Development and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy

ObjectivesTo develop a prognostic prediction MRI-based nomogram model for locally advanced rectal cancer (LARC) treated with neoadjuvant therapy.MethodsThis was a retrospective analysis of 233 LARC (MRI-T stage 3-4 (mrT) and/or MRI-N stage 1-2 (mrN), M0) patients who had undergone neoadjuvant radiot...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Silin Chen, Yuan Tang, Ning Li, Jun Jiang, Liming Jiang, Bo Chen, Hui Fang, Shunan Qi, Jing Hao, Ningning Lu, Shulian Wang, Yongwen Song, Yueping Liu, Yexiong Li, Jing Jin
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	rectal neoplasms neoadjuvant therapy magnetic resonance imaging nomograms prognosis Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/712fc030e4994a3cba7b377725e3b750
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:712fc030e4994a3cba7b377725e3b750
record_format	dspace
spelling	oai:doaj.org-article:712fc030e4994a3cba7b377725e3b7502021-11-15T05:18:31ZDevelopment and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy2234-943X10.3389/fonc.2021.784156https://doaj.org/article/712fc030e4994a3cba7b377725e3b7502021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.784156/fullhttps://doaj.org/toc/2234-943XObjectivesTo develop a prognostic prediction MRI-based nomogram model for locally advanced rectal cancer (LARC) treated with neoadjuvant therapy.MethodsThis was a retrospective analysis of 233 LARC (MRI-T stage 3-4 (mrT) and/or MRI-N stage 1-2 (mrN), M0) patients who had undergone neoadjuvant radiotherapy and total mesorectal excision (TME) surgery with baseline MRI and operative pathology assessments at our institution from March 2015 to March 2018. The patients were sequentially allocated to training and validation cohorts at a ratio of 4:3 based on the image examination date. A nomogram model was developed based on the univariate logistic regression analysis and multivariable Cox regression analysis results of the training cohort for disease-free survival (DFS). To evaluate the clinical usefulness of the nomogram, Harrell’s concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) were conducted in both cohorts.ResultsThe median follow-up times were 43.2 months (13.3–61.3 months) and 32.0 months (12.3–39.5 months) in the training and validation cohorts. Multivariate Cox regression analysis identified MRI-detected extramural vascular invasion (mrEMVI), pathological T stage (ypT) and perineural invasion (PNI) as independent predictors. Lymphovascular invasion (LVI) (which almost reached statistical significance in multivariate regression analysis) and three other independent predictors were included in the nomogram model. The nomogram showed the best predictive ability for DFS (C-index: 0.769 (training cohort) and 0.776 (validation cohort)). It had a good 3-year DFS predictive capacity [area under the curve, AUC=0.843 (training cohort) and 0.771 (validation cohort)]. DCA revealed that the use of the nomogram model was associated with benefits for the prediction of 3-year DFS in both cohorts.ConclusionWe developed and validated a novel nomogram model based on MRI factors and pathological factors for predicting DFS in LARC treated with neoadjuvant therapy. This model has good predictive value for prognosis, which could improve the risk stratification and individual treatment of LARC patients.Silin ChenYuan TangNing LiNing LiJun JiangLiming JiangBo ChenHui FangShunan QiJing HaoNingning LuShulian WangYongwen SongYueping LiuYexiong LiJing JinJing JinFrontiers Media S.A.articlerectal neoplasmsneoadjuvant therapymagnetic resonance imagingnomogramsprognosisNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rectal neoplasms neoadjuvant therapy magnetic resonance imaging nomograms prognosis Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	rectal neoplasms neoadjuvant therapy magnetic resonance imaging nomograms prognosis Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Silin Chen Yuan Tang Ning Li Ning Li Jun Jiang Liming Jiang Bo Chen Hui Fang Shunan Qi Jing Hao Ningning Lu Shulian Wang Yongwen Song Yueping Liu Yexiong Li Jing Jin Jing Jin Development and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy
description	ObjectivesTo develop a prognostic prediction MRI-based nomogram model for locally advanced rectal cancer (LARC) treated with neoadjuvant therapy.MethodsThis was a retrospective analysis of 233 LARC (MRI-T stage 3-4 (mrT) and/or MRI-N stage 1-2 (mrN), M0) patients who had undergone neoadjuvant radiotherapy and total mesorectal excision (TME) surgery with baseline MRI and operative pathology assessments at our institution from March 2015 to March 2018. The patients were sequentially allocated to training and validation cohorts at a ratio of 4:3 based on the image examination date. A nomogram model was developed based on the univariate logistic regression analysis and multivariable Cox regression analysis results of the training cohort for disease-free survival (DFS). To evaluate the clinical usefulness of the nomogram, Harrell’s concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) were conducted in both cohorts.ResultsThe median follow-up times were 43.2 months (13.3–61.3 months) and 32.0 months (12.3–39.5 months) in the training and validation cohorts. Multivariate Cox regression analysis identified MRI-detected extramural vascular invasion (mrEMVI), pathological T stage (ypT) and perineural invasion (PNI) as independent predictors. Lymphovascular invasion (LVI) (which almost reached statistical significance in multivariate regression analysis) and three other independent predictors were included in the nomogram model. The nomogram showed the best predictive ability for DFS (C-index: 0.769 (training cohort) and 0.776 (validation cohort)). It had a good 3-year DFS predictive capacity [area under the curve, AUC=0.843 (training cohort) and 0.771 (validation cohort)]. DCA revealed that the use of the nomogram model was associated with benefits for the prediction of 3-year DFS in both cohorts.ConclusionWe developed and validated a novel nomogram model based on MRI factors and pathological factors for predicting DFS in LARC treated with neoadjuvant therapy. This model has good predictive value for prognosis, which could improve the risk stratification and individual treatment of LARC patients.
format	article
author	Silin Chen Yuan Tang Ning Li Ning Li Jun Jiang Liming Jiang Bo Chen Hui Fang Shunan Qi Jing Hao Ningning Lu Shulian Wang Yongwen Song Yueping Liu Yexiong Li Jing Jin Jing Jin
author_facet	Silin Chen Yuan Tang Ning Li Ning Li Jun Jiang Liming Jiang Bo Chen Hui Fang Shunan Qi Jing Hao Ningning Lu Shulian Wang Yongwen Song Yueping Liu Yexiong Li Jing Jin Jing Jin
author_sort	Silin Chen
title	Development and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy
title_short	Development and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy
title_full	Development and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy
title_fullStr	Development and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy
title_full_unstemmed	Development and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy
title_sort	development and validation of an mri-based nomogram model for predicting disease-free survival in locally advanced rectal cancer treated with neoadjuvant radiotherapy
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/712fc030e4994a3cba7b377725e3b750
work_keys_str_mv	AT silinchen developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT yuantang developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT ningli developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT ningli developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT junjiang developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT limingjiang developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT bochen developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT huifang developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT shunanqi developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT jinghao developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT ningninglu developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT shulianwang developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT yongwensong developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT yuepingliu developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT yexiongli developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT jingjin developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy AT jingjin developmentandvalidationofanmribasednomogrammodelforpredictingdiseasefreesurvivalinlocallyadvancedrectalcancertreatedwithneoadjuvantradiotherapy
_version_	1718428812541165568

Development and Validation of an MRI-Based Nomogram Model for Predicting Disease-Free Survival in Locally Advanced Rectal Cancer Treated With Neoadjuvant Radiotherapy

Ejemplares similares