Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation.

OBJECTIVE: To investigate the natural history of persistent pulmonary pure ground-glass nodules (pGGNs) with deep learning-assisted nodule segmentation. METHODS: Between January 2007 and October 2018, 110 pGGNs from 110 patients with 573 follow-up CT scans were included in this retrospective study. pGGN automatic segmentation was performed on initial and all follow-up CT scans using the Dr. Wise system based on convolution neural networks. Subsequently, pGGN diameter, density, volume, mass, volume doubling time (VDT), and mass doubling time (MDT) were calculated automatically. Enrolled pGGNs were categorized into growth, 52 (47.3%), and non-growth, 58 (52.7%), groups according to volume growth. Kaplan-Meier analyses with the log-rank test and Cox proportional hazards regression analysis were conducted to analyze the cumulative percentages of pGGN growth and identify risk factors for growth. RESULTS: The mean follow-up period of the enrolled pGGNs was 48.7 ± 23.8 months. The median VDT of the 52 pGGNs having grown was 1448 (range, 339-8640) days, and their median MDT was 1332 (range, 290-38,912) days. The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p < 0.001). The growth pattern of pGGNs may conform to the exponential model. Lobulated sign (p = 0.044), initial mean diameter (p < 0.001), volume (p = 0.003), and mass (p = 0.023) predicted pGGN growth. CONCLUSIONS: Persistent pGGNs showed an indolent course. Deep learning can assist in accurately elucidating the natural history of pGGNs. pGGNs with lobulated sign and larger initial diameter, volume, and mass are more likely to grow. KEY POINTS: • The pure ground-glass nodule (pGGN) segmentation accuracy of the Dr. Wise system based on convolution neural networks (CNNs) was 96.5% (573/594). • The median volume doubling time (VDT) of 52 pure ground-glass nodules (pGGNs) having grown was 1448 days (range, 339-8640 days), and their median mass doubling time (MDT) was 1332 days (range, 290-38,912 days). The mean time to growth in volume was 854 ± 675 days (range, 116-2856 days). • The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p values < 0.001). The growth pattern of pure ground-glass nodules may conform to exponential model.

Combination coating of chitosan and anti-CD34 antibody applied on sirolimus-eluting stents can promote endothelialization while reducing neointimal formation.

BACKGROUND: Circulating endothelial progenitor cells (EPCs) capture technology improves endothelialization rates of sirolimus-eluting stents (SES), but the problem of delayed re-endothelialization, as well as endothelial dysfunction, has still not been overcome. Therefore, we investigated whether the combination coating of hyaluronan-chitosan (HC) and anti-CD34 antibody applied on an SES (HCASES) can promote endothelialization, while reducing neointimal formation and inflammation. METHODS: Sirolimus-eluting stents(SES), anti-CD34 antibody stents (GS) and HC-anti-CD34 antibody combined with sirolimus-eluting stents (HCASES) were deployed in 54 normal porcine arteries and harvested for scanning electron microscopy (SEM) and histological analysis. The ratio of endothelial coverage above the stents was evaluated by SEM analysis at 7, 14 and 28 days. The percentage of in-stent stenosis was histologically analyzed at 14 and 28 days. RESULTS: SEM analysis at 7 days showed that endothelial strut coverage was increased in the HCASES group (68±7%) compared with that in the SES group (31±4%, p=0.02). At 14 days, stent surface endothelialization, evaluated by SEM, showed a significantly higher extent of endothelial coverage above struts in the GS (95 ± 2%) and the HCASES groups (87±4%) compared with that in the SES group (51±6%, p=0.02). Histological examination showed that the percentage of stenosis in the HCASES group was not significantly different to that of the SES and GS groups (both p> 0.05). At 28 days, there was no difference in the rates of endothelial coverage between the HCASES and GS groups. The HCASES group showed less stenosis than that in the GS group (P < 0.05), but it was not significantly different from the SES group (P=0.068). CONCLUSIONS: SEM and histology demonstrated that HCASESs can promote re-endothelialization while enhancing antiproliferative effects.