Why Fine-Needle Aspiration Cytology Is Not an Adequate Diagnostic Method for Liver Hydatid Cyst

Malki, Hadj Omar El; Dziri, Chadli; Mejdoubi, Yasser El; Souadka, Amine; Zakri, B.; Mohsine, Raouf; Ifrine, L.; Belkouchi, A.

doi:10.1001/archsurg.142.7.690-a

Cited by 5 publications

(7 citation statements)

References 11 publications

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“…Our developed model based on ultrasonography (as very sensitive and specific tool to diagnose LHC in endemic area) [18,20] and quick examination (Age, presence of jaundice and past history of liver hydatidosis) can be helpful to orientate, with a good accuracy BCC, patients to centres with high hepato-biliary experienced surgeons who may offer the best management for particular complicated cysts. Technichal staff should offer, either pre-operative ERCP or intra-operative cholangiography, intensive care unit, more surgical techniques skills and radiological interventional units.…”

Section: Discussionmentioning

confidence: 99%

“…At the admission to our unit, the diagnosis of LHC was established by clinical history, clinical examination and abdominal ultrsonography for all patients [18]. Radiological investigations revealed: number, localisation, size, Gharbi's classification [20] (type I: pure fluid collection; type II: fluid collection with a split wall (floating membrane); type III: fluid collection with septa (honeycomb image); type IV: heterogeneous echographic patterns; type V: reflecting thick walls), appearance of biliary tract and presence or absence of BCC.…”

Section: Methodsmentioning

confidence: 99%

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Predictive model of biliocystic communication in liver hydatid cysts using classification and regression tree analysis

et al. 2010

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BackgroundIncidence of liver hydatid cyst (LHC) rupture ranged 15%-40% of all cases and most of them concern the bile duct tree. Patients with biliocystic communication (BCC) had specific clinic and therapeutic aspect. The purpose of this study was to determine witch patients with LHC may develop BCC using classification and regression tree (CART) analysisMethodsA retrospective study of 672 patients with liver hydatid cyst treated at the surgery department "A" at Ibn Sina University Hospital, Rabat Morocco. Four-teen risk factors for BCC occurrence were entered into CART analysis to build an algorithm that can predict at the best way the occurrence of BCC.ResultsIncidence of BCC was 24.5%. Subgroups with high risk were patients with jaundice and thick pericyst risk at 73.2% and patients with thick pericyst, with no jaundice 36.5 years and younger with no past history of LHC risk at 40.5%. Our developed CART model has sensitivity at 39.6%, specificity at 93.3%, positive predictive value at 65.6%, a negative predictive value at 82.6% and accuracy of good classification at 80.1%. Discriminating ability of the model was good 82%.Conclusionwe developed a simple classification tool to identify LHC patients with high risk BCC during a routine clinic visit (only on clinical history and examination followed by an ultrasonography). Predictive factors were based on pericyst aspect, jaundice, age, past history of liver hydatidosis and morphological Gharbi cyst aspect. We think that this classification can be useful with efficacy to direct patients at appropriated medical struct's.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Predictive model of biliocystic communication in liver hydatid cysts using classification and regression tree analysis

et al. 2010

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“…Diagnosis tools of LHC at admission to our unit and different surgical procedures used were previously developed in other studies. 2,25,27 After surgery, all patients underwent abdominal US surveillance, at the first month after discharge, every 6 months during the first 4 years and then annually, to detect recurrences. In the case of highly suspected LHC recurrence on the US, abdominal CT scan was performed.…”

Section: Methodsmentioning

confidence: 99%

“…In uncertain cases, FNAC was used to confirm this recurrence. 25 Immunological tests were not routinely used for assessing the diagnosis in this study because they seem to be less sensitive and specific compared with radiologic explorations. 15,17 Recurrence was defined as the appearance of a growing new cyst, undetected by radiologic exploration before the first surgery or by the surgeon during the first procedure, whether in the first location of the hydatid cyst in the liver or in another liver's segment.…”

Section: Methodsmentioning

confidence: 99%

“…2,16,17,24 In some equivocal cases, fine-needle aspiration cytology (FNAC) helps to distinguish between recurrence and residual cavity for patients with initial LHC surgery. 25 Surgery for recurrence of LHC is technically more difficult due to adhesions arising from previous surgeries, 26 which increase considerably the morbidity and mortality rate of this procedure. 11,18 In English literature, few authors tried to discuss recurrent disease problems and their predictive factors.…”

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confidence: 99%

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