Background: The presence of regional lymph node metastasis has an important impact on clinical management and prognostication of patients with oral tongue squamous cell carcinoma (SCC). Approximately 30% to 50% of patients with oral tongue SCC have regional metastasis at diagnosis, but the limited sensibility of the current diagnostic methods used for neck staging does not allow detection of all cases, leaving a significant number of undiagnosed metastasis (occult lymph node metastasis). In this study, we evaluated whether clinicopathologic features and immunohistochemical detection of carcinoma-associated fibroblasts (CAFs) and activin A could be predictive markers for occult lymph node metastasis in oral tongue SCC.
Methods: One hundred ten patients with primary oral tongue SCC, who were classified with early stage tumor (stage I and II) and received surgical treatment with elective neck dissection, were enrolled in the study.
Results: Among all examined features, only high immunohistochemical expression of activin A was significantly associated with presence of occult lymph node metastasis (p = .006). Multivariate survival analysis using the Cox proportional hazard model showed that the expression of activin A was an independent marker of reduced overall survival with a 5-year survival of 89.7% for patients with low expression compared to 76.5% for those with high expression (hazard ratio [HR], 2.44; 95% confidence interval [CI], 1.55-3.85; p = .012).
Conclusion: Our results demonstrated that immunodetection of activin A can be useful for prognostication of oral tongue SCC, revealing patients with occult lymph node metastasis and lower overall survival.
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http://dx.doi.org/10.1002/hed.23627 | DOI Listing |
Eur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine, Nanfang Hospital, Southern Medical University, 1838 Guangzhou North Road, Guangzhou, China.
Purpose: To explore the dynamic and parametric characteristics of [F]F-FAPI-42 PET/CT in lung cancers.
Methods: Nineteen participants with newly diagnosed lung cancer underwent 60-min dynamic [F]F-FAPI-42 PET/CT. Time-activity curves (TAC) were generated for tumors and normal organs, with kinetic parameters (K, K, K, K, K) calculated.
Hum Cell
January 2025
Integrated Head and Neck Oncology Program (DSRG-5), Mazumdar Shaw Medical Foundation, Narayana Health, Bangalore, India.
The study explores the development and characterization of lymph node stromal cell cultures (LNSCs) from patients with oral squamous cell carcinoma (OSCC), highlighting the importance of understanding tumor-node cross-talk for effective prognostic and therapeutic interventions. Herein, we describe the development and characterization of primary lymph node stromal cells (LNSCs, N = 14) from nodes of metastatic and non-metastatic OSCC patients. Primary cultures were established by the explant method from positive (N + ; N = 2), and negative nodes (N0; N = 4) of the metastatic patients (N = 3) as well as negative (N0; N = 8) nodes from non-metastatic (N = 4) patients.
View Article and Find Full Text PDFPathol Int
January 2025
Department of Pathology, Tohoku University Hospital, Sendai, Japan.
Fusobacterium nucleatum is implicated in esophageal cancer; however, its distribution in esophageal cancer tissues remains unknown. This study aimed to clarify the presence and distribution of F. nucleatum in esophageal cancer tissues using fluorescence in situ hybridization (FISH).
View Article and Find Full Text PDFColorectal Dis
January 2025
Colorectal Surgery Unit, General Surgery Department, Marqués de Valdecilla University Hospital, Santander, Spain.
Aim: Complete mesocolic excision (CME) is an oncologically driven technique for treating right colon cancer. While laparoscopic CME is technically demanding and has been associated with more complications, the robotic approach might reduce morbidity. The aim of this study was to assess the safety of stepwise implementation of robotic CME.
View Article and Find Full Text PDFAim: Lymph node metastasis is an adverse prognostic factor in pancreatic ductal adenocarcinoma. However, it remains a challenge to predict lymph node metastasis using preoperative imaging alone. We used machine learning (combining preoperative imaging findings, tumor markers, and clinical information) to create a novel prediction model for lymph node metastasis in resectable pancreatic ductal adenocarcinoma.
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