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@#Lung cancer has brought tough challenges to human health due to its high incidence and mortality rate in the current practice. Nowadays, computed tomography (CT) imaging is still the most preferred diagnostic tool for early screening of lung cancer. However, a great challenge brought from accumulative CT imaging data can not meet the demand of the current clinical practice. As a novel kind of artificial intelligence technique aimed to deal with medical images, a computer-aided diagnosis has been found to provide useful auxiliary information, attenuate the workload of doctors, and significantly improve the efficiency and accuracy for clinical diagnosis of lung cancer. Therefore, an effective combination of computer-aided techniques and CT imaging has increasingly become an active area of investigation in early diagnosis of lung cancer. This review aims to summarize the latest progress on the diagnostic value of computer-aided technology with regard to early stage lung cancer from the perspectives of machine learning and deep learning.
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Objective@#To objectively and subjectively assess and compare the characteristics of monoenergetic images [MEI (+)] and polyenergetic images (PEI) acquired by dual-energy CT (DECT) of patients with breast cancer. @*Materials and Methods@#This retrospective study evaluated the images and data of 42 patients with breast cancer who had undergone dual-phase contrast-enhanced DECT from June to September 2019. One standard PEI, five MEI (+) in 10-kiloelectron volt (keV) intervals (range, 40–80 keV), iodine density (ID) maps, iodine overlay images, and Z effective (Z eff) maps were reconstructed. The contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) were calculated. Multiple quantitative parameters of the malignant breast lesions were compared between the arterial and the venous phase images. Two readers independently assessed lesion conspicuity and performed a morphology analysis. @*Results@#Low keV MEI (+) at 40–50 keV showed increased CNR and SNR breast lesion compared with PEI, especially in the venous phase ([CNR: 40 keV, 20.10; 50 keV, 14.45; vs. PEI, 7.27; p < 0.001], [SNR breast lesion: 40 keV, 21.01; 50 keV, 16.28; vs.PEI, 10.77; p< 0.001]). Multiple quantitative DECT parameters of malignant breast lesions were higher in the venous phase images than in the arterial phase images (p < 0.001). MEI (+) at 40 keV, ID, and Z eff reconstructions yielded the highest Likert scores for lesion conspicuity. The conspicuity of the mass margin and the visual enhancement were significantly better in 40-keV MEI (+) than in the PEI (p = 0.022, p = 0.033, respectively). @*Conclusion@#Compared with PEI, MEI (+) reconstructions at low keV in the venous phase acquired by DECT improved the objective and subjective assessment of lesion conspicuity in patients with malignant breast lesions. MEI (+) reconstruction acquired by DECT may be helpful for the preoperative evaluation of breast cancer.
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Objective@#To objectively and subjectively assess and compare the characteristics of monoenergetic images [MEI (+)] and polyenergetic images (PEI) acquired by dual-energy CT (DECT) of patients with breast cancer. @*Materials and Methods@#This retrospective study evaluated the images and data of 42 patients with breast cancer who had undergone dual-phase contrast-enhanced DECT from June to September 2019. One standard PEI, five MEI (+) in 10-kiloelectron volt (keV) intervals (range, 40–80 keV), iodine density (ID) maps, iodine overlay images, and Z effective (Z eff) maps were reconstructed. The contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) were calculated. Multiple quantitative parameters of the malignant breast lesions were compared between the arterial and the venous phase images. Two readers independently assessed lesion conspicuity and performed a morphology analysis. @*Results@#Low keV MEI (+) at 40–50 keV showed increased CNR and SNR breast lesion compared with PEI, especially in the venous phase ([CNR: 40 keV, 20.10; 50 keV, 14.45; vs. PEI, 7.27; p < 0.001], [SNR breast lesion: 40 keV, 21.01; 50 keV, 16.28; vs.PEI, 10.77; p< 0.001]). Multiple quantitative DECT parameters of malignant breast lesions were higher in the venous phase images than in the arterial phase images (p < 0.001). MEI (+) at 40 keV, ID, and Z eff reconstructions yielded the highest Likert scores for lesion conspicuity. The conspicuity of the mass margin and the visual enhancement were significantly better in 40-keV MEI (+) than in the PEI (p = 0.022, p = 0.033, respectively). @*Conclusion@#Compared with PEI, MEI (+) reconstructions at low keV in the venous phase acquired by DECT improved the objective and subjective assessment of lesion conspicuity in patients with malignant breast lesions. MEI (+) reconstruction acquired by DECT may be helpful for the preoperative evaluation of breast cancer.
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Objective To investigate the relationship between plasma homocysteine (Hcy), Kv1.3 channel and cardiac troponinI (cTnI) in patients with acute ST-segment elevation myocardial infarction (STEMI). Methods According to the level of Hcy, 80 STEMI patients were divided into STEMI with Hhcy group (Hcy > 15 μmol/L, n=41) and control group (STEMI group, Hcy≤15μmol/L, n=39). The Hcy, blood lipid and cTnI were detected with automatic biochemistry analyzer, respectively. Peripheral lymphocytes were isolated by ficoll density gradient centrifugation. Real-time PCR was used to detect mRNA expression of Kv1.3, and Western blot assay was used to detect protein expression of Kv1.3. Results cTnI concentrations were obviously higher in STEMI with Hhcy group than those in STEMI group (μg/L:22.997 ± 5.880 vs. 12.881 ± 6.343;P0.05). The relative expression levels of Kv1.3 mRNA and protein were significantly higher in STEMI with Hhcy group (1.35±0.14, 0.85±0.12) than those in STEMI group (1.00 ± 0.07, 0.64 ± 0.05, P<0.05). Moreover, there was a positive relation between Hcy level and the mRNA and proteinexpression of Kv1.3 channel (r=0.299, r=0.542, P<0.05). There was a positive relation between protein expression levels of Kv1.3 channel and cTnI (r=0.644, P<0.05). Conclusion Our results support that Hcy could exacerbate the concentration of cTnI through playing an important role in the Kv1.3 mRNA and protein expression in lymphocytes.