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

Abstract

PurposeLower dose outside the planned treatment area in lung stereotactic radiotherapy has been linked to increased risk of distant metastasis (DM) possibly due to underdosage of microscopic disease (MDE). Independently, tumour density on pretreatment computed tomography (CT) has been linked to risk of MDE. No studies have investigated the interaction between imaging biomarkers and incidental dose. The interaction would showcase whether the impact of dose on outcome is dependent on imaging and, hence, if imaging could inform which patients require dose escalation outside the gross tumour volume (GTV). We propose an image-based data mining methodology to investigate density–dose interactions radially from the GTV to predict DM with no a priori assumption on location.MethodsDose and density were quantified in 1-mm annuli around the GTV for 199 patients with early-stage lung cancer treated with 60 Gy in 5 fractions. Each annulus was summarised by three density and three dose parameters. For parameter combinations, Cox regressions were performed including a dose–density interaction in independent annuli. Heatmaps were created that described improvement in DM prediction due to the interaction. Regions of significant improvement were identified and studied in overall outcome models.ResultsDose–density interactions were identified that significantly improved prediction for over 50% of bootstrap resamples. Dose and density parameters were not significant when the interaction was omitted. Tumour density variance and high peritumour density were associated with DM for patients with more cold spots (less than 30-Gy EQD2) and non-uniform dose about 3 cm outside of the GTV. Associations identified were independent of the mean GTV dose.ConclusionsPatients with high tumour variance and peritumour density have increased risk of DM if there is a low and non-uniform dose outside the GTV. The dose regions are independent of tumour dose, suggesting that incidental dose may play an important role in controlling occult disease. Understanding such interactions is key to identifying patients who will benefit from dose-escalation. The methodology presented allowed spatial dose–density interactions to be studied at the exploratory stage for the first time. This could accelerate the clinical implementation of imaging biomarkers by demonstrating the impact of incidental dose for tumours of varying characteristics in routine data.

Interval From Simulation Imaging to Treatment Delivery in SABR of Lung Lesions: How Long is Too Long for the Lung?

“…Second, in addition to macroscopic geometric miss, tumor change during the delay may result in a lower and less-uniform dose outside the (true) GTV and underdosage of microscopic disease with the risk of local and distant recurrence. 25 , 26 …”

Predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence

“…All patients were staged with both a CT and 18F-FDG positron emission tomography (PET) scan, but did not always receive a histological diagnosis. Four-dimensional computed tomography scans (4D-CT) and three-dimensional dose distributions were available, as described previously and in Supplementary Material (SM), Section 1 ( 19 ). Clinical data was retrospectively collected from structured e-forms completed in routine practice.…”

“…The radial histogram framework described in our previous work was implemented to measure density and dose at radial distances from the generated GTV for all patients ( 19 ). A 2D cross-histogram of density vs distance in bins of 1mm annuli from -0.5 to 2cm from the GTV was formed for each 4D phase considering lung tissue only.…”

“…Only lung tissue was considered for density metrics so that higher density is not just a surrogate for nearby organs-at-risk. For each patient, a single phase was selected as the most stable compared to neighboring phases and used for density analysis in the remaining analysis (as detailed in previous work) ( 19 , 24 ). Summary curves were then extracted from the 2D histogram for each patient to describe the mean, 90 th percentile, and SD density at the defined distances from the GTV.…”

“…Ignoring risk stratification can lead to incorrectly claiming a lack of association ( 18 ). In our previous work, we developed a Cox-per-radius method to investigate the interaction between imaging density biomarkers and dose in independent annuli surrounding the GTV ( 19 ). In that study we demonstrated that the interaction between CT-based imaging biomarkers and dose far outside the tumor is linked to DM.…”