Literature DB >> 29514017

MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy.

Natally Horvat1, Harini Veeraraghavan1, Monika Khan1, Ivana Blazic1, Junting Zheng1, Marinela Capanu1, Evis Sala1, Julio Garcia-Aguilar1, Marc J Gollub1, Iva Petkovska1.   

Abstract

Purpose To investigate the value of T2-weighted-based radiomics compared with qualitative assessment at T2-weighted imaging and diffusion-weighted (DW) imaging for diagnosis of clinical complete response in patients with rectal cancer after neoadjuvant chemotherapy-radiation therapy (CRT). Materials and Methods This retrospective study included 114 patients with rectal cancer who underwent magnetic resonance (MR) imaging after CRT between March 2012 and February 2016. Median age among women (47 of 114, 41%) was 55.9 years (interquartile range, 45.4-66.7 years) and median age among men (67 of 114, 59%) was 55 years (interquartile range, 48-67 years). Surgical histopathologic analysis was the reference standard for pathologic complete response (pCR). For qualitative assessment, two radiologists reached a consensus. For radiomics, one radiologist segmented the volume of interest on high-spatial-resolution T2-weighted images. A random forest classifier was trained to separate the patients by their outcomes after balancing the number of patients in each response category by using the synthetic minority oversampling technique. Statistical analysis was performed by using the Wilcoxon rank-sum test, McNemar test, and Benjamini-Hochberg method. Results Twenty-one of 114 patients (18%) achieved pCR. The radiomic classifier demonstrated an area under the curve of 0.93 (95% confidence interval [CI]: 0.87, 0.96), sensitivity of 100% (95% CI: 0.84, 1), specificity of 91% (95% CI: 0.84, 0.96), positive predictive value of 72% (95% CI: 0.53, 0.87), and negative predictive value of 100% (95% CI: 0.96, 1). The diagnostic performance of radiomics was significantly higher than was qualitative assessment at T2-weighted imaging or DW imaging alone (P < .02). The specificity and positive predictive values were significantly higher in radiomics than were at combined T2-weighted and DW imaging (P < .0001). Conclusion T2-weighted-based radiomics showed better classification performance compared with qualitative assessment at T2-weighted and DW imaging for diagnosing pCR in patients with locally advanced rectal cancer after CRT. © RSNA, 2018 Online supplemental material is available for this article.

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Year:  2018        PMID: 29514017      PMCID: PMC5978457          DOI: 10.1148/radiol.2018172300

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  42 in total

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Journal:  Eur Radiol       Date:  2016-12-05       Impact factor: 5.315

3.  Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

Authors:  Zhenyu Liu; Xiao-Yan Zhang; Yan-Jie Shi; Lin Wang; Hai-Tao Zhu; Zhenchao Tang; Shuo Wang; Xiao-Ting Li; Jie Tian; Ying-Shi Sun
Journal:  Clin Cancer Res       Date:  2017-09-22       Impact factor: 12.531

4.  Accuracy of MRI in Restaging Locally Advanced Rectal Cancer After Preoperative Chemoradiation.

Authors:  Joris J van den Broek; Floor S W van der Wolf; Max J Lahaye; Luc A Heijnen; Christof Meischl; Martin A Heitbrink; W Hermien Schreurs
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6.  The Swedish rectal cancer registry.

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7.  Distribution of residual cancer cells in the bowel wall after neoadjuvant chemoradiation in patients with rectal cancer.

Authors:  Marjun P Duldulao; Wendy Lee; Leanne Streja; Peiguo Chu; Wenyan Li; Zhenbin Chen; Joseph Kim; Julio Garcia-Aguilar
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8.  Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule.

Authors:  Lan He; Yanqi Huang; Zelan Ma; Cuishan Liang; Changhong Liang; Zaiyi Liu
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9.  Radiomics: Images Are More than Pictures, They Are Data.

Authors:  Robert J Gillies; Paul E Kinahan; Hedvig Hricak
Journal:  Radiology       Date:  2015-11-18       Impact factor: 11.105

10.  "Textural analysis of multiparametric MRI detects transition zone prostate cancer".

Authors:  Harbir S Sidhu; Salvatore Benigno; Balaji Ganeshan; Nikos Dikaios; Edward W Johnston; Clare Allen; Alex Kirkham; Ashley M Groves; Hashim U Ahmed; Mark Emberton; Stuart A Taylor; Steve Halligan; Shonit Punwani
Journal:  Eur Radiol       Date:  2016-09-12       Impact factor: 5.315

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  104 in total

1.  Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement.

Authors:  Ji Eun Park; Donghyun Kim; Ho Sung Kim; Seo Young Park; Jung Youn Kim; Se Jin Cho; Jae Ho Shin; Jeong Hoon Kim
Journal:  Eur Radiol       Date:  2019-07-26       Impact factor: 5.315

2.  Radiogenomics of rectal adenocarcinoma in the era of precision medicine: A pilot study of associations between qualitative and quantitative MRI imaging features and genetic mutations.

Authors:  Natally Horvat; Harini Veeraraghavan; Raphael A Pelossof; Maria Clara Fernandes; Arshi Arora; Monika Khan; Michael Marco; Chin-Tung Cheng; Mithat Gonen; Jennifer S Golia Pernicka; Marc J Gollub; Julio Garcia-Aguillar; Iva Petkovska
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Review 3.  MRI of Rectal Cancer: Tumor Staging, Imaging Techniques, and Management.

Authors:  Natally Horvat; Camila Carlos Tavares Rocha; Brunna Clemente Oliveira; Iva Petkovska; Marc J Gollub
Journal:  Radiographics       Date:  2019-02-15       Impact factor: 5.333

4.  Multiparametric MRI-based Radiomics approaches on predicting response to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer.

Authors:  Yuan Cheng; Yahong Luo; Yue Hu; Zhaohe Zhang; Xingling Wang; Qing Yu; Guanyu Liu; Enuo Cui; Tao Yu; Xiran Jiang
Journal:  Abdom Radiol (NY)       Date:  2021-07-24

5.  Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy.

Authors:  Jia Wang; Xuejun Liu; Bin Hu; Yuanxiang Gao; Jingjing Chen; Jie Li
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6.  Radiomics Approach Outperforms Diameter Criteria for Predicting Pathological Lateral Lymph Node Metastasis After Neoadjuvant (Chemo)Radiotherapy in Advanced Low Rectal Cancer.

Authors:  Ryota Nakanishi; Takashi Akiyoshi; Shigeo Toda; Yu Murakami; Senzo Taguchi; Koji Oba; Yutaka Hanaoka; Toshiya Nagasaki; Tomohiro Yamaguchi; Tsuyoshi Konishi; Shuichiro Matoba; Masashi Ueno; Yosuke Fukunaga; Hiroya Kuroyanagi
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Review 7.  Artificial intelligence in radiation oncology.

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8.  MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer.

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Journal:  Eur Radiol       Date:  2018-11-09       Impact factor: 5.315

Review 9.  Novel imaging techniques of rectal cancer: what do radiomics and radiogenomics have to offer? A literature review.

Authors:  Natally Horvat; David D B Bates; Iva Petkovska
Journal:  Abdom Radiol (NY)       Date:  2019-11

10.  Combining Radiomics and Blood Test Biomarkers to Predict the Response of Locally Advanced Rectal Cancer to Chemoradiation.

Authors:  Seung Hyuck Jeon; Changhoon Song; Eui Kyu Chie; Bohyoung Kim; Young Hoon Kim; Won Chang; Yoon Jin Lee; Joo-Hyun Chung; Jin Beom Chung; Keun-Wook Lee; Sung-Bum Kang; Jae-Sung Kim
Journal:  In Vivo       Date:  2020 Sep-Oct       Impact factor: 2.155

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