Enke Johanna S, Moltz Jan H, D'Anastasi Melvin, Kunz Wolfgang G, Schmidt Christian, Maurus Stefan, Mühlberg Alexander, Katzmann Alexander, Sühling Michael, Hahn Horst, Nörenberg Dominik, Huber Thomas
Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany.
Fraunhofer Institute for Digital Medicine MEVIS, 28359 Bremen, Germany.
Cancers (Basel). 2022 Jan 29;14(3):713. doi: 10.3390/cancers14030713.
The spleen is often involved in malignant lymphoma, which manifests on CT as either splenomegaly or focal, hypodense lymphoma lesions. This study aimed to investigate the diagnostic value of radiomics features of the spleen in classifying malignant lymphoma against non-lymphoma as well as the determination of malignant lymphoma subtypes in the case of disease presence-in particular Hodgkin lymphoma (HL), diffuse large B-cell lymphoma (DLBCL), mantle-cell lymphoma (MCL), and follicular lymphoma (FL). Spleen segmentations of 326 patients (139 female, median age 54.1 +/- 18.7 years) were generated and 1317 radiomics features per patient were extracted. For subtype classification, we created four different binary differentiation tasks and addressed them with a Random Forest classifier using 10-fold cross-validation. To detect the most relevant features, permutation importance was analyzed. Classifier results using all features were: malignant lymphoma vs. non-lymphoma AUC = 0.86 ( < 0.01); HL vs. NHL AUC = 0.75 ( < 0.01); DLBCL vs. other NHL AUC = 0.65 ( < 0.01); MCL vs. FL AUC = 0.67 ( < 0.01). Classifying malignant lymphoma vs. non-lymphoma was also possible using only shape features AUC = 0.77 ( < 0.01), with the most important feature being sphericity. Based on only shape features, a significant AUC could be achieved for all tasks, however, best results were achieved combining shape and textural features. This study demonstrates the value of splenic imaging and radiomic analysis in the diagnostic process in malignant lymphoma detection and subtype classification.
脾脏常累及恶性淋巴瘤,在CT上表现为脾肿大或局灶性低密度淋巴瘤病灶。本研究旨在探讨脾脏的影像组学特征在鉴别恶性淋巴瘤与非淋巴瘤以及在疾病存在时确定恶性淋巴瘤亚型(特别是霍奇金淋巴瘤(HL)、弥漫性大B细胞淋巴瘤(DLBCL)、套细胞淋巴瘤(MCL)和滤泡性淋巴瘤(FL))方面的诊断价值。生成了326例患者(139例女性,中位年龄54.1±18.7岁)的脾脏分割图像,并提取了每位患者的1317个影像组学特征。对于亚型分类,我们创建了四个不同的二元区分任务,并使用随机森林分类器通过10倍交叉验证来处理这些任务。为了检测最相关的特征,分析了排列重要性。使用所有特征的分类器结果为:恶性淋巴瘤与非淋巴瘤AUC = 0.86(<0.01);HL与非霍奇金淋巴瘤AUC = 0.75(<0.01);DLBCL与其他非霍奇金淋巴瘤AUC = 0.65(<0.01);MCL与FL AUC = 0.67(<0.01)。仅使用形状特征也可以对恶性淋巴瘤与非淋巴瘤进行分类,AUC = 0.77(<0.01),最重要 的特征是球形度。仅基于形状特征,所有任务都可以实现显著的AUC,然而,将形状和纹理特征结合起来可获得最佳结果。本研究证明了脾脏成像和影像组学分析在恶性淋巴瘤检测和亚型分类诊断过程中的价值。