The Usefulness of Imaging Quantification in Discriminating Non-Calcified Pulmonary Hamartoma From Adenocarcinoma - Guan, Xiaojun; Wang, Shaoze; Kuang, Pingding; Lu, Haitong; Zhang, Minming; Qian, Dahong; Xu, Xiaojun (Frontiers in Oncology, 2020)
Background: Patients with non-calcified hamartoma were more susceptible to suffer from surgery or needle biopsy for the tough discrimination from lung adenocarcinoma. Radiomics have the ability to quantify the lesion features and potentially improve disease diagnosis. Thus, this study aimed to discriminate non-calcified hamartoma from adenocarcinoma by employing imaging quantification and machine learning. Methods: Forty-two patients with non-calcified hamartoma and 49 patients with adenocarcinoma were retrospectively collected from our institute, that were confirmed by pathology. Manual lesion segmentation, feature quantification (e.g., texture features) and artificial neural network were performed consecutively. Independent t-test was used to conduct the inter-group comparisons of those imaging features. Receiver operating characteristic curve was performed to investigate the discriminating efficacy. Results: Significantly higher contrast, cluster prominence, cluster shade, dissimilarity, energy and entropy in non-calcified hamartoma were observed compared with lung adenocarcinoma. Texture-grey-level co-occurrence matrix showed a well discrimination between non-calcified hamartoma and adenocarcinoma as the detection sensitivity, specificity, accuracy and the area under the curve were 87.22% ± 9.07%, 82.64% ± 8.07%, 85.11% ± 5.40% and 0.942, respectively. Conclusion: Quantifying imaging features is a potentially useful tool for clinical diagnosis. This study demonstrated that non-calcified hamartoma has a heterogeneous distribution of attenuations probably resulting from its complex organizations. And based on this property, imaging quantification could improve discrimination of non-calcified hamartoma from adenocarcinoma.