Literature DB >> 29356631

Does Texture Analysis of MR Images of Breast Tumors Help Predict Response to Treatment?

Massimo Imbriaco1, Renato Cuocolo1.   

Abstract

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Year:  2018        PMID： 29356631     DOI： 10.1148/radiol.2017172454

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


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  7 in total

1.  MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study.

Authors:  Arnaldo Stanzione; Carlo Ricciardi; Renato Cuocolo; Valeria Romeo; Jessica Petrone; Michela Sarnataro; Pier Paolo Mainenti; Giovanni Improta; Filippo De Rosa; Luigi Insabato; Arturo Brunetti; Simone Maurea
Journal:  J Digit Imaging       Date:  2020-08       Impact factor: 4.056

Review 2.  Radiomics: an Introductory Guide to What It May Foretell.

Authors:  Stephanie Nougaret; Hichem Tibermacine; Marion Tardieu; Evis Sala
Journal:  Curr Oncol Rep       Date:  2019-06-25       Impact factor: 5.075

3.  Prediction of high proliferative index in pituitary macroadenomas using MRI-based radiomics and machine learning.

Authors:  Lorenzo Ugga; Renato Cuocolo; Domenico Solari; Elia Guadagno; Alessandra D'Amico; Teresa Somma; Paolo Cappabianca; Maria Laura Del Basso de Caro; Luigi Maria Cavallo; Arturo Brunetti
Journal:  Neuroradiology       Date:  2019-08-02       Impact factor: 2.804

4.  Mask-Guided Convolutional Neural Network for Breast Tumor Prognostic Outcome Prediction on 3D DCE-MR Images.

Authors:  Gengbo Liu; Debasis Mitra; Ella F Jones; Benjamin L Franc; Spencer C Behr; Alex Nguyen; Marjan S Bolouri; Dorota J Wisner; Bonnie N Joe; Laura J Esserman; Nola M Hylton; Youngho Seo
Journal:  J Digit Imaging       Date:  2021-04-22       Impact factor: 4.903

5.  Preliminary study on discriminating HER2 2+ amplification status of breast cancers based on texture features semi-automatically derived from pre-, post-contrast, and subtraction images of DCE-MRI.

Authors:  Lirong Song; Hecheng Lu; Jiandong Yin
Journal:  PLoS One       Date:  2020-06-17       Impact factor: 3.240

6.  Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI.

Authors:  Renato Cuocolo; Lorenzo Ugga; Domenico Solari; Sergio Corvino; Alessandra D'Amico; Daniela Russo; Paolo Cappabianca; Luigi Maria Cavallo; Andrea Elefante
Journal:  Neuroradiology       Date:  2020-07-23       Impact factor: 2.804

7.  Clinical application of the "sellar barrier's concept" for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning analysis.

Authors:  J F Villalonga; D Solari; R Cuocolo; V De Lucia; L Ugga; C Gragnaniello; J I Pailler; A Cervio; A Campero; L M Cavallo; P Cappabianca
Journal:  Front Surg       Date:  2022-09-08
  7 in total

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