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PURPOSE Observed gross tumour volume shrinkage during radiotherapy (RT) raises the question of whether to adapt treatment to changes observed on the acquired images. In the literature, two modes of tumour regression have been described: elastic and non-elastic. These modes of tumour regression will affect the safety of treatment adaptation. This study applies a novel approach, using routine cone-beam computed tomography (CBCT) and deformable image registration to automatically distinguish between elastic and non-elastic tumour regression. METHODS In this retrospective study, hundred and fifty (150) locally advanced non-small cell lung cancer patients treated with 55 Gray of radiotherapy were included. First, the two modes of tumour regression were simulated. For each mode of tumour regression, one timepoint was simulated. Based on the results of simulated data, the approach used for analysis in real patients was developed. CBCTs were non-rigidly registered to the baseline CBCT using a cubic B-spline algorithm, NiftyReg. Next, the Jacobian determinants were computed from the deformation vector fields. To capture local volume changes, ten Jacobian values were sampled perpendicular to the surface of the GTV, across the lung-tumour boundary. From the simulated data, we can distinguish elastic from non-elastic tumour regression by comparing the Jacobian values samples between 5-12.5 mm inside and 5-12.5 mm outside the planning GTV. Finally, morphometric results compared between tumours of different histology. RESULTS Most patients (92.3%) in our cohort showed stable disease in the first week of treatment and non-elastic shrinkage in the later weeks of treatment. At week 2, 125 patients (88%) showed stable disease, 3 patients (2.1%) disease progression and 11 patients (8%) regression. By treatment completion, 91 patients (64%) had stable disease, 1 patient (0.7%) progression and 46 patients (32%) regression. A slight difference in the mode of tumour change was observed between tumours of different histology. CONCLUSION Our novel approach shows that it may be possible to automatically quantify and identify global changes in lung cancer patients during RT, using routine CBCT images. Our results show that different regions of the tumour changes in different ways. Therefore, careful consideration should be taken when adapting RT. This article is protected by copyright. All rights reserved.