Normal Tissue Complication Probability Models Research Articles

Evaluation of the Radiobiological Models Predicting the Radiation-Induced Hypothyroidism in the Partially Irradiated Thyroid Gland of Patients with Breast Cancer

Background: Radiation-induced hypothyroidism (RHT) is one of the side effects that might have an impact on the quality of life of patients with breast cancer treated with radiotherapy. Objectives: The aim of the current study was to evaluate the performances of the Lyman-Kutcher-Burman (LKB) and Log-Logistic models in the prediction of hypothyroidism (HT) as well as the estimation of the model parameters for the incidence of RHT among patients with breast cancer. Methods: Fifty-two patients treated with radiation therapy (RT) for breast cancer were prospectively evaluated. Patients' serum samples [tri-iodothyronine, thyroxine, thyroid-stimulating hormone (TSH), free triiodothyronine, and free thyroxine] were measured before RT and also at a regular time interval until 1 year after the completion of RT. For each patient, dose-volume histograms (DVHs) of the thyroid gland were derived from their treatment planning dataset. Patients whose TSH levels were higher than normal with a decrease in FT4 levels were considered as cases with RHT. The LKB and Log-Logistic radiobiological models were evaluated by comparing them with the resultant follow-up data. The parameters for radiobiological models have been deduced by fitting the models to the follow-up data. The models were fitted in a Bayesian setting and compared according to the widely applicable information criterion (WAIC). Results: Twenty-one (40%) patients developed RHT at a follow-up of 1 year after the end of radiation treatment. The fitted values of D50 for the LKB and Log-Logistic models were 37.71 and 25.50 Gy, respectively for the partially irradiated thyroid of patients with breast cancer. The mean time to the incidence of RHT was obtained at 6.7 months in the studied group. Conclusions: A volumetric effect was found for the thyroid gland in the implemented normal tissue complication probability models. Compared to the follow-up data, the Log-Logistic model was ranked as the best model for predicting the rate of RHT in patients with breast cancer.

Predictive performance of different NTCP techniques for radiation-induced esophagitis in NSCLC patients receiving proton radiotherapy

This study aimed to compare the predictive performance of different modeling methods in developing normal tissue complication probability (NTCP) models for predicting radiation-induced esophagitis (RE) in non–small cell lung cancer (NSCLC) patients receiving proton radiotherapy. The dataset was composed of 328 NSCLC patients receiving passive-scattering proton therapy and 41.6% of the patients experienced ≥ grade 2 RE. Five modeling methods were used to build NTCP models: standard Lyman–Kutcher–Burman (sLKB), generalized LKB (gLKB), multivariable logistic regression using two variable selection procedures-stepwise forward selection (Stepwise-MLR), and least absolute shrinkage and selection operator (LASSO-MLR), and support vector machines (SVM). Predictive performance was internally validated by a bootstrap approach for each modeling method. The overall performance, discriminative ability, and calibration were assessed using the Negelkerke R2, area under the receiver operator curve (AUC), and Hosmer–Lemeshow test, respectively. The LASSO-MLR model showed the best discriminative ability with an AUC value of 0.799 (95% confidence interval (CI): 0.763–0.854), and the best overall performance with a Negelkerke R2 value of 0.332 (95% CI: 0.266–0.486). Both of the optimism-corrected Negelkerke R2 values of the SVM and sLKB models were 0.301. The optimism-corrected AUC of the gLKB model (0.796) was higher than that of the SVM model (0.784). The sLKB model had the smallest optimism in the model variation and discriminative ability. In the context of classification and probability estimation for predicting the NTCP for radiation-induced esophagitis, the MLR model developed with LASSO provided the best predictive results. The simplest LKB modeling had similar or even better predictive performance than the most complex SVM modeling, and it was least likely to overfit the training data. The advanced machine learning approach might have limited applicability in clinical settings with a relatively small amount of data.

Assessment of gene expressions from squamous cell carcinoma of the head and neck to predict radiochemotherapy-related xerostomia and dysphagia

Purpose We tested the hypothesis that gene expressions from biopsies of locally advanced head and neck squamous cell carcinoma (HNSCC) patients can supplement dose-volume parameters to predict dysphagia and xerostomia following primary radiochemotherapy (RCTx). Material and methods A panel of 178 genes previously related to radiochemosensitivity of HNSCC was considered for nanoString analysis based on tumour biopsies of 90 patients with locally advanced HNSCC treated by primary RCTx. Dose-volume parameters were extracted from the parotid, submandibular glands, oral cavity, larynx, buccal mucosa, and lips. Normal tissue complication probability (NTCP) models were developed for acute, late, and for the improvement of xerostomia grade ≥2 and dysphagia grade ≥3 using a cross-validation-based least absolute shrinkage and selection operator (LASSO) approach combined with stepwise logistic regression for feature selection. The final signatures were included in a logistic regression model with optimism correction. Performance was assessed by the area under the receiver operating characteristic curve (AUC). Results NTCP models for acute and late xerostomia and the improvement of dysphagia resulted in optimism-corrected AUC values of 0.84, 0.76, and 0.70, respectively. The minimum dose to the contralateral parotid was selected for both acute and late xerostomia and the minimum dose to the larynx was selected for dysphagia improvement. For the xerostomia endpoints, the following gene expressions were selected: RPA2 (cellular response to DNA damage), TCF3 (salivary gland cells development), GBE1 (glycogen storage and regulation), and MAPK3 (regulation of cellular processes). No gene expression features were selected for the prediction of dysphagia. Conclusion This hypothesis-generating study showed the potential of improving NTCP models using gene expression data for HNSCC patients. The presented models require independent validation before potential application in clinical practice.

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.

A Patient Selection Approach Based on NTCP Models and DVH Parameters for Definitive Proton Therapy in Locally Advanced Sinonasal Cancer Patients.

Simple SummaryThe role of proton therapy as a radiation treatment option for locally advanced sinonasal cancer patients has increased in the last years, showing promising results in terms of clinical outcomes. Definition of strategies to identify patients who would benefit the most from proton therapy in terms of reduced toxicity is highly desirable, due to limited availability and higher costs of this treatment option. The novelty of our in silico study relies on assessing the suitability of a mixed dose volume histograms and normal tissue complication probability model-based approach, aiming at providing, to the scientific community, a patient selection criteria possibly leading the therapeutic choice between proton therapy and advanced photon techniques.(1) Background: In this work, we aim to provide selection criteria based on normal tissue complication probability (NTCP) models and additional explanatory dose-volume histogram parameters suitable for identifying locally advanced sinonasal cancer patients with orbital invasion benefitting from proton therapy. (2) Methods: Twenty-two patients were enrolled, and two advanced radiation techniques were compared: intensity modulated proton therapy (IMPT) and photon volumetric modulated arc therapy (VMAT). Plans were optimized with a simultaneous integrated boost modality: 70 and 56 Gy(RBE) in 35 fractions were prescribed to the high risk/low risk CTV. Several endpoints were investigated, classified for their severity and used as discriminating paradigms. In particular, when NTCP models were already available, a first selection criterion based on the delta-NTCP was adopted. Additionally, an overall analysis in terms of DVH parameters was performed. Furthermore, a second selection criterion based on a weighted sum of the NTCP and DVH was adopted. (3) Results: Four patients out of 22 (18.2%) were suitable for IMPT due to NTCP > 3% for at least one severe toxicity, 4 (18.2%) due to NTCP > 20% for at least three concurrent intermediate toxicities and 16 (72.7%) due to the mixed sum of NTCP and DVH criterion. Since, for some cases, both criteria were contemporary fulfilled, globally 17/22 patients (77.3%) would benefit from IMPT. (4) Conclusions: For this rare clinical scenario, the use of a strategy including DVH parameters and NTCPs when comparing VMAT and IMPT is feasible. We showed that patients affected by sinonasal cancer could profit from IMPT compared to VMAT in terms of optical and central nervous system organs at risk sparing.

A study on predicting cases that would benefit from proton beam therapy in primary liver tumors of less than or equal to 5 cm based on the estimated incidence of hepatic toxicity

BackgroundFor small primary liver tumors, favorable outcomes have been reported with both of proton beam therapy (PBT) and X-ray therapy (XRT). However, no clear criteria have been proposed in the cases for which and when of PBT or XRT has to be used. The aim of this study is to investigate cases that would benefit from PBT based on the predicted rate of hepatic toxicity. Materials and methodsEligible patients were those who underwent PBT for primary liver tumors with a maximum diameter of ≤ 5 cm and Child-Pugh grade A (n = 40). To compare the PBT-plan, the treatment plan using volumetric modulated arc therapy was generated as the XRT-plan. The rate of predicted hepatic toxicity was estimated using five normal tissue complication probability (NTCP) models with three different endpoints. The differences in NTCP values (ΔNTCP) were calculated to determine the relative advantage of PBT. Factors predicting benefits of PBT were analyzed by logistic regression analysis. ResultsFrom the dose-volume histogram comparisons, an advantage of PBT was found in sparing of the normal liver receiving low doses. The factors predicting the benefit of PBT differed depending on the selected NTCP model. From the five models, the total tumor diameter (sum of the target tumors), location (hepatic hilum vs other), and number of tumors (1 vs 2) were significant factors. ConclusionsFrom the radiation-related hepatic toxicity, factors were identified to predict benefits of PBT in primary liver tumors with Child-Pugh grade A, with the maximum tumor diameter of ≤ 5 cm.

Predicting acute severe toxicity for head and neck squamous cell carcinomas by combining dosimetry with a radiosensitivity biomarker: a pilot study.

Radiotherapy (RT) against head and neck squamous cell carcinomas (HNSCC) may lead to severe toxicity in 30-40% of patients. The normal tissue complication probability (NTCP) models, based on dosimetric data refined the normal tissue dose/volume tolerance guidelines. In parallel, the radiation-induced nucleoshuttling (RIANS) of the Ataxia-Telangiectasia Mutated protein (pATM) is a predictive approach of individual intrinsic radiosensitivity. Here, we combined NTCP with RADIODTECT©, a blood assay derived from the RIANS model, to predict RT toxicity in HNSCC patients. RADIODTECT© cutoff values (i.e. 57.8 ng/mL for grade⩾2 toxicity and 46 ng/mL for grade⩾3 toxicity) have been previously assessed. Validation was performed on a prospective cohort of 36 HNSCC patients treated with postoperative RT. Toxicity was graded with the Common Terminology Criteria for Adverse Events (CTCAE) scale and two criteria were considered: grade⩾2 oral mucositis (OM2), grade⩾3 mucositis (OM3) and grade⩾2 dysphagia (DY2), grade⩾3 dysphagia (DY3). pATM quantification was assessed in lymphocytes of HNSCC patients. The discrimination power of the pATM assay was evaluated through the Area Under the Receiver Operator Characteristics Curve (AUC-ROC). Two previously described NTCP models were considered, including the dose to the oral cavity and the mean dose to the parotid glands (OM2 and OM3) and the dose to the oral cavity, to the larynx and the volume of pharyngeal constrictor muscles (DY2 and DY3). Combining NTCP models with RADIODTECT© blood test improved the AUC-ROC. Considering the prediction of mucositis, AUC-ROCNTCP+RADIODTECT©=0.80 was for OM2, and AUC-ROCNTCP+RADIODTECT©=0.78 for OM3. Considering the prediction of acute dysphagia, AUC-ROCNTCP+RADIODTECT©=0.71 for DY2 and for DY3. Combining NTCP models with a radiosensitivity biomarker might significantly improve the prediction of toxicities for HNSCC patients.

Extending the relative seriality formalism for interpretable deep learning of normal tissue complication probability models

We formally demonstrate that the relative seriality (RS) model of normal tissue complication probability (NTCP) can be recast as a simple neural network with one convolutional and one pooling layer. This approach enables us to systematically construct deep relative seriality networks (DRSNs), a new class of mechanistic generalizations of the RS model with radiobiologically interpretable parameters amenable to deep learning. To demonstrate the utility of this formulation, we analyze a simplified example of xerostomia due to irradiation of the parotid gland during alpha radiopharmaceutical therapy. Using a combination of analytical calculations and numerical simulations, we show for both the RS and DRSN cases that the ability of the neural network to generalize without overfitting is tied to ‘stiff’ and ‘sloppy’ directions in the parameter space of the mechanistic model. These results serve as proof-of-concept for radiobiologically interpretable deep learning of NTCP, while simultaneously yielding insight into how such techniques can robustly generalize beyond the training set despite uncertainty in individual parameters.

Radiation Induced Lymphopenia Is Associated With the Effective Dose to the Circulating Immune Cells in Breast Cancer.

BackgroundLymphopenia is a known significant factor for treatment outcome in cancer patients, with underlying risk factor poorly understood in breast cancer. We hypothesize that the effective dose to the circulating immune cells (EDIC) which was related with lymphopenia in lung cancer will also have significant effect for radiation induced lymphopenia (RIL) in patients with breast cancer.Material and MethodsPatients treated with adjuvant radiotherapy (RT) and with complete blood tests within one week from RT end/start (post/preRT) were eligible in this study. Radiation dosimetric factors were collected retrospectively, and EDIC for each patient was calculated based on the doses to lung, heart and total body according to the model description, as previously reported. RIL was defined by the CTCAE5.0 based on postRT peripheral lymphocyte count (PLC). Linear regression was first used to test the correlation between EDIC with post/preRT PLC ratio and postRT PLC, using all these as continuous variables. Normal tissue complication probability (NTCP) was used to develop models that predict the CTCAE graded RIL from EDIC.ResultsA total of 735 patients were eligible. The mean post/preRT PLC ratio was 0.66 (95% CI: 0.64-0.68) and mean EDIC of breast cancer was 1.70Gy (95% CI: 1.64-1.75). Both post/preRT PLC ratio and postRT PLC were significantly correlated with EDIC (P<0.001), with R2 of 0.246. For patients with normal preRT PLC, the post/preRT PLC ratio was better associated with EDIC, and postRT PLC was expressed as PLCpreRT × (0.89 – 0.16 × EDIC). For patients with preRT lymphopenia, postRT PLC was better associated with EDIC and it was 1.1 – 0.17 × EDIC. Using binned EDIC as the dose variable, the bootstrap validated NTCPs fit the data nicely with R2 of 0.93, 0.96, and 0.94 for grade-1, grade-2, and grade-3 RIL, respectively. The corresponding EDIC to induce 50% of grade-1, grade-2 and grade-3 RIL was 1.2, 2.1 and 3.7 Gy, respectively.ConclusionEDIC is a significant factor for RIL in patients with breast cancer, and may be used to compute the risk of lymphopenia in each individual patient with the use of the conventional NTCP modeling. External validation is needed before the EDIC can be used to guide RT plan.

Impact of sarcopenia on acute radiation-induced toxicity in head and neck cancer patients

Background and purposeSarcopenia is related to late radiation-induced toxicities and worse survival in head and neck cancer (HNC) patients. This study tested the hypothesis that sarcopenia improves the performance of current normal tissue complication probability (NTCP) models of radiation-induced acute toxicity in HNC patients. Material/methodsThis was a retrospective analysis in a prospective cohort of HNC patients treated from January 2007 to December 2018 with (chemo)radiotherapy. Planning CT scans were used for evaluating skeletal muscle mass. Characteristics of sarcopenic and non-sarcopenic patients were compared. The impact of sarcopenia was analysed by adding sarcopenia to the linear predictors of current NTCP models predicting physician- and patient-rated acute toxicities. ResultsThe cut-off values of sarcopenia in the study population (n = 977) were established at skeletal muscle index < 42.0 cm2/m2 (men) and < 31.2 cm2/m2 (women), corresponding to the lowest sex-specific quartile. Compared to non-sarcopenic patients, sarcopenic patients were more frequently smokers (61% vs. 48%, p < 0.001), had more often advanced stage of disease (stage III-IV, p = 0.004), higher age (67 vs. 63 years, p < 0.001) and experienced more pretreatment complaints, such as dysphagia (grade ≥ 2, p < 0.001). Sarcopenia remained statistically significant, next to the linear predictor, only for physician-rated grade ≥ 3 dysphagia (week 3–6 during RT, p < 0.01). However, sarcopenia did not improve the performance of these NTCP models (p > 0.99). ConclusionSarcopenia in HNC patients was an independent prognostic factor for radiation-induced physician-rated acute grade ≥ 3 dysphagia, which might be explained by its impact on swallowing muscles. However, addition of sarcopenia did not improve the NTCP model performance.

Stricter Postoperative Oropharyngeal Cancer Radiation Therapy Normal Tissue Dose Constraints Are Feasible

PurposeAlthough dose de-escalation is one proposed strategy to mitigate long-term toxicity in human papillomavirus associated oropharyngeal cancer, applying more stringent normal tissue constraints may be a complementary approach to further reduce toxicity. Our study demonstrates that in a postoperative setting, improving upon nationally accepted constraints is achievable and leads to reductions in normal tissue complication probabilities (NTCP) without compromising disease control. Methods and MaterialsWe identified 92 patients at our institution between 2015 and 2019 with p16+ oropharyngeal cancer who were treated with adjuvant volumetric modulated arc therapy. We included patients treated to postoperative doses and standard volumes (including bilateral neck). Doses delivered to organs at risk were compared with recommended dose constraints from a recent cooperative group head and neck cancer trial of radiation therapy to 60 Gy. We applied validated and published NTCP models for dysphagia, dysgeusia, esophagitis, oral mucositis, and xerostomia relevant to oropharyngeal cancer. ResultsAchievable and delivered mean doses to most normal head and neck tissues were well below national recommended constraints. This translates to notable absolute NTCP reductions for salivary flow (10% improvement in contralateral parotid, 35% improvement in submandibular gland), grade ≥ 2 esophagitis (23% improvement), grade ≥ 3 mucositis (17% improvement), dysgeusia (10% improvement), and dysphagia (8% improvement). Locoregional control at a median follow-up of 26.3 months was 96.7%, with only 3 patients experiencing locoregional recurrence (1 local, 2 regional). ConclusionsModern radiation therapy planning techniques allow for improved normal tissue sparing compared with currently established dose constraints without compromising disease control. These improvements may lead to reduced toxicity in a patient population expected to have favorable long-term outcomes. Stricter constraints can be easily achieved and should be used in conjunction with other evolving efforts to mitigate toxicity.

Dosimetry in the Prediction and Reduction of Acute Skin Toxicity from the Use of Brass Bolus in Postmastectomy Chest Wall Radiotherapy Treatment

Purpose: Brass bolus has been shown to be an acceptable substitute for tissue-equivalent bolus (TEB), and may be advantageous for surface dose delivery to the often irregularly shaped chest wall in postmastectomy radiotherapy (PMRT) [1]. The purpose of this study was to analyse the in vivo TLD skin dose measurements, and use normal tissue complication probability (NTCP) and multivariate logistic regression models for the prediction of acute skin toxicity following PMRT.

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.

Long-Term Evaluation and Normal Tissue Complication Probability (NTCP) Models for Predicting Radiation-Induced Optic Neuropathy after Intensity-Modulated Radiation Therapy (IMRT) for Nasopharyngeal Carcinoma: A Large Retrospective Study in China.

Purpose To quantify the long-term evaluation of optic chiasma (OC) and/or optic nerve(s) (ONs) and to develop predictive models for radiation-induced optic neuropathy (RION) in nasopharyngeal carcinoma after intensity-modulated radiotherapy (IMRT). Methods and Materials A total of 3,662 patients' OC/ONs with full visual acuity and dosimetry data between 2010 and 2015 were identified. Critical dosimetry predictors of RION were chosen by machine learning and penalized regression for survival. A nomogram containing dosimetry and clinical variables was generated for predicting RION-free survival. Results The median follow-up was 71.79 (2.63–120.9) months. Sixty-six eyes in 51 patients (1.39%) developed RION. Two patients were visual field deficient, and 49 patients had visual acuity of less than 0.1 (20/200). The median latency time was 36 (3–90) months. The 3-, 5-, and 8-year cumulative incidence of RION was 0.78%, 1.19%, and 1.97%, respectively. Dmax was the most critical dosimetry variable for RION (AUC: 0.9434, the optimal cutoff: 64.48 Gy). Patients with a Dmax ≥64.48 Gy had a significantly higher risk of RION (HR = 102.25; 95%CI, 24.86–420.59; P < 0.001). Age (>44 years) (HR = 2.234, 95% CI = 1.233–4.051, p = 0.008), advanced T stage (T3 vs. T1-2: HR = 7.516, 95% CI = 1.725–32.767, p=0.007; T4 vs. T1-2: HR = 37.189, 95% CI = 8.796–157.266, P < 0.001), and tumor infiltration/compression of the OC/ONs (HR = 4.572, 95% CI = 1.316–15.874, p=0.017) were significant clinical risk factors of RION. A nomogram comprising age, T stage, tumor infiltration/compression of the OC/ON, and Dmax significantly outperformed the model, with only Dmax predicting RION (C-index: 0.916 vs. 0.880, P < 0.001 in the training set; 0.899 vs. 0.874, P=0.038 in the test set). The nomogram-defined high-risk group had a worse 8-year RION-free survival. Conclusions In the IMRT era, Dmax <60 Gy is safe and represents an acceptable dose constraint for most NPC patients receiving IMRT. A reasonable trade-off for selected patients with unsatisfactory tumor coverage due to proximity to the optic apparatus would be Dmax <65 Gy. Caution should be exercised when treating elderly and advanced T-stage patients or those with tumor infiltration/compression of the OC/ON. Our nomogram shows strong efficacy in predicting RION.

NTCP Modeling for High-Grade Temporal Radionecroses in a Large Cohort of Patients Receiving Pencil Beam Scanning Proton Therapy for Skull Base and Head and Neck Tumors

To develop a normal tissue complication probability model including clinical and dosimetric parameters for high-grade temporal lobe radionecroses (TRN) after pencil beam scanning proton therapy. We included data on 299 patients with skull base and head and neck tumors treated with pencil-beam scan proton therapy, with a total dose of ≥60 GyRBE (relative biological effectiveness) from May 2004 to November 2018. We considered 9 clinical and 27 dosimetric parameters for the structure-wise modeling of high-grade (grade ≥2) TRN. After eliminating strongly cross-correlated variables, we generated logistic regression models using least absolute shrinkage and selection operator regression. We performed bootstrapping to assess parameter selection robustness and evaluated model performance via cross-correlation by assessing the area under the curve of receiver operating characteristic curves and calibration with a Hosmer-Lemeshow test statistic. After a median radiologic follow-up of 51.5 months (range, 4-190), 27 patients (9%) developed grade ≥2 TRN. Eleven patients had bitemporal necrosis, resulting in 38 events in 598 temporal lobes for structure-wise analysis. During our bootstrapping analysis, we found that the highest selection frequency was for prescription dose, followed by age, V40Gy (%), hypertension, and dose to at least 1 cc (D1cc) (Gy) in the temporal lobe. During our cross-validation, we found that age*prescription-dose*D1cc (Gy)*hypertension was superior in all described test statistics. We built full cohort structure-wise and patient-wise models with maximum area under the curve of receiver operating characteristic curves of 0.79 (structure-wise) and 0.76 (patient-wise). While developing a logistic regression normal tissue complication probability model to predict grade ≥2 TRN, the best fit was found for the model containing age, prescription dose, D1cc (Gy), and hypertensive blood pressure as risk factors. External validation will be the next step to improve generalizability and potential introduction into clinical routine.

RBE-weighted dose and its impact on the risk of acute coronary event for breast cancer patients treated with intensity modulated proton therapy.

PurposeTo evaluate the relative biological effectiveness (RBE)‐weighted dose to the heart and to estimate RBE uncertainties when assuming a constant RBE of 1.1, for breast cancer patients receiving intensity‐modulated proton therapy (IMPT). Further, to study the impact of RBE uncertainties on the risk of an acute coronary event (ACE).Material and methodsWe analyzed 20 patients who received IMPT to either the left breast (n = 10) or left chest wall (n = 10) and regional lymph nodes. The Monte Carlo simulation engine, MCsquare, was used to simulate the dose‐averaged linear energy transfer (LETd) map. The RBE‐weighted dose to the heart and its substructures was calculated using three different RBE models. The risk of ACE was estimated per its linear relationship with mean heart dose (MHD) as established by Darby et al.ResultsThe median MHD increased from 1.33 GyRBE assuming an RBE of 1.1 to 1.64, 1.87, and 1.99 GyRBE when using the RBE‐weighted dose models. The median values (and ranges) of the excess absolute risk of ACE were 0.4% (0.1%–0.8%) when assuming an RBE of 1.1, and 0.6% (0.2%–1.0%), 0.6% (0.2%–1.1%), and 0.7% (0.2%–1.1%) with the RBE‐weighted models. For our patient cohort, the maximum excess absolute risk of ACE increased by 0.3% with the RBE‐weighted doses compared to the constant RBE of 1.1, reaching an excess absolute ACE risk of 1.1%. The interpatient LETd variation was small for the relevant high‐dose regions of the heart.ConclusionAll three RBE models predicted a higher biological dose compared to the clinical standard dose assuming a constant RBE of 1.1. An underestimation of the biological dose results in underestimation of the ACE risk. Analyzing the voxel‐by‐voxel biological dose and the LET map alongside clinical outcomes is warranted in the development of a more accurate normal‐tissue complication probability model.

Normal Tissue Complication Probability Modelling for Toxicity Prediction and Patient Selection in Proton Beam Therapy to the Central Nervous System: A Literature Review

Normal tissue complication probability (NTCP) models can guide clinical decision making in radiotherapy. In recent years, they have been used for patient selection for proton beam therapy (PBT) for some anatomical tumour sites. This review synthesizes the published evidence regarding the use of NTCP models to predict the toxicity of PBT, for different end points in patients with brain tumours. A search of Medline and Embase using the Patients, Intervention, Comparison, Outcome (PICO) criteria was undertaken. In total, 37 articles were deemed relevant and were reviewed in detail. Nineteen articles on NTCP modelling of toxicity end points were included. Of these, 11 were comparative NTCP studies of PBT versus conventional photon radiotherapy (XRT), which evaluated differences in plan dosimetry and then assumed that XRT-derived literature estimates of NTCP would be applicable to both. Seven papers derived NTCP models based on PBT outcome data, two of which provided model parameters. Among analysed end points, the reduced risk of secondary tumours with PBT as compared with XRT is estimated – through modelling studies – to be considerable and was highlighted by most authors. For other analysed end points, the clinical benefit of PBT mainly depends on tumour location in relation to organs at risk as well as prescription doses. NTCP models can be useful tools for treatment plan comparison. However, most published toxicity data were derived from XRT cohorts; this review has highlighted the need for further studies relating dose-volume parameters to observed toxicity in PBT-treated patients. Specifically, there is a need for PBT-specific NTCP models that can be implemented in the clinical practice. NTCP models built on robust clinical data for the most common radiotherapy toxicities in the brain would potentially redefine the current indications for PBT.

Prediction of chronic kidney disease in abdominal cancers radiation therapy using the functional assays of normal tissue complication probability models.

The purpose of this study is to predict chronic kidney disease (CKD) in the radiotherapy of abdominal cancers by evaluating clinical and functional assays of normal tissue complication probability (NTCP) models. Radiation renal damage was analyzed in 50 patients with abdominal cancers 12 months after radiotherapy through a clinical estimated glomerular filtration rate (eGFR). According to the common terminology criteria for the scoring system of adverse events, Grade 2 CKD (eGFR ≤30-59 ml/min/1.73 m2) was considered as the radiation therapy endpoint. Modeling and parameter estimation of NTCP models were performed for the Lyman-equivalent uniform dose (EUD), the logit-EUD critical volume (CV), the relative seriality, and the mean dose model. The confidence interval of the fitted parameters was 95%. The parameter value of D50 was obtained 22-38 Gy, and the n and s parameters were equivalent to 0.006 -3 and 1, respectively. According to the Akaike's information criterion, the mean dose model predicts radiation-induced CKD more accurately than the other models. Although the renal medulla consists of many nephrons arranged in parallel, each nephron has a seriality architecture as renal functional subunits. Therefore, based on this principle and modeling results in this study, the whole kidney organs may have a serial-parallel combination or a secret architecture.

Radiobiological modeling of acute esophagitis after radiation therapy of head, neck, and thorax tumors: The influence of chemo-radiation.

The aim of this study was to evaluate the performance of various radiobiological models in predicting the occurrence of acute esophagitis (AE) during radiation therapy (RT) of head, neck, and thoracic tumors with concurrent and sequential chemotherapy. According to recent studies, the probability of AE following RT by normal tissue complication probability models is predictable. A total of 100 patients with nasopharynx, larynx, Hodgkin's lymphoma, spinal metastases, and oral cavity and lung tumors were included in the study. Half of these patients were treated by concurrent chemo-radiotherapy (Con. CRT) and the other half were treated by radiotherapy alone or sequential chemo-radiotherapy (RT + seq. CRT). Radiobiological models of several types were used as follows,: Lyman-generalized equivalent uniform dose (gEUD), Lyman-MED, log-logistic, logit, and logistic. Parameters were estimated using maximum likelihood estimation, and models were compared using Akaike information criteria. Based on follow-up data, the behavior of dose-response curves differed markedly between the Con. CRT and RT + seq. CRT groups. The best fit with clinical results was offered by the Lyman-MED model for the Con. CRT group and the Lyman-gEUD model for the RT + seq. CRT group. Depending on the model used, the parameter of D50 was considerably lower (up to three times) in the Con. CRT group compared to the RT + seq. CRT group. The incidence of AE significantly differed between the two treatment groups in all the models. New parameter estimates could be used for predicting the probability of acute esophagitis after chemo-RT.

The radiobiological effect of using Acuros XB vs anisotropic analytical algorithm on hepatocellular carcinoma stereotactic body radiation therapy.

The purpose of this work was to study the radiobiological effect of using Acuros XB (AXB) vs Analytic Anisotropic Algorithm (AAA) on hepatocellular carcinoma (HCC) stereotactic body radiation therapy (SBRT). Seventy SBRT volumetric modulated arc therapy (VMAT) plans for HCC were calculated using AAA and AXB respectively with the same treatment parameters. Published tumor control probability (TCP) and normal tissue complication probability (NTCP) models were used to quantify the effect of dosimetric difference between AAA and AXB on TCP, NTCP and uncomplicated tumor control probability (UTCP). There was an average decrease of 2.5% in 6-month TCP. Normal liver has the largest average decrease in NTCP which was 59.7%. Bowels followed with 26.6% average decrease in NTCP. Duodenum, stomach and esophagus had 10.2%, 5.1%, and 4.3% average decrease in NTCP. There was an average decrease of 1.8% and up to 7.2% in 6-month UTCP. There was an overall decrease in TCP, NTCP, and UTCP for HCC SBRT plans calculated using AXB compared to AAA which could be clinically significant.