Qiujie Dong, Jinju Sun, Jianping You, He Long, Xin Li, Jun Cheng, Daoxi Hu, Yi Wang, Xiao Chen
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Radiomics features extracted from CT and PET images were screened using interclass correlation coefficients, Pearson correlation analysis and the least absolute shrinkage and selection operator. These selected features were used to calculate the CT and PET rad-scores. Finally, a combined model was constructed using multivariate logistic regression.</p><p><strong>Results: </strong>Tumor type [odds ratio (OR): 3.258, P = 0.012], distance between tumor and pleura (OR: 0.464, P = 0.001), and maximum standardized uptake value (SUVmax) (OR: 1.109, P = 0.002) were used to construct the conventional model. Ten CT radiomics features and six PET radiomics features were used to establish the CT and PET rad-score models. The area under the curve (AUC) value of the combined model (0.824) was higher than conventional model (0.734), CT rad-score model (0.790) and PET rad-score model (0.748) in the training set, and the differences were statistically significant as tested by Delong test (P < 0.05). In the validation set 1 and validation set 2, the combined model exhibited the highest AUC values (0.835 and 0.787), and the difference between the combined model and PET rad-score model (validation set 1: 0.835 vs. 0.747, P = 0.028; validation set 2: 0.787 vs. 0.657, P = 0.043) and CT rad-score model (validation set 2: 0.787 vs. 0.694, P = 0.025) was statistically significant.</p><p><strong>Conclusion: </strong>The combined model based on PET/CT radiomics is an effective and non-invasive tool for preoperative predicting VPI status in IAC patients.</p>","PeriodicalId":11909,"journal":{"name":"European Journal of Nuclear Medicine and Molecular Imaging","volume":" ","pages":""},"PeriodicalIF":7.6000,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predicting visceral pleural invasion in invasive adenocarcinoma with a maximum diameter ≤ 3 cm based on <sup>18</sup>F-FDG PET/CT radiomics.\",\"authors\":\"Qiujie Dong, Jinju Sun, Jianping You, He Long, Xin Li, Jun Cheng, Daoxi Hu, Yi Wang, Xiao Chen\",\"doi\":\"10.1007/s00259-025-07511-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>To investigate the feasibility of <sup>18</sup>F-fluorodeoxyglucose positron emission tomography/computed tomography (<sup>18</sup>F-FDG PET/CT) radiomics in preoperative prediction of visceral pleural invasion (VPI) status in invasive adenocarcinoma (IAC) with a maximum diameter ≤ 3 cm.</p><p><strong>Materials and methods: </strong>A total of 590 IAC patients with a maximum diameter ≤ 3 cm were enrolled and divided into training set (n = 364), validations set 1 (n = 156) and validation set 2 (n = 70). 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The area under the curve (AUC) value of the combined model (0.824) was higher than conventional model (0.734), CT rad-score model (0.790) and PET rad-score model (0.748) in the training set, and the differences were statistically significant as tested by Delong test (P < 0.05). 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引用次数: 0
摘要
目的:探讨18f -氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描(18F-FDG PET/CT)放射组学在最大直径≤3cm的侵袭性腺癌(IAC)术前预测内脏胸膜侵犯(VPI)状态的可行性。材料与方法:共入组590例最大直径≤3cm的IAC患者,分为训练集(n = 364)、验证集1 (n = 156)和验证集2 (n = 70)。基于临床和PET/CT影像特征,通过logistic回归建立常规模型。从CT和PET图像中提取放射组学特征,采用类间相关系数、Pearson相关分析、最小绝对收缩和选择算子进行筛选。这些选择的特征被用来计算CT和PET的评分。最后,利用多元逻辑回归构建组合模型。结果:采用肿瘤类型[比值比(OR): 3.258, P = 0.012]、肿瘤与胸膜距离(OR: 0.464, P = 0.001)、最大标准化摄取值(SUVmax) (OR: 1.109, P = 0.002)构建常规模型。采用10个CT放射组学特征和6个PET放射组学特征建立CT和PET放射组学评分模型。在训练集中,联合模型的曲线下面积(AUC)值(0.824)高于常规模型(0.734)、CT rad-score模型(0.790)和PET rad-score模型(0.748),经Delong检验,差异均有统计学意义(P)。结论:基于PET/CT放射组学的联合模型是预测IAC患者VPI状态的有效、无创工具。
Predicting visceral pleural invasion in invasive adenocarcinoma with a maximum diameter ≤ 3 cm based on 18F-FDG PET/CT radiomics.
Purpose: To investigate the feasibility of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) radiomics in preoperative prediction of visceral pleural invasion (VPI) status in invasive adenocarcinoma (IAC) with a maximum diameter ≤ 3 cm.
Materials and methods: A total of 590 IAC patients with a maximum diameter ≤ 3 cm were enrolled and divided into training set (n = 364), validations set 1 (n = 156) and validation set 2 (n = 70). A conventional model was built based on clinical and PET/CT imaging features by logistic regression. Radiomics features extracted from CT and PET images were screened using interclass correlation coefficients, Pearson correlation analysis and the least absolute shrinkage and selection operator. These selected features were used to calculate the CT and PET rad-scores. Finally, a combined model was constructed using multivariate logistic regression.
Results: Tumor type [odds ratio (OR): 3.258, P = 0.012], distance between tumor and pleura (OR: 0.464, P = 0.001), and maximum standardized uptake value (SUVmax) (OR: 1.109, P = 0.002) were used to construct the conventional model. Ten CT radiomics features and six PET radiomics features were used to establish the CT and PET rad-score models. The area under the curve (AUC) value of the combined model (0.824) was higher than conventional model (0.734), CT rad-score model (0.790) and PET rad-score model (0.748) in the training set, and the differences were statistically significant as tested by Delong test (P < 0.05). In the validation set 1 and validation set 2, the combined model exhibited the highest AUC values (0.835 and 0.787), and the difference between the combined model and PET rad-score model (validation set 1: 0.835 vs. 0.747, P = 0.028; validation set 2: 0.787 vs. 0.657, P = 0.043) and CT rad-score model (validation set 2: 0.787 vs. 0.694, P = 0.025) was statistically significant.
Conclusion: The combined model based on PET/CT radiomics is an effective and non-invasive tool for preoperative predicting VPI status in IAC patients.
期刊介绍:
The European Journal of Nuclear Medicine and Molecular Imaging serves as a platform for the exchange of clinical and scientific information within nuclear medicine and related professions. It welcomes international submissions from professionals involved in the functional, metabolic, and molecular investigation of diseases. The journal's coverage spans physics, dosimetry, radiation biology, radiochemistry, and pharmacy, providing high-quality peer review by experts in the field. Known for highly cited and downloaded articles, it ensures global visibility for research work and is part of the EJNMMI journal family.