Predicting Invasive Non-mucinous Lung Adenocarcinoma IASLC Grading:
A Nomogram Based on Dual-energy CT Imaging and Conventional Features.
10.3779/j.issn.1009-3419.2025.106.24
- Author:
Kaibo ZHU
1
;
Liangna DENG
2
;
Yue HOU
3
;
Lulu XIONG
1
;
Caixia ZHU
4
;
Haisheng WANG
1
;
Junlin ZHOU
1
Author Information
1. Department of Radiology, Lanzhou University Second Hospital, the Second Clinical Medical School, Lanzhou University, Key
Laboratory of Medical Imaging of Gansu Province, Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou 730030, China.
2. Department of Radiology, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing 210009, China.
3. Department of Respiratory Medicine, Lanzhou University Second Hospital, Lanzhou 730030, China.
4. Gansu Medical Device Inspection and Testing Institute, Gansu Province Medical Device Industry Technology Center,
Lanzhou 730300, China.
- Publication Type:Journal Article
- Keywords:
International Association for the Study of Lung Cancer classification;
Invasive non-mucinous pulmonary adenocarcinomas;
Lung neoplasms;
Nomogram;
Spectral computed tomography
- MeSH:
Humans;
Nomograms;
Female;
Male;
Middle Aged;
Tomography, X-Ray Computed/methods*;
Lung Neoplasms/pathology*;
Aged;
Retrospective Studies;
Adenocarcinoma of Lung/pathology*;
Neoplasm Grading;
Adult
- From:
Chinese Journal of Lung Cancer
2025;28(8):585-596
- CountryChina
- Language:Chinese
-
Abstract:
BACKGROUND:Lung adenocarcinoma is an important pathohistologic subtype of non-small cell lung cancer (NSCLC). Invasive non-mucinous pulmonary adenocarcinomas (INMA) tend to have a poor prognosis due to their significant heterogeneity and diverse histologic components. Establishing a histologic grading system for INMA is crucial for evaluating its malignancy. In 2021, the International Association for the Study of Lung Cancer (IASLC) proposed that a new histological grading system could better stratify the prognosis of INMA patients. The aim of this study was to establish a visualized nomogram model to predict INMA IASLC grading preoperatively by means of dual-energy computed tomography (DECT), fractal dimension (FD), clinical features and conventional CT parameters.
METHODS:A total of 112 patients with INMA who underwent preoperative DECT were retrospectively enrolled from March 2021 to January 2025. Patients were categorized into low-intermediate grade and high grade groups based on IASLC grading. The clinical characteristics and conventional CT parameters, including baseline features, biochemical markers, and serum tumor markers, were collected. DECT-derived parameters, including iodine concentration (IC), effective atomic number (eff-Z), and normalized IC (NIC), were collected and determined as NIC ratio (NICr) and fractal dimension (FD). Univariate analysis was employed to compare differences in conventional characteristics and DECT parameters between the two groups. Variables demonstrating statistical significance were subsequently incorporated into a multivariate Logistic regression analysis. A nomogram model integrating clinical data, conventional CT parameters, and DECT parameters was developed to identify independent predictors for IASLC grading of INMA. The discriminatory performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis.
RESULTS:Multivariate analysis identified smoking history [odds ratio (OR)=2.848, P=0.041], lobulation sign (OR=2.163, P=0.004), air bronchogram (OR=7.833, P=0.005), eff-Z in arterial phase (OR=4.266, P<0.001), and IC in arterial phase (OR=1.290, P=0.012) as independent and significant predictors for IASLC grading of INMA. The nomogram model constructed based on these indicators demonstrated optimal predictive performance, achieving an area under the curve (AUC) of 0.804 (95%CI: 0.725-0.883), with specificity and sensitivity of 85.3% and 65.7%, respectively.
CONCLUSIONS:The nomogram model based on clinical features, imaging features and spectral CT parameters have a large potential for application in the preoperative noninvasive assessment of INMA IASLC grading.
