical Cancer.

Standard treatment for locally advanced cervical cancer (LACC) is chemoradiotherapy followed by brachytherapy. Despite radiation therapy advances, the toxicity rate remains significant. In this study, we compared the prediction of toxicity events after radiotherapy for locally advanced cervical cancer (LACC), based on either dose-volume histogram (DVH) parameters or the use of a radiomics approach applied to dose maps at the voxel level. Toxicity scores using the Common Terminology Criteria for Adverse Events (CTCAE v4), spatial dose distributions, and usual clinical predictors for the toxicity of 102 patients treated with chemoradiotherapy followed by brachytherapy for LACC were used in this study. In addition to usual DVH parameters, 91 radiomic features were extracted from rectum, bladder and vaginal 3D dose distributions, after discretization into a fixed bin width of 1 Gy. They were evaluated for predictive modelling of rectal, genitourinary (GU) and vaginal toxicities (grade ≥ 2). Logistic Normal Tissue Complication Probability (NTCP) models were derived using clinical parameters only or combinations of clinical, DVH and radiomics. For rectal acute/late toxicities, the area under the curve (AUC) using clinical parameters was 0.53/0.65, which increased to 0.66/0.63, and 0.76/0.87, with the addition of DVH or radiomics parameters, respectively. For GU acute/late toxicities, the AUC increased from 0.55/0.56 (clinical only) to 0.84/0.90 (+DVH) and 0.83/0.96 (clinical + DVH + radiomics). For vaginal acute/late toxicities, the AUC increased from 0.51/0.57 (clinical only) to 0.58/0.72 (+DVH) and 0.82/0.89 (clinical + DVH + radiomics). The predictive performance of NTCP models based on radiomics features was higher than the commonly used clinical and DVH parameters. Dosimetric radiomics analysis is a promising tool for NTCP modelling in radiotherapy.

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.

Independent external validation using the EORTC HNCG-ROG 1219 DAHANCA trial data of NTCP models for acute oral mucositis

PurposeTo externally validate previously published Normal Tissue Complication Probability (NTCP) models developed by separate teams for grade 3 oral mucositis (g3OM). Materials and methodsTwo models were validated: a logistic model, based on 144 head and neck cancer (HNC) patients receiving induction chemotherapy followed by chemo-IMRT; a multivariable logistic model for prediction of g3OM for 253 patients receiving radical treatment for the head and neck squamous cell carcinoma (HNSCC). The EORTC HNCG-ROG 1219 DAHANCA trial dataset, consisting of 169 patients was used as the validation cohort. This cohort was treated with accelerated fractionated chemo-IMRT, with/without the hypoxic radiosensitizer Nimorazole for HNSCC.External validity was assessed using the scaled Brier score. Calibration was assessed in terms of calibration curves as well as measures of mean and weak calibration. Hosmer-Lemeshow was used for goodness-of-fit test. Discrimination was calculated using the area under the receiver operating curve (AUC-ROC). ResultsThe prevalence of g3OM in the validation cohort (35.5%) was similar to that of two development cohorts, i.e. 38.7% and 31.9% for Bhide logistic and Otter multivariable logistic models respectively. The scaled Brier scores showed good overall model performance. Perfect calibration was observed in the prevalence range of 20% to 40%. AUC-ROC was acceptable in external validation (0.67). The Hosmer-Lemeshow test showed good agreement between predicted and observed outcomes for two models. ConclusionThe NTCP models were validated and lead to valid predictions in a wide range of diverse treatment techniques and patient characteristics, also when Nimorazole is added as hypoxic radiosensitizer.

Identification of patient benefit from proton beam therapy in brain tumour patients based on dosimetric and NTCP analyses

BackgroundThe limited availability of proton beam therapy (PBT) requires individual treatment selection strategies, such as the model-based approach. In this study, we assessed the dosimetric benefit of PBT compared to photon therapy (XRT), analysed the corresponding changes in normal tissue complication probability (NTCP) on a variety of available models, and illustrated model-based patient selection in an in-silico study for patients with brain tumours. MethodsFor 92 patients treated at two PBT centres, volumetric modulated arc therapy treatment plans were retrospectively created for comparison with the clinically applied PBT plans. Several dosimetric parameters for the brain excluding tumour and margins, cerebellum, brain stem, frontal and temporal lobes, hippocampi, cochleae, chiasm, optic nerves, lacrimal glands, lenses, pituitary gland, and skin were compared between both modalities using Wilcoxon signed-rank tests. NTCP differences (ΔNTCP) were calculated for 11 models predicting brain necrosis, delayed recall, temporal lobe injury, hearing loss, tinnitus, blindness, ocular toxicity, cataract, endocrine dysfunction, alopecia, and erythema. A patient was assumed to be selected for PBT if ΔNTCP exceeded a threshold of 10 percentage points for at least one of the side-effects. ResultsPBT substantially reduced the dose in almost all investigated OARs, especially in the low and intermediate dose ranges and for contralateral organs. In general, NTCP predictions were significantly lower for PBT compared to XRT, in particular in ipsilateral organs. Considering ΔNTCP of all models, 80 patients (87.0%) would have been selected for PBT in this in-silico study, mainly due to predictions of a model on delayed recall (51 patients). ConclusionIn this study, substantial dose reductions for PBT were observed, mainly in contralateral organs. However, due to the sigmoidal dose response, NTCP was particularly reduced in ipsilateral organs. This underlines that physical dose-volume parameters alone may not be sufficient to describe the clinical relevance between different treatment techniques and highlights potential benefits of NTCP models. Further NTCP models for different modern treatment techniques are mandatory and existing models have to be externally validated in order to implement the model-based approach in clinical practice for cranial radiotherapy.

Dose escalation guided by 18F-FDG PET/CT for esophageal cancer

ObjectiveTo assess the feasibility of dose escalation guided by 18F-deoxyglucose positron emission tomography/computer tomography (18F-FDG PET/CT) for esophageal cancer (EC). Methods and materialsTen random patients treated with definitive chemoradiotherapy and pre-therapeutic 18F-FDG PET/CT were included in this study. Retrospectively, a threshold of 50% of SUVmax was used to define the high FDG uptake region of the GTV (GTVPET). Three intensity-modulated radiation therapy (IMRT) plans were generated, delivering three dose levels to three different planning target volumes (PTVs). 50.4 ​Gy delivered to PTV50.4 was defined on computed tomography (CT) as Plan50.4, 63 ​Gy was delivered to PTV63 was defined as GTV plus a uniform margin of 0.5 ​cm as Plan63, and 70 ​Gy delivered to PTV70 was defined as GTVPET plus a 0.5 ​cm margin as Plan70. A dosimetric comparison was performed based on normal tissue complication probability (NTCP) for the lung and heart. ResultsClinically acceptable dose escalation failed for 2 of 10 patients in Plan63 due to heart dose constraints. One patient failed heart dose constraint for Plan70. Two patients failed spinal cord constraint for Plan63. Three patients failed lung dose constraints for both Plan63 and Plan70, two of which were even not suitable for Plan50.4 for the same reason.NTCP modeling for lung showed increased risk for Plan63 and Plan70 compared to Plan50.4. The difference between Plan63 and Plan70 was insignificant. NTCP modeling for heart showed an increased risk from 6.38% of Plan50.4 to 8.88% of Plan70 (P ​= ​0.009) or 9.79% of Plan63 (P ​= ​0.007). The risk of heart mortality was significantly higher for Plan63 than Plan70 (P ​= ​0.047). ConclusionsSelective boosting of sub-volumes based on 18F-FDG PET/CT is feasible way with a modest increase in the risk of cardiac and lung toxicities.

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.

Development of a method for generating SNP interaction-aware polygenic risk scores for radiotherapy toxicity

AimTo identify the effect of single nucleotide polymorphism (SNP) interactions on the risk of toxicity following radiotherapy (RT) for prostate cancer (PCa) and propose a new method for polygenic risk score incorporating SNP-SNP interactions (PRSi). Materials and methodsAnalysis included the REQUITE PCa cohort that received external beam RT and was followed for 2 years. Late toxicity endpoints were: rectal bleeding, urinary frequency, haematuria, nocturia, decreased urinary stream. Among 43 literature-identified SNPs, the 30% most strongly associated with each toxicity were tested. SNP-SNP combinations (named SNP-allele sets) seen in ≥10% of the cohort were condensed into risk (RS) and protection (PS) scores, respectively indicating increased or decreased toxicity risk. Performance of RS and PS was evaluated by logistic regression. RS and PS were then combined into a single PRSi evaluated by area under the receiver operating characteristic curve (AUC). ResultsAmong 1,387 analysed patients, toxicity rates were 11.7% (rectal bleeding), 4.0% (urinary frequency), 5.5% (haematuria), 7.8% (nocturia) and 17.1% (decreased urinary stream). RS and PS combined 8 to 15 different SNP-allele sets, depending on the toxicity endpoint. Distributions of PRSi differed significantly in patients with/without toxicity with AUCs ranging from 0.61 to 0.78. PRSi was better than the classical summed PRS, particularly for the urinary frequency, haematuria and decreased urinary stream endpoints. ConclusionsOur method incorporates SNP-SNP interactions when calculating PRS for radiotherapy toxicity. Our approach is better than classical summation in discriminating patients with toxicity and should enable incorporating genetic information to improve normal tissue complication probability models.

Investigating DWI changes in white matter of meningioma patients treated with proton therapy.

To evaluate changes in diffusion and perfusion-related properties of white matter (WM) induced by proton therapy, which is capable of a greater dose sparing to organs at risk with respect to conventional X-ray radiotherapy, and to eventually expose early manifestations of delayed neuro-toxicities. Apparent diffusion coefficient (ADC) and IVIM parameters (D, D* and f) were estimated from diffusion-weighted MRI (DWI) in 46 patients affected by meningioma and treated with proton therapy. The impact on changes in diffusion and perfusion-related WM properties of dose and time, as well as the influence of demographic and pre-treatment clinical information, were investigated through linear mixed-effects models. Decreasing trends in ADC and D were found for WM regions hit by medium-high (30-40Gy(RBE)) and high (>40 Gy(RBE)) doses, which are compatible with diffusion restriction due to radiation-induced cellular injury. Significant influence of dose and time on median ADC changes were observed. Also, D* showed a significant dependency on dose, whereas f consistently showed no dependency on dose and time. Age, gender and surgery extent were also found to affect changes in ADC. These results overall agree with those from studies conducted on cohorts of mixed proton and X-ray radiotherapy patients. Future work should focus on relating our findings with clinical information of co-morbidities and thus exploiting such or more advanced imaging data to build normal tissue complication probability models to better integrate clinical and dose information.

Neurocognitive Effects and Necrosis in Childhood Cancer Survivors Treated With Radiation Therapy: A PENTEC Comprehensive Review

A PENTEC review of childhood cancer survivors who received brain radiation therapy (RT) was performed to develop models that aid in developing dose constraints for RT-associated central nervous system (CNS) morbidities. A comprehensive literature search, through the PENTEC initiative, was performed to identify published data pertaining to 6 specific CNS toxicities in children treated with brain RT. Treatment and outcome data on survivors were extracted and used to generate normal tissue complication probability (NTCP) models. The search identified investigations pertaining to 2 of the 6 predefined CNS outcomes: neurocognition and brain necrosis. For neurocognition, models for 2 post-RT outcomes were developed to (1) calculate the risk for a below-average intelligence quotient (IQ) (IQ <85) and (2) estimate the expected IQ value. The models suggest that there is a 5% risk of a subsequent IQ <85 when 10%, 20%, 50%, or 100% of the brain is irradiated to 35.7, 29.1, 22.2, or 18.1 Gy, respectively (all at 2 Gy/fraction and without methotrexate). Methotrexate (MTX) increased the risk for an IQ <85 similar to a generalized uniform brain dose of 5.9 Gy. The model for predicting expected IQ also includes the effect of dose, age, and MTX. Each of these factors has an independent, but probably cumulative effect on IQ. The necrosis model estimates a 5% risk of necrosis for children after 59.8 Gy or 63.6 Gy (2 Gy/fraction) to any part of the brain if delivered as primary RT or reirradiation, respectively. This PENTEC comprehensive review establishes objective relationships between patient age, RT dose, RT volume, and MTX to subsequent risks of neurocognitive injury and necrosis. A lack of consistent RT data and outcome reporting in the published literature hindered investigation of the other predefined CNS morbidity endpoints.

NTCP modeling and dose\u2013volume correlations for acute xerostomia and dry eye after whole brain radiation

BackgroundWhole brain radiation (WBRT) may lead to acute xerostomia and dry eye from incidental parotid and lacrimal exposure, respectively. We performed a prospective observational study to assess the incidence/severity of this toxicity. We herein perform a secondary analysis relating parotid and lacrimal dosimetric parameters to normal tissue complication probability (NTCP) rates and associated models.MethodsPatients received WBRT to 25–40 Gy in 10–20 fractions using 3D-conformal radiation therapy without prospective delineation of the parotids or lacrimals. Patients completed questionnaires at baseline and 1 month post-WBRT. Xerostomia was assessed using the University of Michigan xerostomia score (scored 0–100, toxicity defined as ≥ 20 pt increase) and xerostomia bother score (scored from 0 to 3, toxicity defined as ≥ 2 pt increase). Dry eye was assessed using the Subjective Evaluation of Symptom of Dryness (SESoD, scored from 0 to 4, toxicity defined as ≥ 2 pt increase). The clinical data were fitted by the Lyman–Kutcher–Burman (LKB) and Relative Seriality (RS) NTCP models.ResultsOf 55 evaluable patients, 19 (35%) had ≥ 20 point increase in xerostomia score, 11 (20%) had ≥ 2 point increase in xerostomia bother score, and 13 (24%) had ≥ 2 point increase in SESoD score. For xerostomia, parotid V10Gy–V20Gy correlated best with toxicity, with AUC 0.68 for xerostomia score and 0.69–0.71 for bother score. The values for the D50, m and n parameters of the LKB model were 22.3 Gy, 0.84 and 1.0 for xerostomia score and 28.4 Gy, 0.55 and 1.0 for bother score, respectively. The corresponding values for the D50, γ and s parameters of the RS model were 23.5 Gy, 0.28 and 0.0001 for xerostomia score and 32.0 Gy, 0.45 and 0.0001 for bother score, respectively. For dry eye, lacrimal V10Gy–V15Gy were found to correlate best with toxicity, with AUC values from 0.67 to 0.68. The parameter values of the LKB model were 53.5 Gy, 0.74 and 1.0, whereas of the RS model were 54.0 Gy, 0.37 and 0.0001, respectively.ConclusionsXerostomia was most associated with parotid V10Gy–V20Gy, and dry eye with lacrimal V10Gy–V15Gy. NTCP models were successfully created for both toxicities and may help clinicians refine dosimetric goals and assess levels of risk in patients receiving palliative WBRT.

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.

Exploratory Investigation of Dose-Linear Energy Transfer (LET) Volume Histogram (DLVH) for Adverse Events Study in Intensity Modulated Proton Therapy (IMPT).

We proposed a novel tool-a dose linear energy transfer (LET)-volume histogram (DLVH)-and performed an exploratory study to investigate rectal bleeding in prostate cancer treated with intensity modulated proton therapy. The DLVH was constructed with dose and LET as 2 axes, and the normalized volume of the structure was contoured in the dose-LET plane as isovolume lines. We defined the DLVH index, DLv%(d,l) (ie, v% of the structure) to have a dose of ≥d Gy and an LET of ≥l keV/μm, similar to the dose-volume histogram index Dv%. Nine patients with prostate cancer with rectal bleeding (Common Terminology Criteria for Adverse Events grade ≥2) were included as the adverse event group, and 48 patients with no complications were considered the control group. A P value map was constructed by comparison of the DLVH indices of all patients between the 2 groups using the Mann-Whitney U test. Dose-LET volume constraints (DLVCs) were derived based on the P value map with a manual selection procedure facilitated by Spearman's correlation tests. The obtained DLVCs were further cross-validated using a multivariate support vector machine (SVM)-based normal tissue complication probability (NTCP) model with an independent testing data set composed of 8 adverse event and 13 control patients. We extracted 2 DLVC constraints. One DLVC was obtained, Vdose/LETboundary:2.5keVμmat 75 Gy to 3.2keVμmat8.65Gy <1.27% (DLVC1), revealing a high LET volume effect. The second DLVC, V(72.2Gy,0keVμm) < 2.23% (DVLC2), revealed a high dose volume effect. The SVM-based NTCP model with 2 DLVCs provided slightly superior performance than using dose only, with an area under the curve of 0.798 versus 0.779 for the testing data set. Our results demonstrated the importance of rectal "hot spots" in both high LET (DLVC1) and high dose (DLVC2) in inducing rectal bleeding. The SVM-based NTCP model confirmed the derived DLVCs as good predictors for rectal bleeding when intensity modulated proton therapy is used to treat prostate cancer.

Vision loss following high-dose proton-based radiotherapy for skull-base chordoma and chondrosarcoma

Background & purposeDose escalation for skull-based chordoma and chondrosarcoma can put critical adjacent structures at risk, specifically the anterior optic pathway. We report the incidence of vision loss following high-dose conformal proton-based radiotherapy. Materials and methodsWe reviewed patients with skull-base chordoma or chondrosarcoma treated with proton-based therapy between 2007 and 2018. We analyzed 148 patients and 283 individual eyes with functional vision at baseline who received a minimum 30GyRBE to 0.1 cm3 of the anterior optic pathway. Eyes were classified as “functionally blind” if visual acuity was 20/200 or worse. Kaplan-Meier and normal tissue complication probability modeling were used to establish the relationship between radiation dose and risk of functional vision loss. ResultsAt last follow-up, 110 of 148 patients were alive with no evidence of disease progression. With a median follow-up of 4.1 years (range, 0.5–12.8), 5 eyes in 3 patients developed functional blindness, with 2 patients developing bilateral blindness. Median time to blindness was 15.2 months. The 5-year incidence of vision loss was 2.1% (95% CI: 0.9–4.9%). On univariate analysis, development of blindness was associated with presence of multiple medical comorbidities (p = 0.0040). While there were no events with a maximum dose < 60GyRBE delivered to the anterior optic pathway, the crude rate was 3.6% over 60GyRBE, with all events occurring between 60-65GyRBE. ConclusionsDespite the high radiotherapy dose delivered to patients with skull-base chordoma and chondrosarcoma, the rate of vision loss is low and no events occurred in those who received a maximum dose under 60GyRBE.

Normal tissue complication probability (NTCP) models for predicting temporal lobe injury after intensity-modulated radiotherapy in nasopharyngeal carcinoma: A large registry-based retrospective study from China

PurposeTo develop predictive models with dosimetric and clinical variables for temporal lobe injury (TLI) in nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT). Materials and methodsData of 8194 NPC patients who received IMRT-based treatment were retrospectively reviewed. TLI was diagnosed by magnetic resonance imaging. Dosimetric factors were selected by penalized regression and machine learning, with area under the receiver operating curve (AUC) calculated. Cox proportional hazards models containing the most predictive dosimetric factor with/without clinical variables were performed. A nomogram was generated as a visualization of Cox regression for predicting TLI-free survival. ResultsDuring median follow-up of 66.8 months (interquartile range [IQR] 54.2–82.2 months), 12.1% of patients (989/8194) developed TLI. Median latency from IMRT to TLI was 36 months (IQR 28–47 months). D0.5cc (dose delivered to 0.5-cm3 temporal-lobe volume) was the most predictive dosimetric factor (AUC: 0.799). Tolerance dose for 5% and 50% probabilities to develop TLI in 5 years were 65.06 Gy (95% confidence interval [CI]: 64.19–65.92) and 89.75 Gy (95% CI: 87.39–92.11), respectively. A nomogram comprising age, T stage, and D0.5cc significantly outperformed the model with only D0.5cc in predicting TLI (C-index: 0.78 vs. 0.737 in train set; 0.775 vs. 0.73 in test set; both P < 0.001). The nomogram-defined high-risk group had worse 5-year TLI-free survival. ConclusionsD0.5cc of 65.06 Gy was the tolerance dose of the temporal lobe. Reducing D0.5cc decreased risk of TLI, especially in older patients with advanced T stage. The nomogram could predict TLI precisely and allow individualized follow-up management.

Modelling of late side-effects following cranial proton beam therapy

BackgroundThe limited availability of proton beam therapy (PBT) requires individual treatment selection strategies that can be based on normal tissue complication probability (NTCP) models. We developed and externally validated NTCP models for common late side-effects following PBT in brain tumour patients to optimise patients’ quality of life. MethodsCohorts from three PBT centres (216 patients) were investigated for several physician-rated endpoints at 12 and 24 months after PBT: alopecia, dry eye syndrome, fatigue, headache, hearing and memory impairment, and optic neuropathy. Dose-volume parameters of associated normal tissues and clinical factors were used for logistic regression modelling in a development cohort. Statistically significant parameters showing high area under the receiver operating characteristic curve (AUC) values in internal cross-validation were externally validated. In addition, analyses of the pooled cohorts and of time-dependent generalised estimating equations including all patient data were performed. ResultsIn the validation study, mild alopecia was related to high dose parameters to the skin [e.g. the dose to 2% of the volume (D2%)] at 12 and 24 months after PBT. Mild hearing impairment at 24 months after PBT was associated with the mean dose to the ipsilateral cochlea. Additionally, the pooled analyses revealed dose–response relations between memory impairment and intermediate to high doses to the remaining brain as well as D2% of the hippocampi. Mild fatigue at 24 months after PBT was associated with D2% to the brainstem as well as with concurrent chemotherapy. Moreover, in generalised estimating equations analysis, dry eye syndrome was associated with the mean dose to the ipsilateral lacrimal gland. ConclusionWe developed and in part validated NTCP models for several common late side-effects following PBT in brain tumour patients. Validation studies are required for further confirmation.

Combining Clinical and Dosimetric Features in a PBS Proton Therapy Cohort to Develop a NTCP Model for Radiation-Induced Optic Neuropathy

Radiation-induced optic neuropathy (RION) is a rare, yet severe complication following radiation therapy for brain, head and neck, or skull-base tumors. Although several risk factors, such as age, metabolic syndrome, and delivered dose, have been identified, we aimed at expanding the understanding of the mechanisms of interplay regarding dosimetry and patient variables leading to the onset of RION with a focus on proton therapy. In this retrospective study, we have investigated proton-specific risk factors by comparing common phenomenological normal tissue complication probability models with a multivariate analysis that includes clinical features on a cohort of patients with skull-base and head and neck cancer treated with pencil beam scanning. Although predictive power of the Lyman-Kutcher-Burman and Poisson models was limited for this data set, the addition of clinical variables such as age, tumor involvement, hypertension, or sex remarkably increased model performance. Based on our assessment, the maximum dose in the optical apparatus is confirmed the most intuitive risk factor. However, above a certain dose threshold, clinical patient characteristics are the deciding factors for the onset of RION. We observed a tendency toward a volume effect that, if confirmed, would imply a benefit for high precision radiation therapy techniques such as proton therapy for the treatment of patients with high clinical risk for RION.

Prostate Stereotactic Body Radiation Therapy: An Overview of Toxicity and Dose Response

Ultrahypofractionationed radiation therapy for prostate cancer is increasingly studied and adopted. The American Association of Physicists in Medicine Working Group on Biological Effects of Hypofractionated Radiotherapy therefore aimed to review studies examining toxicity and quality of life after stereotactic body radiation therapy (SBRT) for prostate cancer and model its effect. We performed a systematic PubMed search of prostate SBRT studies published between 2001 and 2018. Those that analyzed factors associated with late urinary, bowel, or sexual toxicity and/or quality of life were included and reviewed. Normal tissue complication probability modelling was performed on studies that contained detailed dose/volume and outcome data. We found 13 studies that examined urinary effects, 6 that examined bowel effects, and 4 that examined sexual effects. Most studies included patients with low-intermediate risk prostate cancer treated to 35-40 Gy. Most patients were treated with 5 fractions, with several centers using 4 fractions. Endpoints were heterogeneous and included both physician-scored toxicity and patient-reported quality of life. Most toxicities were mild-moderate (eg, grade 1-2) with a very low overall incidence of severe toxicity (eg, grade 3 or higher, usually <3%). Side effects were associated with both dosimetric and non-dosimetric factors. Prostate SBRT appears to be overall well tolerated, with determinants of toxicity that include dosimetric factors and patient factors. Suggested dose constraints include bladder V(Rx Dose)Gy <5-10 cc, urethra Dmax <38-42 Gy, and rectum Dmax <35-38 Gy, though current data do not offer firm guidance on tolerance doses. Several areas for future research are suggested.

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.

Optimal treatment plan adaptation using mid-treatment imaging biomarkers

Previous studies on personalized radiotherapy (RT) have mostly focused on baseline patient stratification, adapting the treatment plan according to mid-treatment anatomical changes, or dose boosting to selected tumor subregions using mid-treatment radiological findings. However, the question of how to find the optimal adapted plan has not been properly tackled. Moreover, the effect of information uncertainty on the resulting adaptation has not been explored.In this paper, we present a framework to optimally adapt radiation therapy treatments to early radiation treatment response estimates derived from pre- and mid-treatment imaging data while considering the information uncertainty. The framework is based on the optimal stopping in radiation therapy (OSRT) framework. Biological response is quantified using tumor control probability (TCP) and normal tissue complication probability (NTCP) models, and these are directly optimized for in the adaptation step. Two adaptation strategies are discussed: (1) uniform dose adaptation and (2) continuous dose adaptation. In the first strategy, the original fluence-map is simply scaled upwards or downwards, depending on whether dose escalation or de-escalation is deemed appropriate based on the mid-treatment response observed from the radiological images. In the second strategy, a full NTCP-TCP-based fluence map re-optimization is performed to achieve the optimal adapted plans.We retrospectively tested the performance of these strategies on 14 canine head and neck cases treated with tomotherapy, using as response biomarker the change in the 3’-deoxy-3’[(18)F]-fluorothymidine (FLT)-PET signals between the pre- and mid-treatment images, and accounting for information uncertainty. Using a 10% uncertainty level, the two adaptation strategies both yield a noteworthy average improvement in guaranteed (worst-case) TCP.

Brainstem NTCP and Dose Constraints for Carbon Ion RT—Application and Translation From Japanese to European RBE-Weighted Dose

Background and PurposeThe Italian National Center of Oncological Hadrontherapy (CNAO) has applied dose constraints for carbon ion RT (CIRT) as defined by Japan’s National Institute of Radiological Sciences (NIRS). However, these institutions use different models to predict the relative biological effectiveness (RBE). CNAO applies the Local Effect Model I (LEM I), which in most clinical situations predicts higher RBE than NIRS’s Microdosimetric Kinetic Model (MKM). Equal constraints therefore become more restrictive at CNAO. Tolerance doses for the brainstem have not been validated for LEM I-weighted dose (D LEM I). However, brainstem constraints and a Normal Tissue Complication Probability (NTCP) model were recently reported for MKM-weighted dose (D MKM), showing that a constraint relaxation to D MKM|0.7 cm3 <30 Gy (RBE) and D MKM|0.1 cm3 <40 Gy (RBE) was feasible. The aim of this work was to evaluate the brainstem NTCP associated with CNAO’s current clinical practice and to propose new brainstem constraints for LEM I-optimized CIRT at CNAO.Material and MethodsWe reproduced the absorbed dose of 30 representative patient treatment plans from CNAO. Subsequently, we calculated both D LEM I and D MKM, and the relationship between D MKM and D LEM I for various brainstem dose metrics was analyzed. Furthermore, the NTCP model developed for D MKM was applied to estimate the NTCPs of the delivered plans.ResultsThe translation of CNAO treatment plans to D MKM confirmed that the former CNAO constraints were conservative compared with D MKM constraints. Estimated NTCPs were 0% for all but one case, in which the NTCP was 2%. The relationship D MKM/D LEM I could be described by a quadratic regression model which revealed that the validated D MKM constraints corresponded to D LEM I|0.7 cm3 <41 Gy (RBE) (95% CI, 38–44 Gy (RBE)) and D LEM I|0.1 cm3 <49 Gy (RBE) (95% CI, 46–52 Gy (RBE)).ConclusionOur study demonstrates that RBE-weighted dose translation is of crucial importance in order to exchange experience and thus harmonize CIRT treatments globally. To mitigate uncertainties involved, we propose to use the lower bound of the 95% CI of the translation estimates, i.e., D LEM I|0.7 cm3 <38 Gy (RBE) and D LEM I|0.1 cm3 <46 Gy (RBE) as brainstem dose constraints for 16 fraction CIRT treatments optimized with LEM I.