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| http://dx.doi.org/10.21037/tlcr.2018.09.12 | DOI Listing |
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The International Association for the Study of Lung Cancer Staging Project: The Database and Proposal for the Revision of the Staging of Pulmonary Neuroendocrine Carcinoma in the Forthcoming Ninth Edition of the TNM Classification for Lung Cancer.
J Thorac Oncol
January 2025
Division of Thoracic Surgery, Keio University School of Medicine, Tokyo, Japan.
Introduction: Pulmonary high-grade neuroendocrine carcinoma (NEC) includes small cell lung carcinoma (SCLC) and large cell neuroendocrine carcinoma (LCNEC). The seventh and eighth editions of the TNM classification for lung cancer confirmed the applicability of this staging system for SCLC. With the proposal of N2 and M1c subcategories for the ninth edition classification, we assessed the applicability to NECs.
View Article and Find Full Text PDFStrategies for implementing an electronic patient-reported outcomes-based symptom management program across six cancer centers.
BMC Health Serv Res
November 2024
Baptist Medical Center, 6019 Walnut Grove Rd, Memphis, TN, 38120, USA.
Article Synopsis
- Electronic patient-reported outcomes (ePROs) can enhance cancer patient care, but integrating them into clinical systems poses significant challenges, requiring technical resources, clinician and patient commitment, and institutional support.* -
- The SIMPRO Research Consortium created and implemented eSyM, an ePRO-based symptom management program across six cancer centers, tracking implementation methods and barriers through established frameworks and tools like REDCap.* -
- Out of 226 documented implementation strategies, 64 unique strategies were identified, with universal strategies that were consistently effective focusing on clinical preparation, training, and patient/clinician engagement being seen as particularly impactful.*
Odysseus Strings His Bow: Incorporating the Burden of Lymph Node Metastasis into Lung Cancer Staging.
Chest
November 2024
Multidisciplinary Thoracic Oncology Program, Baptist Cancer Center, Memphis, TN. Electronic address:
Metrics for Benchmarking Lung Cancer Surgery Quality: Not Waiting for Godot!
Ann Thorac Surg
February 2025
Multidisciplinary Thoracic Oncology Program, Baptist Cancer Center, Memphis, Tennessee. Electronic address:
Significance of Image Reconstruction Parameters for Future Lung Cancer Risk Prediction Using Low-Dose Chest Computed Tomography and the Open-Access Sybil Algorithm.
Invest Radiol
October 2024
From the Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Boston, MA (J.S., A.G., F.J.F.); Harvard Medical School, Boston, MA (J.S., A.E.B.C., S.J.S., L.V.S., F.J.F.); Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA (P.M.); Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA (P.M.); Division of Hematology/Oncology, Department of Medicine, Massachusetts General Hospital, Boston, MA (A.E.B.C., L.V.S.); Department of Medicine, MGH Biostatistics, Massachusetts General Hospital, Boston MA (S.J.S.); and Multidisciplinary Thoracic Oncology Program, Baptist Cancer Center, Memphis, TN (R.U.O.).
Purpose: Sybil is a validated publicly available deep learning-based algorithm that can accurately predict lung cancer risk from a single low-dose computed tomography (LDCT) scan. We aimed to study the effect of image reconstruction parameters and CT scanner manufacturer on Sybil's performance.
Materials And Methods: Using LDCTs of a subset of the National Lung Screening Trial participants, which we previously used for internal validation of the Sybil algorithm (test set), we ran the Sybil algorithm on LDCT series pairs matched on kilovoltage peak, milliampere-seconds, reconstruction interval, reconstruction diameter, and either reconstruction filter or axial slice thickness.
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