MR classification of renal masses with pathologic correlation.

To perform a feature analysis of malignant renal tumors evaluated with magnetic resonance (MR) imaging and to investigate the correlation between MR imaging features and histopathological findings. MR examinations in 79 malignant renal masses were retrospectively evaluated, and a feature analysis wa...

Descripción completa

Detalles Bibliográficos
Publicado en:European Radiology Vol. 18; no. 2; pp. 365 - 376
Autores principales: Pedrosa I, Chou MT, Ngo L, H Baroni R, Genega EM, Galaburda L, Dewolf WC, Rofsky NM, Pedrosa, Ivan, Chou, Mary T, Ngo, Long, H Baroni, Ronaldo, Genega, Elizabeth M, Galaburda, Laura, DeWolf, William C, Rofsky, Neil M
Formato: research Journal Article
Publicado: Springer Nature Feb2008
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105753418&site=ehost-live
header:
  @attributes:
    shortDbName: ccm
    uiTerm: 105753418
    longDbName: CINAHL Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    dissinfo:
    jinfo:
      jid:
        09387994
        NPH
      jtl: European Radiology
      issn: 09387994
      maglogo: N
    pubinfo:
      dt: Feb2008
      vid: 18
      iid: 2
      pid: 237
      pub: Springer Nature
      place: New York, New York
    artinfo:
      ui:
        105753418
        NLM17899106
        2009813995
        10.1007/s00330-007-0757-0
        NLM17899106
        105753418
      ppf: 365
      ppct: 11
      formats:
        fmt:
          @attributes:
            type: P
      tig:
        atl: MR classification of renal masses with pathologic correlation.
      aug:
        au:
          Pedrosa I
          Chou MT
          Ngo L
          H Baroni R
          Genega EM
          Galaburda L
          Dewolf WC
          Rofsky NM
          Pedrosa, Ivan
          Chou, Mary T
          Ngo, Long
          H Baroni, Ronaldo
          Genega, Elizabeth M
          Galaburda, Laura
          DeWolf, William C
          Rofsky, Neil M
        affil: Department of Radiology, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02118, USA
      sug:
        subj:
          Carcinoma, Renal Cell Diagnosis
          Kidney Neoplasms Diagnosis
          Kidney Pathology
          Magnetic Resonance Imaging Methods
          Adult
          Aged
          Aged, 80 and Over
          Carcinoma, Renal Cell Pathology
          Contrast Media Administration and Dosage
          Female
          Image Enhancement Methods
          Kidney Neoplasms Pathology
          Male
          Middle Age
          Neoplasm Staging
          Observer Bias
          Predictive Value of Tests
          Relative Risk
          Retrospective Design
          Sensitivity and Specificity
          Human
          Adult: 19-44 years
          Aged: 65+ years
          Aged, 80 & over
          Middle Aged: 45-64 years
          Female
          Male
      ab: To perform a feature analysis of malignant renal tumors evaluated with magnetic resonance (MR) imaging and to investigate the correlation between MR imaging features and histopathological findings. MR examinations in 79 malignant renal masses were retrospectively evaluated, and a feature analysis was performed. Each renal mass was assigned to one of eight categories from a proposed MRI classification system. The sensitivity and specificity of the MRI classification system to predict the histologic subtype and nuclear grade was calculated. Subvoxel fat on chemical shift imaging correlated to clear cell type (p < 0.05); sensitivity = 42%, specificity = 100%. Large size, intratumoral necrosis, retroperitoneal vascular collaterals, and renal vein thrombosis predicted high-grade clear cell type (p < 0.05). Small size, peripheral location, low intratumoral SI on T2-weighted images, and low-level enhancement were associated with low-grade papillary carcinomas (p < 0.05). The sensitivity and specificity of the MRI classification system for diagnosing low grade clear cell, high-grade clear cell, all clear cell, all papillary, and transitional carcinomas were 50% and 94%, 93% and 75%, 92% and 83%, 80% and 94%, and 100% and 99%, respectively. The MRI feature analysis and proposed classification system help predict the histological type and nuclear grade of renal masses.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
    refInfo:
    holdings:
      @attributes:
        islocal: N