During the 20th century, humanity dreamed of landing on the moon. In the 21st century, pulmonologists dream of safely and efficiently reaching pulmonary nodules for diagnostic purposes (Fig. 1). Thanks to recent technological advances, this goal is increasingly achievable in interventional pulmonology units.
Since its inception, flexible bronchoscopy has been used to evaluate peripheral pulmonary nodules. Baaklini et al. reported an overall diagnostic yield of 60%, significantly lower in benign lesions (35%) and decreasing as lesions were located farther from the pulmonary hilum (82% in central lesions vs. 53% in peripheral lesions). Nodule size was the main determinant of diagnostic performance, with a yield of 23% for lesions ≤2cm, dropping to 14% when these were also peripheral.1
To improve this modest diagnostic yield, complementary techniques such as radial endobronchial ultrasound (rEBUS) were developed, allowing lesion identification via 360° ultrasound imaging.2 Lee et al. described an overall diagnostic yield of 70%, influenced by factors including the bronchus sign, intralesional probe positioning, and lesion size.3
Difficulty accessing lesions beyond the subsegmental bronchi led to the development of new tools, including the ultrathin bronchoscope (overall yield ∼65%) and virtual bronchoscopic navigation, with modest benefits in lesions ≤20mm.4,5 Subsequently, electromagnetic navigation bronchoscopy (ENB) improved access to peripheral lesions, with yields approaching 70%.6,7
However, it should not be forgotten that diagnostic yield differences do not depend solely on guidance technology, as the sampling technique must also be considered. Benn et al. describe a higher diagnostic yield for transbronchial needle aspiration (TBNA) (69%), followed by forceps biopsy (FB) and transbronchial lung cryobiopsy (TBLC) (60%). However, the combined use of the three techniques (TBNA, FB, and TBLC) increases the diagnostic yield from 69% with TBNA alone to 80% with the addition of FB and up to 93% with the incorporation of TBLC.8
The emergence of cone-beam CT (CBCT) and robotic-assisted bronchoscopy (RAB) has overcome historical limitations of conventional navigation,9 since it has enabled intraprocedural confirmation (“tool-in-lesion”).
Recent studies support that intralesional confirmation is a key factor,10 and it is here that the combination of CBCT and RAB represents a substantial shift, integrating guided access to the pulmonary periphery with real-time anatomical verification.9 Current evidence shows diagnostic yields exceeding 85% using this combined strategy, with complication rates below 1%11 despite a median lesion size of 13mm, increased detection of early-stage malignancies,12 and lower complication rates than CT-guided transthoracic needle aspiration.13
From a clinical perspective, this combination may be particularly useful for small (<15mm) or highly peripheral lesions, where intralesional confirmation is critical. However, its implementation is complex, and several factors must be considered: the endoscopist's prior experience, the learning curve, associated costs, and the availability of the necessary technology. Moreover, it should be noted that, although the current evidence is consistent, it derives mainly from non-randomized prospective studies and indirect comparisons, with only a limited number of recent randomized trials – particularly in advanced navigational bronchoscopy, including robotic-assisted platforms – providing higher-level evidence. Nevertheless, several uncertainties remain, underscoring the need for additional multicenter comparative studies to more precisely define the role of robotic bronchoscopy and CBCT-guided strategies in routine clinical practice.
In the 20th century, we attempted to “land” on the nodule almost blindly; today, we navigate with three-dimensional maps, verify our position in real time, and rely on robotic precision. All indications suggest that the 21st century will definitively be the century of peripheral pulmonary nodule diagnosis.
Declaration of generative AI and AI-assisted technologies in the writing processThe illustration included in this editorial was generated using a generative artificial intelligence tool under the supervision of the authors. The authors reviewed and approved the final image and take full responsibility for its content.
Ethical considerationsThis study did not require ethical approval because it did not involve experiments on humans or animals, nor did it include identifiable personal data.
Informed consentThis study did not require informed consent, as it did not involve human participants or the collection of identifiable personal data.
FundingWe declare that there is no funding received for this work.
Authors’ contributionsAll authors contributed to the conception, writing, and revision of the manuscript. All authors have read and approved the final version.
Conflicts of interestWe declare that there is no conflict of interest related to this work.


