Diagnostic success of transthoracic needle biopsy and PET-CT for 1 to 2 cm solid indeterminate pulmonary nodules

A Multicenter, Single-Arm, Prospective Trial Assessing the Diagnostic Yield of Electromagnetic Bronchoscopic and Transthoracic Navigation for Peripheral Pulmonary Nodules

Rationale: Strict adherence to procedural protocols and diagnostic definitions is critical to understand the efficacy of new technologies. Electromagnetic navigational bronchoscopy (ENB) for lung nodule biopsy has been used for decades without a solid understanding of its efficacy, but offers the opportunity for simultaneous tissue acquisition via electromagnetic navigational transthoracic biopsy (EMN-TTNA) and staging via endobronchial ultrasound (EBUS). Objective: To evaluate the diagnostic yield of EBUS, ENB, and EMN-TTNA during a single procedure using a strict a priori definition of diagnostic yield with central pathology adjudication. Methods: A prospective, single-arm trial was conducted at eight centers enrolling participants with pulmonary nodules (<3 cm; without computed tomography [CT]- and/or positron emission tomography-positive mediastinal lymph nodes) who underwent a staged procedure with same-day CT, EBUS, ENB, and EMN-TTNA. The procedure was staged such that, when a diagnosis had been achieved via rapid on-site pathologic evaluation, the procedure was ended and subsequent biopsy modalities were not attempted. A study finding was diagnostic if an independent pathology core laboratory confirmed malignancy or a definitive benign finding. The primary endpoint was the diagnostic yield of the combination of CT, EBUS, ENB, and EMN-TTNA. Measurements and Main Results: A total of 160 participants at 8 centers with a mean nodule size of 18 ± 6 mm were enrolled. The diagnostic yield of the combined procedure was 59% (94 of 160; 95% confidence interval [CI], 51-66%). Nodule regression was found on same-day CT in 2.5% of cases (4 of 160; 95% CI, 0.69-6.3%), and EBUS confirmed malignancy in 7.1% of cases (11 of 156; 95% CI, 3.6-12%). The yield of ENB alone was 49% (74 of 150; 95% CI, 41-58%), that of EMN-TTNA alone was 27% (8 of 30; 95% CI, 12-46%), and that of ENB plus EMN-TTNA was 53% (79 of 150; 95% CI, 44-61%). Complications included a pneumothorax rate of 10% and a 2% bleeding rate. When EMN-TTNA was performed, the pneumothorax rate was 30%. Conclusions: The diagnostic yield for ENB is 49%, which increases to 59% with the addition of same-day CT, EBUS, and EMN-TTNA, lower than in prior reports in the literature. The high complication rate and low diagnostic yield of EMN-TTNA does not support its routine use. Clinical trial registered with www.clinicaltrials.gov (NCT03338049).

Deep convolutional neural network based on CT images of pulmonary nodules in the lungs of adolescent and young adult patients with osteosarcoma

The aim of the present study was to explore the diagnostic value of a deep convolutional neural network (DCNN) model for the diagnosis of pulmonary nodules in adolescent and young adult patients with osteosarcoma. For the present study, 675 chest CT images were retrospectively collected from 109 patients with clinically confirmed osteosarcoma who underwent chest CT examination at Hangzhou Third People's Hospital (Hangzhou, China) from March 2011 to February 2022. CT images were then evaluated using the DCNN and manual models. Subsequently, pulmonary nodules of osteosarcoma were divided into calcified nodules, solid nodules, partially solid nodules and ground glass nodules using the DCNN model. Those patients with osteosarcoma who were diagnosed and treated were followed up to observe dynamic changes in the pulmonary nodules. A total of 3,087 nodules were detected, while 278 nodules were missed compared with those determined using the reference standard given by the consensus of three Experienced radiologists., which was analyzed by two diagnostic radiologists. In the manual model group, 2,442 nodules were detected, while 657 nodules were missed. The DCNN model showed significantly higher sensitivity and specificity compared with the manual model (sensitivity, 0.923 vs. 0.908; specificity, 0.552 vs. 0.351; P<0.05). In addition, the DCNN model yielded an area under the curve (AUC) value of 0.795 [95% confidence interval (CI), 0.743-0.846], outperforming that of the manual model (AUC, 0.687; 95% CI, 0.629-0.732; P<0.05). The film reading time of the DCNN model was also significantly shorter compared with that of the manual model [mean ± standard deviation (SD); 173.25±24.10 vs. 328.32±22.72 sec; P<0.05)]. The AUC of calcified nodules, solid nodules, partially solid nodules and ground glass nodules was calculated to be 0.766, 0.771, 0.761 and 0.796, respectively, using the DCNN model. Using this model, the majority of the pulmonary nodules were detected in patients with osteosarcoma at the initial diagnosis (69/109, 62.3%), and the majority of these were found with multiple pulmonary nodules instead of a single nodule (71/109, 65.1% vs. 38/109, 34.9%). These data suggest that, compared with the manual model, the DCNN model proved to be beneficial for the detection of pulmonary nodules in adolescent and young adult patients with osteosarcoma, which may reduce the time of artificial radiograph reading. In conclusion, the proposed DCNN model, developed using data from 675 chest CT images retrospectively collected from 109 patients with clinically confirmed osteosarcoma, may be used as an effective tool to evaluate pulmonary nodules in patients with osteosarcoma.