基于PET影像组学特征鉴别霍奇金淋巴瘤与侵袭性非霍奇金淋巴瘤诊断分析模型的构建

Construction of a diagnostic model to discriminate Hodgkin lymphoma and aggressive non-Hodgkin lymphoma based on PET radiomic features

    摘要
  • 摘要:
    目的 评估从临床基线18F-氟脱氧葡萄糖（FDG） PET中提取的影像组学特征所建立的诊断分析模型是否有助于鉴别霍奇金淋巴瘤（HL）与侵袭性非霍奇金淋巴瘤（NHL）。
    方法 回顾性分析2016年5月至2021年7月于郑州大学第一附属医院经组织病理学检查证实的182例淋巴瘤患者的临床资料和18F-FDG PET影像资料，其中男性111例、女性71例，中位年龄50岁，年龄范围为4~85岁。所有患者以简单随机抽样法按6∶4的比例分为训练组（110例）和验证组（72例）。使用三维Slicer软件提取8F-FDG PET图像中的影像组学特征，将所提取的训练组患者的影像组学特征进行独立样本t检验，从中筛选出差异有统计学意义的影像组学特征。在训练组中使用最小绝对值收敛和选择算子（LASSO）回归分析对初筛得到的影像组学特征进行再次筛选，获得最佳影像组学参数子集。采用多因素Logistic回归构建预测模型。应用受试者工作特征（ROC）曲线评价影像组学特征诊断模型鉴别诊断HL与NHL的效能。不符合正态分布的计量资料的比较采用Mann-Whitney U秩和检验；计数资料的比较采用χ2检验。
    结果 训练组110例患者中，HL患者33例、NHL患者 77例；验证组72例患者中，HL 患者22例、NHL患者 50例。训练组、验证组HL与NHL患者SUVmax、肿瘤代谢体积、病灶糖酵解总量的差异均无统计学意义（Z=0.232~1.689，均P>0.05）。LASSO回归的最佳λ值为0.030，根据最佳λ值得到13个影像组学特征，经多因素Logistic回归分析筛选出8个影像组学特征。训练组影像组学特征模型ROC的曲线下面积（AUC）为0.880（95%CI：0.802~0.958），灵敏度、特异度、准确率分别为83.5%、65.5%、 78.2%。验证组影像组学特征模型ROC的AUC为0.706（95%CI：0.570~0.842），灵敏度、特异度、准确率分别为70.9%、81.8%、74.2%。
    结论 基于PET的影像组学特征模型在HL和侵袭性NHL的鉴别诊断中具有较大的潜在应用价值。

     

    Abstract:
    Objective To assess whether a diagnostic analysis model based on radiomic features extracted from clinical baseline 18F-fluorodeoxyglucose (FDG) PET is useful in differentiating Hodgkin lymphoma (HL) from aggressive non-Hodgkin lymphoma (NHL).
    Methods Clinical data and 18F-FDG PET imaging data of 182 patients with lymphoma confirmed by histopathological results, who were treated at the First Affiliated Hospital of Zhengzhou University from May 2016 to July 2021, were retrospectively analyzed, including 111 males and 71 females, age 4−85 years old with a median age of 50. All patients were divided into a training group (110 patients) and a validation group (72 patients) in a 6∶4 ratio by simple random sampling. The radiomic features in the 18F-FDG PET images were extracted using 3D Slicer, and the extracted radiomic features of the training groups were subjected to an independent sample t-test, from which statistically significant differences in the radiomic features were screened out. The radiomic features obtained from the initial screening were screened again using least absolute shrinkage and selection operator (LASSO) regression in the training group to obtain the best subset of radiomic parameters. Predictive models were constructed using multivariate Logistic regression analysis. The efficacy of the diagnostic model for differential diagnosis of HL and NHL by radiomic features was evaluated using the receiver operating characteristic (ROC) curve. The Mann-Whitney U rank-sum test was used for comparisons of measurements that did not conform to normal distribution, whereas χ2 test was used for comparisons of count data.
    Results Among the 110 patients in the training group, 33 had HL and 77 had NHL. Among the 72 patients in the validation group, 22 had HL and 50 had NHL. The differences in SUVmax, metabolic tumor volume, total lesion glycolysis between the patients with HL and NHL in the training and validation groups were not statistically significant (Z=0.232−1.689, all P>0.05). The optimal λ-value of the LASSO regression was 0.030, and in accordance with the optimal λ-value of the radiomic parameters, 13 radiomic features were obtained, and 8 radiomic features were screened out by multivariate Logistic regression analysis. The area under curve (AUC) of ROC of the radiomic feature model in the training group was 0.880 (95%CI: 0.802−0.958), and the sensitivity, specificity, and accuracy were 83.5%, 65.5%, and 78.2%. The AUC of ROC of the radiomic feature model in the validation group was 0.706 (95%CI: 0.570−0.842), with sensitivity, specificity, and accuracy of 70.9%, 81.8%, and 74.2%, respectively.
    Conclusion Models based on PET radiomic features has great potential application value in the differential diagnosis of HL and invasive NHL.

     

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