International journal of technology assessment in health care · 2018
PREOPERATIVE MAGNETIC RESONANCE IMAGING DIAGNOSTIC FEATURES OF UTERINE LEIOMYOSARCOMAS: A SYSTEMATIC REVIEW.
СтатьяСистематический обзорНазвание и аннотация: Перевод пока не подготовлен
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Kaganov Helen, Ades Alex, Fraser David Stuart
Аннотация
OBJECTIVES: There are no current established pathognomonic diagnostic features for uterine leiomyosarcomas in the pre- or perioperative setting. Recent inadvertent upstaging of this rare malignancy during laparoscopic morcellation of a presumed fibroid has prompted widespread debate among clinicians regarding the safety of current surgical techniques for management of fibroids. This study aims to conduct a systematic review investigating significant diagnostic features in magnetic resonance imaging (MRI) of uterine leiomyosarcomas.
METHODS: A comprehensive database search was conducted guided by PRISMA recommendations for peer-reviewed publications to November 2017. Parameters available in MRI were compared for reliability and accuracy of diagnosis of leiomyosarcomas. A decision tree algorithm classifier model was constructed to investigate whether T1 and T2 MRI signal intensities are useful indicators.
RESULTS: Nine eligible studies were identified for analysis. There appears to be a significant relationship between histopathological type and T1 and T2 intensity signals (p < .05). A decision tree model analyzing T1 and T2 signal intensity readings supports this trend, with a diagnostic specificity of 77.78 percent for uterine leiomyosarcomas. The apparent diffusion coefficient (ADC) values were not observed to have a significant relationship with tumor pathology (p = .18).
CONCLUSIONS: Various studies have investigated pre- and perioperative techniques in differentiating uterine leiomyosarcoma from benign fibroids. Given the rarity of the malignancy and lack of pathognomonic diagnostic parameters, there is difficulty in establishing definitive criteria. A decision tree model is proposed to aid diagnosis based on MRI signal intensities.
METHODS: A comprehensive database search was conducted guided by PRISMA recommendations for peer-reviewed publications to November 2017. Parameters available in MRI were compared for reliability and accuracy of diagnosis of leiomyosarcomas. A decision tree algorithm classifier model was constructed to investigate whether T1 and T2 MRI signal intensities are useful indicators.
RESULTS: Nine eligible studies were identified for analysis. There appears to be a significant relationship between histopathological type and T1 and T2 intensity signals (p < .05). A decision tree model analyzing T1 and T2 signal intensity readings supports this trend, with a diagnostic specificity of 77.78 percent for uterine leiomyosarcomas. The apparent diffusion coefficient (ADC) values were not observed to have a significant relationship with tumor pathology (p = .18).
CONCLUSIONS: Various studies have investigated pre- and perioperative techniques in differentiating uterine leiomyosarcoma from benign fibroids. Given the rarity of the malignancy and lack of pathognomonic diagnostic parameters, there is difficulty in establishing definitive criteria. A decision tree model is proposed to aid diagnosis based on MRI signal intensities.