방사선의학 웹진

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Abstract
Purpose: Tc-99m dimercaptosuccinic acid (99mTc-DMSA) renal scan is an important tool for the assessment of childhood urinary tract infection (UTI), vesicoureteral reflux (VUR), and renal scarring. We evaluated whether a deep learning (DL) analysis of 99mTc-DMSA renal scans could predict the recurrence of UTI better than conventional clinical factors. Methods: the subjects were 180 paediatric patients diagnosed with UTI, who underwent immediate post-therapeutic 99mTc-DMSA renal scans. The primary outcome was the recurrence of UTI during the follow-up period. For the DL analysis, a convolutional neural network (CNN) model was used. Age, sex, the presence of VUR, the presence of cortical defects on the 99mTc-DMSA renal scan, split renal function (SRF), and DL prediction results were used as independent factors for predicting recurrent UTI. The diagnostic accuracy for predicting recurrent UTI was statistically compared between independent factors. Results: The sensitivity, specificity and accuracy for predicting recurrent UTI were 44.4%, 88.9%, and 82.2% by the presence of VUR; 44.4%, 76.5%, and 71.7% by the presence of cortical defect; 74.1%, 80.4%, and 79.4% by SRF (optimal cut-off = 45.93%); and 70.4%, 94.8%, and 91.1% by the DL prediction results. There were no significant differences in sensitivity between all independent factors (p > 0.05, for all). The specificity and accuracy of the DL prediction results were significantly higher than those of the other factors. Conclusion: DL analysis of 99mTc-DMSA renal scans may be useful for predicting recurrent UTI in paediatric patients. It is an efficient supportive tool to predict poor prognosis without visually demonstrable cortical defects in 99mTc-DMSA renal scans.

(a) 핵의학 전문의 판독 상 신피질 결손이 없는 것으로 진단된 환자이며 요로감염 재발은 없었습니다. 인공신경망은 해당 환자에 대해 요로감염 재발이 없을 것이라 예측하였습니다. (b) 핵의학 전문의 판독 상 다발성 신피질 결손이 진단된 환자이며 요로감염 재발이 있었습니다. 인공신경망은 해당 환자에 대해 요로감염 재발이 있을 것이라 예측하였습니다. (c) 핵의학 전문의 판독 상 신피질 결손이 없는 것으로 진단된 환자이며 요로감염 재발이 있었습니다. 인공신경망은 해당 환자에 대해 요로감염 재발이 있을 것이라 예측하였습니다.

Affiliations

Hyunjong Lee  1 , Beongwoo Yoo  2 , Minki Baek  3 , Joon Young Choi  1
1 Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
2 Sungkyunkwan University School of Medicine, Seoul 06351, Korea.
3 Department of Urology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.