Literature DB >> 32497972

Radiomics in cervical cancer: Current applications and future potential.

Yao Ai1, Haiyan Zhu2, Congying Xie3, Xiance Jin4.   

Abstract

Cervical cancer is the most commonly diagnosed cancer among women. Early diagnosis and prediction will greatly improve the treatment outcome. Many clinical parameters have been used as diagnostic and prognostic factors for cervical cancer patients, including tumor stage, histological type, lymph node status, but with limitations in prediction accuracy. The development of noninvasive biomarker with the potential to provide more specific tumor characterization before treatment begins or during therapy is urgent needed, which may permit clinicians to administer a more individualized anti-cancer treatment. Radiomics is a mathematical-statistical procedure extracting information from medial images, which has the potential for prediction of staging, histological type, node status, relapse and survival in patients with cervical cancer. In this manuscript, we reviewed recent clinical studies and future potential for the application of radiomics in the treatment of patients with cervical cancer, and discussed the current challenges and limitations of radiomics for oncology.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Cervical cancer; Prediction; Radiomics; Recurrence; Survival; Tumor staging

Year:  2020        PMID: 32497972     DOI: 10.1016/j.critrevonc.2020.102985

Source DB:  PubMed          Journal:  Crit Rev Oncol Hematol        ISSN: 1040-8428            Impact factor:   6.312


  9 in total

1.  Staging, recurrence and follow-up of uterine cervical cancer using MRI: Updated Guidelines of the European Society of Urogenital Radiology after revised FIGO staging 2018.

Authors:  Lucia Manganaro; Yulia Lakhman; Nishat Bharwani; Benedetta Gui; Silvia Gigli; Valeria Vinci; Stefania Rizzo; Aki Kido; Teresa Margarida Cunha; Evis Sala; Andrea Rockall; Rosemarie Forstner; Stephanie Nougaret
Journal:  Eur Radiol       Date:  2021-04-14       Impact factor: 5.315

2.  An MRI-based radiomics signature and clinical characteristics for survival prediction in early-stage cervical cancer.

Authors:  Ru-Ru Zheng; Meng-Ting Cai; Li Lan; Xiao Wan Huang; Yun Jun Yang; Martin Powell; Feng Lin
Journal:  Br J Radiol       Date:  2021-11-29       Impact factor: 3.039

3.  PET/MRI and PET/CT Radiomics in Primary Cervical Cancer: A Pilot Study on the Correlation of Pelvic PET, MRI, and CT Derived Image Features.

Authors:  Shadi A Esfahani; Angel Torrado-Carvajal; Barbara Juarez Amorim; David Groshar; Liran Domachevsky; Hanna Bernstine; Dan Stein; Debra Gervais; Onofrio A Catalano
Journal:  Mol Imaging Biol       Date:  2021-10-07       Impact factor: 3.488

4.  MRI-based radiomics for pretreatment prediction of response to concurrent chemoradiotherapy in locally advanced cervical squamous cell cancer.

Authors:  Xiaomiao Zhang; Qi Zhang; Xiaoduo Yu; Xinming Zhao; Yan Chen; Sicong Wang; Jieying Zhang; Jusheng An; Lizhi Xie
Journal:  Abdom Radiol (NY)       Date:  2022-10-12

5.  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

Review 6.  The impact of para-aortic lymph node irradiation on disease-free survival in patients with cervical cancer: A systematic review and meta-analysis.

Authors:  Leslie J H Bukkems; Ina M Jürgenliemk-Schulz; Femke van der Leij; Max Peters; Cornelis G Gerestein; Ronald P Zweemer; Peter S N van Rossum
Journal:  Clin Transl Radiat Oncol       Date:  2022-05-30

7.  Radiomic Score as a Potential Imaging Biomarker for Predicting Survival in Patients With Cervical Cancer.

Authors:  Handong Li; Miaochen Zhu; Lian Jian; Feng Bi; Xiaoye Zhang; Chao Fang; Ying Wang; Jing Wang; Nayiyuan Wu; Xiaoping Yu
Journal:  Front Oncol       Date:  2021-08-16       Impact factor: 6.244

8.  Robustness of radiomics to variations in segmentation methods in multimodal brain MRI.

Authors:  M G Poirot; M W A Caan; H G Ruhe; A Bjørnerud; I Groote; L Reneman; H A Marquering
Journal:  Sci Rep       Date:  2022-10-06       Impact factor: 4.996

9.  MRI accuracy and interobserver agreement in locally advanced cervix carcinoma.

Authors:  Amalia Jacquot; Céline Chauleur; Anne-Sophie Russel-Robillard; Fabien Tinquaut; Sandrine Sotton; Nicolas Magne; Guillaume Etievent
Journal:  Br J Radiol       Date:  2021-07-08       Impact factor: 3.629

  9 in total

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