دورية أكاديمية
Deep Learning-Enabled Quantification of 99m Tc-Pyrophosphate SPECT/CT for Cardiac Amyloidosis.
| العنوان: | Deep Learning-Enabled Quantification of 99m Tc-Pyrophosphate SPECT/CT for Cardiac Amyloidosis. |
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| المؤلفون: | Miller RJH; Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada., Shanbhag A; Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; and.; Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California., Michalowska AM; Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; and., Kavanagh P; Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; and., Liang JX; Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; and., Builoff V; Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; and., Fine NM; Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada., Dey D; Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; and., Berman DS; Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; and., Slomka PJ; Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California; and piotr.slomka@cshs.org. |
| المصدر: | Journal of nuclear medicine : official publication, Society of Nuclear Medicine [J Nucl Med] 2024 Jul 01; Vol. 65 (7), pp. 1144-1150. Date of Electronic Publication: 2024 Jul 01. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Society of Nuclear Medicine Country of Publication: United States NLM ID: 0217410 Publication Model: Electronic Cited Medium: Internet ISSN: 1535-5667 (Electronic) Linking ISSN: 01615505 NLM ISO Abbreviation: J Nucl Med Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Reston, VA : Society of Nuclear Medicine Original Publication: [Chicago, Ill.] : S.N. Turiel & Assoc. |
| مواضيع طبية MeSH: | Technetium Tc 99m Pyrophosphate* , Deep Learning* , Single Photon Emission Computed Tomography Computed Tomography*, Humans ; Female ; Male ; Aged ; Aged, 80 and over ; Cardiomyopathies/diagnostic imaging ; Image Processing, Computer-Assisted ; Amyloid Neuropathies, Familial/diagnostic imaging ; Middle Aged ; Amyloidosis/diagnostic imaging |
| مستخلص: | Transthyretin cardiac amyloidosis (ATTR CA) is increasingly recognized as a cause of heart failure in older patients, with 99m Tc-pyrophosphate imaging frequently used to establish the diagnosis. Visual interpretation of SPECT images is the gold standard for interpretation but is inherently subjective. Manual quantitation of SPECT myocardial 99m Tc-pyrophosphate activity is time-consuming and not performed clinically. We evaluated a deep learning approach for fully automated volumetric quantitation of 99m Tc-pyrophosphate using segmentation of coregistered anatomic structures from CT attenuation maps. Methods: Patients who underwent SPECT/CT 99m Tc-pyrophosphate imaging for suspected ATTR CA were included. Diagnosis of ATTR CA was determined using standard criteria. Cardiac chambers and myocardium were segmented from CT attenuation maps using a foundational deep learning model and then applied to attenuation-corrected SPECT images to quantify radiotracer activity. We evaluated the diagnostic accuracy of target-to-background ratio (TBR), cardiac pyrophosphate activity (CPA), and volume of involvement (VOI) using the area under the receiver operating characteristic curve (AUC). We then evaluated associations with the composite outcome of cardiovascular death or heart failure hospitalization. Results: In total, 299 patients were included (median age, 76 y), with ATTR CA diagnosed in 83 (27.8%) patients. CPA (AUC, 0.989; 95% CI, 0.974-1.00) and VOI (AUC, 0.988; 95% CI, 0.973-1.00) had the highest prediction performance for ATTR CA. The next highest AUC was for TBR (AUC, 0.979; 95% CI, 0.964-0.995). The AUC for CPA was significantly higher than that for heart-to-contralateral ratio (AUC, 0.975; 95% CI, 0.952-0.998; P = 0.046). Twenty-three patients with ATTR CA experienced cardiovascular death or heart failure hospitalization. All methods for establishing TBR, CPA, and VOI were associated with an increased risk of events after adjustment for age, with hazard ratios ranging from 1.41 to 1.84 per SD increase. Conclusion: Deep learning segmentation of coregistered CT attenuation maps is not affected by the pattern of radiotracer uptake and allows for fully automatic quantification of hot-spot SPECT imaging such as 99m Tc-pyrophosphate. This approach can be used to accurately identify patients with ATTR CA and may play a role in risk prediction. (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.) |
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| معلومات مُعتمدة: | R35 HL161195 United States HL NHLBI NIH HHS |
| فهرسة مساهمة: | Keywords: biomarker; cardiac amyloidosis; diagnostic accuracy; quantification; technetium pyrophosphate |
| المشرفين على المادة: | 5L76I61H2B (Technetium Tc 99m Pyrophosphate) |
| SCR Disease Name: | Amyloidosis, Hereditary, Transthyretin-Related |
| تواريخ الأحداث: | Date Created: 20240509 Date Completed: 20240701 Latest Revision: 20240704 |
| رمز التحديث: | 20240704 |
| مُعرف محوري في PubMed: | PMC11218726 |
| DOI: | 10.2967/jnumed.124.267542 |
| PMID: | 38724278 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1535-5667 |
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| DOI: | 10.2967/jnumed.124.267542 |