Multiple analyses suggests texture features can indicate the presence of tumor in the prostate tissue

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dc.contributor.authorSouza, Sérgio Augusto Santana [UNESP]
dc.contributor.authorReis, Leonardo Oliveira
dc.contributor.authorAlves, Allan Felipe Fattori [UNESP]
dc.contributor.authorSilva, Letícia Cotinguiba [UNESP]
dc.contributor.authorMedeiros, Maria Clara Korndorfer
dc.contributor.authorAndrade, Danilo Leite
dc.contributor.authorBillis, Athanase
dc.contributor.authorAmaro, João Luiz [UNESP]
dc.contributor.authorMartins, Daniel Lahan
dc.contributor.authorTrindade, André Petean [UNESP]
dc.contributor.authorMiranda, José Ricardo Arruda [UNESP]
dc.contributor.authorPina, Diana Rodrigues [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.date.accessioned2022-04-29T08:41:29Z
dc.date.available2022-04-29T08:41:29Z
dc.date.issued2022-01-01
dc.description.abstractSeveral studies have demonstrated statistical and texture analysis abilities to differentiate cancerous from healthy tissue in magnetic resonance imaging. This study developed a method based on texture analysis and machine learning to differentiate prostate findings. Forty-eight male patients with PI-RADS classification and subsequent radical prostatectomy histopathological analysis were used as gold standard. Experienced radiologists delimited the regions of interest in magnetic resonance images. Six different groups of images were used to perform multiple analyses (seven analyses variations). Those analyses were outlined by specialists in urology as those of most significant importance for the classification. Forty texture features were extracted from each image and processed with Random Forest, Support Vector Machine, K-Nearest Neighbors, and Naive Bayes. Those seven analyses variation results were described in terms of area under the ROC curve (AUC), accuracy, F-score, precision and sensitivity. The highest AUC (93.7%) and accuracy (88.8%) were obtained when differentiating the group with both MRI and histopathology positive findings against the group with both negative MRI and histopathology. When differentiating the group with both MRI and histopathology positive findings versus the peripheral image zone group the AUC value was 86.6%. When differentiating the group with negative MRI/positive histopathology versus the group with both negative MRI and histopathology the AUC value was 80.7%. The evaluation of statistical and texture analysis promoted very suggestive indications for future work in prostate cancer suspicious regions. The method is fast for both region of interest selection and classification with machine learning and the result brings original contributions in the classification of different groups of patients. This tool is low-cost, and can be used to assist diagnostic decisions.en
dc.description.affiliationSão Paulo State University Júlio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, SP
dc.description.affiliationDepartment of Urology UroScience State University of Campinas Unicamp and Pontifical Catholic University of Campinas PUC-Campinas, Av. John Boyd Dunlop-Jardim Ipaussurama, SP
dc.description.affiliationBotucatu Medical School Clinics Hospital Medical Physics and Radioprotection Nucleus, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, SP
dc.description.affiliationDepartment of Radiology Pontifical Catholic University of Campinas, SP
dc.description.affiliationDepartment of Anatomic Pathology and Urology School of Medical Sciences State University of Campinas (Unicamp)
dc.description.affiliationDepartment of Urology Botucatu Medical School São Paulo State University (UNESP), SP
dc.description.affiliationDepartment of Radiology University of Campinas (UNICAMP), SP
dc.description.affiliationBotucatu Medical School São Paulo State University Júlio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, SP
dc.description.affiliationInstitute of Bioscience São Paulo State University Júlio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, SP
dc.description.affiliationUnespSão Paulo State University Júlio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, SP
dc.description.affiliationUnespBotucatu Medical School Clinics Hospital Medical Physics and Radioprotection Nucleus, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, SP
dc.description.affiliationUnespDepartment of Urology Botucatu Medical School São Paulo State University (UNESP), SP
dc.description.affiliationUnespBotucatu Medical School São Paulo State University Júlio de Mesquita Filho, Av. Prof. Mário Rubens Guimarães Montenegro, s/n - UNESP - Campus de Botucatu, SP
dc.description.affiliationUnespInstitute of Bioscience São Paulo State University Júlio de Mesquita Filho, R. Prof. Dr. Antônio Celso Wagner Zanin, 250 - Distrito de Rubião Junior, SP
dc.identifierhttp://dx.doi.org/10.1007/s13246-022-01118-2
dc.identifier.citationPhysical and Engineering Sciences in Medicine.
dc.identifier.doi10.1007/s13246-022-01118-2
dc.identifier.issn2662-4737
dc.identifier.issn2662-4729
dc.identifier.scopus2-s2.0-85127617059
dc.identifier.urihttp://hdl.handle.net/11449/230681
dc.language.isoeng
dc.relation.ispartofPhysical and Engineering Sciences in Medicine
dc.sourceScopus
dc.subjectHistopathology
dc.subjectMachine learning
dc.subjectMagnetic resonance imaging
dc.subjectProstate cancer
dc.subjectTexture analysis
dc.titleMultiple analyses suggests texture features can indicate the presence of tumor in the prostate tissueen
dc.typeArtigo
unesp.author.orcid0000-0003-1967-1990[12]

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Artigos - Física e Biofísica - IBB
Artigos - Urologia - FMB