Bundschuh Lena, Buermann Jens, Toma Marieta, Schmidt Joachim, Kristiansen Glen, Essler Markus, Bundschuh Ralph Alexander, Prokic Vesna
Klinik und Poliklinik für Nuklearmedizin, Universitätsklinikum Bonn, 53127 Bonn, Germany.
Klinik und Poliklinik für Allgemein-, Viszeral-, Thorax- und Gefäßchirurgie, Universitätsklinikum Bonn, 53127 Bonn, Germany.
Diagnostics (Basel). 2024 Nov 25;14(23):2654. doi: 10.3390/diagnostics14232654.
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.
A total of 20 patients with biopsy-proven lung cancer who underwent [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.
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 ( = 0.0002) than the threshold-based algorithm.
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.
尽管正电子发射断层扫描融入放射治疗治疗计划已成为临床常规操作的一部分，但肿瘤勾画的最佳方法仍存在争议。因此，在本研究中，我们分析了一种基于影像组学特征的新型算法，并结合组织病理学检查，用于非小细胞肺癌患者。
共纳入20例经活检证实为肺癌且在肿瘤切除前接受过[F]氟脱氧葡萄糖正电子发射/计算机断层扫描（FDG-PET/CT）检查的患者。使用基于三种不同影像组学特征的先前报道算法以及基于阈值的算法，在正电子发射断层扫描（PET）数据中对肿瘤进行分割。为了获得金标准结果，在切除后对病变进行测量。然后将病理体积和最大直径与分割算法的结果进行比较。
共分析了20个病变。对于所有算法，分割体积与病理体积相关性良好。一般来说，基于阈值的体积倾向于小于基于影像组学的体积。对于所有病变，传统的基于阈值的分割产生的变异系数与基于病理的体积最为相符；然而，对于大于3立方厘米的病变，基于局部熵的算法表现最佳，其变异系数（=0.0002）明显优于基于阈值的算法。
我们发现，对于小病变，使用传统基于阈值的分割获得的结果与病理体积比较相符。对于大于3立方厘米的病变，基于局部熵的新算法表现最佳。这些发现证实了我们之前模型研究的结果。因此，该算法值得纳入未来研究以进一步证实和应用。
