Literature DB >> 33349519

Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review.

Femke C R Staal1, Denise J van der Reijd1, Marjaneh Taghavi1, Doenja M J Lambregts2, Regina G H Beets-Tan3, Monique Maas4.   

Abstract

Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of lack of a robust biomarker and heterogeneity between and within tumors. The aim of this review was to assess the current possibilities and limitations of radiomics (on computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET]) for the prediction of treatment outcome and long-term outcome in CRC. Medline/PubMed was searched up to August 2020 for studies that used radiomics for the prediction of response to treatment and survival in patients with CRC (based on pretreatment imaging). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and Radiomics Quality Score (RQS) were used for quality assessment. A total of 76 studies met the inclusion criteria and were included for further analysis. Radiomics analyses were performed on MRI in 41 studies, on CT in 30 studies, and on 18F-FDG-PET/CT in 10 studies. Heterogeneous results were reported regarding radiomics methods and included features. High-quality studies (n = 13), consisting mainly of MRI-based radiomics to predict response in rectal cancer, were able to predict response with good performance. Radiomics literature in CRC is highly heterogeneous, but it nonetheless holds promise for the prediction of outcome. The most evidence is available for MRI-based radiomics in rectal cancer. Future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Metastasis; Neoadjuvant chemotherapy; Quantitative imaging analysis; Response

Mesh:

Substances:

Year:  2020        PMID: 33349519     DOI: 10.1016/j.clcc.2020.11.001

Source DB:  PubMed          Journal:  Clin Colorectal Cancer        ISSN: 1533-0028            Impact factor:   4.481


  13 in total

1.  The value of 18F-FDG PET/CT-based radiomics in predicting perineural invasion and outcome in non-metastatic colorectal cancer.

Authors:  Jie Ma; Dong Guo; Wenjie Miao; Yangyang Wang; Lei Yan; Fengyu Wu; Chuantao Zhang; Ran Zhang; Panli Zuo; Guangjie Yang; Zhenguang Wang
Journal:  Abdom Radiol (NY)       Date:  2022-02-26

Review 2.  The importance of MRI for rectal cancer evaluation.

Authors:  Maria Clara Fernandes; Marc J Gollub; Gina Brown
Journal:  Surg Oncol       Date:  2022-03-18       Impact factor: 2.388

Review 3.  Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice.

Authors:  Francesca Coppola; Valentina Giannini; Michela Gabelloni; Jovana Panic; Arianna Defeudis; Silvia Lo Monaco; Arrigo Cattabriga; Maria Adriana Cocozza; Luigi Vincenzo Pastore; Michela Polici; Damiano Caruso; Andrea Laghi; Daniele Regge; Emanuele Neri; Rita Golfieri; Lorenzo Faggioni
Journal:  Diagnostics (Basel)       Date:  2021-04-23

4.  A potential biomarker based on clinical-radiomics nomogram for predicting survival and adjuvant chemotherapy benefit in resected node-negative, early-stage lung adenocarcinoma.

Authors:  Xiaoling Ma; Wenzhi Lv; Cong Wang; Dehao Tu; Jinhan Qiao; Chanyuan Fan; Jiandong Niu; Wen Zhou; Qiuyu Liu; Liming Xia
Journal:  J Thorac Dis       Date:  2022-01       Impact factor: 2.895

Review 5.  Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.

Authors:  Xingping Zhang; Yanchun Zhang; Guijuan Zhang; Xingting Qiu; Wenjun Tan; Xiaoxia Yin; Liefa Liao
Journal:  Front Oncol       Date:  2022-02-17       Impact factor: 6.244

6.  18F-FDG PET Radiomics as Predictor of Treatment Response in Oesophageal Cancer: A Systematic Review and Meta-Analysis.

Authors:  Letizia Deantonio; Maria Luisa Garo; Gaetano Paone; Maria Carla Valli; Stefano Cappio; Davide La Regina; Marco Cefali; Maria Celeste Palmarocchi; Alberto Vannelli; Sara De Dosso
Journal:  Front Oncol       Date:  2022-03-15       Impact factor: 6.244

7.  Precision interventional radiology.

Authors:  Jiansong Ji; Shiji Fang; Weiqian Chen; Zhongwei Zhao; Yongde Cheng
Journal:  J Interv Med       Date:  2021-12-23

8.  Identification of CT Imaging Phenotypes of Colorectal Liver Metastases from Radiomics Signatures-Towards Assessment of Interlesional Tumor Heterogeneity.

Authors:  Hishan Tharmaseelan; Alexander Hertel; Fabian Tollens; Johann Rink; Piotr Woźnicki; Verena Haselmann; Isabelle Ayx; Dominik Nörenberg; Stefan O Schoenberg; Matthias F Froelich
Journal:  Cancers (Basel)       Date:  2022-03-24       Impact factor: 6.639

9.  Radiomic analysis for predicting prognosis of colorectal cancer from preoperative 18F-FDG PET/CT.

Authors:  Lilang Lv; Bowen Xin; Yichao Hao; Ziyi Yang; Junyan Xu; Lisheng Wang; Xiuying Wang; Shaoli Song; Xiaomao Guo
Journal:  J Transl Med       Date:  2022-02-02       Impact factor: 5.531

Review 10.  The Value of 18F-FDG-PET-CT Imaging in Treatment Evaluation of Colorectal Liver Metastases: A Systematic Review.

Authors:  Okker D Bijlstra; Maud M E Boreel; Sietse van Mossel; Mark C Burgmans; Ellen H W Kapiteijn; Daniela E Oprea-Lager; Daphne D D Rietbergen; Floris H P van Velden; Alexander L Vahrmeijer; Rutger-Jan Swijnenburg; J Sven D Mieog; Lioe-Fee de Geus-Oei
Journal:  Diagnostics (Basel)       Date:  2022-03-15
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