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

Spatial dose-distribution-based risk mapping to predict moist desquamation in breast radiotherapy

Objective.A relationship between the regional spatial distribution of skin dose and the development of moist desquamation (MD) was established for patients treated with breast radiotherapy.Approach.A 56-patient dataset was used to develop and validate a dose-distance based metric to predict MD. Dose distributions for the skin were extracted from AcurosXB treatment plans, and patient reported outcomes were used to classify the incidence of MD across the whole breast and then more specifically in the inferior breast. The sensitivity and specificity of the metric was compared against dose-area (A38 Gy ⩽ 50 cm2) and dose-volume (V105% ⩽ 2% of the breast volume) predictive metrics with the same dataset.Main results.With a sensitivity of 70% and a specificity of 72%, the dose-distance metric outperformed the dose-area (45%, 55%) and dose-volume (43%, 56%) predictive metrics. The test performance improves to a sensitivity and specificity of 81% when excluding the full coverage breast support devices that confounded the skin dose identification in the analysis.Significance.This metric offers regional MD prediction and risk mapping to highlight regions at high risk of developing severe skin toxicity and is suitable for implementation within the treatment planning process.This work is based on data acquired for the following clinical trials: ClinicalTrials.gov NCT04543851 and NCT04257396.

Validation study on accuracy of our newly proposed methods for post-therapeutic liver reserve capacity estimation utilizing 99 mTc-GSA scintigraphy prior to carbon-ion radiotherapy

OBJECTIVE

Carbon-ion radiotherapy (CIRT) is known as a promising treatment for liver tumors. However, no method has been established for estimating post-therapeutic residual liver reserve capacity (pRLRC). We previously introduced the estimation method of pRLRC using 99 mTc-galactosyl human serum albumin (99 mTc-GSA) scintigraphy (Yamazaki method). In this study, we developed "Yamazaki-variant" method for pRLRC using dose distribution data of CIRT planning. The purpose of this study was to compare pRLRC calculated by Yamazaki method and Yamazaki-variant in CIRT patients for liver tumors, and to provide the clinical advantages in both methods in terms of estimating ability of pRLRC.

METHODS

Patients who received CIRT for liver tumors in our hospital and underwent 99 mTc-GSA scintigraphy before and 3 months after CIRT, and contrast-enhanced liver MRI within 1 month before CIRT were included. A total of 71 patients, 21 additional to the 50 previously reported, were evaluated. The maximal removal rate of 99 mTc-GSA (GSA-Rmax) was analyzed and the GSA-Rmax of the estimated residual liver (GSA-RL) was calculated using fusion images of MRI and SPECT by Yamazaki method. In Yamazaki-variant, multiple simulations were performed using the dose distribution of the CIRT planning and SPECT fusion images to obtain higher diagnostic accuracy of GSA-RL. Two of these estimates, Validation 1 and Validation 2 by Yamazaki-variant, were used as validation data. GSA-RL and Validation 1 and 2 were compared with GSA-Rmax 3 months after CIRT (post-GSA-Rmax) using linear regression analysis.

RESULTS

GSA-RL, Validation 1 and 2 were calculated as 0.448 ± 0.214, 0.413 ± 0.199 and 0.435 ± 0.208 mg/min, respectively. Post-GSA-Rmax was 0.428 ± 0.220 mg/min. The relationship between post-GSA-Rmax and each parameter was y = 0.02 + 0.90x (R2 = 0.78) for GSA-RL, y = 0.02 + 0.94x (R2 = 0.79) for Estimation 1, y = 0.02 + 0.99x (R2 = 0.80) for Estimation 2, respectively (P <.0001). Both Yamazaki method and Yamazaki-variant showed comparable accurate estimation ability for post-GSA-Rmax.

CONCLUSIONS

The estimation of pRLRC by Yamazaki method and Yamazaki-variant was in good agreement with post-therapeutic liver reserve capacity after CIRT, and there was no difference in the accuracy of the estimation. The usefulness of Yamazaki method, which is simpler and more clinically applicable, was confirmed.

TRIAL REGISTRATION

This study is registered in UMIN Clinical Trials Registry (UMIN-CTR) as UMIN study ID: UMIN000038328 and UMIN000049770, UMIN000038174.

The potential of thermal imaging as an early predictive biomarker of radiation dermatitis during radiotherapy for head and neck cancer: a prospective study

BACKGROUND

Predicting radiation dermatitis (RD), a common radiotherapy toxicity, is essential for clinical decision-making regarding toxicity management. This prospective study aimed to develop and validate a machine-learning model to predict the occurrence of grade ≥ 2 RD using thermal imaging in the early stages of radiotherapy in head and neck cancer.

METHODS

Thermal images of neck skin surfaces were acquired weekly during radiotherapy. A total of 202 thermal images were used to calculate the difference map of neck skin temperature and analyze to extract thermal imaging features. Changes in imaging features during treatment were assessed in the two RD groups, grade ≥ 2 and grade ≤ 1 RD, classified according to the Common Terminology Criteria for Adverse Events (CTCAE) guidelines. Feature importance analysis was performed to select thermal imaging features correlated with grade ≥ 2 RD. A predictive model for grade ≥ 2 RD occurrence was developed using a machine learning algorithm and cross-validated. Area under the receiver-operating characteristic curve (AUC), precision, and sensitivity were used as evaluation metrics.

RESULTS

Of the 202 thermal images, 54 images taken before the occurrence of grade ≥ 2 RD were used to develop the predictive model. Thermal radiomics features related to the homogeneity of image texture were selected as input features of the machine learning model. The gradient boosting decision tree showed an AUC of 0.84, precision of 0.70, and sensitivity of 0.75 in models trained using thermal features acquired before skin dose < 10 Gy. The support vector machine achieved a mean AUC of 0.71, precision of 0.68, and sensitivity of 0.70 for predicting grade ≥ 2 RD using thermal images obtained in the skin dose range of 10-20 Gy.

CONCLUSIONS

Thermal images acquired from patients undergoing radiotherapy for head and neck cancer can be used as an early predictor of grade ≥ 2 RD and may aid in decision support for the management of acute skin toxicity from radiotherapy. However, our results should be interpreted with caution, given the limitations of this study.

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.

Recognizing cisplatin as a potential radiation recall trigger: case report and focused systematic review

We present a case of mild radiation recall dermatitis triggered by cisplatin chemotherapy given simultaneously to re-irradiation. The dermatitis area correlated to skin exposure of the previous radiation therapy, characterizing the reaction clearly as a recall. Cisplatin has not yet been recognized as a potential trigger for recall reactions. Although it was part of several reported multidrug trigger combinations, all review works referred to cisplatin as not suspicious, suggesting the combination partner as the effector. We performed a focused systematic literature review aiming to re-evaluate the real role of cisplatin as a (co-)triggering factor. In total, 30 reported cases were found, 90% triggered by multidrug combinations. The latter tended to cause more severe symptoms. Besides findings supporting the 20 Gy-threshold theory, no correlation between radiation dose and severity or prevalence was found. Recognition of cisplatin as a trigger of the recall phenomenon and its supportive management may prevent unnecessary cessation of systemic chemotherapy. Systematic reporting of recall events as a secondary endpoint of prospective clinical trials applying radiation therapy could support understanding the recall phenomenon.

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 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.