{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:36:50Z","timestamp":1760146610364,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T00:00:00Z","timestamp":1732492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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 [18F]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 (p = 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.<\/jats:p>","DOI":"10.3390\/diagnostics14232654","type":"journal-article","created":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T09:59:34Z","timestamp":1732528774000},"page":"2654","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Tumor Volume Segmentation Algorithm Based on Radiomics Features in FDG-PET in Lung Cancer Patients, Validated Using Surgical Specimens"],"prefix":"10.3390","volume":"14","author":[{"given":"Lena","family":"Bundschuh","sequence":"first","affiliation":[{"name":"Klinik und Poliklinik f\u00fcr Nuklearmedizin, Universit\u00e4tsklinikum Bonn, 53127 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jens","family":"Buermann","sequence":"additional","affiliation":[{"name":"Klinik und Poliklinik f\u00fcr Allgemein-, Viszeral-, Thorax- und Gef\u00e4\u00dfchirurgie, Universit\u00e4tsklinikum Bonn, 53127 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marieta","family":"Toma","sequence":"additional","affiliation":[{"name":"Institut f\u00fcr Pathologie, Universit\u00e4tsklinikum Bonn, 53127 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joachim","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Klinik und Poliklinik f\u00fcr Allgemein-, Viszeral-, Thorax- und Gef\u00e4\u00dfchirurgie, Universit\u00e4tsklinikum Bonn, 53127 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4149-5487","authenticated-orcid":false,"given":"Glen","family":"Kristiansen","sequence":"additional","affiliation":[{"name":"Institut f\u00fcr Pathologie, Universit\u00e4tsklinikum Bonn, 53127 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Markus","family":"Essler","sequence":"additional","affiliation":[{"name":"Klinik und Poliklinik f\u00fcr Nuklearmedizin, Universit\u00e4tsklinikum Bonn, 53127 Bonn, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7291-9372","authenticated-orcid":false,"given":"Ralph Alexander","family":"Bundschuh","sequence":"additional","affiliation":[{"name":"Klinik und Poliklinik f\u00fcr Nuklearmedizin, Universit\u00e4tsklinikum Bonn, 53127 Bonn, Germany"},{"name":"Nuklearmedizin, Medizinische Fakult\u00e4t Augsburg, 86156 Augsburg, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vesna","family":"Prokic","sequence":"additional","affiliation":[{"name":"Department of Physics, University Koblenz, 56070 Koblenz, Germany"},{"name":"University of Applied Science Koblenz, RheinAhrCampus Remagen, 53424 Remagen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1186\/s13014-020-01519-1","article-title":"Recent advances of PET imaging in clinical radiation oncology","volume":"15","author":"Unterrainer","year":"2020","journal-title":"Radiat. 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