scholarly journals Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 696
Author(s):  
Barbara Palumbo ◽  
Francesco Bianconi ◽  
Isabella Palumbo ◽  
Mario Luca Fravolini ◽  
Matteo Minestrini ◽  
...  

In this paper, we investigate the role of shape and texture features from 18F-FDG PET/CT to discriminate between benign and malignant solitary pulmonary nodules. To this end, we retrospectively evaluated cross-sectional data from 111 patients (64 males, 47 females, age = 67.5 ± 11.0) all with histologically confirmed benign (n=39) or malignant (n=72) solitary pulmonary nodules. Eighteen three-dimensional imaging features, including conventional, texture, and shape features from PET and CT were tested for significant differences (Wilcoxon-Mann-Withney) between the benign and malignant groups. Prediction models based on different feature sets and three classification strategies (Classification Tree, k-Nearest Neighbours, and Naïve Bayes) were also evaluated to assess the potential benefit of shape and texture features compared with conventional imaging features alone. Eight features from CT and 15 from PET were significantly different between the benign and malignant groups. Adding shape and texture features increased the performance of both the CT-based and PET-based prediction models with overall accuracy gain being 3.4–11.2 pp and 2.2–10.2 pp, respectively. In conclusion, we found that shape and texture features from 18F-FDG PET/CT can lead to a better discrimination between benign and malignant lung nodules by increasing the accuracy of the prediction models by an appreciable margin.

2017 ◽  
Vol 38 (1) ◽  
pp. 67-75 ◽  
Author(s):  
Zong Ruilong ◽  
Xie Daohai ◽  
Geng Li ◽  
Wang Xiaohong ◽  
Wang Chunjie ◽  
...  

2015 ◽  
Vol 84 (10) ◽  
pp. 2032-2037 ◽  
Author(s):  
Wenbo Li ◽  
Hua Pang ◽  
Qiong Liu ◽  
Jing Zhou

2014 ◽  
Vol 35 (3) ◽  
pp. 260-267 ◽  
Author(s):  
Yusuf Demir ◽  
Berna D. Polack ◽  
Canan Karaman ◽  
Özhan Özdoğan ◽  
Erdem Sürücü ◽  
...  

Medicine ◽  
2018 ◽  
Vol 97 (12) ◽  
pp. e0130 ◽  
Author(s):  
Zhen-Zhen Li ◽  
Ya-Liang Huang ◽  
Hong-Jun Song ◽  
You-Juan Wang ◽  
Yan Huang

2015 ◽  
Vol 25 (7) ◽  
pp. 1837-1844 ◽  
Author(s):  
Liang Zhao ◽  
Li Tong ◽  
Jie Lin ◽  
Kun Tang ◽  
SiSi Zheng ◽  
...  

2019 ◽  
Vol 11 (5) ◽  
pp. 2082-2098 ◽  
Author(s):  
Yuzhu Jia ◽  
Wanfeng Gong ◽  
Zhiping Zhang ◽  
Gaofeng Tu ◽  
Jiapeng Li ◽  
...  

2021 ◽  
Vol 10 (21) ◽  
pp. 5064
Author(s):  
Domenico Albano ◽  
Roberto Gatta ◽  
Matteo Marini ◽  
Carlo Rodella ◽  
Luca Camoni ◽  
...  

The aim of this retrospective study was to investigate the ability of 18 fluorine-fluorodeoxyglucose positron emission tomography/CT (18F-FDG-PET/CT) metrics and radiomics features (RFs) in predicting the final diagnosis of solitary pulmonary nodules (SPN). We retrospectively recruited 202 patients who underwent a 18F-FDG-PET/CT before any treatment in two PET scanners. After volumetric segmentation of each lung nodule, 8 PET metrics and 42 RFs were extracted. All the features were tested for significant differences between the two PET scanners. The performances of all features in predicting the nature of SPN were analyzed by testing three classes of final logistic regression predictive models: two were built/trained through exploiting the separate data from the two scanners, and the other joined the data together. One hundred and twenty-seven patients had a final diagnosis of malignancy, while 64 were of a benign nature. Comparing the two PET scanners, we found that all metabolic features and most of RFs were significantly different, despite the cross correlation being quite similar. For scanner 1, a combination between grey level co-occurrence matrix (GLCM), histogram, and grey-level zone length matrix (GLZLM) related features presented the best performances to predict the diagnosis; for scanner 2, it was GLCM and histogram-related features and metabolic tumour volume (MTV); and for scanner 1 + 2, it was histogram features, standardized uptake value (SUV) metrics, and MTV. RFs had a significant role in predicting the diagnosis of SPN, but their accuracies were directly related to the scanner.


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