CT texture analysis-based nomogram for the preoperative prediction of visceral pleural invasion in cT1N0M0 lung adenocarcinoma: an external validation cohort study

2021 ◽  
Author(s):  
Z. Zuo ◽  
Y. Li ◽  
K. Peng ◽  
X. Li ◽  
Q. Tan ◽  
...  
Keyword(s):  
Cohort Study ◽  
Lung Adenocarcinoma ◽  
Texture Analysis ◽  
Validation Cohort ◽  
External Validation ◽  
Pleural Invasion ◽  
Preoperative Prediction ◽  
Visceral Pleural Invasion ◽  
Ct Texture Analysis

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

scholarly journals Visceral Pleural Invasion Does Not Affect Recurrence or Overall Survival Among Patients With Lung Adenocarcinoma ≤ 2 cm

CHEST Journal ◽  
2013 ◽  
Vol 144 (5) ◽  
pp. 1622-1631 ◽  
Author(s):  
Jun-ichi Nitadori ◽  
Christos Colovos ◽  
Kyuichi Kadota ◽  
Camelia S. Sima ◽  
Inderpal S. Sarkaria ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Pleural Invasion ◽  
Visceral Pleural Invasion

scholarly journals A Clinical-Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Gallbladder Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xingyu Liu ◽  
Xiaoyuan Liang ◽  
Lingxiang Ruan ◽  
Sheng Yan
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Gallbladder Cancer ◽  
Prediction Models ◽  
Validation Cohort ◽  
External Validation ◽  
Training Cohort ◽  
Clinical Variables ◽  
Node Metastasis ◽  
Preoperative Prediction

ObjectivesThe aim of the current study was to develop and validate a nomogram based on CT radiomics features and clinical variables for predicting lymph node metastasis (LNM) in gallbladder cancer (GBC).MethodsA total of 353 GBC patients from two hospitals were enrolled in this study. A Radscore was developed using least absolute shrinkage and selection operator (LASSO) logistic model based on the radiomics features extracted from the portal venous-phase computed tomography (CT). Four prediction models were constructed based on the training cohort and were validated using internal and external validation cohorts. The most effective model was then selected to build a nomogram.ResultsThe clinical-radiomics nomogram, which comprised Radscore and three clinical variables, showed the best diagnostic efficiency in the training cohort (AUC = 0.851), internal validation cohort (AUC = 0.819), and external validation cohort (AUC = 0.824). Calibration curves showed good discrimination ability of the nomogram using the validation cohorts. Decision curve analysis (DCA) showed that the nomogram had a high clinical utility.ConclusionThe findings showed that the clinical-radiomics nomogram based on radiomics features and clinical parameters is a promising tool for preoperative prediction of LN status in patients with GBC.

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