Skin DVHs predict cutaneous toxicity in Head and Neck Cancer patients treated with Tomotherapy

Dose-toxicity surface histogram-based prediction of radiation dermatitis severity and shape

Objective.This study aimed to develop a new approach to predict radiation dermatitis (RD) by using the skin dose distribution in the actual area of RD occurrence to determine the predictive dose by grade.Approach.Twenty-three patients with head and neck cancer treated with volumetric modulated arc therapy were prospectively and retrospectively enrolled. A framework was developed to segment the RD occurrence area in skin photography by matching the skin surface image obtained using a 3D camera with the skin dose distribution. RD predictive doses were generated using the dose-toxicity surface histogram (DTH) calculated from the skin dose distribution within the segmented RD regions classified by severity. We then evaluated whether the developed DTH-based framework could visually predict RD grades and their occurrence areas and shapes according to severity.Main results.The developed framework successfully generated the DTH for three different RD severities: faint erythema (grade 1), dry desquamation (grade 2), and moist desquamation (grade 3); 48 DTHs were obtained from 23 patients: 23, 22, and 3 DTHs for grades 1, 2, and 3, respectively. The RD predictive doses determined using DTHs were 28.9 Gy, 38.1 Gy, and 54.3 Gy for grades 1, 2, and 3, respectively. The estimated RD occurrence area visualized by the DTH-based RD predictive dose showed acceptable agreement for all grades compared with the actual RD region in the patient. The predicted RD grade was accurate, except in two patients.Significance. The developed DTH-based framework can classify and determine RD predictive doses according to severity and visually predict the occurrence area and shape of different RD severities. The proposed approach can be used to predict the severity and shape of potential RD in patients and thus aid physicians in decision making.

A dosiomics model for prediction of radiation-induced acute skin toxicity in breast cancer patients: machine learning-based study for a closed bore linac

BACKGROUND

Radiation induced acute skin toxicity (AST) is considered as a common side effect of breast radiation therapy. The goal of this study was to design dosiomics-based machine learning (ML) models for prediction of AST, to enable creating optimized treatment plans for high-risk individuals.

METHODS

Dosiomics features extracted using Pyradiomics tool (v3.0.1), along with treatment plan-derived dose volume histograms (DVHs), and patient-specific treatment-related (PTR) data of breast cancer patients were used for modeling. Clinical scoring was done using the Common Terminology Criteria for Adverse Events (CTCAE) V4.0 criteria for skin-specific symptoms. The 52 breast cancer patients were grouped into AST 2 + (CTCAE ≥ 2) and AST 2 - (CTCAE < 2) toxicity grades to facilitate AST modeling. They were randomly divided into training (70%) and testing (30%) cohorts. Multiple prediction models were assessed through multivariate analysis, incorporating different combinations of feature groups (dosiomics, DVH, and PTR) individually and collectively. In total, seven unique combinations, along with seven classification algorithms, were considered after feature selection. The performance of each model was evaluated on the test group using the area under the receiver operating characteristic curve (AUC) and f1-score. Accuracy, precision, and recall of each model were also studied. Statistical analysis involved features differences between AST 2 - and AST 2 + groups and cutoff value calculations.

RESULTS

Results showed that 44% of the patients developed AST 2 + after Tomotherapy. The dosiomics (DOS) model, developed using dosiomics features, exhibited a noteworthy improvement in AUC (up to 0.78), when spatial information is preserved in the dose distribution, compared to DVH features (up to 0.71). Furthermore, a baseline ML model created using only PTR features for comparison with DOS models showed the significance of dosiomics in early AST prediction. By employing the Extra Tree (ET) classifiers, the DOS + DVH + PTR model achieved a statistically significant improved performance in terms of AUC (0.83; 95% CI 0.71-0.90), accuracy (0.70), precision (0.74) and sensitivity (0.72) compared to other models.

CONCLUSIONS

This study confirmed the benefit of dosiomics-based ML in the prediction of AST. However, the combination of dosiomics, DVH, and PTR yields significant improvement in AST prediction. The results of this study provide the opportunity for timely interventions to prevent the occurrence of radiation induced AST.

Skin dose-volume predictors of moderate-severe late side effects after whole breast radiotherapy

BACKGROUND

Toxicity after whole breast Radiotherapy is a relevant issue, impacting the quality-of-life of a not negligible number of patients. We aimed to develop a Normal Tissue Complication Probability (NTCP) model predicting late toxicities by combining dosimetric parameters of the breast dermis and clinical factors.

METHODS

The skin structure was defined as the outer CT body contour's 5 mm inner isotropic expansion. It was retrospectively segmented on a large mono-institutional cohort of early-stage breast cancer patients enrolled between 2009 and 2017 (n = 1066). Patients were treated with tangential-field RT, delivering 40 Gy in 15 fractions to the whole breast. Toxicity was reported during Follow-Up (FU) using SOMA/LENT scoring. The study endpoint was moderate-severe late side effects consisting of Fibrosis-Atrophy-Telangiectasia-Pain (FATP G ≥ 2) developed within 42 months after RT completion. A machine learning pipeline was designed with a logistic model combining clinical factors and absolute skin DVH (cc) parameters as output.

RESULTS

The FATP G2 + rate was 3.8 %, with 40/1066 patients experiencing side effects. After the preprocessing of variables, a cross-validation was applied to define the best-performing model. We selected a 4-variable model with Post-Surgery Cosmetic alterations (Odds Ratio, OR = 7.3), Aromatase Inhibitors (as a protective factor with OR = 0.45), V20 Gy (50 % of the prescribed dose, OR = 1.02), and V42 Gy (105 %, OR = 1.09). Factors were also converted into an adjusted V20Gy.

CONCLUSIONS

The association between late reactions and skin DVH when delivering 40 Gy/15 fr was quantified, suggesting an independent role of V20 and V42. Few clinical factors heavily modulate the risk.

A novel approach to predict acute radiation dermatitis in patients with head and neck cancer using a model based on Bayesian probability

PURPOSE

In this study, we aimed to establish a method for predicting the probability of each acute radiation dermatitis (ARD) grade during the head and neck Volumetric Modulated Arc Therapy (VMAT) radiotherapy planning phase based on Bayesian probability.

METHODS

The skin dose volume >50 Gy (V50), calculated using the treatment planning system, was used as a factor related to skin toxicity. The empirical distribution of each ARD grade relative to V50 was obtained from the ARD grades of 119 patients (55, 50, and 14 patients with G1, G2, and G3, respectively) determined by head and neck cancer specialists. Using Bayes' theorem, the Bayesian probabilities of G1, G2, and G3 for each value of V50 were calculated with an empirical distribution. Conversely, V50 was obtained based on the Bayesian probabilities of G1, G2, and G3.

RESULTS

The empirical distribution for each graded patient group demonstrated a normal distribution. The method predicted ARD grades with 92.4 % accuracy and provided a V50 value for each grade. For example, using the graph, we could predict that V50 should be ≤24.5 cm3 to achieve G1 with 70 % probability.

CONCLUSIONS

The Bayesian probability-based ARD prediction method could predict the ARD grade at the treatment planning stage using limited patient diagnostic data that demonstrated a normal distribution. If the probability of an ARD grade is high, skin care can be initiated in advance. Furthermore, the V50 value during treatment planning can provide radiation oncologists with data for strategies to reduce ARD.

Helical tomotherapy and two types of volumetric modulated arc therapy: dosimetric and clinical comparison for several cancer sites

Radiotherapy accelerators have undergone continuous technological developments. We investigated the differences between Radixact™ and VMAT treatment plans. Sixty patients were included in this study. Dosimetric comparison between the Radixact™ and VMAT plans was performed for six cancer sites: whole-brain, head and neck, lymphoma, lung, prostate, and rectum. The VMAT plans were generated with two Elekta linear accelerators (Synergy^® and Versa HD™). The planning target volume (PTV) coverage, organs-at-risk dose constraints, and four dosimetric indexes were considered. The deliverability of the plans was assessed using quality assurance (gamma index evaluation) measurements; clinical judgment was included in the assessment. The mean AAPM TG218 (3%-2 mm, global normalization) gamma index values were 99.4%, 97.8%, and 96.6% for Radixact™, Versa HD™, and Synergy®, respectively. Radixact™ performed better than Versa HD™ in terms of dosimetric indexes, hippocampi D_100%, spinal cord D_max, rectum V_{38.4  Gy}, bladder V_{30 Gy}, and V_{40 Gy}. Versa HD™ saved more of the (lungs-PTV) V_{5 Gy} and (lungs-PTV) D_mean, heart D_mean, breasts V_{4 Gy}, and bowel V_{45 Gy}. Regarding Synergy^®, the head and neck Radixact™ plan saved more of the parotid gland, oral cavity, and supraglottic larynx. From a clinical point of view, for the head and neck, prostate, and rectal sites, the Radixact™ and Versa HD™ plans were similar; Radixact™ plans were preferable for the head and neck and rectum to Synergy^® plans. The quality of linac plans has improved, and differences with tomotherapy have decreased. However, tomotherapy continues to be an essential add-on in multi-machine departments.

Stomach and duodenum dose-volume constraints for locally advanced pancreatic cancer patients treated in 15 fractions in combination with chemotherapy

Purpose

To assess dosimetry predictors of gastric and duodenal toxicities for locally advanced pancreatic cancer (LAPC) patients treated with chemo-radiotherapy in 15 fractions.

Methods

Data from 204 LAPC patients treated with induction+concurrent chemotherapy and radiotherapy (44.25 Gy in 15 fractions) were available. Forty-three patients received a simultaneous integrated boost of 48-58 Gy. Gastric/duodenal Common Terminology Criteria for Adverse Events v. 5 (CTCAEv5) Grade ≥2 toxicities were analyzed. Absolute/% duodenal and stomach dose-volume histograms (DVHs) of patients with/without toxicities were compared: the most predictive DVH points were identified, and their association with toxicity was tested in univariate and multivariate logistic regressions together with near-maximum dose (D0.03) and selected clinical variables.

Results

Toxicity occurred in 18 patients: 3 duodenal (ulcer and duodenitis) and 10 gastric (ulcer and stomatitis); 5/18 experienced both. At univariate analysis, V44cc (duodenum: p = 0.02, OR = 1.07; stomach: p = 0.01, OR = 1.12) and D0.03 (p = 0.07, OR = 1.19; p = 0.008, OR = 1.12) were found to be the most predictive parameters. Stomach/duodenum V44Gy and stomach D0.03 were confirmed at multivariate analysis and found to be sufficiently robust at internal, bootstrap-based validation; the results regarding duodenum D0.03 were less robust. No clinical variables or %DVH was significantly associated with toxicity. The best duodenum cutoff values were V44Gy < 9.1 cc (and D0.03 < 47.6 Gy); concerning the stomach, they were V44Gy < 2 cc and D0.03 < 45 Gy. The identified predictors showed a high negative predictive value (>94%).

Conclusion

In a large cohort treated with hypofractionated radiotherapy for LAPC, the risk of duodenal/gastric toxicities was associated with duodenum/stomach DVH. Constraining duodenum V44Gy < 9.1 cc, stomach V44Gy < 2 cc, and stomach D0.03 < 45 Gy should keep the toxicity rate at approximately or below 5%. The association with duodenum D0.03 was not sufficiently robust due to the limited number of events, although results suggest that a limit of 45-46 Gy should be safe.

IMPT of head and neck cancer: unsupervised machine learning treatment planning strategy for reducing radiation dermatitis

Radiation dermatitis is a major concern in intensity modulated proton therapy (IMPT) for head and neck cancer (HNC) despite its demonstrated superiority over contemporary photon radiotherapy. In this study, dose surface histogram data extracted from forty-four patients of HNC treated with IMPT was used to predict the normal tissue complication probability (NTCP) of skin. Grades of NTCP-skin were clustered using the K-means clustering unsupervised machine learning (ML) algorithm. A new skin-sparing IMPT (IMPT-SS) planning strategy was developed with three major changes and prospectively implemented in twenty HNC patients. Across skin surfaces exposed from 10 (S10) to 70 (S70) GyRBE, the skin's NTCP demonstrated the strongest associations with S50 and S40 GyRBE (0.95 and 0.94). The increase in the NTCP of skin per unit GyRBE is 0.568 for skin exposed to 50 GyRBE as compared to 0.418 for 40 GyRBE. Three distinct clusters were formed, with 41% of patients in G1, 32% in G2, and 27% in G3. The average (± SD) generalised equivalent uniform dose for G1, G2, and G3 clusters was 26.54 ± 6.75, 38.73 ± 1.80, and 45.67 ± 2.20 GyRBE. The corresponding NTCP (%) were 4.97 ± 5.12, 48.12 ± 12.72 and 87.28 ± 7.73 respectively. In comparison to IMPT, new IMPT-SS plans significantly (P < 0.01) reduced SX GyRBE, gEUD, and associated NTCP-skin while maintaining identical dose volume indices for target and other organs at risk. The mean NTCP-skin value for IMPT-SS was 34% lower than that of IMPT. The dose to skin in patients treated prospectively for HNC was reduced by including gEUD for an acceptable radiation dermatitis determined from the local patient population using an unsupervised MLA in the spot map optimization of a new IMPT planning technique. However, the clinical finding of acute skin toxicity must also be related to the observed reduction in skin dose.

A New Approach to Quantify and Grade Radiation Dermatitis Using Deep-Learning Segmentation in Skin Photographs

AIMS

Objective evaluation of radiation dermatitis is important for analysing the correlation between the severity of radiation dermatitis and dose distribution in clinical practice and for reliable reporting in clinical trials. We developed a novel radiation dermatitis segmentation system based on convolutional neural networks (CNNs) to consistently evaluate radiation dermatitis.

MATERIALS AND METHODS

The radiation dermatitis segmentation system is designed to segment the radiation dermatitis occurrence area using skin photographs and skin-dose distribution. A CNN architecture with a dilated convolution layer and skip connection was designed to estimate the radiation dermatitis area. Seventy-three skin photographs obtained from patients undergoing radiotherapy were collected for training and testing. The ground truth of radiation dermatitis segmentation is manually delineated from the skin photograph by an experienced radiation oncologist and medical physicist. We converted the skin photographs to RGB (red-green-blue) and CIELAB (lightness (L^∗), red-green (a^∗) and blue-yellow (b^∗)) colour information and trained the network to segment faint and severe radiation dermatitis using three different input combinations: RGB, RGB + CIELAB (RGBLAB) and RGB + CIELAB + skin-dose distribution (RGBLAB_D). The proposed system was evaluated using the Dice similarity coefficient (DSC), sensitivity, specificity and normalised Matthews correlation coefficient (nMCC). A paired t-test was used to compare the results of different segmentation performances.

RESULTS

Optimal data composition was observed in the network trained for radiation dermatitis segmentation using skin photographs and skin-dose distribution. The average DSC, sensitivity, specificity and nMCC values of RGBLAB_D were 0.62, 0.61, 0.91 and 0.77, respectively, in faint radiation dermatitis, and 0.69, 0.78, 0.96 and 0.83, respectively, in severe radiation dermatitis.

CONCLUSION

Our study showed that CNN-based radiation dermatitis segmentation in skin photographs of patients undergoing radiotherapy can describe radiation dermatitis severity and pattern. Our study could aid in objectifying the radiation dermatitis grading and analysing the reliable correlation between dosimetric factors and the morphology of radiation dermatitis.

Acute radiation dermatitis among patients with nasopharyngeal carcinoma treated with proton beam therapy: Prognostic factors and treatment outcomes

A high incidence of severe acute radiation dermatitis (ARD) has been reported for cancer patients treated by proton beam therapy (PBT). This observational study investigated the prognostic factors and treatment outcomes of ARD among patients with nasopharyngeal carcinoma (NPC) treated with PBT. Fifty-seven patients with newly diagnosed NPC and treated with PBT were enrolled. ARD was recorded weekly based on the criteria of Common Terminology Criteria for Adverse Events version 4.0 at treatment visits (1st to 7th weeks) and 1 week (8th week) and 1 month (11th week) after the completion of PBT. The maximum ARD grade was 1, 2, and 3 in 26 (45.6%), 24 (42.1%), and 7 (12.3%) of the patients, respectively. The peak incidence of grade 2 and 3 ARD was observed during the period of the 6th to 8th weeks. Treatment of ARD included topical corticosteroid alone in 24 (42.1%) patients, topical corticosteroid plus silver sulfadiazine in 33 (57.9%) patients, and non-adhering silicone dressing to cover severe skin wound area in 25 (43.8%) patients. In the 11th week, most grade 2 and 3 ARD had disappeared and 93.0% of the patients had ARD of grade 1 or lower. In the binary logistic regression model, we identified habitual smoking (odds ratio [OR]: 5.2, 95% confidence interval [CI]: 1.3-18.8, P = .012) and N2 to N3 nodal status (OR: 4.9, 95% CI: 1.6-15.4, P = .006) as independent predictors of grade 2 and 3 ARD. The results show ARD is a major concern for patients with NPC treated with PBT, especially those with habitual smoking or advanced nodal status. Topical corticosteroid, silver sulfadiazine, and non-adhering silicone dressing are effective for treating ARD induced by PBT.

Dosimetric Parameters Related to Acute Radiation Dermatitis of Patients with Nasopharyngeal Carcinoma Treated by Intensity-Modulated Proton Therapy

Background: Growing patients with nasopharyngeal carcinoma (NPC) were treated with intensity-modulated proton therapy (IMPT). However, a high probability of severe acute radiation dermatitis (ARD) was observed. The objective of the study is to investigate the dosimetric parameters related to ARD for NPC patients treated with IMPT. Methods: Sixty-two patients with newly diagnosed NPC were analyzed. The ARD was recorded based on the criteria of Common Terminology Criteria for Adverse Events version 4.0. Logistic regression model was performed to identify the clinical and dosimetric parameters related to ARD. Receiver operating characteristic (ROC) curve analysis and the area under the curve (AUC) were used to evaluate the performance of the models. Results: The maximum ARD grade was 1, 2, and 3 in 27 (43.5%), 26 (42.0%), and 9 (14.5%) of the patients, respectively. Statistically significant differences (p < 0.01) in average volume to skin 5 mm with the respective doses were observed in the range 54−62 Cobalt Gray Equivalent (CGE) for grade 2 and 3 versus grade 1 ARD. Smoking habit and N2-N3 status were identified as significant predictors to develop grade 2 and 3 ARD in clinical model, and V58CGE to skin 5 mm as an independent predictor in dosimetric model. After adding the variable of V58CGE to the metric incorporating two parameters of smoking habit and N status, the AUC value of the metric increases from 0.78 (0.66−0.90) to 0.82 (0.72−0.93). The most appropriate cut-off value of V58CGE to skin 5 mm as determined by ROC curve was 5.0 cm3, with a predicted probability of 54% to develop grade 2 and 3 ARD. Conclusion: The dosimetric parameter of V58CGE to skin 5 mm < 5.0 cm3 could be used as a constraint in treatment planning for NPC patients treated by IMPT.

Normal Tissue Complication Probability Model for Acute Radiation Dermatitis in Patients with Head and Neck Cancer Treated with Carbon Ion Radiotherapy

PURPOSE

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 (HNC) treated with carbon ion radiotherapy (CIRT) was developed.

MATERIALS AND METHODS

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 (AUC) in the internal cross-validation and Akaike's corrected Information Criterion (AICc) were examined for model evaluation and selection. The multivariate logistic regression NTCP models were established based on factors with weak correlation.

RESULTS

Tumour volume, planning target volume to the skin, radiation technique and all dose-surface parameters were significantly associated with ARD (P < 0.05). Models with high performance for grade 2-3 ARD were constructed. The most significant prognostic predictors were S_{40 Gy (relative biological effectiveness, RBE)} and S_{20 Gy} [absolute surface area receiving RBE-weighted dose of 40 Gy (RBE) or physical dose of 20 Gy]. The internal cross-validation-based AUCs for models with S_{40 Gy (RBE)} and S_{20 Gy} 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 two factors was not better than that of the univariate models.

CONCLUSIONS

NTCP models for ARD may provide a basis for the development of individualised treatment strategies and reduce the incidence of severe ARD in patients with HNC receiving CIRT. Furthermore, biological and physical dose-surface parameter-based models are comparable. However, further validation with more evaluation parameters is warranted.

A pilot study of a novel method to visualize three-dimensional dose distribution on skin surface images to evaluate radiation dermatitis

Predicting the radiation dose‒toxicity relationship is important for local tumor control and patients’ quality of life. We developed a first intuitive evaluation system that directly matches the three-dimensional (3D) dose distribution with the skin surface image of patients with radiation dermatitis (RD) to predict RD in patients undergoing radiotherapy. Using an RGB-D camera, 82 3D skin surface images (3DSSIs) were acquired from 19 patients who underwent radiotherapy. 3DSSI data acquired included 3D skin surface shape and optical imaging of the area where RD occurs. Surface registration between 3D skin dose (3DSD) and 3DSSI is performed using the iterative closest point algorithm, then reconstructed as a two-dimensional color image. The developed system successfully matched 3DSSI and 3DSD, and visualized the planned dose distribution onto the patient's RD image. The dose distribution pattern was consistent with the occurrence pattern of RD. This new approach facilitated the evaluation of the direct correlation between skin-dose distribution and RD and, therefore, provides a potential to predict the probability of RD and thereby decrease RD severity by enabling informed treatment decision making by physicians. However, the results need to be interpreted with caution due to the small sample size.

Helical tomotherapy: Comparison of Hi-ART and Radixact clinical patient treatments at the Technical University of Munich

The helical tomotherapy (HT) Hi-ART system was installed at our department in April 2007. In July 2018 the first Radixact system in Germany has been launched for clinical use. We present differences, advantages and disadvantages and show future perspectives in patient treatment using two HT devices. We investigate patient characteristics, image quality, radiotherapy treatment specifications and analyze the time effort for treatments with the Hi-ART system from April 2010 until May 2017 and compare it to the data acquired in the first nine months of usage of the Radixact system. Comparing the Hi-ART and Radixact system, the unique option of integrated MVCT image acquisition has experienced distinct improvement in image quality. Time effort for irradiation treatment could be improved resulting in a mean beam on time for craniospinal axis treatment of 636.2 s for the Radixact system compared to 915.9 s for the Hi-ART system. The beneficial use of tomotherapy for complex target volumes is demonstrated by a head and neck tumor case and craniospinal axis treatment. With the Radixact system MVCT image quality has been improved allowing for fast and precise interfraction dose adaptation. The improved time effort for patient treatment could increase the accessibility for clinical usage.

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

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.

Analysis of skin dose distribution for the prediction of severe radiation dermatitis in head and neck squamous cell carcinoma patients treated with concurrent chemo-radiotherapy

BACKGROUND

We investigated whether the pattern of intensity-modulated radiotherapy (IMRT) dose distribution to the skin can be correlated with the development of G3/G4 radiation dermatitis (RD).

METHODS

A frequency-matched cohort analysis was perfomed on patients treated with IMRT and concurrent cisplatin or cetuximab. Risk ratios were obtained by fitting Poisson regression models.

RESULTS

The incidence of G3/G4 RD was 41.1% in 90 patients included (50% vs 36.6% in the cetuximab and cisplatin cohorts, respectively). In multivariate analysis, PS ≥ 1 and weight loss at RT completion >10 kg were the only factors that retained significance. The best dosimetric predictive accuracy was provided by 19.9 cc and 5.8 cc of skin ring 2 mm V50 and V60, respectively (AUC: 0.61 for both).

CONCLUSION

Along with clinical factors, the pattern of dose distribution to a ring structure localized 2 mm below the patient's surface may help predict the development of severe RD.

Monitoring skin dose changes during image-guided helical tomotherapy for head and neck cancer patients

PURPOSE

An increase of skin dose during head and neck cancer (HNC) radiotherapy is potentially dangerous. Aim of this study was to quantify skin dose variation and to assess the need of planning adaptation (ART) to counteract it.

METHODS

Planning CTs of 32 patients treated with helical tomotherapy (HT) according to a Simultaneous Integrated Boost (SIB) technique delivering 54/66 Gy in 30 fractions were deformably co-registered to MVCTs taken at fractions 15 and 30; in addition, the first fraction was also considered. The delivered dose-of-the-day was calculated on the corresponding deformed images. Superficial body layers (SL) were considered as a surrogate for skin, considering a layer thickness of 2 mm. Variations of SL DVH (∆SL) during therapy were quantified, focusing on ∆SL95% (i.e., 62.7 Gy).

RESULTS

Small changes (within ± 1 cc for ∆SL95%) were seen in 15/32 patients. Only 2 patients experienced ∆SL95% > 1 cc in at least one of the two monitored fractions. Negative ∆SL95% > 1 cc (up to 17 cc) were much more common (15/32 patients). The trend of skin dose changes was mostly detected at the first fraction. Negative changes were correlated with the presence of any overlap between PTV and SL at planning and were explained in terms of how the planning system optimizes the PTV dose coverage near the skin. Acute toxicity was associated with planning DVH and this association was not improved if considering DVHs referring to fractions 15/30.

CONCLUSION

About half of the patients treated with SIB with HT for HNC experienced a skin-sparing effect during therapy; only 6% experienced an increase. Our findings do not support skin-sparing ART, while suggesting the introduction of improved skin-sparing planning techniques.