Investigating the diagnostic value of quantitative parameters based on T2-weighted and contrast-enhanced MRI with psoas muscle and outer myometrium as ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Mahrooz Malek, Maryam Rahmani, Seyyedeh Mahdieh Seyyed Ebrahimi, Elnaz Tabibian, Azadeh Alidoosti, Pariya Rahimifar, Setareh Akhavan, Ziba Gandomkar

ABSTRACT

BACKGROUND: Post-hysterectomy histopathological examination is currently the main diagnostic tool for differentiating uterine sarcomas from leiomyomas. This study aimed to investigate the diagnostic accuracy of preoperative quantitative metrics based on T2-weighted sequences and contrast-enhanced MRI (CE-MRI) for distinguishing uterine sarcomas from leiomyomas. MATERIALS AND METHODS: The institutional review board approved the study. Sixty-five women confirmed to have a total of 105 lesions participated. Routine pelvic MRI sequences, T2 map and CE-MRI images were performed preoperatively using a 3 T MR scanner. Six quantitative metrics-T2 mapping parameter, T2 scaled ratio, tumor myometrium contrast ratio on T2, tumor psoas contrast ratio on T2, tumor myometrium contrast-enhanced ratio, and tumor psoas contrast-enhanced ratio-were extracted from the acquired image sets. Chi-square test was used to compare the percentage of malignant lesions with the central necrosis to the corresponding percentage for the benign masses. Using the area under receiver operating characteristic (AUC) curve, the performance of different metrics for distinguishing uterine sarcomas from leiomyomas was measured. Moreover, for each metric, we extracted the optimal cut-off value. The values of sensitivity, specificity, negative predictive value, and positive predictive value were calculted for the classifiers based on different metrics. RESULTS: The average age, average lesion size, and proportion of premenopausal women in benign and malignant groups were comparable in our dataset. The signal intensity of uterine sarcomas at T2-weighted sequences was significantly higher than that of leiomyomas (p < 0.001), while intensity at T1-weighted sequences exhibited no significant difference between the two masses (p = 0.201). Our data also suggested that a central necrosis was ten times more common among malignant lesions compared to benign ones (p < 0.001). Among different metrics, T2 mapping parameter achieved the highest AUC value and accuracy in differentiating two groups. Three measures-T2 scaled ratio, tumor myometrium contrast ratio on T2, and tumor myometrium contrast-enhanced ratio-achieved a sensitivity of 100%, therefore none of the malignant lesions would have been missed if these metrics had been adopted in patient management. CONCLUSIONS: The findings suggested that the evaluated metrics could be useful in the preoperative assessment of myometrial masses to differentiate uterine sarcomas from leiomyomas. The proposed framework has major implications for improving current practice in the management of myometrial masses. More... »

PAGES

20

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s40644-019-0206-8

DOI

http://dx.doi.org/10.1186/s40644-019-0206-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113176203

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30935419


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Clinical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Imam Khomeini Hospital", 
          "id": "https://www.grid.ac/institutes/grid.414574.7", 
          "name": [
            "Department of Radiology, Medical Imaging Center, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), End of Keshavarz Blvd, 1419733141, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Malek", 
        "givenName": "Mahrooz", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imam Khomeini Hospital", 
          "id": "https://www.grid.ac/institutes/grid.414574.7", 
          "name": [
            "Department of Radiology, Medical Imaging Center, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), End of Keshavarz Blvd, 1419733141, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rahmani", 
        "givenName": "Maryam", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imam Khomeini Hospital", 
          "id": "https://www.grid.ac/institutes/grid.414574.7", 
          "name": [
            "Department of Radiology, Medical Imaging Center, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), End of Keshavarz Blvd, 1419733141, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Seyyed Ebrahimi", 
        "givenName": "Seyyedeh Mahdieh", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imam Khomeini Hospital", 
          "id": "https://www.grid.ac/institutes/grid.414574.7", 
          "name": [
            "Department of Radiology, Medical Imaging Center, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), End of Keshavarz Blvd, 1419733141, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tabibian", 
        "givenName": "Elnaz", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imam Khomeini Hospital", 
          "id": "https://www.grid.ac/institutes/grid.414574.7", 
          "name": [
            "Department of Radiology, Medical Imaging Center, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), End of Keshavarz Blvd, 1419733141, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Alidoosti", 
        "givenName": "Azadeh", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Imam Khomeini Hospital", 
          "id": "https://www.grid.ac/institutes/grid.414574.7", 
          "name": [
            "Department of Radiology, Medical Imaging Center, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), End of Keshavarz Blvd, 1419733141, Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rahimifar", 
        "givenName": "Pariya", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tehran University of Medical Sciences", 
          "id": "https://www.grid.ac/institutes/grid.411705.6", 
          "name": [
            "Department of Obstetrics and Gynecology, Tehran University of Medical Sciences (TUMS), Tehran, Iran"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Akhavan", 
        "givenName": "Setareh", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "University of Sydney", 
          "id": "https://www.grid.ac/institutes/grid.1013.3", 
          "name": [
            "The University of Sydney, Discipline of Medical Imaging and Radiation Sciences, Image Optimisation and Perception Group (MIOPeG), Sydney, NSW, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gandomkar", 
        "givenName": "Ziba", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/cncr.28844", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000794421"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0002-9378(90)91298-q", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003407994"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0009-9260(97)80300-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1005116610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-013-2819-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010266994", 
          "https://doi.org/10.1007/s00330-013-2819-9"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jmri.24998", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1010842043"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.diii.2014.11.016", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018970600"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2147/ijwh.s51083", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020194842"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/jmri.24119", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022648511"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0046-8177(93)90186-k", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030276250"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2463/mrms.2014-0023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1034230005"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ygyno.2008.02.026", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036821003"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1472-6874-12-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038045152", 
          "https://doi.org/10.1186/1472-6874-12-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004728-199309000-00018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039371283"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/00004728-199309000-00018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1039371283"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/s0015-0282(98)00193-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041178754"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ygyno.2009.09.023", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044079369"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ajog.2013.12.028", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1046466206"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/aog.0000000000001157", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051116360"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1097/aog.0000000000001157", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051116360"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-009-1471-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051719875", 
          "https://doi.org/10.1007/s00330-009-1471-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-009-1471-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051719875", 
          "https://doi.org/10.1007/s00330-009-1471-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00330-009-1471-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051719875", 
          "https://doi.org/10.1007/s00330-009-1471-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/igc.0b013e31819a1f8f", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061837860"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/igc.0b013e31819a1f8f", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061837860"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/igc.0b013e31819a1f8f", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061837860"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1111/igc.0b013e31819a1f8f", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1061837860"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.11613/bm.2012.031", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1063347969"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.158.5.1566664", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069317207"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.2214/ajr.177.6.1771307", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1069324393"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.5812/iranjradiol.24014", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1073144517"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiographics.19.5.g99se131179", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074522200"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiographics.19.suppl_1.g99oc04s131", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1074533080"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1074666776", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1075301051", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://app.dimensions.ai/details/publication/pub.1078675669", 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiology.171.2.2704819", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1079253853"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiology.161.2.3532190", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1079805371"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1148/radiology.158.2.3753623", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1079933700"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.jvir.2017.02.003", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1084093573"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejrad.2018.11.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109815610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejrad.2018.11.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109815610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejrad.2018.11.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109815610"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/j.ejrad.2018.11.009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109815610"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2019-12", 
    "datePublishedReg": "2019-12-01", 
    "description": "BACKGROUND: Post-hysterectomy histopathological examination is currently the main diagnostic tool for differentiating uterine sarcomas from leiomyomas. This study aimed to investigate the diagnostic accuracy of preoperative quantitative metrics based on T2-weighted sequences and contrast-enhanced MRI (CE-MRI) for distinguishing uterine sarcomas from leiomyomas.\nMATERIALS AND METHODS: The institutional review board approved the study. Sixty-five women confirmed to have a total of 105 lesions participated. Routine pelvic MRI sequences, T2 map and CE-MRI images were performed preoperatively using a 3\u2009T MR scanner. Six quantitative metrics-T2 mapping parameter, T2 scaled ratio, tumor myometrium contrast ratio on T2, tumor psoas contrast ratio on T2, tumor myometrium contrast-enhanced ratio, and tumor psoas contrast-enhanced ratio-were extracted from the acquired image sets. Chi-square test was used to compare the percentage of malignant lesions with the central necrosis to the corresponding percentage for the benign masses. Using the area under receiver operating characteristic (AUC) curve, the performance of different metrics for distinguishing uterine sarcomas from leiomyomas was measured. Moreover, for each metric, we extracted the optimal cut-off value. The values of sensitivity, specificity, negative predictive value, and positive predictive value were calculted for the classifiers based on different metrics.\nRESULTS: The average age, average lesion size, and proportion of premenopausal women in benign and malignant groups were comparable in our dataset. The signal intensity of uterine sarcomas at T2-weighted sequences was significantly higher than that of leiomyomas (p\u2009<\u20090.001), while intensity at T1-weighted sequences exhibited no significant difference between the two masses (p\u2009=\u20090.201). Our data also suggested that a central necrosis was ten times more common among malignant lesions compared to benign ones (p\u2009<\u20090.001). Among different metrics, T2 mapping parameter achieved the highest AUC value and accuracy in differentiating two groups. Three measures-T2 scaled ratio, tumor myometrium contrast ratio on T2, and tumor myometrium contrast-enhanced ratio-achieved a sensitivity of 100%, therefore none of the malignant lesions would have been missed if these metrics had been adopted in patient management.\nCONCLUSIONS: The findings suggested that the evaluated metrics could be useful in the preoperative assessment of myometrial masses to differentiate uterine sarcomas from leiomyomas. The proposed framework has major implications for improving current practice in the management of myometrial masses.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1186/s40644-019-0206-8", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1032121", 
        "issn": [
          "1740-5025", 
          "1470-7330"
        ], 
        "name": "Cancer Imaging", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "19"
      }
    ], 
    "name": "Investigating the diagnostic value of quantitative parameters based on T2-weighted and contrast-enhanced MRI with psoas muscle and outer myometrium as internal references for differentiating uterine sarcomas from leiomyomas at 3T MRI", 
    "pagination": "20", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s40644-019-0206-8"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1113176203"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "ca4f610a302abfdd8e3a60ca817b9bb69861b661acfbb285ea40c7657e9bbc05"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "101172931"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "30935419"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s40644-019-0206-8", 
      "https://app.dimensions.ai/details/publication/pub.1113176203"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-16T06:21", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000377_0000000377/records_106803_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://link.springer.com/10.1186%2Fs40644-019-0206-8"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1186/s40644-019-0206-8'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1186/s40644-019-0206-8'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s40644-019-0206-8'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s40644-019-0206-8'


 

This table displays all metadata directly associated to this object as RDF triples.

215 TRIPLES      21 PREDICATES      62 URIs      21 LITERALS      9 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s40644-019-0206-8 schema:about anzsrc-for:11
2 anzsrc-for:1103
3 schema:author Nf0d15f1ab41e4a6bb8732b2d54d543b6
4 schema:citation sg:pub.10.1007/s00330-009-1471-x
5 sg:pub.10.1007/s00330-013-2819-9
6 sg:pub.10.1186/1472-6874-12-6
7 https://app.dimensions.ai/details/publication/pub.1074666776
8 https://app.dimensions.ai/details/publication/pub.1075301051
9 https://app.dimensions.ai/details/publication/pub.1078675669
10 https://doi.org/10.1002/cncr.28844
11 https://doi.org/10.1002/jmri.24119
12 https://doi.org/10.1002/jmri.24998
13 https://doi.org/10.1016/0002-9378(90)91298-q
14 https://doi.org/10.1016/0046-8177(93)90186-k
15 https://doi.org/10.1016/j.ajog.2013.12.028
16 https://doi.org/10.1016/j.diii.2014.11.016
17 https://doi.org/10.1016/j.ejrad.2018.11.009
18 https://doi.org/10.1016/j.jvir.2017.02.003
19 https://doi.org/10.1016/j.ygyno.2008.02.026
20 https://doi.org/10.1016/j.ygyno.2009.09.023
21 https://doi.org/10.1016/s0009-9260(97)80300-5
22 https://doi.org/10.1016/s0015-0282(98)00193-9
23 https://doi.org/10.1097/00004728-199309000-00018
24 https://doi.org/10.1097/aog.0000000000001157
25 https://doi.org/10.1111/igc.0b013e31819a1f8f
26 https://doi.org/10.1148/radiographics.19.5.g99se131179
27 https://doi.org/10.1148/radiographics.19.suppl_1.g99oc04s131
28 https://doi.org/10.1148/radiology.158.2.3753623
29 https://doi.org/10.1148/radiology.161.2.3532190
30 https://doi.org/10.1148/radiology.171.2.2704819
31 https://doi.org/10.11613/bm.2012.031
32 https://doi.org/10.2147/ijwh.s51083
33 https://doi.org/10.2214/ajr.158.5.1566664
34 https://doi.org/10.2214/ajr.177.6.1771307
35 https://doi.org/10.2463/mrms.2014-0023
36 https://doi.org/10.5812/iranjradiol.24014
37 schema:datePublished 2019-12
38 schema:datePublishedReg 2019-12-01
39 schema:description BACKGROUND: Post-hysterectomy histopathological examination is currently the main diagnostic tool for differentiating uterine sarcomas from leiomyomas. This study aimed to investigate the diagnostic accuracy of preoperative quantitative metrics based on T2-weighted sequences and contrast-enhanced MRI (CE-MRI) for distinguishing uterine sarcomas from leiomyomas. MATERIALS AND METHODS: The institutional review board approved the study. Sixty-five women confirmed to have a total of 105 lesions participated. Routine pelvic MRI sequences, T2 map and CE-MRI images were performed preoperatively using a 3 T MR scanner. Six quantitative metrics-T2 mapping parameter, T2 scaled ratio, tumor myometrium contrast ratio on T2, tumor psoas contrast ratio on T2, tumor myometrium contrast-enhanced ratio, and tumor psoas contrast-enhanced ratio-were extracted from the acquired image sets. Chi-square test was used to compare the percentage of malignant lesions with the central necrosis to the corresponding percentage for the benign masses. Using the area under receiver operating characteristic (AUC) curve, the performance of different metrics for distinguishing uterine sarcomas from leiomyomas was measured. Moreover, for each metric, we extracted the optimal cut-off value. The values of sensitivity, specificity, negative predictive value, and positive predictive value were calculted for the classifiers based on different metrics. RESULTS: The average age, average lesion size, and proportion of premenopausal women in benign and malignant groups were comparable in our dataset. The signal intensity of uterine sarcomas at T2-weighted sequences was significantly higher than that of leiomyomas (p < 0.001), while intensity at T1-weighted sequences exhibited no significant difference between the two masses (p = 0.201). Our data also suggested that a central necrosis was ten times more common among malignant lesions compared to benign ones (p < 0.001). Among different metrics, T2 mapping parameter achieved the highest AUC value and accuracy in differentiating two groups. Three measures-T2 scaled ratio, tumor myometrium contrast ratio on T2, and tumor myometrium contrast-enhanced ratio-achieved a sensitivity of 100%, therefore none of the malignant lesions would have been missed if these metrics had been adopted in patient management. CONCLUSIONS: The findings suggested that the evaluated metrics could be useful in the preoperative assessment of myometrial masses to differentiate uterine sarcomas from leiomyomas. The proposed framework has major implications for improving current practice in the management of myometrial masses.
40 schema:genre research_article
41 schema:inLanguage en
42 schema:isAccessibleForFree true
43 schema:isPartOf N1a1f9ffc16ae4474a682ccfaeb8f53d7
44 N9d642000bcdb46bdb32846b419a7997f
45 sg:journal.1032121
46 schema:name Investigating the diagnostic value of quantitative parameters based on T2-weighted and contrast-enhanced MRI with psoas muscle and outer myometrium as internal references for differentiating uterine sarcomas from leiomyomas at 3T MRI
47 schema:pagination 20
48 schema:productId N1a721647d09e43f9afcdcd33c68e3125
49 N36e08f4c528e4eadad85a634fa4907f0
50 N499135087cd549f5b946474bf1a7f10a
51 N89630a5196a5449891674c234cf616ee
52 Nd9113563314f47a5b27ceea14e059c4b
53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1113176203
54 https://doi.org/10.1186/s40644-019-0206-8
55 schema:sdDatePublished 2019-04-16T06:21
56 schema:sdLicense https://scigraph.springernature.com/explorer/license/
57 schema:sdPublisher N4cd6679d4f8145afa502f4f83d3f4db1
58 schema:url https://link.springer.com/10.1186%2Fs40644-019-0206-8
59 sgo:license sg:explorer/license/
60 sgo:sdDataset articles
61 rdf:type schema:ScholarlyArticle
62 N06340c727e90412285ccb7f305c77b23 schema:affiliation https://www.grid.ac/institutes/grid.411705.6
63 schema:familyName Akhavan
64 schema:givenName Setareh
65 rdf:type schema:Person
66 N076da1e168e244cba21804347ba1cb9e schema:affiliation https://www.grid.ac/institutes/grid.414574.7
67 schema:familyName Malek
68 schema:givenName Mahrooz
69 rdf:type schema:Person
70 N0ca6605213654e678416a44709894493 rdf:first Nbc7697ca711540c99421e7807c1b2cb9
71 rdf:rest Na2a6c0f8bcab44c1b5d129602f832c1c
72 N1a1f9ffc16ae4474a682ccfaeb8f53d7 schema:issueNumber 1
73 rdf:type schema:PublicationIssue
74 N1a721647d09e43f9afcdcd33c68e3125 schema:name dimensions_id
75 schema:value pub.1113176203
76 rdf:type schema:PropertyValue
77 N3058c94b0470458282d1c2e2ef89695b schema:affiliation https://www.grid.ac/institutes/grid.414574.7
78 schema:familyName Rahmani
79 schema:givenName Maryam
80 rdf:type schema:Person
81 N34c80a8a5fd248399dc2098c158910c7 rdf:first Nb203cf5cef224ace930f7c331bc4b75e
82 rdf:rest rdf:nil
83 N36e08f4c528e4eadad85a634fa4907f0 schema:name nlm_unique_id
84 schema:value 101172931
85 rdf:type schema:PropertyValue
86 N499135087cd549f5b946474bf1a7f10a schema:name pubmed_id
87 schema:value 30935419
88 rdf:type schema:PropertyValue
89 N4cd6679d4f8145afa502f4f83d3f4db1 schema:name Springer Nature - SN SciGraph project
90 rdf:type schema:Organization
91 N60036bf2c7cc434aab78e43f3658588a schema:affiliation https://www.grid.ac/institutes/grid.414574.7
92 schema:familyName Alidoosti
93 schema:givenName Azadeh
94 rdf:type schema:Person
95 N66ca1bd3b5984a328aefcc8a097ad128 rdf:first Nccf9958043a248d6bfb8e767247acc16
96 rdf:rest Naa3b8ea9d0df43ad803a513fe1909355
97 N794f703d0f0d4b00b8c49bd811716ec2 rdf:first N3058c94b0470458282d1c2e2ef89695b
98 rdf:rest N66ca1bd3b5984a328aefcc8a097ad128
99 N89630a5196a5449891674c234cf616ee schema:name doi
100 schema:value 10.1186/s40644-019-0206-8
101 rdf:type schema:PropertyValue
102 N90eda02ce7bb4d58b11cca3124f0de24 schema:affiliation https://www.grid.ac/institutes/grid.414574.7
103 schema:familyName Tabibian
104 schema:givenName Elnaz
105 rdf:type schema:Person
106 N91713f720f8a483097952425db02ba96 rdf:first N60036bf2c7cc434aab78e43f3658588a
107 rdf:rest N0ca6605213654e678416a44709894493
108 N9d642000bcdb46bdb32846b419a7997f schema:volumeNumber 19
109 rdf:type schema:PublicationVolume
110 Na2a6c0f8bcab44c1b5d129602f832c1c rdf:first N06340c727e90412285ccb7f305c77b23
111 rdf:rest N34c80a8a5fd248399dc2098c158910c7
112 Naa3b8ea9d0df43ad803a513fe1909355 rdf:first N90eda02ce7bb4d58b11cca3124f0de24
113 rdf:rest N91713f720f8a483097952425db02ba96
114 Nb203cf5cef224ace930f7c331bc4b75e schema:affiliation https://www.grid.ac/institutes/grid.1013.3
115 schema:familyName Gandomkar
116 schema:givenName Ziba
117 rdf:type schema:Person
118 Nbc7697ca711540c99421e7807c1b2cb9 schema:affiliation https://www.grid.ac/institutes/grid.414574.7
119 schema:familyName Rahimifar
120 schema:givenName Pariya
121 rdf:type schema:Person
122 Nccf9958043a248d6bfb8e767247acc16 schema:affiliation https://www.grid.ac/institutes/grid.414574.7
123 schema:familyName Seyyed Ebrahimi
124 schema:givenName Seyyedeh Mahdieh
125 rdf:type schema:Person
126 Nd9113563314f47a5b27ceea14e059c4b schema:name readcube_id
127 schema:value ca4f610a302abfdd8e3a60ca817b9bb69861b661acfbb285ea40c7657e9bbc05
128 rdf:type schema:PropertyValue
129 Nf0d15f1ab41e4a6bb8732b2d54d543b6 rdf:first N076da1e168e244cba21804347ba1cb9e
130 rdf:rest N794f703d0f0d4b00b8c49bd811716ec2
131 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
132 schema:name Medical and Health Sciences
133 rdf:type schema:DefinedTerm
134 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
135 schema:name Clinical Sciences
136 rdf:type schema:DefinedTerm
137 sg:journal.1032121 schema:issn 1470-7330
138 1740-5025
139 schema:name Cancer Imaging
140 rdf:type schema:Periodical
141 sg:pub.10.1007/s00330-009-1471-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1051719875
142 https://doi.org/10.1007/s00330-009-1471-x
143 rdf:type schema:CreativeWork
144 sg:pub.10.1007/s00330-013-2819-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010266994
145 https://doi.org/10.1007/s00330-013-2819-9
146 rdf:type schema:CreativeWork
147 sg:pub.10.1186/1472-6874-12-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038045152
148 https://doi.org/10.1186/1472-6874-12-6
149 rdf:type schema:CreativeWork
150 https://app.dimensions.ai/details/publication/pub.1074666776 schema:CreativeWork
151 https://app.dimensions.ai/details/publication/pub.1075301051 schema:CreativeWork
152 https://app.dimensions.ai/details/publication/pub.1078675669 schema:CreativeWork
153 https://doi.org/10.1002/cncr.28844 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000794421
154 rdf:type schema:CreativeWork
155 https://doi.org/10.1002/jmri.24119 schema:sameAs https://app.dimensions.ai/details/publication/pub.1022648511
156 rdf:type schema:CreativeWork
157 https://doi.org/10.1002/jmri.24998 schema:sameAs https://app.dimensions.ai/details/publication/pub.1010842043
158 rdf:type schema:CreativeWork
159 https://doi.org/10.1016/0002-9378(90)91298-q schema:sameAs https://app.dimensions.ai/details/publication/pub.1003407994
160 rdf:type schema:CreativeWork
161 https://doi.org/10.1016/0046-8177(93)90186-k schema:sameAs https://app.dimensions.ai/details/publication/pub.1030276250
162 rdf:type schema:CreativeWork
163 https://doi.org/10.1016/j.ajog.2013.12.028 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046466206
164 rdf:type schema:CreativeWork
165 https://doi.org/10.1016/j.diii.2014.11.016 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018970600
166 rdf:type schema:CreativeWork
167 https://doi.org/10.1016/j.ejrad.2018.11.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109815610
168 rdf:type schema:CreativeWork
169 https://doi.org/10.1016/j.jvir.2017.02.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1084093573
170 rdf:type schema:CreativeWork
171 https://doi.org/10.1016/j.ygyno.2008.02.026 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036821003
172 rdf:type schema:CreativeWork
173 https://doi.org/10.1016/j.ygyno.2009.09.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044079369
174 rdf:type schema:CreativeWork
175 https://doi.org/10.1016/s0009-9260(97)80300-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005116610
176 rdf:type schema:CreativeWork
177 https://doi.org/10.1016/s0015-0282(98)00193-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041178754
178 rdf:type schema:CreativeWork
179 https://doi.org/10.1097/00004728-199309000-00018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039371283
180 rdf:type schema:CreativeWork
181 https://doi.org/10.1097/aog.0000000000001157 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051116360
182 rdf:type schema:CreativeWork
183 https://doi.org/10.1111/igc.0b013e31819a1f8f schema:sameAs https://app.dimensions.ai/details/publication/pub.1061837860
184 rdf:type schema:CreativeWork
185 https://doi.org/10.1148/radiographics.19.5.g99se131179 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074522200
186 rdf:type schema:CreativeWork
187 https://doi.org/10.1148/radiographics.19.suppl_1.g99oc04s131 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074533080
188 rdf:type schema:CreativeWork
189 https://doi.org/10.1148/radiology.158.2.3753623 schema:sameAs https://app.dimensions.ai/details/publication/pub.1079933700
190 rdf:type schema:CreativeWork
191 https://doi.org/10.1148/radiology.161.2.3532190 schema:sameAs https://app.dimensions.ai/details/publication/pub.1079805371
192 rdf:type schema:CreativeWork
193 https://doi.org/10.1148/radiology.171.2.2704819 schema:sameAs https://app.dimensions.ai/details/publication/pub.1079253853
194 rdf:type schema:CreativeWork
195 https://doi.org/10.11613/bm.2012.031 schema:sameAs https://app.dimensions.ai/details/publication/pub.1063347969
196 rdf:type schema:CreativeWork
197 https://doi.org/10.2147/ijwh.s51083 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020194842
198 rdf:type schema:CreativeWork
199 https://doi.org/10.2214/ajr.158.5.1566664 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069317207
200 rdf:type schema:CreativeWork
201 https://doi.org/10.2214/ajr.177.6.1771307 schema:sameAs https://app.dimensions.ai/details/publication/pub.1069324393
202 rdf:type schema:CreativeWork
203 https://doi.org/10.2463/mrms.2014-0023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034230005
204 rdf:type schema:CreativeWork
205 https://doi.org/10.5812/iranjradiol.24014 schema:sameAs https://app.dimensions.ai/details/publication/pub.1073144517
206 rdf:type schema:CreativeWork
207 https://www.grid.ac/institutes/grid.1013.3 schema:alternateName University of Sydney
208 schema:name The University of Sydney, Discipline of Medical Imaging and Radiation Sciences, Image Optimisation and Perception Group (MIOPeG), Sydney, NSW, Australia
209 rdf:type schema:Organization
210 https://www.grid.ac/institutes/grid.411705.6 schema:alternateName Tehran University of Medical Sciences
211 schema:name Department of Obstetrics and Gynecology, Tehran University of Medical Sciences (TUMS), Tehran, Iran
212 rdf:type schema:Organization
213 https://www.grid.ac/institutes/grid.414574.7 schema:alternateName Imam Khomeini Hospital
214 schema:name Department of Radiology, Medical Imaging Center, Imam Khomeini Hospital Complex (IKHC), Tehran University of Medical Sciences (TUMS), End of Keshavarz Blvd, 1419733141, Tehran, Iran
215 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...