Literature DB >> 32157259

[Radiomics and artificial intelligence: new frontiers in medicine.]

Federica Vernuccio1, Roberto Cannella2, Albert Comelli3, Giuseppe Salvaggio2, Roberto Lagalla2, Massimo Midiri2.   

Abstract

Radiomics is a new frontier of medicine based on the extraction of quantitative data from radiological images which can not be seen by radiologist's naked eye and on the use of these data for the creation of clinical decision support systems. The long-term goal of radiomics is to improve the non-invasive diagnosis of focal and diffuse diseases of different organs by understanding links between extracted quantitative imaging data and the underlying molecular and pathological characteristics of lesions. In the last decade, several studies have highlighted the enormous potential of radiomics in both tumoral and non-tumoral diseases of many organs and systems including brain, lung, breast, gastrointestinal and genitourinary tracts. The enormous potential of radiomics needs to be pursued with the methodological rigor of scientific research and by integrating radiological data with other medical disciplines, in order to improve personalized patient management.

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Year:  2020        PMID: 32157259     DOI: 10.1701/3315.32853

Source DB:  PubMed          Journal:  Recenti Prog Med        ISSN: 0034-1193


  7 in total

Review 1.  Quantitative magnetic resonance imaging for focal liver lesions: bridging the gap between research and clinical practice.

Authors:  Roberto Cannella; Riccardo Sartoris; Jules Grégory; Lorenzo Garzelli; Valérie Vilgrain; Maxime Ronot; Marco Dioguardi Burgio
Journal:  Br J Radiol       Date:  2021-05-14       Impact factor: 3.629

2.  Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis?

Authors:  Tao Peng; JianMing Xiao; Lin Li; BingJie Pu; XiangKe Niu; XiaoHui Zeng; ZongYong Wang; ChaoBang Gao; Ci Li; Lin Chen; Jin Yang
Journal:  Int J Comput Assist Radiol Surg       Date:  2021-10-22       Impact factor: 2.924

3.  Preoperative CT-Based Deep Learning Model for Predicting Risk Stratification in Patients With Gastrointestinal Stromal Tumors.

Authors:  Bing Kang; Xianshun Yuan; Hexiang Wang; Songnan Qin; Xuelin Song; Xinxin Yu; Shuai Zhang; Cong Sun; Qing Zhou; Ying Wei; Feng Shi; Shifeng Yang; Ximing Wang
Journal:  Front Oncol       Date:  2021-09-17       Impact factor: 6.244

4.  Radiomics Analysis on Gadoxetate Disodium-Enhanced MRI Predicts Response to Transarterial Embolization in Patients with HCC.

Authors:  Roberto Cannella; Carla Cammà; Francesco Matteini; Ciro Celsa; Paolo Giuffrida; Marco Enea; Albert Comelli; Alessandro Stefano; Calogero Cammà; Massimo Midiri; Roberto Lagalla; Giuseppe Brancatelli; Federica Vernuccio
Journal:  Diagnostics (Basel)       Date:  2022-05-24

5.  Brain magnetic resonance imaging radiomics features associated with hepatic encephalopathy in adult cirrhotic patients.

Authors:  Gianvincenzo Sparacia; Giuseppe Parla; Roberto Cannella; Giuseppe Mamone; Ioannis Petridis; Luigi Maruzzelli; Vincenzina Lo Re; Mona Shahriari; Alberto Iaia; Albert Comelli; Roberto Miraglia; Angelo Luca
Journal:  Neuroradiology       Date:  2022-04-30       Impact factor: 2.995

Review 6.  New advances in radiomics of gastrointestinal stromal tumors.

Authors:  Roberto Cannella; Ludovico La Grutta; Massimo Midiri; Tommaso Vincenzo Bartolotta
Journal:  World J Gastroenterol       Date:  2020-08-28       Impact factor: 5.742

Review 7.  Advances in liver US, CT, and MRI: moving toward the future.

Authors:  Federica Vernuccio; Roberto Cannella; Tommaso Vincenzo Bartolotta; Massimo Galia; An Tang; Giuseppe Brancatelli
Journal:  Eur Radiol Exp       Date:  2021-12-07
  7 in total

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