|Year : 2021 | Volume
| Issue : 3 | Page : 658-663
Benign pathologies results from lung nodule percutaneous biopsies: How to differentiate true and false benign?
Lei Li1, Xiao-Liang Xu2, Kai Feng3, Xin-Qiang Liu1, Jing Yang1
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
|Date of Submission||26-Aug-2020|
|Date of Decision||13-Oct-2020|
|Date of Acceptance||29-Oct-2020|
|Date of Web Publication||9-Jul-2021|
Department of Oncology, Binzhou Medical University Hospital, Binzhou
Source of Support: None, Conflict of Interest: None
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.
Keywords: Benign, biopsy, false, lung nodule, true
|How to cite this article:|
Li L, Xu XL, Feng K, Liu XQ, Yang J. Benign pathologies results from lung nodule percutaneous biopsies: How to differentiate true and false benign?. J Can Res Ther 2021;17:658-63
|How to cite this URL:|
Li L, Xu XL, Feng K, Liu XQ, Yang J. Benign pathologies results from lung nodule percutaneous biopsies: How to differentiate true and false benign?. J Can Res Ther [serial online] 2021 [cited 2021 Jul 29];17:658-63. Available from: https://www.cancerjournal.net/text.asp?2021/17/3/658/321006
| > Introduction|| |
Lung nodules (LNs) are often diagnosed with highly accurate and minimally invasive computed tomography-guided percutaneous biopsies (CTPB).,,,,,,, CTPB-based pathological results indicating a LN is malignant produce definitive final diagnosis as false-positives are rare.,,,,,, Similarly, a CTPB-based specific benign pathological diagnosis can be definitive in patients with benign tumors or with certain forms of infections,,,,,,, precluding the need for unwarranted surgery. It can be challenging to manage CTPB-based nonspecific benign pathological diagnoses as they are generally not definitive, like in chronic inflammation cases.,,,,,, False-negative CTPB rates ranging from 11% to 16% have been reported.,, Prior analyses have established true- or false-negative pathological predictors in CTPB-based nonspecific benign cases.,, However, no analyses to date have been conducted assessing true-negative LN in CTPB-based nonspecific benign cases.
In this study, we identified reliable true-negative predictors for LN in CTPB-based nonspecific benign cases.
| > Materials and Methods|| |
Our institutional review board approved the present single-center retrospective study. No informed consent was required as the study was retrospective.
From January 2013 to December 2017, a total of 312 patients with LNs underwent CTPB in our center. Among them, we included a 90 patient training group with CTPB-based nonspecific benign pathological results. These patients were used to identify true-negative predictors [Figure 1]. Between January 2018 and June 2019, we included 50 more patients as a validation group to evaluate the reliability of the predictors.
|Figure 1: The receiver operator characteristic curve generated using the predictor from the validation group|
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Lung biopsy decisions were made based on LN management recommendations. Both the training and validation groups used the same inclusion and exclusion criteria.
Study inclusion criteria included patients that had: (a) LNs and (b) CTPB-based benign pathologies. Studies were excluded if patients had (a) distant metastases; (b) LNs lacking any definitive diagnosis; or (c) had CTPB-based specific benign pathologies. Any patients that fall outside of these criteria were excluded from the study.
Computed tomography-guided percutaneous biopsy procedure
A radiologist with >5 years of CTPB experience conducted all procedures using a 16-detector CT (Philips, OH, USA) with the respective tube voltage and current settings at 120 kV and 150 mA/s. The collimation, pitch, rotation time, and FOV were 16 mm × 0.75 mm, 1.063, 0.5 s, and 350 mm, respectively.
LN biopsies were performed in the lung parenchyma using an 18G semi-automatic core needle (Wego, Weihai, China). Computed tomography (CT) scanning was conducted to accurately locate and move the needle tip until it was in contact with the target LN. The LN sample was collected. We routinely obtained more than one specimen. After collection, samples were added to 10% formaldehyde before pathological assessment. Repeated CT scans were used to assess any potential complications associated with the core-needle biopsy procedure.
LNs were any lesions that were round in shape, ≤3 cm in size, surrounded by the pulmonary parenchyma, and showed no other signs of abnormality. Pathologies were used to separate benign CTPBs into specific and nonspecific categories. Specific benign pathologies were attributed to specific infectious pathogens or benign tumors. In contrast, nonspecific benign LNs were those in which fibrosis, inflammatory cell infiltration, or other factors that made it impossible to yield a definitive and specific diagnosis.
CTPB-based benign pathologies were deemed true-negative findings in lesions confirmed as benign following surgical tumor resection, a reduction of >20% in the lesion diameter underwent no anticancer treatment and were stable for at least 1 year (reduction of 1%–19% or no change), or by a specific benign lesion diagnosis upon the pathological assessment of a lung biopsy sample. When these criteria were not met, LNs were ultimately diagnosed as nondiagnostic lesions.
Continuous data are given as means and standard deviations and were compared through Chi-square tests or Fisher's exact test as, when appropriate. Univariate and multivariate logistic regression analyses were employed to identify true-negative predictive factors. All factors yielding a P < 0.1 in the univariate analyses were retained for multivariate analysis. Receiver operating characteristic (ROC) curves were used to analyze predictors. SPSS 16.0 (SPSS Inc., IL, USA) was employed for all statistical analyses. P < 0.05 was the significance threshold.
| > Results|| |
In total, 90 training group patients were subjected to CTPB to characterize their LNs. All 90 LNs presented with CTPB-based nonspecific benign pathologies.
The pathological features of these 90 CTPB-based results included chronic inflammation alone (n = 23), chronic inflammation with fibroplasia (n = 44), chronic inflammation with alveolar epithelial hyperplasia (n = 15), and granulomatous inflammation (n = 8). True-negative and false-negative CTPB-based findings were obtained for 79 and 11 LNs, respectively [Table 1]. These CTPB-based benign findings yielded a negative predictive value (NPV) of 87.8% (79/90) [Figure 1].
|Table 1: Comparison of baseline data between true and false negatives in training group|
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Among the 79 true negatives, CT follow-up confirmed 70 cases [Figure 2], and surgery confirmed the other 9. Of the LNs surgically diagnosed, six were inflammatory pseudotumors and three were hamartomas.
Of the 11 false-negative LNs, re-biopsy and surgery confirmed 8 and 3 cases, respectively. These LNs were diagnosed as adenocarcinoma (n = 9), small-cell lung cancer (n = 1), and squamous cells carcinoma (n = 1).
The re-biopsy or surgery indications for the 20 LNs included: (a) high-risk of lung cancer based on the LN management recommendations (n = 14) and (b) increase in LN size during follow-up (n = 6).
We detected pneumothorax and hemoptysis in 13 (14.4%) and 19 (21.1%) patients, respectively. Conservative treatment using a chest tube was performed on one patient with pneumothorax.
Predictors of true negative
Univariate analyses identified that younger age (P = 0.033, hazard ratio [HR] = 1.088, 95% confidence interval [CI] = 1.007–1.175) and CTPB-based chronic inflammation with fibroplasia (P = 0.021, HR = 0.084, 95% CI = 0.010–0.686) were true-negative predictors. The multivariate analysis further confirmed that younger age (P = 0.019, HR = 1.188, 95% CI = 1.109–1.227) and CTPB-based chronic inflammation with fibroplasia [P = 0.010, HR = 0.056, 95% CI = 0.006–0.501, [Table 2]] remained independent true-negative predictors. The mean age was significantly higher in the false-negative group (64.2 ± 6.1 y vs. 55.8 ± 12.7 y, P = 0.001). CTPB-based chronic inflammation with fibroplasia cases was significantly more common in the true-negative group relative to the false-negative group (54.4% vs. 9.1%, respectively; P = 0.005).
A predictive model was made by combining the two above-mentioned prognostic values as follows: score = −7.975 + 0.112 × age −2.883 × CTPB-based chronic inflammation with fibroplasias (0: no present; 1: present).
The model's predictive ability was gauged using ROC curves. The area under the ROC curve (AUC) was 0.854 [95% CI = 0.762–0.947, P < 0.001, [Figure 3]]. To maximize sensitivity and specificity, we selected a cutoff risk score of −0.1759 (sensitivity = 90.9%, specificity = 77.2%). If the score was greater than or equal to −0.1759, the biopsy result was deemed as a false negative. If the score was <−0.1759, the biopsy result was deemed as a true negative.
|Figure 3: (a) A lung nodule was presented with computed tomographyguided percutaneous biopsy-based benign pathological results. (b) The lung nodule was significantly resolved after 5 months|
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The model's predictive abilities were next confirmed based on clinical findings from the validation group patients (n = 50). We observed no significant differences in baseline data when comparing the validation group to the training group [Table 3]. CTPB-based pathologies were true negative and false negative for 42 and 8 of these patients, respectively. The AUC for the validation group using this model was 0.912 [95% CI = 0.832–0.992, P < 0.001, [Figure 4]].
|Table 3: Comparison of baseline data between training and validation group|
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|Figure 4: The receiver operator characteristic curve generated using the predictor from the training group|
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| > Discussion|| |
In this study, we identified true-negative predictors for LNs with CTPB-based benign pathologies. We determined the training group NPV to be 87.8%. This is within the ranges detected in prior CT-guided lung lesion analyses (83.5%–89%).,, The high NPV value in this study indicated that most CTPB-guided benign pathological results are reliable.
A prior multicenter analysis demonstrated that CTPB-based nondiagnostic true-negative predictors are abscesses detection, organizing pneumonia, and granulomatous inflammation. Similarly, we confirmed CTPB-based granulomatous inflammation for eight detected true-negative cases. However, such inflammation was not identified as a true-negative predictor. This inconsistency is likely attributed to the limited number of CTPB-based granulomatous inflammation cases.
We determined younger age was a true-negative predictor. This finding was consistent with the previous benign and malignant LN differentiation studies.,,,, Age is a key clinical outcome determinant. As somatic cells slowly lose their ability to self-renew with age, cells become less capable of repairing carcinogen-induced epithelial damage. Therefore, a more advanced age is an important factor associated with a greater risk of LN malignancy.
We determined that CTPB-based chronic inflammation with fibroplasia is an accurate true-negative predictor. Fu et al. similarly found CTPB-based chronic inflammation with fibroplasia to be a reliable true-negative predictor, and such fibroplasia and chronic inflammation are linked to the initial stages of granulomatous inflammation or pneumonia.,,
Therefore, detecting CTPB-based chronic inflammation and fibroplasia can be potentially indicative of a true-negative finding.
No association was observed between tumor marker levels and false-negative predictors. A recent meta-analysis revealed that serum carcinoembryonic antigen only achieved moderate diagnostic performance as a differentiating means between malignant and benign LNs, with a pooled sensitivity of 33% and specificity of 92%, respectively. The relationship between tumor markers and LNs can be influenced by many factors, including the tumor marker cutoff values, LN size, tumor stage, or smoking status. Thus, further studies with larger sample sizes are required.
Although some predictors were found in this study, clinicians may be confused about which predictor is more important when used in clinical practice. Therefore, we developed an integrated score that combines the above two predictors to identify true negatives. The AUC indicated good predictive ability (0.854), and a cutoff value of −0.1759 was obtained by calculating the optimum sensitivity and specificity. This predictive model was well fitted to the independent 50 patients' validation group with an AUC of 0.912, demonstrating the accuracy of the model.
There are multiple limitations to the present analysis. For one, this was a retrospective analysis that introduces selection bias. Furthermore, biopsied samples were collected without any guidelines regarding the quantity of material obtained. Although sample amounts were not linked to true-negative findings, this may have the potential to have introduced some degree of bias. Third, patients in the training and validation groups were from different periods that may also result in biased findings.
| > Conclusions|| |
Younger age and CTPB-based chronic inflammation with fibroplasia might indicate the true-negative CTPB-based benign pathologies. Using these factors, we generated a combined score that has a good predictive ability in identifying true negatives among CTPB-based benign LNs.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3]