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A prediction model based on computed tomography characteristics for identifying malignant from benign sub-centimeter

Shu-Lei Cui1, Lin-Lin Qi1, Jia-Ning Liu1

  • 1Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Journal of Thoracic Disease
|August 15, 2024
PubMed
Summary

This study developed a computed tomography (CT) prediction model to differentiate malignant from benign sub-centimeter solid pulmonary nodules (SSPNs). The model, using CT characteristics, showed good performance in distinguishing malignant SSPNs, aiding early diagnosis.

Keywords:
Solitary pulmonary nodulecomputed tomography (CT)differential diagnosislogistic models

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Distinguishing benign from malignant sub-centimeter solid pulmonary nodules (SSPNs) is clinically challenging.
  • Early diagnosis of malignant SSPNs is vital for improving patient survival and prognosis.

Purpose of the Study:

  • To investigate risk factors for malignant SSPNs.
  • To establish and validate a prediction model using computed tomography (CT) characteristics for early diagnosis of malignant SSPNs.

Main Methods:

  • Retrospective analysis of 261 SSPNs (161 malignant, 100 benign) from Center 1, divided into training (n=183) and validation (n=78) sets.
  • Multivariate logistic analysis identified independent predictors of malignancy.
  • External validation performed on 69 SSPNs from Center 2.

Main Results:

  • Four independent predictors identified: tumor-lung interface, spiculation, air bronchogram, and invisibility at the mediastinal window.
  • The prediction model achieved an AUC of 0.875 in the training set, 0.781 in the internal validation set, and 0.873 in the external validation set.
  • High sensitivity and specificity were observed across validation sets, indicating robust predictive efficacy.

Conclusions:

  • A CT-based prediction model can effectively assist in distinguishing malignant from benign SSPNs.
  • This model holds potential for improving the early diagnosis and management of pulmonary nodules.