Biparametric MR signal characteristics can predict histopathological measures of prostate cancer
DC Field | Value | Language |
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dc.contributor.author | To, Minh Nguyen Nhat | - |
dc.contributor.author | Kwak, Jin Tae | - |
dc.date.accessioned | 2022-12-12T00:41:08Z | - |
dc.date.available | 2022-12-12T00:41:08Z | - |
dc.date.created | 2022-12-08 | - |
dc.date.issued | 2022-11 | - |
dc.identifier.issn | 0938-7994 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/147125 | - |
dc.description.abstract | Objectives The aim of this study was to establish a new data-driven metric from MRI signal intensity that can quantify histopathological characteristics of prostate cancer. Methods This retrospective study was conducted on 488 patients who underwent biparametric MRI (bp-MRI), including T2-weighted imaging (T2W) and apparent diffusion coefficient (ADC) of diffusion-weighted imaging, and having biopsy-proven prostate cancer between August 2011 and July 2015. Forty-two of the patients who underwent radical prostatectomy and the rest of 446 patients constitute the labeled and unlabeled datasets, respectively. A deep learning model was built to predict the density of epithelium, epithelial nuclei, stroma, and lumen from bp-MRI, called MR-driven tissue density. On both the labeled validation set and the whole unlabeled dataset, the quality of MR-driven tissue density and its relation to bp-MRI signal intensity were examined with respect to different histopathologic and radiologic conditions using different statistical analyses. Results MR-driven tissue density and bp-MRI of 446 patients were evaluated. MR-driven tissue density was significantly related to bp-MRI (p < 0.05). The relationship was generally stronger in cancer regions than in benign regions. Regarding cancer grades, significant differences were found in the intensity of bp-MRI and MR-driven tissue density of epithelium, epithelial nuclei, and stroma (p < 0.05). Comparing MR true-negative to MR false-positive regions, MR-driven lumen density was significantly different, similar to the intensity of bp-MRI (p < 0.001). Conclusions MR-driven tissue density could serve as a reliable histopathological measure of the prostate on bp-MRI, leading to an improved understanding of prostate cancer and cancer progression. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | TISSUE COMPOSITION | - |
dc.subject | MICROSTRUCTURE | - |
dc.subject | DIAGNOSIS | - |
dc.subject | DENSITY | - |
dc.subject | IMAGES | - |
dc.subject | CELL | - |
dc.subject | ADC | - |
dc.title | Biparametric MR signal characteristics can predict histopathological measures of prostate cancer | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kwak, Jin Tae | - |
dc.identifier.doi | 10.1007/s00330-022-08808-1 | - |
dc.identifier.scopusid | 2-s2.0-85129291990 | - |
dc.identifier.wosid | 000790150800003 | - |
dc.identifier.bibliographicCitation | EUROPEAN RADIOLOGY, v.32, no.11, pp.8027 - 8038 | - |
dc.relation.isPartOf | EUROPEAN RADIOLOGY | - |
dc.citation.title | EUROPEAN RADIOLOGY | - |
dc.citation.volume | 32 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 8027 | - |
dc.citation.endPage | 8038 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | TISSUE COMPOSITION | - |
dc.subject.keywordPlus | MICROSTRUCTURE | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.subject.keywordPlus | DENSITY | - |
dc.subject.keywordPlus | IMAGES | - |
dc.subject.keywordPlus | CELL | - |
dc.subject.keywordPlus | ADC | - |
dc.subject.keywordAuthor | Magnetic resonance imaging | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Pathology | - |
dc.subject.keywordAuthor | Cell count | - |
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