Automatic Lung Cancer Segmentation in 18 FFDG PET/CT Using a Two-Stage Deep Learning Approach / 대한핵의학회잡지

Purpose@#Since accurate lung cancer segmentation is required to determine the functional volume of a tumor in [ 18 F]FDG PET/CT, we propose a two-stage U-Net architecture to enhance the performance of lung cancer segmentation using [ 18 F]FDG PET/CT. @*Methods@#The whole-body [ 18 F]FDG PET/CT scan data of 887 patients with lung cancer were retrospectively used for network training and evaluation. The ground-truth tumor volume of interest was drawn using the LifeX software. The dataset was randomly partitioned into training, validation, and test sets. Among the 887 PET/CT and VOI datasets, 730 were used to train the proposed models, 81 were used as the validation set, and the remaining 76 were used to evaluate the model. In Stage 1, the global U-net receives 3D PET/CT volume as input and extracts the preliminary tumor area, generating a 3D binary volume as output. In Stage 2, the regional U-net receives eight consecutive PET/CT slices around the slice selected by the Global U-net in Stage 1 and generates a 2D binary image as the output. @*Results@#The proposed two-stage U-Net architecture outperformed the conventional one-stage 3D U-Net in primary lung cancer segmentation. The two-stage U-Net model successfully predicted the detailed margin of the tumors, which was determined by manually drawing spherical VOIs and applying an adaptive threshold. Quantitative analysis using the Dice similarity coefficient confirmed the advantages of the two-stage U-Net. @*Conclusion@#The proposed method will be useful for reducing the time and effort required for accurate lung cancer segmentation in [ 18 F]FDG PET/CT.

Computed Tomography (CT) Protocols Associated with Cardiac and Bone Single-Photon Emission Computed Tomography/CT (SPECT/CT) in Korea / 대한핵의학회잡지

Purpose@#Single-photon emission computed tomography/computed tomography (SPECT/CT) is an advanced hybrid nuclear medicine technology that generates both functional and anatomical images in a single study. As utilization of SPECT/CT in Korea has been increasing, the purpose of this study was to survey its application of cardiac and skeletal SPECT/CT imaging for protocol optimization. @*Methods@#We surveyed CT protocols established for cardiac and skeletal SPECT/CT. We searched the guidelines for the CT protocols for SPECT/CT and reviewed the literature recently published. @*Results@#Among 36 hybrid SPECT scanners equipped with four or more multi-channel detector CTs (MDCTs), 18 scanners were used to perform cardiac studies at both very low current CT (30–80 mA; 11.1%) and ultra-low current CT (13–30 mA; 88.9%). Among the 33 canners, very low current (≤ 80 mA) CT or low current CT (80–130 mA) was used in 23.5%, and 41.8% for spine disorders, and in 36.4% or 30.3% for foot/ankle disorders, respectively. In the CT reconstructions, slice thickness of 5 mm for cardiac studies was most commonly used (94.4%); thinner slices (0.6–1.0 mm) for spine and foot/ankle studies were used in 24.2% and 45.5%, respectively. We also reviewed the international guidelines. @*Conclusions@#The results and current recommendations will be helpful for optimizing CT protocols for SPECT/CT. Optimization of SPECT/CT protocols will be required for generating the proper strategy for the specific lesions and clinical purpose.

Spleen Scan for 68Ga-DOTATOC PET-Positive Pancreatic Tail Lesion: Differential Diagnosis of Neuroendocrine Tumor from Accessory Spleen / 대한핵의학회잡지

68Ga-DOTATOC PET/CT is widely used as a functional imaging technique in the detection and characterization of neuroendocrine tumors (NETs). Pancreatic NET and intrapancreatic accessory spleen (IPAS) have similar radiologic characteristics in anatomical imaging and usually show high uptake of 68Ga-DOTATOC. Thus, it is challenging to make a differential diagnosis between NET and IPAS when the tumor-like lesion is located in the pancreatic tail. Here, we present a case of 68Ga-DOTATOC PET-positive pancreatic tail lesion with high arterial enhancement on CT and MRI. Since 99mTc-labeled damaged red blood cell does not accumulate on NET, a negative spleen scan finding was a crucial diagnostic step to decide surgical resection, which was histologically proven as insulinoma. Our case shows a promising role of additional use of spleen scan with SPECT/CT for the differential diagnosis of 68Ga-DOTATOC PET-positive pancreatic NET from the accessory spleen.

Spatial Normalization Using Early-Phase 18FFP-CIT PET for Quantification of Striatal Dopamine Transporter Binding / 대한핵의학회잡지

Purpose@#The precise quantification of dopamine transporter (DAT) density on N-(3-[18F]Fluoropropyl)-2β-carbomethoxy-3β-(4-iodophenyl) nortropane positron emission tomography ([18F]FP-CIT PET) imaging is crucial to measure the degree of striatal DAT loss in patients with parkinsonism. The quantitative analysis requires a spatial normalization process based on a template brain. Since the spatial normalization method based on a delayed-phase PET has limited performance, we suggest an early-phase PET-based method and compared its accuracy, referring to the MRI-based approach as a gold standard. @*Methods@#A total of 39 referred patients from the movement disorder clinic who underwent dual-phase [18F]FP-CIT PET and took MRI within 1 year were retrospectively analyzed. The three spatial normalization methods were applied for quantification of [18F]FP-CIT PET-MRI-based anatomical normalization, PET template-based method based on delayed PET, and that based on early PET. The striatal binding ratios (BRs) were compared, and voxelwise paired t tests were implemented between different methods. @*Results@#The early image-based normalization showed concordant patterns of putaminal [18F]FP-CIT binding with an MRI-based method. The BRs of the putamen from the MRI-based approach showed higher agreement with early image- than delayed image-based method as presented by Bland-Altman plots and intraclass correlation coefficients (early image-based, 0.980; delayed image-based, 0.895). The voxelwise test exhibited a smaller volume of significantly different counts in putamen between brains processed by early image and MRI compared to that between delayed image and MRI. @*Conclusion@#The early-phase [18F]FP-CIT PET can be utilized for spatial normalization of delayed PET image when the MRI image is unavailable and presents better performance than the delayed template-based method in quantitation of putaminal binding ratio.

Relationship of EGFR Mutation to Glucose Metabolic Activityand Asphericity of Metabolic Tumor Volumein Lung Adenocarcinoma / 대한핵의학회잡지

Purpose@#EGFR-mutation (EGFR-mt) is a major oncogenic driver mutation in lung adenocarcinoma (ADC) and is more oftenobserved in Asian population. In lung ADC, some radiomics parameters of FDG PET have been reported to be associated withEGFR-mt. Here, the associations between EGFR-mt and PET parameters, particularly asphericity (ASP), were evaluated inAsian population. @*Methods@#Lung ADC patients who underwent curative surgical resection as the first treatment were retrospectively enrolled.EGFR mutation was defined as exon 19 deletion and exon 21 point mutation and was evaluated using surgical specimens. OnFDG PET, image parameters of maximal standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesionglycolysis (TLG), and ASP were obtained. The parameters were compared between EGFR-mt and wild type (EGFR-wt) groups,and the relationships between these PET parameters and EGFR-mt were evaluated. @*Results@#A total of 64 patients (median age 66 years, M:F = 34:30) were included in the analysis, and 29 (45%) patients showedEGFR-mt. In EGFR-mt group, all the image parameters of SUVmax, MTV, TLG, and ASP were significantly lower than inEGFR-wt group (all adjusted P< 0.050). In univariable logistic regression, SUVmax (P= 0.003) and ASP (P= 0.010) weresignificant determinants for EGFR-mt, whereasMTV was not (P= 0.690). Multivariate analysis revealed that SUVmax and ASPare independent determinants for EGFR-mt, regardless of inclusion of MTV in the analysis (P< 0.05). @*Conclusion@#In Asian NSCLC/ADC patients, SUVmax, MTV, and ASP on FDG PET are significantly related to EGFR mutationstatus. Particularly, low SUVmax and ASP are independent determinants for EGFR-mt.

KSNM/KSID/KOSHIC Guidance for Nuclear Medicine Department Against the Coronavirus Disease 2019 (COVID-19) Pandemic / 대한핵의학회잡지

The dramatic spread of Coronavirus Disease 2019 (COVID-19) has profound impacts on every continent and life. Due to humanto-human transmission of COVID-19, nuclear medicine staffs also cannot escape the risk of infection from workplaces. Everystaff in the nuclear medicine department must prepare for and respond to COVID-19 pandemic which tailored to the characteristicsof our profession. This article provided the guidance prepared by the Korean Society of Nuclear Medicine (KSNM) incooperation with the Korean Society of Infectious Disease (KSID) and Korean Society for Healthcare-Associated InfectionControl and Prevention (KOSHIC) in managing the COVID-19 pandemic for the nuclear medicine department.We hope that thisguidance will support every practice in nuclear medicine during this chaotic period.

Thyroid Metastasis from Breast and Lung Cancer in Patients with Underlying Hashimoto’s Thyroiditis

Metastatic disease involving the thyroid gland is uncommon. Thyroid metastases has been previously described from several primary cancers of lung, breast, and kidney. Because of the lower incidence and ambiguous clinical significance, it is not easy to consider thyroid metastasis and decide the optimal time for performing diagnostic examination. Here, we reported two cases of metastatic diseases of thyroid in patients who had underlying Hashimoto’s thyroiditis: a 39-year-old woman who had thyroid metastasis of breast cancer with underlying Hashimoto’s thyroiditis, and a 44-year-old woman with metastatic lung cancer.

Amyloid PET Quantification Via End-to-End Training of a Deep Learning / 대한핵의학회잡지

PURPOSE@#Although quantification of amyloid positron emission tomography (PET) is important for evaluating patients with cognitive impairment, its routine clinical use is hampered by complicated preprocessing steps and required MRI. Here, we suggested a one-step quantification based on deep learning using native-space amyloid PET images of different radiotracers acquired from multiple centers.@*METHODS@#Amyloid PET data of the Alzheimer Disease Neuroimaging Initiative (ADNI) were used for this study. A training/validation consists of 850 florbetapir PET images. Three hundred sixty-six florbetapir and 89 florbetaben PET images were used as test sets to evaluate the model. Native-space amyloid PET images were used as inputs, and the outputs were standardized uptake value ratios (SUVRs) calculated by the conventional MR-based method.@*RESULTS@#The mean absolute errors (MAEs) of the composite SUVR were 0.040, 0.060, and 0.050 of training/validation and test sets for florbetapir PETand a test set for florbetaben PET, respectively. The agreement of amyloid positivity measured by Cohen's kappa for test sets of florbetapir and florbetaben PET were 0.87 and 0.89, respectively.@*CONCLUSION@#We suggest a one-step quantification method for amyloid PET via a deep learning model. The model is highly reliable to quantify the amyloid PET regardless of multicenter images and various radiotracers.

Radiomics in Oncological PET/CT: a Methodological Overview / 대한핵의학회잡지

Radiomics is a medical imaging analysis approach based on computer-vision. Metabolic radiomics in particular analyses the spatial distribution patterns of molecular metabolism on PET images. Measuring intratumoral heterogeneity via image is one of the main targets of radiomics research, and it aims to build a image-based model for better patient management. The workflow of radiomics using texture analysis follows these steps: 1) imaging (image acquisition and reconstruction); 2) preprocessing (segmentation & quantization); 3) quantification (texture matrix design & texture feature extraction); and 4) analysis (statistics and/or machine learning). The parameters or conditions at each of these steps are effect on the results. In statistical testing or modeling, problems such as multiple comparisons, dependence on other variables, and high dimensionality of small sample size data should be considered. Standardization of methodology and harmonization of image quality are one of the most important challenges with radiomics methodology. Even though there are current issues in radiomics methodology, it is expected that radiomics will be clinically useful in personalized medicine for oncology.

Amyloid PET Quantification Via End-to-End Training of a Deep Learning / 대한핵의학회잡지

PURPOSE: Although quantification of amyloid positron emission tomography (PET) is important for evaluating patients with cognitive impairment, its routine clinical use is hampered by complicated preprocessing steps and required MRI. Here, we suggested a one-step quantification based on deep learning using native-space amyloid PET images of different radiotracers acquired from multiple centers.METHODS: Amyloid PET data of the Alzheimer Disease Neuroimaging Initiative (ADNI) were used for this study. A training/validation consists of 850 florbetapir PET images. Three hundred sixty-six florbetapir and 89 florbetaben PET images were used as test sets to evaluate the model. Native-space amyloid PET images were used as inputs, and the outputs were standardized uptake value ratios (SUVRs) calculated by the conventional MR-based method.RESULTS: The mean absolute errors (MAEs) of the composite SUVR were 0.040, 0.060, and 0.050 of training/validation and test sets for florbetapir PETand a test set for florbetaben PET, respectively. The agreement of amyloid positivity measured by Cohen's kappa for test sets of florbetapir and florbetaben PET were 0.87 and 0.89, respectively.CONCLUSION: We suggest a one-step quantification method for amyloid PET via a deep learning model. The model is highly reliable to quantify the amyloid PET regardless of multicenter images and various radiotracers.

Radiomics in Oncological PET/CT: a Methodological Overview / 대한핵의학회잡지

Radiomics is a medical imaging analysis approach based on computer-vision. Metabolic radiomics in particular analyses the spatial distribution patterns of molecular metabolism on PET images. Measuring intratumoral heterogeneity via image is one of the main targets of radiomics research, and it aims to build a image-based model for better patient management. The workflow of radiomics using texture analysis follows these steps: 1) imaging (image acquisition and reconstruction); 2) preprocessing (segmentation & quantization); 3) quantification (texture matrix design & texture feature extraction); and 4) analysis (statistics and/or machine learning). The parameters or conditions at each of these steps are effect on the results. In statistical testing or modeling, problems such as multiple comparisons, dependence on other variables, and high dimensionality of small sample size data should be considered. Standardization of methodology and harmonization of image quality are one of the most important challenges with radiomics methodology. Even though there are current issues in radiomics methodology, it is expected that radiomics will be clinically useful in personalized medicine for oncology.

Application of Quantitative Indexes of FDG PET to Treatment Response Evaluation in Indolent Lymphoma / 대한핵의학회잡지

PURPOSE@#Although ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is a standard imaging modality for response evaluation in FDG-avid lymphoma, there is a controversy using FDG PET in indolent lymphoma. The purpose of this study was to investigate the effectiveness of quantitative indexes on FDG PET in response evaluation of the indolent lymphoma.@*METHODS@#Fifty-seven indolent lymphoma patients who completed chemotherapy were retrospectively enrolled. FDG PET/computed tomography (CT) scans were performed at baseline, interim, and end of treatment (EOT). Response was determined by Lugano classification, and progression-free survival (PFS) by follow-up data. Maximumstandardized uptake value (SUV(max)), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured in the single hottest lesion (target A) or five hottest lesions (target B). Their efficacies regarding response evaluation and PFS prediction were evaluated.@*RESULTS@#On EOT PET, SUV(max), and MTVof both targets were well associated with visual analysis. Changes between initial and EOT PET were not significantly different between CR and non-CR groups. On interim PET, SUV(max), and %ΔSUV(max) in both targets were significantly different between CR and non-CR groups. For prediction of PFS, most tested indexes were significant on EOT and interim PET, with SUVmax being the most significant prognostic factor.@*CONCLUSION@#Quantitative indexes of FDG PET are well associated with Lugano classification in indolent lymphoma. SUV(max) measured in the single hottest lesion can be effective in response evaluation and prognosis prediction on interim and EOT PET.

Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions / 대한핵의학회잡지

Recent advances in deep learning have impacted various scientific and industrial fields. Due to the rapid application of deep learning in biomedical data, molecular imaging has also started to adopt this technique. In this regard, it is expected that deep learning will potentially affect the roles of molecular imaging experts as well as clinical decision making. This review firstly offers a basic overview of deep learning particularly for image data analysis to give knowledge to nuclear medicine physicians and researchers. Because of the unique characteristics and distinctive aims of various types of molecular imaging, deep learning applications can be different from other fields. In this context, the review deals with current perspectives of deep learning in molecular imaging particularly in terms of development of biomarkers. Finally, future challenges of deep learning application for molecular imaging and future roles of experts in molecular imaging will be discussed.

Application of Quantitative Indexes of FDG PET to Treatment Response Evaluation in Indolent Lymphoma / 대한핵의학회잡지

PURPOSE: Although ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is a standard imaging modality for response evaluation in FDG-avid lymphoma, there is a controversy using FDG PET in indolent lymphoma. The purpose of this study was to investigate the effectiveness of quantitative indexes on FDG PET in response evaluation of the indolent lymphoma.METHODS: Fifty-seven indolent lymphoma patients who completed chemotherapy were retrospectively enrolled. FDG PET/computed tomography (CT) scans were performed at baseline, interim, and end of treatment (EOT). Response was determined by Lugano classification, and progression-free survival (PFS) by follow-up data. Maximumstandardized uptake value (SUV(max)), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were measured in the single hottest lesion (target A) or five hottest lesions (target B). Their efficacies regarding response evaluation and PFS prediction were evaluated.RESULTS: On EOT PET, SUV(max), and MTVof both targets were well associated with visual analysis. Changes between initial and EOT PET were not significantly different between CR and non-CR groups. On interim PET, SUV(max), and %ΔSUV(max) in both targets were significantly different between CR and non-CR groups. For prediction of PFS, most tested indexes were significant on EOT and interim PET, with SUVmax being the most significant prognostic factor.CONCLUSION: Quantitative indexes of FDG PET are well associated with Lugano classification in indolent lymphoma. SUV(max) measured in the single hottest lesion can be effective in response evaluation and prognosis prediction on interim and EOT PET.

Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions / 대한핵의학회잡지

Recent advances in deep learning have impacted various scientific and industrial fields. Due to the rapid application of deep learning in biomedical data, molecular imaging has also started to adopt this technique. In this regard, it is expected that deep learning will potentially affect the roles of molecular imaging experts as well as clinical decision making. This review firstly offers a basic overview of deep learning particularly for image data analysis to give knowledge to nuclear medicine physicians and researchers. Because of the unique characteristics and distinctive aims of various types of molecular imaging, deep learning applications can be different from other fields. In this context, the review deals with current perspectives of deep learning in molecular imaging particularly in terms of development of biomarkers. Finally, future challenges of deep learning application for molecular imaging and future roles of experts in molecular imaging will be discussed.