MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer.
Purpose: To investigative the performance of MRI-radiomics analysis derived from T2WI and apparent diffusion coefficients (ADC) images before and after neoadjuvant chemoradiation therapy (nCRT) separately or simultaneously for predicting post-nCRT lymph node status in patients with locally advanced...
| Publicado en: | Abdominal Radiology Vol. 48; no. 7; pp. 2270 - 2284 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2023
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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=163964781&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 163964781 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jul2023 vid: 48 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 163964781 163210492 10.1007/s00261-023-03910-4 163964781 ppf: 2270 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer. aug: au: Fang, Zhu Pu, Hong Chen, Xiao-li Yuan, Yi Zhang, Feng Li, Hang affil: Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, 32# Second Section of First Ring Road, Qingyang District, 610070, Chengdu, Sichuan, China sug: ab: Purpose: To investigative the performance of MRI-radiomics analysis derived from T2WI and apparent diffusion coefficients (ADC) images before and after neoadjuvant chemoradiation therapy (nCRT) separately or simultaneously for predicting post-nCRT lymph node status in patients with locally advanced rectal cancer (LARC). Materials and Methods: Eighty-three patients (training cohort, n = 57; validation cohort, n = 26) with LARC between June 2017 and December 2022 were retrospectively enrolled. All the radiomics features were extracted from volume of interest on T2WI and ADC images from baseline and post-nCRT MRI. Delta-radiomics features were defined as the difference between radiomics features before and after nCRT. Seven clinical-radiomics models were constructed by combining the most predictive radiomics signatures and clinical parameters selected from support vector machine. Receiver operating characteristic curve (ROC) was used to evaluate the performance of models. The optimum model-based LNM was applied to assess 5-years disease-free survival (DFS) using Kaplan–Meier analysis. The end point was clinical or radiological locoregional recurrence or distant metastasis during postoperative follow-up. Results: Clinical-deltaADC radiomics combined model presented good performance for predicting post-CRT LNM in the training (AUC = 0.895,95%CI:0.838–0.953) and validation cohort (AUC = 0.900,95%CI:0.771–1.000). Clinical-deltaADC radiomics-postT2WI radiomics combined model also showed good performances (AUC = 0.913,95%CI:0.838–0.953) in the training and (AUC = 0.912,95%CI:0.771–1.000) validation cohort. As for subgroup analysis, clinical-deltaADC radiomics combined model showed good performance predicting LNM in ypT0-T2 (AUC = 0.827;95%CI:0.649–1.000) and ypT3-T4 stage (AUC = 0.934;95%CI:0.864–1.000). In ypT0-T2 stage, clinical-deltaADC radiomics combined model-based LNM could assess 5-years DFS (P = 0.030). Conclusion: Clinical-deltaADC radiomics combined model could predict post-nCRT LNM, and this combined model-based LNM was associated with 5-years DFS in ypT0-T2 stage. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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