Modeling the risk of radiation-induced acute esophagitis for combined Washington University and RTOG trial 93-11 lung cancer patients

Factors associated with acute esophagitis during radiation therapy for lung cancer

INTRODUCTION

Limiting acute esophagitis remains a clinical challenge during the treatment of locally advanced non-small cell lung cancer (NSCLC).

METHODS

Demographic, dosimetric, and acute toxicity data were prospectively collected for patients undergoing definitive radiation therapy +/- chemotherapy for stage II-III NSCLC from 2012 to 2022 across a statewide consortium. Logistic regression models were used to characterize the risk of grade 2 + and 3 + esophagitis as a function of dosimetric and clinical covariates. Multivariate regression models were fitted to predict the 50 % risk of grade 2 esophagitis and 3 % risk of grade 3 esophagitis.

RESULTS

Of 1760 patients, 84.2 % had stage III disease and 85.3 % received concurrent chemotherapy. 79.2 % of patients had an ECOG performance status ≤ 1. Overall rates of acute grade 2 + and 3 + esophagitis were 48.4 % and 2.2 %, respectively. On multivariate analyses, performance status, mean esophageal dose (MED) and minimum dose to the 2 cc of esophagus receiving the highest dose (D2cc) were significantly associated with grade 2 + and 3 + esophagitis. Concurrent chemotherapy was associated with grade 2 + but not grade 3 + esophagitis. For all patients, MED of 29 Gy and D2cc of 61 Gy corresponded to a 3 % risk of acute grade 3 + esophagitis. For patients receiving chemotherapy, MED of 22 Gy and D2cc of 50 Gy corresponded to a 50 % risk of acute grade 2 + esophagitis.

CONCLUSIONS

Performance status, concurrent chemotherapy, MED and D2cc are associated with acute esophagitis during definitive treatment of NSCLC. Models that quantitatively account for these factors can be useful in individualizing radiation plans.

Using inflammatory indexes and clinical parameters to predict radiation esophagitis in patients with small-cell lung cancer undergoing chemoradiotherapy

OBJECTIVE

Radiation esophagitis (RE) is a common adverse effect in small cell lung cancer (SCLC) patients undergoing thoracic radiotherapy. We aim to develop a novel nomogram to predict the acute severe RE (grade≥2) receiving chemoradiation in SCLC patients.

MATERIALS AND METHODS

the risk factors were analyzed by logistic regression, and a nomogram was constructed based on multivariate analysis results. The clinical value of the model was evaluated using the area under the receiver operating curve (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). The correlations of inflammation indexes were assessed using Spearman correlation analysis.

RESULTS

Eighty-four of 187 patients (44.9%) developed grade ≥2 RE. Univariate analysis indicated that concurrent chemoradiotherapy (CCRT, p < 0.001), chemotherapy cycle (p = 0.097), system inflammation response index (SIRI, p = 0.048), prognostic-nutrition index (PNI, p = 0.073), platelets-lymphocyte radio (PLR, p = 0.026), platelets-albumin ratio (PAR, p = 0.029) were potential predictors of RE. In multivariate analysis, CCRT [p < 0.001; OR, 3.380; 95% CI, 1.767-6.465], SIRI (p = 0.047; OR, 0.436; 95% CI, 0.192-0.989), and PAR (p = 0.036; OR, 2.907; 95% CI, 1.071-7.891) were independent predictors of grade ≥2 RE. The AUC of nomogram was 0.702 (95% CI, 0.626-0.778), which was greater than each independent predictor (CCRT: 0.645; SIRI: 0.558; PAR: 0.559). Calibration curves showed high coherence between the predicted and actual observation RE, and DCA displayed satisfactory clinical utility.

CONCLUSION

In this study, CCRT, SIRI, and PAR were independent predictors for RE (grade ≥2) in patients with SCLC receiving chemoradiotherapy. We developed and validated a predictive model through these factors. The developed nomogram with superior prediction ability can be used as a quantitative model to predict RE.

A New Nomogram and Risk Stratification of Brain Metastasis by Clinical and Inflammatory Parameters in Stage III Small Cell Lung Cancer Without Prophylactic Cranial Irradiation

Background

This study was conducted to determine risk factors for developing brain metastasis (BM) and to predict brain metastasis free survival (BMFS) and overall survival (OS) by combining several clinical parameters and inflammatory indexes.

Materials and Methods

A nomogram and risk stratification were developed based on multivariate analysis results. The prognostic index (PI) predicting the high risk of BM was calculated by multiplying the weighted factor (β coefficient) with each variable.

Results

Thirty-two of one hundred patients (32.0%) developed BM. Multivariate cox regression analysis revealed that concurrent chemoradiotherapy (CCRT; hazard ratio (HR), 3.356; p = 0.020), monocyte–lymphocyte ratio (MLR; HR, 4.511; p = 0.002), neutrophil–lymphocyte ratio (NLR; HR, 4.023; p = 0.033), and prognostic-nutrition index (PNI; HR, 2.902; p = 0.018) were independent prognostic factors of BMFS. The nomogram has good accuracy in predicting BMFS, and the C-index was 0.73. The ROC curve showed that these risk factors have good discriminant ability. Similarly, tumor location (HR, 1.675; p = 0.035) and MLR (HR, 2.076; p = 0.013) were independent prognostic factors of OS. In the subgroup analysis of OS, the good group had a better prognosis than the other groups. Risk stratification by PI: the high-risk group had worse BMFS than the low-risk group, which also has certain practical significance for clinical practice in OS.

Conclusion

We developed a nomogram and corresponding risk stratification in stage III SCLC patients who developed BM. This model and risk stratification can help clinicians improve patient treatment management and better deliver personalized therapy.

Stricter Postoperative Oropharyngeal Cancer Radiotherapy Normal Tissue Dose Constraints Are Feasible: Stricter Postoperative Oropharyngeal Radiotherapy Constraints

PURPOSE

While 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 MATERIALS

We identified 92 patients at our institution between 2015-2019 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 to recommended dose constraints from a recent cooperative group head and neck cancer trial of radiotherapy to 60 Gy. We applied validated and published NTCP models for dysphagia, dysgeusia, esophagitis, oral mucositis and xerostomia relevant to oropharyngeal cancer.

RESULTS

Achievable 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).

CONCLUSIONS

Modern radiotherapy planning techniques allow for improved normal tissue sparing compared to 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.

Deep learning driven predictive treatment planning for adaptive radiotherapy of lung cancer

BACKGROUND AND PURPOSE

To develop a novel deep learning algorithm of sequential analysis, Seq2Seq, for predicting weekly anatomical changes of lung tumor and esophagus during definitive radiotherapy, incorporate the potential tumor shrinkage into a predictive treatment planning paradigm, and improve the therapeutic ratio.

METHODS AND MATERIALS

Seq2Seq starts with the primary tumor and esophagus observed on the planning CT to predict their geometric evolution during radiotherapy on a weekly basis, and subsequently updates the predictions with new snapshots acquired via weekly CBCTs. Seq2Seq is equipped with convolutional long short term memory to analyze the spatial-temporal changes of longitudinal images, trained and validated using a dataset including sixty patients. Predictive plans were optimized according to each weekly prediction and made ready for weekly deployment to mitigate the clinical burden of online weekly replanning.

RESULTS

Seq2Seq tracks structural changes well: DICE between predicted and actual weekly tumor and esophagus were (0.83 ± 0.10, 0.79 ± 0.14, 0.78 ± 0.12, 0.77 ± 0.12, 0.75 ± 0.12, 0.71 ± 0.17), and (0.72 ± 0.16, 0.73 ± 0.11, 0.75 ± 0.08, 0.74 ± 0.09, 0.72 ± 0.14, 0.71 ± 0.14), respectively, while the average Hausdorff distances were within 2 mm. Evaluating dose to the actual weekly tumor and esophagus, a 4.2 Gy reduction in esophagus mean dose while maintaining 60 Gy tumor coverage was achieved with the predictive weekly plans, compared to the plan optimized using the initial tumor and esophagus alone, primarily due to noticeable tumor shrinkage during radiotherapy.

CONCLUSION

It is feasible to predict the longitudinal changes of tumor and esophagus with the Seq2Seq, which could lead to improving the efficiency and effectiveness of lung adaptive radiotherapy.

Early Prediction of Acute Esophagitis for Adaptive Radiation Therapy

PURPOSE

Acute esophagitis (AE) is a common dose-limiting toxicity in radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). We developed an early AE prediction model from weekly accumulated esophagus dose and its associated local volumetric change.

METHODS AND MATERIALS

Fifty-one patients with LA-NSCLC underwent treatment with intensity modulated radiation therapy to 60 Gy in 2-Gy fractions with concurrent chemotherapy and weekly cone beam computed tomography (CBCT). Twenty-eight patients (55%) developed grade ≥2 AE (≥AE2) at a median of 4 weeks after the start of radiation therapy. For early ≥AE2 prediction, the esophagus on CBCT of the first 2 weeks was deformably registered to the planning computed tomography images, and weekly esophagus dose was accumulated. Week 1-to-week 2 (w1→w2) esophagus volume changes including maximum esophagus expansion (MEex%) and volumes with ≥x% local expansions (VEx%; x = 5, 10, 15) were calculated from the Jacobian map of deformation vector field gradients. Logistic regression model with 5-fold cross-validation was built using combinations of the accumulated mean esophagus doses (MED) and the esophagus change parameters with the lowest P value in univariate analysis. The model was validated on an additional 18 and 11 patients with weekly CBCT and magnetic resonance imaging (MRI), respectively, and compared with models using only planned mean dose (MEDPlan). Performance was assessed using area under the curve (AUC) and Hosmer-Lemeshow test (PHL).

RESULTS

Univariately, w1→w2 VE10% (P = .004), VE5% (P = .01) and MEex% (P = .02) significantly predicted ≥AE2. A model combining MEDW2 and w1→w2 VE10% had the best performance (AUC = 0.80; PHL = 0.43), whereas the MEDPlan model had a lower accuracy (AUC = 0.67; PHL = 0.26). The combined model also showed high accuracy in the CBCT (AUC = 0.78) and MRI validations (AUC = 0.75).

CONCLUSIONS

A CBCT-based, cross-validated, and internally validated model on MRI with a combination of accumulated esophagus dose and local volume change from the first 2 weeks of chemotherapy significantly improved AE prediction compared with conventional models using only the planned dose. This model could inform plan adaptation early to lower the risk of esophagitis.

Treatment planning and outcomes effects of reducing the preferred mean esophagus dose for conventionally fractionated non-small cell lung cancer radiotherapy

Based on an analysis of published literature, our department recently lowered the preferred mean esophagus dose (MED) constraint for conventionally fractionated (2 Gy/fraction in approximately 30 fractions) treatment of locally advanced non-small cell lung cancer (LA-NSCLC) with the goal of reducing the incidence of symptomatic acute esophagitis (AE). The goal of the change was to encourage treatment planners to achieve a MED close to 21 Gy while still permitting MED to go up to the previous guideline of 34 Gy in difficult cases. We compared all our suitable LA-NSCLC patients treated with plans from one year before through one year after the constraint change. The primary endpoint for this study was achievability of the new constraint by the planners; the secondary endpoint was reduction in symptomatic AE. Planners were able to achieve the new constraint in statistically significantly more cases during the year following its explicit implementation than in the year before (P = 0.0025). Furthermore, 38% of patients treated after the new constraint developed symptomatic AE during their treatment as opposed to 48% of the patients treated before. This is a clinically desirable endpoint although the observed difference was not statistically significant. A subsequent power calculation suggests that this is due to the relatively small number of patients in the study.

Predictive Modeling of Thoracic Radiotherapy Toxicity and the Potential Role of Serum Alpha-2-Macroglobulin

Background: To investigate the impact of alpha-2-macroglobulin (A2M), a suspected intrinsic radioprotectant, on radiation pneumonitis and esophagitis using multifactorial predictive models. Materials and Methods: Baseline A2M levels were obtained for 258 patients prior to thoracic radiotherapy (RT). Dose-volume characteristics were extracted from treatment plans. Spearman's correlation (Rs) test was used to correlate clinical and dosimetric variables with toxicities. Toxicity prediction models were built using least absolute shrinkage and selection operator (LASSO) logistic regression on 1,000 bootstrapped datasets. Results: Grade ≥2 esophagitis and pneumonitis developed in 61 (23.6%) and 36 (14.0%) patients, respectively. The median A2M level was 191 mg/dL (range: 94-511). Never/former/current smoker status was 47 (18.2%)/179 (69.4%)/32 (12.4%). We found a significant negative univariate correlation between baseline A2M levels and esophagitis (Rs = -0.18/p = 0.003) and between A2M and smoking status (Rs = 0.13/p = 0.04). Further significant parameters for grade ≥2 esophagitis included age (Rs = -0.32/p < 0.0001), chemotherapy use (Rs = 0.56/p < 0.0001), dose per fraction (Rs = -0.57/p < 0.0001), total dose (Rs = 0.35/p < 0.0001), and several other dosimetric variables with Rs > 0.5 (p < 0.0001). The only significant non-dosimetric parameter for grade ≥2 pneumonitis was sex (Rs = -0.32/p = 0.037) with higher risk for women. For pneumonitis D15 (lung) (Rs = 0.19/p = 0.006) and D45 (heart) (Rs = 0.16/p = 0.016) had the highest correlation. LASSO models applied on the validation data were statistically significant and resulted in areas under the receiver operating characteristic curve of 0.84 (esophagitis) and 0.78 (pneumonitis). Multivariate predictive models did not require A2M to reach maximum predictive power. Conclusion: This is the first study showing a likely association of higher baseline A2M values with lower risk of radiation esophagitis and with smoking status. However, the baseline A2M level was not a significant risk factor for radiation pneumonitis.

Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy

In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians.

Dosimetric comparison of helical tomotherapy and hybrid (3DCRT-VMAT) technique for locally advanced non-small cell lung cancer

Aim:

The purpose of the present study is to compare hybrid [three-dimensional conformal radiation therapy-volumetric-modulated arc therapy (3DCRT-VMAT)] and helical tomotherapy (HT) techniques in terms of both planning target volume (PTV) and organs at risk (OARs) in the plans we made in locally advanced non-small cell lung cancer (NSCLC) patients

Material and methods:

Radiotherapy was planned for 15 locally advanced NSCLC patients with 2 different techniques. Large tumours with positive mediastinal lymph nodes were preferred. The prescription dose was determined as 60 Gy at 30 fractions.

Results:

Mean PTV volume was 602·5 cc (range: 265–1461). Mean total lung volume was 4264 cc (range: 1885–6803). Homogeneity index, Dmean, Dmax, D2 and V105 were found to be lower in HT, V100, total monitor units (MU) and total beam on time were found to be lower in the hybrid plan. Total lung Dmean was found to be 17 Gy in both techniques. V10 value was 42·85 in the hybrid plan and 48·67 in HT (p = 0·037). Heart Dmean was 14·5 Gy in the hybrid plan and 18·7 in HT (p < 0·001), and V30 values were 18·1 and 22·9, respectively (p = 0·009).

Conclusion:

Suitable dose coverage and OAR doses can be provided with both techniques. Especially the opposite lung, heart and oesophagus doses can be kept lower with the hybrid plan, and lower MU and shorter beam on time can be provided.

Library of deep-learning image segmentation and outcomes model-implementations

An open-source library of implementations for deep-learning-based image segmentation and outcomes models based on radiotherapy and radiomics is presented. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. Inclusion of deep-learning-based image segmentation and outcomes models in the same library provides a fully automated and reproduceable pipeline to estimate prognosis. The library was developed with the Computational Environment for Radiological Research (CERR) software platform. Centralizing model implementations in CERR builds upon its rich set of radiotherapy and radiomics tools and caters to the world-wide user base. CERR provides well-validated feature extraction pipelines for radiotherapy dosimetry and radiomics with fine control over the calculation settings, allowing users to select appropriate parameters used in model derivation. Models for automatic image segmentation are distributed via containers, allowing them to be deployed with a variety of scientific computing architectures. The library includes implementations of popular DVH-based models outlined in the Quantitative Analysis of Normal Tissue Effects in the Clinic effort and recently published literature. Radiomics models include features from the Image Biomarker Standardization Initiative and application-specific features found to be relevant across multiple sites and image modalities. The library is distributed as a module within CERR at https://www.github.com/cerr/CERR under the GNU-GPL copyleft with additional restrictions on clinical and commercial use and provision to dual license in future.

Toward personalized dose-prescription in locally advanced non-small cell lung cancer: Validation of published normal tissue complication probability models

PURPOSE

To identify published normal tissue complication probability (NTCP) models suitable for patient-specific dose-prescription in locally advanced non-small cell lung cancer (LA-NSCLC) through in-house validation.

MATERIAL AND METHODS

From eight previously published candidate NTCP models (≥grade 2 acute esophagitis and radiation pneumonitis; AE2, RP2), patient-specific dose-responses were calculated using model variables and fractionation-corrected doses for 241 LA-NSCLC patients treated with chemo-IMRT to 50-80 Gy@1.8-2.0 Gy between 2004 and 2014 (AE2/RP2 rate: 50%/12%). A model was judged final if it significantly predicted AE2 or RP2 (p ≤ 0.05), was discriminative and well calibrated (AUC > 0.60; Hosmer-Lemeshow test p_HL > 0.05), which were assessed as the median over 1000 bootstrap samples.

RESULTS

Models for AE2 had superior discrimination to RP2 models (AUC = 0.63-0.65 vs. 0.51-0.65). The final AE2 model included mean esophageal dose and concurrent chemotherapy (AUC = 0.65; p < 0.0001). The final RP2 model was a slightly adjusted version of the RP2 model with the best discrimination, and included age, mean lung dose, and pulmonary comorbidity (AUC = 0.73; p < 0.0001).

CONCLUSION

Of the eight investigated and published NTCP models, one model successfully described AE2 and one slightly adjusted model successfully described RP2 in the independent cohort. Estimates from these two NTCP models will, therefore, be considered internally when prescribing patient-specific doses in LA-NSCLC patients.

A Quantitative Clinical Decision-Support Strategy Identifying Which Patients With Oropharyngeal Head and Neck Cancer May Benefit the Most From Proton Radiation Therapy

PURPOSE

Developing a quantitative decision-support strategy estimating the impact of normal tissue complications from definitive radiation therapy (RT) for head and neck cancer (HNC). We developed this strategy to identify patients with oropharyngeal HNC who may benefit most from receiving proton RT.

METHODS AND MATERIALS

Recent normal tissue complication probability (NTCP) models for dysphagia, esophagitis, hypothyroidism, xerostomia, and oral mucositis were used to estimate NTCP for 33 patients with oropharyngeal HNC previously treated with photon intensity modulated radiation therapy (IMRT). Comparative proton therapy plans were generated using clinical protocols for HNC RT at a collaborating proton center. Organ-at-risk (OAR) doses from photon and proton RT plans were used to calculate NTCPs; Monte Carlo sampling 10,000 times was used for each patient to account for model parameter uncertainty. The latency and duration of each complication were modeled from calculated NTCP, accounting for age-, sex-, smoking- and p16-specific conditional survival probability. Complications were then assigned quality-adjustment factors based on severity to calculate quality-adjusted life years (QALYs) lost from each complication.

RESULTS

Based on our institutional-delivered photon IMRT doses and the achievable proton therapy doses, the average QALY reduction from all HNC RT complications for photon and proton therapy was 1.52 QALYs versus 1.15 QALYs, with proton therapy sparing 0.37 QALYs on average (composite 95% confidence interval, 0.27-2.53 QALYs). Long-term complications (dysphagia and xerostomia) contributed most to the QALY reduction. The QALYs spared with proton RT varied considerably among patients, ranging from 0.06 to 0.84 QALYs. Younger patients with p16-positive tumors who smoked ≤10 pack-years may benefit most from proton therapy, although this finding should be validated using larger patient series. A sensitivity analysis reducing photon IMRT doses to all OARs by 20% resulted in no overall estimated benefit with proton therapy with -0.02 QALYs spared, although some patients still had an estimated benefit in this scenario, ranging from -0.50 to 0.43 QALYs spared.

CONCLUSIONS

This quantitative decision-support strategy allowed us to identify patients with oropharyngeal cancer who might benefit the most from proton RT, although the estimated benefit of proton therapy ultimately depends on the OAR doses achievable with modern photon IMRT solutions. These results can help radiation oncologists and proton therapy centers optimize resource allocation and improve quality of life for patients with HNC.

Revisiting the dose constraints for head and neck OARs in the current era of IMRT

Head and neck cancer poses a particular challenge in radiation therapy, whilst being an effective treatment modality it requires very high doses of radiation to provide effective therapy. This is further complicated by the fact that the head and neck region contains a large number of radiosensitive tissues, often resulting in patients experiencing debilitating normal tissue complications. In the era of intensity-modulated radiation therapy (IMRT) treatments can be delivered using non-uniform dose distributions selectively aimed at reducing the dose to critical organs-at-risk while still adequately covering the tumor target. Dose-volume constraints for the different risk organs play a vital role in one's ability to devise the best IMRT treatment plan for a head and neck cancer patient. To this end, it is pivotal to have access to the latest and most relevant dose constraints available and as such the goal of this review is to provide a summary of suggested dose-volume constraints for head and neck cancer RT that have been published after the QUANTEC reports were made available in early 2010.

Prediction of Radiation Esophagitis in Non-Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels

Radiation esophagitis (RE) is a common adverse event associated with radiotherapy for non-small cell lung cancer (NSCLC). While plasma cytokine levels have been correlated with other forms of radiation-induced toxicity, their association with RE has been less well studied. We analyzed data from 126 patients treated on 4 prospective clinical trials. Logistic regression models based on combinations of dosimetric factors [maximum dose to 2 cubic cm (D2cc) and generalized equivalent uniform dose (gEUD)], clinical variables, and pretreatment plasma levels of 30 cytokines were developed. Cross-validated estimates of area under the receiver operating characteristic curve (AUC) and log likelihood were used to assess prediction accuracy. Dose-only models predicted grade 3 RE with AUC values of 0.750 (D2cc) and 0.727 (gEUD). Combining clinical factors with D2cc increased the AUC to 0.779. Incorporating pretreatment cytokine measurements, modeled as direct associations with RE and as potential interactions with the dose-esophagitis association, produced AUC values of 0.758 and 0.773, respectively. D2cc and gEUD correlated with grade 3 RE with odds ratios (ORs) of 1.094/Gy and 1.096/Gy, respectively. Female gender was associated with a higher risk of RE, with ORs of 1.09 and 1.112 in the D2cc and gEUD models, respectively. Older age was associated with decreased risk of RE, with ORs of 0.992/year and 0.991/year in the D2cc and gEUD models, respectively. Combining clinical with dosimetric factors but not pretreatment cytokine levels yielded improved prediction of grade 3 RE compared to prediction by dose alone. Such multifactorial modeling may prove useful in directing radiation treatment planning.

Systematic Review of Normal Tissue Complication Models Relevant to Standard Fractionation Radiation Therapy of the Head and Neck Region Published After the QUANTEC Reports

There has recently been an increasing interest in model-based evaluation and comparison of different treatment options in radiation oncology studies. This is partly driven by the considerable technical advancements in radiation therapy of the last decade, leaving radiation oncologists with a multitude of options to consider. In lieu of randomized trials comparing all of these different treatment options for varying indications, which is unfeasible, treatment evaluations based on normal tissue complication probability (NTCP) models offer a practical alternative. The Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) effort, culminating in a number of reports published in 2010, provided a basis for many of the since-implemented dose-response models and dose-volume constraints and was a key component for model-based treatment evaluations. Given that 7 years have passed since the QUANTEC publications and that patient-reported outcomes have emerged as an important consideration in recent years, an updated summary of the published radiation dose-response literature, which includes a focus on patient-reported quality of life outcomes, is warranted. Here we provide a systematic review of quantitative dose-response models published after January 1, 2010 for endpoints relevant to radiation therapy for head and neck cancer, because these patients are typically at risk for a variety of treatment-induced normal tissue complications.

Different definitions of esophagus influence esophageal toxicity prediction for esophageal cancer patients administered simultaneous integrated boost versus standard-dose radiation therapy

We aim to evaluate whether different definitions of esophagus (DEs) impact on the esophageal toxicity prediction for esophageal cancer (EC) patients administered intensity-modulated radiation therapy with simultaneous integrated boost (SIB-IMRT) vs. standard-dose IMRT (SD-IMRT). The esophagus for 21 patients diagnosed with primary EC were defined in the following four ways: the whole esophagus, including the tumor (ESO_whole); ESO_whole within the treatment field (ESO_infield); ESO_infield, excluding the tumor (ESO_{infield-tumor}) and ESO_whole, excluding the tumor (ESO_whole-tumor). The difference in the dose variation, acute esophageal toxicity (AET) and late esophageal toxicity (LET) of four DEs were compared. We found that the mean esophageal dose for ESO_whole, ESO_infield, ESO_{infield-tumor} and ESO_whole-tumor were increased by 7.2 Gy, 10.9 Gy, 4.6 Gy and 2.0 Gy, respectively, in the SIB-IMRT plans. Radiobiological models indicated that a grade ≥ 2 AET was 2.9%, 3.1%, 2.2% and 1.6% higher on average with the Kwint model and 14.6%, 13.2%, 7.2% and 3.4% higher with the Wijsman model for the four DEs. A grade ≥ 3 AET increased by 4.3%, 7.2%, 4.2% and 1.2%, respectively. Additionally, the predicted LET increased by 0.15%, 0.39%, 1.2 × 10⁻²% and 1.5 × 10⁻³%. Our study demonstrates that different DEs influence the esophageal toxicity prediction for EC patients administered SIB-IMRT vs. SD-IMRT treatment.

Radiobiological evaluation of simultaneously dose-escalated versus non-escalated intensity-modulated radiation therapy for patients with upper thoracic esophageal cancer

OBJECTIVE

To compare the radiobiological response between simultaneously dose-escalated and non-escalated intensity-modulated radiation therapy (DE-IMRT and NE-IMRT) for patients with upper thoracic esophageal cancer (UTEC) using radiobiological evaluation.

METHODS

Computed tomography simulation data sets for 25 patients pathologically diagnosed with primary UTEC were used in this study. DE-IMRT plan with an escalated dose of 64.8 Gy/28 fractions to the gross tumor volume (GTV) and involved lymph nodes from 25 patients pathologically diagnosed with primary UTEC, was compared to an NE-IMRT plan of 50.4 Gy/28 fractions. Dose-volume metrics, tumor control probability (TCP), and normal tissue complication probability for the lung and spinal cord were compared. In addition, the risk of acute esophageal toxicity (AET) and late esophageal toxicity (LET) were also analyzed.

RESULTS

Compared with NE-IMRT plan, we found the DE-IMRT plan resulted in a 14.6 Gy dose escalation to the GTV. The tumor control was predicted to increase by 31.8%, 39.1%, and 40.9% for three independent TCP models. The predicted incidence of radiation pneumonitis was similar (3.9% versus 3.6%), and the estimated risk of radiation-induced spinal cord injury was extremely low (<0.13%) in both groups. Regarding the esophageal toxicities, the estimated grade ≥2 and grade ≥3 AET predicted by the Kwint model were increased by 2.5% and 3.8%. Grade ≥2 AET predicted using the Wijsman model was increased by 14.9%. The predicted incidence of LET was low (<0.51%) in both groups.

CONCLUSION

Radiobiological evaluation reveals that the DE-IMRT dosing strategy is feasible for patients with UTEC, with significant gains in tumor control and minor or clinically acceptable increases in radiation-induced toxicities.

Independent test of a model to predict severe acute esophagitis

PURPOSE

Treatment planning factors are known to affect the risk of severe acute esophagitis during thoracic radiation therapy. We tested a previously published model to predict the risk of severe acute esophagitis on an independent data set.

METHODS AND MATERIALS

The data set consists of data from patients who had recoverable treatment plans and received definitive radiation therapy for non-small cell carcinoma of the lung at a single institution between November 2004 and January 2010. Complete esophagus dose-volume and available clinical information was extracted using our in-house software. The previously published model was a logistic function with a combination of mean esophageal dose and use of concurrent chemotherapy. In addition to testing the previous model, we used a novel, machine learning-based method to build a maximally predictive model.

RESULTS

Ninety-four patients (81.7%) developed Common Terminology Criteria for Adverse Events, Version 4, Grade 2 or more severe esophagitis (Grade 2: n = 79 and Grade 3: n = 15). Univariate analysis revealed that the most statistically significant dose-volume parameters included percentage of esophagus volume receiving ≥40 to 60 Gy, minimum dose to the highest 20% of esophagus volume (D20) to D35, and mean dose. Other significant predictors included concurrent chemotherapy and patient age. The previously published model predicted risk effectively with a Spearman's rank correlation coefficient (r_s) of 0.43 (P < .001) with good calibration (Hosmer-Lemeshow goodness of fit: P = .537). A new model that was built from the current data set found the same variables, yielding an r_s of 0.43 (P < .001) with a logistic function of 0.0853 × mean esophageal dose [Gy] + 1.49 × concurrent chemotherapy [1/0] - 1.75 and Hosmer-Lemeshow P = .659. A novel preconditioned least absolute shrinkage and selection operator method yielded an average r_s of 0.38 on 100 bootstrapped data sets.

CONCLUSIONS

The previously published model was validated on an independent data set and determined to be nearly as predictive as the best possible two-parameter logistic model even though it overpredicted risk systematically. A novel, machine learning-based model using a bootstrapping approach showed reasonable predictive power.

Advances in dosimetry and biological predictors of radiation-induced esophagitis

OBJECTIVE

To summarize the research progress about the dosimetry and biological predictors of radiation-induced esophagitis.

METHODS

We performed a systematic literature review addressing radiation esophagitis in the treatment of lung cancer published between January 2009 and May 2015 in the PubMed full-text database index systems.

RESULTS

Twenty-eight eligible documents were included in the final analysis. Many clinical factors were related to the risk of radiation esophagitis, such as elder patients, concurrent chemoradiotherapy, and the intense radiotherapy regimen (hyperfractionated radiotherapy or stereotactic body radiotherapy). The parameters including Dmax, Dmean, V20, V30, V50, and V55 may be valuable in predicting the occurrence of radiation esophagitis in patients receiving concurrent chemoradiotherapy. Genetic variants in inflammation-related genes are also associated with radiation-induced toxicity.

CONCLUSION

Dosimetry and biological factors of radiation-induced esophagitis provide clinical information to decrease its occurrence and grade during radiotherapy. More prospective studies are warranted to confirm their prediction efficacy.

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

BACKGROUND AND PURPOSE

The majority of normal-tissue complication probability (NTCP) models for acute esophageal toxicity (AET) in advanced stage non-small cell lung cancer (AS-NSCLC) patients treated with (chemo-)radiotherapy are based on three-dimensional conformal radiotherapy (3D-CRT). Due to distinct dosimetric characteristics of intensity-modulated radiation therapy (IMRT), 3D-CRT based models need revision. We established a multivariable NTCP model for AET in 149 AS-NSCLC patients undergoing IMRT.

MATERIALS AND METHODS

An established model selection procedure was used to develop an NTCP model for Grade ⩾2 AET (53 patients) including clinical and esophageal dose-volume histogram parameters.

RESULTS

The NTCP model predicted an increased risk of Grade ⩾2 AET in case of: concurrent chemoradiotherapy (CCR) [adjusted odds ratio (OR) 14.08, 95% confidence interval (CI) 4.70-42.19; p<0.001], increasing mean esophageal dose [Dmean; OR 1.12 per Gy increase, 95% CI 1.06-1.19; p<0.001], female patients (OR 3.33, 95% CI 1.36-8.17; p=0.008), and ⩾cT3 (OR 2.7, 95% CI 1.12-6.50; p=0.026). The AUC was 0.82 and the model showed good calibration.

CONCLUSIONS

A multivariable NTCP model including CCR, Dmean, clinical tumor stage and gender predicts Grade ⩾2 AET after IMRT for AS-NSCLC. Prior to clinical introduction, the model needs validation in an independent patient cohort.

Normal tissue complication models for clinically relevant acute esophagitis (≥ grade 2) in patients treated with dose differentiated accelerated radiotherapy (DART-bid)

Background

One of the primary dose-limiting toxicities during thoracic irradiation is acute esophagitis (AE). The aim of this study is to investigate dosimetric and clinical predictors for AE grade ≥ 2 in patients treated with accelerated radiotherapy for locally advanced non-small cell lung cancer (NSCLC).

Patients and methods

66 NSCLC patients were included in the present analysis: 4 stage II, 44 stage IIIA and 18 stage IIIB. All patients received induction chemotherapy followed by dose differentiated accelerated radiotherapy (DART-bid). Depending on size (mean of three perpendicular diameters) tumors were binned in four dose groups: <2.5 cm 73.8 Gy, 2.5–4.5 cm 79.2 Gy, 4.5–6 cm 84.6 Gy, >6 cm 90 Gy. Patients were treated in 3D target splitting technique. In order to estimate the normal tissue complication probability (NTCP), two Lyman models and the cutoff-logistic regression model were fitted to the data with AE ≥ grade 2 as statistical endpoint. Inter-model comparison was performed with the corrected Akaike information criterion (AIC_c), which calculates the model’s quality of fit (likelihood value) in relation to its complexity (i.e. number of variables in the model) corrected by the number of patients in the dataset. Toxicity was documented prospectively according to RTOG.

Results

The median follow up was 686 days (range 84–2921 days), 23/66 patients (35 %) experienced AE ≥ grade 2. The actuarial local control rates were 72.6 % and 59.4 % at 2 and 3 years, regional control was 91 % at both time points. The Lyman-MED model (D50 = 32.8 Gy, m = 0.48) and the cutoff dose model (D_c = 38 Gy) provide the most efficient fit to the current dataset. On multivariate analysis V38 (volume of the esophagus that receives 38 Gy or above, 95 %-CI 28.2–57.3) was the most significant predictor of AE ≥ grade 2 (HR = 1.05, CI 1.01–1.09, p = 0.007).

Conclusion

Following high-dose accelerated radiotherapy the rate of AE ≥ grade 2 is slightly lower than reported for concomitant radio-chemotherapy with the additional benefit of markedly increased loco-regional tumor control. In the current patient cohort the most significant predictor of AE was found to be V38. A second clinically useful parameter in treatment planning may be MED (mean esophageal dose).

Electronic supplementary material

The online version of this article (doi:10.1186/s13014-015-0429-1) contains supplementary material, which is available to authorized users.

Radiobiological determination of dose escalation and normal tissue toxicity in definitive chemoradiation therapy for esophageal cancer

PURPOSE

This study investigated the trade-off in tumor coverage and organ-at-risk sparing when applying dose escalation for concurrent chemoradiation therapy (CRT) of mid-esophageal cancer, using radiobiological modeling to estimate local control and normal tissue toxicity.

METHODS AND MATERIALS

Twenty-one patients with mid-esophageal cancer were selected from the SCOPE1 database (International Standard Randomised Controlled Trials number 47718479), with a mean planning target volume (PTV) of 327 cm(3). A boost volume, PTV2 (GTV + 0.5 cm margin), was created. Radiobiological modeling of tumor control probability (TCP) estimated the dose required for a clinically significant (+20%) increase in local control as 62.5 Gy/25 fractions. A RapidArc (RA) plan with a simultaneously integrated boost (SIB) to PTV2 (RA62.5) was compared to a standard dose plan of 50 Gy/25 fractions (RA50). Dose-volume metrics and estimates of normal tissue complication probability (NTCP) for heart and lungs were compared.

RESULTS

Clinically acceptable dose escalation was feasible for 16 of 21 patients, with significant gains (>18%) in tumor control from 38.2% (RA50) to 56.3% (RA62.5), and only a small increase in predicted toxicity: median heart NTCP 4.4% (RA50) versus 5.6% (RA62.5) P<.001 and median lung NTCP 6.5% (RA50) versus 7.5% (RA62.5) P<.001.

CONCLUSIONS

Dose escalation to the GTV to improve local control is possible when overlap between PTV and organ-at-risk (<8% heart volume and <2.5% lung volume overlap for this study) generates only negligible increase in lung or heart toxicity. These predictions from radiobiological modeling should be tested in future clinical trials.

Predicting esophagitis after chemoradiation therapy for non-small cell lung cancer: an individual patient data meta-analysis

PURPOSE

Concurrent chemoradiation therapy (CCRT) improves survival compared with sequential treatment for locally advanced non-small cell lung cancer, but it increases toxicity, particularly radiation esophagitis (RE). Validated predictors of RE for clinical use are lacking. We performed an individual-patient-data meta-analysis to determine factors predictive of clinically significant RE.

METHODS AND MATERIALS

After a systematic review of the literature, data were obtained on 1082 patients who underwent CCRT, including patients from Europe, North America, Asia, and Australia. Patients were randomly divided into training and validation sets (2/3 vs 1/3 of patients). Factors predictive of RE (grade≥2 and grade≥3) were assessed using logistic modeling, with the concordance statistic (c statistic) used to evaluate the performance of each model.

RESULTS

The median radiation therapy dose delivered was 65 Gy, and the median follow-up time was 2.1 years. Most patients (91%) received platinum-containing CCRT regimens. The development of RE was common, scored as grade 2 in 348 patients (32.2%), grade 3 in 185 (17.1%), and grade 4 in 10 (0.9%). There were no RE-related deaths. On univariable analysis using the training set, several baseline factors were statistically predictive of RE (P<.05), but only dosimetric factors had good discrimination scores (c>.60). On multivariable analysis, the esophageal volume receiving ≥60 Gy (V60) alone emerged as the best predictor of grade≥2 and grade≥3 RE, with good calibration and discrimination. Recursive partitioning identified 3 risk groups: low (V60<0.07%), intermediate (V60 0.07% to 16.99%), and high (V60≥17%). With use of the validation set, the predictive model performed inferiorly for the grade≥2 endpoint (c=.58) but performed well for the grade≥3 endpoint (c=.66).

CONCLUSIONS

Clinically significant RE is common, but life-threatening complications occur in <1% of patients. Although several factors are statistically predictive of RE, the V60 alone provides the best predictive ability. Efforts to reduce the V60 should be prioritized, with further research needed to identify and validate new predictive factors.

Dosimetric and clinical predictors of acute esophagitis in lung cancer patients in Turkey treated with radiotherapy

BACKGROUND

The purpose of this study was to determine the clinical and dosimetric factors associated with acute esophagitis (AE) in lung cancer patients treated with conformal radiotherapy (RT) in Turkey.

MATERIALS AND METHODS

In this retrospective review 104 lung cancer patients were examined. Esophagitis grades were verified weekly during treatment, and at 1 week, and 1 and 2 months afterwards. The clinical parameters included patient age, gender, tumor pathology, number of chemotherapy treatments before RT, concurrent chemotherapy, radiation dose, tumor response to RT, tumor localization, interruption of RT, weight loss, tumor and nodal stage and tumor volume. The following dosimetric parameters were analyzed for correlation of AE: The maximum (Dmax) and mean (Dmean) doses delivered to the esophagus, the percentage of esophagus volume receiving ≥10 Gy (V10), ≥20 Gy (V20), ≥30 Gy (V30), ≥35 Gy (V35), ≥40 Gy (V40), ≥45 Gy (V45), ≥50 Gy (V50) and ≥60 Gy (V60).

RESULTS

Fifty-five patients (52.9%) developed AE. Maximum grades of AE were recorded: Grade 1 in 51 patients (49%), and Grade 2 in 4 patients (3.8%). Clinical factors had no statistically significant influence on the incidence of AE. In terms of dosimetric findings, correlation analyses demonstrated a significant association between AE and Dmax (>5117 cGy), Dmean (>1487 cGy) and V10-60 (percentage of volume receiving >10 to 60 Gy). The most significant relationship between RT and esophagitis were in Dmax (>5117 cGy) (p=0.002) and percentage of esophageal volume receiving >30 Gy (V30>31%) (p=0.008) in the logistic regression analysis.

CONCLUSIONS

The maximum dose esophagus greater than 5117 cGy and approximately one third (31%) of the esophageal volume receiving >30 Gy was the most statistically significant predictive factor associated with esophagitis due to RT.

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

BACKGROUND

Hypothyroidism is a frequent late side effect of radiation therapy of the cervical region. Purpose of this work is to develop multivariate normal tissue complication probability (NTCP) models for radiation-induced hypothyroidism (RHT) and to compare them with already existing NTCP models for RHT.

METHODS

Fifty-three patients treated with sequential chemo-radiotherapy for Hodgkin's lymphoma (HL) were retrospectively reviewed for RHT events. Clinical information along with thyroid gland dose distribution parameters were collected and their correlation to RHT was analyzed by Spearman's rank correlation coefficient (Rs). Multivariate logistic regression method using resampling methods (bootstrapping) was applied to select model order and parameters for NTCP modeling. Model performance was evaluated through the area under the receiver operating characteristic curve (AUC). Models were tested against external published data on RHT and compared with other published NTCP models.

RESULTS

If we express the thyroid volume exceeding X Gy as a percentage (Vx(%)), a two-variable NTCP model including V30(%) and gender resulted to be the optimal predictive model for RHT (Rs = 0.615, p < 0.001. AUC = 0.87). Conversely, if absolute thyroid volume exceeding X Gy (Vx(cc)) was analyzed, an NTCP model based on 3 variables including V30(cc), thyroid gland volume and gender was selected as the most predictive model (Rs = 0.630, p < 0.001. AUC = 0.85). The three-variable model performs better when tested on an external cohort characterized by large inter-individuals variation in thyroid volumes (AUC = 0.914, 95% CI 0.760-0.984). A comparable performance was found between our model and that proposed in the literature based on thyroid gland mean dose and volume (p = 0.264).

CONCLUSIONS

The absolute volume of thyroid gland exceeding 30 Gy in combination with thyroid gland volume and gender provide an NTCP model for RHT with improved prediction capability not only within our patient population but also in an external cohort.

Monte Carlo role in radiobiological modelling of radiotherapy outcomes

Radiobiological models are essential components of modern radiotherapy. They are increasingly applied to optimize and evaluate the quality of different treatment planning modalities. They are frequently used in designing new radiotherapy clinical trials by estimating the expected therapeutic ratio of new protocols. In radiobiology, the therapeutic ratio is estimated from the expected gain in tumour control probability (TCP) to the risk of normal tissue complication probability (NTCP). However, estimates of TCP/NTCP are currently based on the deterministic and simplistic linear-quadratic formalism with limited prediction power when applied prospectively. Given the complex and stochastic nature of the physical, chemical and biological interactions associated with spatial and temporal radiation induced effects in living tissues, it is conjectured that methods based on Monte Carlo (MC) analysis may provide better estimates of TCP/NTCP for radiotherapy treatment planning and trial design. Indeed, over the past few decades, methods based on MC have demonstrated superior performance for accurate simulation of radiation transport, tumour growth and particle track structures; however, successful application of modelling radiobiological response and outcomes in radiotherapy is still hampered with several challenges. In this review, we provide an overview of some of the main techniques used in radiobiological modelling for radiotherapy, with focus on the MC role as a promising computational vehicle. We highlight the current challenges, issues and future potentials of the MC approach towards a comprehensive systems-based framework in radiobiological modelling for radiotherapy.