Literature DB >> 31978538

PET/CT radiomics in breast cancer: Mind the step.

Martina Sollini1, Luca Cozzi2, Gaia Ninatti3, Lidija Antunovic4, Lara Cavinato4, Arturo Chiti1, Margarita Kirienko5.   

Abstract

The aim of the present review was to assess the current status of positron emission tomography/computed tomography (PET/CT) radiomics research in breast cancer, and in particular to analyze the strengths and weaknesses of the published papers in order to identify challenges and suggest possible solutions and future research directions. Various combinations of the terms "breast", "radiomic", "PET", "radiomics", "texture", and "textural" were used for the literature search, extended until 8 July 2019, within the PubMed/MEDLINE database. Twenty-six articles fulfilling the inclusion/exclusion criteria were retrieved in full text and analyzed. The studies had technical and clinical objectives, including diagnosis, biological characterization (correlation with histology, molecular subtypes and IHC marker expression), prediction of response to neoadjuvant chemotherapy, staging, and outcome prediction. We reviewed and discussed the selected investigations following the radiomics workflow steps related to the clinical, technical, analysis, and reporting issues. Most of the current evidence on the clinical role of PET/CT radiomics in breast cancer is at the feasibility level. Harmonized methods in image acquisition, post-processing and features calculation, predictive models and classifiers trained and validated on sufficiently representative datasets, adherence to consensus guidelines, and transparent reporting will give validity and generalizability to the results.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Breast cancer; Image mining steps; Positron emission tomography/computed tomography; Radiomics workflow; Texture analysis

Year:  2020        PMID: 31978538     DOI: 10.1016/j.ymeth.2020.01.007

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  8 in total

1.  Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions.

Authors:  Ober Van Gómez; Joaquin L Herraiz; José Manuel Udías; Alexander Haug; Laszlo Papp; Dania Cioni; Emanuele Neri
Journal:  Cancers (Basel)       Date:  2022-06-14       Impact factor: 6.575

2.  Development of a Radiomics Prediction Model for Histological Type Diagnosis in Solitary Pulmonary Nodules: The Combination of CT and FDG PET.

Authors:  Mengmeng Yan; Weidong Wang
Journal:  Front Oncol       Date:  2020-09-15       Impact factor: 6.244

3.  Developing diagnostic assessment of breast lumpectomy tissues using radiomic and optical signatures.

Authors:  Samuel S Streeter; Brady Hunt; Rebecca A Zuurbier; Wendy A Wells; Keith D Paulsen; Brian W Pogue
Journal:  Sci Rep       Date:  2021-11-08       Impact factor: 4.379

Review 4.  Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms.

Authors:  Jesus A Basurto-Hurtado; Irving A Cruz-Albarran; Manuel Toledano-Ayala; Mario Alberto Ibarra-Manzano; Luis A Morales-Hernandez; Carlos A Perez-Ramirez
Journal:  Cancers (Basel)       Date:  2022-07-15       Impact factor: 6.575

5.  Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer.

Authors:  Margarita Kirienko; Martina Sollini; Marinella Corbetta; Emanuele Voulaz; Noemi Gozzi; Matteo Interlenghi; Francesca Gallivanone; Isabella Castiglioni; Rosanna Asselta; Stefano Duga; Giulia Soldà; Arturo Chiti
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-05-07       Impact factor: 9.236

6.  AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis.

Authors:  V Romeo; P Clauser; S Rasul; P Kapetas; P Gibbs; P A T Baltzer; M Hacker; R Woitek; T H Helbich; K Pinker
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-08-10       Impact factor: 10.057

7.  Can Radiomics Analyses in 18F-FDG PET/CT Images of Primary Breast Carcinoma Predict Hormone Receptor Status?

Authors:  Mine Araz; Çiğdem Soydal; Pınar Gündüz; Ayça Kırmızı; Batuhan Bakırarar; Serpil Dizbay Sak; Elgin Özkan
Journal:  Mol Imaging Radionucl Ther       Date:  2022-02-02

8.  Exploratory Analysis of 18F-3'-deoxy-3'-fluorothymidine (18F-FLT) PET/CT-Based Radiomics for the Early Evaluation of Response to Neoadjuvant Chemotherapy in Patients With Locally Advanced Breast Cancer.

Authors:  Lorenzo Fantini; Maria Luisa Belli; Irene Azzali; Emiliano Loi; Andrea Bettinelli; Giacomo Feliciani; Emilio Mezzenga; Anna Fedeli; Silvia Asioli; Giovanni Paganelli; Anna Sarnelli; Federica Matteucci
Journal:  Front Oncol       Date:  2021-06-24       Impact factor: 6.244

  8 in total

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