Background Transthyretin amyloidosis (ATTR) is an occasional incidental finding on bone scintigraphy. We studied its prognostic impact in elderly patients. Methods The study population consisted of 2000 patients aged over 70 years who underwent bone scintigraphies with clinical indications in three nuclear medicine departments (Kymenlaakso, Jorvi and Meilahti hospitals) in Finland. All studies were performed using 99mTechnetium labeled hydroxymethylene diphosphonate (HMDP). ATTR was suspected in patients with ≥grade 2 Perugini grade uptake (grade 0-3). Heart-to-contralateral ratio (H/CL) of ≥ 1.30 was considered positive for ATTR. The overall and cardiovascular mortality were obtained from the Finnish National Statistical Service. Results There were a total of 1014 deaths (51%) and 177 cardiovascular deaths (9%) during median follow-up of 4 ± 2 years. ATTR was suspected in 69 patients (3.6%) of which 54 (2.7%) had grade 2 and 15 (.8%) had grade 3 uptake and in 47 patients (2.4%) by H/CL ratio. In multivariate analyses age, bone metastasis, H/CL ratio and grade 3 uptake were independent predictors of overall and cardiovascular mortality. Grade 2 uptake was a predictor of cardiovascular mortality. Conclusions A suspected ATTR as an incidental finding on bone scintigraphy predicts elevated overall and cardiovascular mortality in elderly patients.

Background Transthyretin amyloidosis (ATTR) is a progressive disease which can be diagnosed non-invasively using bone avid [99mTc]-labeled radiotracers. Thus, ATTR is also an occasional incidental finding on bone scintigraphy. In this study, we trained convolutional neural networks (CNN) to automatically detect and classify ATTR from scintigraphy images. The study population consisted of 1334 patients who underwent [99mTc]-labeled hydroxymethylene diphosphonate (HMDP) scintigraphy and were visually graded using Perugini grades (grades 0–3). A total of 47 patients had visual grade ≥ 2 which was considered positive for ATTR. Two custom-made CNN architectures were trained to discriminate between the four Perugini grades of cardiac uptake. The classification performance was compared to four state-of-the-art CNN models. Results Our CNN models performed better than, or equally well as, the state-of-the-art models in detection and classification of cardiac uptake. Both models achieved area under the curve (AUC) ≥ 0.85 in the four-class Perugini grade classification. Accuracy was good in detection of negative vs. positive ATTR patients (grade < 2 vs grade ≥ 2, AUC > 0.88) and high-grade cardiac uptake vs. other patients (grade < 3 vs. grade 3, AUC = 0.94). Maximum activation maps demonstrated that the automated deep learning models were focused on detecting the myocardium and not extracardiac features. Conclusion Automated convolutional neural networks can accurately detect and classify different grades of cardiac uptake on bone scintigraphy. The CNN models are focused on clinically relevant image features. Automated screening of bone scintigraphy images using CNN could improve the early diagnosis of ATTR.

Introduction: Computed tomography perfusion (CTP) imaging has become an important tool in evaluating acute recanalization treatment candidates. Large clinical trials have successfully used RAPID automated imaging analysis software for quantifying ischemic core and penumbra, yet other commercially available software vendors are also on the market. We evaluated the possible difference in ischemic core and perfusion lesion volumes and the agreement rate of target mismatch between OLEA, MIStar, and Syngo.Via versus RAPID software in acute recanalization treatment candidates. Patients and methods: All consecutive stroke-code patients with baseline CTP RAPID imaging at Helsinki University Hospital during 8/2018–9/2021 were included. Ischemic core was defined as cerebral blood flow <30% than the contralateral hemisphere and within the area of delay time (DT) >3s with MIStar. Perfusion lesion volume was defined as DT > 3 s (MIStar) and Tmax > 6 s with all other software. A perfusion mismatch ratio of ⩾1.8, a perfusion lesion volume of ⩾15 mL, and ischemic core <70 mL was defined as target mismatch. The mean pairwise differences of the core and perfusion lesion volumes between software were calculated using the Bland-Altman method and the agreement of target mismatch between software using the Pearson correlation. Results: A total of 1606 patients had RAPID perfusion maps, 1222 of which had MIStar, 596 patients had OLEA, and 349 patients had Syngo.Via perfusion maps available. Each software was compared with simultaneously analyzed RAPID software. MIStar showed the smallest core difference compared with RAPID (−2 mL, confidence interval (CI) from −26 to 22), followed by OLEA (2 mL, CI from −33 to 38). Perfusion lesion volume differed least with MIStar (4 mL, CI from −62 to 71) in comparison with RAPID, followed by Syngo.Via (6 mL, CI from −94 to 106). MIStar had the best agreement rate with target mismatch of RAPID followed by OLEA and Syngo.Via. Discussion and conclusion: Comparison of RAPID with three other automated imaging analysis software showed variance in ischemic core and perfusion lesion volumes and in target mismatch.