Current Practices and Novel Techniques in the Diagnosis and Management of Neuroendocrine Tumors of Unknown Primary

被引:11
作者
Hendifar, Andrew E. [1 ]
Ramirez, Robert A. [2 ]
Anthony, Lowell B. [3 ]
Liu, Eric [4 ]
机构
[1] Cedars Sinai Med Ctr, Dept Med, Los Angeles, CA 90048 USA
[2] Ochsner Med Ctr, Dept Hematol Oncol, New Orleans, LA USA
[3] Univ Kentucky, Div Med Oncol, Markey Canc Ctr, Lexington, KY USA
[4] Rocky Mt Canc Ctr, Neuroendocrine Inst, Denver, CO USA
关键词
neuroendocrine tumors; gene expression profiling; Ga-68-DOTATATE; neoplasms; unknown primary; EMISSION TOMOGRAPHY/COMPUTED TOMOGRAPHY; 92-GENE CANCER CLASSIFIER; PRIMARY SITE; LIVER METASTASES; GA-68-DOTATATE PET/CT; CONSENSUS-GUIDELINES; CARCINOMA; ORIGIN; IDENTIFICATION; EPIDEMIOLOGY;
D O I
10.1097/MPA.0000000000001391
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
Neuroendocrine tumors (NETs) comprise a heterogeneous group of neoplasms in which tumor staging/prognosis and response to treatments depend heavily on accurate and timely identification of the anatomic primary site or NET subtype. Despite recent technological advancements and use of multiple diagnostic modalities, 10% to 14% of newly diagnosed NETs are not fully characterized based on subtype or anatomic primary site. Inability to fully characterize NETs of unknown primary may cause delays in surgical intervention and limit potential treatment options. To address this unmet need, clinical validity and utility are being demonstrated for novel approaches that improve NET subtype or anatomic primary site identification. Functional imaging using Ga-68-radiolabeled DOTATATE positron emission tomography/computed tomography has been shown to overcome some false-positive and resolution issues associated with octreotide scanning and computed tomography/magnetic resonance imaging. Using a genomic approach, molecular tumor classification based on differential gene expression has demonstrated high diagnostic accuracy in blinded validation studies of different NET types and subtypes. Given the widespread availability of these technologies, we propose an algorithm for the workup of NETs of unknown primary that integrates these approaches. Including these technologies in the standard workup will lead to better NET subtype identification and improved treatment optimization for patients.
引用
收藏
页码:1111 / 1118
页数:8
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