Clinical Applications of Radiomics in Nuclear Medicine

Abstract

Radiomics is an emerging field of artificial intelligence that focuses on the extraction and analysis of quantitative features such as intensity, shape, texture and spatial relationships from medical images. These features, often imperceptible to the human eye, can reveal complex patterns and biological insights. They can also be combined with clinical data to create predictive models using machine learning to improve disease characterization in nuclear medicine. This review article examines the current state of radiomics in nuclear medicine and shows its potential to improve patient care. Selected clinical applications for diseases such as cancer, neurodegenerative diseases, cardiovascular problems and thyroid diseases are examined. The article concludes with a brief classification in terms of future perspectives and strategies for linking research findings to clinical practice.

Keywords

PET - SPECT - Artificial Intelligence (AI) - Machine Learning - Cancer - Neurodegenerative Disease - Cardiovascular Disease - Thyroid Diseases

Publikationsverlauf

Eingereicht: 21. September 2023

Angenommen: 12. Oktober 2023

Artikel online veröffentlicht:
07. November 2023

• References

• 1 Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016; 278: 563-577 DOI: 10.1148/radiol.2015151169. (PMID: 26579733)
• 2 Lambin P, Leijenaar RTH, Deist TM. et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14: 749-762 DOI: 10.1038/nrclinonc.2017.141.
• 3 Lambin P, Rios-Velazquez E, Leijenaar R. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48: 441-446 DOI: 10.1016/j.ejca.2011.11.036.
• 4 Mayerhoefer ME, Materka A, Langs G. et al. Introduction to Radiomics. J Nucl Med 2020; 61: 488-495 DOI: 10.2967/jnumed.118.222893. (PMID: 32060219)
• 5 Lohmann P, Galldiks N, Kocher M. et al. Radiomics in neuro-oncology: Basics, workflow, and applications. Methods 2021; 188: 112-121 DOI: 10.1016/j.ymeth.2020.06.003. (PMID: 32522530)
• 6 Mamdani H, Matosevic S, Khalid AB. et al. Immunotherapy in Lung Cancer: Current Landscape and Future Directions. Front Immunol 2022; 13: 823618 DOI: 10.3389/fimmu.2022.823618. (PMID: 35222404)
• 7 McLaughlin J, Han G, Schalper KA. et al. Quantitative Assessment of the Heterogeneity of PD-L1 Expression in Non-Small-Cell Lung Cancer. JAMA Oncol 2016; 2: 46-54 DOI: 10.1001/jamaoncol.2015.3638. (PMID: 26562159)
• 8 Zhang M, Bao Y, Rui W. et al. Performance of (18)F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer. Front Oncol 2020; 10: 568857 DOI: 10.3389/fonc.2020.568857.
• 9 Monaco L, De Bernardi E, Bono F. et al. The “digital biopsy” in non-small cell lung cancer (NSCLC): a pilot study to predict the PD-L1 status from radiomics features of [18F]FDG PET/CT. Eur J Nucl Med Mol Imaging 2022; 49: 3401-3411 DOI: 10.1007/s00259-022-05783-z.
• 10 Valentinuzzi D, Vrankar M, Boc N. et al. 18F]FDG PET immunotherapy radiomics signature (iRADIOMICS. Radiol Oncol 2020; 54: 285-294 DOI: 10.2478/raon-2020-0042. (PMID: 32726293)
• 11 Galldiks N, Wollring M, Werner JM. et al. An updated review on the diagnosis and assessment of post-treatment relapse in brain metastases using PET. Expert Rev Neurother 2022; 22: 915-921 DOI: 10.1080/14737175.2022.2162880. (PMID: 36563186)
• 12 Langen KJ, Galldiks N, Hattingen E. et al. Advances in neuro-oncology imaging. Nat Rev Neurol 2017; 13: 279-289 DOI: 10.1038/nrneurol.2017.44. (PMID: 28387340)
• 13 Wang K, Qiao Z, Zhao X. et al. Individualized discrimination of tumor recurrence from radiation necrosis in glioma patients using an integrated radiomics-based model. Eur J Nucl Med Mol Imaging 2020; 47: 1400-1411 DOI: 10.1007/s00259-019-04604-0.
• 14 Hotta M, Minamimoto R, Miwa K. 11C-methionine-PET for differentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifier. Sci Rep 2019; 9: 15666 DOI: 10.1038/s41598-019-52279-2. (PMID: 31666650)
• 15 Lohmann P, Kocher M, Ceccon G. et al. Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis. Neuroimage Clin 2018; 20: 537-542 DOI: 10.1016/j.nicl.2018.08.024.
• 16 Muller M, Winz O, Gutsche R. et al. Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression. J Neurooncol 2022; 159: 519-529 DOI: 10.1007/s11060-022-04089-2.
• 17 Bray F, Ferlay J, Soerjomataram I. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68: 394-424 DOI: 10.3322/caac.21492. (PMID: 30207593)
• 18 Eiber M, Weirich G, Holzapfel K. et al. Simultaneous (68)Ga-PSMA HBED-CC PET/MRI Improves the Localization of Primary Prostate Cancer. Eur Urol 2016; 70: 829-836 DOI: 10.1016/j.eururo.2015.12.053. (PMID: 26795686)
• 19 Cohen MS, Hanley RS, Kurteva T. et al. Comparing the Gleason prostate biopsy and Gleason prostatectomy grading system: the Lahey Clinic Medical Center experience and an international meta-analysis. Eur Urol 2008; 54: 371-381 DOI: 10.1016/j.eururo.2008.03.049. (PMID: 18395322)
• 20 Solari EL, Gafita A, Schachoff S. et al. The added value of PSMA PET/MR radiomics for prostate cancer staging. Eur J Nucl Med Mol Imaging 2022; 49: 527-538 DOI: 10.1007/s00259-021-05430-z. (PMID: 34255130)
• 21 Moazemi S, Erle A, Lutje S. et al. Estimating the Potential of Radiomics Features and Radiomics Signature from Pretherapeutic PSMA-PET-CT Scans and Clinical Data for Prediction of Overall Survival When Treated with (177)Lu-PSMA. Diagnostics (Basel) 2021; 11 DOI: 10.3390/diagnostics11020186.
• 22 Lukasiewicz S, Czeczelewski M, Forma A. et al. Breast Cancer-Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies-An Updated Review. Cancers (Basel) 2021; 13 DOI: 10.3390/cancers13174287. (PMID: 34503097)
• 23 Antunovic L, De Sanctis R, Cozzi L. et al. PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy. Eur J Nucl Med Mol Imaging 2019; 46: 1468-1477 DOI: 10.1007/s00259-019-04313-8. (PMID: 30915523)
• 24 Chen K, Wang J, Li S. et al. Predictive value of (18)F-FDG PET/CT-based radiomics model for neoadjuvant chemotherapy efficacy in breast cancer: a multi-scanner/center study with external validation. Eur J Nucl Med Mol Imaging 2023; 50: 1869-1880 DOI: 10.1007/s00259-023-06150-2.
• 25 Mitchell AJ, Shiri-Feshki M. Rate of progression of mild cognitive impairment to dementia--meta-analysis of 41 robust inception cohort studies. Acta Psychiatr Scand 2009; 119: 252-265 DOI: 10.1111/j.1600-0447.2008.01326.x. (PMID: 19236314)
• 26 Jiang J, Wang M, Alberts I. et al. Using radiomics-based modelling to predict individual progression from mild cognitive impairment to Alzheimer’s disease. Eur J Nucl Med Mol Imaging 2022; 49: 2163-2173 DOI: 10.1007/s00259-022-05687-y.
• 27 Ding Y, Zhao K, Che T. et al. Quantitative Radiomic Features as New Biomarkers for Alzheimer’s Disease: An Amyloid PET Study. Cereb Cortex 2021; 31: 3950-3961 DOI: 10.1093/cercor/bhab061. (PMID: 33884402)
• 28 Hughes AJ, Daniel SE, Ben-Shlomo Y. et al. The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain 2002; 125: 861-870 DOI: 10.1093/brain/awf080. (PMID: 11912118)
• 29 Sun J, Cong C, Li X. et al. Identification of Parkinson’s disease and multiple system atrophy using multimodal PET/MRI radiomics. Eur Radiol 2023; DOI: 10.1007/s00330-023-10003-9. (PMID: 37535155)
• 30 Comte V, Schmutz H, Chardin D. et al. Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT. Eur J Nucl Med Mol Imaging 2022; 49: 3787-3796 DOI: 10.1007/s00259-022-05816-7. (PMID: 35567626)
• 31 Ragosta M, Bishop AH, Lipson LC. et al. Comparison between angiography and fractional flow reserve versus single-photon emission computed tomographic myocardial perfusion imaging for determining lesion significance in patients with multivessel coronary disease. Am J Cardiol 2007; 99: 896-902 DOI: 10.1016/j.amjcard.2006.11.035.
• 32 Degtiarova G, Garefa C, Boehm R. et al. Radiomics for the detection of diffusely impaired myocardial perfusion: A proof-of-concept study using 13N-ammonia positron emission tomography. J Nucl Cardiol 2023; 30: 1474-1483 DOI: 10.1007/s12350-022-03179-y. (PMID: 36600174)
• 33 Zhang X, Sun T, Liu E. et al. Development and evaluation of a radiomics model of resting (13)N-ammonia positron emission tomography myocardial perfusion imaging to predict coronary artery stenosis in patients with suspected coronary heart disease. Ann Transl Med 2022; 10: 1167 DOI: 10.21037/atm-22-4692.
• 34 Giovanella L, Milan L, Piccardo A. et al. Radiomics analysis improves (18)FDG PET/CT-based risk stratification of cytologically indeterminate thyroid nodules. Endocrine 2022; 75: 202-210 DOI: 10.1007/s12020-021-02856-1. (PMID: 34468949)
• 35 Ceriani L, Milan L, Virili C. et al. Radiomics Analysis of [(18)F]-Fluorodeoxyglucose-Avid Thyroid Incidentalomas Improves Risk Stratification and Selection for Clinical Assessment. Thyroid 2021; 31: 88-95 DOI: 10.1089/thy.2020.0224. (PMID: 32517585)
• 36 Ko WS, Kim SJ. Prediction of Malignant Thyroid Nodules Using 18 F-FDG PET/CT-Based Radiomics Features in Thyroid Incidentalomas. Clin Nucl Med 2023; 48: 497-504 DOI: 10.1097/RLU.0000000000004637. (PMID: 37001129)
• 37 Lohmann P, Franceschi E, Vollmuth P. et al. Radiomics in neuro-oncological clinical trials. The Lancet Digital Health 2022; DOI: 10.1016/s2589-7500(22)00144-3. (PMID: 36182633)
• 38 Pfaehler E, Zhovannik I, Wei L. et al. A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features. Phys Imaging Radiat Oncol 2021; 20: 69-75 DOI: 10.1016/j.phro.2021.10.007. (PMID: 34816024)
• 39 Zwanenburg A. Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging 2019; 46: 2638-2655 DOI: 10.1007/s00259-019-04391-8. (PMID: 31240330)
• 40 Traverso A, Wee L, Dekker A. et al. Repeatability and Reproducibility of Radiomic Features: A Systematic Review. Int J Radiat Oncol Biol Phys 2018; 102: 1143-1158 DOI: 10.1016/j.ijrobp.2018.05.053. (PMID: 30170872)
• 41 Huang EP, O’Connor JPB, McShane LM. et al. Criteria for the translation of radiomics into clinically useful tests. Nat Rev Clin Oncol 2023; 20: 69-82 DOI: 10.1038/s41571-022-00707-0.
• 42 Gutsche R, Scheins J, Kocher M. et al. Evaluation of FET PET Radiomics Feature Repeatability in Glioma Patients. Cancers 2021; 13 DOI: 10.3390/cancers13040647. (PMID: 33562803)
• 43 Werner RA, Habacha B, Lutje S. et al. Lack of repeatability of radiomic features derived from PET scans: Results from a (18) F-DCFPyL test-retest cohort. Prostate 2023; 83: 547-554 DOI: 10.1002/pros.24483.
• 44 Zwanenburg A, Vallieres M, Abdalah MA. et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology 2020; 295: 328-338 DOI: 10.1148/radiol.2020191145. (PMID: 32154773)
• 45 Collins GS, Dhiman P, Andaur Navarro CL. et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open 2021; 11: e048008 DOI: 10.1136/bmjopen-2020-048008.
• 46 Liu X, Cruz Rivera S, Moher D. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Lancet Digit Health 2020; 2: e537-e548 DOI: 10.1016/S2589-7500(20)30218-1.
• 47 Cruz Rivera S, Liu X, Chan AW. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 2020; 26: 1351-1363 DOI: 10.1038/s41591-020-1037-7.