Introduction: Management of the neck in oral cavity squamous cell carcinoma (OCSCC) is essential to oncologic control and survival. The rates of lymph node metastasis (LNM) vary based on oral cavity tumor site and stage and influence treatment decisions. The aim of this paper was to describe clinical LNM for different tumor subsites and stages of surgically managed OCSCC.
Methods: We conducted a retrospective analysis of 25,846 surgically managed OCSCC patients from the National Cancer Database (NCDB) stratified by tumor subsite and clinical T-stage. For cN + patients, rates of pathologic LNM and absence of pathologic LNM were determined. For cN0 patients, outcomes included the rates of elective neck dissection (END) and occult LNM and predictors of occult LNM determined by a multivariable logistic regression model.
Results: A total of 25,846 patients (59.1% male, mean age 61.9 years) met inclusion criteria with primary tumor sites including oral tongue (50.8%), floor of mouth (21.2%), lower alveolus (7.6%), buccal mucosa (6.7%), retromolar area (4.9%), upper alveolus (3.6%), hard palate (2.7%), and mucosal lip (2.5%). Among all sites, clinical N+ rates increased with T-stage (8.9% T1, 28.0% T2, 51.6% T3, 52.5% T4); these trends were preserved across subsites. Among patients with cN + disease, the overall rate of concordant positive pathologic LNM was 80.1% and the rate of discordant negative pathologic LNM was 19.6%, which varied based on tumor site and stage. In the overall cohort of cN0 patients, 59.9% received END, and the percentage of patients receiving END increased with higher tumor stage. Occult LNM among those cN0 was found in 25.1% of END cases, with the highest rates in retromolar (28.8%) and oral tongue (27.5%) tumors. Multivariable regression demonstrated significantly increased rates of occult LNM for higher T stage (T2 OR: 2.1 [1.9-2.4]; T3 OR: 3.0 [2.5-3.7]; T4 OR: 2.7 [2.2-3.2]), positive margins (OR: 1.4 [1.2-1.7]), and positive lymphovascular invasion (OR: 5.1 [4.4-5.8]).
Conclusions: Management of the neck in OCSCC should be tailored based on primary tumor factors and considered for early-stage tumors.
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http://dx.doi.org/10.1159/000534491 | DOI Listing |
J Cancer Res Ther
December 2024
Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, P. R. China.
Background: Endoscopic submucosal dissection (ESD) is a standardized procedure for intramucosal and slightly invasive submucosal colorectal cancers (CRC). However, the role of ESD for T1b (depth of submucosal invasion: ≥1,000 μm) CRC remains unclear. This study aimed to investigate the long-term efficacy and safety of ESD for T1b CRC.
View Article and Find Full Text PDFWorld J Urol
January 2025
Department of Urology, Renmin Hospital of Wuhan University, 99 Zhang Zhi-dong Road, Wuhan, Hubei, 430060, P.R. China.
Purpose: To develop a deep learning (DL) model based on primary tumor tissue to predict the lymph node metastasis (LNM) status of muscle invasive bladder cancer (MIBC), while validating the prognostic value of the predicted aiN score in MIBC patients.
Methods: A total of 323 patients from The Cancer Genome Atlas (TCGA) were used as the training and internal validation set, with image features extracted using a visual encoder called UNI. We investigated the ability to predict LNM status while assessing the prognostic value of aiN score.
Gut Liver
January 2025
Department of Gastroenterology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Background/aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC.
Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC.
Thorac Cancer
January 2025
Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of MedicalSciences and Peking Union Medical College, Beijing, China.
Objectives: This study aimed to analyze lymph node metastasis (LNM) distribution in superficial esophageal squamous cell carcinoma (ESCC) and its impact factors on survival.
Methods: We reviewed 241 pT1N+ ESCC cases between February 2012 and April 2022 from 10 Chinese hospitals with a high volume of esophageal cancer (EC). We analyzed clinicopathological data to identify overall survival (OS) risk factors and LNM distribution in relation to tumor invasion depth.
Sci Rep
January 2025
Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.
Pathology provides the definitive diagnosis, and Artificial Intelligence (AI) tools are poised to improve accuracy, inter-rater agreement, and turn-around time (TAT) of pathologists, leading to improved quality of care. A high value clinical application is the grading of Lymph Node Metastasis (LNM) which is used for breast cancer staging and guides treatment decisions. A challenge of implementing AI tools widely for LNM classification is domain shift, where Out-of-Distribution (OOD) data has a different distribution than the In-Distribution (ID) data used to train the model, resulting in a drop in performance in OOD data.
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