Journal Article

Regression models for predicting physical and EQD2 plan parameters of two methods of hybrid planning for stage III NSCLC

Shao, Yan, Kong, Qing, Duan, Yanhua, Xu, Zhiyong, Gan, Wutian, Zhou, Yongkang, Chen, Hua, Huang, Ying, Feng, Aihui, Gu, Hengle, Wang, Hao

2021

Overview

Background/purpose To establish regression models of physical and equivalent dose in 2 Gy per fraction (EQD 2 ) plan parameters of two kinds of hybrid planning for stage III NSCLC. Methods Two kinds of hybrid plans named conventional fraction radiotherapy & stereotactic body radiotherapy (C&S) and conventional fraction radiotherapy & simultaneous integrated boost (C&SIB) were retrospectively made for 20 patients with stage III NSCLC. Prescription dose of C&S plans was 2 Gy × 30f for planning target volume of lymph node (PTV LN ) and 12.5 Gy × 4f for planning target volume of primary tumor (PTV PT ), while prescription dose of C&SIB plans was 2 Gy × 26f for PTV LN and sequential 2 Gy × 4f for PTV LN combined with 12.5 Gy × 4f for PTV PT . Regression models of physical and EQD 2 plan parameters were established based on anatomical geometry features for two kinds of hybrid plans. The features were mainly characterized by volume ratio, min distance and overlapping slices thickness of two structures. The possibilities of regression models of EQD 2 plan parameters were verified by spearman’s correlation coefficients between physical and EQD 2 plan parameters, and the influence on the consistence of fitting goodness between physical and EQD 2 models was investigated by the correlations between physical and EQD 2 plan parameters. Finally, physical and EQD 2 models predictions were compared with plan parameters for two new patients. Results Physical and EQD 2 plan parameters of PTV LN CI 60Gy have shown strong positive correlations with PTV LN volume and min distance (PT to LN) , and strong negative correlations with PTV PT volume for two kinds of hybrid plans. PTV (PT+LN) CI 60Gy is not only correlated with above three geometry features, but also negatively correlated with overlapping slices thickness (PT and LN) . When neck lymph node metastasis was excluded from PTV LN volume, physical and EQD 2 total lung V 20 showed a high linear correlation with corrected volume ratio (LN to total lung). Meanwhile, physical total lung mean dose (MLD) had a high linear correlation with corrected volume ratio (LN to total lung) , while EQD 2 total lung MLD was not only affected by corrected volume ratio (LN to total lung) but also volume ratio (PT to total lung). Heart D 5 , D 30 and mean dose (MHD) would be more susceptible to overlapping structure (heart and LN) . Min distance (PT to ESO) may be an important feature for predicting EQD 2 esophageal max dose for hybrid plans. It’s feasible for regression models of EQD 2 plan parameters, and the consistence of the fitting goodness of physical and EQD 2 models had a positive correlation with spearman’s correlation coefficients between physical and EQD 2 plan parameters. For total lung V 20 , ipsilateral lung V 20 , and ipsilateral lung MLD, the models could predict that C&SIB plans were higher than C&S plans for two new patients. Conclusion The regression models of physical and EQD 2 plan parameters were established with at least moderate fitting goodness in this work, and the models have a potential to predict physical and EQD 2 plan parameters for two kinds of hybrid planning.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Biomedical and Life Sciences / Biomedicine / Cancer Research / Care and treatment / Correlation coefficient / Correlation coefficients / Dosage / EQD2 plan parameter / Esophagus / Geometry / Hybrid planning / Imaging / Lung cancer / Lung cancer, Non-small cell / Lungs / Lymph nodes / Lymphatic system / Medical care / Metastases / Methods / Needs assessment / Non-small cell lung carcinoma / Oncology / Parameters / Patients / Physical plan parameter / Planning / Radiation dosimetry / Radiation Physics / Radiation therapy / Radiology / Radiotherapy / Regression analysis / Regression model / Regression models / Spinal cord / Stage III NSCLC / Thickness