This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
Predictive Model for Oral Mucositis of Nasopharyngeal Carcinoma Patients Receiving Chemo-radiotherapy
Yuanyuan Chen
Zhejiang Cancer Hospital
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Purpose
To analyze risk factors for severe acute oral mucositis of nasopharyngeal carcinoma patients (NPCs) receiving chemo-radiotherapy and build predictive models.
Methods
270 NPCs 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 divided into severe mucositis group (grade 3) and non-severe mucositis group (grade = 1, 2) according to RTOG criteria. Statistical analyses were completed by IBM SPSS Statistics 25.0 and IBM SPSS Modeler 18.0.
Results
Intermediate to high Vx (%) were strongly associated with severe oral mucositis (V40-V70(%)). Multivariate analysis showed that V55 (%) was the most important predictor for severe oral mucositis followed by overweight and retropharyngeal lymph node region irradiation (RLN). Two predictive models were built based on these two methods. AUC of OCC and MSC based model in training set were 0.786 both. Higher AUC of MSC-base model was observed in validation set when compared to OCC (0.721 vs. 0.622).
Conclusion
Dosimetric parameter is the most important predictive factors for severe oral mucositis in nasopharyngeal carcinoma patients during chemo-radiation. Of the two models generated in this study, performance of MSC-based model in validation data is mildly better than OCC.
Keywords
Nasopharyngeal carcinoma, oral mucositis, radiotherapy, dosimetric parameter, predictive model
Figure 1
Figure 2
Figure 3
This is a list of supplementary files associated with this preprint. Click to download.
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 10 Jun, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Learn more about our company.
This is a preprint. It has not completed peer review.
Research article
Predictive Model for Oral Mucositis of Nasopharyngeal Carcinoma Patients Receiving Chemo-radiotherapy
Yuanyuan Chen
Zhejiang Cancer Hospital
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 10 Jun, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Purpose
To analyze risk factors for severe acute oral mucositis of nasopharyngeal carcinoma patients (NPCs) receiving chemo-radiotherapy and build predictive models.
Methods
270 NPCs 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 divided into severe mucositis group (grade 3) and non-severe mucositis group (grade = 1, 2) according to RTOG criteria. Statistical analyses were completed by IBM SPSS Statistics 25.0 and IBM SPSS Modeler 18.0.
Results
Intermediate to high Vx (%) were strongly associated with severe oral mucositis (V40-V70(%)). Multivariate analysis showed that V55 (%) was the most important predictor for severe oral mucositis followed by overweight and retropharyngeal lymph node region irradiation (RLN). Two predictive models were built based on these two methods. AUC of OCC and MSC based model in training set were 0.786 both. Higher AUC of MSC-base model was observed in validation set when compared to OCC (0.721 vs. 0.622).
Conclusion
Dosimetric parameter is the most important predictive factors for severe oral mucositis in nasopharyngeal carcinoma patients during chemo-radiation. Of the two models generated in this study, performance of MSC-based model in validation data is mildly better than OCC.
Figure 1
Figure 2
Figure 3