학술논문
Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis
Document Type
Original Paper
Author
Hunter, Benjamin; Argyros, Christos; Inglese, Marianna; Linton-Reid, Kristofer; Pulzato, Ilaria; Nicholson, Andrew G.; Kemp, Samuel V.; L. Shah, Pallav; Molyneaux, Philip L.; McNamara, Cillian; Burn, Toby; Guilhem, Emily; Mestas Nuñez, Marcos; Hine, Julia; Choraria, Anika; Ratnakumar, Prashanthi; Bloch, Susannah; Jordan, Simon; Padley, Simon; Ridge, Carole A.; Robinson, Graham; Robbie, Hasti; Barnett, Joseph; Silva, Mario; Desai, Sujal; Lee, Richard W.; Aboagye, Eric O.; Devaraj, Anand
Source
British Journal of Cancer. 129(12):1949-1955
Subject
Language
English
ISSN
0007-0920
1532-1827
1532-1827
Abstract
Background: Methods to improve stratification of small (≤15 mm) lung nodules are needed. We aimed to develop a radiomics model to assist lung cancer diagnosis.Methods: Patients were retrospectively identified using health records from January 2007 to December 2018. The external test set was obtained from the national LIBRA study and a prospective Lung Cancer Screening programme. Radiomics features were extracted from multi-region CT segmentations using TexLab2.0. LASSO regression generated the 5-feature small nodule radiomics-predictive-vector (SN-RPV). K-means clustering was used to split patients into risk groups according to SN-RPV. Model performance was compared to 6 thoracic radiologists. SN-RPV and radiologist risk groups were combined to generate “Safety-Net” and “Early Diagnosis” decision-support tools.Results: In total, 810 patients with 990 nodules were included. The AUC for malignancy prediction was 0.85 (95% CI: 0.82–0.87), 0.78 (95% CI: 0.70–0.85) and 0.78 (95% CI: 0.59–0.92) for the training, test and external test datasets, respectively. The test set accuracy was 73% (95% CI: 65–81%) and resulted in 66.67% improvements in potentially missed [8/12] or delayed [6/9] cancers, compared to the radiologist with performance closest to the mean of six readers.Conclusions: SN-RPV may provide net-benefit in terms of earlier cancer diagnosis.