18F-FDG PET Radiomics As Predictor of Treatment Response in Oesophageal Cancer: A Systematic Review and Meta-Analysis

Overview

Journal Front Oncol

Specialty Oncology

Date 2022 Apr 4

PMID 35371989

Authors

Letizia Deantonio

Maria Luisa Garo

Gaetano Paone

Maria Carla Valli

Stefano Cappio

Davide La Regina

Marco Cefali

Maria Celeste Palmarocchi

Alberto Vannelli

Sara De Dosso

Affiliations

Soon will be listed here.

Abstract

Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42021274636.

Citing Articles

Gastro-Esophageal Cancer: Can Radiomic Parameters from Baseline F-FDG-PET/CT Predict the Development of Distant Metastatic Disease?.

Hinzpeter R, Mirshahvalad S, Kulanthaivelu R, Kohan A, Ortega C, Metser U Diagnostics (Basel). 2024; 14(11).

PMID: 38893731 PMC: 11171817. DOI: 10.3390/diagnostics14111205.

CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis.

Liu L, Liao H, Zhao Y, Yin J, Wang C, Duan L Front Oncol. 2024; 14:1267596.

PMID: 38577325 PMC: 10993774. DOI: 10.3389/fonc.2024.1267596.

Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma.

Geng X, Zhang Y, Li Y, Cai Y, Liu J, Geng T Br J Radiol. 2024; 97(1155):652-659.

PMID: 38268475 PMC: 11027331. DOI: 10.1093/bjr/tqae009.

Radiomics features of computed tomography and magnetic resonance imaging for predicting response to transarterial chemoembolization in hepatocellular carcinoma: a meta-analysis.

Feng L, Chen Q, Huang L, Long L Front Oncol. 2023; 13:1194200.

PMID: 37519801 PMC: 10374837. DOI: 10.3389/fonc.2023.1194200.

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation.

Zhong J, Lu J, Zhang G, Mao S, Chen H, Yin Q Insights Imaging. 2023; 14(1):111.

PMID: 37336830 PMC: 10279606. DOI: 10.1186/s13244-023-01437-2.

References

Staal F, van der Reijd D, Taghavi M, Lambregts D, Beets-Tan R, Maas M . Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review. Clin Colorectal Cancer. 2020; 20(1):52-71. DOI: 10.1016/j.clcc.2020.11.001. View

Alfieri S, Romano R, Bologna M, Calareso G, Corino V, Mirabile A . Prognostic role of pre-treatment magnetic resonance imaging (MRI)-based radiomic analysis in effectively cured head and neck squamous cell carcinoma (HNSCC) patients. Acta Oncol. 2021; 60(9):1192-1200. DOI: 10.1080/0284186X.2021.1924401. View

Hanley J, McNeil B . The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982; 143(1):29-36. DOI: 10.1148/radiology.143.1.7063747. View

Sah B, Owczarczyk K, Siddique M, Cook G, Goh V . Radiomics in esophageal and gastric cancer. Abdom Radiol (NY). 2018; 44(6):2048-2058. PMC: 6934409. DOI: 10.1007/s00261-018-1724-8. View

Duong C, Hicks R, Weih L, Drummond E, Leong T, Michael M . FDG-PET status following chemoradiotherapy provides high management impact and powerful prognostic stratification in oesophageal cancer. Eur J Nucl Med Mol Imaging. 2006; 33(7):770-8. DOI: 10.1007/s00259-005-0040-z. View

Murakami Y, Kawahara D, Tani S, Kubo K, Katsuta T, Imano N . Predicting the Local Response of Esophageal Squamous Cell Carcinoma to Neoadjuvant Chemoradiotherapy by Radiomics with a Machine Learning Method Using F-FDG PET Images. Diagnostics (Basel). 2021; 11(6). PMC: 8227132. DOI: 10.3390/diagnostics11061049. View

Wesdorp N, Hellingman T, Jansma E, van Waesberghe J, Boellaard R, Punt C . Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging. 2020; 48(6):1785-1794. PMC: 8113210. DOI: 10.1007/s00259-020-05142-w. View

Uhlenhopp D, Then E, Sunkara T, Gaduputi V . Epidemiology of esophageal cancer: update in global trends, etiology and risk factors. Clin J Gastroenterol. 2020; 13(6):1010-1021. DOI: 10.1007/s12328-020-01237-x. View

Beukinga R, Hulshoff J, van Dijk L, Muijs C, Burgerhof J, Kats-Ugurlu G . Predicting Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer with Textural Features Derived from Pretreatment F-FDG PET/CT Imaging. J Nucl Med. 2016; 58(5):723-729. DOI: 10.2967/jnumed.116.180299. View

10.

Park C . Can Artificial Intelligence Fix the Reproducibility Problem of Radiomics?. Radiology. 2019; 292(2):374-375. DOI: 10.1148/radiol.2019191154. View

11.

Chatterton B, Ho Shon I, Baldey A, Lenzo N, Patrikeos A, Kelley B . Positron emission tomography changes management and prognostic stratification in patients with oesophageal cancer: results of a multicentre prospective study. Eur J Nucl Med Mol Imaging. 2008; 36(3):354-61. DOI: 10.1007/s00259-008-0959-y. View

12.

Moher D, Liberati A, Tetzlaff J, Altman D . Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009; 6(7):e1000097. PMC: 2707599. DOI: 10.1371/journal.pmed.1000097. View

13.

Appelt A, Elhaminia B, Gooya A, Gilbert A, Nix M . Deep Learning for Radiotherapy Outcome Prediction Using Dose Data - A Review. Clin Oncol (R Coll Radiol). 2021; 34(2):e87-e96. DOI: 10.1016/j.clon.2021.12.002. View

14.

Rishi A, Zhang G, Yuan Z, Sim A, Song E, Moros E . Pretreatment CT and F-FDG PET-based radiomic model predicting pathological complete response and loco-regional control following neoadjuvant chemoradiation in oesophageal cancer. J Med Imaging Radiat Oncol. 2020; 65(1):102-111. DOI: 10.1111/1754-9485.13128. View

15.

Gillies R, Kinahan P, Hricak H . Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015; 278(2):563-77. PMC: 4734157. DOI: 10.1148/radiol.2015151169. View

16.

Bedenne L, Michel P, Bouche O, Milan C, Mariette C, Conroy T . Chemoradiation followed by surgery compared with chemoradiation alone in squamous cancer of the esophagus: FFCD 9102. J Clin Oncol. 2007; 25(10):1160-8. DOI: 10.1200/JCO.2005.04.7118. View

17.

Morris L, Riaz N, Desrichard A, Senbabaoglu Y, Hakimi A, Makarov V . Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget. 2016; 7(9):10051-63. PMC: 4891103. DOI: 10.18632/oncotarget.7067. View

18.

Yip S, Coroller T, Sanford N, Mamon H, Aerts H, Berbeco R . Relationship between the Temporal Changes in Positron-Emission-Tomography-Imaging-Based Textural Features and Pathologic Response and Survival in Esophageal Cancer Patients. Front Oncol. 2016; 6:72. PMC: 4810033. DOI: 10.3389/fonc.2016.00072. View

19.

Cao J, Zhang S . Multiple comparison procedures. JAMA. 2014; 312(5):543-4. DOI: 10.1001/jama.2014.9440. View

20.

Sung H, Ferlay J, Siegel R, Laversanne M, Soerjomataram I, Jemal A . Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021; 71(3):209-249. DOI: 10.3322/caac.21660. View