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- Maria Antonietta Mazzei, Leila Khader, Alfredo Cirigliano, Nevada Cioffi Squitieri, Susanna Guerrini, Beatrice Forzoni, Daniele Marrelli, Franco Roviello, Francesco Giuseppe Mazzei, and Luca Volterrani.
- Section of Radiological Sciences, Department of Medical, Surgical and Neuro Sciences, University of Siena, Viale Bracci 10, 53100, Siena, Italy, mamazzei@gmail.com.
- Abdom Imaging. 2013 Dec 1; 38 (6): 1422-30.
PurposeTo evaluate the accuracy of MDCT in the preoperative definition of Peritoneal Cancer Index (PCI) in patients with advanced ovarian cancer who underwent a peritonectomy and hyperthermic intraperitoneal chemotherapy (HIPEC) after neoadjuvant chemotherapy to obtain a pre-surgery prognostic evaluation and a prediction of optimal cytoreduction surgery.Materials And MethodsPre-HIPEC CT examinations of 43 patients with advanced ovarian cancer after neoadjuvant chemotherapy were analyzed by two radiologists. The PCI was scored according to the Sugarbaker classification, based on lesion size and distribution. The results were compared with macroscopic and histologic data after peritonectomy and HIPEC. To evaluate the accuracy of MDCT to detect and localize peritoneal carcinomatosis, both patient-level and regional-level analyses were conducted. A correlation between PCI CT and histologic values for each patient was searched according to the PCI grading.ResultsConsidering the patient-level analysis, CT shows a sensitivity, specificity, PPV, NPV, and an accuracy in detecting the peritoneal carcinomatosis of 100 %, 40 %, 93 % 100 %, and 93 %, respectively. Considering the regional level analysis, a sensitivity, specificity, PPV, NPV, and diagnostic accuracy of 72 %, 80 %, 66 %, 84 %, and 77 %, respectively were obtained for the correlation between CT and histology.ConclusionOur results encourage the use of MDCT as the only technique sufficient to select patients with peritoneal carcinomatosis for cytoreductive surgery and HIPEC on the condition that a CT examination will be performed using a dedicated protocol optimized to detect minimal peritoneal disease and CT images will be analyzed by an experienced reader.
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