Robotic-assisted bronchoscopy for histopathologic subtyping of primary lung adenocarcinoma Journal Article


Authors: Kalchiem-Dekel, O.; Rakočević, R.; Toumbacaris, N.; Tan, K. S.; Nadig, T. R.; Adusumilli, P. S.; Dycoco, J.; Lee, R. P.; Oberg, C. L.; Gray, K. D.; Park, B. J.; Rocco, G.; Chaft, J. E.; Solomon, S. B.; Jones, D. R.; Chawla, M.; Husta, B. C.; Baine, M. K.; Bott, M. J.
Article Title: Robotic-assisted bronchoscopy for histopathologic subtyping of primary lung adenocarcinoma
Abstract: Background: The identification of high-grade patterns and mucinous features of invasive primary lung adenocarcinoma on biopsy specimens can have implications on therapeutic decisions across all stages of disease. Shape sensing robotic-assisted bronchoscopy (ssRAB) is an emerging modality for the concomitant diagnosis and staging of lung cancer. We evaluated the performance of ssRAB for adenocarcinoma pattern identification, and particularly high-grade patterns, as well as the histopathologic concordance between biopsy and surgical resection specimens. Methods: Patients with lung adenocarcinoma diagnosed via ssRAB forceps or cryobiopsy specimens between October 2019 and December 2023 were included in the analysis. Biopsy specimens were evaluated for the identification of histopathologic patterns and mucinous features. A generalized linear mixed model quantified the association between pre- and intra-operative factors and successful pattern identification on biopsy. The concordance between high-grade patterns and mucinous features on ssRAB-acquired biopsy and poorly differentiated grade and mucinous features on subsequent surgical resection was determined. Results: A total of 242 ssRAB-acquired specimens were included in the final analysis. The biopsy specimen was sufficient to identify adenocarcinoma histopathologic patterns in 71 %. In a multivariable analysis, sampling by cryobiopsy was positively associated with pattern identification (OR 3.54, CI: 1.02–12.30; P = 0.04), as compared with forceps biopsy. A corresponding surgical resection specimen was available in 66 cases. The sensitivity, specificity, positive, and negative predictive values of biopsy were 63, 72, 61, and 74 %, respectively for the presurgical detection of poorly differentiated adenocarcinoma, and 87, 100, 100, and 96 %, respectively for the presurgical detection of mucinous features. Conclusion: This study is the first to report the performance of ssRAB-acquired biopsy for identification of adenocarcinoma patterns and its concordance with surgical resection. Our findings align with those previously reported for percutaneous lung biopsy. ssRAB emerges as a viable tool for the identification of adenocarcinoma patterns. Future studies are needed to confirm these findings in larger patient cohorts. © 2025 Elsevier B.V., All rights reserved.
Keywords: human tissue; aged; major clinical study; clinical feature; histopathology; cancer grading; adenocarcinoma; lung cancer; risk factor; lung adenocarcinoma; preoperative period; intraoperative period; bronchoscopy; lung biopsy; predictive value; human; male; female; article; cryobiopsy; robotic assisted bronchoscopy; robot-assisted procedure
Journal Title: Lung Cancer
Volume: 207
ISSN: 0169-5002
Publisher: Elsevier Ireland Ltd.  
Date Published: 2025-09-01
Start Page: 108681
Language: English
DOI: 10.1016/j.lungcan.2025.108681
PROVIDER: scopus
PMCID: PMC12379800
PUBMED: 40749261
DOI/URL:
Notes: The MSK Cancer Center Support Grant (P30 CA008748) is acknowledge in the PDF -- Corresponding authors is MSK author: Or Kalchiem-Dekel -- Source: Scopus
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MSK Authors
  1. Jamie Erin Chaft
    293 Chaft
  2. Mohit Chawla
    49 Chawla
  3. Bernard J Park
    268 Park
  4. Robert Piljae Lee
    32 Lee
  5. Stephen Solomon
    429 Solomon
  6. Matthew Bott
    139 Bott
  7. Joseph Dycoco
    47 Dycoco
  8. David Randolph Jones
    422 Jones
  9. Kay See   Tan
    246 Tan
  10. Gaetano Rocco
    135 Rocco
  11. Katherine D. Gray
    29 Gray
  12. Marina K Baine
    60 Baine
  13. Bryan C. Husta
    18 Husta
  14. Tejaswi Ramananda Nadig
    5 Nadig
  15. Catherine L. Oberg
    2 Oberg