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Goertz L, Hohenstatt S, Vollherbst DF, Weyland CS, Nikoubashman O, Styczen H, Gronemann C, Weiss D, Kaschner M, Pflaeging M, Siebert E, Zopfs D, Kottlors J, Pennig L, Schlamann M, Bohner G, Liebig T, Turowski B, Dorn F, Deuschl C, Wiesmann M, Möhlenbruch MA, Kabbasch C. Safety and efficacy of coated flow diverters in the treatment of ruptured intracranial aneurysms: a retrospective multicenter study. J Neurointerv Surg 2024:jnis-2024-021516. [PMID: 38569886 DOI: 10.1136/jnis-2024-021516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
BACKGROUND This multicenter study evaluated the safety and efficacy of coated flow diverters (cFDs) for the treatment of ruptured intracranial aneurysms. METHODS Consecutive patients treated with different cFDs for ruptured aneurysms under tirofiban at eight neurovascular centers between 2016 and 2023 were retrospectively analyzed. The majority of patients were loaded with dual antiplatelet therapy after the treatment. Aneurysm occlusion was determined using the O'Kelly-Marotta (OKM) grading scale. Primary outcome measures were major procedural complications and aneurysmal rebleeding during hospitalization. RESULTS The study included 60 aneurysms (posterior circulation: 28 (47%)) with a mean size of 5.8±4.7 mm. Aneurysm morphology was saccular in 28 (47%), blister-like in 12 (20%), dissecting in 13 (22%), and fusiform in 7 (12%). Technical success was 100% with a mean of 1.1 cFDs implanted per aneurysm. Adjunctive coiling was performed in 11 (18%) aneurysms. Immediate contrast retention was observed in 45 (75%) aneurysms. There was 1 (2%) major procedural complication (a major stroke, eventually leading to death) and no aneurysmal rebleeding. A good outcome (modified Rankin Scale 0-2) was achieved in 40 (67%) patients. At a mean follow-up of 6 months, 27/34 (79%) aneurysms were completely occluded (OKM D), 3/34 (9%) had an entry remnant (OKM C), and 4/34 (12%) had residual filling (OKM A or B). There was 1 (3%) severe in-stent stenosis during follow-up that was treated with balloon angioplasty. CONCLUSIONS Treatment of ruptured aneurysms with cFDs was reasonably safe and efficient and thus represents a valid treatment option, especially for complex cases.
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Affiliation(s)
- Lukas Goertz
- Department of Radiology and Neuroradiology, University of Cologne, Faculty of Medicine and University Hospital, Cologne, Germany
| | - Sophia Hohenstatt
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Dominik F Vollherbst
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | | | - Omid Nikoubashman
- Department of Neuroradiology, University Hospital Aachen, Aachen, Germany
| | - Hanna Styczen
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | | | - Daniel Weiss
- Department of Neuroradiology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Marius Kaschner
- Department of Neuroradiology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Muriel Pflaeging
- Department of Neuroradiology, University Hospital Munich (LMU), Munich, Germany
| | - Eberhard Siebert
- Department of Neuroradiology, University Hospital Berlin (Charité), Berlin, Germany
| | - David Zopfs
- Department of Radiology and Neuroradiology, University of Cologne, Faculty of Medicine and University Hospital, Cologne, Germany
| | - Jonathan Kottlors
- Department of Radiology and Neuroradiology, University of Cologne, Faculty of Medicine and University Hospital, Cologne, Germany
| | - Lenhard Pennig
- Department of Radiology and Neuroradiology, University of Cologne, Faculty of Medicine and University Hospital, Cologne, Germany
| | - Marc Schlamann
- Department of Radiology and Neuroradiology, University of Cologne, Faculty of Medicine and University Hospital, Cologne, Germany
| | - Georg Bohner
- Department of Neuroradiology, University Hospital Berlin (Charité), Berlin, Germany
| | - Thomas Liebig
- Department of Neuroradiology, University Hospital Munich (LMU), Munich, Germany
| | - Bernd Turowski
- Department of Neuroradiology, University Hospital Düsseldorf, Düsseldorf, Germany
| | - Franziska Dorn
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Cornelius Deuschl
- Institute for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Martin Wiesmann
- Department of Neuroradiology, University Hospital Aachen, Aachen, Germany
| | - Markus A Möhlenbruch
- Department of Neuroradiology, University Hospital Heidelberg, Heidelberg, Germany
| | - Christoph Kabbasch
- Department of Radiology and Neuroradiology, University of Cologne, Faculty of Medicine and University Hospital, Cologne, Germany
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Gertz RJ, Dratsch T, Bunck AC, Lennartz S, Iuga AI, Hellmich MG, Persigehl T, Pennig L, Gietzen CH, Fervers P, Maintz D, Hahnfeldt R, Kottlors J. Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy. Radiology 2024; 311:e232714. [PMID: 38625012 DOI: 10.1148/radiol.232714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), may assist in generating reports. Purpose To assess effectiveness of GPT-4 in identifying common errors in radiology reports, focusing on performance, time, and cost-efficiency. Materials and Methods In this retrospective study, 200 radiology reports (radiography and cross-sectional imaging [CT and MRI]) were compiled between June 2023 and December 2023 at one institution. There were 150 errors from five common error categories (omission, insertion, spelling, side confusion, and other) intentionally inserted into 100 of the reports and used as the reference standard. Six radiologists (two senior radiologists, two attending physicians, and two residents) and GPT-4 were tasked with detecting these errors. Overall error detection performance, error detection in the five error categories, and reading time were assessed using Wald χ2 tests and paired-sample t tests. Results GPT-4 (detection rate, 82.7%;124 of 150; 95% CI: 75.8, 87.9) matched the average detection performance of radiologists independent of their experience (senior radiologists, 89.3% [134 of 150; 95% CI: 83.4, 93.3]; attending physicians, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; residents, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; P value range, .522-.99). One senior radiologist outperformed GPT-4 (detection rate, 94.7%; 142 of 150; 95% CI: 89.8, 97.3; P = .006). GPT-4 required less processing time per radiology report than the fastest human reader in the study (mean reading time, 3.5 seconds ± 0.5 [SD] vs 25.1 seconds ± 20.1, respectively; P < .001; Cohen d = -1.08). The use of GPT-4 resulted in lower mean correction cost per report than the most cost-efficient radiologist ($0.03 ± 0.01 vs $0.42 ± 0.41; P < .001; Cohen d = -1.12). Conclusion The radiology report error detection rate of GPT-4 was comparable with that of radiologists, potentially reducing work hours and cost. © RSNA, 2024 See also the editorial by Forman in this issue.
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Affiliation(s)
- Roman Johannes Gertz
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Thomas Dratsch
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Alexander Christian Bunck
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Simon Lennartz
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Andra-Iza Iuga
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Martin Gunnar Hellmich
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Thorsten Persigehl
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Lenhard Pennig
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Carsten Herbert Gietzen
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Philipp Fervers
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - David Maintz
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Robert Hahnfeldt
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
| | - Jonathan Kottlors
- From the Institute of Diagnostic and Interventional Radiology (R.J.G., T.D., A.C.B., S.L., A.I.I., T.P., L.P., C.H.G., P.F., D.M., R.H., J.K.) and Institute of Medical Statistics and Bioinformatics (M.G.H.), Faculty of Medicine, University Hospital Cologne, University of Cologne, Kerpener Strasse 62, 50937 Cologne, Germany
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Zaeske C, Zopfs D, Laukamp K, Lennartz S, Kottlors J, Goertz L, Stetefeld H, Hof M, Abdullayev N, Kabbasch C, Schlamann M, Schönfeld M. Immediate angiographic control after intra-arterial nimodipine administration underestimates the vasodilatory effect. Sci Rep 2024; 14:6154. [PMID: 38486099 PMCID: PMC10940303 DOI: 10.1038/s41598-024-56807-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 03/11/2024] [Indexed: 03/18/2024] Open
Abstract
Intra-arterial nimodipine administration is a widely used rescue therapy for cerebral vasospasm. Although it is known that its effect sets in with delay, there is little evidence in current literature. Our aim was to prove that the maximal vasodilatory effect is underestimated in direct angiographic controls. We reviewed all cases of intra-arterial nimodipine treatment for subarachnoid hemorrhage-related cerebral vasospasm between January 2021 and December 2022. Inclusion criteria were availability of digital subtraction angiography runs before and after nimodipine administration and a delayed run for the most affected vessel at the end of the procedure to decide on further escalation of therapy. We evaluated nimodipine dose, timing of administration and vessel diameters. Delayed runs were performed in 32 cases (19 patients) with a mean delay of 37.6 (± 16.6) min after nimodipine administration and a mean total nimodipine dose of 4.7 (± 1.2) mg. Vessel dilation was more pronounced in delayed vs. immediate controls, with greater changes in spastic vessel segments (n = 31: 113.5 (± 78.5%) vs. 32.2% (± 27.9%), p < 0.0001) vs. non-spastic vessel segments (n = 32: 23.1% (± 13.5%) vs. 13.3% (± 10.7%), p < 0.0001). In conclusion intra-arterially administered nimodipine seems to exert a delayed vasodilatory effect, which should be considered before escalation of therapy.
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Affiliation(s)
- Charlotte Zaeske
- Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpenerstr. 62, 50937, Cologne, Germany.
| | - David Zopfs
- Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpenerstr. 62, 50937, Cologne, Germany
| | - Kai Laukamp
- Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpenerstr. 62, 50937, Cologne, Germany
| | - Simon Lennartz
- Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpenerstr. 62, 50937, Cologne, Germany
| | - Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpenerstr. 62, 50937, Cologne, Germany
| | - Lukas Goertz
- Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpenerstr. 62, 50937, Cologne, Germany
| | - Henning Stetefeld
- Department of Neurology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Marion Hof
- Department of Neurosurgery, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Nuran Abdullayev
- Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpenerstr. 62, 50937, Cologne, Germany
| | - Christoph Kabbasch
- Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpenerstr. 62, 50937, Cologne, Germany
| | - Marc Schlamann
- Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpenerstr. 62, 50937, Cologne, Germany
| | - Michael Schönfeld
- Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Kerpenerstr. 62, 50937, Cologne, Germany
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4
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Goertz L, Pflaeging M, Gronemann C, Zopfs D, Kottlors J, Schlamann M, Dorn F, Liebig T, Kabbasch C. Aneurysm Treatment With the Pipeline Vantage Embolization Device in Retrospective Evaluation: Periprocedural Results from the Pipe-VADER Study. World Neurosurg 2024; 183:e210-e217. [PMID: 38101543 DOI: 10.1016/j.wneu.2023.12.057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 12/10/2023] [Indexed: 12/17/2023]
Abstract
OBJECTIVE The Pipeline Vantage Embolization Device is a fourth-generation flow diverter with an antithrombotic coating and a reduced profile compared to previous Pipeline versions. The objective of this study was to evaluate the procedural feasibility, safety, and efficacy of this device. METHODS The Pipe-VADER study was designed as a retrospective, observational study of consecutive patients treated with the Vantage at 3 neurovascular centers. Patient and aneurysm characteristics, procedural parameters, early complications, and extent of postinterventional contrast retention were analyzed on an intention-to-treat basis. RESULTS Twenty-eight patients with 31 aneurysms (median size: 5.0 mm, posterior circulation: 4 [12.9%], ruptured: 5 [16.1%]) were included. The technical success rate was 100%, with multiple stents used in 4/30 (13.3%) procedures. Of the 30 procedures, adjunctive coiling was performed in 3 (10.0%) and balloon angioplasty in 2 (6.7%). Median procedure time was 62 minutes. Procedural ischemic stroke occured in 4 (13.3%) cases, whereof 2 were major strokes (6.6%). There were no hemorrhagic complications. Initial contrast retention was observed in 29/31 (93.5%) aneurysms. All 27 overstented side vessels were patent at the end of the procedure. Short-term follow-up (median: 5 months) showed complete and favorable occlusion rates of 70% (14/20) and 80% (16/20), respectively. CONCLUSIONS The new Pipeline Vantage appears to be safe and feasible for the treatment of intracranial aneurysms and warrants further evaluation.
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Affiliation(s)
- Lukas Goertz
- Department of Radiology and Neuroradiology, University of Cologne, Faculty of Medicine and University Hospital, Cologne, Germany.
| | - Muriel Pflaeging
- Department of Neuroradiology, University Hospital Munich (LMU), Munich, Germany
| | | | - David Zopfs
- Department of Radiology and Neuroradiology, University of Cologne, Faculty of Medicine and University Hospital, Cologne, Germany
| | - Jonathan Kottlors
- Department of Radiology and Neuroradiology, University of Cologne, Faculty of Medicine and University Hospital, Cologne, Germany
| | - Marc Schlamann
- Department of Radiology and Neuroradiology, University of Cologne, Faculty of Medicine and University Hospital, Cologne, Germany
| | - Franziska Dorn
- Department of Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Thomas Liebig
- Department of Neuroradiology, University Hospital Munich (LMU), Munich, Germany
| | - Christoph Kabbasch
- Department of Radiology and Neuroradiology, University of Cologne, Faculty of Medicine and University Hospital, Cologne, Germany
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Steuwe A, Kamp B, Afat S, Akinina A, Aludin S, Bas EG, Berger J, Bohrer E, Brose A, Büttner SM, Ehrengut C, Gerwing M, Grosu S, Gussew A, Güttler F, Heinrich A, Jiraskova P, Kloth C, Kottlors J, Kuennemann MD, Liska C, Lubina N, Manzke M, Meinel FG, Meyer HJ, Mittermeier A, Persigehl T, Schmill LP, Steinhardt M, The Racoon Study Group, Antoch G, Valentin B. Standardization of a CT Protocol for Imaging Patients with Suspected COVID-19-A RACOON Project. Bioengineering (Basel) 2024; 11:207. [PMID: 38534481 DOI: 10.3390/bioengineering11030207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 02/09/2024] [Accepted: 02/15/2024] [Indexed: 03/28/2024] Open
Abstract
CT protocols that diagnose COVID-19 vary in regard to the associated radiation exposure and the desired image quality (IQ). This study aims to evaluate CT protocols of hospitals participating in the RACOON (Radiological Cooperative Network) project, consolidating CT protocols to provide recommendations and strategies for future pandemics. In this retrospective study, CT acquisitions of COVID-19 patients scanned between March 2020 and October 2020 (RACOON phase 1) were included, and all non-contrast protocols were evaluated. For this purpose, CT protocol parameters, IQ ratings, radiation exposure (CTDIvol), and central patient diameters were sampled. Eventually, the data from 14 sites and 534 CT acquisitions were analyzed. IQ was rated good for 81% of the evaluated examinations. Motion, beam-hardening artefacts, or image noise were reasons for a suboptimal IQ. The tube potential ranged between 80 and 140 kVp, with the majority between 100 and 120 kVp. CTDIvol was 3.7 ± 3.4 mGy. Most healthcare facilities included did not have a specific non-contrast CT protocol. Furthermore, CT protocols for chest imaging varied in their settings and radiation exposure. In future, it will be necessary to make recommendations regarding the required IQ and protocol parameters for the majority of CT scanners to enable comparable IQ as well as radiation exposure for different sites but identical diagnostic questions.
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Affiliation(s)
- Andrea Steuwe
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Benedikt Kamp
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Saif Afat
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Alena Akinina
- Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), 06120 Halle, Germany
| | - Schekeb Aludin
- Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein Campus Kiel, 24105 Kiel, Germany
| | - Elif Gülsah Bas
- Department of Diagnostic and Interventional Radiology, University Hospital of Marburg, 35043 Marburg, Germany
| | - Josephine Berger
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076 Tuebingen, Germany
| | - Evelyn Bohrer
- Department of Diagnostic and Interventional Radiology, University Hospital Giessen, Justus Liebig University, Klinikstr. 33, 35392 Giessen, Germany
| | - Alexander Brose
- Department of Diagnostic and Interventional Radiology, University Hospital Giessen, Justus Liebig University, Klinikstr. 33, 35392 Giessen, Germany
| | - Susanne Martina Büttner
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Constantin Ehrengut
- Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103 Leipzig, Germany
| | - Mirjam Gerwing
- Clinic of Radiology, University of Münster, 48149 Münster, Germany
| | - Sergio Grosu
- Department of Radiology, LMU University Hospital, LMU Munich, 81377 Munich, Germany
| | - Alexander Gussew
- Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), 06120 Halle, Germany
| | - Felix Güttler
- Department of Radiology, Jena University Hospital, Friedrich Schiller University, 07747 Jena, Germany
| | - Andreas Heinrich
- Department of Radiology, Jena University Hospital, Friedrich Schiller University, 07747 Jena, Germany
| | - Petra Jiraskova
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany
| | - Christopher Kloth
- Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany
| | - Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | | | - Christian Liska
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Nora Lubina
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, Stenglinstraße 2, 86156 Augsburg, Germany
| | - Mathias Manzke
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Schillingallee 36, 18057 Rostock, Germany
| | - Felix G Meinel
- Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Schillingallee 36, 18057 Rostock, Germany
| | - Hans-Jonas Meyer
- Department of Diagnostic and Interventional Radiology, University of Leipzig Medical Center, Liebigstraße 20, 04103 Leipzig, Germany
| | - Andreas Mittermeier
- Department of Radiology, LMU University Hospital, LMU Munich, 81377 Munich, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Lars-Patrick Schmill
- Department of Radiology and Neuroradiology, University Hospital Schleswig-Holstein Campus Kiel, 24105 Kiel, Germany
| | - Manuel Steinhardt
- Institute of Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, 81675 Munich, Germany
| | | | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Birte Valentin
- Department of Diagnostic and Interventional Radiology, Medical Faculty and University Hospital, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
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Kottlors J, Bratke G, Rauen P, Kabbasch C, Persigehl T, Schlamann M, Lennartz S. Feasibility of Differential Diagnosis Based on Imaging Patterns Using a Large Language Model. Radiology 2023; 308:e231167. [PMID: 37404149 DOI: 10.1148/radiol.231167] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/06/2023]
Affiliation(s)
- Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Grischa Bratke
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Philip Rauen
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Christoph Kabbasch
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Marc Schlamann
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Simon Lennartz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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Gertz RJ, Bunck AC, Lennartz S, Dratsch T, Iuga AI, Maintz D, Kottlors J. GPT-4 for Automated Determination of Radiological Study and Protocol based on Radiology Request Forms: A Feasibility Study. Radiology 2023; 307:e230877. [PMID: 37310247 DOI: 10.1148/radiol.230877] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Published under a CC BY 4.0 license.
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Affiliation(s)
- Roman Johannes Gertz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Alexander Christian Bunck
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Simon Lennartz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Thomas Dratsch
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Andra-Iza Iuga
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - David Maintz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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Fervers P, Fervers F, Rinneburger M, Weisthoff M, Kottlors J, Reimer R, Zopfs D, Celik E, Maintz D, Große-Hokamp N, Persigehl T. Physiological iodine uptake of the spine's bone marrow in dual-energy computed tomography - using artificial intelligence to define reference values based on 678 CT examinations of 189 individuals. Front Endocrinol (Lausanne) 2023; 14:1098898. [PMID: 37274340 PMCID: PMC10235812 DOI: 10.3389/fendo.2023.1098898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 05/09/2023] [Indexed: 06/06/2023] Open
Abstract
Purpose The bone marrow's iodine uptake in dual-energy CT (DECT) is elevated in malignant disease. We aimed to investigate the physiological range of bone marrow iodine uptake after intravenous contrast application, and examine its dependence on vBMD, iodine blood pool, patient age, and sex. Method Retrospective analysis of oncological patients without evidence of metastatic disease. DECT examinations were performed on a spectral detector CT scanner in portal venous contrast phase. The thoracic and lumbar spine were segmented by a pre-trained neural network, obtaining volumetric iodine concentration data [mg/ml]. vBMD was assessed using a phantomless, CE-certified software [mg/cm3]. The iodine blood pool was estimated by ROI-based measurements in the great abdominal vessels. A multivariate regression model was fit with the dependent variable "median bone marrow iodine uptake". Standardized regression coefficients (β) were calculated to assess the impact of each covariate. Results 678 consecutive DECT exams of 189 individuals (93 female, age 61.4 ± 16.0 years) were evaluated. AI-based segmentation provided volumetric data of 97.9% of the included vertebrae (n=11,286). The 95th percentile of bone marrow iodine uptake, as a surrogate for the upper margin of the physiological distribution, ranged between 4.7-6.4 mg/ml. vBMD (p <0.001, mean β=0.50) and portal vein iodine blood pool (p <0.001, mean β=0.43) mediated the strongest impact. Based thereon, adjusted reference values were calculated. Conclusion The bone marrow iodine uptake demonstrates a distinct profile depending on vBMD, iodine blood pool, patient age, and sex. This study is the first to provide the adjusted reference values.
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Affiliation(s)
- Philipp Fervers
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - Florian Fervers
- Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, Germany
| | - Miriam Rinneburger
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - Mathilda Weisthoff
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - Jonathan Kottlors
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - Robert Reimer
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - David Zopfs
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - Erkan Celik
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - David Maintz
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - Nils Große-Hokamp
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
| | - Thorsten Persigehl
- University Cologne, Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, Cologne, Germany
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Kottlors J, Fervers P, Geißen S, Gertz RJ, Bremm J, Rinneburger M, Weisthoff M, Shahzad R, Maintz D, Persigehl T. Morphological appearance of the B.1.1.7 mutation of the novel coronavirus 2 (SARS-CoV-2) in chest CT. Quant Imaging Med Surg 2023; 13:1058-1070. [PMID: 36819239 PMCID: PMC9929392 DOI: 10.21037/qims-22-718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 12/01/2022] [Indexed: 01/15/2023]
Abstract
Background Diagnosing a coronavirus disease 2019 (COVID-19) infection with high specificity in chest computed tomography (CT) imaging is considered possible due to distinctive imaging features of COVID-19 pneumonia. Since other viral non-COVID pneumonia show mostly a different distribution pattern, it is reasonable to assume that the patterns observed caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are a consequence of its genetically encoded molecular properties when interacting with the respiratory tissue. As more mutations of the initial SARS-CoV-2 wild-type with varying aggressiveness have been detected in the course of 2021, it became obvious that its genome is in a state of transformation and therefore a potential modification of the specific morphological appearance in CT may occur. The aim of this study was to quantitatively analyze the morphological differences of the SARS-CoV-2-B.1.1.7 mutation and wildtype variant in CT scans of the thorax. Methods We analyzed a dataset of 140 patients, which was divided into pneumonias caused by n=40 wildtype variants, n=40 B.1.1.7 variants, n=20 bacterial pneumonias, n=20 viral (non-COVID) pneumonias, and a test group of n=20 unremarkable CT examinations of the thorax. Semiautomated 3D segmentation of the lung tissue was performed for quantification of lung pathologies. The extent, ratio, and specific distribution of inflammatory affected lung tissue in each group were compared in a multivariate group analysis. Results Lung segmentation revealed significant difference between the extent of ground glass opacities (GGO) or consolidation comparing SARS-CoV-2 wild-type and B.1.1.7 variant. Wildtype and B.1.1.7 variant showed both a symmetric distribution pattern of stage-dependent GGO and consolidation within matched COVID-19 stages. Viral non-COVID pneumonias had significantly fewer consolidations than the bacterial, but also than the COVID-19 B.1.1.7 variant groups. Conclusions CT based segmentation showed no significant difference between the morphological appearance of the COVID-19 wild-type variant and the SARS-CoV-2 B.1.1.7 mutation. However, our approach allowed a semiautomatic quantification of bacterial and viral lung pathologies. Quantitative CT image analyses, such as the one presented, appear to be an important component of pandemic preparedness considering an organism with ongoing genetic change, to describe a potential arising change in CT morphological appearance of possible new upcoming COVID-19 variants of concern.
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Affiliation(s)
- Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Philipp Fervers
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Simon Geißen
- Division of Cardiology, Pneumology, Angiology and Intensive Care, University of Cologne (UOC), Cologne, Germany
| | - Roman Johannes Gertz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Johannes Bremm
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Miriam Rinneburger
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Mathilda Weisthoff
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Rahil Shahzad
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany;,Innovative Technology, Philips Healthcare, Aachen, Germany
| | - David Maintz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne (UOC), Cologne, Germany
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Fervers P, Zaeske C, Rauen P, Iuga AI, Kottlors J, Persigehl T, Sonnabend K, Weiss K, Bratke G. Conventional and Deep-Learning-Based Image Reconstructions of Undersampled K-Space Data of the Lumbar Spine Using Compressed Sensing in MRI: A Comparative Study on 20 Subjects. Diagnostics (Basel) 2023; 13:diagnostics13030418. [PMID: 36766523 PMCID: PMC9914543 DOI: 10.3390/diagnostics13030418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/20/2023] [Accepted: 01/21/2023] [Indexed: 01/25/2023] Open
Abstract
Compressed sensing accelerates magnetic resonance imaging (MRI) acquisition by undersampling of the k-space. Yet, excessive undersampling impairs image quality when using conventional reconstruction techniques. Deep-learning-based reconstruction methods might allow for stronger undersampling and thus faster MRI scans without loss of crucial image quality. We compared imaging approaches using parallel imaging (SENSE), a combination of parallel imaging and compressed sensing (COMPRESSED SENSE, CS), and a combination of CS and a deep-learning-based reconstruction (CS AI) on raw k-space data acquired at different undersampling factors. 3D T2-weighted images of the lumbar spine were obtained from 20 volunteers, including a 3D sequence (standard SENSE), as provided by the manufacturer, as well as accelerated 3D sequences (undersampling factors 4.5, 8, and 11) reconstructed with CS and CS AI. Subjective rating was performed using a 5-point Likert scale to evaluate anatomical structures and overall image impression. Objective rating was performed using apparent signal-to-noise and contrast-to-noise ratio (aSNR and aCNR) as well as root mean square error (RMSE) and structural-similarity index (SSIM). The CS AI 4.5 sequence was subjectively rated better than the standard in several categories and deep-learning-based reconstructions were subjectively rated better than conventional reconstructions in several categories for acceleration factors 8 and 11. In the objective rating, only aSNR of the bone showed a significant tendency towards better results of the deep-learning-based reconstructions. We conclude that CS in combination with deep-learning-based image reconstruction allows for stronger undersampling of k-space data without loss of image quality, and thus has potential for further scan time reduction.
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Affiliation(s)
- Philipp Fervers
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
- Correspondence:
| | - Charlotte Zaeske
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
| | - Philip Rauen
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
| | - Andra-Iza Iuga
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
| | - Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
| | | | - Kilian Weiss
- Philips GmbH Market DACH, 22335 Hamburg, Germany
| | - Grischa Bratke
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, 50937 Cologne, Germany
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Meng F, Kottlors J, Shahzad R, Liu H, Fervers P, Jin Y, Rinneburger M, Le D, Weisthoff M, Liu W, Ni M, Sun Y, An L, Huai X, Móré D, Giannakis A, Kaltenborn I, Bucher A, Maintz D, Zhang L, Thiele F, Li M, Perkuhn M, Zhang H, Persigehl T. AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study. Eur Radiol 2022; 33:4280-4291. [PMID: 36525088 PMCID: PMC9755771 DOI: 10.1007/s00330-022-09335-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 11/03/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022]
Abstract
OBJECTIVES Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.
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Affiliation(s)
- Fanyang Meng
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Jonathan Kottlors
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Rahil Shahzad
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Innovative Technology, Philips Healthcare, Aachen, Germany
| | - Haifeng Liu
- Department of Radiology, Wuhan No. 1 Hospital, Wuhan, China
| | - Philipp Fervers
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Yinhua Jin
- Department of Radiology, Ningbo Hwamei Hospital, University of Chinese Academy of Sciences, Wuhan, China
| | - Miriam Rinneburger
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Dou Le
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Mathilda Weisthoff
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Wenyun Liu
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Mengzhe Ni
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Ye Sun
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Liying An
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | | | - Dorottya Móré
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Athanasios Giannakis
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Isabel Kaltenborn
- Institute for Diagnostic and Interventional Radiology, Frankfurt University Hospital, Frankfurt, Germany
| | - Andreas Bucher
- Institute for Diagnostic and Interventional Radiology, Frankfurt University Hospital, Frankfurt, Germany
| | - David Maintz
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Lei Zhang
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Frank Thiele
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Innovative Technology, Philips Healthcare, Aachen, Germany
| | - Mingyang Li
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China
| | - Michael Perkuhn
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Innovative Technology, Philips Healthcare, Aachen, Germany
| | - Huimao Zhang
- Department of Radiology, The First Hospital of Ji Lin University, No. 1 Xinmin Street, Changchun, 130012, China.
| | - Thorsten Persigehl
- Institute for Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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12
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Fervers P, Fervers F, Jaiswal A, Rinneburger M, Weisthoff M, Pollmann-Schweckhorst P, Kottlors J, Carolus H, Lennartz S, Maintz D, Shahzad R, Persigehl T. Assessment of COVID-19 lung involvement on computed tomography by deep-learning-, threshold-, and human reader-based approaches—an international, multi-center comparative study. Quant Imaging Med Surg 2022; 12:5156-5170. [PMID: 36330188 PMCID: PMC9622452 DOI: 10.21037/qims-22-175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 08/09/2022] [Indexed: 11/12/2022]
Abstract
Background The extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia, quantified on computed tomography (CT), is an established biomarker for prognosis and guides clinical decision-making. The clinical standard is semi-quantitative scoring of lung involvement by an experienced reader. We aim to compare the performance of automated deep-learning- and threshold-based methods to the manual semi-quantitative lung scoring. Further, we aim to investigate an optimal threshold for quantification of involved lung in COVID pneumonia chest CT, using a multi-center dataset. Methods In total 250 patients were included, 50 consecutive patients with RT-PCR confirmed COVID-19 from our local institutional database, and another 200 patients from four international datasets (n=50 each). Lung involvement was scored semi-quantitatively by three experienced radiologists according to the established chest CT score (CCS) ranging from 0–25. Inter-rater reliability was reported by the intraclass correlation coefficient (ICC). Deep-learning-based segmentation of ground-glass and consolidation was obtained by CT Pulmo Auto Results prototype plugin on IntelliSpace Discovery (Philips Healthcare, The Netherlands). Threshold-based segmentation of involved lung was implemented using an open-source tool for whole-lung segmentation under the presence of severe pathologies (R231CovidWeb, Hofmanninger et al., 2020) and consecutive quantitative assessment of lung attenuation. The best threshold was investigated by training and testing a linear regression of deep-learning and threshold-based results in a five-fold cross validation strategy. Results Median CCS among 250 evaluated patients was 10 [6–15]. Inter-rater reliability of the CCS was excellent [ICC 0.97 (0.97–0.98)]. Best attenuation threshold for identification of involved lung was −522 HU. While the relationship of deep-learning- and threshold-based quantification was linear and strong (r2deep-learningvs.threshold=0.84), both automated quantification methods translated to the semi-quantitative CCS in a non-linear fashion, with an increasing slope towards higher CCS (r2deep-learningvs.CCS= 0.80, r2thresholdvs.CCS=0.63). Conclusions The manual semi-quantitative CCS underestimates the extent of COVID pneumonia in higher score ranges, which limits its clinical usefulness in cases of severe disease. Clinical implementation of fully automated methods, such as deep-learning or threshold-based approaches (best threshold in our multi-center dataset: −522 HU), might save time of trained personnel, abolish inter-reader variability, and allow for truly quantitative, linear assessment of COVID lung involvement.
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Affiliation(s)
- Philipp Fervers
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Florian Fervers
- Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, Germany
| | - Astha Jaiswal
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Miriam Rinneburger
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Mathilda Weisthoff
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | | | - Jonathan Kottlors
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Heike Carolus
- Philips CT Clinical Science, Philips Healthcare, Hamburg, Germany
| | - Simon Lennartz
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - David Maintz
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Rahil Shahzad
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
- Philips GmbH Innovative Technologies, Philips Healthcare, Aachen, Germany
| | - Thorsten Persigehl
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
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13
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Fervers P, Kottlors J, Persigehl T, Lennartz S, Maus V, Fischer S, Styczen H, Deuschl C, Schlamann M, Mpotsaris A, Zubel S, Schroeter M, Maintz D, Fink GR, Abdullayev N. Meaningful use of imaging resources to rule out cerebral venous sinus thrombosis after ChAdOx1 COVID-19 vaccination: Evaluation of the AHA diagnostic algorithm with a clinical cohort and a systematic data review. J Clin Neurosci 2022; 102:5-12. [PMID: 35687921 PMCID: PMC9167954 DOI: 10.1016/j.jocn.2022.05.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 05/30/2022] [Accepted: 05/31/2022] [Indexed: 11/26/2022]
Abstract
Vaccine-induced immune thrombotic thrombocytopenia (VITT) with cerebral venous thrombosis (CVST) is an improbable (0.0005%), however potentially lethal complication after ChAdOx1 vaccination. On the other hand, headache is among the most frequent side effects of ChAdOx1 (29.3%). In September 2021, the American Heart Association (AHA) suggested a diagnostic workflow to facilitate risk-adapted use of imaging resources for patients with neurological symptoms after ChAdOx1. We aimed to evaluate the AHA workflow in a retrospective patient cohort presenting at four primary care hospitals in Germany for neurological complaints after ChAdOx1. Scientific literature was screened for case reports of VITT with CVST after ChAdOx1, published until September 1st, 2021. One-hundred-thirteen consecutive patients (77 female, mean age 38.7 +/− 11.9 years) were evaluated at our institutes, including one case of VITT with CVST. Further 228 case reports of VITT with CVST are published in recent literature, which share thrombocytopenia (225/227 reported) and elevated d-dimer levels (100/101 reported). The AHA workflow would have recognized all VITT cases with CVST (100% sensitivity), the number needed to diagnose (NND) was 1:113. Initial evaluation of thrombocytopenia or elevated d-dimer levels would have lowered the NND to 1:68, without cost of sensitivity. Hence, we suggest that in case of normal thrombocyte and d-dimer levels, the access to further diagnostics should be limited by the established clinical considerations regardless of vaccination history.
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Zäske C, Zopfs D, Laukamp K, Kottlors J, Goertz L, Schafigh D, Neuschmelting H, Abdullayev N, Kabbasch C, Schlamann M, Schönfeld M. Intraarterielle Applikation von Nimodipin während der stent-gestützten mechanischen Thrombektomie: Sicherheit und Effektivität. ROFO-FORTSCHR RONTG 2022. [DOI: 10.1055/s-0042-1749871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- C Zäske
- Universitätsklinikum Köln, Institut f. diagn. u. intervent. Radiologie, Köln
| | - D Zopfs
- Institut für diagnostische und interventionelle Radiologie, Universitätsklinikum Köln, Köln
| | - K Laukamp
- Institut für diagnostische und interventionelle Radiologie, Universitätsklinikum Köln, Köln
| | - J Kottlors
- Institut für diagnostische und interventionelle Radiologie, Universitätsklinikum Köln, Köln
| | - L Goertz
- Institut für diagnostische und interventionelle Radiologie, Universitätsklinikum Köln, Köln
| | - D Schafigh
- Institut für diagnostische und interventionelle Radiologie, Universitätsklinikum Köln, Köln
| | - H Neuschmelting
- Institut für diagnostische und interventionelle Radiologie, Universitätsklinikum Köln, Köln
| | - N Abdullayev
- Institut für diagnostische und interventionelle Radiologie, Universitätsklinikum Köln, Köln
| | - C Kabbasch
- Institut für diagnostische und interventionelle Radiologie, Universitätsklinikum Köln, Köln
| | - M Schlamann
- Institut für diagnostische und interventionelle Radiologie, Universitätsklinikum Köln, Köln
| | - M Schönfeld
- Institut für diagnostische und interventionelle Radiologie, Universitätsklinkum Köln, Köln
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15
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Gertz RJ, Gerhardt F, Kröger JR, Shahzad R, Caldeira L, Kottlors J, Große Hokamp N, Maintz D, Rosenkranz S, Bunck AC. Spectral Detector CT-Derived Pulmonary Perfusion Maps and Pulmonary Parenchyma Characteristics for the Semiautomated Classification of Pulmonary Hypertension. Front Cardiovasc Med 2022; 9:835732. [PMID: 35391852 PMCID: PMC8982082 DOI: 10.3389/fcvm.2022.835732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 02/02/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesTo evaluate the usefulness of spectral detector CT (SDCT)-derived pulmonary perfusion maps and pulmonary parenchyma characteristics for the semiautomated classification of pulmonary hypertension (PH).MethodsA total of 162 consecutive patients with right heart catheter (RHC)-proven PH of different aetiologies as defined by the current ESC/ERS guidelines who underwent CT pulmonary angiography (CTPA) on SDCT and 20 patients with an invasive rule-out of PH were included in this retrospective study. Semiautomatic lung segmentation into normal and malperfused areas based on iodine density (ID) as well as automatic, virtual non-contrast-based emphysema quantification were performed. Corresponding volumes, histogram features and the ID SkewnessPerfDef-Emphysema-Index (δ-index) accounting for the ratio of ID distribution in malperfused lung areas and the proportion of emphysematous lung parenchyma were computed and compared between groups.ResultsPatients with PH showed a significantly greater extent of malperfused lung areas as well as stronger and more homogenous perfusion defects. In group 3 and 4 patients, ID skewness revealed a significantly more homogenous ID distribution in perfusion defects than in all other subgroups. The δ-index allowed for further subclassification of subgroups 3 and 4 (p < 0.001), identifying patients with chronic thromboembolic PH (CTEPH, subgroup 4) with high accuracy (AUC: 0.92, 95%-CI, 0.85–0.99).ConclusionAbnormal pulmonary perfusion in PH can be detected and quantified by semiautomated SDCT-based pulmonary perfusion maps. ID skewness in malperfused lung areas, and the δ-index allow for a classification of PH subgroups, identifying groups 3 and 4 patients with high accuracy, independent of reader expertise.
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Affiliation(s)
- Roman Johannes Gertz
- Department of Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- *Correspondence: Roman Johannes Gertz
| | - Felix Gerhardt
- Department of Cardiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jan Robert Kröger
- Department of Radiology, Neuroradiology, and Nuclear Medicine, Johannes Wesling University Hospital, Ruhr University Bochum, Bochum, Germany
| | - Rahil Shahzad
- Department of Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Clinical Applications Research, Philips GmbH Innovative Technologies, Aachen, Germany
| | - Liliana Caldeira
- Department of Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Jonathan Kottlors
- Department of Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nils Große Hokamp
- Department of Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - David Maintz
- Department of Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Stephan Rosenkranz
- Department of Cardiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Alexander Christian Bunck
- Department of Radiology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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16
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Celik E, Nelles C, Kottlors J, Fervers P, Goertz L, Pinto dos Santos D, Achenbach T, Maintz D, Persigehl T. Quantitative determination of pulmonary emphysema in follow-up LD-CTs of patients with COVID-19 infection. PLoS One 2022; 17:e0263261. [PMID: 35113939 PMCID: PMC8812925 DOI: 10.1371/journal.pone.0263261] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 01/15/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose To evaluate the association between the coronavirus disease 2019 (COVID-19) and post-inflammatory emphysematous lung alterations on follow-up low-dose CT scans. Methods Consecutive patients with proven COVID-19 infection and a follow-up CT were retrospectively reviewed. The severity of pulmonary involvement was classified as mild, moderate and severe. Total lung volume, emphysema volume and the ratio of emphysema/-to-lung volume were quantified semi-automatically and compared inter-individually between initial and follow-up CT and to a control group of healthy, age- and sex-matched patients. Lung density was further assessed by drawing circular regions of interest (ROIs) into non-affected regions of the upper lobes. Results A total of 32 individuals (mean age: 64 ± 13 years, 12 females) with at least one follow-up CT (mean: 52 ± 66 days, range: 5–259) were included. In the overall cohort, total lung volume, emphysema volume and the ratio of lung-to-emphysema volume did not differ significantly between the initial and follow-up scans. In the subgroup of COVID-19 patients with > 30 days of follow-up, the emphysema volume was significantly larger as compared to the subgroup with a follow-up < 30 days (p = 0.045). Manually measured single ROIs generally yielded lower attenuation values prior to COVID-19 pneumonia, but the difference was not significant between groups (all p > 0.05). Conclusion COVID-19 patients with a follow-up CT >30 days showed significant emphysematous lung alterations. These findings may help to explain the long-term effect of COVID-19 on pulmonary function and warrant validation by further studies.
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Affiliation(s)
- Erkan Celik
- Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
- * E-mail:
| | - Christian Nelles
- Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
| | - Jonathan Kottlors
- Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
| | - Philipp Fervers
- Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
| | - Lukas Goertz
- Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
| | - Daniel Pinto dos Santos
- Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
| | - Tobias Achenbach
- Department of Diagnostic and Interventional Radiology, Lahn-Dill-Kliniken, Wetzlar, Germany
| | - David Maintz
- Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
| | - Thorsten Persigehl
- Faculty of Medicine and University Hospital Cologne, Department of Diagnostic and Interventional Radiology, University of Cologne, Cologne, Germany
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17
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Fervers P, Fervers F, Kottlors J, Lohneis P, Pollman-Schweckhorst P, Zaytoun H, Rinneburger M, Maintz D, Große Hokamp N. Feasibility of artificial intelligence–supported assessment of bone marrow infiltration using dual-energy computed tomography in patients with evidence of monoclonal protein — a retrospective observational study. Eur Radiol 2021; 32:2901-2911. [PMID: 34921619 PMCID: PMC9038860 DOI: 10.1007/s00330-021-08419-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 09/30/2021] [Accepted: 10/17/2021] [Indexed: 12/20/2022]
Abstract
Abstract
Objectives
To demonstrate the feasibility of an automated, non-invasive approach to estimate bone marrow (BM) infiltration of multiple myeloma (MM) by dual-energy computed tomography (DECT) after virtual non-calcium (VNCa) post-processing.
Methods
Individuals with MM and monoclonal gammopathy of unknown significance (MGUS) with concurrent DECT and BM biopsy between May 2018 and July 2020 were included in this retrospective observational study. Two pathologists and three radiologists reported BM infiltration and presence of osteolytic bone lesions, respectively. Bone mineral density (BMD) was quantified CT-based by a CE-certified software. Automated spine segmentation was implemented by a pre-trained convolutional neural network. The non-fatty portion of BM was defined as voxels > 0 HU in VNCa. For statistical assessment, multivariate regression and receiver operating characteristic (ROC) were conducted.
Results
Thirty-five patients (mean age 65 ± 12 years; 18 female) were evaluated. The non-fatty portion of BM significantly predicted BM infiltration after adjusting for the covariable BMD (p = 0.007, r = 0.46). A non-fatty portion of BM > 0.93% could anticipate osteolytic lesions and the clinical diagnosis of MM with an area under the ROC curve of 0.70 [0.49–0.90] and 0.71 [0.54–0.89], respectively. Our approach identified MM-patients without osteolytic lesions on conventional CT with a sensitivity and specificity of 0.63 and 0.71, respectively.
Conclusions
Automated, AI-supported attenuation assessment of the spine in DECT VNCa is feasible to predict BM infiltration in MM. Further, the proposed method might allow for pre-selecting patients with higher pre-test probability of osteolytic bone lesions and support the clinical diagnosis of MM without pathognomonic lesions on conventional CT.
Key Points
• The retrospective study provides an automated approach for quantification of the non-fatty portion of bone marrow, based on AI-supported spine segmentation and virtual non-calcium dual-energy CT data.
• An increasing non-fatty portion of bone marrow is associated with a higher infiltration determined by invasive biopsy after adjusting for bone mineral density as a control variable (p = 0.007, r = 0.46).
• The non-fatty portion of bone marrow might support the clinical diagnosis of multiple myeloma when conventional CT images are negative (sensitivity 0.63, specificity 0.71).
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18
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Fervers P, Celik E, Bratke G, Maintz D, Baues C, Ruffing S, Pollman-Schweckhorst P, Kottlors J, Lennartz S, Große Hokamp N. Radiotherapy Response Assessment of Multiple Myeloma: A Dual-Energy CT Approach With Virtual Non-Calcium Images. Front Oncol 2021; 11:734819. [PMID: 34646776 PMCID: PMC8504158 DOI: 10.3389/fonc.2021.734819] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 09/01/2021] [Indexed: 12/19/2022] Open
Abstract
Background Life expectancy of patients with multiple myeloma (MM) has increased over the past decades, underlining the importance of local tumor control and avoidance of dose-dependent side effects of palliative radiotherapy (RT). Virtual noncalcium (VNCa) imaging from dual-energy computed tomography (DECT) has been suggested to estimate cellularity and metabolic activity of lytic bone lesions (LBLs) in MM. Objective To explore the feasibility of RT response monitoring with DECT-derived VNCa attenuation measurements in MM. Methods Thirty-three patients with 85 LBLs that had been irradiated and 85 paired non-irradiated LBLs from the same patients were included in this retrospective study. Irradiated and non-irradiated LBLs were measured by circular regions of interest (ROIs) on conventional and VNCa images in a total of 216 follow-up measurements (48 before and 168 after RT). Follow-ups were rated as therapy response, stable disease, or local progression according to the MD Anderson criteria. Receiver operating characteristic (ROC) analysis was performed to discriminate irradiated vs. non-irradiated and locally progressive vs. stable/responsive LBLs using absolute attenuation post-irradiation and percentage attenuation change for patients with pre-irradiation DECT, if available. Results Attenuation of LBLs decreased after RT depending on the time that had passed after irradiation [absolute thresholds for identification of irradiated LBLs 30.5–70.0 HU [best area under the curve [AUC] 0.75 (0.59–0.91)] and -77.0 to -22.5 HU [best AUC 0.85 (0.65–1.00)]/-50% and -117% to -167% proportional change of attenuation on conventional and VNCa images, respectively]. VNCa CT was significantly superior for identification of RT effects in LBLs with higher calcium content [best VNCa AUC 0.96 (0.91–1.00), best conventional CT AUC 0.64 (0.45–0.83)]. Thresholds for early identification of local irradiation failure were >20.5 HU on conventional CT [AUC 0.78 (0.68–0.88)] and >-27 HU on VNCa CT [AUC 0.83 (0.70–0.96)]. Conclusion Therapy response of LBLs after RT can be monitored by VNCa imaging based on regular myeloma scans, which yields potential for optimizing the lesion-specific radiation dose for local tumor control. Decreasing attenuation indicates RT response, while above threshold attenuation of LBLs precedes local irradiation failure.
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Affiliation(s)
- Philipp Fervers
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
| | - Erkan Celik
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
| | - Grischa Bratke
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
| | - David Maintz
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
| | - Christian Baues
- Department of Radiotherapy and Cyberknife Center, University Hospital of Cologne, Cologne, Germany
| | - Simon Ruffing
- Department of Radiotherapy and Cyberknife Center, University Hospital of Cologne, Cologne, Germany
| | | | - Jonathan Kottlors
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
| | - Simon Lennartz
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
| | - Nils Große Hokamp
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
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19
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Kottlors J, Große Hokamp N, Fervers P, Bremm J, Fichter F, Persigehl T, Safarov O, Maintz D, Tritt S, Abdullayev N. Early extrapulmonary prognostic features in chest computed tomography in COVID-19 pneumonia: Bone mineral density is a relevant predictor for the clinical outcome - A multicenter feasibility study. Bone 2021; 144:115790. [PMID: 33301962 PMCID: PMC7720732 DOI: 10.1016/j.bone.2020.115790] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 12/01/2020] [Accepted: 12/02/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Besides throat-nose swab polymerase chain reaction (PCR), unenhanced chest computed tomography (CT) is a recommended diagnostic tool for early detection and quantification of pulmonary changes in COVID-19 pneumonia caused by the novel corona virus. Demographic factors, especially age and comorbidities, are major determinants of the outcome in COVID-19 infection. This study examines the extra pulmonary parameter of bone mineral density (BMD) from an initial chest computed tomography as an associated variable of pre-existing comorbidities like chronic lung disease or demographic factors to determine the later patient's outcome, in particular whether treatment on an intensive care unit (ICU) was necessary in infected patients. METHODS We analyzed 58 PCR-confirmed COVID-19 infections that received an unenhanced CT at admission at one of the included centers. In addition to the extent of pulmonary involvement, we performed a phantomless assessment of bone mineral density of thoracic vertebra 9-12. RESULTS In a univariate regression analysis BMD was found to be a significant predictor of the necessity for intensive care unit treatment of COVID-19 patients. In the subgroup requiring intensive care treatment within the follow-up period a significantly lower BMD was found. In a multivariate logistic regression model considering gender, age and CT measurements of bone mineral density, BMD was eliminated from the regression analysis as a significant predictor. CONCLUSION Phantomless assessed BMD provides prognostic information on the necessity for ICU treatment in course of COVID-19 pneumonia. We recommend using the measurement of BMD in an initial CT image to facilitate a potentially better prediction of severe patient outcomes within the 22 days after an initial CT scan. Consequently, in the present sample, additional bone density analysis did not result in a prognostic advantage over simply considering age. Significantly larger patient cohorts with a more homogenous patient age should be performed in the future to illustrate potential effects. CLINICAL RELEVANCE While clinical capacities such as ICU beds and ventilators are more crucial than ever to help manage the current global corona pandemic, this work introduces an approach that can be used in a cost-effective way to help determine the amount of these rare clinical resources required in the near future.
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Affiliation(s)
- Jonathan Kottlors
- Institute of Diagnostic and Interventional Radiology, University Hospital of Cologne, Germany.
| | - Nils Große Hokamp
- Institute of Diagnostic and Interventional Radiology, University Hospital of Cologne, Germany.
| | - Philipp Fervers
- Institute of Diagnostic and Interventional Radiology, University Hospital of Cologne, Germany.
| | - Johannes Bremm
- Institute of Diagnostic and Interventional Radiology, University Hospital of Cologne, Germany.
| | - Florian Fichter
- Institute of Diagnostic and Interventional Radiology, University Hospital of Cologne, Germany.
| | - Thorsten Persigehl
- Institute of Diagnostic and Interventional Radiology, University Hospital of Cologne, Germany.
| | | | - David Maintz
- Institute of Diagnostic and Interventional Radiology, University Hospital of Cologne, Germany.
| | | | - Nuran Abdullayev
- Institute of Diagnostic and Interventional Radiology, University Hospital of Cologne, Germany.
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20
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Fervers P, Kottlors J, Zopfs D, Bremm J, Maintz D, Safarov O, Tritt S, Abdullayev N, Persigehl T. Calcification of the thoracic aorta on low-dose chest CT predicts severe COVID-19. PLoS One 2020; 15:e0244267. [PMID: 33362199 PMCID: PMC7757863 DOI: 10.1371/journal.pone.0244267] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 12/08/2020] [Indexed: 12/29/2022] Open
Abstract
Background Cardiovascular comorbidity anticipates poor prognosis of SARS-CoV-2 disease (COVID-19) and correlates with the systemic atherosclerotic transformation of the arterial vessels. The amount of aortic wall calcification (AWC) can be estimated on low-dose chest CT. We suggest quantification of AWC on the low-dose chest CT, which is initially performed for the diagnosis of COVID-19, to screen for patients at risk of severe COVID-19. Methods Seventy consecutive patients (46 in center 1, 24 in center 2) with parallel low-dose chest CT and positive RT-PCR for SARS-CoV-2 were included in our multi-center, multi-vendor study. The outcome was rated moderate (no hospitalization, hospitalization) and severe (ICU, tracheal intubation, death), the latter implying a requirement for intensive care treatment. The amount of AWC was quantified with the CT vendor's software. Results Of 70 included patients, 38 developed a moderate, and 32 a severe COVID-19. The average volume of AWC was significantly higher throughout the subgroup with severe COVID-19, when compared to moderate cases (771.7 mm3 (Q1 = 49.8 mm3, Q3 = 3065.5 mm3) vs. 0 mm3 (Q1 = 0 mm3, Q3 = 57.3 mm3)). Within multivariate regression analysis, including AWC, patient age and sex, as well as a cardiovascular comorbidity score, the volume of AWC was the only significant regressor for severe COVID-19 (p = 0.004). For AWC > 3000 mm3, the logistic regression predicts risk for a severe progression of 0.78. If there are no visually detectable AWC risk for severe progression is 0.13, only. Conclusion AWC seems to be an independent biomarker for the prediction of severe progression and intensive care treatment of COVID-19 already at the time of patient admission to the hospital; verification in a larger multi-center, multi-vendor study is desired.
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Affiliation(s)
- Philipp Fervers
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
- * E-mail:
| | - Jonathan Kottlors
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - David Zopfs
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Johannes Bremm
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - David Maintz
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Orkhan Safarov
- Department of Radiology, Helios Dr. Horst Schmidt Kliniken Wiesbaden, Wiesbaden, Germany
| | - Stephanie Tritt
- Department of Radiology, Helios Dr. Horst Schmidt Kliniken Wiesbaden, Wiesbaden, Germany
| | - Nuran Abdullayev
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
| | - Thorsten Persigehl
- Department of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, University Cologne, Cologne, Germany
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21
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Kottlors J, Maus V, Mpotsaris A, Onur ÖA, Liebig T, Kabbasch C, Borggrefe J. Thrombus Enhancement Is a Predictor of Clinical Outcome in Acute Ischemic Stroke after Mechanical Thrombectomy. Cerebrovasc Dis 2019; 46:270-278. [PMID: 30645999 DOI: 10.1159/000495419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 11/12/2018] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Ex vivo computed tomography (CT) studies of artificial blood thrombi showed that contrast enhancement (CE) is determined by fibrin-content, while unenhanced density is associated with red blood cells. Thus, the present study investigates patient outcome in association with combined thrombus density measures in native and contrast-enhanced CT (CECT) of acute ischemic stroke patients. METHODS This retrospective study includes 137 patients with M1 occlusions treated by mechanical thrombectomy (MT) between 2010 and 2016. Clinical outcome was determined with modified Rankin Scale (mRS) at 90 days. Differentiation of complete and incomplete large vessel occlusion (CLVO/ILVO) was based on CT and angiography. Two blinded readers classified blood thrombi based on native non-enhanced CT (NECT) as (a) hypo-, (b) iso-, and (c) hyperdense and in CECT angio measurements as (d) not-enhancing, (e) intermediate and (f) enhancing. To make sure that the mean is not represented in any of the maximum/minimum groups, thresholds in both cases were selected in a way that all values within one SD around the mean value form the isodense/intermediate group. In addition, the CE per se was correlated with the outcome. Correlations between imaging and clinical scales were performed with Spearman's Rho. For the group testing Pearson chi-square test, Mann-Whitney U, as well parametric and nonparametric one-factor ANOVA "Kruskal-Wallis" test including Bonferroni correction for multiple tests ware used. RESULTS Twenty-three patients with ILVO (16.8%) differed significantly from patients with CLVO in mRS at admission (median 4 vs. 5) and after 90 days (median 1 vs. 4; p < 0.05) and thus were excluded. In the ILVO cohort, the classification according to NECT did not show statistical difference between hypo-, iso- and hyperdense CLVOs in regard to outcome. Classification of CLVOs according to CECT allowed an outcome prediction between the intermediate (median 3) and enhancing group (median 5) and between the enhancing and non-enhancing group (median 3; both p < 0.05) with a correlation of 291 between CE and higher mRS after 90 days (p < 0.005). CONCLUSIONS CE of thrombi - especially in a range from over 18.4 to 40.35 Hounsfield Units - is an independent predictor of poor clinical outcome in patients undergoing MT due to acute middle cerebral artery occlusion.
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Affiliation(s)
- Jonathan Kottlors
- Uniklinik Köln, Institut für Diagnostische und Interventionelle Radiologie, Cologne, Germany,
| | - Volker Maus
- Universitätsmedizin Göttingen, Department of Diagnostic and Interventional Neuroradiology, Cologne, Germany
| | - Anastasios Mpotsaris
- RWTH Aachen, Klinik für Diagnostische und Interventionelle Neuroradiologie, Cologne, Germany
| | - Özgür A Onur
- Uniklinik Köln, Zentrum für Neurologie und Psychiatrie, Cologne, Germany
| | - Thomas Liebig
- LMU München, Institut für Diagnostische und Interventionelle Neuroradiologie, Cologne, Germany
| | - Christoph Kabbasch
- Uniklinik Köln, Institut für Diagnostische und Interventionelle Radiologie, Cologne, Germany
| | - Jan Borggrefe
- Uniklinik Köln, Institut für Diagnostische und Interventionelle Radiologie, Cologne, Germany
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22
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Borggrefe J, Kottlors J, Mirza M, Neuhaus VF, Abdullayev N, Maus V, Kabbasch C, Maintz D, Mpotsaris A. Differentiation of Clot Composition Using Conventional and Dual-Energy Computed Tomography. Clin Neuroradiol 2017; 28:515-522. [PMID: 28536753 DOI: 10.1007/s00062-017-0599-3] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 05/10/2017] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND PURPOSE In unenhanced computed tomography (CT) of acute ischemic stroke, the density of occluding clots is associated with the content of red blood cells and successful recanalization with stent thrombectomy. However, no CT marker for fibrin content is established. In order to improve clot diagnostics, we conducted an in vitro study to investigate thrombus composition of histologically defined ovine blood clots with unenhanced and contrast-enhanced CT using spectral detector CT (SDCT). METHODS Ovine blood clot types containing defined amounts of red blood cells (RBC; pure fibrin clots: RBC 0% ± 0, fibrin 100% ± 0), mixed clots (RBC 35.1% ± 4.11, fibrin 79.2% ± 5.6) and red clots (RBC 99.05% ± 1.14, fibrin 0.95% ± 1.14) were scanned in a SDCT (IQon®, Philips, Amsterdam, The Netherlands) (a) in a tube containing saline, (b) 5 min and (c) 3 days after exposure to a 1:50 dilution of iohexol (Accupaque-350®, GE-Healthcare, Boston, MA, USA). The attenuation of the clots was measured in Hounsfield units (HU) in conventional CT datasets as well as virtual noncontrast reconstructions (VNC) of nonenhanced and contrast-enhanced SDCT in a blinded and randomized fashion. Statistical analysis was conducted with ANOVA, Spearman's correlation, linear and multivariable regression models. RESULTS In unenhanced scans, clots differed in density with linear interrelation (fibrin 23.6 ± 1.1, mixed 34.9 ± 1.6, red 46.7 ± 1.6, mean HU ± SD). The blood clots did not show any overlap of density in the native scans and VNC at different time points (p < 0.0001 for each setting and clot type). However, they could not be differentiated after initial contrast exposure (fibrin 108.5 ± 7.8, mixed 105.3 ± 3.5, red 104.8 ± 3.8, mean HU ± SD). After prolonged exposure, the fibrin rich clots showed a significant increase of density due to further uptake of contrast medium (fibrin 163.6 ± 3.6, mixed 138.3 ± 4.1, red 109.6 ± 5.4, mean HU ± SD). In multivariable models, native CT density and contrast enhancement were independent variables associated with thrombus type (p < 0.01 each). CONCLUSION The fibrin content in blood clots is strongly associated with contrast uptake. As previously shown, the density of the clot formations in native CT scans is dependent on the RBC. Our data show that CT density and relative enhancement of clots are independent determinants of clot composition. Using both variables in the CT workup of acute ischemic stroke has the potential to have a decisive impact on patient stratification for treatment.
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Affiliation(s)
- Jan Borggrefe
- Institut für Diagnostische und Interventionelle Radiologie, Uniklinik Köln, Kerpener Str. 62, 60937, Cologne, Germany.
| | - Jonathan Kottlors
- Institut für Diagnostische und Interventionelle Radiologie, Uniklinik Köln, Kerpener Str. 62, 60937, Cologne, Germany
| | - Mahmood Mirza
- Clinical Science, Neuravi Ltd, llybrit Business Park, H91 K5YD, Galway, Ireland
| | - Victor-Frederic Neuhaus
- Institut für Diagnostische und Interventionelle Radiologie, Uniklinik Köln, Kerpener Str. 62, 60937, Cologne, Germany
| | - Nuran Abdullayev
- Institut für Diagnostische und Interventionelle Radiologie, Uniklinik Köln, Kerpener Str. 62, 60937, Cologne, Germany
| | - Volker Maus
- Institut für Diagnostische und Interventionelle Radiologie, Uniklinik Köln, Kerpener Str. 62, 60937, Cologne, Germany
| | - Christoph Kabbasch
- Institut für Diagnostische und Interventionelle Radiologie, Uniklinik Köln, Kerpener Str. 62, 60937, Cologne, Germany
| | - David Maintz
- Institut für Diagnostische und Interventionelle Radiologie, Uniklinik Köln, Kerpener Str. 62, 60937, Cologne, Germany
| | - Anastasios Mpotsaris
- Institut für Diagnostische und Interventionelle Radiologie, Uniklinik Köln, Kerpener Str. 62, 60937, Cologne, Germany
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Borggrefe J, Kottlors J, Mirza M, Maus V, Kabbasch C, Neuhaus V, Abdullayev N, Maintz D, Mpotsaris A. Differenzierung der Zusammensetzung von Thromben in der Spektral-Detector Computertomografie. ROFO-FORTSCHR RONTG 2017. [DOI: 10.1055/s-0037-1600380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- J Borggrefe
- Uniklinik Köln, Institut für Diagnostische und Interventionelle Radiologie, Köln
| | - J Kottlors
- Uniklinik Köln, Institut für Diagnostische und Interventionelle Radiologie, Köln
| | - M Mirza
- Neuravi Ltd., Clinical Research, Galway, Ireland
| | - V Maus
- Uniklinik Köln, Institut für Diagnostische und Interventionelle Radiologie, Köln
| | - C Kabbasch
- Uniklinik Köln, Institut für Diagnostische und Interventionelle Radiologie, Köln
| | - V Neuhaus
- Uniklinik Köln, Institut für Diagnostische und Interventionelle Radiologie, Köln
| | - N Abdullayev
- Uniklinik Köln, Institut für Diagnostische und Interventionelle Radiologie, Köln
| | - D Maintz
- Uniklinik Köln, Institut für Diagnostische und Interventionelle Radiologie, Köln
| | - A Mpotsaris
- Uniklinik Köln, Institut für Diagnostische und Interventionelle Radiologie, Köln
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