Related Experiment Video
Updated: May 15, 2026
Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules
Published on: October 13, 2023
Quantitative peri-lesional densitometry mapping via thin-slice volume rendering enhances differentiation of
Sifan Chen1, Ke Zhang1, Min Zhao1
1Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Objectives:
Pneumonic-type lung cancer (PTLC) poses significant diagnostic challenges owing to its overlapping computed tomography (CT) imaging appearance with inflammatory pneumonia. We developed and validated a novel diagnostic model that integrates densitometry-derived thresholds into modified thin-slab volume rendering (tsVR) to differentiate PTLC from inflammatory pneumonia. We further evaluated and compared the diagnostic performance of this tsVR approach against conventional CT feature-based diagnosis.
Materials And Methods:
This retrospective study enrolled 383 patients (193 PTLC, 190 pneumonia) for model development (training/internal validation cohorts, 7:3 ratio) and 61 patients as external validation cohort. Peri-lesional densitometric analysis identified voxel-level and individual-level CT density thresholds translated into modified tsVR parameters to enhance the peri-lesional visualization. Discriminating capabilities of tsVR were assessed by four radiologists against original CT feature-based diagnosis using the area under curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), F1-score, F2-score and Matthews correlation coefficient (MCC), followed by logistical regression analyses assessing independent influencing factors for diagnostic accuracy.
Results:
The tsVR model achieved superior performance over original CT feature-based diagnosis across all metrics, with internal validation AUC 0.86 (sensitivity 0.90, specificity 0.82, accuracy 0.86, F2 score 0.89) and external validation AUC 0.81 (sensitivity 0.90, specificity 0.73, accuracy 0.82, F2 score 0.87). Logistical regression analyses confirmed robustness of tsVR in diagnostic accuracy across scanners, scanning doses and reconstruction protocols, with good inter-observer agreement (κ=0.713).
Conclusion:
The tsVR significantly improves the diagnostic efficacy in discriminating PTLC from inflammatory pneumonia, demonstrating clinical applicability and robust stability.
