Predictive Model and Precaution for Oral Mucositis During Chemo-Radiotherapy in Nasopharyngeal Carcinoma Patients

Abstract

Purpose: To explore risk factors for severe acute oral mucositis of nasopharyngeal carcinoma (NPC) patients receiving chemo-radiotherapy, build predictive models and determine preventive measures. Methods and Materials: Two hundred and seventy NPC patients receiving radical chemo-radiotherapy were included. Oral mucosa structure was contoured by oral cavity contour (OCC) and mucosa surface contour (MSC) methods. Oral mucositis during treatment was prospectively evaluated and divided into severe mucositis group (grade ≥ 3) and non-severe mucositis group (grade < 3) according to RTOG Acute Reaction Scoring System. Nineteen clinical features and nineteen dosimetric parameters were included in analysis, least absolute shrinkage and selection operator (LASSO) logistic regression model was used to construct a risk score (RS) system. Results: Two predictive models were built based on the two delineation methods. MSC based model is more simplified one, it includes body mass index (BMI) classification before radiation, retropharyngeal lymph node (RLN) area irradiation status and MSC V55%, RS = −1.480 + (0.021 × BMI classification before RT) + (0.126 × RLN irradiation) + (0.052 × MSC V55%). The cutoff of MSC based RS is −1.011, with an area under curve (AUC) of 0.737 (95%CI: 0.672-0.801), a specificity of 0.595 and a sensitivity of 0.786. OCC based model involved more variables, RS= −4.805+ (0.152 × BMI classification before RT) + (0.080 × RT Technique) + (0.097 × Concurrent Nimotuzumab) + (0.163 × RLN irradiation) + (0.028 × OCC V15%) + (0.120 × OCC V60%). The cutoff of OCC based RS is −0.950, with an AUC of 0.767 (95%CI: 0.702-0.831), a specificity of 0.602 and a sensitivity of 0.819. Analysis in testing set shown higher AUC of MSC based model than that of OCC based model (AUC: 0.782 vs 0.553). Analysis in entire set shown AUC in these two method-based models were close (AUC: 0.744 vs 0.717).

Efficacy and safety of thalidomide in preventing oral mucositis in patients with nasopharyngeal carcinoma undergoing concurrent chemoradiotherapy: A multicenter, open‐label, randomized controlled trial

“…OM manifests clinically as erythematous and ulcerative lesions as well as pain in the oral mucosa; this affects patients' diet and nutrition intake, causes weight loss, and decreases their quality of life 6 . Severe oral mucositis (SOM; World Health Organization grade 3 or higher) often leads to treatment interruption and prolonged treatment periods, which result in decreased therapeutic efficacy in patients with NPC 7 . Currently, there is no acknowledged standard treatment for preventing OM, and the Multinational Association of Supportive Care in Cancer/International Society of Oral Oncology (MASCC/ISOO) Clinical Practice Guidelines, which were updated in 2020, are the leading clinical practice guidelines for this adverse event (AE) 8 .…”

Treatment of Yunnan Baiyao plus Kangfuxin Solution Reduces Inflammatory Response and Prevents Patients with Nasopharyngeal Carcinoma against Radiation-Induced Oral Mucositis

“…# indicates P < 0:05 compared with before treatment, and @ indicates P < 0:05 compared with the control group. 5 Journal of Nanomaterials used in clinical antibacterial treatment for oral mucositis [15]. Dexamethasone can reduce the response of tissue to inflammatory factors and help to control inflammation in oral mucositis [16].…”

“…Oral mucosa is stratified squamous epithelium with rapid renewal and high radiosensitivity. Oral mucositis is a common adverse reaction of NPC after radiotherapy [5]. Radiotherapy-induced oral mucositis are characterized by dry mouth, taste dysfunction, oral mucosal congestion, edema, erosion, and ulcer, which seriously affect the patient's eating function, eventually lead to malnutrition and electrolyte disorder [6].…”

Predicting severe radiation-induced oral mucositis in head and neck cancer patients using integrated baseline CT radiomic, dosimetry, and clinical features: A machine learning approach

“…It should be noted, however, the prediction of the severity of RIOM on a case-by-case basis is highly challenging [ 10 ]. Also, the strict use of the present dose-volume constraints does not prevent severe OM in some patients [ 17 ]. Taken together, no NTCP model has been developed to confidently guide clinical decision-making until now.…”