Consolidative chemotherapy after definitive concurrent chemoradiotherapy for esophageal squamous cell carcinoma patients: a population based cohort study.

BACKGROUND: The role of consolidative chemotherapy (CCT) for locally advanced esophageal squamous cell carcinoma (LA-ESCC) patients treated with definitive concurrent chemoradiotherapy (dCCRT) is unclear. We aimed to compare the overall survival (OS) of those treated with vs without CCT via a population based approach. METHODS: Eligible LA-ESCC patients diagnosed between 2011 and 2017 were identified via the Taiwan Cancer Registry. We used propensity score (PS) weighting to balance observable potential confounders between groups. The hazard ratio (HR) of death and incidence of esophageal cancer mortality (IECM) were compared between those with vs without CCT. We also evaluated the OS in supplementary analyses via alternative approaches. RESULTS: Our primary analysis consisted of 368 patients in whom covariates were well balanced after PS weighting. The HR of death when CCT was compared to without was 0.67 (95% confidence interval 0.52-0.86, P = 0.002). The HR of IECM was 0.66 (P = 0.04). The HR of OS remained similarly in favor of CCT in supplementary analyses. CONCLUSIONS: We found that CCT was associated with significantly improved OS for LA-ESCC patients treated with dCCRT. Randomized controlled trials were needed to confirm this finding.

Experience of sorafenib treatment in differentiated thyroid cancer from Taiwan.

BACKGROUND: Sorafenib has been shown to prolong the progression free survival (PFS) of advanced radioiodine (RAI) refractory differentiated thyroid cancer (DTC) and has been approved by the FDA as the result of the phase III DECISION trial. Sorafenib has been reimbursed for the treatment of RAI refractory DTC in Taiwan since Jan 2017. High percentage of adverse events (AE) was noted in DECISION trial. We conducted a study to show the real-world experience of sorafenib in Taiwan. METHODS: We retrospectively collected the clinical data, including dose, AE, and PFS of sorafenib, of the DTC patients who received sorafenib treatment in National Cheng Kung University Hospital and China Medical University Hospital by chart review from 2012 to 2018. RESULTS: Thirty-six advanced DTC patients with progression were included in this study. The starting dose of sorafenib in most patients was 200 mg twice daily and the mean daily maintenance dose was 433 mg. Five patients had partial response (13.9%) and 28 patients had stable disease (77.8%). The median PFS was 17.3 months (95% confidence interval: 11.9-33.6 months). Daily maintenance dose ≥ 600 mg was associated with better PFS (median PFS, not reached). The most common toxicity of sorafenib was hand foot skin reaction (69%), followed by diarrhea (42%), and skin rash (33%). Most of the toxicities were grade I/II. CONCLUSION: Higher maintenance dose of sorafenib is associated with longer PFS while starting from half dose is feasible to minimize the incidence of high grade toxicities in the real-world use of sorafenib.

Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose positron emission tomography/computed tomography.

BACKGROUND: We designed a deep learning model for assessing 18F-FDG PET/CT for early prediction of local and distant failures for patients with locally advanced cervical cancer. METHODS: All 142 patients with cervical cancer underwent 18F-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets. In each round of k-fold cross-validation, a well-trained proposed model and a slice-based optimal threshold were derived from a training set and used to classify each slice set in the test set into the categories of with or without local or distant failure. The classification results of each tumor were aggregated to summarize a tumor-based prediction result. RESULTS: In total, 21 and 26 patients experienced local and distant failures, respectively. Regarding local recurrence, the tumor-based prediction result summarized from all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively. CONCLUSION: This is the first study to use deep learning model for assessing 18F-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients. KEY POINTS: • This is the first study to use deep learning model for assessing 18 F-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients. • All 142 patients with cervical cancer underwent 18 F-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets. • For local recurrence, all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively.

Development of a novel radioligand for imaging 18-kD translocator protein (TSPO) in a rat model of Parkinson's disease.

PURPOSE: The inflammation reaction in the brain may stimulate damage repair or possibly lead to secondary brain injury. It is often associated with activated microglia, which would overexpress 18-kDa translocator protein (TSPO). In this study, we successfully developed a new TSPO radioligand, [18F]-2-(4-fluoro-2-(p-tolyloxy)phenyl)-1,2-dihydroisoquinolin-3(4H)-one ([18F]FTPQ), and evaluate its potential to noninvasively detect brain changes in a rat model of Parkinson's disease (PD). PROCEDURES: The precursor (8) for [18F]FTPQ preparation was synthesized via six steps. Radiofluorination was carried out in the presence of a copper catalyst, and the crude product was purified by high-performance liquid chromatography (HPLC) to give the desired [18F]FTPQ. The rat model of PD was established by the injection of 6-OHDA into the right hemisphere of male 8-week-old Sprague-Dawley rats. MicroPET/CT imaging and immunohistochemistry (IHC) were performed to characterize the biological properties of [18F]FTPQ. RESULTS: The overall chemical yield for the precursor (8) was around 14% after multi-step synthesis. The radiofluorination efficiency of [18F]FTPQ was 60 ± 5%. After HPLC purification, the radiochemical purity was higher than 98%. The overall radiochemical yield was approximately 19%. The microPET/CT images demonstrated apparent striatum accumulation in the brains of PD rats at the first 30 min after intravenous injection of [18F]FTPQ. Besides, longitudinal imaging found the uptake of [18F]FTPQ in the brain may reflect the severity of PD. The radioactivity accumulated in the ipsilateral hemisphere of PD rats at 1, 2, and 3 weeks after 6-OHDA administration was 1.84 ± 0.26, 3.43 ± 0.45, and 5.58 ± 0.72%ID/mL, respectively. IHC revealed that an accumulation of microglia/macrophages and astrocytes in the 6-OHDA-injected hemisphere. CONCLUSIONS: In this study, we have successfully synthesized [18F]FTPQ with acceptable radiochemical yield and demonstrated the feasibility of [18F]FTPQ as a TSPO radioligand for the noninvasive monitoring the disease progression of PD.

[18]Fluorodeoxyglucose Positron Emission Tomography for the Textural Features of Cervical Cancer Associated with Lymph Node Metastasis and Histological Type.

BACKGROUND: In this study, we investigated the correlation between the lymph node (LN) status or histological types and textural features of cervical cancers on 18F-fluorodeoxyglucose positron emission tomography/computed tomography. METHODS: We retrospectively reviewed the imaging records of 170 patients with International Federation of Gynecology and Obstetrics stage IB-IVA cervical cancer. Four groups of textural features were studied in addition to the maximum standardized uptake value (SUVmax), metabolic tumor volume, and total lesion glycolysis (TLG). Moreover, we studied the associations between the indices and clinical parameters, including the LN status, clinical stage, and histology. Receiver operating characteristic curves were constructed to evaluate the optimal predictive performance among the various textural indices. Quantitative differences were determined using the Mann-Whitney U test. Multivariate logistic regression analysis was performed to determine the independent factors, among all the variables, for predicting LN metastasis. RESULTS: Among all the significant indices related to pelvic LN metastasis, homogeneity derived from the gray-level co-occurrence matrix (GLCM) was the sole independent predictor. By combining SUVmax, the risk of pelvic LN metastasis can be scored accordingly. The TLGmean was the independent feature of positive para-aortic LNs. Quantitative differences between squamous and nonsquamous histology can be determined using short-zone emphasis (SZE) from the gray-level size zone matrix (GLSZM). CONCLUSION: This study revealed that in patients with cervical cancer, pelvic or para-aortic LN metastases can be predicted by using textural feature of homogeneity from the GLCM and TLGmean, respectively. SZE from the GLSZM is the sole feature associated with quantitative differences between squamous and nonsquamous histology.

Correlation of pretreatment 18F-FDG PET tumor textural features with gene expression in pharyngeal cancer and implications for radiotherapy-based treatment outcomes.

PURPOSE: This study investigated the correlation of the matrix heterogeneity of tumors on 18F-fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) with gene-expression profiling in patients with pharyngeal cancer and determined the prognostic factors for radiotherapy-based treatment outcomes. METHODS: We retrospectively reviewed the records of 57 patients with stage III-IV oropharyngeal or hypopharyngeal cancer who had completed definitive therapy. Four groups of the textural features as well as 31 indices were studied in addition to maximum standard uptake value, metastatic tumor volume, and total lesion glycolysis. Immunohistochemical data from pretreatment biopsy specimens (Glut1, CAIX, VEGF, HIF-1α, EGFR, Ki-67, Bcl-2, CLAUDIN-4, YAP-1, c-Met, and p16) were analyzed. The relationships between the indices and genomic expression were studied, and the robustness of various textural features relative to cause-specific survival and primary relapse-free survival was analyzed. RESULTS: The overexpression of VEGF was positively associated with the increased values of the matrix heterogeneity obtained using gray-level nonuniformity for zone (GLNUz) and run-length nonuniformity (RLNU). Advanced T stage (p = 0.01, hazard ratio [HR] = 3.38), a VEGF immunoreactive score of >2 (p = 0.03, HR = 2.79), and a higher GLNUz value (p = 0.04, HR = 2.51) were prognostic factors for low cause-specific survival, whereas advanced T stage, a HIF-1α staining percentage of ≥80%, and a higher GLNUz value were prognostic factors for low primary-relapse free survival. CONCLUSIONS: The overexpression of VEGF was associated with the increased matrix index of GLNUz and RLNU. For patients with pharyngeal cancer requiring radiotherapy, the treatment outcome can be stratified according to the textural features, T stage, and biomarkers.

Immunohistochemical overexpression of hypoxia-induced factor 1α associated with slow reduction in 18fluoro-2-deoxy-D-glucose uptake for chemoradiotherapy in patients with pharyngeal cancer.

BACKGROUND: This study examined genomic factors associated with a reduction in 18fluoro-2-deoxy-D-glucose (FDG) uptake during positron emission tomography-computed tomography (PET-CT) for definitive chemoradiotherapy (CRT) in patients with pharyngeal cancer. METHODS: The pretreatment and interim PET-CT images of 25 patients with advanced pharyngeal cancers receiving definitive CRT were prospectively evaluated. The maximum standardized uptake value (SUVmax) of the interim PET-CT and the reduction ratio of the SUVmax (SRR) between the two images were measured. Genomic data from pretreatment incisional biopsy specimens (SLC2A1, CAIX, VEGF, HIF1A, BCL2, Claudin-4, YAP1, MET, MKI67, and EGFR) were analyzed using tissue microarrays. Differences in FDG uptake and SRRs between tumors with low and high gene expression were examined using the Mann-Whitney test. Cox regression analysis was performed to examine the effects of variables on local control. RESULTS: The SRR of the primary tumors (SRR-P) was 0.59 ± 0.31, whereas the SRR of metastatic lymph nodes (SRR-N) was 0.54 ± 0.32. Overexpression of HIF1A was associated with a high iSUVmax of the primary tumor (P < 0.001) and neck lymph node (P = 0.04) and a low SRR-P (P = 0.02). Multivariate analysis revealed that patients who had tumors with low SRR-P or high HIF1A expression levels showed inferior local control. CONCLUSION: In patients with pharyngeal cancer requiring CRT, HIF1A overexpression was positively associated with high interim SUVmax or a slow reduction in FDG uptake. Prospective trials are needed to determine whether the local control rate can be stratified using the HIF1A level as a biomarker and SRR-P.

New Trends in Radionuclide Myocardial Perfusion Imaging.

UNLABELLED: Radionuclide myocardial perfusion imaging (MPI) with single photon emission computed tomography (SPECT) has been widely used clinically as one of the major functional imaging modalities for patients with coronary artery disease (CAD) for decades. Ample evidence has supported the use of MPI as a useful and important tool in the diagnosis, risk stratification and treatment planning for CAD. Although popular in the United States, MPI has become the most frequently used imaging modality among all nuclear medicine tests in Taiwan. However, it should be acknowledged that MPI SPECT does have its limitations. These include false-positive results due to certain artifacts, false-negative due to balanced ischemia, complexity and adverse reaction arising from current pharmacological stressors, time consuming nature of the imaging procedure, no blood flow quantitation and relatively high radiation exposure. The purpose of this article was to review the recent trends in nuclear cardiology, including the utilization of positron emission tomography (PET) for MPI, new stressor, new SPECT camera with higher resolution and higher sensitivity, dynamic SPECT protocol for blood flow quantitation, new software of phase analysis for evaluation of LV dyssynchrony, and measures utilized for reducing radiation exposure of MPI. KEY WORDS: Coronary artery disease • Myocardial flow reserve • Myocardial perfusion imaging • Phase analysis • PET • SPECT.

[18F]-Fluorodeoxyglucose Positron Emission Tomography Standardized Uptake Value as a Predictor of Adjuvant Chemotherapy Benefits in Patients With Nasopharyngeal Carcinoma.

BACKGROUND: The role of adjuvant chemotherapy for the treatment of nasopharyngeal carcinoma (NPC) is controversial, and the identification of adequate predictive factors is warranted. Therefore, we aimed to investigate whether the mean standardized uptake value (SUV) measured on [(18)F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) could predict the survival benefits for NPC patients that receive adjuvant chemotherapy. MATERIALS AND METHODS: The data for 174 NPC patients who underwent PET/computed tomography before chemoradiation between January 2004 and January 2012 were reviewed. The SUV75% was recorded for primary tumors. All patients received intensity-modulated radiotherapy and cisplatin-based chemotherapy. Adjuvant chemotherapy consisted of 3 cycles of 75 mg/m(2) cisplatin and 1,000 mg/m(2) fluorouracil for 4 days. RESULTS: The optimal cutoff value was 8.35 for SUV75%, with 112 (64.4%) patients having lower SUV75% and 62 (35.6%) having higher SUV75%. Patients with lower SUV75% had significantly better 5-year overall survival (OS) and distant metastasis-free survival. Multivariate analysis revealed that tumor stage, SUV75%, and adjuvant chemotherapy were significant prognostic factors for OS. Patients with higher SUV75% had significantly higher 5-year OS rates with adjuvant chemotherapy than without adjuvant chemotherapy (84.3% vs. 32.4%, respectively; p < .001). However, in the lower SUV75% group, no differences in 5-year OS were observed between patients who received and those who did not receive adjuvant chemotherapy (92.4% vs. 93.3%, respectively; p = .682). CONCLUSION: The SUV75% on FDG PET for primary tumors could successfully identify NPC patients who may benefit from adjuvant chemotherapy.

Performance of ChatGPT incorporated chain-of-thought method in bilingual nuclear medicine physician board examinations.

Objective: This research explores the performance of ChatGPT, compared to human doctors, in bilingual, Mandarin Chinese and English, medical specialty exam in Nuclear Medicine in Taiwan. Methods: The study employed generative pre-trained transformer (GPT-4) and integrated chain-of-thoughts (COT) method to enhance performance by triggering and explaining the thinking process to answer the question in a coherent and logical manner. Questions from the Taiwanese Nuclear Medicine Specialty Exam served as the basis for testing. The research analyzed the correctness of AI responses in different sections of the exam and explored the influence of question length and language proportion on accuracy. Results: AI, especially ChatGPT with COT, exhibited exceptional capabilities in theoretical knowledge, clinical medicine, and handling integrated questions, often surpassing, or matching human doctor performance. However, AI struggled with questions related to medical regulations. The analysis of question length showed that questions within the 109-163 words range yielded the highest accuracy. Moreover, an increase in the proportion of English words in questions improved both AI and human accuracy. Conclusions: This research highlights the potential and challenges of AI in the medical field. ChatGPT demonstrates significant competence in various aspects of medical knowledge. However, areas like medical regulations require improvement. The study also suggests that AI may help in evaluating exam question difficulty and maintaining fairness in examinations. These findings shed light on AI role in the medical field, with potential applications in healthcare education, exam preparation, and multilingual environments. Ongoing AI advancements are expected to further enhance AI utility in the medical domain.

Early prediction of distant metastasis in patients with uterine cervical cancer treated with definitive chemoradiotherapy by deep learning using pretreatment [ 18 F]fluorodeoxyglucose positron emission tomography/computed tomography.

OBJECTIVES: A deep learning (DL) model using image data from pretreatment [ 18 F]fluorodeoxyglucose ([ 18 F] FDG)-PET or computed tomography (CT) augmented with a novel imaging augmentation approach was developed for the early prediction of distant metastases in patients with locally advanced uterine cervical cancer. METHODS: This study used baseline [18F]FDG-PET/CT images of newly diagnosed uterine cervical cancer patients. Data from 186 to 25 patients were analyzed for training and validation cohort, respectively. All patients received chemoradiotherapy (CRT) and follow-up. PET and CT images were augmented by using three-dimensional techniques. The proposed model employed DL to predict distant metastases. Receiver operating characteristic (ROC) curve analysis was performed to measure the model's predictive performance. RESULTS: The area under the ROC curves of the training and validation cohorts were 0.818 and 0.830 for predicting distant metastasis, respectively. In the training cohort, the sensitivity, specificity, and accuracy were 80.0%, 78.0%, and 78.5%, whereas, the sensitivity, specificity, and accuracy for distant failure were 73.3%, 75.5%, and 75.2% in the validation cohort, respectively. CONCLUSION: Through the use of baseline [ 18 F]FDG-PET/CT images, the proposed DL model can predict the development of distant metastases for patients with locally advanced uterine cervical cancer treatment by CRT. External validation must be conducted to determine the model's predictive performance.

Bivalent mRNA COVID-19 Vaccine-Related Pericarditis on 18 F-FDG PET/CT.

ABSTRACT: A 13-year-old boy was suspected with pericarditis after a second booster dose of bivalent mRNA COVID-19 vaccine. After specific preparation for cardiac inflammation with carbohydrate-free, high-fat diet, the 18 F-FDG PET/CT successfully demonstrated simultaneous presentation of vaccination-related axillary lymphadenopathy and pericarditis without the interference of physiological myocardial uptake.

Bone Metastases Lesion Segmentation on Breast Cancer Bone Scan Images with Negative Sample Training.

The use of deep learning methods for the automatic detection and quantification of bone metastases in bone scan images holds significant clinical value. A fast and accurate automated system for segmenting bone metastatic lesions can assist clinical physicians in diagnosis. In this study, a small internal dataset comprising 100 breast cancer patients (90 cases of bone metastasis and 10 cases of non-metastasis) and 100 prostate cancer patients (50 cases of bone metastasis and 50 cases of non-metastasis) was used for model training. Initially, all image labels were binary. We used the Otsu thresholding method or negative mining to generate a non-metastasis mask, thereby transforming the image labels into three classes. We adopted the Double U-Net as the baseline model and made modifications to its output activation function. We changed the activation function to SoftMax to accommodate multi-class segmentation. Several methods were used to enhance model performance, including background pre-processing to remove background information, adding negative samples to improve model precision, and using transfer learning to leverage shared features between two datasets, which enhances the model's performance. The performance was investigated via 10-fold cross-validation and computed on a pixel-level scale. The best model we achieved had a precision of 69.96%, a sensitivity of 63.55%, and an F1-score of 66.60%. Compared to the baseline model, this represents an 8.40% improvement in precision, a 0.56% improvement in sensitivity, and a 4.33% improvement in the F1-score. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

Artificial Intelligence of Object Detection in Skeletal Scintigraphy for Automatic Detection and Annotation of Bone Metastases.

BACKGROUND: When cancer has metastasized to bone, doctors must identify the site of the metastases for treatment. In radiation therapy, damage to healthy areas or missing areas requiring treatment should be avoided. Therefore, it is necessary to locate the precise bone metastasis area. The bone scan is a commonly applied diagnostic tool for this purpose. However, its accuracy is limited by the nonspecific character of radiopharmaceutical accumulation. The study evaluated object detection techniques to improve the efficacy of bone metastases detection on bone scans. METHODS: We retrospectively examined the data of 920 patients, aged 23 to 95 years, who underwent bone scans between May 2009 and December 2019. The bone scan images were examined using an object detection algorithm. RESULTS: After reviewing the image reports written by physicians, nursing staff members annotated the bone metastasis sites as ground truths for training. Each set of bone scans contained anterior and posterior images with resolutions of 1024 × 256 pixels. The optimal dice similarity coefficient (DSC) in our study was 0.6640, which differs by 0.04 relative to the optimal DSC of different physicians (0.7040). CONCLUSIONS: Object detection can help physicians to efficiently notice bone metastases, decrease physician workload, and improve patient care.

Imaging prediction of KRAS mutation in patients with rectal cancer through deep metric learning using pretreatment [18F]Fluorodeoxyglucose positron emission tomography/computed tomography.

OBJECTIVES: To predict KRAS mutation in rectal cancer (RC) through computer vision of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) by using metric learning (ML). METHODS: This study included 160 patients with RC who had undergone preoperative PET/CT. KRAS mutation was identified through polymerase chain reaction analysis. This model combined ML with the deep-learning framework to analyze PET data with or without CT images. The Batch Balance Wrapper framework and K-fold cross-validation were employed during the learning process. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance. RESULTS: Genetic alterations in KRAS were identified in 82 (51%) tumors. Both PET and CT images were used, and the proposed model had an area under the ROC curve of 0.836 for its ability to predict a mutation status. The sensitivity, specificity, and accuracy were 75.3%, 79.3%, and 77.5%, respectively. When PET images alone were used, the area under the curve was 0.817, whereas the sensitivity, specificity, and accuracy were 73.2%, 79.6%, and 76.2%, respectively. CONCLUSIONS: The ML model presented herein revealed that baseline 18F-FDG PET/CT images could provide supplemental information to determine KRAS mutation in RC. Additional studies are required to maximize the predictive accuracy. ADVANCES IN KNOWLEDGE: The results of the ML model presented herein indicate that baseline 18F-FDG PET/CT images could provide supplemental information for determining KRAS mutation in RC.The predictive accuracy of the model was 77.5% when both image types were used and 76.2% when PET images alone were used. Additional studies are required to maximize the predictive accuracy.

Use of pretreatment metabolic tumour volumes to predict the outcome of pharyngeal cancer treated by definitive radiotherapy.

PURPOSE: The aim of the study was to investigate the predictive role of pretreatment metabolic volume (MTV) in pharyngeal cancer (PC) patients treated with definitive (chemo) radiotherapy. METHODS: This retrospective analysis enrolled 64 patients with PC treated with (chemo) radiotherapy. All patients received pretreatment fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT. Four PET segmentation methods were used, namely applying an isocontour at a standardized uptake value (SUV) of either 2.5 or 3.0 (MTV2.5 and MTV3.0) or using fixed thresholds of either 40 or 50 % (MTV40 %, MTV50 %) of the maximum intratumoural FDG activity. Disease-free survival (DFS) and primary relapse-free survival (PRFS) were examined according to cutoffs of the median values for each MTV and the gross tumour volume (GTVp). Independent prognosticators were identified by Cox regression analysis. RESULTS: With a median follow-up of 24 months, 19 patients died, and 26 patients experienced tumour relapse at primary sites. Multivariate analysis of the DFS showed that MTV2.5 > 13.6 ml was the only predictor of relapse [p = 0.011, hazard ratio = 2.69, 95 % confidence interval (CI) 1.25-5.76]. The independent predictor for PRFS was MTV2.5 > 13.6 ml (p = 0.003, hazard ratio = 3.76, 95 % CI 1.57-8.92), whereas GTVp > 15.5 ml had a marginal impact on PRFS (p = 0.06, hazard ratio = 3.54, 95 % CI 0.97-11.85). Patients having tumours with MTV2.5 > 13.6 ml had a significantly inferior 2-year PRFS compared with patients who had lower MTV2.5 tumours (39 vs 72 %, respectively, p = 0.001). CONCLUSION: For PC patients treated with definitive (chemo)radiotherapy, pretreatment MTV2.5 volume achieved the best predictive value for primary recurrence, and the same value was also a prognosticator for DFS.

Rare "Burned-Out" Testicular Mixed Germ Cell Tumor on 18 F-FDG PET/CT.

ABSTRACT: Spontaneous regression of testicular mixed germ cell tumor is rare and is also called burned-out testicular tumor. We herein present the case of a 20-year-old man who was initially diagnosed with metastatic embryonal carcinoma. 18 F-FDG PET/CT demonstrated apparent metastases in the lymph node regions and both lungs. A covert right testicular lesion was noted according to the features on the CT component of PET/CT, which was subsequently confirmed as burned-out testicular mixed germ cell tumor.

18F-Fluciclovine Uptake in Intramuscular Injecting Site of Antiandrogen.

ABSTRACT: 18F-fluciclovine, a radiolabeled amino acid analog, has been approved by US Food and Drug Administration for detecting lesions of biochemical recurrence of prostate adenocarcinoma with PET/CT. However, it is not specific for prostate cancer and has been found to be present in variety of malignant and benign etiologies. We herein present an interesting case of the incidental finding of increasing uptake of 18F-fluciclovine related to intramuscular injection of antiandrogen.

Prediction of All-Cause Mortality Based on Stress/Rest Myocardial Perfusion Imaging (MPI) Using Deep Learning: A Comparison between Image and Frequency Spectra as Input.

BACKGROUND: Cardiovascular management and risk stratification of patients is an important issue in clinics. Patients who have experienced an adverse cardiac event are concerned for their future and want to know the survival probability. METHODS: We trained eight state-of-the-art CNN models using polar maps of myocardial perfusion imaging (MPI), gender, lung/heart ratio, and patient age for 5-year survival prediction after an adverse cardiac event based on a cohort of 862 patients who had experienced adverse cardiac events and stress/rest MPIs. The CNN model outcome is to predict a patient's survival 5 years after a cardiac event, i.e., two classes, either yes or no. RESULTS: The best accuracy of all the CNN prediction models was 0.70 (median value), which resulted from ResNet-50V2, using image as the input in the baseline experiment. All the CNN models had better performance after using frequency spectra as the input. The accuracy increment was about 7~9%. CONCLUSIONS: This is the first trial to use pure rest/stress MPI polar maps and limited clinical data to predict patients' 5-year survival based on CNN models and deep learning. The study shows the feasibility of using frequency spectra rather than images, which might increase the performance of CNNs.

Real-World Testing of a Machine Learning-Derived Visual Scale for Tc99m TRODAT-1 for Diagnosing Lewy Body Disease: Comparison with a Traditional Approach Using Semiquantification.

Objectives: Abnormal dopamine transporter (DAT) uptake is an important biomarker for diagnosing Lewy body disease (LBD), including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). We evaluated a machine learning-derived visual scale (ML-VS) for Tc99m TRODAT-1 from one center and compared it with the striatal/background ratio (SBR) using semiquantification for diagnosing LBD in two other centers. Patients and Methods: This was a retrospective analysis of data from a history-based computerized dementia diagnostic system. MT-VS and SBR among normal controls (NCs) and patients with PD, PD with dementia (PDD), DLB, or Alzheimer's disease (AD) were compared. Results: We included 715 individuals, including 122 NCs, 286 patients with PD, 40 with AD, 179 with DLB, and 88 with PDD. Compared with NCs, patients with PD exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. Compared with the AD group, PDD and DLB groups exhibited a significantly higher prevalence of abnormal DAT uptake using all methods. The distribution of ML-VS was significantly different between PD and NC, DLB and AD, and PDD and AD groups (all p < 0.001). The correlation coefficient of ML-VS/SBR in all participants was 0.679. Conclusions: The ML-VS designed in one center is useful for differentiating PD from NC, DLB from AD, and PDD from AD in other centers. Its correlation with traditional approaches using different scanning machines is also acceptable. Future studies should develop models using data pools from multiple centers for increasing diagnostic accuracy.