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ORIGINAL ARTICLE
Year : 2021  |  Volume : 17  |  Issue : 3  |  Page : 658-663

Benign pathologies results from lung nodule percutaneous biopsies: How to differentiate true and false benign?


1 Department of Oncology, Binzhou Medical University Hospital, Binzhou, China
2 Department of Pediatric Surgery, Binzhou Medical University Hospital, Binzhou, China
3 Department of Cardio-Thoracic Surgery, Binzhou People's Hospital, Binzhou, China

Correspondence Address:
Jing Yang
Department of Oncology, Binzhou Medical University Hospital, Binzhou
China
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jcrt.JCRT_1245_20

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Objectives: The objective was to identify predictors of true negatives in lung nodules (LNs) with computed tomography-guided percutaneous biopsy (CTPB)-based benign pathological results. Materials and Methods: We included 90 total patients between January 2013 and December 2017 that had CTPB-based nonspecific benign pathologies and used these patients as a training group to accurately identify true-negative predictors. A validation group of 50 patients from January 2018 to June 2019 to confirm predictor reliability. Results: CTPB was conducted on 90 LNs from the training group. True-negative and false-negative CTPB-based pathologies were obtained for 79 and 11 LNs, respectively. CTPB-based benign results had a negative predictive value of 87.8% (79/90). Univariate and multivariate analyses revealed younger age (P = 0.019) and CTPB-based chronic inflammation with fibroplasia (P = 0.010) to be true-negative predictors. A predictive model was made by combining these two prognostic values as follows: score = −7.975 + 0.112 × age −2.883 × CTPB-based chronic inflammation with fibroplasia (0: no present; 1: present). The area under receiver operator characteristic (ROC) curve was 0.854 (P < 0.001). To maximize sensitivity and specificity, we selected a cutoff risk score of −0.1759. The application of this model to the validation group yielded an area under the ROC curve of 0.912 (P < 0.001). Conclusions: Our predictive model showed good predictive ability for identifying true negatives among CTPB-based benign pathological results.


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