Ermittlung aussagekräftiger Bildgebungsbiomarker: Klinische Anwendungen bei Mamma- und Prostatakarzinomen^[*]

Zusammenfassung

Hintergrund Bildgebungsbiomarker sind quantitative Parameter aus bildgebenden Modalitäten, welche nicht-invasiv erhoben werden und Aussagen über physiologische und pathophysiologische Abläufe zulassen, wobei diese aus einzelnen (monoparametrisch) oder mehreren Parametern (bi- bzw. multiparametrisch) bestehen können.

Methode Die vorliegende Übersichtsarbeit soll den Stand der Technik zur Quantifizierung von multimodalen und multiparametrischen Bildgebungsbiomarkern vorstellen. Hierbei wird die Nutzung von Biomarkern mittels künstlicher Intelligenz thematisiert und die klinische Anwendung von bildgebenden Biomarkern bei Mamma- und Prostatakarzinomen erläutert. Für die Anfertigung des Übersichtsartikels wurde basierend auf Pubmed, Web of Science und Google Scholar eine ausführliche Literaturrecherche durchgeführt. Die Ergebnisse wurden hinsichtlich Stimmigkeit und Allgemeingültigkeit ausgewertet und diskutiert.

Ergebnisse und Schlussfolgerung Die Quantifizierung von unterschiedlichen bildgebenden Biomarkern erfolgt aus der Nutzung komplementärer Bildgebungsmodalitäten (multimodal) radiologischer und nuklearmedizinischer Techniken bzw. von Hybridverfahren. Aus diesen Techniken werden Parameter auf morphologischer (z. B. Größe), funktioneller (z. B. Vaskularisierung oder Diffusion), metabolischer (z. B. Glukosestoffwechsel) und molekularer (z. B. Expression des Prostataspezifischen Membranantigens, PSMA) Ebene bestimmt. Die Integration und Wichtung von bildgebenden Biomarkern erfolgt zunehmend mit der künstlichen Intelligenz, wobei Algorithmen des maschinellen Lernens genutzt werden. Auf diesem Wege nimmt die klinische Anwendung von bildgebenden Biomarkern zu, was anhand der Diagnostik von Mamma- und Prostatakarzinomen erläutert wird.

Kernaussagen 

• Bildgebungsbiomarker sind quantitative Parameter zur Erfassung von physiologischen und pathophysiologischen Abläufen.

• Die Integration von bildgebenden Biomarkern aus der multimodalen und multiparametrischen Bildgebung erfolgt über Algorithmen der künstlichen Intelligenz.

• Quantitative Parameter der Bildgebung sind grundlegender Bestandteil der Diagnostik aller Tumorentitäten, wie beispielsweise für Mamma- und Prostatakarzinome.

Zitierweise

• Bäuerle T, Dietzel M, Pinker K et al. Identification of impactful imaging biomarker: Clinical applications for breast and prostate carcinoma. Fortschr Röntgenstr 2024; 196: 354 – 362

Keywords

quantification - artificial intelligence - imaging - breast cancer - prostate cancer - biomarker

^* Ein Artikel der AG Methodik und Forschung.

Publication History

Received: 14 April 2023

Accepted: 19 August 2023

Article published online:
09 November 2023

• References

• 1 European Society of Radiology. ESR Statement on the Validation of Imaging Biomarkers. Insights Imaging 2020; 4 (01) 76
• 2 deSouza NM, van der Lugt A, Deroose CM. et al. Standardised lesion segmentation for imaging biomarker quantitation: a consensus recommendation from ESR and EORTC. Insights Imaging 2022; 13: 159
• 3 Alberich-Bayarri A, Neri E, Martí-Bonmatí L. Imaging biomarkers and imaging biobanks. In: Ranschaert E, Morozov S, Algra P. Hrsg. Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks. Springer Nature; 2019: 119-126
• 4 Pfaehler E, Burggraaff C, Kramer G. et al. PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability. PLoS One 2020; 15 (03) e0230901
• 5 Yip SSF, Parmar C, Blezek D. et al. Application of the 3D slicer chest imaging platform segmentation algorithm for large lung nodule delineation. PLoS One 2017; 12 (06) e0178944
• 6 Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016; 278 (02) 563-577
• 7 Patel JL, Goyal RK. Applications of artificial neural networks in medical science. Curr Clin Pharmacol 2007; 2 (03) 217-226
• 8 Han T, Pedersoli KJ, Zimmermann F. et al. Image prediction of disease progression for osteoarthritis by style-based manifold extrapolation. Nat Mach Intell 2022; 4: 1029-1039
• 9 Ellmann S, Wenkel E, Dietzel M. et al. Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in equivocal breast lesions. PLoS One 2020; 15 (01) e0228446
• 10 Ellmann S, Schlicht M, Dietzel M. et al. Computer-aided diagnosis in multiparametric MRI of the prostate: An open-access online tool for lesion classification with high accuracy. Cancers 2020; 12 (09) E2366
• 11 Molyneux PD, Miller DH, Filippi M. et al. Visual analysis of serial T2-weighted MRI in multiple sclerosis: intra- and interobserver reproducibility. Neuroradiology 1999; 41 (12) 882-888
• 12 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
• 13 Chernyak V, Fowler KJ, Kamaya A. et al. Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology 2018; 289 (03) 816-830
• 14 Eisenhauer EA, Therasse P, Bogaerts J. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009; 45 (02) 228-247
• 15 Jiang Y, You K, Qiu X. et al. Tumor volume predicts local recurrence in early rectal cancer treated with radical resection: A retrospective observational study of 270 patients. Int J Surg 2018; 49: 68-73
• 16 Sung H, Ferlay J, Siegel RL. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021; 71 (03) 209-249
• 17 Mann RM, Cho N, Moy L. Breast MRI: State of the Art. Radiology 2019; 292 (03) 520-536
• 18 Coffey K, Jochelson MS. Contrast-enhanced mammography in breast cancer screening. Eur J Radiol 2022; 156: 110513
• 19 Dietzel M, Trimboli RM, Zanardo M. et al. The potential of predictive and prognostic breast MRI (P2-bMRI). Eur Radiol Exp 2022; 6 (01) 42
• 20 McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 2006; 15 (06) 1159-1169
• 21 Bakker MF, de Lange SV, Pijnappel RM. et al. Supplemental MRI Screening for Women with Extremely Dense Breast Tissue. N Engl J Med 2019; 381 (22) 2091-2102
• 22 Mann RM, Athanasiou A, Baltzer PAT. et al. Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI). Eur Radiol 2022; 32 (06) 4036-4045
• 23 Kontos D, Winham SJ, Oustimov A. et al. Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment. Radiology 2019; 290 (01) 41-49
• 24 Pinto Dos Santos D, Dietzel M, Baessler B. A decade of radiomics research: are images really data or just patterns in the noise?. Eur Radiol 2021; 31 (01) 1-4
• 25 Gamble P, Jaroensri R, Wang H. et al. Determining breast cancer biomarker status and associated morphological features using deep learning. Commu Med 2021; 1: 14
• 26 Baltzer P, Mann RM, Iima M. et al. Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group. Eur Radiol 2020; 30 (03) 1436-1450
• 27 Dietzel M, Baltzer PAT. How to use the Kaiser score as a clinical decision rule for diagnosis in multiparametric breast MRI: a pictorial essay. Insights Imaging 2018; 9 (03) 325-335
• 28 Tofts PS, Brix G, Buckley DL. et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging 1999; 10 (03) 223-232
• 29 Nicolini A, Ferrari P, Duffy MJ. Prognostic and predictive biomarkers in breast cancer: Past, present and future. Semin Cancer Biol 2018; 52 (01) 56-73
• 30 Marinovich ML, Macaskill P, Irwig L. et al. Agreement between MRI and pathologic breast tumor size after neoadjuvant chemotherapy, and comparison with alternative tests: individual patient data meta-analysis. BMC Cancer 2015; 15: 662
• 31 Dietzel M, Schulz-Wendtland R, Ellmann S. et al. Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer. Sci Rep 2020; 10 (01) 3664
• 32 Baltzer PAT, Krug KB. et al. Evidence-Based and Structured Diagnosis in Breast MRI using the Kaiser Score. Fortschr Röntgenstr 2022; 194 (11) 1216-1228
• 33 Clauser P, Krug B, Bickel H. et al. Diffusion-weighted Imaging Allows for Downgrading MR BI-RADS 4 Lesions in Contrast-enhanced MRI of the Breast to Avoid Unnecessary Biopsy. Clin Cancer Res 2021; 27 (07) 1941-1948
• 34 Kasivisvanathan V, Rannikko AS, Borghi M. et al. MRI-Targeted or Standard Biopsy for Prostate-Cancer Diagnosis. N Engl J Med 2018; 378: 1767-1777
• 35 Rouviere O, Puech P, Renard-Penna R. et al. Use of prostate systematic and targeted biopsy on the basis of multiparametric MRI in biopsy-naive patients (MRI-FIRST): a prospective, multicentre, paired diagnostic study. Lancet Oncol 2019; 20: 100-109
• 36 Ahmed HU, El-Shater BosailyA, Brown LC. et al. Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 2017; 389: 815-822
• 37 Cooperberg MR, Carroll PR, Klotz L. Active surveillance for prostate cancer: progress and promise. J Clin Oncol 2011; 29: 3669-3676
• 38 Hegde JV, Mulkern RV, Panych LP. et al. Multiparametric MRI of prostate cancer: an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer. J Magn Reson Imaging 2013; 37: 1035-1054
• 39 Kaneko M, Lenon MSL, Storino Ramacciotti L. et al. Multiparametric ultrasound of prostate: role in prostate cancer diagnosis. Ther Adv Urol 2022; 14
• 40 Grey ADR, Scott R, Shah B. et al. Multiparametric ultrasound versus multiparametric MRI to diagnose prostate cancer (CADMUS): a prospective, multicentre, paired-cohort, confirmatory study. Lancet Oncol 2022; 23: 428-438
• 41 Turkbey B, Rosenkrantz AB, Haider MA. et al. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol 2019; 76: 340-351
• 42 Caglic I, Sushentsev N, Gnanapragasam VJ. et al. MRI-derived PRECISE scores for predicting pathologically-confirmed radiological progression in prostate cancer patients on active surveillance. Eur Radiol 2021; 31: 2696-2705
• 43 Knoedler JJ, Karnes RJ, Thompson RH. et al. The association of tumor volume with mortality following radical prostatectomy. Prostate Cancer Prostatic Dis 2014; 17: 144-148
• 44 Barentsz JO, Richenberg J, Clements R. et al. ESUR prostate MR guidelines 2012. Eur Radiol 2012; 22: 746-757
• 45 Breit HC, Block TK, Winkel DJ. et al. Revisiting DCE-MRI: Classification of Prostate Tissue Using Descriptive Signal Enhancement Features Derived From DCE-MRI Acquisition With High Spatiotemporal Resolution. Invest Radiol 2021; 56: 553-562
• 46 Tavakoli AA, Hielscher T, Badura P. et al. Contribution of Dynamic Contrast-enhanced and Diffusion MRI to PI-RADS for Detecting Clinically Significant Prostate Cancer. Radiology 2023; 306: 186-199
• 47 Panda A, O'Connor G, Lo WC. et al. Targeted Biopsy Validation of Peripheral Zone Prostate Cancer Characterization With Magnetic Resonance Fingerprinting and Diffusion Mapping. Invest Radiol 2019; 54: 485-493
• 48 Pang Y, Turkbey B, Bernardo M. et al. Intravoxel incoherent motion MR imaging for prostate cancer: an evaluation of perfusion fraction and diffusion coefficient derived from different b-value combinations. Magn Reson Med 2013; 69: 553-562
• 49 Tavakoli AA, Kuder TA, Tichy D. et al. Measured Multipoint Ultra-High b-Value Diffusion MRI in the Assessment of MRI-Detected Prostate Lesions. Invest Radiol 2021; 56: 94-102
• 50 Bonekamp D, Kohl S, Wiesenfarth M. et al. Radiomic Machine Learning for Characterization of Prostate Lesions with MRI: Comparison to ADC Values. Radiology 2018; 289: 128-137
• 51 Zwanenburg A, Vallières M, Abdalah MA. et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology 2020; 295: 328-338
• 52 Zhang KS, Schelb P, Netzer N. et al. Pseudoprospective Paraclinical Interaction of Radiology Residents With a Deep Learning System for Prostate Cancer Detection: Experience, Performance, and Identification of the Need for Intermittent Recalibration. Invest Radiol 2022; 57: 601-612
• 53 Dietzel M, Clauser P, Kapetas P. et al. Images Are Data: A Breast Imaging Perspective on a Contemporary Paradigm. Fortschr Röntgenstr 2021; 193 (08) 898-908