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Alexander KC, Ikonomidis JS, Akerman AW. New Directions in Diagnostics for Aortic Aneurysms: Biomarkers and Machine Learning. J Clin Med 2024; 13:818. [PMID: 38337512 PMCID: PMC10856211 DOI: 10.3390/jcm13030818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
This review article presents an appraisal of pioneering technologies poised to revolutionize the diagnosis and management of aortic aneurysm disease, with a primary focus on the thoracic aorta while encompassing insights into abdominal manifestations. Our comprehensive analysis is rooted in an exhaustive survey of contemporary and historical research, delving into the realms of machine learning (ML) and computer-assisted diagnostics. This overview draws heavily upon relevant studies, including Siemens' published field report and many peer-reviewed publications. At the core of our survey lies an in-depth examination of ML-driven diagnostic advancements, dissecting an array of algorithmic suites to unveil the foundational concepts anchoring computer-assisted diagnostics and medical image processing. Our review extends to a discussion of circulating biomarkers, synthesizing insights gleaned from our prior research endeavors alongside contemporary studies gathered from the PubMed Central database. We elucidate the prevalent challenges and envisage the potential fusion of AI-guided aortic measurements and sophisticated ML frameworks with the computational analyses of pertinent biomarkers. By framing current scientific insights, we contemplate the transformative prospect of translating fundamental research into practical diagnostic tools. This narrative not only illuminates present strides, but also forecasts promising trajectories in the clinical evaluation and therapeutic management of aortic aneurysm disease.
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Affiliation(s)
| | | | - Adam W. Akerman
- Department of Surgery, Division of Cardiothoracic Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (K.C.A.); (J.S.I.)
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Zamirpour S, Boskovski MT, Pirruccello JP, Pace WA, Hubbard AE, Leach JR, Ge L, Tseng EE. Sex differences in ascending aortic size reporting and growth on chest computed tomography and magnetic resonance imaging. Clin Imaging 2024; 105:110021. [PMID: 37992628 DOI: 10.1016/j.clinimag.2023.110021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 10/16/2023] [Accepted: 11/02/2023] [Indexed: 11/24/2023]
Abstract
PURPOSE Diameter-based guidelines for prophylactic repair of ascending aortic aneurysms have led to routine aortic evaluation in chest imaging. Despite sex differences in aneurysm outcomes, there is little understanding of sex-specific aortic growth rates. Our objective was to evaluate sex-specific temporal changes in radiologist-reported aortic size as well as sex differences in aortic reporting. METHOD In this cohort study, we queried radiology reports of chest computed tomography or magnetic resonance imaging at an academic medical center from 1994 to 2022, excluding type A dissection. Aortic diameter was extracted using a custom text-processing algorithm. Growth rates were estimated using mixed-effects modeling with fixed terms for sex, age, and imaging modality, and patient-level random intercepts. Sex, age, and modality were evaluated as predictors of aortic reporting by logistic regression. RESULTS This study included 89,863 scans among 46,622 patients (median [interquartile range] age, 64 [52-73]; 22,437 women [48%]). Aortic diameter was recorded in 14% (12,722/89,863 reports). Temporal trends were analyzed in 7194 scans among 1998 patients (age, 68 [60-75]; 677 women [34%]) with ≥2 scans. Aortic growth rate was significantly higher in women (0.22 mm/year [95% confidence interval 0.17-0.28] vs. 0.09 mm/year [0.06-0.13], respectively). Aortic reporting was significantly less common in women (odds ratio, 0.54; 95% CI, 0.52-0.56; p < 0.001). CONCLUSIONS While aortic growth rates were small overall, women had over twice the growth rate of men. Aortic dimensions were much less frequently reported in women than men. Sex-specific standardized assessment of aortic measurements may be needed to address sex differences in aneurysm outcomes.
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Affiliation(s)
- Siavash Zamirpour
- Division of Adult Cardiothoracic Surgery, Department of Surgery, University of California San Francisco, San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA; School of Medicine, University of California San Francisco, CA, USA
| | - Marko T Boskovski
- Division of Adult Cardiothoracic Surgery, Department of Surgery, University of California San Francisco, San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | - James P Pirruccello
- Division of Cardiology, Department of Medicine, University of California San Francisco, USA; Institute for Human Genetics, University of California San Francisco, USA
| | - William A Pace
- Division of Adult Cardiothoracic Surgery, Department of Surgery, University of California San Francisco, San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA; School of Medicine, University of California San Francisco, CA, USA
| | - Alan E Hubbard
- Division of Biostatistics, School of Public Health, University of California Berkeley, USA
| | - Joseph R Leach
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | - Liang Ge
- Division of Adult Cardiothoracic Surgery, Department of Surgery, University of California San Francisco, San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | - Elaine E Tseng
- Division of Adult Cardiothoracic Surgery, Department of Surgery, University of California San Francisco, San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA.
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Lo Piccolo F, Hinck D, Segeroth M, Sperl J, Cyriac J, Yang S, Rapaka S, Bremerich J, Sauter AW, Pradella M. Impact of retraining a deep learning algorithm for improving guideline-compliant aortic diameter measurements on non-gated chest CT. Eur J Radiol 2023; 168:111093. [PMID: 37716024 DOI: 10.1016/j.ejrad.2023.111093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 08/21/2023] [Accepted: 09/08/2023] [Indexed: 09/18/2023]
Abstract
PURPOSE/OBJECTIVE Reliable detection of thoracic aortic dilatation (TAD) is mandatory in clinical routine. For ECG-gated CT angiography, automated deep learning (DL) algorithms are established for diameter measurements according to current guidelines. For non-ECG gated CT (contrast enhanced (CE) and non-CE), however, only a few reports are available. In these reports, classification as TAD is frequently unreliable with variable result quality depending on anatomic location with the aortic root presenting with the worst results. Therefore, this study aimed to explore the impact of re-training on a previously evaluated DL tool for aortic measurements in a cohort of non-ECG gated exams. METHODS & MATERIALS A cohort of 995 patients (68 ± 12 years) with CE (n = 392) and non-CE (n = 603) chest CT exams was selected which were classified as TAD by the initial DL tool. The re-trained version featured improved robustness of centerline fitting and cross-sectional plane placement. All cases were processed by the re-trained DL tool version. DL results were evaluated by a radiologist regarding plane placement and diameter measurements. Measurements were classified as correctly measured diameters at each location whereas false measurements consisted of over-/under-estimation of diameters. RESULTS We evaluated 8948 measurements in 995 exams. The re-trained version performed 8539/8948 (95.5%) of diameter measurements correctly. 3765/8948 (42.1%) of measurements were correct in both versions, initial and re-trained DL tool (best: distal arch 655/995 (66%), worst: Aortic sinus (AS) 221/995 (22%)). In contrast, 4456/8948 (49.8%) measurements were correctly measured only by the re-trained version, in particular at the aortic root (AS: 564/995 (57%), sinotubular junction: 697/995 (70%)). In addition, the re-trained version performed 318 (3.6%) measurements which were not available previously. A total of 228 (2.5%) cases showed false measurements because of tilted planes and 181 (2.0%) over-/under-segmentations with a focus at AS (n = 137 (14%) and n = 73 (7%), respectively). CONCLUSION Re-training of the DL tool improved diameter assessment, resulting in a total of 95.5% correct measurements. Our data suggests that the re-trained DL tool can be applied even in non-ECG-gated chest CT including both, CE and non-CE exams.
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Affiliation(s)
- Francesca Lo Piccolo
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland.
| | - Daniel Hinck
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland.
| | - Martin Segeroth
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland.
| | - Jonathan Sperl
- Siemens Healthineers, Siemensstraße 1, 91301 Forchheim, Germany.
| | - Joshy Cyriac
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland.
| | - Shan Yang
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland.
| | - Saikiran Rapaka
- Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540, United States.
| | - Jens Bremerich
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland.
| | - Alexander W Sauter
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland; Department of Radiology, Kantonsspital Baden, Im Ergel 1, 5404 Baden, Switzerland; Department of Radiology, University Hospital Tuebingen, Hoppe-Seyler-Straße 3, 7207 Tuebingen, Germany.
| | - Maurice Pradella
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031 Basel, Switzerland.
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