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.
Biomarkers ; Clinical Decision-Making ; Learning ; Machine Learning ; Molecular Imaging ; Nuclear Medicine ; Precision Medicine ; Statistics as Topic

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.

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.

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.
Diagnostic Imaging ; Humans ; Metabolism ; Population Characteristics ; Positron-Emission Tomography and Computed Tomography ; Precision Medicine ; Sample Size

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.

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.

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.
Classification ; Disease-Free Survival ; Drug Therapy ; Follow-Up Studies ; Glycolysis ; Humans ; Lymphoma ; Positron-Emission Tomography ; Prognosis ; Retrospective Studies ; Tumor Burden

Solid pseudopapillary tumor (SPT) of the pancreas is a neoplasm with low malignant potential. It is often challenging to diagnose SPT due to its nonspecific clinical and radiological features, and ­[18F]FDOPA is effective in diagnosing SPT, particularly in differentiating SPT from benign conditions such as splenosis. A 55-year-old woman underwent distal pancreatectomy and splenectomy for histologically confirmed SPT. She was also initially diagnosed with splenosis. During follow-up, sizes of multiple nodular lesions were increased, raising the possibility of peritoneal seeding of SPT. For diagnosis, a spleen scan and SPECT/CT were performed using 99mTc-labeled damaged red blood cells, which showed no uptake in the peritoneal nodules. Subsequent ­[18F]FDOPA PET/CT revealed ­[18F]FDOPA-avidity of the nodules. The patient underwent tumor resection surgery, and the nodules were pathologically confirmed as SPT.

Methods: 150 amyloid brain PET images were visually assessed by experts and categorized as negative and positive. Standardized uptake value ratio (SUVR) was calculated with cerebellum grey matter as the reference region, and receiver operating characteristic (ROC) and precision-recall (PR) analysis for BTXBrain-Amyloid were conducted. For comparison, same image processing and analysis was performed using Statistical Parametric Mapping (SPM) program. In addition, to evaluate the spatial normalization (SN) performance, mutual information (MI) between MRI template and spatially normalized PET images was calculated and SPM group analysis was conducted. Results: Both BTXBrain and SPM methods discriminated between negative and positive groups. However, BTXBrain exhibited lower SUVR standard deviation (0.06 and 0.21 for negative and positive, respectively) than SPM method (0.11 and 0.25). In ROC analysis, BTXBrain had an AUC of 0.979, compared to 0.959 for SPM, while PR curves showed an AUC of 0.983 for BTXBrain and 0.949 for SPM. At the optimal cut-off, the sensitivity and specificity were 0.983 and 0.921 for BTXBrain and 0.917 and 0.921 for SPM12, respectively. MI evaluation also favored BTXBrain (0.848 vs. 0.823), indicating improved SN. In SPM group analysis, BTXBrain exhibited higher sensitivity in detecting basal ganglia differences between negative and positive groups. Conclusion BTXBrain-Amyloid outperformed SPM in clinical performance evaluation, also demonstrating superior SN and improved detection of deep brain differences. These results suggest the potential of BTXBrain-Amyloid as a valuable tool for clinical amyloid PET image evaluation.

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.