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J Cancer Res Ther. 2019;15(7):1464-1470. doi: 10.4103/jcrt.JCRT_109_19.

Nonspecific benign pathological results on computed tomography-guided lung biopsy: A predictive model of true negatives.

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Department of Radiology, Xuzhou Central Hospital, Xuzhou, China.
Department of Clinical Laboratory, Clinical Laboratory, Yuhuangding Hospital, Yantai, China.



The aim of this study is to develop a predictive model for identifying true negatives among nonspecific benign results on computed tomography-guided lung biopsy.

Materials and Methods:

This was a single-center retrospective study. Between December 2013 and May 2016, a total of 126 patients with nonspecific benign biopsy results were used as the training group to create a predictive model of true-negative findings. Between June 2016 and June 2017, additional 56 patients were used as the validation group to test the constructed model.


In the training group, a total of 126 lesions from 126 patients were biopsied. Biopsies from 106 patients were true negatives and 20 were false-negatives. Univariate and multivariate logistic regression analyses were identified a biopsy result of "chronic inflammation with fibroplasia" as a predictor of true-negative results (P = 0.013). Abnormal neuron-specific enolase (NSE) level (P = 0.012) and pneumothorax during the lung biopsy (P = 0.021) were identified as predictors of false-negative results. A predictive model was developed as follows: Risk score = -0.437 + 2.637 × NSE level + 1.687 × pneumothorax - 1.82 × biopsy result of "chronic inflammation with fibroplasia." The area under the receiver operator characteristic (ROC) curve was 0.78 (P < 0.001). To maximize sensitivity and specificity, we selected a cutoff risk score of -0.029. When the model was used on the validation group, the area under the ROC curve was 0.766 (P = 0.005).


Our predictive model showed good predictive ability for identifying true negatives among nonspecific benign lung biopsy results.


False-negative; lung biopsy; nonspecific benign; true-negative

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