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Biomedical And Clinical Sciences
Oncology And Carcinogenesis
Predictive And Prognostic Markers
Preoperative Markers For Identifying Ct ≤2 Cm Solid Nodules Of Lung Adenocarcinoma Based On Image Deep Learning.

Home
Research Domains
Biomedical And Clinical Sciences
Oncology And Carcinogenesis
Predictive And Prognostic Markers
Preoperative Markers For Identifying Ct ≤2 Cm Solid Nodules Of Lung Adenocarcinoma Based On Image Deep Learning.

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Preoperative markers for identifying CT ≤2 cm solid nodules of lung adenocarcinoma based on image deep learning.

Zhen Gao¹, Shang Liu¹, Xiao Li¹

¹Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Shandong First Medical University, Jinan, PR China.

Thoracic Cancer

|October 2, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Preoperative identification of solid-pattern lung adenocarcinoma using imaging and clinical factors is crucial. A three-dimensional proportion of solid component (PSC) of 72% effectively predicts this aggressive subtype, aiding surgical planning.

Keywords:

lung adenocarcinoma proportion of solid component solid pattern adenocarcinoma

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Area of Science:

Thoracic oncology
Pulmonary medicine
Radiology

Background:

Solid-pattern lung adenocarcinoma is a highly malignant subtype.
Preoperative identification is vital for surgical approach and prognosis in small lung cancers.
Transitioning surgical techniques necessitate accurate subtype diagnosis.

Purpose of the Study:

To identify preoperative diagnostic factors for solid-pattern lung adenocarcinoma.
To develop a predictive model for surgical planning.
To improve patient outcomes through precise diagnosis.

Main Methods:

Analysis of 1489 patients with clinical stage IA1-2 lung adenocarcinoma.
Deep learning for lung imaging features and clinical characteristics.
LASSO regression, decision tree analysis, logistic model, and nomogram construction.

Restricted cubic spline (RCS) analysis for optimal cutoff points.

Main Results:

Three-dimensional proportion of solid component (PSC), sex, and smoking status are key diagnostic factors.
The logistic model achieved an AUC of 0.85.
A PSC of ≥72% indicated a 4.6-fold increase in solid adenocarcinoma.
A PSC cutoff of 72% demonstrated good predictive value.

Conclusions:

Preoperative diagnosis of solid-pattern adenocarcinoma is feasible using imaging and clinical data.
Accurate diagnosis assists thoracic surgeons in developing precise surgical plans.
Improved diagnostic accuracy can lead to better patient outcomes.