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MRI-based radiomic features of the urinary bladder wall identify patients with moderate-to-severe international prostate symptom score.
Shahait, Mohammed; Usamentiaga, Ruben; Tong, Yubing; Sandberg, Alex; Lee, David I; Udupa, Jayaram K; Torigian, Drew A.
Afiliação
  • Shahait M; Consultant of Urology, Dubai, UAE.
  • Usamentiaga R; Department of Computer Science and Engineering, University of Oviedo, Gijon, Spain.
  • Tong Y; Medical Image Processing Group, Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Goddard Building, 6th Floor, Rm 601W, Philadelphia, PA, 19104, USA.
  • Sandberg A; Temple Medical School, Temple University, Philadelphia, PA, USA.
  • Lee DI; Department of Urology, University of California Irvine, Irvine, CA, USA.
  • Udupa JK; Medical Image Processing Group, Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Goddard Building, 6th Floor, Rm 601W, Philadelphia, PA, 19104, USA.
  • Torigian DA; Medical Image Processing Group, Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Goddard Building, 6th Floor, Rm 601W, Philadelphia, PA, 19104, USA. drew.torigian@pennmedicine.upenn.edu.
World J Urol ; 42(1): 375, 2024 Jun 13.
Article em En | MEDLINE | ID: mdl-38872048
ABSTRACT

BACKGROUND:

The International Prostate Symptom Score (IPSS) is a patient-reported measurement to assess the lower urinary tract symptoms of bladder outlet obstruction. Bladder outlet obstruction induces molecular and morphological alterations in the urothelium, suburothelium, detrusor smooth muscle cells, detrusor extracellular matrix, and nerves. We sought to analyze MRI-based radiomics features of the urinary bladder wall and their association with IPSS.

METHOD:

In this retrospective study, 87 patients who had pelvic MRI scans were identified. A biomarker discovery approach based on the optimal biomarker (OBM) method was used to extract features of the bladder wall from MR images, including morphological, intensity-based, and texture-based features, along with clinical variables. Mathematical models were created using subsets of features and evaluated based on their ability to discriminate between low and moderate-to-severe IPSS (less than 8 vs. equal to or greater than 8).

RESULTS:

Of the 7,666 features per patient, four highest-ranking optimal features were derived (all texture-based features), which provided a classification accuracy of 0.80 with a sensitivity, specificity, and area under the receiver operating characteristic curve of 0.81, 0.81, and 0.87, respectively.

CONCLUSION:

A highly independent set of urinary bladder wall features derived from MRI scans were able to discriminate between patients with low vs. moderate-to-severe IPSS with accuracy of 80%. Such differences in MRI-based properties of the bladder wall in patients with varying IPSS's might reflect differences in underlying molecular and morphological alterations that occur in the setting of chronic bladder outlet obstruction.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Bexiga Urinária / Obstrução do Colo da Bexiga Urinária / Índice de Gravidade de Doença / Imageamento por Ressonância Magnética Limite: Aged / Humans / Male / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Bexiga Urinária / Obstrução do Colo da Bexiga Urinária / Índice de Gravidade de Doença / Imageamento por Ressonância Magnética Limite: Aged / Humans / Male / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article