Solitary ground-glass opacity nodules of stage IA pulmonary adenocarcinoma: combination of 18F-FDG PET/CT and high-resolution computed tomography features to predict invasive adenocarcinoma

Zhou, Jun; Li, Yanli; Zhang, Yiqiu; Liu, Guobing; Tan, Hui; Hu, Yan; Xiao, Jie; Shi, Hongcheng

doi:10.18632/oncotarget.15577

Cited by 24 publications

(18 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results are consistent with previous studies. Shi et al revealed that there existed huge differences in the CT measurements of solitary GGNs between AIS-MIA and IAC [7]. Different from our study, they combined the AIS and MIA into one group.…”

Section: Discussioncontrasting

confidence: 79%

“…: (1) nodule patterns: pure ground-glass nodule (PGGN) or mixed ground-glass nodule (MGGN);(2) nodule shapes (round/oval shape or irregular shape); (3) pleural invasion (or not); (4) vascular invasion (or not); (5) lymphatic invasion (or not); (6) necrosis (or not);(7) diameter of the nodule (largest diameter on the axial section) ; (8) diameter of the solid component (largest diameter of solid component on the axial section; (9) proportion of the solid component (< 50% or > 50%); (10) the border of the nodule was defined or undefined; (11) the margin of the nodule (smooth, lobulate, or spiculate); (12) vascular convergence sign (or not); (13) air-bronchograms (direct involvement of bronchiole, in the GGO component or in the solid component, no); (14) vacuole sign (or not, pointlike/translucent/low-density shadows); (15) pleural indentation (or not). Besides, CT radiodensity values were recorded (for PGGN, the value of GGO component; for MGGN, the value of solid component).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Features Based on Thin-section Computed Tomography Identify the Type of lung Adenocarcinoma

Li¹,

Zheng²,

Liu³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: To investigate whether thin-section computed tomography (TSCT) features may efficiently guide the invasiveness basedclassification of lung adenocarcinoma. Methods: Totally, 316 lung adenocarcinoma patients (from 2011-2015) were divided into three groups: 56 adenocarcinoma in situ (AIS), 98 minimally invasive adenocarcinoma (MIA), and 162 invasive adenocarcinoma (IAC) according their pathological results. Their TSCT features, including nodule pattern, shape, pleural invasion, solid proportion, border, margin, vascular convergence, air bronchograms, vacuole sign, pleural indentation, diameter, solid diameter, and CT values of ground-glass nodules (GGN) were analyzed. Pearson’s chi-square test, Fisher’s exact test and One-way ANOVA were adopted tocomparebetweengroups. Receiver operating characteristic (ROC) analysis wereperformedto assess its value for prediction and diagnosis. Results: Patients with IAC were significantly elder than those in AIS or MIA group,and more MIA patients had a smoking history than AIS and IAC. No recurrence happened in the AIS and MIA groups, while 4.3% recurrences were confirmed in the IAC group. As for TSCT variables, we found AIS group showed dominantly higher 91.07%PGGN pattern and 87.50% round/oval nodules than that in MIA and IAC group. In contrast, MIA group showed more cases with undefined border and vascular convergence than AIS and IAC group. Importantly, IAC group uniquely showed higher frequency of pleural invasion compared with MIA and AIS group. The majority of patients (82.1%) in IAC group showed ≥ 50% solid proportion. We found diameter and solid diameter of the lesions were notably larger in the IAC group compared with AIS and MIA groupin quantitative aspect. In addition, for MGGNs, the CT values of ground-glass opacity (GGO) and ground-glass opacity solid portion (GGO-solid) were both higher in the IAC group than AIS and MIA. Finally, we also observed that smooth margin took a dominant proportion in the AIS group while most cases in the IAC group had a lobulate margin. Patients in MIA and IAC group shared higher level of air bronchograms and vacuole signs than AIS group. Conclusions: The unique features in different groups identified by TSCT had diagnosis value for lung adenocarcinoma.

show abstract

Section: Discussioncontrasting

confidence: 79%

mentioning

confidence: 99%

Features Based on Thin-section Computed Tomography Identify the Type of lung Adenocarcinoma

Li¹,

Zheng²,

Liu³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Additionally, the napkin-ring sign, plaque CT value and degree of coronary artery stenosis were also analyzed by multivariate logistic regression analysis. The statistical method was reported previously (21). Briefly, a logistic regression equation was formed by multivariate regression analysis, and the exact pair-matched data of the napkin-ring sign and plaque CT value of the 68 plaques were backtested via the equation to compute exact probabilities.…”

Section: Discussionmentioning

confidence: 99%

The diagnosis of coronary plaque stability by multi-slice computed tomography coronary angiography

Song

Zhou

et al. 2018

J. Thorac. Dis.

Self Cite

View full text Add to dashboard Cite

Background: Coronary computed tomographic angiography is a robust non-invasive method to assess coronary artery disease (CAD) and analyze coronary plaque stability, especially for the non-calcified plaques.The aim of this study was to investigate the differential characteristics between the unstable coronary plaques and the stable coronary plaques using multi-slice computed tomography (MSCT). Methods: Sixty patients with coronary heart disease (37 unstable plaques and 31 stable plaques) were included. The napkin ring thickness, napkin-ring sign, plaque CT attenuation and degree of lumen stenosis were retrospectively analyzed. The diagnostic performances of MSCT were determined to predict the unstable plaques. The difference was statistically significant if P<0.05. Results:The napkin ring thickness of the unstable plaques was thinner than that of the stable plaques (P<0.05). The napkin-ring sign was more frequently observed in the unstable group (89.2%) than the stable group (22.6%, P<0.05). The average CT value of the unstable plaques (26.8±17.8 HU) was lower than that of the stable plaques (68.5±25.5 HU, P<0.05). The unstable plaques had more severe lumen stenosis or occlusion (70.3%) than the stable plaques (41.9%, P<0.05). The measurable napkin ring thickness of the plaques with a cutoff value of 0.8 mm and an accuracy of 89.5% was one independent factor to predict unstable plaques. The optimal combined threshold of the napkin-ring sign and/or the plaque CT value of 53 HU with an accuracy of 80.9% was to predict unstable plaques. Conclusions:The optimal combined threshold of the napkin-ring sign and/or the plaque CT value ≤53 HU may be a good indicator to predict the unstable plaques in patients with CAD. The subgroup of measurable napkin ring thickness of the non-calcified plaques may also be an independent factor to predict the unstable plaques in patients with CAD.

show abstract

“…Evolutions in the radiological features analysis of the nodule would probably help us determining lesion invasiveness and surgical requires (13,14).…”

Section: Introductionmentioning

confidence: 99%

Planning and marking for thoracoscopic anatomical segmentectomies

et al. 2018

View full text Add to dashboard Cite

Although sublobar resection (SLR) for treating non-small cell lung carcinoma (NSCLC) is still controversial, thoracoscopic segmentectomy is rising. Performing it by closed chest surgery is complex as it means confirming the location of the lesion, identifying vascular and bronchial structures, preserving venous drainage of adjacent segments, severing the intersegmental plane and ensuring an oncological safety margin with no manual palpation and different landmarks. Accurate planning is mandatory. We discuss in this article the interest of 3D reconstruction and mapping technics to enhance safety and reliability of these procedures.

show abstract

Solitary ground-glass opacity nodules of stage IA pulmonary adenocarcinoma: combination of 18F-FDG PET/CT and high-resolution computed tomography features to predict invasive adenocarcinoma

Cited by 24 publications

References 42 publications

Features Based on Thin-section Computed Tomography Identify the Type of lung Adenocarcinoma

Features Based on Thin-section Computed Tomography Identify the Type of lung Adenocarcinoma

The diagnosis of coronary plaque stability by multi-slice computed tomography coronary angiography

Planning and marking for thoracoscopic anatomical segmentectomies

Contact Info

Product

Resources

About