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.
Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis / Hunter, B.; Argyros, C.; Inglese, M.; Linton-Reid, K.; Pulzato, I.; Nicholson, A. G.; Kemp, S. V.; L. Shah, P.; Molyneaux, P. L.; Mcnamara, C.; Burn, T.; Guilhem, E.; Mestas Nunez, M.; Hine, J.; Choraria, A.; Ratnakumar, P.; Bloch, S.; Jordan, S.; Padley, S.; Ridge, C. A.; Robinson, G.; Robbie, H.; Barnett, J.; Silva, M.; Desai, S.; Lee, R. W.; Aboagye, E. O.; Devaraj, A.. - In: BRITISH JOURNAL OF CANCER. - ISSN 0007-0920. - 129:12(2023), pp. 1949-1955. [10.1038/s41416-023-02480-y]
Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis
Silva M.Investigation
;
2023-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
