An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images
Abstract Background This study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monoc...
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BMC
2024-10-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-024-01467-2 |
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| author | Zhong-Yan Ma Hai-lin Zhang Fa-jin Lv Wei Zhao Dan Han Li-chang Lei Qin Song Wei-wei Jing Hui Duan Shao-Lei Kang |
| author_facet | Zhong-Yan Ma Hai-lin Zhang Fa-jin Lv Wei Zhao Dan Han Li-chang Lei Qin Song Wei-wei Jing Hui Duan Shao-Lei Kang |
| author_sort | Zhong-Yan Ma |
| collection | DOAJ |
| description | Abstract Background This study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs. Methods Non-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong’s test was used to compare the CPIs group with the VMIs group. Results When distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong’s test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05). Conclusion The AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT. |
| format | Article |
| id | doaj-art-2fcc9dc177544c45b111a56c9a4cd86f |
| institution | OA Journals |
| issn | 1471-2342 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-2fcc9dc177544c45b111a56c9a4cd86f2025-08-20T02:18:32ZengBMCBMC Medical Imaging1471-23422024-10-0124111110.1186/s12880-024-01467-2An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic imagesZhong-Yan Ma0Hai-lin Zhang1Fa-jin Lv2Wei Zhao3Dan Han4Li-chang Lei5Qin Song6Wei-wei Jing7Hui Duan8Shao-Lei Kang9Department of Radiology, First Affiliated Hospital of Kunming Medical UniversityDepartment of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical UniversityDepartment of Radiology, First Affiliated Hospital of Chongqing Medical UniversityDepartment of Radiology, First Affiliated Hospital of Kunming Medical UniversityDepartment of Radiology, First Affiliated Hospital of Kunming Medical UniversityDepartment of Radiology, First Affiliated Hospital of Kunming Medical UniversityDepartment of Radiology, First Affiliated Hospital of Kunming Medical UniversityDepartment of Radiology, First Affiliated Hospital of Chongqing Medical UniversityDepartment of Radiology, First Affiliated Hospital of Kunming Medical UniversityDepartment of Radiology, First Affiliated Hospital of Kunming Medical UniversityAbstract Background This study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs. Methods Non-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong’s test was used to compare the CPIs group with the VMIs group. Results When distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong’s test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05). Conclusion The AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.https://doi.org/10.1186/s12880-024-01467-2Pulmonary ground-glass noduleArtificial intelligenceDeep learningDual-layer detector spectralComputed tomographyVirtual monochromatic images |
| spellingShingle | Zhong-Yan Ma Hai-lin Zhang Fa-jin Lv Wei Zhao Dan Han Li-chang Lei Qin Song Wei-wei Jing Hui Duan Shao-Lei Kang An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images BMC Medical Imaging Pulmonary ground-glass nodule Artificial intelligence Deep learning Dual-layer detector spectral Computed tomography Virtual monochromatic images |
| title | An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images |
| title_full | An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images |
| title_fullStr | An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images |
| title_full_unstemmed | An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images |
| title_short | An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images |
| title_sort | artificial intelligence algorithm for the detection of pulmonary ground glass nodules on spectral detector ct performance on virtual monochromatic images |
| topic | Pulmonary ground-glass nodule Artificial intelligence Deep learning Dual-layer detector spectral Computed tomography Virtual monochromatic images |
| url | https://doi.org/10.1186/s12880-024-01467-2 |
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