Abstract
Radiation pneumonitis (RP) is one of the major toxicities of thoracic radiation therapy. RP incidence has been proven to be closely associated with the dosimetric factors and normal tissue control possibility (NTCP) factors. However, because these factors only utilize limited information of the dose distribution, the prediction abilities of these factors are modest. We adopted the dosiomics method for RP prediction. The dosiomics method first extracts spatial features of the dose distribution within ipsilateral, contralateral, and total lungs, and then uses these extracted features to construct prediction model via univariate and multivariate logistic regression (LR). The dosiomics method is validated using 70 non-small cell lung cancer (NSCLC) patients treated with volumetric modulated arc therapy (VMAT) radiotherapy. Dosimetric and NTCP factors based prediction models are also constructed to compare with the dosiomics features based prediction model. For the dosimetric, NTCP and dosiomics factors/features, the most significant single factors/features are the mean dose, parallel/serial (PS) NTCP and gray level co-occurrence matrix (GLCM) contrast of ipsilateral lung, respectively. And the area under curve (AUC) of univariate LR is 0.665, 0.710 and 0.709, respectively. The second significant factors are V5 of contralateral lung, equivalent uniform dose (EUD) derived from PS NTCP of contralateral lung and the low gray level run emphasis of gray level run length matrix (GLRLM) of total lungs. The AUC of multivariate LR is improved to 0.676, 0.744, and 0.782, respectively. The results demonstrate that the univariate LR of dosiomics features has approximate predictive ability with NTCP factors, and the multivariate LR outperforms both the dosimetric and NTCP factors. In conclusion, the spatial features of dose distribution extracted by the dosiomics method effectively improves the prediction ability.
Highlights
Radiation pneumonitis (RP) is one of the major toxicities of thoracic radiation therapy
The prediction ability of the dosiomics features is validated using 70 non-small cell lung cancer (NSCLC) patients treated with volumetric modulated arc therapy (VMAT) radiotherapy, and further evaluated by comparing with the dosimetric and normal tissue control possibility (NTCP) factors
Twenty and sixteen features, which are commonly used in radiomics studies, are derived from gray level co-occurrence matrix (GLCM) and gray level run length matrix (GLRLM), respectively
Summary
Radiation pneumonitis (RP) is one of the major toxicities of thoracic radiation therapy. Palma et al analyzed 836 patient cases from international institutions and concluded that symptomatic RP is associated with V20, and fatal RP associated with the mean dose per day during treatment [5] Those studies demonstrate that the dosimetric factors are associated with RP incidence. The normal tissue complication probability (NTCP) model utilizes all information of the DVH curve by compressing the entire curve to a single factor with dose response functions (DRFs). By investigating the prediction ability of dosimetric and NTCP factors, it is reasonable to hypothesize that if the spatial information of dose distribution were utilized properly, the prediction ability should be further improved. The prediction ability of the dosiomics features is validated using 70 non-small cell lung cancer (NSCLC) patients treated with volumetric modulated arc therapy (VMAT) radiotherapy, and further evaluated by comparing with the dosimetric and NTCP factors
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