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Background: Lymph node metastases are associated with poor prognosis in oral cavity squamous cell carcinoma (OCSCC). In other cancers, clinical guidelines on the number of lymph nodes removed during primary surgery, lymph node yield (LNY), exist. Here, we evaluated the prognostic capacity of LNY on regional failure, locoregional recurrence, and disease‐free survival (DFS) in patients with OCSCC treated by primary neck surgery. Methods: This retrospective cohort study took place at Sunnybrook Health Sciences Centre in Toronto, Canada and involved a chart review of all adult patients with treatment‐naive OCSCC undergoing primary neck dissection. For each outcome, we first used the maximally selected rank statistics and an optimism‐corrected concordance to identify an optimal threshold of LNY. We then used a multivariable Cox proportional hazards model to assess the association between high LNY (>threshold) and each outcome. Results: Among the 579 patients with OCSCC receiving primary neck dissection, 61.7% (n = 357) were male with a mean age of 62.9 years (standard deviation: 13.1) at cancer diagnosis. When adjusting for sociodemographic and clinical factors, LNY >15 was significantly associated with improved DFS (adjusted HR [aHR]: 0.73, 95% CI: 0.54–0.98), locoregional recurrence (aHR: 0.68, 95% CI: 0.49–0.95), and regional failure (aHR: 0.61, 95% CI: 0.39–0.93). Conclusions: Our study findings suggested high LNY to be a strong independent predictor of various patient‐level quality of surgical care metrics. The optimal LNY we found (15) was lower than the conventionally recommended (18), which calls for further research to establish validity in practice. [ABSTRACT FROM AUTHOR]

Background: For oral cavity squamous cell carcinoma (OSCC), extent of extranodal extension (ENE) (minor, ≤2 mm; major, >2 mm) is differentially prognostic, whereas limitations exist with the 8th edition of American Joint Committee on Cancer/International Union Against Cancer TNM N‐classification (TNM‐8‐N). Methods: Resected OSCC patients at four centers were included and extent of ENE was recorded. Thresholds for optimal overall survival (OS) discrimination of lymph node (LN) features were established. After dividing into training and validation sets, two new N‐classifications were created using 1) recursive partitioning analysis (RPA), and 2) adjusted hazard ratios (aHRs) and were ranked against TNM‐8‐N and two published proposals. Results: A total of 1460 patients were included (pN0: 696; pN+: 764). Of the pN+ cases, 135 (18%) had bilateral/contralateral LNs; 126 (17%) and 244 (32%) had minor and major ENE, and two (0.3%) had LN(s) >6 cm without ENE (N3a). LN number (1 and >1 vs. 0: aHRs, 1.92 [95% confidence interval (CI), 1.44–2.55] and 3.21 [95% CI, 2.44–4.22]), size (>3 vs. ≤3 cm: aHR, 1.88 [95% CI, 1.44–2.45]), and ENE extent (major vs. minor: aHR, 1.40 [95% CI, 1.05–1.87]) were associated with OS, whereas presence of contralateral LNs was not (aHR, 1.05 [95% CI, 0.81–1.36]). The aHR proposal provided optimal performance with these changes to TNM‐8‐N: 1) stratification of ENE extent, 2) elimination of N2c and 6‐cm threshold, and 3) stratification of N2b by 3 cm threshold. Conclusion: A new N‐classification improved staging performance compared to TNM‐8‐N, by stratifying by ENE extent, eliminating the old N2c category and the 6 cm threshold, and by stratifying multiple nodes by size. There are several limitations in the most recent TNM N classification system including categories with small sample size and categories that have limited prognostic value. The authors proposed and validated a new N classification system that overcomes these limitations and outperforms the previous iteration of TNM. [ABSTRACT FROM AUTHOR]

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Importance: Accurate, timely, and cost-effective methods for staging oropharyngeal cancers are crucial for patient prognosis and treatment decisions, but staging documentation is often inaccurate or incomplete. With the emergence of artificial intelligence in medicine, data abstraction may be associated with reduced costs but increased efficiency and accuracy of cancer staging., Objective: To evaluate an algorithm using an artificial intelligence engine capable of extracting essential information from medical records of patients with oropharyngeal cancer and assigning tumor, nodal, and metastatic stages according to American Joint Committee on Cancer eighth edition guidelines., Design, Setting, and Participants: This retrospective diagnostic study was conducted among a convenience sample of 806 patients with oropharyngeal squamous cell carcinoma. Medical records of patients with staged oropharyngeal squamous cell carcinomas who presented to a single tertiary care center between January 1, 2010, and August 1, 2020, were reviewed. A ground truth cancer stage dataset and comprehensive staging rule book consisting of 135 rules encompassing p16 status, tumor, and nodal and metastatic stage were developed. Subsequently, 4 distinct models were trained: model T (entity relationship extraction) for anatomical location and invasion state, model S (numerical extraction) for lesion size, model M (sequential classification) for metastasis detection, and a p16 model for p16 status. For validation, results were compared against ground truth established by expert reviewers, and accuracy was reported. Data were analyzed from March to November 2023., Main Outcomes and Measures: The accuracy of algorithm cancer stages was compared with ground truth., Results: Among 806 patients with oropharyngeal cancer (mean [SD] age, 63.6 [10.6] years; 651 males [80.8%]), 421 patients (52.2%) were positive for human papillomavirus. The artificial intelligence engine achieved accuracies of 55.9% (95% CI, 52.5%-59.3%) for tumor, 56.0% (95% CI, 52.5%-59.4%) for nodal, and 87.6% (95% CI, 85.1%-89.7%) for metastatic stages and 92.1% (95% CI, 88.5%-94.6%) for p16 status. Differentiation between localized (stages 1-2) and advanced (stages 3-4) cancers achieved 80.7% (95% CI, 77.8%-83.2%) accuracy., Conclusion and Relevance: This study found that tumor and nodal staging accuracies were fair to good and excellent for metastatic stage and p16 status, with clinical relevance in assigning optimal treatment and reducing toxic effect exposures. Further model refinement and external validation with electronic health records at different institutions are necessary to improve algorithm accuracy and clinical applicability.