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.

Normal tissue complication probability models of hypothyroidism after radiotherapy for breast cancer.

Purpose: We aimed to develop Lyman-Kutcher-Burman (LKB) and multivariable normal tissue complication probability (NTCP) models to predict the risk of radiation-induced hypothyroidism (RIHT) in breast cancer patients. Materials and methods: A total of 1,063 breast cancer patients who underwent whole breast irradiation between 2009 and 2016 were analyzed. Individual dose-volume histograms were used to generate LKB and multivariable logistic regression models. LKB model was fit using the thyroid radiation dose-volume parameters. A multivariable model was constructed to identify potential dosimetric and clinical parameters associated with RIHT. Internal validation was conducted using bootstrapping techniques, and model performance was evaluated using the area under the curve (AUC) and Hosmer-Lemeshow (HL) goodness-of-fit test. Results: RIHT developed in 4 % of patients with a median follow-up of 77.7 months. LKB and multivariable NTCP models exhibited significant agreement between the predicted and observed results (HL P values > 0.05). The multivariable NTCP model outperformed the LKB model in predicting RIHT (AUC 0.62 vs. 0.54). In the multivariable model, systemic therapy, age, and percentage of thyroid volume receiving ≥ 10 Gy (V10) were significant prognostic factors for RIHT. The cumulative incidence of RIHT was significantly higher in patients who exceeded the cut-off values for all three risk predictors (systemic therapy, age ≥ 40 years, and thyroid V10 ≥ 26 %, P < 0.005). Conclusions: Systemic therapy, age, and V10 of the thyroid were identified as strong risk factors for the development of RIHT. Our NTCP models provide valuable insights to clinicians for predicting and preventing hypothyroidism by identifying high-risk patients.

Development and Validation of a Normal Tissue Complication Probability Model for Lymphedema After Radiation Therapy in Breast Cancer.

PURPOSE: To develop and test a multivariable normal tissue complication probability (NTCP) model predicting lymphedema in patients with breast cancer receiving radiation therapy. METHODS AND MATERIALS: We retrospectively reviewed 1345 patients with breast cancer who received radiation therapy from 2 independent institutions. The patients were divided into a training cohort (institution A, n = 368, all treated with 3-dimensional conformal external beam radiation therapy [RT] with 2 Gy/fraction) and an external validation cohort (institution B, n = 977, treated either with 3-dimensional conformal external beam RT or with volumetric modulated RT and either with 1.8-2.0 Gy/fraction or with 2.67 Gy/fraction). Axillary-lateral thoracic vessel juncture (ALTJ) was delineated. The multivariable model was generated using dosimetric and clinical parameters. The performance of the model was comprehensively validated internally and externally. RESULTS: During a median follow-up of 78.7 months for the entire cohort, 97 patients (7.2%) developed lymphedema. The multivariable model that took into account the number of lymph nodes dissected, as well as the volume of the ALTJ receiving a dose ≥35 Gy equivalent doses in 2-Gy fractions (ALTJ V35), showed good agreement between predicted and observed results for both internal and external validation (Hosmer-Lemeshow P value > .05). The area under the receiver operating characteristic curve (AUC) and negative log-likelihood values for the multivariable NTCP model were 0.89 and 0.19 in internal validation and 0.83 and 0.19 in external validation. In addition, the multivariable model performance was acceptable for hypofractionated regimens (AUC 0.70) and volumetric modulated arc therapy (AUC 0.69). The number of lymph nodes dissected and ALTJ V35 were found to be the most important factors influencing lymphedema after radiation therapy. CONCLUSIONS: We first developed and validated the multivariable NTCP model for the lymphedema incidence in patients with breast cancer after radiation therapy. The multivariable NTCP model showed excellent performance and robustness in predicting lymphedema in both internal and completely independent external validations. The multivariable model for lymphedema prediction was robust and reliable for different treatment modalities and fractionation regimens.

Novel framework for determining TPS-calculated doses corresponding to detector locations using 3D camera inin vivosurface dosimetry.

Purpose. To address the shortcomings of current procedures for evaluating the measured-to-planned dose agreement inin vivodosimetry (IVD), this study aimed to develop an accurate and efficient novel framework to identify the detector location placed on a patient's skin surface using a 3D camera and determine the planned dose at the same anatomical position corresponding to the detector location.Methods. Breast cancer treatment was simulated using an anthropomorphic adult female phantom (ATOM 702D; CIRS, Norfolk, VA, USA). An optically stimulated luminescent dosimeter was used for surface dose measurements (MyOSLchip, RadPro International GmbH, Germany) at six IVD points. Three-dimensional surface imaging (3DSI) of the phantom with the detector was performed in the treatment position using a 3D camera. The developed framework, iSMART, was designed to import 3DSI and treatment planning data for determining the position of the IVD detectors in the 3D treatment planning DICOM image. The clinical usefulness of iSMART was evaluated in terms of accuracy and efficiency, for comparison with the results obtained using cone-beam computed tomography (CBCT) image guidance.Results. The relative dose difference between the planned doses determined using iSMART and CBCT images displayed similar accuracies (within approximately ±2.0%) at all detector locations. The relative dose differences between the planned and measured doses at the six detector locations ranged from -4.8% to 3.1% for the CBCT images and -3.5% to 2.1% for iSMART. The total time required to read the planned doses at six detector locations averaged at 8.1 and 0.8 min for the CBCT images and iSMART, respectively.Conclusions. The proposed framework can improve the robustness of IVD analyses and aid in accurate and efficient evaluations of the measured-to-planned dose agreement.

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.