Predicting the Risk of Malignancy of Lung Nodules Diagnosed as Indeterminate on Radial Endobronchial Ultrasound-Guided Biopsy

Routine radiology-pathology concordance evaluation of CT-guided percutaneous lung biopsies increases the number of cancers identified

BACKGROUND

Routine concordance evaluation between pathology and imaging findings was introduced for CT-guided biopsies.

PURPOSE

To analyze malignancy rate in concordant, discordant, and indeterminate non-malignant results of CT-guided lung biopsies.

METHODS

Concordance between pathology results and imaging findings of consecutive patients undergoing CT-guided lung biopsy between 7/1/2016 and 9/30/2021 was assessed during routine meetings by procedural radiologists. Concordant was defined as pathology consistent with imaging findings; discordant was used when pathology could not explain imaging findings; indeterminate when pathology could explain imaging findings but there was concern for malignancy. Recommendations for discordant and indeterminate were provided. All the malignant results were concordant. Pathology of repeated biopsy, surgical sample, or follow-up was considered reference standard.

RESULTS

Consecutive 828 CT-guided lung biopsies were performed on 795 patients (median age 70 years, IQR 61-77), 423/828 (51%) women. On pathology, 224/828 (27%) were non-malignant. Among the non-malignant, radiology-pathology concordance determined 138/224 (62%) to be concordant with imaging findings, 54/224 (24%) discordant, and 32/224 (14%) indeterminate. When compared to the reference standard, 33/54 (61%) discordant results, 6/30 (20%) indeterminate, and 3/133 (2%) concordant were malignant. The prevalence of malignancy in the three groups was significantly different (p < 0.001). Time to diagnosis was significantly different between patients who reached the diagnosis with imaging follow-up (median 114 days, IQR 69-206) compared to repeat biopsy (33 days, IQR 18-133) (p = 0.01).

CONCLUSION

Routine radiology-pathology concordance evaluation of CT-guided lung biopsy correctly identifies patients at high risk for missed diagnosis of malignancy. Repeat biopsy is the fastest method to reach diagnosis.

CLINICAL RELEVANCE STATEMENT

A routine radiology-pathology concordance assessment identifies patients with non-malignant CT-guided lung biopsy result who are at greater risk of missed diagnosis of malignancy.

KEY POINTS

• A routine radiology-pathology concordance evaluation of CT-guided lung biopsies classified 224 non-malignant results as concordant, discordant, or indeterminate. • The percentage of malignancy on follow-up was significantly different in concordant (2%), discordant (61%), and indeterminate (20%) (p < 0.001). • Time to definitive diagnosis was significantly shorter with repeat biopsy (33 days), compared to imaging follow-up (114 days), p = 0.01.

Chinese expert consensus on cone-beam CT-guided diagnosis, localization and treatment for pulmonary nodules

Cone-beam computed tomography (CBCT) system can provide real-time 3D images and fluoroscopy images of the region of interest during the operation. Some systems can even offer augmented fluoroscopy and puncture guidance. The use of CBCT for interventional pulmonary procedures has grown significantly in recent years, and numerous clinical studies have confirmed the technology's efficacy and safety in the diagnosis, localization, and treatment of pulmonary nodules. In order to optimize and standardize the technical specifications of CBCT and guide its application in clinical practice, the consensus statement has been organized and written in a collaborative effort by the Professional Committee on Interventional Pulmonology of China Association for Promotion of Health Science and Technology.

Lung cancer risk prediction models based on pulmonary nodules: A systematic review

BACKGROUND

Screening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models.

METHODS

The keywords "lung cancer," "lung neoplasms," "lung tumor," "risk," "lung carcinoma" "risk," "predict," "assessment," and "nodule" were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed.

RESULTS

A total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single-center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets.

CONCLUSION

The existing models showed good discrimination for identifying high-risk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required to improve the quality of deep learning models, particularly for the Asian population.