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.

Hunter, B., Argyros, C., Inglese, M., Linton-Reid, K., Pulzato, I., Nicholson, A.g., et al. (2023). Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis. BRITISH JOURNAL OF CANCER [10.1038/s41416-023-02480-y].

Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis

Inglese M.;
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.
2023
Pubblicato
Rilevanza internazionale
Articolo
Sì, ma tipo non specificato
Settore FIS/07
Settore ING-INF/06
English
Hunter, B., Argyros, C., Inglese, M., Linton-Reid, K., Pulzato, I., Nicholson, A.g., et al. (2023). Radiomics-based decision support tool assists radiologists in small lung nodule classification and improves lung cancer early diagnosis. BRITISH JOURNAL OF CANCER [10.1038/s41416-023-02480-y].
Hunter, B; Argyros, C; Inglese, M; Linton-Reid, K; Pulzato, I; Nicholson, Ag; Kemp, Sv; L. Shah, P; Molyneaux, Pl; Mcnamara, C; Burn, T; Guilhem, E; M...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/345388
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