Patient feature based dosimetric Pareto front prediction in esophageal cancer radiotherapy. Issue 2 (29th January 2015)

• Record Type: Journal Article
• Authors: Wang, Jiazhou; Jin, Xiance; Zhao, Kuaike; Peng, Jiayuan; Xie, Jiang; Chen, Junchao; Zhang, Zhen; Studenski, Matthew; Hu, Weigang
• Is Part Of: Medical physics. Volume 42:Issue 2(2015)
• Page Start: 1005
• Page End: 1011
• Publication Date: 2015-01-29
• Subjects: cancer -- cardiology -- dosimetry -- feature extraction -- lung -- Pareto analysis -- principal component analysis -- radiation therapy -- regression analysis -- tumours; Dose‐volume analysis -- Simulation -- Probability theory, stochastic processes, and statistics -- Cancer; Radiation therapy -- Scintigraphy; Pareto front -- esophagus -- dosimetric prediction -- model training; Dosimetry -- Heart -- Lungs -- Magnetohydrodynamics -- Cancer -- Medical treatment planning -- Intensity modulated radiation therapy -- Databases; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4906252
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To investigate the feasibility of the dosimetric Pareto front (PF) prediction based on patient's anatomic and dosimetric parameters for esophageal cancer patients. Methods: Eighty esophagus patients in the authors' institution were enrolled in this study. A total of 2928 intensity‐modulated radiotherapy plans were obtained and used to generate PF for each patient. On average, each patient had 36.6 plans. The anatomic and dosimetric features were extracted from these plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose, and PTV homogeneity index were recorded for each plan. Principal component analysis was used to extract overlap volume histogram (OVH) features between PTV and other organs at risk. The full dataset was separated into two parts; a training dataset and a validation dataset. The prediction outcomes were the MHD and MLD. The spearman's rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The stepwise multiple regression method was used to fit the PF. The cross validation method was used to evaluate the model. Results: With 1000 repetitions, the mean prediction error of the MHD was 469 cGy. The most correlated factor was the first principal components of the OVH between heart and PTV and the overlap between heart and PTV in Z ‐axis. The mean prediction error of the MLD was 284 cGy. The most correlated factors were the first principal components of the OVH between heart and PTV and the overlap between lung and PTV in Z ‐axis. Conclusions: It is feasible to use patients' anatomic and dosimetric features to generate a predicted Pareto front. Additional samples and further studies are required improve the prediction model.