Applying Radiomics to Predict Pathology of Postchemotherapy Retroperitoneal Nodal Masses in Germ Cell Tumors | Zendy

Jeremy Lewin | Zendy; Paul Dufort | Zendy; Jaydeep Halankar | Zendy; Martin O’Malley | Zendy; Michael Jewett | Zendy; Robert J. Hamilton | Zendy; Abha A. Gupta | Zendy; Armando J. Lorenzo | Zendy; Jeffrey Traubici | Zendy; Madhur Nayan | Zendy; Ricardo Leão | Zendy; Padraig Warde | Zendy; Peter Chung | Zendy; L. Cartwright | Zendy; Joan Sweet | Zendy; Aaron R. Hansen | Zendy; Ur Metser | Zendy; Philippe L. Bédard | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Applying Radiomics to Predict Pathology of Postchemotherapy Retroperitoneal Nodal Masses in Germ Cell Tumors

Author(s) -

Jeremy Lewin,

Paul Dufort,

Jaydeep Halankar,

Martin O’Malley,

Michael Jewett,

Robert J. Hamilton,

Abha A. Gupta,

Armando J. Lorenzo,

Jeffrey Traubici,

Madhur Nayan,

Ricardo Leão,

Padraig Warde,

Peter Chung,

L. Cartwright,

Joan Sweet,

Aaron R. Hansen,

Ur Metser,

Philippe L. Bédard

Publication year - 2018

Publication title -

jco clinical cancer informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.188

H-Index - 12

ISSN - 2473-4276

DOI - 10.1200/cci.18.00004

Subject(s) - radiomics , medicine , germ cell tumors , teratoma , fibrosis , magnetic resonance imaging , radiology , chemotherapy , pathology

After chemotherapy, approximately 50% of patients with metastatic testicular germ cell tumors (GCTs) who undergo retroperitoneal lymph node dissections (RPNLDs) for residual masses have fibrosis. Radiomics uses image processing techniques to extract quantitative textures/features from regions of interest (ROIs) to train a classifier that predicts outcomes. We hypothesized that radiomics would identify patients with a high likelihood of fibrosis who may avoid RPLND.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research