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Article

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

1
Section of Nuclear Medicine and Health Physics, Department of Surgical and Biomedical Sciences, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy
2
Department of Engineering, Università degli Studi di Perugia, Via Goffredo Duranti 93, 06135 Perugia, Italy
3
Section of Radiation Oncology, Department of Surgical and Biomedical Sciences, Università degli Studi di Perugia, Piazza Lucio Severi 1, 06132 Perugia, Italy
4
Unit of Nuclear Medicine, Department of Medical, Surgical and Experimental Sciences, Università degli Studi di Sassari, Viale San Pietro 8, 07100 Sassari, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Diagnostics 2020, 10(9), 696; https://doi.org/10.3390/diagnostics10090696
Received: 19 August 2020 / Revised: 10 September 2020 / Accepted: 10 September 2020 / Published: 15 September 2020
(This article belongs to the Special Issue Artificial Intelligence in Lung Diseases)
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. View Full-Text
Keywords: solitary pulmonary nodule; shape; texture; radiomics solitary pulmonary nodule; shape; texture; radiomics
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MDPI and ACS Style

Palumbo, B.; Bianconi, F.; Palumbo, I.; Fravolini, M.L.; Minestrini, M.; Nuvoli, S.; Stazza, M.L.; Rondini, M.; Spanu, A. 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, 10, 696. https://doi.org/10.3390/diagnostics10090696

AMA Style

Palumbo B, Bianconi F, Palumbo I, Fravolini ML, Minestrini M, Nuvoli S, Stazza ML, Rondini M, Spanu A. 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; 10(9):696. https://doi.org/10.3390/diagnostics10090696

Chicago/Turabian Style

Palumbo, Barbara, Francesco Bianconi, Isabella Palumbo, Mario L. Fravolini, Matteo Minestrini, Susanna Nuvoli, Maria L. Stazza, Maria Rondini, and Angela Spanu. 2020. "Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation" Diagnostics 10, no. 9: 696. https://doi.org/10.3390/diagnostics10090696

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