Radiomics in Medical Imaging: Pitfalls and Challenges in Clinical Management

Overview

Journal Jpn J Radiol

Publisher Springer

Specialty Radiology

Date 2022 Mar 28

PMID 35344132

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

Affiliations

Soon will be listed here.

Abstract

Background: Radiomics and radiogenomics are two words that recur often in language of radiologists, nuclear doctors and medical physicists especially in oncology field. Radiomics is the technique of medical images analysis to extract quantitative data that are not detected by human eye.

Methods: This article is a narrative review on Radiomics in Medical Imaging. In particular, the review exposes the process, the limitations related to radiomics, and future prospects are discussed.

Results: Several studies showed that radiomics is very promising. However, there were some critical issues: poor standardization and generalization of radiomics results, data-quality control, repeatability, reproducibility, database balancing and issues related to model overfitting.

Conclusions: Radiomics procedure should made considered all pitfalls and challenges to obtain robust and reproducible results that could be generalized in other patients cohort.

Citing Articles

Radiomics in Dermatological Optical Coherence Tomography (OCT): Feature Repeatability, Reproducibility, and Integration into Diagnostic Models in a Prospective Study.

Widaatalla Y, Wolswijk T, Khan M, Halilaj I, Mosterd K, Woodruff H Cancers (Basel). 2025; 17(5).

PMID: 40075619 PMC: 11899706. DOI: 10.3390/cancers17050768.

Overlooked and underpowered: a meta-research addressing sample size in radiomics prediction models for binary outcomes.

Zhong J, Liu X, Lu J, Yang J, Zhang G, Mao S Eur Radiol. 2025; 35(3):1146-1156.

PMID: 39789271 PMC: 11835977. DOI: 10.1007/s00330-024-11331-0.

Distinguishing lymphoma from benign lymph node diseases in fever of unknown origin using PET/CT radiomics.

Zhang X, Jing F, Hu Y, Tian C, Zhang J, Li S EJNMMI Res. 2024; 14(1):106.

PMID: 39537895 PMC: 11561199. DOI: 10.1186/s13550-024-01171-w.

Radiomics in radiology: What the radiologist needs to know about technical aspects and clinical impact.

Ferrari R, Trinci M, Casinelli A, Treballi F, Leone E, Caruso D Radiol Med. 2024; 129(12):1751-1765.

PMID: 39472389 DOI: 10.1007/s11547-024-01904-w.

Sinonasal adenoid cystic carcinoma: preoperative apparent diffusion coefficient histogram analysis in prediction of prognosis and Ki-67 proliferation status.

Cheng J, Liu Q, Wang Y, Zhan Y, Wang Y, Shen D Jpn J Radiol. 2024; 43(3):389-401.

PMID: 39382794 DOI: 10.1007/s11604-024-01676-3.

References

Lambin P, Leijenaar R, Deist T, Peerlings J, de Jong E, van Timmeren J . Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017; 14(12):749-762. DOI: 10.1038/nrclinonc.2017.141. View

Limkin E, Sun R, Dercle L, Zacharaki E, Robert C, Reuze S . Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. 2017; 28(6):1191-1206. DOI: 10.1093/annonc/mdx034. View

Agazzi G, Ravanelli M, Roca E, Medicina D, Balzarini P, Pessina C . CT texture analysis for prediction of EGFR mutational status and ALK rearrangement in patients with non-small cell lung cancer. Radiol Med. 2021; 126(6):786-794. DOI: 10.1007/s11547-020-01323-7. View

Palumbo P, Masedu F, De Cataldo C, Cannizzaro E, Bruno F, Pradella S . Real-world clinical validity of cardiac magnetic resonance tissue tracking in primitive hypertrophic cardiomyopathy. Radiol Med. 2021; 126(12):1532-1543. DOI: 10.1007/s11547-021-01432-x. View

Granata V, Grassi R, Fusco R, Setola S, Belli A, Ottaiano A . Intrahepatic cholangiocarcinoma and its differential diagnosis at MRI: how radiologist should assess MR features. Radiol Med. 2021; 126(12):1584-1600. DOI: 10.1007/s11547-021-01428-7. View

Santone A, Brunese M, Donnarumma F, Guerriero P, Mercaldo F, Reginelli A . Radiomic features for prostate cancer grade detection through formal verification. Radiol Med. 2021; 126(5):688-697. DOI: 10.1007/s11547-020-01314-8. View

Granata V, Fusco R, Costa M, Picone C, Cozzi D, Moroni C . Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients. Cancers (Basel). 2021; 13(16). PMC: 8393664. DOI: 10.3390/cancers13163992. View

Granata V, Fusco R, Barretta M, Picone C, Avallone A, Belli A . Radiomics in hepatic metastasis by colorectal cancer. Infect Agent Cancer. 2021; 16(1):39. PMC: 8173908. DOI: 10.1186/s13027-021-00379-y. View

Fusco R, Granata V, Mazzei M, Di Meglio N, Del Roscio D, Moroni C . Quantitative imaging decision support (QIDS) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan. Cancer Control. 2021; 28:1073274820985786. PMC: 8482708. DOI: 10.1177/1073274820985786. View

10.

Petralia G, Summers P, Agostini A, Ambrosini R, Cianci R, Cristel G . Dynamic contrast-enhanced MRI in oncology: how we do it. Radiol Med. 2020; 125(12):1288-1300. DOI: 10.1007/s11547-020-01220-z. View

11.

Ria F, Samei E . Is regulatory compliance enough to ensure excellence in medicine?. Radiol Med. 2020; 125(9):904-905. DOI: 10.1007/s11547-020-01171-5. View

12.

Zhang A, Song J, Ma Z, Chen T . Combined dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted imaging to predict neoadjuvant chemotherapy effect in FIGO stage IB2-IIA2 cervical cancers. Radiol Med. 2020; 125(12):1233-1242. DOI: 10.1007/s11547-020-01214-x. View

13.

Crimi F, Capelli G, Spolverato G, Bao Q, Florio A, Milite Rossi S . MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). Radiol Med. 2020; 125(12):1216-1224. DOI: 10.1007/s11547-020-01215-w. View

14.

Kirienko M, Ninatti G, Cozzi L, Voulaz E, Gennaro N, Barajon I . Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas. Radiol Med. 2020; 125(10):951-960. DOI: 10.1007/s11547-020-01188-w. View

15.

Zhang L, Kang L, Li G, Zhang X, Ren J, Shi Z . Computed tomography-based radiomics model for discriminating the risk stratification of gastrointestinal stromal tumors. Radiol Med. 2020; 125(5):465-473. DOI: 10.1007/s11547-020-01138-6. View

16.

Gurgitano M, Angileri S, Roda G, Liguori A, Pandolfi M, Ierardi A . Interventional Radiology ex-machina: impact of Artificial Intelligence on practice. Radiol Med. 2021; 126(7):998-1006. PMC: 8050998. DOI: 10.1007/s11547-021-01351-x. View

17.

Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E . A deep look into radiomics. Radiol Med. 2021; 126(10):1296-1311. PMC: 8520512. DOI: 10.1007/s11547-021-01389-x. View

18.

Wei J, Jiang H, Gu D, Niu M, Fu F, Han Y . Radiomics in liver diseases: Current progress and future opportunities. Liver Int. 2020; 40(9):2050-2063. PMC: 7496410. DOI: 10.1111/liv.14555. View

20.

Nardone V, Reginelli A, Grassi R, Boldrini L, Vacca G, DIppolito E . Delta radiomics: a systematic review. Radiol Med. 2021; 126(12):1571-1583. DOI: 10.1007/s11547-021-01436-7. View

21.

Shaw B, Daskareh M, Gholamrezanezhad A . The lingering manifestations of COVID-19 during and after convalescence: update on long-term pulmonary consequences of coronavirus disease 2019 (COVID-19). Radiol Med. 2020; 126(1):40-46. PMC: 7529085. DOI: 10.1007/s11547-020-01295-8. View