Novel multiparametric MRI-based radiomics in preoperative prediction of perirectal fat invasion in rectal cancer.
Objectives: To investigate the feasibility and efficacy of a nomogram that combines clinical and radiomic features of magnetic resonance imaging (MRI) for preoperative perirectal fat invasion (PFI) prediction in rectal cancer. Methods: This was a retrospective study. A total of 363 patients from two...
| Publicado en: | Abdominal Radiology Vol. 48; no. 2; pp. 471 - 486 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Feb2023
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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=161717350&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 161717350 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Feb2023 vid: 48 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 161717350 160732242 10.1007/s00261-022-03759-z 161717350 ppf: 471 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Novel multiparametric MRI-based radiomics in preoperative prediction of perirectal fat invasion in rectal cancer. aug: au: Wang, Hui Chen, Xiaoyong Ding, Jingfeng Deng, Shuitang Mao, Guoqun Tian, Shuyuan Zhu, Xiandi Ao, Weiqun affil: Department of Radiology, Tongde Hospital of Zhejiang Province, No. 234, Gucui Road, 310012, Hangzhou, Zhejiang, China sug: ab: Objectives: To investigate the feasibility and efficacy of a nomogram that combines clinical and radiomic features of magnetic resonance imaging (MRI) for preoperative perirectal fat invasion (PFI) prediction in rectal cancer. Methods: This was a retrospective study. A total of 363 patients from two centers were included in the study. Patients in the first center were randomly divided into training cohort (n = 212) and internal validation cohort (n = 91) at the ratio of 7:3. Patients in the second center were allocated to the external validation cohort (n = 60). Among the training cohort, the numbers of patients who were PFI positive and PFI negative were 108 and 104, respectively. The radiomics features of preoperative T2-weighted images, diffusion-weighted images and enhanced T1-weighted images were extracted, and the total Radscore of each patient was obtained. We created Clinic model and Radscore model, respectively, according to clinical data or Radscore only. And that, we assembled the combined model using the clinical data and Radscore. We used DeLong's test, receiver operating characteristic, calibration and decision curve analysis to assess the models' performance. Results: The three models had good performance. Clinic model and Radscore model showed equivalent performance with AUCs of 0.85, 0.82 (accuracy of 81%, 81%) in the training cohort, AUCs of 0.78, 0.86 (accuracy of 74%, 84%) in the internal cohort, and 0.84, 0.84 (accuracy of 80%, 82%) in the external cohort without statistical difference (DeLong's test, p > 0.05). AUCs and accuracy of Combined model were 0.89 and 87%, 0.90 and 88%, and 0.90 and 88% in the three cohorts, respectively, which were higher than that of Clinic model and Radscore model, but only in the training cohort with a statistical difference (DeLong's test, p < 0.05). The calibration curves of the nomogram exhibited acceptable consistency, and the decision curve analysis indicated higher net benefit in clinical practice. Conclusion: A nomogram combining clinical and radiomic features of MRI to compute the probability of PFI in rectal cancer was developed and validated. It has the potential to serve as a preoperative biomarker for predicting pathological PFI of rectal cancer. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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