Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy

Abstract

Purpose . 4D-CT is routine imaging for lung cancer patients treated with stereotactic body radiotherapy. No studies have investigated optimal 4D phase selection for radiomics. We aim to determine how phase data should be used to identify prognostic biomarkers for distant failure, and test whether stability assessment is required. A phase selection approach will be developed to aid studies with different 4D protocols and account for patient differences. Methods . 186 features were extracted from the tumour and peritumour on all phases for 258 patients. Feature values were selected from phase features using four methods: (A) mean across phases, (B) median across phases, (C) 50% phase, and (D) the most stable phase (closest in value to two neighbours), coined personalised selection. Four levels of stability assessment were also analysed, with inclusion of: (1) all features, (2) stable features across all phases, (3) stable features across phase and neighbour phases, and (4) features averaged over neighbour phases. Clinical-radiomics models were built for twelve combinations of feature type and assessment method. Model performance was assessed by concordance index (c-index) and fraction of new information from radiomic features. Results . The most stable phase spanned the whole range but was most often near exhale. All radiomic signatures provided new information for distant failure prediction. The personalised model had the highest c-index (0.77), and 58% of new information was provided by radiomic features when no stability assessment was performed. Conclusion . The most stable phase varies per-patient and selecting this improves model performance compared to standard methods. We advise the single most stable phase should be determined by minimising feature differences to neighbour phases. Stability assessment over all phases decreases performance by excessively removing features. Instead, averaging of neighbour phases should be used when stability is of concern. The models suggest that higher peritumoural intensity predicts distant failure.

Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR

“…The counterintuitive result of Region 1 is explained by considering the mean EQD2 dose inside each of the regions identified by the dose parameter (Supplementary Figure 13). In region 1, the median value is 55 Gy with range 74-92 Gy, which is significantly higher than 9 Gy (17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32) in R2 and 14 Gy (22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41) in R3. Considering the difference in dose scales, a large SD in this region would not lead to underdosage of microscopic disease.…”

“…Planning four-dimensional CT (4D-CT) scans [described in Davey et al. ( 18 )] and 3D dose distributions were available for all patients. Patients received a dose of 54 Gy in 3 fractions, or 60 Gy in 5 or 8 fractions, with further planning details included in Supplementary Material , Section 1.…”

Robust image-based risk predictions from the deep learning of lung tumors in motion

“…Previous work has shown that tumor motion can alter the radiomic feature space in discrete ways 21,22,27 . However, whether motion-associated variations are deterministic, capturing true changes in tumor composition, or stochastic, representing "noise" to be dampened remained unknown.…”

“…Although the documented instability in the classical radiomic feature space created by tumor motion has been established 21,22,27 , it remains unclear if these variations are clinically meaningful. Moreover, motion-related variations in some radiomic features may not be relevant to DL models.…”

“…In addition to these potential sources of variation, tumor motion has been shown to impact radiomic feature stability. Lung tumors, in particular, can have significant motion during respiration 20 and radiomic features extracted from free breathing (FB) computed tomography (CT) 21 or four-dimensional (4D) CT scans can be affected by motion 22 . Tumor motion could be mitigated with active breathing control (breath hold) and use of abdominal compression techniques 12 .…”