Literature DB >> 27842663

Can Diffusion-weighted Magnetic Resonance Imaging Predict Survival in Patients with Cervical Cancer? A Meta-Analysis.

Yu-Ting Wang1, Ying-Chun Li2, Long-Lin Yin3, Hong Pu4.   

Abstract

OBJECTIVE: Although diffusion-weighted magnetic resonance imaging (DWI) has been widely used in the diagnosis of cervical cancer, whether it can predict disease recurrence or survival remains inconclusive. This study aimed to systematically evaluate whether DWI can serve as a reliable prognostic predictor in patients with cervical cancer.
METHODS: PubMed, the MEDLINE database and the Cochrane Library were searched for DWI studies with >12 months of prognostic data in patients with cervical cancer. Endpoints included tumor recurrence and death. Methodological quality was assessed using the Quality in Prognostic Studies (QUIPS) tool. Combined estimates of hazard ratios (HRs) were derived.
RESULTS: Nine studies involving a total of 796 patients (mean/median age from 45.0 years to 62.9 years) met the inclusion criteria. Methodological quality was relatively high. Eight of the nine studies employed apparent diffusion coefficient (ADC) as an indicator of DWI results. Using disease-free survival (DFS) as an outcome measure, nine studies yielded a combined HR of 1.55 (95% confidence interval (CI): 1.23-1.95), and seven studies that employed pretreatment DWI yielded a combined HR of 1.50 (95% CI: 1.03-2.19), which indicated that unfavorable DWI results were associated with an approximately 1.50-1.55-fold higher risk of tumor recurrence. The two studies investigating the impact of DWI results on overall survival (OS) reported HRs of 7.20 and 2.17, respectively.
CONCLUSION: DWI may serve as a predictor of tumor recurrence in patients with cervical cancer as showed by meta-analysis, and the quantified ADC as a suitable candidate indicator.
Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Keywords:  ADC; Cervical cancer; DWI; Disease free survival; MRI; Recurrence

Mesh:

Year:  2016        PMID: 27842663     DOI: 10.1016/j.ejrad.2016.10.011

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  7 in total

1.  Combination of Radiomics and Machine Learning with Diffusion-Weighted MR Imaging for Clinical Outcome Prognostication in Cervical Cancer.

Authors:  Ankush Jajodia; Ayushi Gupta; Helmut Prosch; Marius Mayerhoefer; Swarupa Mitra; Sunil Pasricha; Anurag Mehta; Sunil Puri; Arvind Chaturvedi
Journal:  Tomography       Date:  2021-08-05

2.  Role of Functional Magnetic Resonance Imaging Derived Parameters as Imaging Biomarkers and Correlation with Clinicopathological Features in Carcinoma of Uterine Cervix.

Authors:  Ramireddy Jeba Karunya; Putta Tharani; Subhashini John; Ramani Manoj Kumar; Saikat Das
Journal:  J Clin Diagn Res       Date:  2017-08-01

3.  Intravoxel Incoherent Motion (IVIM) MR Quantification in Locally Advanced Cervical Cancer (LACC): Preliminary Study on Assessment of Tumor Aggressiveness and Response to Neoadjuvant Chemotherapy.

Authors:  Miriam Dolciami; Silvia Capuani; Veronica Celli; Alessandra Maiuro; Angelina Pernazza; Innocenza Palaia; Violante Di Donato; Giusi Santangelo; Stefania Maria Rita Rizzo; Paolo Ricci; Carlo Della Rocca; Carlo Catalano; Lucia Manganaro
Journal:  J Pers Med       Date:  2022-04-15

Review 4.  Diffusion magnetic resonance imaging: A molecular imaging tool caught between hope, hype and the real world of "personalized oncology".

Authors:  Abhishek Mahajan; Sneha S Deshpande; Meenakshi H Thakur
Journal:  World J Radiol       Date:  2017-06-28

5.  Comparison of DWI and 18F-FDG PET/CT for assessing preoperative N-staging in gastric cancer: evidence from a meta-analysis.

Authors:  Mingxu Luo; Hongmei Song; Gang Liu; Yikai Lin; Lintao Luo; Xin Zhou; Bo Chen
Journal:  Oncotarget       Date:  2017-09-19

6.  Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis.

Authors:  Shin-Hyung Park; Myong Hun Hahm; Bong Kyung Bae; Gun Oh Chong; Shin Young Jeong; Sungdae Na; Sungmoon Jeong; Jae-Chul Kim
Journal:  Radiat Oncol       Date:  2020-04-20       Impact factor: 3.481

7.  Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer.

Authors:  Mengjie Fang; Yangyang Kan; Di Dong; Tao Yu; Nannan Zhao; Wenyan Jiang; Lianzhen Zhong; Chaoen Hu; Yahong Luo; Jie Tian
Journal:  Front Oncol       Date:  2020-05-05       Impact factor: 6.244

  7 in total

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