Artificial intelligence-based prediction of oral mucositis in patients with head-and-neck cancer: A prospective observational study utilizing a thermographic approach
Ruchika Thukral, Ashwani Kumar Aggarwal, Ajat Shatru Arora, Tapas Dora, Sankalp Sancheti- Cancer Research
- Oncology (nursing)
- Drug Guides
- Oncology
ABSTRACT
JOURNAL/crsat/04.03/02201859-202306020-00003/figure1/v/2023-08-03T140821Z/r/image-tiff
JOURNAL/crsat/04.03/02201859-202306020-00003/figure2/v/2023-08-03T140821Z/r/image-tiff
Background:
In patients with locally advanced head-and-neck squamous cell carcinoma (HNSCC), chemoradiotherapy improves outcomes. Radiotherapy commonly causes mucositis, which can significantly impede treatment and reduce the patient’s quality of life. Patients with severe mucocutaneous toxicity will show significant changes in thermal intensity early on, when compared to identically treated counterparts without toxicity.
Objective:
We aimed to assess the accuracy of the automated computer-aided deep learning approach in predicting the occurrence of oral mucositis in patients with HNSCC undergoing radiotherapy alone or with concurrent chemotherapy.
Materials and Methods:
This prospective observational study was conducted over four weeks in September 2021 in the Department of Radiotherapy at the Homi Bhabha Cancer Hospital, Sangrur (Punjab, India). We enrolled patients with HNSCC who were planned for radical intent radiotherapy, with or without concurrent chemotherapy. Using an automated deep learning technique, we analyzed the images taken with a FLIR-E60 thermal camera on the same day that patients received radiotherapy, with or without chemotherapy. Thermal images were binarily classified into two grades, that is, Grade 0 (absence of mucositis) and Grade I (asymptomatic or mild symptoms of mucositis). The dataset was split into training and testing cohorts, with a split ratio of 0.8. Accuracy was calculated as the ratio of correct predicted or classified instances to the total number of instances in the dataset. Accuracy was categorized as testing accuracy and training accuracy.
Results:
A total of 386 thermal images from 50 patients were acquired. Of these, 308 images (79.8%) were used for the training set and 78 (20.2%) for the testing set. There were 206 images (53.4%) with Grade 0 mucositis and 180 (46.6%) with Grade I. There was a significant difference in the thermal profile of patients with Grade 0 and Grade I images;
Conclusion:
The deep learning approach-based analysis of thermal images can be a useful technique for predicting oral mucositis at an early stage in treatment, thus helping in intensifying supportive care. The model has been tested on a diverse dataset, and its performance in terms of accuracy validates the proposed model.