Evidence map›Paper›PMID 38872048›Full record

ArticleWorld journal of urology2024

MRI-based radiomic features of the urinary bladder wall identify patients with moderate-to-severe international prostate symptom score.

Mohammed Shahait, Ruben Usamentiaga, Yubing Tong, Alex Sandberg, David I Lee, Jayaram K Udupa, Drew A Torigian

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Article in World journal of urology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Mohammed ShahaitConsultant of Urology, Dubai, UAE.ORCID http://orcid.org/0000-0003-2609-5629
Ruben UsamentiagaDepartment of Computer Science and Engineering, University of Oviedo, Gijon, Spain.
Yubing TongMedical Image Processing Group, Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Goddard Building, 6th Floor, Rm 601W, Philadelphia, PA, 19104, USA.
Alex SandbergTemple Medical School, Temple University, Philadelphia, PA, USA.
David I LeeDepartment of Urology, University of California Irvine, Irvine, CA, USA.
Jayaram K UdupaMedical Image Processing Group, Department of Radiology, University of Pennsylvania, 3710 Hamilton Walk, Goddard Building, 6th Floor, Rm 601W, Philadelphia, PA, 19104, USA.
Drew A TorigianMedical 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodIn 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).

resultsOf 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.

conclusionA 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.

Indexed as

Magnetic Resonance ImagingSeverity of Illness IndexUrinary BladderUrinary Bladder Neck ObstructionAgedHumansLower Urinary Tract SymptomsMaleMiddle AgedRadiomicsRetrospective StudiesSymptom AssessmentMachine learningMRIRadiomicsUrinary bladder wall

Identifiers

PMID38872048
PMCPMC11176201

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.