Identification of modes of tumor regression in non‐small cell lung cancer patients during radiotherapy. Issue 1 (29th November 2021)

• Record Type: Journal Article
• Authors: Amugongo, Lameck Mbangula; Green, Andrew; Cobben, David; van Herk, Marcel; McWilliam, Alan; Osorio, Eliana Vasquez
• Is Part Of: Medical physics. Volume 49:Issue 1(2022)
• Page Start: 370
• Page End: 381
• Publication Date: 2021-11-29
• Subjects: Jacobian maps -- NSCLC -- radiotherapy -- tumor regression; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15320
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose: Observed gross tumor volume (GTV) 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 tumor regression have been described: elastic and non‐elastic. These modes of tumor 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 tumor regression. Methods: In this retrospective study, 150 locally advanced non‐small cell lung cancer patients treated with 55 Gray of radiotherapy were included. First, the two modes of tumor regression were simulated. For each mode of tumor 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, 10 Jacobian values were sampled perpendicular to the surface of the GTV, across the lung‐tumor boundary. From the simulated data, we can distinguish elastic from non‐elastic tumor regression by comparing the Jacobian values samples between 5 and 12.5 mm inside and 5 and 12.5 mm outside the planning GTV. Finally, morphometric results were compared between tumors of different histologies. 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, three patients (2.1%) disease progression, and 11 patients (8%) regression. By treatment completion, 91 patients (64%) had stable disease, one patient (0.7%) progression and 46 patients (32%) regression. A slight difference in the mode of tumor change was observed between tumors of different histologies. 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 tumor change in different ways. Therefore, careful consideration should be taken when adapting RT.