Subscribe to RSS
DOI: 10.1055/s-0043-1776426
Advancing Diagnostics and Patient Care: The Role of Biomarkers in Radiology
Abstract
The integration of biomarkers into medical practice has revolutionized the field of radiology, allowing for enhanced diagnostic accuracy, personalized treatment strategies, and improved patient care outcomes. This review offers radiologists a comprehensive understanding of the diverse applications of biomarkers in medicine. By elucidating the fundamental concepts, challenges, and recent advancements in biomarker utilization, it will serve as a bridge between the disciplines of radiology and epidemiology. Through an exploration of various biomarker types, such as imaging biomarkers, molecular biomarkers, and genetic markers, I outline their roles in disease detection, prognosis prediction, and therapeutic monitoring. I also discuss the significance of robust study designs, blinding, power and sample size calculations, performance metrics, and statistical methodologies in biomarker research. By fostering collaboration between radiologists, statisticians, and epidemiologists, I hope to accelerate the translation of biomarker discoveries into clinical practice, ultimately leading to improved patient care.
Publication History
Article published online:
08 February 2024
© 2024. Thieme. All rights reserved.
Thieme Medical Publishers, Inc.
333 Seventh Avenue, 18th Floor, New York, NY 10001, USA
-
References
- 1 Obuchowski NA, Huang E, deSouza NM. et al. A framework for evaluating the technical performance of multiparameter quantitative imaging biomarkers (mp-QIBs). Acad Radiol 2023; 30 (02) 147-158
- 2 Andre F, Ismaila N, Henry NL. et al. Use of biomarkers to guide decisions on adjuvant systemic therapy for women with early-stage invasive breast cancer: ASCO Clinical Practice Guideline Update—Integration of Results From TAILORx. J Clin Oncol 2019; 37 (22) 1956-1964
- 3 Chaddad A, Kucharczyk MJ, Daniel P. et al. Radiomics in glioblastoma: current status and challenges facing clinical implementation. Front Oncol 2019; 9: 374
- 4 Bodalal Z, Trebeschi S, Nguyen-Kim TDL, Schats W, Beets-Tan R. Radiogenomics: bridging imaging and genomics. Abdom Radiol (NY) 2019; 44 (06) 1960-1984
- 5 Relling MV, Evans WE. Pharmacogenomics in the clinic. Nature 2015; 526 (7573) 343-350
- 6 Schwaederle M, Parker BA, Schwab RB. et al. Precision oncology: the UC San Diego Moores Cancer Center PREDICT experience. Mol Cancer Ther 2016; 15 (04) 743-752
- 7 Zhang Q, Liu W, Luo SB. et al. Development of a prognostic five-gene signature for diffuse lower-grade glioma patients. Front Neurol 2021; 12: 633390
- 8 O'Connor JP, Aboagye EO, Adams JE. et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 2017; 14 (03) 169-186
- 9 Aerts HJ, Velazquez ER, Leijenaar RT. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5: 4006 . Published correction appears in Nat Commun 2014;5:4644
- 10 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 (04) 441-446
- 11 Pfister DG, Haddad RI, Worden FP. et al. Biomarkers predictive of response to pembrolizumab in head and neck cancer. Cancer Med 2023; 12 (06) 6603-6614
- 12 Tang Q, Chen Y, Li X. et al. The role of PD-1/PD-L1 and application of immune-checkpoint inhibitors in human cancers. Front Immunol 2022; 13: 964442
- 13 Nielsen TO, Leung SCY, Riaz N. et al. Ki67 assessment protocol as an integral biomarker for avoiding radiotherapy in the LUMINA breast cancer trial. Histopathology 2023 ; August 23 ( Epub ahead of print)
- 14 Yeung KT, More S, Woodward B, Velculescu V, Husain H. Circulating tumor DNA for mutation detection and identification of mechanisms of resistance in non-small cell lung cancer. Mol Diagn Ther 2017; 21 (04) 375-384
- 15 Jack Jr CR, Bennett DA, Blennow K. et al; Contributors. NIA-AA Research Framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement 2018; 14 (04) 535-562
- 16 Klunk WE, Engler H, Nordberg A. et al. Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann Neurol 2004; 55 (03) 306-319
- 17 Henschke CI, McCauley DI, Yankelevitz DF. et al. Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 1999; 354 (9173) 99-105
- 18 Gafson A, Giovannoni G, Hawkes CH. The diagnostic criteria for multiple sclerosis: from Charcot to McDonald. Mult Scler Relat Disord 2012; 1 (01) 9-14
- 19 Lehman CD, Wellman RD, Buist DS, Kerlikowske K, Tosteson AN, Miglioretti DL. Breast Cancer Surveillance Consortium. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med 2015; 175 (11) 1828-1837
- 20 Barentsz JO, Richenberg J, Clements R. et al; European Society of Urogenital Radiology. ESUR prostate MR guidelines 2012. Eur Radiol 2012; 22 (04) 746-757
- 21 Ginsburg SB, Algohary A, Pahwa S. et al. Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: preliminary findings from a multi-institutional study. J Magn Reson Imaging 2017; 46 (01) 184-193
- 22 Gugliandolo SG, Pepa M, Isaksson LJ. et al. MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218). Eur Radiol 2021; 31 (02) 716-728
- 23 Chiu FY, Yen Y. Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives. Biomark Res 2023; 11 (01) 35
- 24 Chitalia RD, Kontos D. Role of texture analysis in breast MRI as a cancer biomarker: a review. J Magn Reson Imaging 2019; 49 (04) 927-938
- 25 Giger ML, Karssemeijer N, Schnabel JA. Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu Rev Biomed Eng 2013; 15: 327-357
- 26 Loomba R, Adams LA. Advances in non-invasive assessment of hepatic fibrosis. Gut 2020; 69 (07) 1343-1352
- 27 Kim YS, Jang YN, Song JS. Comparison of gradient-recalled echo and spin-echo echo-planar imaging MR elastography in staging liver fibrosis: a meta-analysis. Eur Radiol 2018; 28 (04) 1709-1718
- 28 Li W, Le NN, Onishi N. et al; I-Spy Imaging Working Group, I-Spy Investigator Network. Diffusion-weighted MRI for predicting pathologic complete response in neoadjuvant immunotherapy. Cancers (Basel) 2022; 14 (18) 4436
- 29 Rosenkrantz AB, Mannelli L, Kong X. et al. Prostate cancer: utility of fusion of T2-weighted and high b-value diffusion-weighted images for peripheral zone tumor detection and localization. J Magn Reson Imaging 2011; 34 (01) 95-100
- 30 Provenzale JM, Wang GR, Brenner T, Petrella JR, Sorensen AG. Comparison of permeability in high-grade and low-grade brain tumors using dynamic susceptibility contrast MR imaging. AJR Am J Roentgenol 2002; 178 (03) 711-716
- 31 Evangelista L, Sepulcri M, Pasello G. PET/CT and the response to immunotherapy in lung cancer. Curr Radiopharm 2020; 13 (03) 177-184
- 32 Linning E, Lu L, Li L, Yang H, Schwartz LH, Zhao B. Radiomics for classifying histological subtypes of lung cancer based on multiphasic contrast-enhanced computed tomography. J Comput Assist Tomogr 2019; 43 (02) 300-306
- 33 Barrington SF, Mikhaeel NG, Kostakoglu L. et al. Role of imaging in the staging and response assessment of lymphoma: consensus of the International Conference on Malignant Lymphomas Imaging Working Group. J Clin Oncol 2014; 32 (27) 3048-3058 . Published correction appears in J Clin Oncol 2016;34(21):2562
- 34 van Oosterom AT, Judson I, Verweij J. et al; European Organisation for Research and Treatment of Cancer Soft Tissue and Bone Sarcoma Group. Safety and efficacy of imatinib (STI571) in metastatic gastrointestinal stromal tumours: a phase I study. Lancet 2001; 358 (9291) 1421-1423
- 35 O'Connor JP, Jackson A, Parker GJ, Jayson GC. DCE-MRI biomarkers in the clinical evaluation of antiangiogenic and vascular disrupting agents. Br J Cancer 2007; 96 (02) 189-195
- 36 Wielema M, Sijens PE, Dijkstra H. et al. Diffusion weighted imaging of the breast: performance of standardized breast tumor tissue selection methods in clinical decision making. PLoS One 2021; 16 (01) e0245930
- 37 Joo I, Lee JM, Han JK, Choi BI. Intravoxel incoherent motion diffusion-weighted MR imaging for monitoring the therapeutic efficacy of the vascular disrupting agent CKD-516 in rabbit VX2 liver tumors. Radiology 2014; 272 (02) 417-426
- 38 Planche V, Manjon JV, Mansencal B. et al. Structural progression of Alzheimer's disease over decades: the MRI staging scheme. Brain Commun 2022; 4 (03) fcac109
- 39 Li F, Sone S, Abe H, Macmahon H, Doi K. Malignant versus benign nodules at CT screening for lung cancer: comparison of thin-section CT findings. Radiology 2004; 233 (03) 793-798
- 40 Park JE, Kim HS, Park KJ, Kim SJ, Kim JH, Smith SA. Pre- and posttreatment glioma: comparison of amide proton transfer imaging with MR spectroscopy for biomarkers of tumor proliferation. Radiology 2016; 278 (02) 514-523
- 41 Brown M, Park AS, Shayya RF, Wolfson T, Su HI, Chang RJ. Ovarian imaging by magnetic resonance in adolescent girls with polycystic ovary syndrome and age-matched controls. J Magn Reson Imaging 2013; 38 (03) 689-693
- 42 Białecki J, Bartosz P, Marczyński W, Zając J. Usefulness of ultrasonography in the diagnosis of hematoma after primary hip arthroplasty. J Ultrason 2017; 17 (70) 149-153
- 43 Choi JW, Lee JM, Lee DH. et al. Radiofrequency ablation using a separable clustered electrode for the treatment of hepatocellular carcinomas: a randomized controlled trial of a dual-switching monopolar mode versus a single-switching monopolar mode. Korean J Radiol 2021; 22 (02) 179-188
- 44 Kumar AD, Durham DD, Lane L, Perera P, Rivera MP, Henderson LM. Randomized control trial of unconditional versus conditional incentives to increase study enrollment rates in participants at increased risk of lung cancer. J Clin Epidemiol 2022; 141: 11-17
- 45 Alexander FE. The Edinburgh Randomized Trial of Breast Cancer Screening. J Natl Cancer Inst Monogr 1997; (22) 31-35
- 46 Johnson KE, Miller B, Juvancic-Heltzel JA. et al. Agreement between ultrasound and dual-energy X-ray absorptiometry in assessing percentage body fat in college-aged adults. Clin Physiol Funct Imaging 2014; 34 (06) 493-496
- 47 Lv Y, Chen H, Luo B. et al. Transjugular intrahepatic portosystemic shunt with or without gastro-oesophageal variceal embolisation for the prevention of variceal rebleeding: a randomised controlled trial. Lancet Gastroenterol Hepatol 2022; 7 (08) 736-746 . Published correction appears in Lancet Gastroenterol Hepatol 2022;7(8):704
- 48 Bhatia K, Guest W, Lee H. et al. Radial vs. femoral artery access for procedural success in diagnostic cerebral angiography: a randomized clinical trial. Clin Neuroradiol 2021; 31 (04) 1083-1091
- 49 Russell KM, Champion VL, Monahan PO. et al. Randomized trial of a lay health advisor and computer intervention to increase mammography screening in African American women. Cancer Epidemiol Biomarkers Prev 2010; 19 (01) 201-210
- 50 Druy EM, Bettmann MA, Jeans W. A double-blind study of iopromide 300 for peripheral arteriography. Results of a multi-institutional comparison of iopromide with iohexol and iopamidol. Invest Radiol 1994; 29 (Suppl. 01) S102-S105 ; discussion S106
- 51 Kamalipour H, Bagheri M, Kamali K, Taleie A, Yarmohammadi H. Lateral neck radiography for prediction of difficult orotracheal intubation. Eur J Anaesthesiol 2005; 22 (09) 689-693
- 52 Zutshi V, Makkar B, Garg A, Batra S. Transvaginal sonography versus cystoscopy for detecting urinary bladder invasion in early stage cervical cancer. J Clin Diagn Res 2017; 11 (02) QC01-QC03
- 53 Yang H, Seon J, Sung PS. et al. Dexamethasone prophylaxis to alleviate postembolization syndrome after transarterial chemoembolization for hepatocellular carcinoma: a randomized, double-blinded, placebo-controlled study. J Vasc Interv Radiol 2017; 28 (11) 1503-1511.e2
- 54 Baffour FI, Ferrero A, Aird GA. et al. Evolving role of dual-energy CT in the clinical workup of gout: a retrospective study. AJR Am J Roentgenol 2022; 218 (06) 1041-1050
- 55 Maltenfort M. Type I, type II, and occasionally type III: how can we go wrong?. J Spinal Disord Tech 2015; 28 (05) 189
- 56 Peterson SJ, Foley S. Clinician's guide to understanding effect size, alpha level, power, and sample size. Nutr Clin Pract 2021; 36 (03) 598-605
- 57 Armstrong RA. When to use the Bonferroni correction. Ophthalmic Physiol Opt 2014; 34 (05) 502-508
- 58 Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Roy. Statist. Soc. Ser. B57 1995; (01) 289-300
- 59 Zhang S, Cao J, Ahn C. Sample size calculation for before-after experiments with partially overlapping cohorts. Contemp Clin Trials 2018; 64: 274-280
- 60 Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 2016; 15 (02) 155-163 . Published correction appears in J Chiropr Med 2017;16(4):346
- 61 Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977; 33 (01) 159-174
- 62 Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; 1 (8476) 307-310
- 63 McGraw KO, Wong SP. Forming inferences about some intraclass correlation coefficients. Psychol Methods 1996; 1 (01) 30-46
- 64 Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas 1960; 20 (01) 37-46
- 65 Altman DG. Practical Statistics for Medical Research. Boca Raton, FL: Chapman & Hall/CRC; 1990
- 66 Sim J, Wright CC. The kappa statistic in reliability studies: use, interpretation, and sample size requirements. Phys Ther 2005; 85 (03) 257-268
- 67 Zhang Z, Castelló A. Principal components analysis in clinical studies. Ann Transl Med 2017; 5 (17) 351
- 68 Hunt RJ. Percent agreement, Pearson's correlation, and kappa as measures of inter-examiner reliability. J Dent Res 1986; 65 (02) 128-130
- 69 Altman DG, Bland JM. Diagnostic tests. 1: Sensitivity and specificity. BMJ 1994; 308 (6943) 1552
- 70 Altman DG, Bland JM. Diagnostic tests 2: Predictive values. BMJ 1994; 309 (6947) 102
- 71 DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988; 44 (03) 837-845
- 72 Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making 2006; 26 (06) 565-574
- 73 Fawcett T. An introduction to ROC analysis. Pattern Recognit Lett 2006; 27 (08) 861-874
- 74 Greiner M, Pfeiffer D, Smith RD. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 2000; 45 (1–2): 23-41
- 75 Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol 2003; 56 (11) 1129-1135
- 76 Davis J, Goadrich M. The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd International Conference on Machine Learning, 2006. Available at: https://www.biostat.wisc.edu/~page/rocpr.pdf . Accessed October 10, 2023
- 77 Flach PA. Machine Learning: The Art and Science of Algorithms That Make Sense of Data. Cambridge, UK: Cambridge University Press; 2012
- 78 Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Inf Process Manage 2009; 45 (04) 427-437
- 79 Van Rijsbergen CJ. Information Retrieval. Oxford, UK: Butterworth-Heinemann; 1979
- 80 Powers DM. Evaluation: from precision, recall and F1 to ROC, informedness, markedness and correlation. J Mach Learn Technol 2011; 2 (01) 37-63
- 81 Nilsson NJ. Artificial Intelligence: A New Synthesis. San Francisco, CA: Morgan Kaufmann; 1998
- 82 Bishop CM. Pattern Recognition and Machine Learning. New York, NY: Springer; 2006
- 83 Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med 2022; 28 (01) 31-38
- 84 Martin-Carreras T, Li H, Cooper K, Fan Y, Sebro R. Radiomic features from MRI distinguish myxomas from myxofibrosarcomas. BMC Med Imaging 2019; 19 (01) 67
- 85 Elmahdy M, Sebro R. Opportunistic screening for osteoporosis using CT scans of the knee: a pilot study. Stud Health Technol Inform 2023; 302: 909-910
- 86 Sebro R, De la Garza-Ramos C. Utilizing machine learning for opportunistic screening for low BMD using CT scans of the cervical spine. J Neuroradiol 2023; 50 (03) 293-301
- 87 Sebro R, De la Garza-Ramos C, Peterson JJ. Detecting whether L1 or other lumbar levels would be excluded from DXA bone mineral density analysis during opportunistic CT screening for osteoporosis using machine learning. Int J CARS 2023 ; May 23 ( Epub ahead of print)
- 88 Sebro R, De la Garza-Ramos C. Support vector machines are superior to principal components analysis for selecting the optimal bones' CT attenuations for opportunistic screening for osteoporosis using CT scans of the foot or ankle. Osteoporos Sarcopenia 2022; 8 (03) 112-122
- 89 Sebro R, De la Garza-Ramos C. Machine learning for the prediction of osteopenia/osteoporosis using the CT attenuation of multiple osseous sites from chest CT. Eur J Radiol 2022; 155: 110474
- 90 Linna N, Kahn Jr CE. Applications of natural language processing in radiology: A systematic review. Int J Med Inform 2022; 163: 104779
- 91 Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18 (08) 500-510
- 92 Shen Y, Heacock L, Elias J. et al. ChatGPT and other large language models are double-edged swords. Radiology 2023; 307 (02) e230163
- 93 Andaur Navarro CL, Damen JAA, Takada T. et al. Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review. BMJ 2021; 375 (2281) n2281
- 94 Sackett DL, Haynes RB, Guyatt GH, Tugwell P. Clinical Epidemiology: A Basic Science for Clinical Medicine. Philadelphia, PA: Lippincott Williams & Wilkins; 1991
- 95 Hosmer DW, Lemeshow S, Sturdivant RX. Applied Logistic Regression. New York, NY: John Wiley & Sons; 2013
- 96 Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. New York, NY: Springer; 2019
- 97 Bossuyt PM, Reitsma JB, Bruns DE. et al; STARD Group. STARD 2015: An updated list of essential items for reporting diagnostic accuracy studies. Radiology 2015; 277 (03) 826-832
- 98 Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 2015; 162 (01) 55-63 . Published correction appears in Ann Intern Med 2015;162(8):600
- 99 McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM. Statistics Subcommittee of the NCI-EORTC Working Group on Cancer Diagnostics. Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 2005; 97 (16) 1180-1184
- 100 Schulz KF, Altman DG, Moher D. CONSORT Group. CONSORT 2010 statement: updated guidelines for reporting parallel group randomized trials. Ann Intern Med 2010; 152 (11) 726-732
- 101 Moher D, Hopewell S, Schulz KF. et al; Consolidated Standards of Reporting Trials Group. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. J Clin Epidemiol 2010; 63 (08) e1-e37 . Published correction appears in J Clin Epidemiol 2012;65(3):351