Surgical Approach to Liver Metastases in GEP-NET in a Tertiary Reference Center.
Frederike ButzAgata DukaczewskaHenning JannEva Maria TeegenLisa ReinhardGeorg LurjeJohann PratschkePeter E GoretzkiWenzel SchöningMartina T MoglPublished in: Cancers (2023)
Indications for liver resection in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NET) vary from liver resection with curative intent to tumor debulking or tissue sampling for histopathological characterization. With increasing expertise, the number of minimally invasive liver surgeries (MILS) in GEP-NET patients has increased. However, the influence on the oncological outcome has hardly been described. The clinicopathological data of patients who underwent liver resection for hepatic metastases of GEP-NET at the Department of Surgery, Charité-Universitätsmedizin Berlin, were analyzed. Propensity score matching (PSM) was performed to compare MILS with open liver surgery (OLS). In total, 22 patients underwent liver surgery with curative intent, and 30 debulking surgeries were analyzed. Disease-free survival (DFS) was longer than progression-free survival (PFS) (10 vs. 24 months), whereas overall survival (OS) did not differ significantly ( p = 0.588). Thirty-nine (75%) liver resections were performed as OLS, and thirteen (25%) as MILS. After PSM, a shorter length of hospital stay was found for the MILS group (14 vs. 10 d, p = 0.034), while neither DFS/PFS nor OS differed significantly. Both curative intended and cytoreductive resection of hepatic GEP-NET metastases achieved excellent outcomes. MILS led to a reduced length of hospital, while preserving a good oncological outcome.
Keyphrases
- minimally invasive
- free survival
- end stage renal disease
- ejection fraction
- prognostic factors
- chronic kidney disease
- newly diagnosed
- healthcare
- rectal cancer
- peritoneal dialysis
- squamous cell carcinoma
- emergency department
- coronary artery bypass
- radiation therapy
- neuroendocrine tumors
- prostate cancer
- percutaneous coronary intervention
- patient reported
- machine learning
- surgical site infection