A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 176

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML

File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

CT Predictors of Visceral Pleural Invasion in Patients with Non-Small Cell Lung Cancers 30 mm or Smaller. | LitMetric

CT Predictors of Visceral Pleural Invasion in Patients with Non-Small Cell Lung Cancers 30 mm or Smaller.

Radiology

From the Department of Radiology, Harbin Medical University Cancer Hospital, Harbin, China (Q.S., P.L.); and Department of Radiology, Icahn School of Medicine at Mount Sinai, 1 Gustave Levy Pl, New York, NY 10029 (Q.S., P.L., J.Z., R.Y., Y.Z., D.F.Y., C.I.H.).

Published: January 2024

Background CT-defined visceral pleural invasion (VPI) is an important indicator of prognosis for non-small cell lung cancer (NSCLC). However, there is a lack of studies focused on small subpleural NSCLCs (≤30 mm). Purpose To identify CT features predictive of VPI in patients with subpleural NSCLCs 30 mm or smaller. Materials and Methods This study is a retrospective review of patients enrolled in the Initiative for Early Lung Cancer Research on Treatment (IELCART) at Mount Sinai Hospital between July 2014 and February 2023. Subpleural nodules 30 mm or smaller were classified into two groups: a pleural-attached group and a pleural-tag group. Preoperative CT features suggestive of VPI were evaluated for each group separately. Multivariable logistic regression analysis adjusted for sex, age, nodule size, and smoking status was used to determine predictive factors for VPI. Model performance was analyzed with the area under the receiver operating characteristic curve (AUC), and models were compared using Akaike information criterion (AIC). Results Of 379 patients with NSCLC with subpleural nodules, 37 had subsolid nodules and 342 had solid nodules. Eighty-eight patients (22%) had documented VPI, all in solid nodules. Of the 342 solid nodules (46% in male patients, 54% in female patients; median age, 71 years; IQR: 66, 76), 226 were pleural-attached nodules and 116 were pleural-tag nodules. VPI was more frequent for pleural-attached nodules than for pleural-tag nodules (31% [69 of 226] vs 16% [19 of 116], = .005). For pleural-attached nodules, jellyfish sign (odds ratio [OR], 21.60; < .001), pleural thickening (OR, 6.57; < .001), and contact surface area (OR, 1.05; = .01) independently predicted VPI. The jellyfish sign led to a better VPI prediction (AUC, 0.84; 95% CI: 0.78, 0.90). For pleural-tag nodules, multiple tags to different pleura surfaces enabled independent prediction of VPI (OR, 9.30; = .001). Conclusions For patients with solid NSCLC (≤30 mm), CT predictors of VPI were the jellyfish sign, pleural thickening, contact surface area (pleural-attached nodules), and multiple tags to different pleura surfaces (pleural-tag nodules). © RSNA, 2024 See also the editorial by Nishino in this issue.

Download full-text PDF

Source
http://dx.doi.org/10.1148/radiol.231611DOI Listing

Publication Analysis

Top Keywords

pleural-attached nodules
16
pleural-tag nodules
16
nodules
14
solid nodules
12
jellyfish sign
12
vpi
10
visceral pleural
8
pleural invasion
8
patients
8
non-small cell
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!