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[Potential of radiomics and artificial intelligence in myeloma imaging : Development of automatic, comprehensive, objective skeletal analyses from whole-body imaging data]. Radiologe 2021; 62:44-50. [PMID: 34889968 DOI: 10.1007/s00117-021-00940-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/22/2021] [Indexed: 10/19/2022]
Abstract
CLINICAL/METHODICAL ISSUE Multiple myeloma can affect the complete skeleton, which makes whole-body imaging necessary. With the current assessment of these complex datasets by radiologists, only a small part of the accessible information is assessed and reported. STANDARD RADIOLOGICAL METHODS Depending on the question and availability, computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) is performed and the results are then visually examined by radiologists. METHODOLOGICAL INNOVATIONS A combination of automatic skeletal segmentation using artificial intelligence and subsequent radiomics analysis of each individual bone have the potential to provide automatic, comprehensive, and objective skeletal analyses. PERFORMANCE A few automatic skeletal segmentation algorithms for CT already show promising results. In addition, first studies indicate correlations between radiomics features of bone and bone marrow with established disease markers and therapy response. ACHIEVEMENTS Artificial intelligence (AI) and radiomics algorithms for automatic skeletal analysis from whole-body imaging are currently in an early phase of development.
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Wennmann M, Hielscher T, Kintzelé L, Menze BH, Langs G, Merz M, Sauer S, Kauczor HU, Schlemmer HP, Delorme S, Goldschmidt H, Weinhold N, Hillengass J, Weber MA. Analyzing Longitudinal wb-MRI Data and Clinical Course in a Cohort of Former Smoldering Multiple Myeloma Patients: Connections between MRI Findings and Clinical Progression Patterns. Cancers (Basel) 2021; 13:961. [PMID: 33668879 PMCID: PMC7956649 DOI: 10.3390/cancers13050961] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 02/22/2021] [Indexed: 11/16/2022] Open
Abstract
The purpose of this study was to analyze size and growth dynamics of focal lesions (FL) as well as to quantify diffuse infiltration (DI) in untreated smoldering multiple myeloma (SMM) patients and correlate those MRI features with timepoint and cause of progression. We investigated 199 whole-body magnetic resonance imaging (wb-MRI) scans originating from longitudinal imaging of 60 SMM patients and 39 computed tomography (CT) scans for corresponding osteolytic lesions (OL) in 17 patients. All FLs >5 mm were manually segmented to quantify volume and growth dynamics, and DI was scored, rating four compartments separately in T1- and fat-saturated T2-weighted images. The majority of patients with at least two FLs showed substantial spatial heterogeneity in growth dynamics. The volume of the largest FL (p = 0.001, c-index 0.72), the speed of growth of the fastest growing FL (p = 0.003, c-index 0.75), the DI score (DIS, p = 0.014, c-index 0.67), and its dynamic over time (DIS dynamic, p < 0.001, c-index 0.67) all significantly correlated with the time to progression. Size and growth dynamics of FLs correlated significantly with presence/appearance of OL in CT within 2 years after the respective MRI assessment (p = 0.016 and p = 0.022). DIS correlated with decrease of hemoglobin (p < 0.001). In conclusion, size and growth dynamics of FLs correlate with prognosis and local bone destruction. Connections between MRI findings and progression patterns (fast growing FL-OL; DIS-hemoglobin decrease) might enable more precise diagnostic and therapeutic approaches for SMM patients in the future.
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Affiliation(s)
- Markus Wennmann
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (H.-P.S.); (S.D.)
- Diagnostic and Interventional Radiology, University Hospital Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany; (L.K.); (H.-U.K.); (M.-A.W.)
| | - Thomas Hielscher
- Division of Biostatistics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany;
| | - Laurent Kintzelé
- Diagnostic and Interventional Radiology, University Hospital Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany; (L.K.); (H.-U.K.); (M.-A.W.)
| | - Bjoern H. Menze
- Department of Computer Science, Technical University of Munich, Boltzmannstrasse 3, 85748 Garching, Germany;
| | - Georg Langs
- Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Laboratory, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria;
| | - Maximilian Merz
- Department of Medicine V, Multiple Myeloma Section, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany; (M.M.); (S.S.); (H.G.); (N.W.)
- National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
| | - Sandra Sauer
- Department of Medicine V, Multiple Myeloma Section, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany; (M.M.); (S.S.); (H.G.); (N.W.)
| | - Hans-Ulrich Kauczor
- Diagnostic and Interventional Radiology, University Hospital Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany; (L.K.); (H.-U.K.); (M.-A.W.)
| | - Heinz-Peter Schlemmer
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (H.-P.S.); (S.D.)
| | - Stefan Delorme
- Division of Radiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; (H.-P.S.); (S.D.)
| | - Hartmut Goldschmidt
- Department of Medicine V, Multiple Myeloma Section, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany; (M.M.); (S.S.); (H.G.); (N.W.)
- National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany
| | - Niels Weinhold
- Department of Medicine V, Multiple Myeloma Section, University Hospital Heidelberg, Im Neuenheimer Feld 410, 69120 Heidelberg, Germany; (M.M.); (S.S.); (H.G.); (N.W.)
| | - Jens Hillengass
- Department of Medicine, Roswell Park Comprehensive Cancer Center, Elm and Carlton Streets, Buffalo, NY 14263, USA;
| | - Marc-André Weber
- Diagnostic and Interventional Radiology, University Hospital Heidelberg, Im Neuenheimer Feld 110, 69120 Heidelberg, Germany; (L.K.); (H.-U.K.); (M.-A.W.)
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057 Rostock, Germany
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Kleesiek J, Murray JM, Strack C, Prinz S, Kaissis G, Braren R. [Artificial intelligence and machine learning in oncologic imaging]. DER PATHOLOGE 2020; 41:649-658. [PMID: 33052431 DOI: 10.1007/s00292-020-00827-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Machine learning (ML) is entering many areas of society, including medicine. This transformation has the potential to drastically change medicine and medical practice. These aspects become particularly clear when considering the different stages of oncologic patient care and the involved interdisciplinary and intermodality interactions. In recent publications, computers-in collaboration with humans or alone-have been outperforming humans regarding tumor identification, tumor classification, estimating prognoses, and evaluation of treatments. In addition, ML algorithms, e.g., artificial neural networks (ANNs), which constitute the drivers behind many of the latest achievements in ML, can deliver this level of performance in a reproducible, fast, and inexpensive manner. In the future, artificial intelligence applications will become an integral part of the medical profession and offer advantages for oncologic diagnostics and treatment.
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Affiliation(s)
- Jens Kleesiek
- AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Deutschland. .,German Cancer Consortium (DKTK), Heidelberg, Deutschland. .,Institut für Künstliche Intelligenz in der Medizin (IKIM), Universitätsklinikum Essen, Girardetstr. 6, 45131, Essen, Deutschland.
| | - Jacob M Murray
- AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Deutschland.,Heidelberg University, Heidelberg, Deutschland
| | - Christian Strack
- AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Deutschland.,Heidelberg University, Heidelberg, Deutschland
| | - Sebastian Prinz
- AG Computational Radiology, Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Deutschland.,Heidelberg University, Heidelberg, Deutschland
| | - Georgios Kaissis
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, München, Deutschland
| | - Rickmer Braren
- German Cancer Consortium (DKTK), Heidelberg, Deutschland.,Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, München, Deutschland
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