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NTCP Models for Severe Radiation Induced Dermatitis After IMRT or Proton Therapy for Thoracic Cancer Patients.

ABSTRACT: Radiation therapy (RT) of thoracic cancers may cause severe radiation dermatitis (RD), which impacts on the quality of a patient's life. Aim of this study was to analyze the incidence of acute RD and develop normal tissue complication probability (NTCP) models for severe RD in thoracic cancer patients treated with Intensity-Modulated RT (IMRT) or Passive Scattering Proton Therapy (PSPT). We analyzed 166 Non-Small-Cell Lung Cancer (NSCLC) patients prospectively treated at a single institution with IMRT (103 patients) or PSPT (63 patients). All patients were treated to a prescribed dose of 60 to 74 Gy in conventional daily fractionation with concurrent chemotherapy. RD was scored according to CTCAE v3 scoring system. For each patient, the epidermis structure (skin) was automatically defined by an in house developed segmentation algorithm. The absolute dose-surface histogram (DSH) of the skin were extracted and normalized using the Body Surface Area (BSA) index as scaling factor. Patient and treatment-related characteristics were analyzed. The Lyman-Kutcher-Burman (LKB) NTCP model recast for DSH and the multivariable logistic model were adopted. Models were internally validated by Leave-One-Out method. Model performance was evaluated by the area under the receiver operator characteristic curve, and calibration plot parameters. Fifteen of 166 (9%) patients developed severe dermatitis (grade 3). RT technique did not impact RD incidence. Total gross tumor volume (GTV) size was the only non dosimetric variable significantly correlated with severe RD (p = 0.027). Multivariable logistic modeling resulted in a single variable model including S 20Gy, the relative skin surface receiving more than 20 Gy (OR = 31.4). The cut off for S 20Gy was 1.1% of the BSA. LKB model parameters were TD50 = 9.5 Gy, m = 0.24, n = 0.62. Both NTCP models showed comparably high prediction and calibration performances. Despite skin toxicity has long been considered a potential limiting factor in the clinical use of PSPT, no significant differences in RD incidence was found between RT modalities. Once externally validated, the availability of NTCP models for prediction of severe RD may advance treatment planning optimization.

SUBMITTER: Palma G

PROVIDER: S-EPMC7090153 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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NTCP Models for Severe Radiation Induced Dermatitis After IMRT or Proton Therapy for Thoracic Cancer Patients.

Palma Giuseppe G Monti Serena S Conson Manuel M Xu Ting T Hahn Stephen S Durante Marco M Mohan Radhe R Liao Zhongxing Z Cella Laura L

Frontiers in oncology 20200317

Radiation therapy (RT) of thoracic cancers may cause severe radiation dermatitis (RD), which impacts on the quality of a patient's life. Aim of this study was to analyze the incidence of acute RD and develop normal tissue complication probability (NTCP) models for severe RD in thoracic cancer patients treated with Intensity-Modulated RT (IMRT) or Passive Scattering Proton Therapy (PSPT). We analyzed 166 Non-Small-Cell Lung Cancer (NSCLC) patients prospectively treated at a single institution wit ...[more]

PMID: 32257950

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Project description:PurposeTo 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 materialsA 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.ResultsThe 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.ConclusionsIn 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.

Project description: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.

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Project description:Chronic radiation dermatitis is a late side effect of skin irradiation, which may deteriorate patients' quality of life. There is a lack of precise data about its incidence; however, several risk factors may predispose to the development of this condition. It includes radiotherapy dose, fractionation, technique, concurrent systemic therapy, comorbidities, and personal and genetic factors. Chronic radiation dermatitis is mostly caused by the imbalance of proinflammatory and profibrotic cytokines. Clinical manifestation includes changes in skin appearance, wounds, ulcerations, necrosis, fibrosis, and secondary cancers. The most severe complication of irradiation is extensive radiation-induced fibrosis (RIF). RIF can manifest in many ways, such as skin induration and retraction, lymphedema or restriction of joint motion. Diagnosis of chronic radiation dermatitis is usually made by clinical examination. In case of unclear clinical manifestation, a biopsy and histopathological examination are recommended to exclude secondary malignancy. The most effective prophylaxis of chronic radiation dermatitis is the use of proper radiation therapy techniques to avoid unnecessary irradiation of healthy skin. Treatment of chronic radiation dermatitis is demanding. The majority of the interventions are based only on clinical practice. Telangiectasia may be treated with pulse dye laser therapy. Chronic postirradiation wounds need special dressings. In case of necrosis or severe ulceration, surgical intervention may be considered. Management of RIF should be complex. Available methods are rehabilitative care, pharmacotherapy, hyperbaric oxygen therapy, and laser therapy. Future challenges include the assessment of late skin toxicity in modern irradiation techniques. Special attention should be paid on genomics and radiomics that allow scientists and clinicians to select patients who are at risk of the development of chronic radiation dermatitis. Novel treatment methods and clinical trials are strongly needed to provide more efficacious therapies.

Dataset Information

NTCP Models for Severe Radiation Induced Dermatitis After IMRT or Proton Therapy for Thoracic Cancer Patients.

Publications

NTCP Models for Severe Radiation Induced Dermatitis After IMRT or Proton Therapy for Thoracic Cancer Patients.

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