scholarly journals Ultra-High-Resolution CT Features of Pulmonary Nodules Correlate with Visceral Pleural Invasion in Early Stage Lung Adenocarcinoma

2021 ◽  
Author(s):  
Lu Li ◽  
Huimin Li ◽  
Jiangfeng Pan ◽  
Zhenwei Chen ◽  
Xiaorong Chen ◽  
...  
Keyword(s):  
High Resolution ◽  
Lung Adenocarcinoma ◽  
Early Stage ◽  
Tnm Classification ◽  
Area Under The Curve ◽  
Pulmonary Nodules ◽  
High Resolution Computed Tomography ◽  
Solid Ratio ◽  
Pleural Invasion ◽  
Visceral Pleural Invasion

Abstract Backgroundvisceral pleural invasion (VPI) is an important prognostic factor in early stage lung adenocarcinoma, which can affect the TNM Classification of Tumors.PurposeTo investigate whether ultra-high-resolution computed tomography (U-HRCT) features can predict VPI of early stage pulmonary nodules contacting the interlobar pleura.Material and MethodsA total of 126 patients with lung adenocarcinoma (age, 24-77 years) confirmed by surgical pathology were retrospectively enrolled. All patients underwent U-HRCT scan and were divided into two groups according to pulmonary nodular type: pure (pGGN) and mixed (mGGN). Clinical features were recorded, and U-HRCT features were manually measured using PHILIPS EBW V4.5.5. Univariate and multivariate logistic regression were used to determine factors that can significantly predict VPI. ResultsU-HRCT and three-dimensional orthogonal post-processing method could better display the relationship between GGNs and interlobar fissures. Among all patients, fifteen patients (12%) had VPI. None of the patients with pGGN had VPI. In the mGGN group, the solid ratio (odds ratio [OR]=1.275, 95% CI 1.1-1.478; P=0.001) and solid diameter (OR=1.139, 95% CI 1.06-2.346; P=0.046) were independent risk factors for VPI in early stage lung adenocarcinoma. For VPI diagnosis, the area under the curve, sensitivity, and specificity of the solid ratio and solid diameter were 0.803, 80%, and 75% and 0.807, 80%, and 80.36%, respectively.ConclusionU-HRCT can display GGNs and interlobar fissures in detail. VPI was not detected in patients with pGGN. In patients with mGGNs, a solid diameter >6mm and solid ratio >38% can be independent predictors of VPI, which may be helpful in surgical decision-making.

scholarly journals Prediction of the Growth Rate of Early-Stage Lung Adenocarcinoma by Radiomics

2021 ◽  
Vol 11 ◽  
Author(s):  
Mingyu Tan ◽  
Weiling Ma ◽  
Yingli Sun ◽  
Pan Gao ◽  
Xuemei Huang ◽  
...  
Keyword(s):  
Growth Rate ◽  
Lung Adenocarcinoma ◽  
Early Stage ◽  
Model Performance ◽  
Area Under The Curve ◽  
Pulmonary Nodules ◽  
Region Of Interest ◽  
Radiographic Features ◽  
Preoperative Ct ◽  
Validation Set

ObjectivesTo investigate the value of imaging in predicting the growth rate of early lung adenocarcinoma.MethodsFrom January 2012 to June 2018, 402 patients with pathology-confirmed lung adenocarcinoma who had two or more thin-layer CT follow-up images were retrospectively analyzed, involving 407 nodules. Two complete preoperative CT images and complete clinical data were evaluated. Training and validation sets were randomly assigned according to an 8:2 ratio. All cases were divided into fast-growing and slow-growing groups. Researchers extracted 1218 radiomics features from each volumetric region of interest (VOI). Then, radiomics features were selected by repeatability analysis and Analysis of Variance (ANOVA); Based on the Univariate and multivariate analyses, the significant radiographic features is selected in training set. A decision tree algorithm was conducted to establish the radiographic model, radiomics model and the combined radiographic-radiomics model. Model performance was assessed by the area under the curve (AUC) obtained by receiver operating characteristic (ROC) analysis.ResultsSixty-two radiomics features and one radiographic features were selected for predicting the growth rate of pulmonary nodules. The combined radiographic-radiomics model (AUC 0.78) performed better than the radiographic model (0.727) and the radiomics model (0.710) in the validation set.ConclusionsThe model has good clinical application value and development prospects to predict the growth rate of early lung adenocarcinoma through the combined radiographic-radiomics model.

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

Air-Containing Space in Lung Adenocarcinoma: High-Resolution Computed Tomography Findings

2002 ◽  
Vol 26 (6) ◽  
pp. 1026-1031 ◽  
Author(s):  
Atsushi Nambu ◽  
Kazuyuki Miyata ◽  
Katsura Ozawa ◽  
Masahisa Miyazawa ◽  
Yuuko Taguchi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Lung Adenocarcinoma ◽  
High Resolution Computed Tomography

scholarly journals Use of Autofluorescence to Intraoperatively Diagnose Visceral Pleural Invasion From Frozen Sections in Patients With Lung Adenocarcinoma 2 cm or Less

2019 ◽  
Vol 152 (5) ◽  
pp. 608-615
Author(s):  
Huikang Xie ◽  
Hang Su ◽  
Donglai Chen ◽  
Dong Xie ◽  
Chenyang Dai ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Accurate Method ◽  
Sublobar Resection ◽  
Frozen Sections ◽  
Surgical Decision ◽  
Pleural Invasion ◽  
Visceral Pleural Invasion ◽  
Lung Adenocarcinomas ◽  
The Impact ◽  
Risk Factor For Recurrence

Abstract Objectives We prospectively investigate the accuracy of frozen sections for diagnosing visceral pleural invasion (VPI) by autofluorescence and evaluated its usefulness in sublobar resection. Methods We included patients with lung adenocarcinoma 2 cm or less to evaluate the diagnostic performance of autofluorescence for VPI in frozen sections via a fluorescence microscope. Furthermore, the impact of VPI on patients treated with sublobar resection was assessed in another cohort. Results A total of 112 patients were enrolled. The accuracy, sensitivity, and specificity of autofluorescence for VPI diagnosis was 95.5%, 86.8%, and 100%, respectively. Sublobar resection was an independent risk factor for recurrence in patients with lung adenocarcinomas 2 cm or less with VPI positivity (hazard ratio, 3.30; P = .023), whereas it was not in those with VPI negativity. Conclusions Using autofluorescence in frozen sections appears to be an accurate method for diagnosing VPI, which is helpful for surgical decision making.

Recurrence Pattern of Pathologic Stage I Lung Adenocarcinoma With Visceral Pleural Invasion

2017 ◽  
Vol 103 (4) ◽  
pp. 1126-1131 ◽  
Author(s):  
Dhihintia Jiwangga ◽  
Sukki Cho ◽  
Kwhanmien Kim ◽  
Sanghoon Jheon
Keyword(s):  
Lung Adenocarcinoma ◽  
Stage I ◽  
Recurrence Pattern ◽  
Pathologic Stage ◽  
Pleural Invasion ◽  
Visceral Pleural Invasion
Sign in / Sign up

Export Citation Format

Share Document