IMPORTANCE: The inclusion of data about the presence of metastatic neck nodes with extracapsular spread (ECS) in the neck dissection improves the prognostic classification of patients with head and neck squamous cell carcinoma (HNSCC). OBJECTIVE: To evaluate the prognostic capacity of ECS in patients with HNSCC, and to analyze the usefulness of including this information in the pathological classification of patients treated with a neck dissection. DESIGN: Retrospective unicenter study performed from 1985 through 2007. SETTING: Tertiary referral center. PARTICIPANTS: A total of 1190 patients with HNSCC treated with a neck dissection. INTERVENTION: Unilateral or bilateral neck dissection . MAIN OUTCOMES AND MEASURES: Adjusted survival and local, regional, and distant metastases-free survival. Patients were classified according to a recursive partitioning analysis (RPA) method, considering pN category and number of neck nodes with ECS as the independent variables. RESULTS: Five-year adjusted survival for patients without metastatic nodes in the neck dissection (pN0) was 85.5%, for patients with neck node metastases without ECS (pN+/ECS-) it was 62.5%, and for patients with neck node metastases with ECS (pN+/ECS+) it was 29.9%. There were significant differences in survival between patients with pN0 lesions and pN+/ECS- (P < .001), and between patients with pN+/ECS- and those with pN+/ECS+ (P < .001). According to the RPA method, we propose classifying patients according to 4 categories: category I, pN0 lesions; category II, pN1/ECS+ or pN+/ECS-; category III, pN2-3/1 node and ECS+; and category IV, pN2-3/2 or more nodes and ECS+. The RPA-derived classification achieved a better prognostic discrimination than the pTNM classification. CONCLUSIONS AND RELEVANCE: The inclusion of information about ECS in the neck dissection improved the prognostic classification of patients with HNSCC in relation to the pTNM classification.
IMPORTANCE: The inclusion of data about the presence of metastatic neck nodes with extracapsular spread (ECS) in the neck dissection improves the prognostic classification of patients with head and neck squamous cell carcinoma (HNSCC). OBJECTIVE: To evaluate the prognostic capacity of ECS in patients with HNSCC, and to analyze the usefulness of including this information in the pathological classification of patients treated with a neck dissection. DESIGN: Retrospective unicenter study performed from 1985 through 2007. SETTING: Tertiary referral center. PARTICIPANTS: A total of 1190 patients with HNSCC treated with a neck dissection. INTERVENTION: Unilateral or bilateral neck dissection . MAIN OUTCOMES AND MEASURES: Adjusted survival and local, regional, and distant metastases-free survival. Patients were classified according to a recursive partitioning analysis (RPA) method, considering pN category and number of neck nodes with ECS as the independent variables. RESULTS: Five-year adjusted survival for patients without metastatic nodes in the neck dissection (pN0) was 85.5%, for patients with neck node metastases without ECS (pN+/ECS-) it was 62.5%, and for patients with neck node metastases with ECS (pN+/ECS+) it was 29.9%. There were significant differences in survival between patients with pN0 lesions and pN+/ECS- (P < .001), and between patients with pN+/ECS- and those with pN+/ECS+ (P < .001). According to the RPA method, we propose classifying patients according to 4 categories: category I, pN0 lesions; category II, pN1/ECS+ or pN+/ECS-; category III, pN2-3/1 node and ECS+; and category IV, pN2-3/2 or more nodes and ECS+. The RPA-derived classification achieved a better prognostic discrimination than the pTNM classification. CONCLUSIONS AND RELEVANCE: The inclusion of information about ECS in the neck dissection improved the prognostic classification of patients with HNSCC in relation to the pTNM classification.
Authors: R C Hoesli; M L Ludwig; N L Michmerhuizen; A J Rosko; M E Spector; J C Brenner; A C Birkeland Journal: Eur J Surg Oncol Date: 2016-12-15 Impact factor: 4.424
Authors: Matthew L Hedberg; Gerald Goh; Simion I Chiosea; Julie E Bauman; Maria L Freilino; Yan Zeng; Lin Wang; Brenda B Diergaarde; William E Gooding; Vivian W Y Lui; Roy S Herbst; Richard P Lifton; Jennifer R Grandis Journal: J Clin Invest Date: 2015-11-30 Impact factor: 14.808