Abstract

BackgroundThe primary aim was to test the hypothesis that deriving pre-treatment 3D magnetic resonance tumour volume (mrTV) quantification improves performance characteristics for the prediction of loco-regional failure compared with standard maximal tumour diameter (1D) assessment in patients with squamous cell carcinoma of the anus undergoing chemoradiotherapy.MethodsWe performed an early evaluation case-control study at two UK centres (2007–2014) in 39 patients with loco-regional failure (cases), and 41 patients disease-free at 3 years (controls). mrTV was determined using the summation of areas method (Volsum). Reproducibility was assessed using intraclass concordance correlation (ICC) and Bland-Altman limits of agreements. We derived receiver operating curves using logistic regression models and expressed accuracy as area under the curve (ROCAUC).ResultsThe median time per patient for Volsum quantification was 7.00 (inter-quartile range, IQR: 0.57–12.48) minutes. Intra and inter-observer reproducibilities were generally good (ICCs from 0.79 to 0.89) but with wide limits of agreement (intra-observer: − 28 to 31%; inter-observer: − 28 to 46%). Median mrTVs were greater for cases (32.6 IQR: 21.5–53.1 cm3) than controls (9.9 IQR: 5.7–18.1 cm3, p < 0.0001). The ROCAUC for mrT-size predicting loco-regional failure was 0.74 (95% CI: 0.63–0.85) improving to 0.82 (95% CI: 0.72–0.92) when replaced with mrTV (test for ROC differences, p = 0.024).ConclusionPreliminary results suggest that the replacement of mrTV for mrT-size improves prediction of loco-regional failure after chemoradiotherapy for squamous cell carcinoma of the anus. However, mrTV calculation is time consuming and variation in its reproducibility are drawbacks with the current technology.

Highlights

  • The primary aim was to test the hypothesis that deriving pre-treatment Three Dimensional (3D) magnetic resonance tumour volume quantification improves performance characteristics for the prediction of loco-regional failure compared with standard maximal tumour diameter (1D) assessment in patients with squamous cell carcinoma of the anus undergoing chemoradiotherapy

  • We evaluated for accuracies compared with Volsum using the ROCAUC method

  • We utilised other indicators of performance characteristics, using the methods described by Pencina et al [23] which derives two characteristics – the Integrated Discriminatory Improvement (IDI), an index of improvements in sensitivity relative to specificity, and Net Reclassification Improvement (NRI), an index of net change in events versus non-events detected, which in turn focuses on medical decision making

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Summary

Methods

In accordance with the CRUK/EORTC imaging biomarker consensus statement, this was a two-centre Domain 2 validation study evaluating performance characteristics, reproducibility and whether the biomarker is ‘fit for purpose’ [15]. Selection for case-control study From the retrospective two-centre clinical databases, all 40 patients with LRF from 2007 to 2014 undergoing CRT and with measureable anal tumours were selected as cases (one outlier volume later excluded). All contiguous areas of tumour were contoured together including nodal masses that had coalesced with the tumour or contiguous areas of extramural vascular invasion (EMVI) This was required to account for difficulties in defining a plane between the entities and to allow consistency of approach. We tested whether volume estimation derived from 1D tumour dimensions is a valid estimate of TV, using ellipsoid and elliptical cylinder equations and measured 1D diameters (Fig. 1c & d), where d1, d2 and d3 are the maximal diameters of the primary tumour measured in the AP, LR and CC planes. We utilised other indicators of performance characteristics, using the methods described by Pencina et al [23] which derives two characteristics – the Integrated Discriminatory Improvement (IDI), an index of improvements in sensitivity relative to specificity, and Net Reclassification Improvement (NRI), an index of net change in events versus non-events detected, which in turn focuses on medical decision making

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