Abstract
Radiomics analysis has been widely applied in cancer research and has demonstrated its potential to improve patient care. The process of radiomics analysis involves several steps, starting with image acquisition and preprocessing, followed by segmentation. Radiomics features are then extracted, which include shape, intensity, texture, and statistical measures, among others. These features are then subjected to machine learning algorithms to identify patterns and relationships between features and clinical endpoints. In addition to radiomics features, other features such as deep features or other imaging biomarkers can be extracted from the image. Standardization emerges as a crucial aspect in radiomics analysis, ensuring consistency and reproducibility. The Imaging Biomarkers Standardization Initiative (IBSI) provides guidelines for radiomics feature extraction.
Deep learning models have emerged as a promising alternative to feature-based models. These models learn features automatically, which can help overcome the limitations of feature-based models that are sensitive to inter-scanner and inter-protocol variability.
Radiomics is a rapidly growing field that has the potential to transform medical imaging and improve patient outcomes. By providing quantitative information on tissue structure and function, radiomics analysis can help clinicians make more informed treatment decisions and develop personalized treatment strategies.
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References
Chetan MR, Gleeson FV (2021) Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol 31(2):1049–1058. https://doi.org/10.1007/s00330-020-07141-9. Epub 2020 Aug 18. PMID: 32809167; PMCID: PMC7813733
Du P, Liu X, Shen L, Wu X, Chen J, Chen L, Cao A, Geng D (2023) Prediction of treatment response in patients with brain metastasis receiving stereotactic radiosurgery based on pre-treatment multimodal MRI radiomics and clinical risk factors: A machine learning model. Front Oncol 13:1114194. https://doi.org/10.3389/fonc.2023.1114194. PMID: 36994193; PMCID: PMC10040663
Huynh LM, Hwang Y, Taylor O, Baine MJ (2023) The use of MRI-derived radiomic models in prostate cancer risk stratification: a critical review of contemporary literature. Diagnostics (Basel) 13(6):1128. 13(6):1128. https://doi.org/10.3390/diagnostics13061128. PMID: 36980436; PMCID: PMC10047271
Zhang Y, Yang Y, Ning G, Wu X, Yang G, Li Y (2023) Contrast computed tomography-based radiomics is correlation with COG risk stratification of neuroblastoma. Abdom Radiol (NY) https://doi.org/10.1007/s00261-023-03875-4. Epub ahead of print. PMID: 36951989
Cui Y, Li Z, Xiang M, Han D, Yin Y, Ma C (2022) Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures. Radiat Oncol. 17(1):212. https://doi.org/10.1186/s13014-022-02186-0. PMID: 36575480; PMCID: PMC9795769
Chu F, Liu Y, Liu Q, Li W, Jia Z, Wang C, Wang Z, Lu S, Li P, Zhang Y, Liao Y, Xu M, Yao X, Wang S, Liu C, Zhang H, Wang S, Yan X, Kamel IR, Sun H, Yang G, Zhang Y, Qu J (2022) Development and validation of MRI-based radiomics signatures models for prediction of disease-free survival and overall survival in patients with esophageal squamous cell carcinoma. Eur Radiol. 32(9):5930–5942. https://doi.org/10.1007/s00330-022-08776-6. Epub 2022 Apr 6. PMID: 35384460
Chen W, Qiao X, Yin S, Zhang X, Xu X (2022) Integrating Radiomics with Genomics for Non-Small Cell Lung Cancer Survival Analysis. J Oncol. 2022:5131170. https://doi.org/10.1155/2022/5131170. PMID: 36065309; PMCID: PMC9440821
Feng Q, Ding Z (2020) MRI Radiomics Classification and Prediction in Alzheimer’s Disease and Mild Cognitive Impairment: A Review. Curr Alzheimer Res. 17(3):297–309. https://doi.org/10.2174/1567205017666200303105016. PMID: 32124697
Pujadas ER, Raisi-Estabragh Z, Szabo L, McCracken C, Morcillo CI, Campello VM, Martín-Isla C, Atehortua AM, Vago H, Merkely B, Maurovich-Horvat P, Harvey NC, Neubauer S, Petersen SE, Lekadir K 2022 Prediction of incident cardiovascular events using machine learning and CMR radiomics. Eur Radiol https://doi.org/10.1007/s00330-022-09323-z. Epub ahead of print. PMID: 36512045
Gabryś HS, Gote-Schniering J, Brunner M, Bogowicz M, Blüthgen C, Frauenfelder T, Guckenberger M, Maurer B, Tanadini-Lang S 2022 Transferability of radiomic signatures from experimental to human interstitial lung disease. Front Med (Lausanne). 9:988927. https://doi.org/10.3389/fmed.2022.988927. PMID: 36465941; PMCID: PMC9712180
Cho YH, Seo JB, Lee SM, Kim N, Yun J, Hwang JE, Lee JS, Oh YM, Do Lee S, Loh LC, Ong CK (2021) Radiomics approach for survival prediction in chronic obstructive pulmonary disease. Eur Radiol 31(10):7316–7324. https://doi.org/10.1007/s00330-021-07747-7. Epub 2021 Apr 13. PMID: 33847809.
Martí-Bonmatí Luis and Alberich-Bayarri, A (2018) Imaging biomarkers development and clinical integration. Cham: springer international publishing.
Jha AK, Mithun S, Jaiswar V, Sherkhane UB, Purandare NC, Prabhash K, Rangarajan V, Dekker A, Wee L, Traverso A (2021) Repeatability and reproducibility study of radiomic features on a phantom and human cohort. Sci Rep 11(1):2055. https://doi.org/10.1038/s41598-021-81. 526–8. PMID: 33479392; PMCID: PMC7820018.
Liu R, Elhalawani H, Radwan Mohamed AS, Elgohari B, Court L, Zhu H, Fuller CD (2019) Stability analysis of CT radiomic features with respect to segmentation variation in oropharyngeal cancer. Clin transl radiat oncol. 21:11–18. https://doi.org/10.1016/j.ctro.2019.11.005. PMID: 31886423; PMCID: PMC6920497.
Leithner D, Schöder H, Haug A, Vargas HA, Gibbs P, Häggström I, Rausch I, Weber M, Becker AS, Schwartz J, Mayerhoefer ME (2022) impact of combat harmonization on pet radiomics-based tissue classification: a dual-cen2ter PET/MRI and PET/CT Study. J Nucl Med. 63(10):1611–1616. https://doi.org/10.2967/jnumed.121.263102. Epub 2022 Feb 24. PMID: 35210300; PMCID: PMC9536705.
Cabini RF, Brero F, Lancia A, Stelitano C, Oneta O, Ballante E, Puppo E, Mariani M, Alì E, Bartolomeo V, Montesano M, Merizzoli E, Aluia D, Agustoni F, Stella GM, Sun R, Bianchini L, Deutsch E, Figini S, Bortolotto C, Preda L, Lascialfari A, Filippi AR (2022). Preliminary report on harmonization of features extraction process using the ComBat tool in the multi-center “Blue Sky Radiomics” study on stage III unresectable NSCLC. Insights Imaging 13(1):38. https://doi.org/10.1186/s13244-022-01171-1. PMID: 35254525; PMCID: PMC8901939
Zeineldin RA, Karar ME, Elshaer Z, Coburger J, Wirtz CR, Burgert O, Mathis-Ullrich F (2022) Explainability of deep neural networks for MRI analysis of brain tumors. Int J Comput Assist Radiol Surg 17(9):1673–1683. https://doi.org/10.1007/s11548-022-02619-x. Epub 2022 Apr 23. PMID: 35460019; PMCID: PMC9463287.
Zerunian M, Pucciarelli F, Caruso D, Polici M, Masci B, Guido G, De Santis D, Polverari D, Principessa D, Benvenga A, Iannicelli E, Laghi A (2022) Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation. Radiol Med 127(10):1098–1105. https://doi.org/10.1007/s11547-022-01539-9. Epub 2022 Sep 7. PMID: 36070066; PMCID: PMC9512724
Tixier F, Jaouen V, Hognon C, Gallinato O, Colin T, Visvikis D (2021) Evaluation of conventional and deep learning based image harmonization methods in radiomics studies. Phys Med Biol 66(24). https://doi.org/10.1088/1361-6560/ac39e5. PMID: 34781280
Shafiq-Ul-Hassan M, Zhang GG, Latifi K, Ullah G, Hunt DC, Balagurunathan Y, Abdalah MA, Schabath MB, Goldgof DG, Mackin D, Court LE, Gillies RJ, Moros EG (2017) Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels. Med Phys 44(3):1050–1062. https://doi.org/10.1002/mp.12123. PMID: 28112418; PMCID: PMC5462462
Covert EC, Fitzpatrick K, Mikell J, Kaza RK, Millet JD, Barkmeier D, Gemmete J, Christensen J, Schipper MJ, Dewaraja YK (2022) Intra- and inter-operator variability in MRI-based manual segmentation of HCC lesions and its impact on dosimetry. EJNMMI Phys 9(1):90. https://doi.org/10.1186/s40658-022-00515-6. PMID: 36542239; PMCID: PMC9772368
Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR, Davatzikos C, Depeursinge A, Desseroit MC, Dinapoli N, Dinh CV, Echegaray S, El Naqa I, Fedorov AY, Gatta R, Gillies RJ, Goh V, Götz M, Guckenberger M, Ha SM, Hatt M, Isensee F, Lambin P, Leger S, Leijenaar RTH, Lenkowicz J, Lippert F, Losnegård A, Maier-Hein KH, Morin O, Müller H, Napel S, Nioche C, Orlhac F, Pati S, Pfaehler EAG, Rahmim A, Rao AUK, Scherer J, Siddique MM, Sijtsema NM, Socarras Fernandez J, Spezi E, Steenbakkers RJHM, Tanadini-Lang S, Thorwarth D, Troost EGC, Upadhaya T, Valentini V, van Dijk LV, van Griethuysen J, van Velden FHP, Whybra P, Richter C, Löck S (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2):328–338. https://doi.org/10.1148/radiol.2020191145. Epub 2020 Mar 10. PMID: 32154773; PMCID: PMC7193906
Li W (2015) “Automatic segmentation of liver tumor in CT images with deep convolutional neural networks”. J Comput Commun 3(11):146
Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y et al (2014) “The multimodal brain tumor image segmentation benchmark (BRATS)”. IEEE Trans Med Imaging 34(10):1993–2024
Wang S, Zhou M, Liu Z, Liu Z, Gu D, Zang Y, Dong D, Gevaert O, Tian J (2017) “Central focused convolutional neural networks: developing a data-driven model for lung nodule segmentation”. Med Image Anal 40:172–183
Huynh BQ, Li H, Giger ML (2016) Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging 3(3):034501
Paul R et al (2016) Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography 2(4):388–395
Summers RM, Johnson CD, Pusanik LM, Malley JD, Youssef, AM, Reed JE (2001) Automated polyp detection at CT colonography: feasibility assessment in a human population. Radiology 219(1):51–59
Wang Y, Sun L, Ma K, Fang J (2018) Breast cancer microscope image classification based on CNN with image deformation. In Image Analysis and Recognition: 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal 27–29;2018. Proceedings 15 (pp. 845–852). Springer International Publishing
Dehmeshki J, Amin H, Valdivieso M, Ye X (2008) Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach. IEEE transactions on medical imaging 27(4):467–480.
Szegedy C et al (2015) Going deeper with convolutions. En Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1–9
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
He K et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770–778
Zhu Y et al (2019) A deep learning radiomics model for preoperative grading in meningioma. Eur J Radiol 116:128–134
Zheng X et al (2020) Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun 11:1–9
Afshar P et al (2019) From handcrafted to deep-learning-based cancer radiomics: challenges and opportunities. IEEE Signal Process Mag 36(4):132–160
Echaniz O, Graña M (2017) Ongoing work on deep learning for lung cancer prediction. In: Biomedical applications based on natural and artificial computing: international work-conference on the interplay between natural and artificial computation, IWINAC 2017, Corunna, Spain, June 19–23, 2017, Proceedings, Part II. Springer International Publishing, pp 42–48
Fu L et al (2017) Automatic detection of lung nodules: false positive reduction using convolution neural networks and handcrafted features. In: Medical imaging 2017: computer-aided diagnosis. SPIE, pp 60–67
Hassan AH, Wahed ME, Metwally MS, Atiea MA (2022) A hybrid approach for classification breast cancer histopathology images. Frontiers in scientific research and technology 3(1):1–10
Liu S et al (2017) Pulmonary nodule classification in lung cancer screening with three-dimensional convolutional neural networks. J Med Imaging 4(4):041308
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Jimenez-Pastor, A., Urbanos-García, G. (2023). How to Extract Radiomic Features from Imaging. In: Alberich-Bayarri, Á., Bellvís-Bataller, F. (eds) Basics of Image Processing. Imaging Informatics for Healthcare Professionals. Springer, Cham. https://doi.org/10.1007/978-3-031-48446-9_3
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