Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation.
Purpose: To predict microsatellite instability (MSI) status of colon cancer on preoperative CT imaging using radiomic analysis. Methods: This retrospective study involved radiomic analysis of preoperative CT imaging of patients who underwent resection of stage II–III colon cancer from 2004 to 2012....
| Publicado en: | Abdominal Radiology Vol. 44; no. 11; pp. 3755 - 3764 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=139439214&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139439214 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Nov2019 vid: 44 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139439214 143922462 10.1007/s00261-019-02117-w 139439214 ppf: 3755 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation. aug: au: Golia Pernicka, Jennifer S. Gagniere, Johan Chakraborty, Jayasree Yamashita, Rikiya Nardo, Lorenzo Creasy, John M. Petkovska, Iva Do, Richard R. K. Bates, David D. B. Paroder, Viktoriya Gonen, Mithat Weiser, Martin R. Simpson, Amber L. Gollub, Marc J. affil: Body Imaging Service, Department of Radiology, Evelyn H. Lauder Breast Center, Memorial Sloan Kettering Cancer Center, 300 East 66th St., Suite 757, 10065, New York, NY, USA sug: ab: Purpose: To predict microsatellite instability (MSI) status of colon cancer on preoperative CT imaging using radiomic analysis. Methods: This retrospective study involved radiomic analysis of preoperative CT imaging of patients who underwent resection of stage II–III colon cancer from 2004 to 2012. A radiologist blinded to MSI status manually segmented the tumor region on CT images. 254 Intensity-based radiomic features were extracted from the tumor region. Three prediction models were developed with (1) only clinical features, (2) only radiomic features, and (3) "combined" clinical and radiomic features. Patients were randomly separated into training (n = 139) and test (n = 59) sets. The model was constructed from training data only; the test set was reserved for validation only. Model performance was evaluated using AUC, sensitivity, specificity, PPV, and NPV. Results: Of the total 198 patients, 134 (68%) patients had microsatellite stable tumors and 64 (32%) patients had MSI tumors. The combined model performed slightly better than the other models, predicting MSI with an AUC of 0.80 for the training set and 0.79 for the test set (specificity = 96.8% and 92.5%, respectively), whereas the model with only clinical features achieved an AUC of 0.74 and the model with only radiomic features achieved an AUC of 0.76. The model with clinical features alone had the lowest specificity (70%) compared with the model with radiomic features alone (95%) and the combined model (92.5%). Conclusions: Preoperative prediction of MSI status via radiomic analysis of preoperative CT adds specificity to clinical assessment and could contribute to personalized treatment selection. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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