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Development and validation of a CT-based comprehensive nomogram for differentiating benign from malignant

Ke Zhang1, Wei-Wei Jing1, Jin Jiang1

  • 1Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Journal of Thoracic Disease
|November 13, 2025
PubMed
Summary

A new nomogram integrating radiomic and clinical features accurately distinguishes benign from malignant subcentimeter solid pulmonary nodules (SSPNs). This tool aids early diagnosis and treatment decisions for SSPNs.

Keywords:
RadiomicsSubcentimeter solid pulmonary nodules (SSPNs)computed tomography imaging (CT imaging)nomogramperi-tumor

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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Distinguishing benign from malignant subcentimeter solid pulmonary nodules (SSPNs) is a clinical challenge.
  • Accurate early diagnosis is crucial for effective treatment strategies.

Purpose of the Study:

  • To develop a predictive nomogram for SSPNs by integrating radiomic features and clinical risk factors.
  • To improve diagnostic accuracy and aid clinical decision-making for SSPNs.

Main Methods:

  • Retrospective analysis of 415 patients with SSPNs.
  • Development of clinical and radiologic models using machine learning algorithms.
  • Integration of optimal models into a comprehensive nomogram for performance evaluation using AUC, calibration curves, and DCA.

Main Results:

  • Multiplanar volume rendering (MPVR)-maximum diameter and margin were identified as independent predictors of malignancy.
  • The comprehensive nomogram achieved high AUC values (0.965 in training, 0.966 in test set) for distinguishing benign from malignant SSPNs.
  • The nomogram demonstrated superior clinical utility and calibration compared to other models.

Conclusions:

  • The developed nomogram integrating radiomic features (IntraPeri3mm model) and clinical factors (MPVR-maximum diameter) shows optimal predictive efficiency for SSPNs.
  • This tool provides a valuable scientific basis for early diagnosis and treatment of SSPNs.
  • MPVR-maximum diameter is a significant independent factor in predicting SSPN malignancy.