Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning-Reference-Cited by-同舟云学术

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Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning

Published:2022-08-11 Issue:11 Volume:47 Page:3645-3659
ISSN:2366-0058
Container-title:Abdominal Radiology
language:en
Short-container-title:Abdom Radiol

Author:

Abbaspour Samira,Abdollahi Hamid,Arabalibeik Hossein,Barahman Maedeh,Arefpour Amir Mohammad,Fadavi Pedram,Ay Mohammadreza,Mahdavi Seied Rabi^ORCID

Funder

Pharmaceutical Sciences Research Center, Tehran University of Medical Sciences

Publisher

Springer Science and Business Media LLC

Subject

Urology,Gastroenterology,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

https://link.springer.com/content/pdf/10.1007/s00261-022-03625-y.pdf

Reference49 articles.

1. Chen L-D, Wang W, Xu J-B, et al. Assessment of rectal tumors with shear-wave elastography before surgery: Comparison with endorectal US. Radiology. 2017;285(1):279–292.

2. Chen L Da, Liang JY, Wu H, et al. Multiparametric radiomics improve prediction of lymph node metastasis of rectal cancer compared with conventional radiomics. Life Sci. 2018;208:55–63.

3. Cui Y, Yang X, Shi Z, et al. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol. 2019;29(3):1211–1220.

4. Pizzi AD, Chiarelli AM, Chiacchiaretta P, et al. OPEN MRI ‑ based clinical ‑ radiomics model predicts tumor response before treatment in locally advanced rectal cancer. Sci Rep. 2021;1–11.

5. Chen L-D, Li W, Xian M-F, et al. Preoperative prediction of tumour deposits in rectal cancer by an artificial neural network–based US radiomics model. Eur Radiol. 2020;30(4):1969–1979.

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3. Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis;La radiologia medica;2024-03-21

4. Development and validation of a multi-modal ultrasomics model to predict response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer;BMC Medical Imaging;2024-03-18

5. The role of radiomics in computed tomography, magnetic resonance imaging, and ultrasound for renal tumors’ diagnoses: A systematic review;Journal of Radiation Research and Applied Sciences;2024-03

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