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99mTc-MAG3 diuresis renography in differentiating renal obstruction: Using statistical parameters as new quantifiable indices.
Suriyanto, S; Ng, E Y K; Ng, C E David; Yan, Xuexian Sean; Verma, N K.
Afiliación
  • Suriyanto S; NTU Institute for Health Technologies, Interdisciplinary Graduate School, Nanyang Technological University, Singapore; School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological Univers
  • Ng EYK; School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore. Electronic address: mykng@ntu.edu.sg.
  • Ng CED; Department of Nuclear Medicine and Molecular Imaging, Singapore General Hospital, Singapore; Duke-NUS Medical School, Singapore. Electronic address: david.ng.c.e@singhealth.com.sg.
  • Yan XS; Department of Nuclear Medicine and Molecular Imaging, Singapore General Hospital, Singapore; Duke-NUS Medical School, Singapore. Electronic address: sean.yan.x.x@singhealth.com.sg.
  • Verma NK; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore. Electronic address: nkverma@ntu.edu.sg.
Comput Biol Med ; 112: 103371, 2019 09.
Article en En | MEDLINE | ID: mdl-31404720
OBJECTIVE: The aim of this study was to research, develop and assess the feasibility of using basic statistical parameters derived from renogram, "mean count value (MeanCV) and "median count value (MedianCV)", as novel indices in the diagnosis of renal obstruction through diuresis renography. SUBJECTS AND METHODS: First, we re-digitalized and normalized 132 renograms from 74 patients in order to derive the MeanCV and MedianCV. To improve the performance of the parameters, we extrapolated renograms by a two-compartmental modeling. After that, the cutoff points for diagnosis using each modified parameter were set and the sensitivity and specificity were calculated in order to determine the best variants of MeanCV and MedianCV that could differentiate renal obstruction status into 3 distinct classes - i) unobstructed, ii) slightly obstructed, and iii) heavily obstructed. RESULTS: The modified MeanCV and MedianCV derived from extended renograms predicted the severity of the renal obstruction. The most appropriate variants of MeanCV and MedianCV were found to be the MeanCV50 and the MedianCV60. The cutoff points of MeanCV50 in separating unobstructed and obstructed classes as well as slightly and heavily obstructed classes were 0.50 and 0.72, respectively. The cutoff points of MedianCV60 in separating unobstructed and obstructed classes as well as slightly and heavily obstructed classes were 0.35 and 0.69, respectively. Notably, MeanCV50 and MedianCV60 were not significantly influenced by either age or gender. CONCLUSIONS: The MeanCV50 and the MedianCV60 derived from a renogram could be incorporated with other quantifiable parameters to form a system that could provide a highly accurate diagnosis of renal obstructions.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Interpretación de Imagen Asistida por Computador / Renografía por Radioisótopo / Tecnecio Tc 99m Mertiatida / Radiofármacos / Enfermedades Renales Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Revista: Comput Biol Med Año: 2019 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Algoritmos / Interpretación de Imagen Asistida por Computador / Renografía por Radioisótopo / Tecnecio Tc 99m Mertiatida / Radiofármacos / Enfermedades Renales Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adult / Female / Humans / Male / Middle aged Idioma: En Revista: Comput Biol Med Año: 2019 Tipo del documento: Article