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신무헌,최준영,김선욱,김정한,조영석 대한핵의학회 2021 핵의학 분자영상 Vol.55 No.6
Purpose 99mTc-labeled sestamibi scintigraphy combined with single-photon emission computed tomography (SPECT) has ahigh positive predictive value for localizing hyperfunctioning parathyroid lesions in primary hyperparathyroidism (pHPT) butrelatively low sensitivity and specificity in secondary hyperparathyroidism (sHPT) and tertiary hyperparathyroidism (tHPT). The purpose of this study is to investigate the usefulness of 99mTc-sestamibi scintigraphy in persistent hyperparathyroidismafter kidney transplant (KT). Methods Retrospectively evaluated 50 patients who received parathyroidectomy after KT at a single medical center. Theparathyroid lesion with the highest sestamibi uptake intensity of a patient was graded from 0 to 3. Uptake intensity wasanalyzed in correlation with parathyroid hormone (PTH), calcium, ionized calcium, phosphorus, and vitamin D. Results Per-patient analysis, 43 patients had hyperplasia, 6 patients had adenomas, and 1 patient had a carcinoma. Only 3patients with hyperplasia did not demonstrate any sestamibi uptake in the parathyroid scans. Out of the 148 pathologicallyconfirmed parathyroid lesions, SPECT/CT images were able to identify 89 lesions (60%) and planar images of 71 lesions(48%). The average of sestamibi uptake intensity was mild at grade 1.6. Uptake intensity showed a positive correlation withparathyroid hormone (PTH) level but not with phosphorus, calcium, ionized calcium, or vitamin D levels. The largest lesionshowed a high positive predictive value, especially in lesions with a diameter over 1.0 cm. Conclusions Regardless of relatively low and less discrete uptake in KT patients, it well depicts the largest and the mosthyperfunctioning lesion.
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Radiogenomics Based on PET Imaging
박용진,신무헌,문승환 대한핵의학회 2020 핵의학 분자영상 Vol.54 No.3
Radiogenomics or imaging genomics is a novel omics strategy of associating imaging data with genetic information, which has the potential to advance personalized medicine. Imaging features extracted from PET or PET/CT enable assessment of in vivo functional and physiological activity and provide comprehensive tumor information non-invasively. However, PET features are considered secondary to features on conventional imaging, and there has not yet been a review of the radiogenomic approach using PET features. This review article summarizes the current state of PET-based radiogenomic research for cancer, which discusses some of its limitations and directions for future study.
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박용진,배지훈,신무헌,현승협,조영석,최연성,최준영,이경한,김병태,문승환 대한핵의학회 2019 핵의학 분자영상 Vol.53 No.2
Purpose We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora.We evaluated the diagnostic performance of these models. Methods Between January 2016 and September 2017, 250 patients with epiphora who underwent dacryocystography (DCG) and lacrimal scintigraphy (LS) were included in the study.We developed five different predictive models usingMLtools, Pythonbased TensorFlow, R, and Microsoft Azure Machine Learning Studio (MAMLS). A total of 27 clinical characteristics and parameters including variables related to epiphora (VE) and variables related to dacryocystography (VDCG) were used as input data. Apart from this, we developed two predictive convolutional neural network (CNN) models for diagnosing LS images. We conducted this study using supervised learning. Results Among 500 eyes of 250 patients, 59 eyes had anatomical obstruction, 338 eyes had functional obstruction, and the remaining 103 eyes were normal. For the data set that excluded VE and VDCG, the test accuracies in Python-based TensorFlow, R, multiclass logistic regression in MAMLS, multiclass neural network in MAMLS, and nuclear medicine physician were 81.70%, 80.60%, 81.70%, 73.10%, and 80.60%, respectively. The test accuracies of CNN models in three-class classification diagnosis and binary classification diagnosis were 72.00% and 77.42%, respectively. Conclusions ML-based predictive models using different programming languages and different computing platforms were useful for classifying clinical diagnoses in patients with epiphora and were similar to a clinician’s diagnostic ability.
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