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Robotic-assisted Bronchoscopy Combined with Multimodal Imaging for Targeted Lung Cryobiopsies
Published on: July 19, 2024
Time, tissue, and team: Reframing transbronchial biopsy in lung transplantation as an integrated diagnostic system
Francesca Lunardi1, Jason Christie2
1University of Pavia, Pavia, Italy; Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
None:
Despite remarkable advances in transplant pathology, molecular diagnostics, imaging, and biomarker discovery, uncertainty remains an intrinsic feature of lung allograft evaluation. Traditionally, this uncertainty has been attributed to sampling limitations, overlapping histologic patterns, imperfect diagnostic criteria, insufficiently specific biomarkers, and interobserver variability. While these factors undoubtedly contribute, they overlook a fundamental dimension of clinical practice: diagnosis is not generated by pathological findings in a vacuum but through communication among the disciplines that produce, interpret, and integrate clinical evidence. In lung transplantation, histologic findings acquire meaning only when interpreted alongside temporal context, radiologic evolution, microbiologic and immunologic data, graft function, and therapeutic interventions. Diagnostic certainty therefore depends not on tissue alone, but on the integration of complementary expertise. Failures in team communication may represent an underrecognized source of diagnostic variability, contributing as much to uncertainty as the biological and technical limitations of current diagnostic tools. Using the conceptual framework of Time, Tissue, and Team, we argue that multidisciplinary dialogue should be regarded as an integral component of diagnosis in lung transplantation. This framework has implications for pathology reporting, multidisciplinary team structure, specialist training, and the integration of emerging technologies, including molecular diagnostics and artificial intelligence. Ultimately, the next major advances in lung transplant diagnosis may depend not only on generating better data, but on integrating them better through multidisciplinary discussion.