A Predictive Model for False-Negative Results in Ultrasound-Guided Percutaneous Transthoracic Needle Lung Biopsy

This study aimed to develop a post-procedural predictive model for assessing the risk of false-negative results in ultrasound-guided percutaneous transthoracic needle lung biopsy (US-PTLB).

Two prospective cohorts were designed for model development and validation. Patients scheduled for US-PTLB underwent B-mode ultrasound (B-US), color Doppler flow imaging (CDFI), ultrasound elastography, and contrast-enhanced ultrasound (CEUS) of the lesions, with the final diagnosis confirmed through comprehensive evaluation. Risk factors associated with false-negative results were identified, and multivariate logistic regression was used to construct the predictive model. The model's performance was further evaluated in an independent cohort to assess its impact on reducing the incidence of false-negative results through targeted interventions.

The US-PTLB false-negative risk prediction model was constructed using data from 129 patients, of whom 35 (29.1%) were ultimately diagnosed with false-negative results. Predictors included age, lesion size, elasticity score, lesion necrosis, and enhancement intensity on CEUS. The model demonstrated excellent discrimination, with an area under the curve of 0.922, sensitivity of 88.6%, and specificity of 90.4%. Internal validation in 70 independently collected patients confirmed robust model performance. Application of the model in 423 patients, coupled with second biopsies for high-risk patients, led to a significant reduction in the incidence of false-negative results.

This predictive model, combining clinical parameters with multimodal ultrasound features, serves as a robust post-procedural tool for objectively assessing false-negative risk in ultrasound-guided percutaneous transthoracic needle lung biopsy. Its clinical application enables early risk stratification, minimizes false-negative rates, and enhances diagnostic precision.