Radiomics handles imaging biomarker from high-throughput feature extraction through complex pattern recognition that is difficult for human to process. Recent medical paradigms are rapidly changing to personalized medicine, including molecular targeted therapy, immunotherapy, and theranostics, and the importance of biomarkers for these is growing day by day. Even though biopsy continues to gold standard for tumor assessment in personalized medicine, imaging is expected to complement biopsy because it allows whole tumor evaluation, whole body evaluation, and non-invasive and repetitive evaluation. Radiomics is known as a useful method to get imaging biomarkers related to intratumor heterogeneity in molecular targeted therapy as well as one-size-fits-all therapy. It is also expected to be useful in new paradigms such as immunotherapy and somatostatin receptor (SSTR) or prostate-specific membrane antigen (PSMA)-targeted theranostics. Radiomics research should move to multimodality (CT, MR, PET, etc.), multicenter, and prospective studies from current single modality, single institution, and retrospective studies. Image-quality harmonization, intertumor heterogeneity, and integrative analysis of information from different scales are thought to be important keywords in future radiomics research. It is clear that radiomics will play an important role in personalized medicine.
Biomarkers ; Biopsy ; Complement System Proteins ; Humans ; Immunotherapy ; Membranes ; Methods ; Molecular Targeted Therapy ; Population Characteristics ; Precision Medicine ; Prospective Studies ; Receptors, Somatostatin ; Retrospective Studies ; Theranostic Nanomedicine ; Weights and Measures

Radiomics handles imaging biomarker from high-throughput feature extraction through complex pattern recognition that is difficult for human to process. Recent medical paradigms are rapidly changing to personalized medicine, including molecular targeted therapy, immunotherapy, and theranostics, and the importance of biomarkers for these is growing day by day. Even though biopsy continues to gold standard for tumor assessment in personalized medicine, imaging is expected to complement biopsy because it allows whole tumor evaluation, whole body evaluation, and non-invasive and repetitive evaluation. Radiomics is known as a useful method to get imaging biomarkers related to intratumor heterogeneity in molecular targeted therapy as well as one-size-fits-all therapy. It is also expected to be useful in new paradigms such as immunotherapy and somatostatin receptor (SSTR) or prostate-specific membrane antigen (PSMA)-targeted theranostics. Radiomics research should move to multimodality (CT, MR, PET, etc.), multicenter, and prospective studies from current single modality, single institution, and retrospective studies. Image-quality harmonization, intertumor heterogeneity, and integrative analysis of information from different scales are thought to be important keywords in future radiomics research. It is clear that radiomics will play an important role in personalized medicine.

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.

Objective: 177 Lutetium [Lu] Ludotadipep is a novel prostate-specific membrane antigen targeting therapeutic agent with an albumin motif added to increase uptake in the tumors. We assessed the biodistribution and dosimetry of [ 177 Lu]Ludotadipep in patients with metastatic castration-resistant prostate cancer (mCRPC). Materials and Methods: Data from 25 patients (median age, 73 years; range, 60–90) with mCRPC from a phase I study with activity escalation design of single administration of [ 177 Lu]Ludotadipep (1.85, 2.78, 3.70, 4.63, and 5.55 GBq) were assessed. Activity in the salivary glands, lungs, liver, kidneys, and spleen was estimated from whole-body scan and abdominal SPECT/CT images acquired at 2, 24, 48, 72, and 168 h after administration of [ 177 Lu]Ludotadipep. Red marrow activity was calculated from blood samples obtained at 3, 10, 30, 60, and 180 min, and at 24, 48, and 72 h after administration. Organand tumor-based absorbed dose calculations were performed using IDAC-Dose 2.1. Results: Absorbed dose coefficient (mean ± standard deviation) of normal organs was 1.17 ± 0.81 Gy/GBq for salivary glands, 0.05 ± 0.02 Gy/GBq for lungs, 0.14 ± 0.06 Gy/GBq for liver, 0.77 ± 0.28 Gy/GBq for kidneys, 0.12 ± 0.06 Gy/GBq for spleen, and 0.07 ± 0.02 Gy/GBq for red marrow. The absorbed dose coefficient of the tumors was 10.43 ± 7.77 Gy/GBq. Conclusion [ 177 Lu]Ludotadipep is expected to be safe at the dose of 3.7 GBq times 6 cycles planned for a phase II clinical trial with kidneys and bone marrow being the critical organs, and shows a high tumor absorbed dose.

Radioiodine activity required for remnant thyroid ablation is of great concern, to avoid unnecessary exposure to radiation and minimize adverse effects. We investigated clinical outcomes of remnant thyroid ablation with a low radioiodine activity in Korean patients with low to intermediate-risk thyroid cancer. For remnant thyroid ablation, 176 patients received radioiodine of 1.1 GBq, under a standard thyroid hormone withdrawal and a low iodine diet protocol. Serum levels of thyroid stimulating hormone stimulated thyroglobulin (off-Tg) and thyroglobulin-antibody (Tg-Ab), and a post-therapy whole body scan (RxWBS) were evaluated. Completion of remnant ablation was considered when there was no visible uptake on RxWBS and undetectable off-Tg (<1.0 ng/mL). Various factors including age, off-Tg, and histopathology were analyzed to predict ablation success rates. Of 176 patients, 68.8% (n = 121) who achieved successful remnant ablation were classified into Group A, and the remaining 55 were classified into Group B. Group A presented with significantly lower off-Tg at the first radioiodine administration (pre-ablative Tg) than those of Group B (1.2 +/- 2.3 ng/mL vs. 6.2 +/- 15.2 ng/mL, P = 0.027). Pre-ablative Tg was the only significant factor related with ablation success rates. Diagnostic performances of pre-ablative Tg < 10.0 ng/mL were sensitivity of 99.1%, specificity of 14.0%, positive predictive value of 71.1%, and negative predictive value of 87.5%, respectively. Single administration of low radioiodine activity could be sufficient for remnant thyroid ablation in patients with low to intermediate-risk thyroid cancer. Pre-ablative Tg with cutoff value of 10.0 ng/mL is a promising factor to predict successful remnant ablation.
Adolescent ; Adult ; Aged ; Female ; Humans ; Iodine Radioisotopes/*therapeutic use ; Male ; Middle Aged ; Republic of Korea ; Thyroglobulin/blood/immunology ; Thyroid Gland/*pathology/*radiation effects ; Thyroid Neoplasms/*radiotherapy ; Thyrotropin/blood ; Treatment Outcome ; Young Adult

OBJECTIVE: We investigated the prognostic value of intratumoral [18F]fluorodeoxyglucose (FDG) uptake heterogeneity (IFH) derived from positron emission tomography/computed tomography (PET/CT) in patients with cervical cancer. METHODS: Patients with uterine cervical cancer of the International Federation of Obstetrics and Gynecology (FIGO) stage IB to IIA were imaged with [18F]FDG PET/CT before radical surgery. PET/CT parameters such as maximum and average standardized uptake values (SUV(max) and SUV(avg)), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and IFH were assessed. Regression analyses were used to identify clinicopathological and imaging variables associated with progression-free survival (PFS). RESULTS: We retrospectively reviewed clinical data of 85 eligible patients. Median PFS was 32 months (range, 6 to 83 months), with recurrence observed in 14 patients (16.5%). IFH at an SUV of 2.0 was correlated with primary tumor size (p<0.001), SUV(tumor) (p<0.001), MTV(tumor) (p<0.001), TLG(tumor) (p<0.001), depth of cervical invasion (p<0.001), and negatively correlated with age (p=0.036). Tumor recurrence was significantly associated with TLG(tumor) (p<0.001), MTV(tumor) (p=0.001), SUV(LN) (p=0.004), IFH (p=0.005), SUV(tumor) (p=0.015), and FIGO stage (p=0.015). Multivariate analysis identified that IFH (p=0.028; hazard ratio, 756.997; 95% CI, 2.047 to 279,923.191) was the only independent risk factor for recurrence. The Kaplan-Meier survival graphs showed that PFS significantly differed in groups categorized based on IFH (p=0.013, log-rank test). CONCLUSION: Preoperative IFH was significantly associated with cervical cancer recurrence. [18F]FDG based heterogeneity may be a useful and potential predicator of patient recurrence before treatment.
Adult ; Aged ; Aged, 80 and over ; Carcinoma, Squamous Cell/metabolism/*radionuclide imaging/secondary ; Disease-Free Survival ; Female ; Fluorodeoxyglucose F18/*pharmacokinetics ; Glycolysis ; Humans ; Middle Aged ; Multimodal Imaging ; Neoplasm Invasiveness ; Neoplasm Recurrence, Local/metabolism/radionuclide imaging ; Neoplasm Staging ; *Positron-Emission Tomography ; Predictive Value of Tests ; Radiopharmaceuticals/*pharmacokinetics ; Retrospective Studies ; Tomography, X-Ray Computed ; Tumor Burden ; Uterine Cervical Neoplasms/metabolism/pathology/*radionuclide imaging

The pathological features of Alzheimer's disease are senile plaques which are aggregates of β-amyloid peptides and neurofibrillary tangles in the brain. Neurofibrillary tangles are aggregates of hyperphosphorylated tau proteins, and these induce various other neurodegenerative diseases, such as progressive supranuclear palsy, corticobasal degeneration, frontotemporal lobar degeneration, frontotemporal dementia and parkinsonism linked to chromosome 17 (FTDP-17), and chronic traumatic encephalopathy. In the case of Alzheimer's disease, the measurement of neurofibrillary tangles associated with cognitive decline is suitable for differential diagnosis, disease progression assessment, and to monitor the effects of therapeutic treatment. This review discusses considerations for the development of tau ligands for imaging and summarizes the results of the first-in-human and preclinical studies of the tau tracers that have been developed thus far. The development of tau ligands for imaging studies will be helpful for differential diagnosis and for the development of therapeutic treatments for tauopathies including Alzheimer's disease.
Alzheimer Disease ; Brain ; Brain Injury, Chronic ; Chromosomes, Human, Pair 17 ; Diagnosis, Differential ; Disease Progression ; Frontotemporal Dementia ; Frontotemporal Lobar Degeneration ; Ligands ; Neurodegenerative Diseases ; Neurofibrillary Tangles ; Parkinsonian Disorders ; Peptides ; Plaque, Amyloid ; Supranuclear Palsy, Progressive ; tau Proteins ; Tauopathies

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.

The pathological features of Alzheimer's disease are senile plaques which are aggregates of β-amyloid peptides and neurofibrillary tangles in the brain. Neurofibrillary tangles are aggregates of hyperphosphorylated tau proteins, and these induce various other neurodegenerative diseases, such as progressive supranuclear palsy, corticobasal degeneration, frontotemporal lobar degeneration, frontotemporal dementia and parkinsonism linked to chromosome 17 (FTDP-17), and chronic traumatic encephalopathy. In the case of Alzheimer's disease, the measurement of neurofibrillary tangles associated with cognitive decline is suitable for differential diagnosis, disease progression assessment, and to monitor the effects of therapeutic treatment. This review discusses considerations for the development of tau ligands for imaging and summarizes the results of the first-in-human and preclinical studies of the tau tracers that have been developed thus far. The development of tau ligands for imaging studies will be helpful for differential diagnosis and for the development of therapeutic treatments for tauopathies including Alzheimer's disease.