Literature DB >> 33922483

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

Francesca Coppola1, Valentina Giannini2, Michela Gabelloni3, Jovana Panic2, Arianna Defeudis2, Silvia Lo Monaco1, Arrigo Cattabriga1, Maria Adriana Cocozza1, Luigi Vincenzo Pastore1, Michela Polici4, Damiano Caruso4, Andrea Laghi4, Daniele Regge2,5, Emanuele Neri3, Rita Golfieri1, Lorenzo Faggioni3.   

Abstract

While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.

Entities:  

Keywords:  deep learning; magnetic resonance imaging; neoadjuvant chemoradiation therapy; personalized medicine; radiomics; rectal cancer; surgery

Year:  2021        PMID: 33922483     DOI: 10.3390/diagnostics11050756

Source DB:  PubMed          Journal:  Diagnostics (Basel)        ISSN: 2075-4418


  72 in total

1.  Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer.

Authors:  Liheng Liu; Yuhui Liu; Liang Xu; Zhenjiang Li; Han Lv; Ningning Dong; Wenwu Li; Zhenghan Yang; Zhenchang Wang; Erhu Jin
Journal:  J Magn Reson Imaging       Date:  2016-09-22       Impact factor: 4.813

2.  Prognostic significance of tumor regression after preoperative chemoradiotherapy for rectal cancer.

Authors:  Claus Rödel; Peter Martus; Thomas Papadoupolos; Laszlo Füzesi; Martin Klimpfinger; Rainer Fietkau; Torsten Liersch; Werner Hohenberger; Rudolf Raab; Rolf Sauer; Christian Wittekind
Journal:  J Clin Oncol       Date:  2005-10-24       Impact factor: 44.544

3.  Use of magnetic resonance imaging in rectal cancer patients: Society of Abdominal Radiology (SAR) rectal cancer disease-focused panel (DFP) recommendations 2017.

Authors:  Marc J Gollub; Supreeta Arya; Regina Gh Beets-Tan; Gregory dePrisco; Mithat Gonen; Kartik Jhaveri; Zahra Kassam; Harmeet Kaur; David Kim; Andrea Knezevic; Elena Korngold; Chandana Lall; Neeraj Lalwani; D Blair Macdonald; Courtney Moreno; Stephanie Nougaret; Perry Pickhardt; Shannon Sheedy; Mukesh Harisinghani
Journal:  Abdom Radiol (NY)       Date:  2018-11

4.  Preoperative high-resolution magnetic resonance imaging can identify good prognosis stage I, II, and III rectal cancer best managed by surgery alone: a prospective, multicenter, European study.

Authors:  Fiona G M Taylor; Philip Quirke; Richard J Heald; Brendan Moran; Lennart Blomqvist; Ian Swift; David J Sebag-Montefiore; Paris Tekkis; Gina Brown
Journal:  Ann Surg       Date:  2011-04       Impact factor: 12.969

5.  Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings.

Authors:  Yiqun Sun; Panpan Hu; Jiazhou Wang; Lijun Shen; Fan Xia; Gan Qing; Weigang Hu; Zhen Zhang; Chao Xin; Weijun Peng; Tong Tong; Yajia Gu
Journal:  J Magn Reson Imaging       Date:  2018-02-13       Impact factor: 4.813

6.  MRI-Diagnosed Tumor Deposits and EMVI Status Have Superior Prognostic Accuracy to Current Clinical TNM Staging in Rectal Cancer.

Authors:  Amy C Lord; Nigel D'Souza; Annabel Shaw; Zena Rokan; Brendan Moran; Muti Abulafi; Shahnawaz Rasheed; Anuradha Chandramohan; Alison Corr; Ian Chau; Gina Brown
Journal:  Ann Surg       Date:  2020-09-15       Impact factor: 13.787

7.  Radiomics-Based Preoperative Prediction of Lymph Node Status Following Neoadjuvant Therapy in Locally Advanced Rectal Cancer.

Authors:  Xuezhi Zhou; Yongju Yi; Zhenyu Liu; Zhiyang Zhou; Bingjia Lai; Kai Sun; Longfei Li; Liyu Huang; Yanqiu Feng; Wuteng Cao; Jie Tian
Journal:  Front Oncol       Date:  2020-05-11       Impact factor: 6.244

8.  Magnetic Resonance-Based Texture Analysis Differentiating KRAS Mutation Status in Rectal Cancer.

Authors:  Ji Eun Oh; Min Ju Kim; Joohyung Lee; Bo Yun Hur; Bun Kim; Dae Yong Kim; Ji Yeon Baek; Hee Jin Chang; Sung Chan Park; Jae Hwan Oh; Sun Ah Cho; Dae Kyung Sohn
Journal:  Cancer Res Treat       Date:  2019-05-07       Impact factor: 4.679

9.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.

Authors:  Hugo J W L Aerts; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Carvalho; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin
Journal:  Nat Commun       Date:  2014-06-03       Impact factor: 14.919

Review 10.  Noninvasive Biomarkers of Colorectal Cancer: Role in Diagnosis and Personalised Treatment Perspectives.

Authors:  Gianluca Pellino; Gaetano Gallo; Pierlorenzo Pallante; Raffaella Capasso; Alfonso De Stefano; Isacco Maretto; Umberto Malapelle; Shengyang Qiu; Stella Nikolaou; Andrea Barina; Giuseppe Clerico; Alfonso Reginelli; Antonio Giuliani; Guido Sciaudone; Christos Kontovounisios; Luca Brunese; Mario Trompetto; Francesco Selvaggi
Journal:  Gastroenterol Res Pract       Date:  2018-06-13       Impact factor: 2.260

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  11 in total

1.  Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer.

Authors:  Giuseppe Filitto; Francesca Coppola; Nico Curti; Enrico Giampieri; Daniele Dall'Olio; Alessandra Merlotti; Arrigo Cattabriga; Maria Adriana Cocozza; Makoto Taninokuchi Tomassoni; Daniel Remondini; Luisa Pierotti; Lidia Strigari; Dajana Cuicchi; Alessandra Guido; Karim Rihawi; Antonietta D'Errico; Francesca Di Fabio; Gilberto Poggioli; Alessio Giuseppe Morganti; Luigi Ricciardiello; Rita Golfieri; Gastone Castellani
Journal:  Cancers (Basel)       Date:  2022-04-29       Impact factor: 6.575

2.  MRI-Based Radiomics Features to Predict Treatment Response to Neoadjuvant Chemotherapy in Locally Advanced Rectal Cancer: A Single Center, Prospective Study.

Authors:  Bi-Yun Chen; Hui Xie; Yuan Li; Xin-Hua Jiang; Lang Xiong; Xiao-Feng Tang; Xiao-Feng Lin; Li Li; Pei-Qiang Cai
Journal:  Front Oncol       Date:  2022-05-12       Impact factor: 5.738

3.  Hybrid Deep Learning Approach for Automatic Detection in Musculoskeletal Radiographs.

Authors:  Gurpreet Singh; Darpan Anand; Woong Cho; Gyanendra Prasad Joshi; Kwang Chul Son
Journal:  Biology (Basel)       Date:  2022-04-26

4.  Combining Clinicopathology, IVIM-DWI and Texture Parameters for a Nomogram to Predict Treatment Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients.

Authors:  Rixin Su; Shusheng Wu; Hao Shen; Yaolin Chen; Jingya Zhu; Yu Zhang; Haodong Jia; Mengge Li; Wenju Chen; Yifu He; Fei Gao
Journal:  Front Oncol       Date:  2022-05-27       Impact factor: 5.738

5.  Preoperative T and N Restaging of Rectal Cancer After Neoadjuvant Chemoradiotherapy: An Accuracy Comparison Between MSCT and MRI.

Authors:  Wenjuan Liu; Yuyi Li; Xue Zhang; Jia Li; Jing Sun; Han Lv; Zhenchang Wang
Journal:  Front Oncol       Date:  2022-01-21       Impact factor: 6.244

6.  Deep learning-based pelvic levator hiatus segmentation from ultrasound images.

Authors:  Zeping Huang; Enze Qu; Yishuang Meng; Man Zhang; Qiuwen Wei; Xianghui Bai; Xinling Zhang
Journal:  Eur J Radiol Open       Date:  2022-03-24

7.  The Value of Intravoxel Incoherent Motion Diffusion-Weighted Magnetic Resonance Imaging Combined With Texture Analysis of Evaluating the Extramural Vascular Invasion in Rectal Adenocarcinoma.

Authors:  Fei Gao; Bin Shi; Peipei Wang; Chuanbin Wang; Xin Fang; Jiangning Dong; Tingting Lin
Journal:  Front Oncol       Date:  2022-03-03       Impact factor: 6.244

8.  Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients.

Authors:  Jia Wang; Jingjing Chen; Ruizhi Zhou; Yuanxiang Gao; Jie Li
Journal:  BMC Cancer       Date:  2022-04-19       Impact factor: 4.638

9.  Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images.

Authors:  Lorenzo Faggioni; Michela Gabelloni; Fabrizio De Vietro; Jessica Frey; Vincenzo Mendola; Diletta Cavallero; Rita Borgheresi; Lorenzo Tumminello; Jorge Shortrede; Riccardo Morganti; Veronica Seccia; Francesca Coppola; Dania Cioni; Emanuele Neri
Journal:  Eur J Radiol Open       Date:  2022-06-18

10.  Radiomic Cancer Hallmarks to Identify High-Risk Patients in Non-Metastatic Colon Cancer.

Authors:  Damiano Caruso; Michela Polici; Marta Zerunian; Antonella Del Gaudio; Emanuela Parri; Maria Agostina Giallorenzi; Domenico De Santis; Giulia Tarantino; Mariarita Tarallo; Filippo Maria Dentice di Accadia; Elsa Iannicelli; Giovanni Maria Garbarino; Giulia Canali; Paolo Mercantini; Enrico Fiori; Andrea Laghi
Journal:  Cancers (Basel)       Date:  2022-07-15       Impact factor: 6.575

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