Literature DB >> 34865190

Delta radiomics: a systematic review.

Valerio Nardone1, Alfonso Reginelli2, Roberta Grassi1, Luca Boldrini3, Giovanna Vacca1, Emma D'Ippolito1, Salvatore Annunziata3, Alessandra Farchione3, Maria Paola Belfiore1, Isacco Desideri4, Salvatore Cappabianca1.   

Abstract

BACKGROUND: Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches.
METHODS: Eligible articles were searched in Embase, PubMed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with three key search terms: "radiomics", "texture", and "delta". Studies were analysed using QUADAS-2 and the RQS tool.
RESULTS: Forty-eight studies were finally included. The studies were divided into preclinical/methodological (five studies, 10.4%); rectal cancer (six studies, 12.5%); lung cancer (twelve studies, 25%); sarcoma (five studies, 10.4%); prostate cancer (three studies, 6.3%), head and neck cancer (six studies, 12.5%); gastrointestinal malignancies excluding rectum (seven studies, 14.6%), and other disease sites (four studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%.
CONCLUSIONS: Delta radiomics shows potential benefit for several clinical endpoints in oncology (differential diagnosis, prognosis and prediction of treatment response, and evaluation of side effects). Nevertheless, the studies included in this systematic review suffer from the bias of overall low quality, so that the conclusions are currently heterogeneous, not robust, and not replicable. Further research with prospective and multicentre studies is needed for the clinical validation of delta radiomics approaches.
© 2021. Italian Society of Medical Radiology.

Entities:  

Keywords:  Delta radiomics; Meta-analysis; Oncology; Precision medicine; Radiomics; Radiotherapy; Texture analysis

Mesh:

Year:  2021        PMID: 34865190     DOI: 10.1007/s11547-021-01436-7

Source DB:  PubMed          Journal:  Radiol Med        ISSN: 0033-8362            Impact factor:   3.469


  75 in total

1.  Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs.

Authors:  Yu Gao; Anusha Kalbasi; William Hsu; Dan Ruan; Jie Fu; Jiaxin Shao; Minsong Cao; Chenyang Wang; Fritz C Eilber; Nicholas Bernthal; Susan Bukata; Sarah M Dry; Scott D Nelson; Mitchell Kamrava; John Lewis; Daniel A Low; Michael Steinberg; Peng Hu; Yingli Yang
Journal:  Phys Med Biol       Date:  2020-08-27       Impact factor: 3.609

2.  The role of delta radiomics in gastric cancer.

Authors:  Maria Antonietta Mazzei; Valerio Nardone; Letizia Di Giacomo; Giulio Bagnacci; Francesco Gentili; Paolo Tini; Daniele Marrelli; Luca Volterrani
Journal:  Quant Imaging Med Surg       Date:  2018-08

3.  Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: a feasibility study.

Authors:  Laura Filograna; Jacopo Lenkowicz; Francesco Cellini; Nicola Dinapoli; Stefania Manfrida; Nicola Magarelli; Antonio Leone; Cesare Colosimo; Vincenzo Valentini
Journal:  Radiol Med       Date:  2018-09-06       Impact factor: 3.469

4.  Texture analysis versus conventional MRI prognostic factors in predicting tumor response to neoadjuvant chemotherapy in patients with locally advanced cancer of the uterine cervix.

Authors:  Maria Ciolina; Valeria Vinci; Laura Villani; Silvia Gigli; Matteo Saldari; Pierluigi Benedetti Panici; Giorgia Perniola; Andrea Laghi; Carlo Catalano; Lucia Manganaro
Journal:  Radiol Med       Date:  2019-06-28       Impact factor: 3.469

5.  Progressive Desmoid Tumor: Radiomics Compared With Conventional Response Criteria for Predicting Progression During Systemic Therapy-A Multicenter Study by the French Sarcoma Group.

Authors:  Amandine Crombé; Michèle Kind; Isabelle Ray-Coquard; Nicolas Isambert; Christine Chevreau; Thierry André; Celeste Lebbe; Axel Le Cesne; Emmanuelle Bompas; Sophie Piperno-Neumann; Esma Saada; Amine Bouhamama; Jean-Yves Blay; Antoine Italiano
Journal:  AJR Am J Roentgenol       Date:  2020-09-29       Impact factor: 3.959

6.  Radiomics: a critical step towards integrated healthcare.

Authors:  Zuhir Bodalal; Stefano Trebeschi; Regina Beets-Tan
Journal:  Insights Imaging       Date:  2018-11-12

7.  Serial T2-Weighted Magnetic Resonance Images Acquired on a 1.5 Tesla Magnetic Resonance Linear Accelerator Reveal Radiomic Feature Variation in Organs at Risk: An Exploratory Analysis of Novel Metrics of Tissue Response in Prostate Cancer.

Authors:  Joshua W Lorenz; Diane Schott; Lisa Rein; Farshad Mostafaei; George Noid; Colleen Lawton; Meena Bedi; X A Li; Christopher J Schultz; Eric Paulson; William A Hall
Journal:  Cureus       Date:  2019-04-20

Review 8.  Deep Learning: A Review for the Radiation Oncologist.

Authors:  Luca Boldrini; Jean-Emmanuel Bibault; Carlotta Masciocchi; Yanting Shen; Martin-Immanuel Bittner
Journal:  Front Oncol       Date:  2019-10-01       Impact factor: 6.244

9.  3D bone texture analysis as a potential predictor of radiation-induced insufficiency fractures.

Authors:  Valerio Nardone; Paolo Tini; Stefania Croci; Salvatore Francesco Carbone; Lucio Sebaste; Tommaso Carfagno; Giuseppe Battaglia; Pierpaolo Pastina; Giovanni Rubino; Maria Antonietta Mazzei; Luigi Pirtoli
Journal:  Quant Imaging Med Surg       Date:  2018-02

Review 10.  Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment.

Authors:  Valerio Nardone; Luca Boldrini; Roberta Grassi; Davide Franceschini; Ilaria Morelli; Carlotta Becherini; Mauro Loi; Daniela Greto; Isacco Desideri
Journal:  Cancers (Basel)       Date:  2021-07-17       Impact factor: 6.639

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  23 in total

1.  Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases.

Authors:  Vincenza Granata; Roberta Fusco; Federica De Muzio; Carmen Cutolo; Sergio Venanzio Setola; Roberta Grassi; Francesca Grassi; Alessandro Ottaiano; Guglielmo Nasti; Fabiana Tatangelo; Vincenzo Pilone; Vittorio Miele; Maria Chiara Brunese; Francesco Izzo; Antonella Petrillo
Journal:  Radiol Med       Date:  2022-03-26       Impact factor: 3.469

Review 2.  A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers.

Authors:  Simone Vicini; Chandra Bortolotto; Marco Rengo; Daniela Ballerini; Davide Bellini; Iacopo Carbone; Lorenzo Preda; Andrea Laghi; Francesca Coppola; Lorenzo Faggioni
Journal:  Radiol Med       Date:  2022-06-30       Impact factor: 6.313

Review 3.  Role of Texture Analysis in Oropharyngeal Carcinoma: A Systematic Review of the Literature.

Authors:  Eleonora Bicci; Cosimo Nardi; Leonardo Calamandrei; Michele Pietragalla; Edoardo Cavigli; Francesco Mungai; Luigi Bonasera; Vittorio Miele
Journal:  Cancers (Basel)       Date:  2022-05-16       Impact factor: 6.575

4.  Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases.

Authors:  Vincenza Granata; Roberta Fusco; Federica De Muzio; Carmen Cutolo; Sergio Venanzio Setola; Federica Dell'Aversana; Francesca Grassi; Andrea Belli; Lucrezia Silvestro; Alessandro Ottaiano; Guglielmo Nasti; Antonio Avallone; Federica Flammia; Vittorio Miele; Fabiana Tatangelo; Francesco Izzo; Antonella Petrillo
Journal:  Radiol Med       Date:  2022-06-02       Impact factor: 6.313

5.  Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT texture analysis.

Authors:  Riccardo De Robertis; Luca Geraci; Luisa Tomaiuolo; Luca Bortoli; Alessandro Beleù; Giuseppe Malleo; Mirko D'Onofrio
Journal:  Radiol Med       Date:  2022-09-04       Impact factor: 6.313

6.  Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery.

Authors:  Valerio Nardone; Alfonso Reginelli; Roberta Grassi; Giovanna Vacca; Giuliana Giacobbe; Antonio Angrisani; Alfredo Clemente; Ginevra Danti; Pierpaolo Correale; Salvatore Francesco Carbone; Luigi Pirtoli; Lorenzo Bianchi; Angelo Vanzulli; Cesare Guida; Roberto Grassi; Salvatore Cappabianca
Journal:  Cancers (Basel)       Date:  2022-06-18       Impact factor: 6.575

Review 7.  Radiomics in medical imaging: pitfalls and challenges in clinical management.

Authors:  Roberta Fusco; Vincenza Granata; Giulia Grazzini; Silvia Pradella; Alessandra Borgheresi; Alessandra Bruno; Pierpaolo Palumbo; Federico Bruno; Roberta Grassi; Andrea Giovagnoni; Roberto Grassi; Vittorio Miele; Antonio Barile
Journal:  Jpn J Radiol       Date:  2022-03-28       Impact factor: 2.701

Review 8.  Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer.

Authors:  Getao Du; Yun Zeng; Dan Chen; Wenhua Zhan; Yonghua Zhan
Journal:  Jpn J Radiol       Date:  2022-10-19       Impact factor: 2.701

9.  An update on radiomics techniques in primary liver cancers.

Authors:  Vincenza Granata; Roberta Fusco; Sergio Venazio Setola; Igino Simonetti; Diletta Cozzi; Giulia Grazzini; Francesca Grassi; Andrea Belli; Vittorio Miele; Francesco Izzo; Antonella Petrillo
Journal:  Infect Agent Cancer       Date:  2022-03-04       Impact factor: 2.965

10.  CT-Based Radiomics Analysis to Predict Histopathological Outcomes Following Liver Resection in Colorectal Liver Metastases.

Authors:  Vincenza Granata; Roberta Fusco; Sergio Venanzio Setola; Federica De Muzio; Federica Dell' Aversana; Carmen Cutolo; Lorenzo Faggioni; Vittorio Miele; Francesco Izzo; Antonella Petrillo
Journal:  Cancers (Basel)       Date:  2022-03-24       Impact factor: 6.639

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