PURPOSE: EBUS-TBNA is an established modality for the nodal staging in lung cancer, nevertheless the effective acquisition of scope handling and puncture technique is a still challenge. We present our protocol of EBUS-TBNA learning system and evaluate outcome in terms of ability of cytologic diagnosis and of sampling histological tissue.
METHODS: We designed 5-step learning system as follows; 1)preparation, 2)insertion of probe, 3)sonographic observation, 4)assistance of TBNA and 5)operator of TBNA. Each trainee must accomplish the first 4 steps before beginning step-5. In step-5, EBUS-TBNA was performed in turn in trainee and supervisors who had experiences of EBUS-TBNA over 100 cases. The immediate diagnosis was made through on-site screening by a cytologist in every puncture. Accuracy of diagnosis and success of histological sampling were recorded in each trial with a control of those in the corresponding supervisor.
RESULTS: All 9 trainees entered to step-5 after passing steps 1 to 4 during 5 to 10 trials. A total of 154 nodes had been punctured in step-5. The mean size of nodes in sonography was 13 mm in total, in which that was 15 mm in early phase (≤15 experienced nodes) and 12 mm in late phase (>15). Supervisors could make a diagnosis and histological sampling in all nodes tried. In cytologic diagnosis, accordance rate between supervisors and trainees was 94% in total, and this rate tended to increase from early phase (92%) to late phase (95%). Rate of success in histological sampling was 86% in total, and the rate is likely to be dependent to the nodal size tried not to number of experiences. The respective success rates in early and late phases were 90 % and 82 % whereas the respective sizes of nodes in success and failure were 14mm and 10mm.
CONCLUSIONS: Our learning system for EBUS-TBNA is almost satisfactory, however, histological sampling still remains as an issue for starters.
CLINICAL IMPLICATIONS: EBUS-TBNA would become accessible through our learning system.
DISCLOSURE: The following authors have nothing to disclose: Yuichi Sakairi, Fumie Saegusa, Hidehisa Hoshino, Takurou Kometani, Takekazu Iwata, Teruaki Mizobuchi, Yasumitsu Moriya, Shigetoshi Yoshida, Yuichi Takiguchi, Ichiro Yoshino
No Product/Research Disclosure Information