Case Details

Lesion Characteristics

Lesion Size (diameter): 20 mm

Lesion Location: Right Middle Lobe (RML)

Bronchus Sign: No

Visible on Fluoro: No

Case Information

Final Pathology Report: Endometrial Cancer

Background

A 75-year-old female with a history of endometrial cancer (resected in 2018)

presented with a new lung nodule in the right middle lobe. Despite being

asymptomatic and there being a low likelihood of endometrial cancer relapse,Å the

nodule’s absence in prior imaging warranted further investigation. Due to its central

location and lack of a bronchus sign, a transbronchial needle aspiration (TBNA) using

the LungVision® (Body Vision Medical) platform was chosen.Ç

The Procedure

Under general anesthesia, TBNA was attempted at the site using the LungVision

system for real-time visualization. The lesion was accessed “off-road” using standard

biopsy forceps inserted through a puncture site. Localization was confirmed using

fluoroscopic imaging in three different planes using LungVision. Histopathology

revealed a recurrence of endometrial cancer. The patient was presented to the

tumor board and initiated on platinum-based chemotherapy and immunotherapy.

Conclusion

The integration of artificial intelligence (AI) into bronchoscopy has significantly enhanced diagnostic precision for peripheral lung nodules. This case presents a patient in whom the LungVision AI-driven augmented fluoroscopy and real time 3D imaging was employed to safely navigate challenging anatomical scenarios and achieve an accurate diagnosis. The case underscore the potential of AI enabled augmented fluoroscopy and advanced imaging in addressing limitations of traditional bronchoscopy techniques and improving outcomes in pulmonary oncology. This case highlights the value of the LungVision platform in obtaining diagnostic samples safely without being limited by lesions that have a bronchus sign at or adjacent to the lesion. The platform’s precise imaging capabilities facilitated “off-road” sampling. This can greatly expand the pool ofnodules that can be targeted for biopsy.

	Coronal	Axial	Sagittal
Pre-operative CT
LungVision AI Tomo intraoperative image

Fig 1. Paired images showing pre-operative CT scan of Right Middle Lobe (RML) pulmonary lesion in coronal, axial, and sagittal planes and corresponding AI Tomo 3D tomographic images captured intraoperatively using LungVision prior to introduction of the transbronchial needle.

The LungVision platform’s use of a proprietary artificial-intelligence-driven imaging algorithm that enables augmented fluoroscopy and real-time, 3D tomographic imaging with a standard 2D C-arm fluoroscope represents a transformative advancement in diagnostic bronchoscopy. By enabling precise navigation, multimodal sampling and the biopsy of lesions that historically have been challenging in location and size, the system enhances diagnostic yield while minimizing patient risk in a comparatively cost-effective fashion. This case demonstrates the platform’s potential to become an invaluable tool in the era of lung screening by providing a scalable means for cost-effectively and definitively diagnosing lung patients in which a suspicious pulmonary nodule is identified and thus improving clinical outcomes in pulmonary oncology.

References

1. Siegenthaler F, Lindemann K, Epstein E, Rau TT, Nastic D, Ghaderi M, Rydberg F, Mueller MD, Carlson J, Imboden S. Time to first recurrence, pattern of recurrence, and survival after recurrence in endometrial cancer according to the molecular classification. Gynecol Oncol. 2022 May;165(2):230-238. doi: 10.1016/j.ygyno.2022.02.024. Epub 2022 Mar 8. PMID: 35277281

2. Ali MS, Sethi J, Taneja A, Musani A, Maldonado F. Computed Tomography Bronchus Sign and the Diagnostic Yield of Guided Bronchoscopy for Peripheral Pulmonary Lesions. A Systematic Review and Meta-Analysis. Ann Am Thorac Soc. 2018 Aug;15(8):978-987. doi: 10.1513/AnnalsATS.201711-856OC. PMID: 29877715.

About Dr. Björn Schwick Dr. med. José Miguel Sodi Luna

Dr. Björn Schwick

Chief Physician

Department of Pulmonology

Luisenhospital Aachen

Aachen, Germany

Dr. med. José Miguel Sodi Luna

Senior Physician

Department of Pulmonology

Luisenhospital Aachen

Aachen, Germany