A method to combine target volume data from 3D and 4D planned thoracic radiotherapy patient cohorts for machine learning applications

Publication date: Available online 6 December 2017
Source:Radiotherapy and Oncology
Author(s): Corinne Johnson, Gareth Price, Jonathan Khalifa, Corinne Faivre-Finn, Andre Dekker, Christopher Moore, Marcel van Herk
Background and purposeThe gross tumour volume (GTV) is predictive of clinical outcome and consequently features in many machine-learned models. 4D-planning, however, has prompted substitution of the GTV with the internal gross target volume (iGTV). We present and validate a method to synthesise GTV data from the iGTV, allowing the combination of 3D and 4D planned patient cohorts for modelling.Material and methodsExpert delineations in 40 non-small cell lung cancer patients were used to develop linear fit and erosion methods to synthesise the GTV volume and shape. Quality was assessed using Dice Similarity Coefficients (DSC) and closest point measurements; by calculating dosimetric features; and by assessing the quality of random forest models built on patient populations with and without synthetic GTVs.ResultsVolume estimates were within the magnitudes of inter-observer delineation variability. Shape comparisons produced mean DSCs of 0.8817 and 0.8584 for upper and lower lobe cases, respectively. A model trained on combined true and synthetic data performed significantly better than models trained on GTV alone, or combined GTV and iGTV data.ConclusionsAccurate synthesis of GTV size from the iGTV permits the combination of lung cancer patient cohorts, facilitating machine learning applications in thoracic radiotherapy.



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