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18F-FDG PET/CT-based radiomics model for predicting the degree of pathological differentiation in non-small cell lung cancer: a multicentre study

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机构： [1]Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China. [2]The Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China. [3]Sichuan Science City Hospital, Mianyang, Sichuan 621000, China. [4]Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha 410005, China. [5]Center for Molecular Imaging Probe, Hunan Province Key Laboratory of Tumour Cellular and Molecular Pathology, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.

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To explore the value of 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)/computed tomography (CT)-based radiomics model for predicting the degree of pathological differentiation in non-small-cell lung cancer (NSCLC).Clinical characteristics of 182 NSCLC patients from four centres were collected, and radiomics features were extracted from 18F-FDG PET/CT images. Three logistic regression prediction models were established: clinical model; radiomics model; and nomogram combining radiomics signatures and clinical features. The predictive ability of the models was assessed using receiver operating characteristics curve analysis.Patients from centre 1 were assigned randomly to the training and internal validation cohorts (7:3 ratio); patients from centres 2-4 served as the external validation cohort. The area under the curve (AUC) values for the clinical model in the training, internal validation, and external validation cohort were 0.74 (95% confidence interval [CI] = 0.64-0.84), 0.64 (95% CI = 0.46-0.81), and 0.74 (95% CI = 0.60-0.88), respectively. In the training (AUC: 0.84 [95% CI = 0.77-0.92]), internal validation (AUC: 0.81 [95% CI = 0.67-0.95]), and external validation cohorts (AUC: 0.74 [95% CI = 0.58-0.89]), the radiomics model showed good predictive ability for differentiation. Compared to the clinical and radiomics models, the nomogram has relatively better diagnostic performance, and the AUC values for nomogram in the training, internal validation, and external validation cohort were 0.86 (95% CI = 0.78-0.93), 0.83 (95% CI = 0.70-0.96), and 0.77 (95% CI = 0.62-0.92), respectively.The 18F-FDG PET/CT-based radiomics model showed good ability for predicting the degree of differentiation of NSCLC. The nomogram combining the radiomics signature and clinical features has relatively better diagnostic performance.Copyright © 2023. Published by Elsevier Ltd.

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出版当年[2023]版：

大类 | 3 区医学

小类 | 3 区核医学

最新[2023]版：

大类 | 3 区医学

小类 | 3 区核医学

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出版当年[2023]版：

Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

最新[2023]版：

Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子： 2.1 最新[2023版] 2.2 最新五年平均 2.1 出版当年[2023版] 0 出版当年五年平均 2.1 出版前一年[2023版]

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第一作者机构： [1]Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.

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通讯作者：

通讯机构： [1]Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China. [5]Center for Molecular Imaging Probe, Hunan Province Key Laboratory of Tumour Cellular and Molecular Pathology, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China. [*1]Center for Molecular Imaging Probe, Hunan Province Key Laboratory of Tumour Cellular and Molecular Pathology, Cancer Research Institute, Hengyang Medical School, University of South China, 28 West Changsheng Road, Hengyang, Hunan 421001, China. [*2]Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, No. 35 Jiefang Road, Zhengxiang Area, Hengyang, Hunan 421001, China

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