Literature DB >> 36016617

Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer.

Yun Qin1, Li-Hua Zhu1, Wei Zhao1, Jun-Jie Wang2, Hao Wang2,3.   

Abstract

By breaking the traditional medical image analysis framework, precision medicine-radiomics has attracted much attention in the past decade. The use of various mathematical algorithms offers radiomics the ability to extract vast amounts of detailed features from medical images for quantitative analysis and analyzes the confidential information related to the tumor in the image, which can establish valuable disease diagnosis and prognosis models to support personalized clinical decisions. This article summarizes the application of radiomics and dosiomics in radiation oncology. We focus on the application of radiomics in locally advanced rectal cancer and also summarize the latest research progress of dosiomics in radiation tumors to provide ideas for the treatment of future related diseases, especially 125I CT-guided radioactive seed implant brachytherapy.
Copyright © 2022 Qin, Zhu, Zhao, Wang and Wang.

Entities:  

Keywords:  deep learning; dosiomics; machine learning; radiomics; rectal cancer

Year:  2022        PMID: 36016617      PMCID: PMC9395725          DOI: 10.3389/fonc.2022.913683

Source DB:  PubMed          Journal:  Front Oncol        ISSN: 2234-943X            Impact factor:   5.738


  73 in total

1.  Locally recurrent rectal cancer: what the radiologist should know.

Authors:  Dhakshinamoorthy Ganeshan; Stephanie Nougaret; Elena Korngold; Gaiane M Rauch; Courtney C Moreno
Journal:  Abdom Radiol (NY)       Date:  2019-11

2.  Delta radiomics for rectal cancer response prediction using low field magnetic resonance guided radiotherapy: an external validation.

Authors:  Davide Cusumano; Luca Boldrini; Poonam Yadav; Gao Yu; Bindu Musurunu; Giuditta Chiloiro; Antonio Piras; Jacopo Lenkowicz; Lorenzo Placidi; Angela Romano; Viola De Luca; Claudio Votta; Brunella Barbaro; Maria Antonietta Gambacorta; Michael F Bassetti; Yingli Yang; Luca Indovina; Vincenzo Valentini
Journal:  Phys Med       Date:  2021-04-23       Impact factor: 2.685

Review 3.  MRI of Rectal Cancer: An Overview and Update on Recent Advances.

Authors:  Kartik S Jhaveri; Hooman Hosseini-Nik
Journal:  AJR Am J Roentgenol       Date:  2015-07       Impact factor: 3.959

Review 4.  Radiomics: the process and the challenges.

Authors:  Virendra Kumar; Yuhua Gu; Satrajit Basu; Anders Berglund; Steven A Eschrich; Matthew B Schabath; Kenneth Forster; Hugo J W L Aerts; Andre Dekker; David Fenstermacher; Dmitry B Goldgof; Lawrence O Hall; Philippe Lambin; Yoganand Balagurunathan; Robert A Gatenby; Robert J Gillies
Journal:  Magn Reson Imaging       Date:  2012-08-13       Impact factor: 2.546

5.  Prognostic value of the texture analysis parameters of the initial computed tomographic scan for response to neoadjuvant chemoradiation therapy in patients with locally advanced rectal cancer.

Authors:  Benjamin Vandendorpe; Carole Durot; Loïc Lebellec; Marie-Cécile Le Deley; Dienabou Sylla; André-Michel Bimbai; Kocéila Amroun; Fabrice Ramiandrisoa; Abel Cordoba; Xavier Mirabel; Christine Hoeffel; David Pasquier; Stéphanie Servagi-Vernat
Journal:  Radiother Oncol       Date:  2019-03-27       Impact factor: 6.280

6.  Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer.

Authors:  Jie Fu; Xinran Zhong; Ning Li; Ritchell Van Dams; John Lewis; Kyunghyun Sung; Ann C Raldow; Jing Jin; X Sharon Qi
Journal:  Phys Med Biol       Date:  2020-04-02       Impact factor: 3.609

7.  Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases.

Authors:  Aiqian Wu; Yongbao Li; Mengke Qi; Xingyu Lu; Qiyuan Jia; Futong Guo; Zhenhui Dai; Yuliang Liu; Chaomin Chen; Linghong Zhou; Ting Song
Journal:  Oral Oncol       Date:  2020-03-06       Impact factor: 5.337

8.  Predicting poor response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer: Model constructed using pre-treatment MRI features of structured report template.

Authors:  Xiaofeng Tang; Wu Jiang; Haojiang Li; Fei Xie; Annan Dong; Lizhi Liu; Li Li
Journal:  Radiother Oncol       Date:  2020-04-08       Impact factor: 6.280

9.  Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer.

Authors:  Jean-Emmanuel Bibault; Philippe Giraud; Martin Housset; Catherine Durdux; Julien Taieb; Anne Berger; Romain Coriat; Stanislas Chaussade; Bertrand Dousset; Bernard Nordlinger; Anita Burgun
Journal:  Sci Rep       Date:  2018-08-22       Impact factor: 4.379

10.  MRI-Based Radiomics Predicts Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

Authors:  Xiaoping Yi; Qian Pei; Youming Zhang; Hong Zhu; Zhongjie Wang; Chen Chen; Qingling Li; Xueying Long; Fengbo Tan; Zhongyi Zhou; Wenxue Liu; Chenglong Li; Yuan Zhou; Xiangping Song; Yuqiang Li; Weihua Liao; Xuejun Li; Lunquan Sun; Haiping Pei; Chishing Zee; Bihong T Chen
Journal:  Front Oncol       Date:  2019-06-26       Impact factor: 6.244

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