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Characterization of Spatial Properties of Dosimetric Data for Voxel-Based Analyses: Disentangling Contributions From Heart and Lung Substructures to Radiation Induced Toxicities

R Mohan3*, S Monti1, A Stanzione2, T Xu3, M Durante4, G Palma1, Z Liao3, L Cella1, (1) Italian National Research Council, Napoli, IT, (2) University Of Naples "Federico II", Napoli, IT, (3) UT MD Anderson Cancer Center, Houston, TX, (4) GSI Helmholtz Centre for Heavy Ion Research, Darmstadt, DE

Presentations

(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Purpose:
To propose a novel strategy for the characterization of dose maps (DMs) properties that impact on significance maps from Voxel-based analyses (VBAs) evaluating local dose-response patterns via voxelwise statistical analysis on spatially normalized DMs.

Methods:
DMs of 178 lung cancer patients, treated with IMRT or protons, were normalized on XCAT digital phantom [Segars 2010]. We analyzed:
1. the uniformity of voxelwise mean (µ) and standard deviation (s) of DMs over patients, which determines the homogeneity of VBA statistical power;
2. the probabilistic independent component analysis (PICA) [Beckmann & Smith 2005], blindly inferring the number of statistically-significant independent maps (model order) that generate the DMs;
3. the connectogram [Irimia 2012], linking pairs of substructures by Spearman correlations (Rs) between their mean doses. We analyzed the cardiac substructures included in XCAT and the lung subregions segmented by a radiologist.
Points 2-3 elucidate the spatial resolution of the significance map from VBA for a given effect.

Results:
The contrast over the 80% of the analyzed volume was 0.8 for µ-map and 0.5 for s-map. PICA detected 43 dose clusters homogenously spread across the thorax. Connectograms showed that, while doses to main structures (cardiac chambers and lung lobes) were weakly correlated (Rs²<0.2), Rs² between adjacent lobe segments or chambers and related walls can reach 0.8.

Conclusion:
The homogeneity of the s map and the spread of PICA clusters suggests a uniform power of possible VBAs on the dataset.
PICA order, comparable with the cohort size, hints that a large number of DMs contributes to split the analyzed volume into independent patches that could highlight via VBAs distinct dose-response correlations.
Connectograms showed that the dataset can barely supports a radiobiological differentiation between the tiniest substructures.
The proposed characterization should be ancillary to any dosimetric VBA for a clear insight on the inference limits.

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