2017
DOI: 10.1007/s10147-017-1222-y
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Ultrasound-based logistic regression model LR2 versus magnetic resonance imaging for discriminating between benign and malignant adnexal masses: a prospective study

Abstract: The IOTA LR2 model had a similar sensitivity to MRI for discriminating between benign and malignant tumors and a higher specificity compared with MRI. Our findings suggest that the IOTA LR2 model, either alone or in conjunction with MRI, should be included in preoperative evaluation of adnexal masses.

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Cited by 15 publications
(14 citation statements)
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“…Several reports have found that magnetic resonance imaging (MRI), alone or in combination with computed tomography (CT), predicts accurately the presence of peritoneal carcinomatosis in patients undergoing preoperative evaluation for cytoreductive surgery, particularly when the assessment is carried out by an experienced radiologist 114–117 . Recently, a prospective study reported higher specificity of the IOTA LR2 model compared with subjective interpretation of MRI findings by an experienced radiologist, as well as similar sensitivities for both imaging modalities for discriminating between benign and malignant tumors 118 . The addition of diffusion‐weighted techniques to conventional imaging modalities has been shown in multiple pooled studies to increase diagnostic accuracy in discriminating between benign tumors and ovarian cancer, especially in the Caucasian population, with data even suggesting a value in predicting resectability 119–123 .…”
Section: Resultsmentioning
confidence: 99%
“…Several reports have found that magnetic resonance imaging (MRI), alone or in combination with computed tomography (CT), predicts accurately the presence of peritoneal carcinomatosis in patients undergoing preoperative evaluation for cytoreductive surgery, particularly when the assessment is carried out by an experienced radiologist 114–117 . Recently, a prospective study reported higher specificity of the IOTA LR2 model compared with subjective interpretation of MRI findings by an experienced radiologist, as well as similar sensitivities for both imaging modalities for discriminating between benign and malignant tumors 118 . The addition of diffusion‐weighted techniques to conventional imaging modalities has been shown in multiple pooled studies to increase diagnostic accuracy in discriminating between benign tumors and ovarian cancer, especially in the Caucasian population, with data even suggesting a value in predicting resectability 119–123 .…”
Section: Resultsmentioning
confidence: 99%
“…[114][115][116][117] Recently, a prospective study reported higher specificity of the IOTA LR2 model compared with subjective interpretation of MRI findings by an experienced radiologist, as well as similar sensitivities for both imaging modalities for discriminating between benign and malignant tumors. 118 The addition of diffusion-weighted techniques to conventional imaging modalities has been shown in multiple pooled studies to increase diagnostic accuracy in discriminating between benign tumors and ovarian cancer, especially in the Caucasian population, with data even suggesting a value in predicting resectability. [119][120][121][122][123] However, the true extent of such a benefit needs to be validated further in multicenter large-scale prospective randomized studies, which are currently being designed or underway.…”
Section: Tumor Markersmentioning
confidence: 99%
“…Recently, a prospective study reported higher specificity of the IOTA LR2 model compared with subjective interpretation of MRI findings by an experienced radiologist, as well as similar sensitivities for both imaging modalities for discriminating between benign and malignant tumours (Shimada et al, 2018). The addition of diffusion-weighted techniques to conventional imaging modalities has been shown in multiple pooled studies to increase diagnostic accuracy in discriminating between benign tumours and ovarian cancer, especially in the Caucasian population, with data even suggesting a value in predicting resectability (Dai et al, 2019;Espada et al, 2013;Meng et al, 2016;Michielsen et al, 2017;Rizzo et al, 2020).…”
Section: Magnetic Resonance Imagingmentioning
confidence: 99%