Szumowski Piotr, Szklarzewski Artur, Żukowski Łukasz, Abdelrazek Saeid, Mojsak Małgorzata, Porębska Katarzyna, Sierko Ewa, Myśliwiec Janusz
Department of Nuclear Medicine, Medical University of Bialystok, M. Skłodowskiej-Curie St. 24A, 15-276 Bialystok, Poland.
Department of Nuclear Medicine, Comprehensive Cancer Center of Białystok, 15-027 Bialystok, Poland.
J Clin Med. 2021 Apr 1;10(7):1430. doi: 10.3390/jcm10071430.
The paper presents a pre-processing method which, based on positron-emission tomography (PET) images of F-fluorodeoxyglucose ([18F] FDG) hypermetabolic pulmonary nodules, makes it possible to obtain additional visual characteristics and use them to enhance the specificity of imaging.
A retrospective analysis of 69 FDG-PET/CT scans of solitary hypermetabolic pulmonary nodules (40 cases of lung cancer and 29 benign tumours), where in each case, the standardised uptake value of the hottest voxel within the defined volume of interest was greater than 2.5 (SUVmax > 2.5). No diagnosis could be made based on these SUVmax values. All of the PET DICOM images were transformed by means of the pre-processing method for contouring the uptake levels of [18F] FDG (PCUL-FDG). Next, a multidimensional comparative analysis was conducted using a synthetic variable obtained by calculating the similarities based on the generalised distance measure for non-metric scaling (GDM2) from the pattern object. The calculations were performed with the use of the R language.
The PCUL-FDG method revealed 73.9% hypermetabolic nodules definitively diagnosed as either benign or malignant lesions. As for the other 26.1% of the nodules, there was uncertainty regarding their classification (some had features suggesting malignancy, while the characteristics of others made it impossible to confirm malignancy with a high degree of certainty).
Application of the PCUL-FDG method enhances the specificity of PET in imaging solitary hypermetabolic pulmonary nodules. Images obtained using the PCUL-FDG method can serve as point of departure for automatic analysis of PET data based on convolutional neural networks.
本文提出了一种预处理方法，该方法基于氟脱氧葡萄糖（[18F] FDG）代谢增高的肺结节的正电子发射断层扫描（PET）图像，能够获取额外的视觉特征并利用这些特征提高成像的特异性。
对69例孤立性代谢增高的肺结节的FDG-PET/CT扫描进行回顾性分析（40例肺癌和29例良性肿瘤），在每种情况下，定义的感兴趣体积内最热体素的标准化摄取值均大于2.5（SUVmax > 2.5）。基于这些SUVmax值无法做出诊断。所有PET DICOM图像均通过用于勾勒[18F] FDG摄取水平的预处理方法（PCUL-FDG）进行转换。接下来，使用通过基于模式对象的非度量缩放广义距离度量（GDM2）计算相似度而获得的综合变量进行多维比较分析。计算使用R语言进行。
PCUL-FDG方法明确诊断出73.9%的代谢增高结节为良性或恶性病变。至于其他26.1%的结节，其分类存在不确定性（一些具有提示恶性的特征，而其他结节的特征使其无法高度确定地确认恶性）。
PCUL-FDG方法的应用提高了PET对孤立性代谢增高肺结节成像的特异性。使用PCUL-FDG方法获得的图像可作为基于卷积神经网络自动分析PET数据的出发点。
