Literature DB >> 1307638

[A study of intraoperative rapid frozen section diagnosis focusing on accuracy and quality assessment].

N Nemoto1, I Sakurai, S Baba, S Gotoh, H Osada.   

Abstract

Accuracy of frozen section (FS) diagnoses was investigated in a consecutive series of 1129 intraoperative consultations. In our series, the correct diagnosis including clinically not serious minor errors was made in 96.9% of the cases. Whereas the accuracy ratio of FS diagnosis for unknown pathologic process decreased to 92.2%. Among all the types of tissues, central nervous system was shown to be the most frequently handled for FS, followed by lung/bronchus, breast, liver/biliary tract, lymph node and so forth. The requesting ratio of FS, on the other hand, tended to be high in the following specialties; neurosurgery (46.5%), thoracic surgery (19.0%), general surgery, (10.0%). Deferred diagnosis with a provisional diagnosis and misinterpretations in histology typing without serious clinical problem accounted for 2.7% and 3.1% respectively. Causes of erroneous diagnoses seemed to be multifactorial, such as inappropriate sampling, diagnosis on poor quality histology sections, lack of clinical information, lack of enough experience in FS practice of pathologists, or a combination of more than two of them, though inevitable cases showing minimal cytological and structural atypia were included. Intraoperative consultation by FS diagnosis is now essential to serve a good quality medication to patients. It is thus necessary that to establish a tight peer review system and also to provide an education program with regard to practice in FS diagnosis particularly for young pathologists.

Entities:  

Mesh:

Year:  1992        PMID: 1307638

Source DB:  PubMed          Journal:  Rinsho Byori        ISSN: 0047-1860


  2 in total

1.  Intraoperative pathology consultation: error, cause and impact.

Authors:  Etienne Mahe; Shamim Ara; Mona Bishara; Annie Kurian; Syeda Tauqir; Nafisa Ursani; Pooja Vasudev; Tariq Aziz; Cathy Ross; Alice Lytwyn
Journal:  Can J Surg       Date:  2013-06       Impact factor: 2.089

2.  Deep Neural Network for Differentiation of Brain Tumor Tissue Displayed by Confocal Laser Endomicroscopy.

Authors:  Andreas Ziebart; Denis Stadniczuk; Veronika Roos; Miriam Ratliff; Andreas von Deimling; Daniel Hänggi; Frederik Enders
Journal:  Front Oncol       Date:  2021-05-11       Impact factor: 6.244

  2 in total

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