TOXICAN: a guide for grading dermatological adverse events of cancer treatments

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.

A survey of accuracy of nurses' clinical judgement of cutaneous graft-versus-host disease in Japan

AIM

We examined accuracy of nurses' clinical judgement of graft-versus-host-disease (GVHD) symptoms and related factors using Common Terminology Criteria for Adverse Events (CTCAE) for patients who developed chronic cutaneous GVHD after haematopoietic stem cell transplants.

DESIGN

Cross-sectional design using nationwide survey.

METHODS

A questionnaire survey based on Tanner's clinical judgement model to assess patients with chronic cutaneous GVHD using CTCAE was used. Free-text descriptions and statistical analyses of relationship between correct responses and demographic data were performed.

RESULTS

The rate of correct responses for main symptoms of skin GVHD was < 50%; there was no statistical significance between correct responses and demographic data, knowledge about GVHD and collaborative practice with physicians. The accuracy of cutaneous GVHD clinical judgements was not directly related to nurses' background. Educational opportunities that reinforce nurses' abilities to reflect on knowledge and experiences to interpret patient symptoms are essential for improving accuracy of clinical judgement.