A Tumor Volume Segmentation Algorithm Based on Radiomics Features in FDG-PET in Lung Cancer Patients, Validated Using Surgical Specimens
Background: Although the integration of positron emission tomography into radiation therapy treatment planning has become part of clinical routine, the best method for tumor delineation is still a matter of debate. In this study, therefore, we analyzed a novel, radiomics-feature-based algorithm in c...
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MDPI AG
2024-11-01
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| author | Lena Bundschuh Jens Buermann Marieta Toma Joachim Schmidt Glen Kristiansen Markus Essler Ralph Alexander Bundschuh Vesna Prokic |
| author_facet | Lena Bundschuh Jens Buermann Marieta Toma Joachim Schmidt Glen Kristiansen Markus Essler Ralph Alexander Bundschuh Vesna Prokic |
| author_sort | Lena Bundschuh |
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| description | Background: Although the integration of positron emission tomography into radiation therapy treatment planning has become part of clinical routine, the best method for tumor delineation is still a matter of debate. In this study, therefore, we analyzed a novel, radiomics-feature-based algorithm in combination with histopathological workup for patients with non-small-cell lung cancer. Methods: A total of 20 patients with biopsy-proven lung cancer who underwent [<sup>18</sup>F]fluorodeoxyglucose positron emission/computed tomography (FDG-PET/CT) examination before tumor resection were included. Tumors were segmented in positron emission tomography (PET) data using previously reported algorithms based on three different radiomics features, as well as a threshold-based algorithm. To obtain gold-standard results, lesions were measured after resection. Pathological volumes and maximal diameters were then compared with the results of the segmentation algorithms. Results: A total of 20 lesions were analyzed. For all algorithms, segmented volumes correlated well with pathological volumes. In general, the threshold-based volumes exhibited a tendency to be smaller than the radiomics-based volumes. For all lesions, conventional threshold-based segmentation produced coefficients of variation which corresponded best with pathologically based volumes; however, for lesions larger than 3 ccm, the algorithm based on Local Entropy performed best, with a significantly better coefficient of variation (<i>p</i> = 0.0002) than the threshold-based algorithm. Conclusions: We found that, for small lesions, results obtained using conventional threshold-based segmentation compared well with pathological volumes. For lesions larger than 3 ccm, the novel algorithm based on Local Entropy performed best. These findings confirm the results of our previous phantom studies. This algorithm is therefore worthy of inclusion in future studies for further confirmation and application. |
| format | Article |
| id | doaj-art-cd7f16b248dd42be9790a7d886b8a015 |
| institution | DOAJ |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Diagnostics |
| spelling | doaj-art-cd7f16b248dd42be9790a7d886b8a0152025-08-20T02:50:19ZengMDPI AGDiagnostics2075-44182024-11-011423265410.3390/diagnostics14232654A Tumor Volume Segmentation Algorithm Based on Radiomics Features in FDG-PET in Lung Cancer Patients, Validated Using Surgical SpecimensLena Bundschuh0Jens Buermann1Marieta Toma2Joachim Schmidt3Glen Kristiansen4Markus Essler5Ralph Alexander Bundschuh6Vesna Prokic7Klinik und Poliklinik für Nuklearmedizin, Universitätsklinikum Bonn, 53127 Bonn, GermanyKlinik und Poliklinik für Allgemein-, Viszeral-, Thorax- und Gefäßchirurgie, Universitätsklinikum Bonn, 53127 Bonn, GermanyInstitut für Pathologie, Universitätsklinikum Bonn, 53127 Bonn, GermanyKlinik und Poliklinik für Allgemein-, Viszeral-, Thorax- und Gefäßchirurgie, Universitätsklinikum Bonn, 53127 Bonn, GermanyInstitut für Pathologie, Universitätsklinikum Bonn, 53127 Bonn, GermanyKlinik und Poliklinik für Nuklearmedizin, Universitätsklinikum Bonn, 53127 Bonn, GermanyKlinik und Poliklinik für Nuklearmedizin, Universitätsklinikum Bonn, 53127 Bonn, GermanyDepartment of Physics, University Koblenz, 56070 Koblenz, GermanyBackground: Although the integration of positron emission tomography into radiation therapy treatment planning has become part of clinical routine, the best method for tumor delineation is still a matter of debate. In this study, therefore, we analyzed a novel, radiomics-feature-based algorithm in combination with histopathological workup for patients with non-small-cell lung cancer. Methods: A total of 20 patients with biopsy-proven lung cancer who underwent [<sup>18</sup>F]fluorodeoxyglucose positron emission/computed tomography (FDG-PET/CT) examination before tumor resection were included. Tumors were segmented in positron emission tomography (PET) data using previously reported algorithms based on three different radiomics features, as well as a threshold-based algorithm. To obtain gold-standard results, lesions were measured after resection. Pathological volumes and maximal diameters were then compared with the results of the segmentation algorithms. Results: A total of 20 lesions were analyzed. For all algorithms, segmented volumes correlated well with pathological volumes. In general, the threshold-based volumes exhibited a tendency to be smaller than the radiomics-based volumes. For all lesions, conventional threshold-based segmentation produced coefficients of variation which corresponded best with pathologically based volumes; however, for lesions larger than 3 ccm, the algorithm based on Local Entropy performed best, with a significantly better coefficient of variation (<i>p</i> = 0.0002) than the threshold-based algorithm. Conclusions: We found that, for small lesions, results obtained using conventional threshold-based segmentation compared well with pathological volumes. For lesions larger than 3 ccm, the novel algorithm based on Local Entropy performed best. These findings confirm the results of our previous phantom studies. This algorithm is therefore worthy of inclusion in future studies for further confirmation and application.https://www.mdpi.com/2075-4418/14/23/2654PET/CTradiomicsradiation therapy treatment planningtumor volume segmentation |
| spellingShingle | Lena Bundschuh Jens Buermann Marieta Toma Joachim Schmidt Glen Kristiansen Markus Essler Ralph Alexander Bundschuh Vesna Prokic A Tumor Volume Segmentation Algorithm Based on Radiomics Features in FDG-PET in Lung Cancer Patients, Validated Using Surgical Specimens Diagnostics PET/CT radiomics radiation therapy treatment planning tumor volume segmentation |
| title | A Tumor Volume Segmentation Algorithm Based on Radiomics Features in FDG-PET in Lung Cancer Patients, Validated Using Surgical Specimens |
| title_full | A Tumor Volume Segmentation Algorithm Based on Radiomics Features in FDG-PET in Lung Cancer Patients, Validated Using Surgical Specimens |
| title_fullStr | A Tumor Volume Segmentation Algorithm Based on Radiomics Features in FDG-PET in Lung Cancer Patients, Validated Using Surgical Specimens |
| title_full_unstemmed | A Tumor Volume Segmentation Algorithm Based on Radiomics Features in FDG-PET in Lung Cancer Patients, Validated Using Surgical Specimens |
| title_short | A Tumor Volume Segmentation Algorithm Based on Radiomics Features in FDG-PET in Lung Cancer Patients, Validated Using Surgical Specimens |
| title_sort | tumor volume segmentation algorithm based on radiomics features in fdg pet in lung cancer patients validated using surgical specimens |
| topic | PET/CT radiomics radiation therapy treatment planning tumor volume segmentation |
| url | https://www.mdpi.com/2075-4418/14/23/2654 |
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