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Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics
DC Field | Value | Language |
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dc.contributor.author | 강정현 | - |
dc.contributor.author | 박은정 | - |
dc.contributor.author | 유영훈 | - |
dc.contributor.author | 이강영 | - |
dc.contributor.author | 이재훈 | - |
dc.contributor.author | 이혜선 | - |
dc.contributor.author | 전태주 | - |
dc.contributor.author | 김소영 | - |
dc.date.accessioned | 2023-07-12T02:44:18Z | - |
dc.date.available | 2023-07-12T02:44:18Z | - |
dc.date.issued | 2023-05 | - |
dc.identifier.issn | 0513-5796 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/195385 | - |
dc.description.abstract | Purpose: We investigated the feasibility of preoperative 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics with machine learning to predict microsatellite instability (MSI) status in colorectal can cer (CRC) patients. Materials and Methods: Altogether, 233 patients with CRC who underwent preoperative FDG PET/CT were enrolled and divided into training (n=139) and test (n=94) sets. A PET-based radiomics signature (rad_score) was established to predict the MSI status in patients with CRC. The predictive ability of the rad_score was evaluated using the area under the receiver operating character istic curve (AUROC) in the test set. A logistic regression model was used to determine whether the rad_score was an independent predictor of MSI status in CRC. The predictive performance of rad_score was compared with conventional PET parameters. Results: The incidence of MSI-high was 15 (10.8%) and 10 (10.6%) in the training and test sets, respectively. The rad_score was constructed based on the two radiomic features and showed similar AUROC values for predicting MSI status in the training and test sets (0.815 and 0.867, respectively; p=0.490). Logistic regression analysis revealed that the rad_score was an independent pre dictor of MSI status in the training set. The rad_score performed better than metabolic tumor volume when assessed using the AUROC (0.867 vs. 0.794, p=0.015). Conclusion: Our predictive model incorporating PET radiomic features successfully identified the MSI status of CRC, and it also showed better performance than the conventional PET image parameters. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | Yonsei University | - |
dc.relation.isPartOf | YONSEI MEDICAL JOURNAL | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Colorectal Neoplasms* / diagnostic imaging | - |
dc.subject.MESH | Colorectal Neoplasms* / genetics | - |
dc.subject.MESH | Fluorodeoxyglucose F18* | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Machine Learning | - |
dc.subject.MESH | Microsatellite Instability | - |
dc.subject.MESH | Positron Emission Tomography Computed Tomography | - |
dc.subject.MESH | Retrospective Studies | - |
dc.title | Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Surgery (외과학교실) | - |
dc.contributor.googleauthor | Soyoung Kim | - |
dc.contributor.googleauthor | Jae-Hoon Lee | - |
dc.contributor.googleauthor | Eun Jung Park | - |
dc.contributor.googleauthor | Hye Sun Lee | - |
dc.contributor.googleauthor | Seung Hyuk Baik | - |
dc.contributor.googleauthor | Tae Joo Jeon | - |
dc.contributor.googleauthor | Kang Young Lee | - |
dc.contributor.googleauthor | Young Hoon Ryu | - |
dc.contributor.googleauthor | Jeonghyun Kang | - |
dc.identifier.doi | 10.3349/ymj.2022.0548 | - |
dc.contributor.localId | A00080 | - |
dc.contributor.localId | A04572 | - |
dc.contributor.localId | A02485 | - |
dc.contributor.localId | A02640 | - |
dc.contributor.localId | A03093 | - |
dc.contributor.localId | A03312 | - |
dc.contributor.localId | A03557 | - |
dc.relation.journalcode | J02813 | - |
dc.identifier.eissn | 1976-2437 | - |
dc.identifier.pmid | 37114635 | - |
dc.subject.keyword | Colorectal cancer | - |
dc.subject.keyword | image analysis | - |
dc.subject.keyword | machine learning | - |
dc.subject.keyword | microsatellite instability | - |
dc.subject.keyword | positron emission tomography | - |
dc.contributor.alternativeName | Kang, Jeonghyun | - |
dc.contributor.affiliatedAuthor | 강정현 | - |
dc.contributor.affiliatedAuthor | 박은정 | - |
dc.contributor.affiliatedAuthor | 유영훈 | - |
dc.contributor.affiliatedAuthor | 이강영 | - |
dc.contributor.affiliatedAuthor | 이재훈 | - |
dc.contributor.affiliatedAuthor | 이혜선 | - |
dc.contributor.affiliatedAuthor | 전태주 | - |
dc.citation.volume | 64 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 320 | - |
dc.citation.endPage | 326 | - |
dc.identifier.bibliographicCitation | YONSEI MEDICAL JOURNAL, Vol.64(5) : 320-326, 2023-05 | - |
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