Literature DB >> 29050064

Bilateral anatomic resection of the ventral parts of the paramedian sectors of the liver with total caudate lobectomy for deeply/centrally located liver tumors: a new technique maximizing both oncological and surgical safety.

Junichi Shindoh1, Yujiro Nishioka1, Masaji Hashimoto1.   

Abstract

Systematic resection of the tumor-bearing portal territory is reportedly correlated with an improved survival of patients with liver tumors, especially in hepatocellular carcinoma. Despite advances in surgical management, however, anatomic resection of deeply/centrally located tumors remains a challenging procedure not only with technical difficulty but also because of decreased hepatic functional reserve frequently observed due to underlying liver disease. In this report, we have reported a novel technique that allows a promising approach for deeply/centrally located tumors with maximizing both the surgical and oncological safety. Bilateral anatomic resection of the ventral parts of the paramedian sectors (BVPM) offers a sufficient surgical window for safe access to the perihilar region. This technique is based on Hjortsjo's theory for liver anatomy and enables systematic removal of the 3rd-order portal territories. In addition, the current technique is advantageous in minimizing the loss of the normal liver parenchyma without leaving ischemia or congestion in the future liver remnant. Of the seven consecutive patients who were treated with this procedure, all the patients achieved R0 resection with acceptable rate of major morbidity (1/7, 14%). The BVPM may offer a safe and maximized chance of curative resection for deeply/centrally located liver tumors.
© 2017 Japanese Society of Hepato-Biliary-Pancreatic Surgery.

Entities:  

Keywords:  Anatomic resection; Caudate lobe; Central hepatectomy; Liver resection; Middle hepatic vein

Mesh:

Year:  2017        PMID: 29050064     DOI: 10.1002/jhbp.507

Source DB:  PubMed          Journal:  J Hepatobiliary Pancreat Sci        ISSN: 1868-6974            Impact factor:   7.027


  1 in total

1.  Image Processing and Machine Learning-Based Classification and Detection of Liver Tumor.

Authors:  V Durga Prasad Jasti; Enagandula Prasad; Manish Sawale; Shivlal Mewada; Manoj L Bangare; Pushpa M Bangare; Sunil L Bangare; F Sammy
Journal:  Biomed Res Int       Date:  2022-07-26       Impact factor: 3.246

  1 in total

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