The Impact of Radiomics in Diagnosis and Staging of Pancreatic Cancer

Overview

Journal Ther Adv Gastrointest Endosc

Publisher Sage Publications

Date 2022 Mar 28

PMID 35342883

Authors

Calogero Casa

Antonio Piras

Andrea DAviero

Francesco Preziosi

Silvia Mariani

Davide Cusumano

Angela Romano

Ivo Boskoski

Jacopo Lenkowicz

Nicola Dinapoli

Francesco Cellini

Maria Antonietta Gambacorta

Vincenzo Valentini

Gian Carlo Mattiucci

Luca Boldrini

Affiliations

Soon will be listed here.

Abstract

Introduction: Pancreatic cancer (PC) is one of the most aggressive tumours, and better risk stratification among patients is required to provide tailored treatment. The meaning of radiomics and texture analysis as predictive techniques are not already systematically assessed. The aim of this study is to assess the role of radiomics in PC.

Methods: A PubMed/MEDLINE and Embase systematic review was conducted to assess the role of radiomics in PC. The search strategy was 'radiomics [All Fields] AND ("pancreas" [MeSH Terms] OR "pancreas" [All Fields] OR "pancreatic" [All Fields])' and only original articles referred to PC in humans in the English language were considered.

Results: A total of 123 studies and 183 studies were obtained using the mentioned search strategy on PubMed and Embase, respectively. After the complete selection process, a total of 56 papers were considered eligible for the analysis of the results. Radiomics methods were applied in PC for assessment technical feasibility and reproducibility aspects analysis, risk stratification, biologic or genomic status prediction and treatment response prediction.

Discussion: Radiomics seems to be a promising approach to evaluate PC from diagnosis to treatment response prediction. Further and larger studies are required to confirm the role and allowed to include radiomics parameter in a comprehensive decision support system.

Citing Articles

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Multiparametric Magnetic Resonance Imaging Findings of the Pancreas: A Comparison in Patients with Type 1 and 2 Diabetes.

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Exploring radiomic features of lateral cerebral ventricles in postmortem CT for postmortem interval estimation.

De-Giorgio F, Guerreri M, Gatta R, Bergamin E, De Vita V, Mancino M Int J Legal Med. 2024; 139(2):667-677.

PMID: 39702800 DOI: 10.1007/s00414-024-03396-9.

Advancements in Early Detection and Screening Strategies for Pancreatic Cancer: From Genetic Susceptibility to Novel Biomarkers.

Shah Y, Dahiya D, Tiwari A, Kumar H, Gangwani M, Ali H J Clin Med. 2024; 13(16).

PMID: 39200847 PMC: 11355237. DOI: 10.3390/jcm13164706.

Pancreatic cancer: from early detection to personalized treatment approaches.

Khayat S, Choudhary K, Claude Nshimiyimana J, Gurav J, Hneini A, Nazir A Ann Med Surg (Lond). 2024; 86(5):2866-2872.

PMID: 38694319 PMC: 11060269. DOI: 10.1097/MS9.0000000000002011.

References

Gruzdev I, Zamyatina K, Tikhonova V, Kondratyev E, Glotov A, Karmazanovsky G . Reproducibility of CT texture features of pancreatic neuroendocrine neoplasms. Eur J Radiol. 2020; 133:109371. DOI: 10.1016/j.ejrad.2020.109371. View

Shi Y, Zhu H, Liu Y, Wei Y, Qin X, Zhang X . Radiomics Analysis Based on Diffusion Kurtosis Imaging and T2 Weighted Imaging for Differentiation of Pancreatic Neuroendocrine Tumors From Solid Pseudopapillary Tumors. Front Oncol. 2020; 10:1624. PMC: 7473210. DOI: 10.3389/fonc.2020.01624. View

Qiu J, Yin J, Qian W, Liu J, Huang Z, Yu H . A Novel Multiresolution-Statistical Texture Analysis Architecture: Radiomics-Aided Diagnosis of PDAC Based on Plain CT Images. IEEE Trans Med Imaging. 2020; 40(1):12-25. DOI: 10.1109/TMI.2020.3021254. View

Bian Y, Li J, Cao K, Fang X, Jiang H, Ma C . Magnetic resonance imaging radiomic analysis can preoperatively predict G1 and G2/3 grades in patients with NF-pNETs. Abdom Radiol (NY). 2020; 46(2):667-680. DOI: 10.1007/s00261-020-02706-0. View

Vanella G, Capurso G, Boskoski I, Bossi E, Signorelli C, Ciceri F . How to get away with COVID-19: endoscopy during post-peak pandemic. A perspective review. Therap Adv Gastroenterol. 2020; 13:1756284820965070. PMC: 7548539. DOI: 10.1177/1756284820965070. View

Bian Y, Guo S, Jiang H, Gao S, Shao C, Cao K . Relationship Between Radiomics and Risk of Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma. Pancreas. 2019; 48(9):1195-1203. PMC: 6830946. DOI: 10.1097/MPA.0000000000001404. View

Tang T, Li X, Zhang Q, Guo C, Zhang X, Lao M . Development of a Novel Multiparametric MRI Radiomic Nomogram for Preoperative Evaluation of Early Recurrence in Resectable Pancreatic Cancer. J Magn Reson Imaging. 2019; 52(1):231-245. PMC: 7317738. DOI: 10.1002/jmri.27024. View

Ryan D, Hong T, Bardeesy N . Pancreatic adenocarcinoma. N Engl J Med. 2014; 371(11):1039-49. DOI: 10.1056/NEJMra1404198. View

Nasief H, Zheng C, Schott D, Hall W, Tsai S, Erickson B . A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer. NPJ Precis Oncol. 2019; 3:25. PMC: 6778189. DOI: 10.1038/s41698-019-0096-z. View

10.

Fesinmeyer M, Austin M, Li C, De Roos A, Bowen D . Differences in survival by histologic type of pancreatic cancer. Cancer Epidemiol Biomarkers Prev. 2005; 14(7):1766-73. DOI: 10.1158/1055-9965.EPI-05-0120. View

11.

Zhou H, Han Y, Lu J, Wei J, Guo J, Zhu H . Radiomics Facilitates Candidate Selection for Irradiation Stents Among Patients With Unresectable Pancreatic Cancer. Front Oncol. 2019; 9:973. PMC: 6776612. DOI: 10.3389/fonc.2019.00973. View

12.

Yamashita R, Perrin T, Chakraborty J, Chou J, Horvat N, Koszalka M . Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation. Eur Radiol. 2019; 30(1):195-205. PMC: 7127865. DOI: 10.1007/s00330-019-06381-8. View

13.

Cusumano D, Dinapoli N, Boldrini L, Chiloiro G, Gatta R, Masciocchi C . Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer. Radiol Med. 2017; 123(4):286-295. DOI: 10.1007/s11547-017-0838-3. View

14.

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

15.

Valentini V, Boldrini L, Damiani A, Muren L . Recommendations on how to establish evidence from auto-segmentation software in radiotherapy. Radiother Oncol. 2014; 112(3):317-20. DOI: 10.1016/j.radonc.2014.09.014. View

16.

Park S, Chu L, Hruban R, Vogelstein B, Kinzler K, Yuille A . Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features. Diagn Interv Imaging. 2020; 101(9):555-564. DOI: 10.1016/j.diii.2020.03.002. View

17.

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

18.

Zhang Y, Lobo-Mueller E, Karanicolas P, Gallinger S, Haider M, Khalvati F . CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging. BMC Med Imaging. 2020; 20(1):11. PMC: 6998249. DOI: 10.1186/s12880-020-0418-1. View

19.

Fionda B, Boldrini L, DAviero A, Lancellotta V, Gambacorta M, Kovacs G . Artificial intelligence (AI) and interventional radiotherapy (brachytherapy): state of art and future perspectives. J Contemp Brachytherapy. 2020; 12(5):497-500. PMC: 7701925. DOI: 10.5114/jcb.2020.100384. View

20.

Ferlay J, Colombet M, Soerjomataram I, Dyba T, Randi G, Bettio M . Cancer incidence and mortality patterns in Europe: Estimates for 40 countries and 25 major cancers in 2018. Eur J Cancer. 2018; 103:356-387. DOI: 10.1016/j.ejca.2018.07.005. View