18F-FDG PET/CT radiomic features for predicting the subtypes of non-small-cell lung cancer(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2019年第3期
- Page:
- 311-315
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- 18F-FDG PET/CT radiomic features for predicting the subtypes of non-small-cell lung cancer
- Author(s):
- SHA Xue1; GONG Guanzhong2; DENG Hongbin3; QIU Qingtao2; LI Dengwang1; YIN Yong1; 2
- 1. Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, School of Physics and Electronics, Shandong Normal University, Ji’nan 250358, China; 2. Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Ji’nan 250117, China; 3. The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210011, China
- Keywords:
- ; Keywords: non-small-cell lung cancer; PET/CT; radiomics; pathological subtype
- PACS:
- R734.2；R445
- DOI:
- DOI:10.3969/j.issn.1005-202X.2019.03.013
- Abstract:
- Abstract: Objective To investigate the feasibility of using pretreatment 18F-FDG PET/CT radiomic features to predict the pathological subtypes of non-small-cell lung cancer (NSCLC). Methods The pretreatment 18F -FDG PET/CT images of 100 NSCLC patients, including 60 adenocarcinoma (ADC) patients and 40 squamous cell carcinoma (SqCC) patients, were analyzed retrospectively. After the gross tumor volume was delineated on PET images, the metabolic parameters and texture parameters were extracted from gross tumor volume. Pearson correlation coefficients and receiver operating characteristic curve were used to assess the performances of the predictive features in the prediction of the pathological subtypes of NSCLC, and to calculate the sensitivity, specificity and optimal threshold of these features. Results Of 107 features extracted in this study, 87 features reflected the differences between ADC and SqCC (P<0.05). Among the 87 features, there were 8 features related to the pathological subtypes (r>0.4), and their AUC values were all higher than 0.7. Three features with the best predictive performance, namely inverse difference moment, homogeneity and short-zone emphasis, were selected as predictive factors. The AUC values of the 3 predictive factors reached 0.770, 0.768 and 0.754, respectively, and their sensitivity and specificity were 0.949 and 0.475, 0.795 and 0.607, 0.821 and 0.639, respectively. Conclusion Tumor heterogeneity reflected in the radiomic features of ADC and SqCC is expected to provide an efficient and non-invasive detection method for the diagnosis of tumor subtypes.
Last Update: 2019-03-25