Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis

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.

Keywords: dose distribution; dosiomics; logistic regression; pneumonitis prediction; radiomics.

Figure 1

Intermediate results of dosiomics method. (A) 3D dose distribution, (B) GLCM, and (C) GLRLM of total lung.

Figure 2

Mean Spearman correlation of 1,000 bootstrap samples. (A–C) mean Spearman correlation of dosimetric factors, NTCP factors, and dosiomics features. (D) zoomed correlation of the features extracted from ipsilateral and total lungs. All the 4 figures are diagonal symmetric. For (A–C), the features are sorted in the order of ipsilateral, contralateral, and total lungs from left to right and from top to down. In (D), the features are sorted the order of GLCM and GLRLM. The correlation of the features of the optimal combination are denoted with red circle. Cor, correlation; Emp, emphasis; GL, gray level; Homo, homogeneity; LRun, long run; SRun, short run; N-uni, non-uniformity; RL, run length.