Abstract
Objective
To construct and validate conventional and radiomics models based on dual-layer spectral CT radiomics for preoperative prediction of lung ground glass nodules (GGNs) invasiveness.
Materials and methods
A retrospective study was conducted on 176 GGNs patients who underwent chest non-contrast enhancement scan on dual-layer spectral detector CT at our hospital within 2 weeks before surgery. Patients were randomized into the training cohort and testing cohort. Clinical features, imaging features and spectral quantitative parameters were collected to establish a conventional model. Radiomics models were established by extracting 1781 radiomics features form regions of interest of each spectral image [120 kVp poly energetic images (PI), 60 keV images and electron density maps], respectively. After selecting the optimal radiomic features and integrating multiple machine learning models, the conventional model, PI model, 60 keV model, electron density (ED) model and combined model based on multimodal spectral images were finally established. The performance of these models was assessed through the evaluation of discrimination, calibration, and clinical application.
Results
In the conventional model, age, vacuole sign, 60 keV and ED were independent risk factors of invasiveness. The combined model using logistic regression-least absolute shrinkage and selection operator classifiers was the optimal model with a higher area under the curve of the training (0.961, 95% confidence interval, CI: 0.932–0.991) and testing set (0.944, 0.890–0.999).
Conclusion
The combined models are helpful to predict the invasiveness of GGNs before surgery and guide the individualized treatment of patients.
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Availability of data and materials
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Acknowledgements
The author would wish to express our heartfelt thanks to the staff of the Department of Thoracic Surgery and Pathology of our hospital.
Funding
The work described in this paper was partially supported by the National Natural Science Foundation of China (Grant number 81971573) and the Suzhou Gusu Medical Youth Talent (Grant number GSWS2020019) and Jiangsu Provincial Key Medical Discipline (Grant number JSDW202242).
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CY is responsible for data generation, data analysis and manuscript writing. XH, SY, LYQ and YLF participated in the patient's film reading and feature extraction. YLF contributes to screening patients for study eligibility. The DH was involved in the generation of data and the drafting of manuscripts and was responsible for ideation, supervision, project management and funding acquisition. All authors have read and approved the final draft.
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This study was performed in line with the principles of the Declaration of Helsinki. The Institutional Ethics Review Board approved this retrospective study and waived the requirement for written informed consent (No: 233).
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Chang, Y., **ng, H., Shang, Y. et al. Preoperative predicting invasiveness of lung adenocarcinoma manifesting as ground-glass nodules based on multimodal images of dual-layer spectral detector CT radiomics models. J Cancer Res Clin Oncol 149, 15425–15438 (2023). https://doi.org/10.1007/s00432-023-05311-y
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DOI: https://doi.org/10.1007/s00432-023-05311-y