PURPOSE: To determine retrospectively the sensitivity and specificity of computed tomography (CT) for the differentiation of perihepatic metastases with and those without liver parenchymal invasion (LPI) in patients with ovarian cancer by using interpretations of radiologists with different experience levels and staging laparotomy and pathologic examination findings as the reference standards. MATERIALS AND METHODS: Institutional review board approval and waiver of informed consent were obtained for this HIPAA-compliant study; 121 patients with ovarian cancer (age range, 29-94 years; mean age, 57.8 years) formed the study group. Two radiologists blinded to patient clinical data (radiologist 1, 6 months of experience; radiologist 2, 2 years 6 months of experience) retrospectively and independently recorded presence of perihepatic metastases, liver regions involved, and presence of LPI by perihepatic metastases visible on CT images. Sensitivities and specificities for detecting the presence of perihepatic metastases and liver regions involved and sensitivities for detecting LPI were calculated. kappa Statistics were used to analyze interradiologist agreement. RESULTS: Pathologic examination results showed 66 perihepatic metastases in 43 (36%) of 121 patients. Sixty (91%) of 66 perihepatic metastases did not show signs of LPI and six (9%) did. Sensitivity and specificity combinations for radiologists 1 and 2 were 56% and 87% and 86% and 99%, respectively, for detecting the presence of perihepatic metastases and 46% and 97% and 82% and 100%, respectively, for determining liver regions involved. Radiologists 1 and 2 had sensitivities of 35% and 80%, respectively, for detecting regions with perihepatic metastases without LPI and sensitivities of 50% and 100%, respectively, for detecting regions with perihepatic metastases with LPI. CONCLUSION: CT can be used to detect perihepatic metastases in patients with ovarian cancer and allows for distinction between metastases that invade the liver and those that do not.
PURPOSE: To determine retrospectively the sensitivity and specificity of computed tomography (CT) for the differentiation of perihepatic metastases with and those without liver parenchymal invasion (LPI) in patients with ovarian cancer by using interpretations of radiologists with different experience levels and staging laparotomy and pathologic examination findings as the reference standards. MATERIALS AND METHODS: Institutional review board approval and waiver of informed consent were obtained for this HIPAA-compliant study; 121 patients with ovarian cancer (age range, 29-94 years; mean age, 57.8 years) formed the study group. Two radiologists blinded to patient clinical data (radiologist 1, 6 months of experience; radiologist 2, 2 years 6 months of experience) retrospectively and independently recorded presence of perihepatic metastases, liver regions involved, and presence of LPI by perihepatic metastases visible on CT images. Sensitivities and specificities for detecting the presence of perihepatic metastases and liver regions involved and sensitivities for detecting LPI were calculated. kappa Statistics were used to analyze interradiologist agreement. RESULTS: Pathologic examination results showed 66 perihepatic metastases in 43 (36%) of 121 patients. Sixty (91%) of 66 perihepatic metastases did not show signs of LPI and six (9%) did. Sensitivity and specificity combinations for radiologists 1 and 2 were 56% and 87% and 86% and 99%, respectively, for detecting the presence of perihepatic metastases and 46% and 97% and 82% and 100%, respectively, for determining liver regions involved. Radiologists 1 and 2 had sensitivities of 35% and 80%, respectively, for detecting regions with perihepatic metastases without LPI and sensitivities of 50% and 100%, respectively, for detecting regions with perihepatic metastases with LPI. CONCLUSION: CT can be used to detect perihepatic metastases in patients with ovarian cancer and allows for distinction between metastases that invade the liver and those that do not.
Authors: Sunila Pradeep; Seung W Kim; Sherry Y Wu; Masato Nishimura; Pradeep Chaluvally-Raghavan; Takahito Miyake; Chad V Pecot; Sun-Jin Kim; Hyun Jin Choi; Farideh Z Bischoff; Julie Ann Mayer; Li Huang; Alpa M Nick; Carolyn S Hall; Cristian Rodriguez-Aguayo; Behrouz Zand; Heather J Dalton; Thiruvengadam Arumugam; Ho Jeong Lee; Hee Dong Han; Min Soon Cho; Rajesha Rupaimoole; Lingegowda S Mangala; Vasudha Sehgal; Sang Cheul Oh; Jinsong Liu; Ju-Seog Lee; Robert L Coleman; Prahlad Ram; Gabriel Lopez-Berestein; Isaiah J Fidler; Anil K Sood Journal: Cancer Cell Date: 2014-07-14 Impact factor: 31.743
Authors: Tsz-Lun Yeung; Cecilia S Leung; Kay-Pong Yip; Chi Lam Au Yeung; Stephen T C Wong; Samuel C Mok Journal: Am J Physiol Cell Physiol Date: 2015-07-29 Impact factor: 4.249
Authors: Sai Kiran Sharma; Kuntal K Sevak; Sebastien Monette; Sean D Carlin; James C Knight; Frank R Wuest; Evis Sala; Brian M Zeglis; Jason S Lewis Journal: J Nucl Med Date: 2016-02-02 Impact factor: 10.057