Literature DB >> 27436029

Limited accuracy of DCE-MRI in identification of pathological complete responders after chemoradiotherapy treatment for rectal cancer.

Marc J Gollub1, Tong Tong2, Martin Weiser3, Junting Zheng4, Mithat Gonen4, Kristen L Zakian5.   

Abstract

OBJECTIVES: To examine whether post-chemoradiotherapy (CRT) DCE-MRI can identify rectal cancer patients with pathologic complete response (pCR).
METHODS: From a rectal cancer surgery database 2007-2014, 61 consecutive patients that met the following inclusion criteria were selected for analysis: (1) stage II/III primary rectal adenocarcinoma; (2) received CRT; (3) underwent surgery (4); underwent rectal DCE-MRI on a 1.5-T MRI scanner. Two experienced radiologists, in consensus, drew regions of interest (ROI) on the sagittal DCE-MRI image in the tumour bed. These were exported from ImageJ to in-house Matlab code for modelling using the Tofts model. K trans, K ep and v e values were compared to pathological response.
RESULTS: Of the 61 initial patients, 37 had data considered adequate for fitting to obtain perfusion parameters. Among the 13 men and 24 women, median age 53 years, there were 8 pCR (22 %). K trans could not distinguish patients with pCR. For patients with 90 % or greater response, mean K trans and K ep values were statistically significant (p = 0.032 and 0.027, respectively). Using a cutoff value of K trans = 0.25 min-1, the AUC was 0.71.
CONCLUSION: K trans could be used to identify patients with 90 % or more response to chemoradiotherapy for rectal cancer with an AUC of 0.7. KEY POINTS: • Chemoradiotherapy for rectal cancer causes decreased blood flow and permeability in the tumour bed. • Lower values of blood flow and permeability correlate with good tumour response. • K trans of 0.25min -1 best identifies patients with ≥90 % response with AUC 0.71.

Entities:  

Keywords:  Chemoradiotherapy; Complete response; DCE-MRI; Ktrans; Rectal cancer

Mesh:

Substances:

Year:  2016        PMID: 27436029      PMCID: PMC5570543          DOI: 10.1007/s00330-016-4493-1

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  8 in total

1.  Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

Authors:  Yanfen Cui; Xiaotang Yang; Zhongqiang Shi; Zhao Yang; Xiaosong Du; Zhikai Zhao; Xintao Cheng
Journal:  Eur Radiol       Date:  2018-08-20       Impact factor: 5.315

Review 2.  Current controversy, confusion, and imprecision in the use and interpretation of rectal MRI.

Authors:  Marc J Gollub; Chandana Lall; Neeraj Lalwani; Michael H Rosenthal
Journal:  Abdom Radiol (NY)       Date:  2019-11

3.  Quantitating whole lesion tumor biology in rectal cancer MRI: taking a lesson from FDG-PET tumor metrics.

Authors:  Marc J Gollub; Andreas M Hotker; Kaitlin M Woo; Yousef Mazaheri; Mithat Gonen
Journal:  Abdom Radiol (NY)       Date:  2018-07

4.  Diagnostic Performance of Vascular Permeability and Texture Parameters for Evaluating the Response to Neoadjuvant Chemoradiotherapy in Patients With Esophageal Squamous Cell Carcinoma.

Authors:  Wenbing Ji; Jian Wang; Rongzhen Zhou; Minke Wang; Weizhen Wang; Peipei Pang; Min Kong; Chao Zhou
Journal:  Front Oncol       Date:  2021-05-18       Impact factor: 6.244

5.  Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer.

Authors:  Yuqiang Li; Wenxue Liu; Qian Pei; Lilan Zhao; Cenap Güngör; Hong Zhu; Xiangping Song; Chenglong Li; Zhongyi Zhou; Yang Xu; Dan Wang; Fengbo Tan; Pei Yang; Haiping Pei
Journal:  Cancer Med       Date:  2019-10-22       Impact factor: 4.452

Review 6.  MRI Evaluation of Complete Response of Locally Advanced Rectal Cancer After Neoadjuvant Therapy: Current Status and Future Trends.

Authors:  Qiaoyu Xu; Yanyan Xu; Hongliang Sun; Tao Jiang; Sheng Xie; Bee Yen Ooi; Yi Ding
Journal:  Cancer Manag Res       Date:  2021-06-01       Impact factor: 3.989

7.  Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction.

Authors:  Guolin Ye; Suqun He; Ruilin Pan; Lewei Zhu; Dan Zhou; RuiLiang Lu
Journal:  J Healthc Eng       Date:  2022-02-23       Impact factor: 2.682

8.  Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer.

Authors:  Zhuokai Zhuang; Zongchao Liu; Juan Li; Xiaolin Wang; Peiyi Xie; Fei Xiong; Jiancong Hu; Xiaochun Meng; Meijin Huang; Yanhong Deng; Ping Lan; Huichuan Yu; Yanxin Luo
Journal:  J Transl Med       Date:  2021-06-10       Impact factor: 5.531

  8 in total

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