Rice Thomas W, Ishwaran Hemant, Hofstetter Wayne L, Schipper Paul H, Kesler Kenneth A, Law Simon, Lerut E M R, Denlinger Chadrick E, Salo Jarmo A, Scott Walter J, Watson Thomas J, Allen Mark S, Chen Long-Qi, Rusch Valerie W, Cerfolio Robert J, Luketich James D, Duranceau Andre, Darling Gail E, Pera Manuel, Apperson-Hansen Carolyn, Blackstone Eugene H
*Cleveland Clinic, Cleveland, OH †University of Miami, Miami, FL ‡University of Texas, MD Anderson Cancer Center, Houston, TX §Oregon Health and Science Center, Portland, OR ¶Indiana University, Indianapolis, IN ||Queen Mary Hospital, The University of Hong Kong, People's Republic of China **University Hospital Leuven, Leuven, Belgium ††Medical University of South Carolina, Charleston, SC ‡‡Helsinki University Hospital, Helsinki, Finland §§Fox Chase Cancer Center, Philadelphia, PA ¶¶University of Rochester, Rochester, New York, NY ||||Mayo Clinic, Rochester, MN ***West China Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China †††Memorial Sloan-Kettering Cancer Center, New York, NY ‡‡‡University of Alabama at Birmingham, Birmingham, AL §§§University of Pittsburgh School of Medicine, Pittsburgh, PA ¶¶¶University of Montreal, Montreal, Canada ||||||Toronto General Hospital, Toronto, Canada ****Hospital Universitario del Mar, Institut Hospital del Mar d'Investigacions Mèdiques, Universitat Autònoma de Barcelona, Barcelona, Spain.
Ann Surg. 2017 Jan;265(1):122-129. doi: 10.1097/SLA.0000000000001594.
To identify the associations of lymph node metastases (pN+), number of positive nodes, and pN subclassification with cancer, treatment, patient, geographic, and institutional variables, and to recommend extent of lymphadenectomy needed to accurately detect pN+ for esophageal cancer.
Limited data and traditional analytic techniques have precluded identifying intricate associations of pN+ with other cancer, treatment, and patient characteristics.
Data on 5806 esophagectomy patients from the Worldwide Esophageal Cancer Collaboration were analyzed by Random Forest machine learning techniques.
pN+, number of positive nodes, and pN subclassification were associated with increasing depth of cancer invasion (pT), increasing cancer length, decreasing cancer differentiation (G), and more regional lymph nodes resected. Lymphadenectomy necessary to accurately detect pN+ is 60 for shorter, well-differentiated cancers (<2.5 cm) and 20 for longer, poorly differentiated ones.
In esophageal cancer, pN+, increasing number of positive nodes, and increasing pN classification are associated with deeper invading, longer, and poorly differentiated cancers. Consequently, if the goal of lymphadenectomy is to accurately define pN+ status of such cancers, few nodes need to be removed. Conversely, superficial, shorter, and well-differentiated cancers require a more extensive lymphadenectomy to accurately define pN+ status.
确定淋巴结转移（pN+）、阳性淋巴结数量及pN亚分类与癌症、治疗、患者、地理和机构变量之间的关联，并推荐准确检测食管癌pN+所需的淋巴结清扫范围。
有限的数据和传统分析技术妨碍了对pN+与其他癌症、治疗及患者特征之间复杂关联的识别。
采用随机森林机器学习技术分析了来自全球食管癌协作组的5806例食管癌切除患者的数据。
pN+、阳性淋巴结数量及pN亚分类与癌症浸润深度增加（pT）、癌症长度增加、癌症分化程度降低（G）及切除的区域淋巴结增多相关。对于较短、高分化癌症（<2.5 cm），准确检测pN+所需的淋巴结清扫数为60个；对于较长、低分化癌症，所需清扫数为20个。
在食管癌中，pN+、阳性淋巴结数量增加及pN分类增加与浸润更深、更长及低分化癌症相关。因此，如果淋巴结清扫的目标是准确界定此类癌症的pN+状态，则只需清扫少数淋巴结。相反，对于浅表、较短及高分化癌症，需要更广泛的淋巴结清扫以准确界定pN+状态。
