Background: Virtual-assisted lung mapping (VAL-MAP), a bronchoscopic multi-spot dye-marking technique, was tested for its ability to resect ground glass nodules (GGNs) in sublobar lung resections.
Methods: All patients were prospectively registered in the multi-institutional lung mapping (MIL-MAP) study using VAL-MAP. The data were retrospectively analyzed, focusing on GGNs. GGN characteristics, pathological findings, operation type, and the surgical contribution of VAL-MAP were evaluated.
Results: The 370 GGNs in 299 patients included 257 pure and 113 mixed GGNs. There were 146 wedge resections (43.6%), 99 simple segmentectomies (29.6%), and 60 complex segmentectomies (18.0%). The largest number of marks were used in complex segmentectomy (4.05±0.74), followed by simple segmentectomy (3.35±0.97) and wedge resection (2.96±0.80). The overall successful resection rate was 98.6%. Multiple [2-5] GGNs were concurrently targeted by VAL-MAP in 53 patients (17.7%) with 123 GGNs. Two concurrent resections were conducted in 36 patients (12.1%), most commonly wedge resection and segmentectomies (21 patients). Among 190 sub-centimeter GGNs, 24 out of 51 GGNs ≤5 mm in diameter (47.1%) and 113 of 139 GGNs >5 mm in diameter (81.3%) were primary lung cancer (P<0.0001). Regarding the contribution of VAL-MAP to successful resection, wedge resection and pure GGNs were graded higher than both other resection types and mixed GGNs.
Conclusions: VAL-MAP enabled thoracoscopic limited resection of GGNs. Its multiple marks facilitated resections of multi-centric GGNs. Resected suspicious GGNs >5 mm in diameter are likely to be lung cancer. VAL-MAP may impact decision-making regarding the indications and type of surgery for suspicious small GGNs.
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http://dx.doi.org/10.21037/jtd.2018.05.13 | DOI Listing |
JACC CardioOncol
December 2024
Department of Radiation Oncology, Massachusetts General Hospital, Boston, Massachusetts, USA.
JACC Cardiovasc Imaging
January 2025
Department of Radiology and Imaging Sciences and Krannert Cardiovascular Research Center, Indiana University School of Medicine, Indianapolis, Indiana, USA. Electronic address:
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Hybrid Technology Hub, Centre of Excellence, Institute of Basic Medical Sciences, University of Oslo, 0372 Oslo, Norway.
: Tumor organoid and tumor-on-chip (ToC) platforms replicate aspects of the anatomical and physiological states of tumors. They, therefore, serve as models for investigating tumor microenvironments, metastasis, and immune interactions, especially for precision drug testing. To map the changing research diversity and focus in this field, we performed a quality-controlled text analysis of categorized academic publications and clinical studies.
View Article and Find Full Text PDFDiagnostics (Basel)
December 2024
College of Computer Science and Engineering, Taibah University, Medina 41477, Saudi Arabia.
Computer-aided diagnostic systems have achieved remarkable success in the medical field, particularly in diagnosing malignant tumors, and have done so at a rapid pace. However, the generalizability of the results remains a challenge for researchers and decreases the credibility of these models, which represents a point of criticism by physicians and specialists, especially given the sensitivity of the field. This study proposes a novel model based on deep learning to enhance lung cancer diagnosis quality, understandability, and generalizability.
View Article and Find Full Text PDFGene
January 2025
Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India; Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology (IGIB), New Delhi, India. Electronic address:
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