Automatic Quantitative Assessment for Diagnostic and Therapeutic Response in Rodent Myocardial Infarct Model.

The purpose of this study was to investigate the most appropriate methodological approach for the automatic measurement of rodent myocardial infarct polar map using histogram-based thresholding and unsupervised deep learning (DL)-based segmentation. A rat myocardial infarction model was induced by ligation of the left coronary artery. Positron emission tomography (PET) was performed 60 min after the administration of 18F-fluoro-deoxy-glucose (18F-FDG), and PET was performed after injecting 64Cu-pyruvaldehyde-bis(N4-methylthiosemicarbazone). Single photon emission computed tomography was performed 60 min after injection of 99mTc-hexakis-2-methoxyisobutylisonitrile and 201Tl. Delayed contrast-enhanced magnetic resonance imaging was performed after injecting Gd-DTPA-BMA. Three types of thresholding methods (naive thresholding, Otsu's algorithm, and multi-Gaussian mixture model (MGMM)) were used. DL segmentation methods were based on a convolution neural network and trained with constraints on feature similarity and spatial continuity of the response map extracted from images by the network. The relative infarct sizes measured by histology and estimated R2 for 18F-FDG were 0.8477, 0.7084, 0.8353, and 0.9024 for naïve thresholding, Otsu's algorithm, MGMM, and DL segmentation, respectively. DL-based method improved the accuracy of MI size assessment.

A Phase II Study of 131I-rituximab for Patients With Relapsed or Refractory Marginal Zone Lymphoma.

Background: Radioimmunotherapy (RIT) is a rare treatment option for relapsed or refractory B-cell non-Hodgkin's lymphoma (NHL). We investigated the safety and efficacy of 131I-rituximab in patients with relapsed or refractory marginal zone lymphomas. Methods: Patients with pathologically confirmed marginal zone lymphoma who relapsed or were resistant to prior therapy were enrolled. The patients received 250 mg/m2 of unlabeled rituximab immediately before receiving a therapeutic 131I-rituximab dose. The primary endpoint was the objective response rate (ORR), and the secondary endpoints were toxicity assessment, progression-free survival (PFS), and overall survival (OS). Results: Ten patients (median age = 57.5 years; range = 32-71) were included. Owing to poor enrollment, only 10 of the initially intended 25 patients were included in the study, rendering it unfeasible to perform the primary endpoint analysis. Before RIT, patients received chemotherapy, with 40% (n = 4) receiving rituximab therapy. Median PFS and OS were 18.9 months (95% confidence interval [CI]: 0.0-38.9) and 100.0 months (95% CI: 39.8-160.1), respectively. The ORR was 90%, and the duration of response was 29.7 months (95% CI: 0.0-61.3). Considering a median follow-up of 78.5 months (95% CI: 42.7-114.3), 4 patients (40%) were diagnosed with secondary malignancy. Hematological toxicities were common treatment-related adverse events, and 60% and 50% of the patients experienced grade 3 to 4 thrombocytopenia and neutropenia, respectively. Conclusions: 131I-rituximab showed marked efficacy in patients with relapsed or refractory marginal zone lymphoma, with a considerable risk of secondary malignancies during long-term follow-up. Radioimmunotherapy is not a recommended treatment option for relapsed or refractory marginal zone lymphoma but may be considered when other treatment options are not feasible.

Deep Learning-Based Delayed PET Image Synthesis from Corresponding Early Scanned PET for Dosimetry Uptake Estimation.

The acquisition of in vivo radiopharmaceutical distribution through imaging is time-consuming due to dosimetry, which requires the subject to be scanned at several time points post-injection. This study aimed to generate delayed positron emission tomography images from early images using a deep-learning-based image generation model to mitigate the time cost and inconvenience. Eighteen healthy participants were recruited and injected with [18F]Fluorodeoxyglucose. A paired image-to-image translation model, based on a generative adversarial network (GAN), was used as the generation model. The standardized uptake value (SUV) mean of the generated image of each organ was compared with that of the ground-truth. The least square GAN and perceptual loss combinations displayed the best performance. As the uptake time of the early image became closer to that of the ground-truth image, the translation performance improved. The SUV mean values of the nominated organs were estimated reasonably accurately for the muscle, heart, liver, and spleen. The results demonstrate that the image-to-image translation deep learning model is applicable for the generation of a functional image from another functional image acquired from normal subjects, including predictions of organ-wise activity for specific normal organs.

Emerging role of vascular burden in AT(N) classification in individuals with Alzheimer's and concomitant cerebrovascular burdens.

OBJECTIVES: Alzheimer's disease (AD) is characterised by amyloid-beta accumulation (A), tau aggregation (T) and neurodegeneration (N). Vascular (V) burden has been found concomitantly with AD pathology and has synergistic effects on cognitive decline with AD biomarkers. We determined whether cognitive trajectories of AT(N) categories differed according to vascular (V) burden. METHODS: We prospectively recruited 205 participants and classified them into groups based on the AT(N) system using neuroimaging markers. Abnormal V markers were identified based on the presence of severe white matter hyperintensities. RESULTS: In A+ category, compared with the frequency of Alzheimer's pathological change category (A+T-), the frequency of AD category (A+T+) was significantly lower in V+ group (31.8%) than in V- group (64.4%) (p=0.004). Each AT(N) biomarker was predictive of cognitive decline in the V+ group as well as in the V- group (p<0.001). Additionally, the V+ group showed more severe cognitive trajectories than the V- group in the non-Alzheimer's pathological changes (A-T+, A-N+; p=0.002) and Alzheimer's pathological changes (p<0.001) categories. CONCLUSION: The distribution and longitudinal outcomes of AT(N) system differed according to vascular burdens, suggesting the importance of incorporating a V biomarker into the AT(N) system.

Radioimmunotherapy with 131 I-rituximab for patients with relapsed or refractory follicular or mantle cell lymphoma.

AIM: This study aimed to evaluate the safety and efficacy of 131 I-rituximab in patients with relapsed or refractory follicular or mantle cell lymphoma. METHODS: Twenty-four patients with relapsed or refractory follicular or mantle cell lymphoma were administered unlabeled rituximab (70 mg) immediately before receiving a therapeutic dose of 131 I-rituximab. Contrast-enhanced 18F-fluorodeoxyglucose positron emission tomography/computed tomography was used a month later to assess tumor response. RESULTS: This study enrolled 24 patients between June 2012 and 2022. Depending on how they responded to radioimmunotherapy (RIT), 131 I-rituximab was administered one to five times. Of the 24 patients, 9 achieved complete response after RIT and 8 achieved partial response. The median progression-free and overall survival was 5.9 and 37.9 months, respectively. During the follow-up period of 64.2 months, three patients were diagnosed with a secondary malignancy. Among treatment-related adverse events, hematologic toxicities were common, and grade 3-4 thrombocytopenia and neutropenia were reported in 66.6% of cases. CONCLUSION: 131 I-rituximab has an effective and favorable safety profile in patients with relapsed or refractory follicular lymphoma and mantle cell lymphoma. This suggests that RIT may also be considered a treatment option for patients with relapsed or refractory follicular lymphoma and mantle cell lymphoma.

Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes.

This study aimed to identify a distant-recurrence image biomarker in NSCLC by investigating correlations between heterogeneity functional gene expression and fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography (18F-FDG PET) image features of NSCLC patients. RNA-sequencing data and 18F-FDG PET images of 53 patients with NSCLC (19 with distant recurrence and 34 without recurrence) from The Cancer Imaging Archive and The Cancer Genome Atlas Program databases were used in a combined analysis. Weighted correlation network analysis was performed to identify gene groups related to distant recurrence. Genes were selected for functions related to distant recurrence. In total, 47 image features were extracted from PET images as radiomics. The relationship between gene expression and image features was estimated using a hypergeometric distribution test with the Pearson correlation method. The distant recurrence prediction model was validated by a random forest (RF) algorithm using image texture features and related gene expression. In total, 37 gene modules were identified by gene-expression pattern with weighted gene co-expression network analysis. The gene modules with the highest significance were selected (p-value < 0.05). Nine genes with high protein-protein interaction and area under the curve (AUC) were identified as hub genes involved in the proliferation function, which plays an important role in distant recurrence of cancer. Four image features (GLRLM_SRHGE, GLRLM_HGRE, SUVmean, and GLZLM_GLNU) and six genes were identified to be correlated (p-value < 0.1). AUCs (accuracy: 0.59, AUC: 0.729) from the 47 image texture features and AUCs (accuracy: 0.767, AUC: 0.808) from hub genes were calculated using the RF algorithm. AUCs (accuracy: 0.783, AUC: 0.912) from the four image texture features and six correlated genes and AUCs (accuracy: 0.738, AUC: 0.779) from only the four image texture features were calculated using the RF algorithm. The four image texture features validated by heterogeneity group gene expression were found to be related to cancer heterogeneity. The identification of these image texture features demonstrated that advanced prediction of NSCLC distant recurrence is possible using the image biomarker.

The role of 18F-FDG PET/CT in patients with diffuse large B-cell lymphoma after radioimmunotherapy using 131I-rituximab as consolidation therapy.

PURPOSE: To evaluate the prognostic value of pretreatment 18F-FDG PET/CT after consolidation therapy of 131I-rituximab in patients with diffuse large B-cell lymphoma (DLBCL) who had acquired complete remission after receiving chemotherapy. METHODS: Patients who were diagnosed with DLBCL via histologic confirmation were retrospectively reviewed. All patients had achieved complete remission after 6 to 8 cycles of R-CHOP (rituximab, cyclophosphamide, vincristine, doxorubicin, and prednisolone) chemotherapy after which they underwent consolidation treatment with 131I-rituximab. 18F-FDG PET/CT scans were performed before R-CHOP for initial staging. The largest diameter of tumor, maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained from pretreatment 18F-FDG PET/CT scans. Receiver-operating characteristic curves analysis was introduced for assessing the optimal criteria. Kaplan-Meier curve survival analysis was performed to evaluate both relapse free survival (RFS) and overall survival (OS). RESULTS: A total of 15 patients (12 males and 3 females) with a mean age of 56 (range, 30-73) years were enrolled. The median follow-up period of these patients was 73 months (range, 11-108 months). Four (27%) patients relapsed. Of them, three died during follow-up. Median values of the largest tumor size, highest SUVmax, MTV, and TLG were 5.3 cm (range, 2.0-16.4 cm), 20.2 (range, 11.1-67.4), 231.51 (range, 15-38.34), and 1277.95 (range, 238.37-10341.04), respectively. Patients with SUVmax less than or equal to 16.9 showed significantly worse RFS than patients with SUVmax greater than 16.9 (5-year RFS rate: 60% vs. 100%, p = 0.008). Patients with SUVmax less than or equal to 16.9 showed significantly worse OS than patients with SUVmax greater than 16.9 (5-year OS rate: 80% vs. 100% p = 0.042). CONCLUSION: Higher SUVmax at pretreatment 18F-FDG PET/CT was associated with better relapse free survival and overall survival in DLBCL patients after consolidation therapy with 131I-rituximab. However, because this study has a small number of patients, a phase 3 study with a larger number of patients is needed for clinical application in the future.

The Prediction of HER2-Targeted Treatment Response Using 64Cu-Tetra-Azacyclododecanetetra-Acetic Acid (DOTA)-Trastuzumab PET/CT in Metastatic Breast Cancer: A Case Report.

A 45-year-old woman diagnosed with breast cancer reported disease progression in the form of metastatic lung and recurrent breast lesions following chemotherapy and human epidermal growth factor receptor 2 (HER2)-targeted therapy. The patient underwent 64Cu-tetra-azacyclododecanetetra-acetic acid (DOTA)-trastuzumab positron emission tomography/computed tomography (PET/CT) to evaluate the HER2 expression status. 64Cu-DOTA-trastuzumab accumulated in the left breast and lymph nodes but not in the lung lesions. Following trastuzumab emtansine treatment, there was a significant improvement in the lesions with 64Cu-DOTA-trastuzumab accumulation. However, the lesions that did not accumulate 64Cu-DOTA-trastuzumab aggravated. Therefore, it was concluded that 64Cu-DOTA-trastuzumab PET/CT can be used to predict the outcome of HER2-targeted treatment by evaluating HER2 expression in breast cancer patients.

KSNM60 in Non-thyroidal Radionuclide Therapy: Leaping into the Future.

This year, the Korean Society of Nuclear Medicine (KSNM) is celebrating its 60th anniversary. Treatment, as well as diagnosis, has played a very important role in the development of nuclear medicine. Since I-131 was used for thyroid therapy in 1959, other radionuclide therapy is still being used, and attempts to use new radionuclide are increasing. In this review, we briefly summarize and introduce the therapies such as radioimmunotherapy, transarterial radioembolization, radionuclide therapy for neuroendocrine tumors, peptide receptor radionuclide therapy, control of metastatic bone pain, radiation synovectomy, radionuclide brachytherapy, alpha particle therapy, and boron neutron capture therapy, which has been being attempted so far in the field of nuclear medicine.

Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Using Convolutional Neural Network of Tumor Center 18F-FDG PET Images.

We compared the accuracy of prediction of the response to neoadjuvant chemotherapy (NAC) in osteosarcoma patients between machine learning approaches of whole tumor utilizing fluorine-18fluorodeoxyglucose (18F-FDG) uptake heterogeneity features and a convolutional neural network of the intratumor image region. In 105 patients with osteosarcoma, 18F-FDG positron emission tomography/computed tomography (PET/CT) images were acquired before (baseline PET0) and after NAC (PET1). Patients were divided into responders and non-responders about neoadjuvant chemotherapy. Quantitative 18F-FDG heterogeneity features were calculated using LIFEX version 4.0. Receiver operating characteristic (ROC) curve analysis of 18F-FDG uptake heterogeneity features was used to predict the response to NAC. Machine learning algorithms and 2-dimensional convolutional neural network (2D CNN) deep learning networks were estimated for predicting NAC response with the baseline PET0 images of the 105 patients. ML was performed using the entire tumor image. The accuracy of the 2D CNN prediction model was evaluated using total tumor slices, the center 20 slices, the center 10 slices, and center slice. A total number of 80 patients was used for k-fold validation by five groups with 16 patients. The CNN network test accuracy estimation was performed using 25 patients. The areas under the ROC curves (AUCs) for baseline PET maximum standardized uptake value (SUVmax), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and gray level size zone matrix (GLSZM) were 0.532, 0.507, 0.510, and 0.626, respectively. The texture features test accuracy of machine learning by random forest and support vector machine were 0.55 and 0. 54, respectively. The k-fold validation accuracy and validation accuracy were 0.968 ± 0.01 and 0.610 ± 0.04, respectively. The test accuracy of total tumor slices, the center 20 slices, center 10 slices, and center slices were 0.625, 0.616, 0.628, and 0.760, respectively. The prediction model for NAC response with baseline PET0 texture features machine learning estimated a poor outcome, but the 2D CNN network using 18F-FDG baseline PET0 images could predict the treatment response before prior chemotherapy in osteosarcoma. Additionally, using the 2D CNN prediction model using a tumor center slice of 18F-FDG PET images before NAC can help decide whether to perform NAC to treat osteosarcoma patients.

Preliminary Radiogenomic Evidence for the Prediction of Metastasis and Chemotherapy Response in Pediatric Patients with Osteosarcoma Using 18F-FDF PET/CT, EZRIN and KI67.

Chemotherapy response and metastasis prediction play important roles in the treatment of pediatric osteosarcoma, which is prone to metastasis and has a high mortality rate. This study aimed to estimate the prediction model using gene expression and image texture features. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images of 52 pediatric osteosarcoma patients were used to estimate the machine learning algorithm. An appropriate algorithm was selected by estimating the machine learning accuracy. 18F-FDG PET/CT images of 21 patients were selected for prediction model development based on simultaneous KI67 and EZRIN expression. The prediction model for chemotherapy response and metastasis was estimated using area under the curve (AUC) maximum image texture features (AUC_max) and gene expression. The machine learning algorithm with the highest test accuracy in chemotherapy response and metastasis was selected using the random forest algorithm. The chemotherapy response and metastasis test accuracy with image texture features was 0.83 and 0.76, respectively. The highest test accuracy and AUC of chemotherapy response with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.89, respectively. The highest test accuracy and AUC of metastasis with AUC_max, KI67, and EZRIN were estimated to be 0.85 and 0.8, respectively. The metastasis prediction accuracy increased by 10% using radiogenomics data.

A Phase 0 Microdosing PET/CT Study Using O-[18F]Fluoromethyl-d-Tyrosine in Normal Human Brain and Brain Tumor.

PURPOSE: The aim of the present study was to obtain information about distribution, radiation dosimetry, toxicity, and pharmacokinetics of O-[18F]fluoromethyl-d-tyrosine (d-18F-FMT), an amino acid PET tracer, in patients with brain tumors. PATIENTS AND METHODS: A total of 6 healthy controls (age = 19-25 years, 3 males and 3 females) with brain PET images and radiation dosimetry and 12 patients (median age = 60 years, 6 males and 6 females) with primary (n = 5) or metastatic brain tumor (n = 7) were enrolled. We acquired 60-minute dynamic brain PET images after injecting 370 MBq of d-18F-FMT. Time-activity curves of d-18F-FMT uptake in normal brain versus brain tumors and tumor-to-background ratio were analyzed for each PET data set. RESULTS: Normal cerebral uptake of d-18F-FMT decreased from 0 to 5 minutes after injection, but gradually increased from 10 to 60 minutes. Tumoral uptake of d-18F-FMT reached a peak before 30 minutes. Tumor-to-background ratio peaked at less than 15 minutes for 8 patients and more than 15 minutes for 4 patients. The mean effective dose was calculated to be 13.2 µSv/MBq. CONCLUSIONS: Using d-18F-FMT as a PET radiotracer is safe. It can distinguish brain tumor from surrounding normal brain tissues with a high contrast. Early-time PET images of brain tumors should be acquired because the tumor-to-background ratio tended to reach a peak within 15 minutes after injection.

A preliminary clinical trial to evaluate 64Cu-NOTA-Trastuzumab as a positron emission tomography imaging agent in patients with breast cancer.

BACKGROUND: The purpose of this study was to evaluate both the biodistribution and safety of 64Cu-1,4,7-triazacyclononane-1,4,7-triacetic acid (NOTA)-Trastuzumab, a novel 64Cu-labeled positron emission tomography (PET) tracer for human epidermal growth factor receptor 2 (HER2) in patients with breast cancer. METHODS: PET images at 1, 24, and 48 h after 296 MBq of 64Cu-NOTA-Trastuzumab injection were obtained from seven patients with breast cancer. Both the primary tumors' and metastatic lesions' maximum standardized uptake value (SUVmax) was evaluated. The mean SUVmax (SUVmean) was evaluated in the other organs, including the blood pool, liver, kidney, muscle, spleen, bladder, and the lungs, as well as the bones. Moreover, the internal radiation dosimetry was calculated using the OLINDA/EXM software. Safety was assessed based on feedback regarding adverse reactions and safety-related issues within 1 month after 64Cu-NOTA-Trastuzumab administration. RESULTS: 64Cu-NOTA-Trastuzumab PET images showed that the overall SUVmean values in each organ negatively correlated with time. The liver's average SUVmean values were measured at 5.3 ± 0.7, 4.8 ± 0.6, and 4.4 ± 0.5 on 1 h, 24 h, and 48 h after injection, respectively. The average SUVmean blood values were measured at 13.1 ± 0.9, 9.1 ± 1.2, and 7.1 ± 1.9 on 1 h, 24 h, and 48 h after injection, respectively. The SUVmax of HER2-positive tumors was relatively higher than HER2-negative tumors (8.6 ± 5.1 and 5.2 ± 2.8 on 48 h after injection, respectively). Tumor-to-background ratios were higher in the HER2-positive tumors than in the HER2-negative tumors. No adverse events related to 64Cu-NOTA-Trastuzumab were reported. The calculated effective dose with a 296 MBq injection of 64Cu-NOTA-Trastuzumab was 2.96 mSv. The highest absorbed dose was observed in the liver (0.076 mGy/MBq), followed by the spleen (0.063 mGy/MBq), kidney (0.044 mGy/MBq), and heart wall (0.044 mGy/MBq). CONCLUSIONS: 64Cu-NOTA-Trastuzumab showed a specific uptake at the HER2-expressing tumors, thus making it a feasible and safe monitoring tool of HER2 tumor status in patients with breast cancer. TRIAL REGISTRATION: CRIS, KCT0002790. Registered 02 February 2018, https://cris.nih.go.kr.

A microdose clinical trial to evaluate [18F]Florastamin as a positron emission tomography imaging agent in patients with prostate cancer.

PURPOSE: To evaluate the biodistribution of [18F]Florastamin, a novel 18F-labelled positron emission tomography (PET) tracer for prostate-specific membrane antigen (PSMA) for the diagnosis of prostate cancer. METHODS: PET was performed for five healthy controls and 10 patients with prostate cancer at 0, 10, 30, 70, and 120 mins after injecting 370 MBq of [18F]Florastamin. The maximum standardised uptake value (SUVmax) was evaluated in the primary tumour. The mean SUVmax (SUVmean) was evaluated in normal organs. Furthermore, the residence time was evaluated by assessing radioactivity in each organ. The internal radiation dosimetry was calculated using the OLINDA/EXM software. RESULTS: The SUVmax in primary tumours increased with time. A favourable tumour to background ratio was also observed over time. Multiple lymph nodes and bone metastases were also evaluated and showed a similar pattern to SUVmax in the primary tumour. In one patient, a tiny lymph node metastasis was identified using [18F]Florastamin PET, which was not observed using other modalities, and was histologically confirmed. The highest absorbed dose was observed in the kidney (0.062 ± 0.015 mGy/MBq), followed by the bladder (0.032 ± 0.013 mGy/MBq), liver (0.022 ± 0.006 mGy/MBq), and salivary gland (0.018 ± 0.006 mGy/MBq). The effective dose with a 370 MBq injection of [18F]Florastamin was 1.81 mSv. No adverse events related to [18F]Florastamin were reported. CONCLUSION: We identified a novel PSMA-targeted PET ligand, [18F]Florastamin, for imaging prostate cancer. [18F]Florastamin showed a high SUVmax and relatively high tumour to background ratio in both primary tumour and metastatic lesions, which suggests its high sensitivity to detect tumours without any adverse events. TRIAL REGISTRATION: KCT0003924 registered at https://cris.nih.go.kr/ .

Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning.

This study aimed to investigate the predictive efficacy of positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) for the pathological response of advanced breast cancer to neoadjuvant chemotherapy (NAC). The breast PET/MRI image deep learning model was introduced and compared with the conventional methods. PET/CT and MRI parameters were evaluated before and after the first NAC cycle in patients with advanced breast cancer [n = 56; all women; median age, 49 (range 26-66) years]. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were obtained with the corresponding baseline values (SUV0, MTV0, and TLG0, respectively) and interim PET images (SUV1, MTV1, and TLG1, respectively). Mean apparent diffusion coefficients were obtained from baseline and interim diffusion MR images (ADC0 and ADC1, respectively). The differences between the baseline and interim parameters were measured (ΔSUV, ΔMTV, ΔTLG, and ΔADC). Subgroup analysis was performed for the HER2-negative and triple-negative groups. Datasets for convolutional neural network (CNN), assigned as training (80%) and test datasets (20%), were cropped from the baseline (PET0, MRI0) and interim (PET1, MRI1) images. Histopathologic responses were assessed using the Miller and Payne system, after three cycles of chemotherapy. Receiver operating characteristic curve analysis was used to assess the performance of the differentiating responders and non-responders. There were six responders (11%) and 50 non-responders (89%). The area under the curve (AUC) was the highest for ΔSUV at 0.805 (95% CI 0.677-0.899). The AUC was the highest for ΔSUV at 0.879 (95% CI 0.722-0.965) for the HER2-negative subtype. AUC improved following CNN application (SUV0:PET0 = 0.652:0.886, SUV1:PET1 = 0.687:0.980, and ADC1:MRI1 = 0.537:0.701), except for ADC0 (ADC0:MRI0 = 0.703:0.602). PET/MRI image deep learning model can predict pathological responses to NAC in patients with advanced breast cancer.

The correlation of neuropsychological evaluation with 11C-PiB and 18F-FC119S amyloid PET in mild cognitive impairment and Alzheimer disease.

For the diagnosis of mild cognitive impairment (MCI) and Alzheimer disease (AD), variable neuroimaging and neuropsychological tests have been used. We aimed to evaluate the correlation of neuropsychological domain with new amyloid positron emission tomography (PET) study and to validate the availability of new PET tracer.We enrolled 20 patients who underwent C-PiB-PET/CT, new PET tracer F-FC119S PET/CT from November, 2014 to July, 2015. Among them, 10 patients were diagnosed with AD and 10 patients with MCI. The current version of Seoul Neuropsychological Screening Battery (SNSB) II was performed for cognitive evaluation. Each parameter of SNSB was compared between 2 patient groups. Spearman correlation analysis between value of SNSB domain and standardized uptake value ratio (SUVR) of PET was also performed.The AD group presented significant poor z-score in Korean-Boston Naming Test(K-BNT) (P = .01),copy score of Rey Complex Figure Test (RCFT) (P = .049), immediate (P = .028)and delayed memory of Seoul Verbal Learning Test (SVLT) (P = .028), recognition of RCFT (P = .004), "animal" of Controlled Oral Word Association Test (COWAT) (P = .041), color reading of Korean-Color Word Stroop test (K-CWST) (P = .014), and Digit Symbol Coding (DSC) (P = .007) compared with MCI group. That means, except attention domain, all other cognitive domains were relatively impaired in AD compared with MCI. In correlation analysis, we found that poor performances on copy score of RCFT in MCI groups were associated with great beta amyloid burden in frontal area in both C-PiB-PET/CT and F-FC119S PET/CT. In AD group, F-FC119S PET presented more extensive correlation in each cognitive domain with multiple cortical areas compared with C-PiB-PET.The degree of amyloid burden assessed on F-FC119S PET was significantly correlated with neuropsychological test in AD, and also MCI patients. The combination of neuropsychological evaluation with novel F-FC119S PET/CT can be used for valid biomarker for MCI and AD.

Clinical Usefulness of 18F-FC119S Positron-Emission Tomography as an Auxiliary Diagnostic Method for Dementia: An Open-Label, Single-Dose, Evaluator-Blind Clinical Trial.

BACKGROUND AND PURPOSE: The aim of this study was to determine the diagnostic performance and safety of a new ¹8F-labeled amyloid tracer, ¹8F-FC119S. METHODS: This study prospectively recruited 105 participants, comprising 53 with Alzheimer's disease (AD) patients, 16 patients with dementia other than AD (non-AD), and 36 healthy controls (HCs). In the first screening visit, the Seoul Neuropsychological Screening Battery cognitive function test was given to the dementia group, while HC subjects completed the Korean version of the Mini Mental State Examination. Individuals underwent ¹8F-FC119S PET, ¹8F-fluorodeoxyglucose (FDG) PET, and brain MRI. The diagnostic performance of ¹8F-FC119S PET for AD was compared to a historical control (comprising previously reported and currently used amyloid-beta PET agents), ¹8F-FDG PET, and MRI. The standardized uptake value (SUV) ratio (ratio of the cerebral cortical SUV to the cerebellar SUV) was measured for each PET data set to provide semiquantitative analysis. All adverse effects during the clinical trial periods were monitored. RESULTS: Visual assessments of the ¹8F-FC119S PET data revealed a sensitivity of 92% and a specificity of 84% in detecting AD. ¹8F-FC119S PET demonstrated equivalent or better diagnostic performance for AD detection than the historical control, ¹8F-FDG PET (sensitivity of 80.0% and specificity of 76.0%), and MRI (sensitivity of 98.0% and specificity of 50.0%). The SUV ratios differed significantly between AD patients and the other groups, at 1.44±0.17 (mean±SD) for AD, 1.24±0.09 for non-AD, and 1.21±0.08 for HC. No clinically significant adverse effects occurred during the trial periods. CONCLUSIONS: ¹8F-FC119S PET provides high sensitivity and specificity in detecting AD and therefore may be considered a useful diagnostic tool for AD.

Early response monitoring of neoadjuvant chemotherapy using [18F]FDG PET can predict the clinical outcome of extremity osteosarcoma.

BACKGROUND: To propose a personalized therapeutic approach in osteosarcoma treatment, we assessed whether sequential [18F]FDG PET/CT (PET/CT) could predict the outcome of patients with osteosarcoma of the extremities after one cycle and two cycles of neoadjuvant chemotherapy. METHODS: A total of 73 patients with AJCC stage II extremity osteosarcoma treated with 2 cycles of neoadjuvant chemotherapy, surgery, and adjuvant chemotherapy were retrospectively analyzed in this study. All patients underwent PET/CT before (PET0), after 1 cycle (PET1), and after the completion of neoadjuvant chemotherapy (PET2), respectively. Maximum standardized uptake value (SUVmax) (corrected for body weight) and the % changes of SUVmax were calculated, and histological responses were evaluated after surgery. Receiver-operating characteristic (ROC) curve analyses and the Cox proportional hazards models were used to analyze whether imaging and clinicopathologic parameters could predict event-free survival (EFS). RESULTS: A total of 36 patients (49.3%) exhibited a poor histologic response and 17 patients (23.3%) showed events (metastasis in 15 and local recurrence in 2). SUVmax on PET2 (SUV2), the percentage change of SUVmax between PET0 and PET1 (Δ%SUV01), and between PET0 and PET2 (Δ%SUV02) most accurately predicted events using the ROC curve analysis. SUV2 (relative risk, 8.86; 95% CI, 2.25-34.93), Δ%SUV01 (relative risk, 5.97; 95% CI, 1.47-24.25), and Δ%SUV02 (relative risk, 6.00; 95% CI, 1.16-30.91) were independent predicting factors for EFS with multivariate analysis. Patients with SUV2 over 5.9 or Δ%SUV01 over - 39.8% or Δ%SUV02 over - 54.1% showed worse EFS rates than others (p < 0.05). CONCLUSIONS: PET evaluation after 1 cycle of presurgical chemotherapy can predict the clinical outcome of extremity osteosarcoma. [18F]FDG PET, which shows a potential role in the early evaluation of the modification of timing of local control, can be a useful modality for early response monitoring of neoadjuvant chemotherapy.

Metastasis risk prediction model in osteosarcoma using metabolic imaging phenotypes: A multivariable radiomics model.

BACKGROUND: Osteosarcoma (OS) is the most common primary bone tumor affecting humans and it has extreme heterogeneity. Despite modern therapy, it recurs in approximately 30-40% of patients initially diagnosed with no metastatic disease, with the long-term survival rates of patients with recurrent OS being generally 20%. Thus, early prediction of metastases in OS management plans is crucial for better-adapted treatments and survival rates. In this study, a radiomics model for metastasis risk prediction in OS was developed and evaluated using metabolic imaging phenotypes. METHODS AND FINDINGS: The subjects were eighty-three patients with OS, and all were treated with surgery and chemotherapy for local control. All patients underwent a pretreatment 18F-FDG-PET scan. Forty-five features were extracted from the tumor region. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved cross validation in the following four steps leading to final prediction model construction: (1) feature set reduction and selection; (2) model coefficients computation through train and validation processing; and (3) prediction performance estimation. The multivariable logistic regression model was developed using two radiomics features, SUVmax and GLZLM-SZLGE. The trained and validated multivariable logistic model based on probability of endpoint (P) = 1/ (1+exp (-Z)) was Z = -1.23 + 1.53*SUVmax + 1.68*GLZLM-SZLGE with significant p-values (SUVmax: 0.0462 and GLZLM_SZLGE: 0.0154). The final multivariable logistic model achieved an area under the curve (AUC) receiver operating characteristics (ROC) curve of 0.80, a sensitivity of 0.66, and a specificity of 0.88 in cross validation. CONCLUSIONS: The SUVmax and GLZLM-SZLGE from metabolic imaging phenotypes are independent predictors of metastasis risk assessment. They show the association between 18F-FDG-PET and metastatic colonization knowledge. The multivariable model developed using them could improve patient outcomes by allowing aggressive treatment in patients with high metastasis risk.

Comparisons of 131I-rituximab treatment responses in patients with aggressive lymphoma and indolent lymphoma.

OBJECTIVE: We evaluated the changes in treatment response over time after single 131I-rituximab radioimmunotherapy (RIT) according to non-Hodgkin lymphoma (NHL) types. METHODS: Fifteen aggressive and 21 indolent lymphoma cases undergoing RIT were evaluated. All patients underwent 18F-FDG-PET-CT before and 5 days, 1, and 3 months after RIT. The maximum standardized uptake value (SUV) and the sum of the products of the longest perpendicular diameters of tumours (SPD) were evaluated. Treatment responses were evaluated 1 and 3 months after RIT RESULTS: In aggressive lymphoma, SUV decreased at 5 days after RIT but increased after that. SPD decreased at 1 month but significantly increased at 3 months. Complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD) at 1 month after RIT were changed to PD at 3 months after RIT. In indolent lymphoma, the SUV decreased continuously until 1 month after RIT. The SPD significantly decreased at 1 month and tended to further decrease to 3 months. CR, PR, SD, and PD at 1 month after RIT were achieved in 0, 8, 13, and 0 cases, respectively. Among the 13 SD cases, one changed to CR, three changed to PR, and nine had not changed at 3 months after RIT. CONCLUSIONS: The treatment response to single RIT differed depending on NHL type. These findings suggest a need to establish an optimal treatment regimen based on NHL aggressiveness.