Integration of clinical parameters and deep learning based ROI enhancement in Tc-99m PYP scintigraphy for predicting SPECT/CT-adjudicated Perugini categories in suspected transthyretin cardiac amyloidosis

Technetium-99 m pyrophosphate (Tc-99 m PYP) planar scintigraphy is widely used for evaluating transthyretin cardiac amyloidosis (ATTR-CM). However, interpreting equivocal Perugini Grade 1 cardiac uptake remains a significant clinical challenge due to overlapping blood-pool activity and low signal-to-noise ratio. We aimed to develop and validate a multimodal deep learning framework to predict expert SPECT/CT adjudicated Perugini categories from planar imaging and pre-procedural clinical variables, with particular attention to Grade 1 examinations. In this multicenter study utilizing data from the Taiwan Multicenter TTCA-PYP Registry ( N = 438), we developed a multimodal neural network. The framework processes physician-verified cardiac planar ROIs (32 × 128 pixels) with a truncated Wide-ResNet50-2 encoder and 23 routine clinical variables with a frozen TabPFN embedding, aligned by a CLIP-style contrastive loss and fused by cross-attention, to predict SPECT/CT-adjudicated Perugini categories (Grade 0, Grade 1, Grades 2–3). Genetic testing status and study design were not inputs. Evaluation used 10 repetitions of a hospital- and grade-stratified assessment set with 10-fold cross-validation, decision curve analysis, and leave-one-center-out testing of the two centers with all grades (FEMH, NTUH). Overall accuracy was higher with the multimodal model than with imaging only (0.826 vs. 0.751; paired difference + 0.075, 95% CI 0.034 to 0.117), whereas macro-AUC was similar (0.925 vs. 0.917). In Grade 1, precision increased from 0.536 to 0.694 (difference + 0.159, 95% CI 0.056 to 0.261) and recall decreased from 0.704 to 0.683 (difference − 0.022, 95% CI − 0.120 to 0.077); F1-score and PR-AUC differences were less certain. Intervals used a Nadeau-Bengio correction for repeated resampling. With FEMH or NTUH held out, accuracy was 0.704 and 0.889, but Grade 1 recall was 0.312 in FEMH and Grade 0 recall 0.071 in NTUH. The multimodal framework may serve as an adjunct for standardizing expert imaging-based Perugini classification from planar Tc-99 m PYP examinations. It does not establish an etiologic diagnosis of ATTR-CM, and Grade 1 predictions remain indeterminate findings requiring monoclonal protein assessment and, where appropriate, tomographic or further clinical evaluation. Performance did not transfer uniformly to held-out centers, and independent validation is required.

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Journal
BMC Medical Imaging
Published
2026-11-06
DOI
https://doi.org/10.1186/s12880-026-03036-1
Primary Topic
Amyloidosis: Diagnosis, Treatment, Outcomes
Type
article
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article

Integration of clinical parameters and deep learning based ROI enhancement in Tc-99m PYP scintigraphy for predicting SPECT/CT-adjudicated Perugini categories in suspected transthyretin cardiac amyloidosis

Yu-An Yen, Wen-Hwa Wang, Szu-Ying Tsai, Kao Chia-Hung et al.
3 links
BMC Medical Imaging
Amyloidosis: Diagnosis, Treatment, Outcomes
article

Integration of clinical parameters and deep learning based ROI enhancement in Tc-99m PYP scintigraphy for predicting SPECT/CT-adjudicated Perugini categories in suspected transthyretin cardiac amyloidosis

Yu-An Yen, Wen-Hwa Wang, Szu-Ying Tsai, Kao Chia-Hung, Chi‐Lun Ko, De-Xiang Ou, Wen-Chung Yu, Chih-Hung Lai, Jhih-Yuan Shih, Chung-Lieh Hung, Mei-Fang Cheng, Chung-Ming Chen, Cheng-Hau Huang, Shan-Ying Wang, on behalf of the TTCA-PYP investigators, Yen-Hung Lin, Lien-Hsin Hu, Yen-Wen Wu, Kuan-Cheng Chang, Wei-Ting Chang
article en

Abstract

Technetium-99 m pyrophosphate (Tc-99 m PYP) planar scintigraphy is widely used for evaluating transthyretin cardiac amyloidosis (ATTR-CM). However, interpreting equivocal Perugini Grade 1 cardiac uptake remains a significant clinical challenge due to overlapping blood-pool activity and low signal-to-noise ratio. We aimed to develop and validate a multimodal deep learning framework to predict expert SPECT/CT adjudicated Perugini categories from planar imaging and pre-procedural clinical variables, with particular attention to Grade 1 examinations. In this multicenter study utilizing data from the Taiwan Multicenter TTCA-PYP Registry ( N = 438), we developed a multimodal neural network. The framework processes physician-verified cardiac planar ROIs (32 × 128 pixels) with a truncated Wide-ResNet50-2 encoder and 23 routine clinical variables with a frozen TabPFN embedding, aligned by a CLIP-style contrastive loss and fused by cross-attention, to predict SPECT/CT-adjudicated Perugini categories (Grade 0, Grade 1, Grades 2–3). Genetic testing status and study design were not inputs. Evaluation used 10 repetitions of a hospital- and grade-stratified assessment set with 10-fold cross-validation, decision curve analysis, and leave-one-center-out testing of the two centers with all grades (FEMH, NTUH). Overall accuracy was higher with the multimodal model than with imaging only (0.826 vs. 0.751; paired difference + 0.075, 95% CI 0.034 to 0.117), whereas macro-AUC was similar (0.925 vs. 0.917). In Grade 1, precision increased from 0.536 to 0.694 (difference + 0.159, 95% CI 0.056 to 0.261) and recall decreased from 0.704 to 0.683 (difference − 0.022, 95% CI − 0.120 to 0.077); F1-score and PR-AUC differences were less certain. Intervals used a Nadeau-Bengio correction for repeated resampling. With FEMH or NTUH held out, accuracy was 0.704 and 0.889, but Grade 1 recall was 0.312 in FEMH and Grade 0 recall 0.071 in NTUH. The multimodal framework may serve as an adjunct for standardizing expert imaging-based Perugini classification from planar Tc-99 m PYP examinations. It does not establish an etiologic diagnosis of ATTR-CM, and Grade 1 predictions remain indeterminate findings requiring monoclonal protein assessment and, where appropriate, tomographic or further clinical evaluation. Performance did not transfer uniformly to held-out centers, and independent validation is required.

BMC Medical Imaging
National Sun Yat-sen University (TW), National Yang Ming Chiao Tung University (TW), National Taiwan University (TW), China Medical University (TW), Mackay Memorial Hospital (TW), Taipei Veterans General Hospital (TW), Mackay Medical University (TW), National Yang Ming University Hospital (TW), Taichung Veterans General Hospital (TW), Far Eastern Memorial Hospital (TW), China Medical University Hospital (TW), National Taiwan University Hospital (TW), Chi Mei Medical Center (TW), Kaohsiung Veterans General Hospital (TW), Yuan Ze University (TW)
Ministry of Science and Technology, Taiwan
Good health and well-being
Openalex Percentile: Top 11%
Amyloidosis: Diagnosis, Treatment, Outcomes
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