Cargando…
Histogram analysis based on multi-parameter MR imaging as a biomarker to predict lymph node metastasis in T3 stage rectal cancer
BACKGROUND: Preoperative identification of rectal cancer lymph node status is crucial for patient prognosis and treatment decisions. Rectal magnetic resonance imaging (MRI) plays an essential role in the preoperative staging of rectal cancer, but its ability to predict lymph node metastasis (LNM) is...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8609786/ https://www.ncbi.nlm.nih.gov/pubmed/34809615 http://dx.doi.org/10.1186/s12880-021-00706-0 |
_version_ | 1784602982062489600 |
---|---|
author | Zhou, Yang Yang, Rui Wang, Yuan Zhou, Meng Zhou, Xueyan Xing, JiQing Wang, Xinxin Zhang, Chunhui |
author_facet | Zhou, Yang Yang, Rui Wang, Yuan Zhou, Meng Zhou, Xueyan Xing, JiQing Wang, Xinxin Zhang, Chunhui |
author_sort | Zhou, Yang |
collection | PubMed |
description | BACKGROUND: Preoperative identification of rectal cancer lymph node status is crucial for patient prognosis and treatment decisions. Rectal magnetic resonance imaging (MRI) plays an essential role in the preoperative staging of rectal cancer, but its ability to predict lymph node metastasis (LNM) is insufficient. This study explored the value of histogram features of primary lesions on multi-parametric MRI for predicting LNM of stage T3 rectal carcinoma. METHODS: We retrospectively analyzed 175 patients with stage T3 rectal cancer who underwent preoperative MRI, including diffusion-weighted imaging (DWI) before surgery. 62 patients were included in the LNM group, and 113 patients were included in the non-LNM group. Texture features were calculated from histograms derived from T2 weighted imaging (T2WI), DWI, ADC, and T2 maps. Stepwise logistic regression analysis was used to screen independent predictors of LNM from clinical features, imaging features, and histogram features. Predictive performance was evaluated by receiver operating characteristic (ROC) curve analysis. Finally, a nomogram was established for predicting the risk of LNM. RESULTS: The clinical, imaging and histogram features were analyzed by stepwise logistic regression. Preoperative carbohydrate antigen 199 level (p = 0.009), MRN stage (p < 0.001), (T2WI)Kurtosis (p = 0.010), (DWI)Mode (p = 0.038), (DWI)CV (p = 0.038), and (T2-map)P5 (p = 0.007) were independent predictors of LNM. These factors were combined to form the best predictive model. The model reached an area under the ROC curve (AUC) of 0.860, with a sensitivity of 72.8% and a specificity of 85.5%. CONCLUSION: The histogram features on multi-parametric MRI of the primary tumor in rectal cancer were related to LN status, which is helpful for improving the ability to predict LNM of stage T3 rectal cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-021-00706-0. |
format | Online Article Text |
id | pubmed-8609786 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-86097862021-11-23 Histogram analysis based on multi-parameter MR imaging as a biomarker to predict lymph node metastasis in T3 stage rectal cancer Zhou, Yang Yang, Rui Wang, Yuan Zhou, Meng Zhou, Xueyan Xing, JiQing Wang, Xinxin Zhang, Chunhui BMC Med Imaging Research BACKGROUND: Preoperative identification of rectal cancer lymph node status is crucial for patient prognosis and treatment decisions. Rectal magnetic resonance imaging (MRI) plays an essential role in the preoperative staging of rectal cancer, but its ability to predict lymph node metastasis (LNM) is insufficient. This study explored the value of histogram features of primary lesions on multi-parametric MRI for predicting LNM of stage T3 rectal carcinoma. METHODS: We retrospectively analyzed 175 patients with stage T3 rectal cancer who underwent preoperative MRI, including diffusion-weighted imaging (DWI) before surgery. 62 patients were included in the LNM group, and 113 patients were included in the non-LNM group. Texture features were calculated from histograms derived from T2 weighted imaging (T2WI), DWI, ADC, and T2 maps. Stepwise logistic regression analysis was used to screen independent predictors of LNM from clinical features, imaging features, and histogram features. Predictive performance was evaluated by receiver operating characteristic (ROC) curve analysis. Finally, a nomogram was established for predicting the risk of LNM. RESULTS: The clinical, imaging and histogram features were analyzed by stepwise logistic regression. Preoperative carbohydrate antigen 199 level (p = 0.009), MRN stage (p < 0.001), (T2WI)Kurtosis (p = 0.010), (DWI)Mode (p = 0.038), (DWI)CV (p = 0.038), and (T2-map)P5 (p = 0.007) were independent predictors of LNM. These factors were combined to form the best predictive model. The model reached an area under the ROC curve (AUC) of 0.860, with a sensitivity of 72.8% and a specificity of 85.5%. CONCLUSION: The histogram features on multi-parametric MRI of the primary tumor in rectal cancer were related to LN status, which is helpful for improving the ability to predict LNM of stage T3 rectal cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12880-021-00706-0. BioMed Central 2021-11-22 /pmc/articles/PMC8609786/ /pubmed/34809615 http://dx.doi.org/10.1186/s12880-021-00706-0 Text en © The Author(s) 2021, corrected publication 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zhou, Yang Yang, Rui Wang, Yuan Zhou, Meng Zhou, Xueyan Xing, JiQing Wang, Xinxin Zhang, Chunhui Histogram analysis based on multi-parameter MR imaging as a biomarker to predict lymph node metastasis in T3 stage rectal cancer |
title | Histogram analysis based on multi-parameter MR imaging as a biomarker to predict lymph node metastasis in T3 stage rectal cancer |
title_full | Histogram analysis based on multi-parameter MR imaging as a biomarker to predict lymph node metastasis in T3 stage rectal cancer |
title_fullStr | Histogram analysis based on multi-parameter MR imaging as a biomarker to predict lymph node metastasis in T3 stage rectal cancer |
title_full_unstemmed | Histogram analysis based on multi-parameter MR imaging as a biomarker to predict lymph node metastasis in T3 stage rectal cancer |
title_short | Histogram analysis based on multi-parameter MR imaging as a biomarker to predict lymph node metastasis in T3 stage rectal cancer |
title_sort | histogram analysis based on multi-parameter mr imaging as a biomarker to predict lymph node metastasis in t3 stage rectal cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8609786/ https://www.ncbi.nlm.nih.gov/pubmed/34809615 http://dx.doi.org/10.1186/s12880-021-00706-0 |
work_keys_str_mv | AT zhouyang histogramanalysisbasedonmultiparametermrimagingasabiomarkertopredictlymphnodemetastasisint3stagerectalcancer AT yangrui histogramanalysisbasedonmultiparametermrimagingasabiomarkertopredictlymphnodemetastasisint3stagerectalcancer AT wangyuan histogramanalysisbasedonmultiparametermrimagingasabiomarkertopredictlymphnodemetastasisint3stagerectalcancer AT zhoumeng histogramanalysisbasedonmultiparametermrimagingasabiomarkertopredictlymphnodemetastasisint3stagerectalcancer AT zhouxueyan histogramanalysisbasedonmultiparametermrimagingasabiomarkertopredictlymphnodemetastasisint3stagerectalcancer AT xingjiqing histogramanalysisbasedonmultiparametermrimagingasabiomarkertopredictlymphnodemetastasisint3stagerectalcancer AT wangxinxin histogramanalysisbasedonmultiparametermrimagingasabiomarkertopredictlymphnodemetastasisint3stagerectalcancer AT zhangchunhui histogramanalysisbasedonmultiparametermrimagingasabiomarkertopredictlymphnodemetastasisint3stagerectalcancer |