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  5. Radio-Dosiomics Evaluation of Peritumoral Volumes to Predict Radiation Pneumonitis

Radio-Dosiomics Evaluation of Peritumoral Volumes to Predict Radiation Pneumonitis

N Chopra1*, T Dou2 , G Sharp3 , H Aerts4 , R Mak5 , (1) Massachusetts General Hospital, Melrose, MA, (2) Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, (3) Massachusetts General Hospital, Boston, MA, (4) Dana-Farber/Brigham Womens Cancer Center, Boston, MA, (5) Brigham and Women's Hospital and Dana-Farber Cance, Boston,

Presentations

(Sunday, 7/14/2019) 1:00 PM - 2:00 PM

Room: Stars at Night Ballroom 2-3

Purpose: To evaluate the prognostic power of radiomics (CT) and dosiomics (TPS Dose Image) features using segmentations of Vâ‚‚â‚€ and Vâ‚… peritumoral dose distributions to predict radiation pneumonitis (RP) in a large cohort of 701 locally advanced non-small cell lung cancer patients.

Methods: Planning CT and TPS thoracic dose data was used to subtract the tumor volume and generate dose-level (Vâ‚‚â‚€ and Vâ‚…) based peritumoral volumes registered to CT pulmonary volumes. Only patients that completed a curative treatment course were included (median dose = 64 Gy). The endpoint was CTCAE grade>=2 RP (n=77). Radiomics and dosiomics features were computed for the ipsilateral Vâ‚‚â‚€ and Vâ‚… CT and dose volumes using the open-source PyRadiomics toolkit. Feature selection was performed for each data set using the Minimum Redundancy Maximum Relevance (mRMR) algorithm and classification performance was evaluated for top 15 features using the AUC statistic. For comparison, conventional DVH parameters including lung Vâ‚‚â‚€ and Vâ‚… mean lung dose (MLD), and pack years were incorporated in the univariate analysis.

Results: We find that the performance of radiomics and dosiomics Vâ‚‚â‚€ & Vâ‚… peritumoral features for the ipsilateral lung is comparable to MLD in predicting RP. MLD was statistically significant at predicting RP with AUC = 0.674. The AUC statistic for the statistically significant (p < 0.05) Vâ‚‚â‚€ radiomics predictors ranged from 0.589 to 0.644 and dosiomics predictors ranged from 0.582 to 0.628. The AUC statistic for statistically significant (p < 0.05) Vâ‚… radiomics predictors ranged from 0.593 to 0.647. Only one of the Vâ‚… dosiomic features was statistically significant with AUC = 0.611.

Conclusion: Univariately, radiomics and dosiomics features from the peritumoral regions are comparably predictive to clinical predictors i.e MLD. A multivariate model combining both radiomics & dosiomics predictors to further enhance the predictive power for RP must be explored.

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