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

Angela Davey,Corinne Faivre-Finn,Marcel Van Herk,Alan Mcwilliam

doi:10.3389/fonc.2022.838155

Angela Davey, Corinne Faivre-Finn + Show 2 more

Open Access

https://doi.org/10.3389/fonc.2022.838155

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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.

Highlights

Stereotactic body radiotherapy (SABR) is standard of care for patients with early-stage non-small cell lung cancer (NSCLC) who are not eligible for surgery due to refusal or ill health [1]
We found that high tumour density variability and high peritumour density are associated with metastasis but only for patients who receive low and non-uniform doses ~3 cm from the gross tumour volume (GTV)
The interaction between imaging biomarkers and dose has been under-investigated in radiotherapy research

Summary

Introduction

Stereotactic body radiotherapy (SABR) is standard of care for patients with early-stage non-small cell lung cancer (NSCLC) who are not eligible for surgery due to refusal or ill health [1]. To keep the dose conformal, the clinical target volume (CTV) is generally omitted as the dosimetric penumbra is believed to provide adequate coverage of microscopic disease extensions (MDE) that cannot be visualised on standard imaging [2, 3]. Depending on the prescription dose, approximately 6 mm of MDE coverage will be provided by the dose fall-off outside the planned treatment area [4]; it is believed that at least 2.6 cm is required for adequate coverage in 90% of patients [5]. The impact of inadequate coverage of MDE was demonstrated in a study where a biologically equivalent dose (EQD2) of less than 21 Gy outside the planning target volume (PTV) was associated with increased risk of DM [9]. There is an implicit assumption that inadequate coverage of MDE is associated with the same increase in risk of DM for all patients, but not all patients have extensive microscopic disease. It is more likely that there will be no increase in risk for those with limited MDE, and to model this interaction, a predictive biomarker is required

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