Hematocrit and lactate trends help predict outcomes in trauma independent of CT and other clinical parameters.

Background: Hematocrit and lactate have an established role in trauma as indicators of bleeding and cell death, respectively. The wide availability of CT imaging and clinical data poses the question of how these can be used in combination to predict outcomes. Purpose: To assess the utility of hematocrit or lactate trends in predicting intensive care unit (ICU) admission and hospital length of stay (LOS) in patients with torso trauma combined with clinical parameters and injury findings on CT. Materials and Methods: This was a single-center retrospective study of adults with torso trauma in one year. Trends were defined as a unit change per hour. CT findings and clinical parameters were explanatory variables. Outcomes were ICU admission and hospital LOS. Multivariate logistic and negative binomial regression models were used to calculate the odds ratio (OR) and incident rate ratio (IRR). Results: Among 840 patients, 561 (72% males, age 39 ± 18) were included, and 168 patients (30%) were admitted to the ICU. Decreasing hematocrit trend [OR 2.54 (1.41-4.58), p = 0.002] and increasing lactate trend [OR 3.85 (1.35-11.01), p = 0.012] were associated with increased odds of ICU admission. LOS median was 2 (IQR: 1-5) days. Decreasing hematocrit trend [IRR 1.37 (1.13-1.66), p = 0.002] and increasing lactate trend [2.02 (1.43-2.85), p < 0.001] were associated with longer hospital LOS. Conclusion: Hematocrit and lactate trends may be helpful in predicting ICU admission and LOS in torso trauma independent of organ injuries on CT, age, or admission clinical parameters.

Prediction of the local treatment outcome in patients with oropharyngeal squamous cell carcinoma using deep learning analysis of pretreatment FDG-PET images.

BACKGROUND: This study aimed to assess the utility of deep learning analysis using pretreatment FDG-PET images to predict local treatment outcome in oropharyngeal squamous cell carcinoma (OPSCC) patients. METHODS: One hundred fifty-four OPSCC patients who received pretreatment FDG-PET were included and divided into training (n = 102) and test (n = 52) sets. The diagnosis of local failure and local progression-free survival (PFS) rates were obtained from patient medical records. In deep learning analyses, axial and coronal images were assessed by three different architectures (AlexNet, GoogLeNET, and ResNet). In the training set, FDG-PET images were analyzed after the data augmentation process for the diagnostic model creation. A multivariate clinical model was also created using a binomial logistic regression model from a patient's clinical characteristics. The test data set was subsequently analyzed for confirmation of diagnostic accuracy. Assessment of local PFS rates was also performed. RESULTS: Training sessions were successfully performed with an accuracy of 74-89%. ROC curve analyses revealed an AUC of 0.61-0.85 by the deep learning model in the test set, whereas it was 0.62 by T-stage, 0.59 by clinical stage, and 0.74 by a multivariate clinical model. The highest AUC (0.85) was obtained with deep learning analysis of ResNet architecture. Cox proportional hazards regression analysis revealed deep learning-based classification by a multivariate clinical model (P < .05), and ResNet (P < .001) was a significant predictor of the treatment outcome. In the Kaplan-Meier analysis, the deep learning-based classification divided the patient's local PFS rate better than the T-stage, clinical stage, and a multivariate clinical model. CONCLUSIONS: Deep learning-based diagnostic model with FDG-PET images indicated its possibility to predict local treatment outcomes in OPSCCs.

Deep learning analysis using FDG-PET to predict treatment outcome in patients with oral cavity squamous cell carcinoma.

OBJECTIVE: To assess the utility of deep learning analysis using 18F-fluorodeoxyglucose (FDG) uptake by positron emission tomography (PET/CT) to predict disease-free survival (DFS) in patients with oral cavity squamous cell carcinoma (OCSCC). METHODS: One hundred thirteen patients with OCSCC who received pretreatment FDG-PET/CT were included. They were divided into training (83 patients) and test (30 patients) sets. The diagnosis of treatment control/failure and the DFS rate were obtained from patients' medical records. In deep learning analyses, three planes of axial, coronal, and sagittal FDG-PET images were assessed by ResNet-101 architecture. In the training set, image analysis was performed for the diagnostic model creation. The test data set was subsequently analyzed for confirmation of diagnostic accuracy. T-stage, clinical stage, and conventional FDG-PET parameters (the maximum and mean standardized uptake value (SUVmax and SUVmean), heterogeneity index, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were also assessed with determining the optimal cutoff from training dataset and then validated their diagnostic ability from test dataset. RESULTS: In dividing into patients with treatment control and failure, the highest diagnostic accuracy of 0.8 was obtained using deep learning classification, with a sensitivity of 0.8, specificity of 0.8, positive predictive value of 0.89, and negative predictive value of 0.67. In the Kaplan-Meier analysis, the DFS rate was significantly different only with the analysis of deep learning-based classification (p < .01). CONCLUSIONS: Deep learning-based diagnosis with FDG-PET images may predict treatment outcome in patients with OCSCC. KEY POINTS: • Deep learning-based diagnosis of FDG-PET images showed the highest diagnostic accuracy to predict the treatment outcome in patients with oral cavity squamous cell carcinoma. • Deep learning-based diagnosis was shown to differentiate patients between good and poor disease-free survival more clearly than conventional T-stage, clinical stage, and conventional FDG-PET-based parameters.

Prediction of the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma by FDG-PET imaging dataset using deep learning analysis: A hypothesis-generating study.

PURPOSE: To assess the diagnostic accuracy of imaging-based deep learning analysis to differentiate between human papillomavirus (HPV) positive and negative oropharyngeal squamous cell carcinomas (OPSCCs) using FDG-PET images. METHODS: One hundred and twenty patients with OPSCC who underwent pretreatment FDG-PET/CT were included and divided into the training 90 patients and validation 30 patients cohorts. In the training session, 2160 FDG-PET images were analyzed after data augmentation process by a deep learning technique to create a diagnostic model to discriminate between HPV-positive and HPV-negative OPSCCs. Validation cohort data were subsequently analyzed for confirmation of diagnostic accuracy in determining HPV status by the deep learning-based diagnosis model. In addition, two radiologists evaluated the validation cohort image-data to determine the HPV status based on each tumor's imaging findings. RESULTS: In deep learning analysis with training session, the diagnostic model using training dataset was successfully created. In the validation session, the deep learning diagnostic model revealed sensitivity of 0.83, specificity of 0.83, positive predictive value of 0.88, negative predictive value of 0.77, and diagnostic accuracy of 0.83, while the visual assessment by two radiologists revealed 0.78, 0.5, 0.7, 0.6, and 0.67 (reader 1), and 0.56, 0.67, 0.71, 0.5, and 0.6 (reader 2), respectively. Chi square test showed a significant difference between deep learning- and radiologist-based diagnostic accuracy (reader 1: P = 0.016, reader 2: P = 0.008). CONCLUSIONS: Deep learning diagnostic model with FDG-PET imaging data can be useful as one of supportive tools to determine the HPV status in patients with OPSCC.

ACR-ACNM Practice Parameter for the Performance of Dopamine Transporter (DaT) Single Photon Emission Computed Tomography (SPECT) Imaging for Movement Disorders.

This American College of Radiology and American College of Nuclear Medicine joint clinical practice parameter is for performance of dopamine transporter single photon emission computed tomography (SPECT) imaging, for patients with movement disorders. Parkinsonian syndrome (PS) consists of a group of neurodegenerative diseases including Parkinson disease (PD), progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal degeneration (CBD), and dementia with Lewy bodies (DLB). Accurate diagnosis of PS is critical for clinical management. An important diagnostic dilemma is the differentiation of PS and non-neurodegenerative disorders, such as essential tremor (ET) or drug-induced tremor, due to the overlap of clinical symptoms. The management approach to these conditions is distinctly different. An abnormal iodine-123 ioflupane SPECT scan suggests a decreased amount of dopamine transporter in the striatum, that is, a diagnosis of nigrostriatal neurodegenerative PS, whereas a normal scan suggests ET or other nondegenerative parkinsonism (drug-induced, vascular, or psychogenic).

An amylin analog used as a challenge test for Alzheimer's disease.

INTRODUCTION: Preclinical studies demonstrate the potential of amylin in the diagnosis of Alzheimer's disease (AD). We aimed to lay the foundation for repurposing the amylin analog and a diabetes drug, pramlintide, for AD in humans. METHODS: We administered a single subcutaneous injection of 60 µg of pramlintide to nondiabetic subjects under fasting conditions. RESULTS: None of the participants developed hypoglycemia after the injection of pramlintide. The pramlintide challenge induced a significant surge of amyloid-ß peptide and a decrease in total tau in the plasma of AD subjects but not in control participants. The pramlintide injection provoked an increase in interleukin 1 receptor antagonist and a decrease in retinol-binding protein 4, which separates AD subjects from control subjects. DISCUSSION: Pramlintide use appeared to be safe in the absence of diabetes. The biomarker changes as a result of the pramlintide challenge, which distinguished AD from control subjects and mild cognitive impairment.

Use of 18F-FDG PET/CT as a predictive biomarker of outcome in patients with head-and-neck non-squamous cell carcinoma.

OBJECTIVE: The purpose of this article is to establish whether pretreatment (18)F-FDG uptake predicts disease-free survival (DFS) and overall survival in patients with head-and-neck non-squamous cell carcinoma (SCC). MATERIALS AND METHODS: Eighteen patients (six women and 12 men; mean [± SD] age at diagnosis, 57.89 ± 13.54 years) with head-and-neck non-SCC were included. Tumor FDG uptake was measured by the maximum standardized uptake value (SUV(max)) and was corrected for background liver FDG uptake to derive the corrected SUV(max). Receiver operating characteristic analyses were used to predict the optimal corrected SUV(max) cutoffs for respective outcomes of DFS (i.e., absence of recurrence) and death. RESULTS: The mean corrected SUV(max) of the 18 head-and-neck tumors was 5.63 ± 3.94 (range, 1.14-14.29). The optimal corrected SUV(max) cutoff for predicting DFS and overall survival was 5.79. DFS and overall survival were significantly higher among patients with corrected SUV(max) < 6 than among patients with corrected SUV(max) ≥ 6. The mean DFS for patients with corrected SUV(max) < 6 was 25.7 ± 11.14 months, and the mean DFS for patients with corrected SUV(max) ≥ 6 was 7.88 ± 7.1 months (p < 0.018). Among patients with corrected SUV(max) < 6, none died, and the mean length of follow-up for this group was 35.2 ± 9.96 months. All of the patients who died had corrected SUV(max) ≥ 6, and the overall survival for this group was 13.28 ± 12.89 months (p < 0.001). CONCLUSION: FDG uptake, as measured by corrected SUV(max), may be a predictive imaging biomarker for DFS and overall survival in patients with head-and-neck non-SCC.

CT arthrography, MR arthrography, PET, and scintigraphy in osteoarthritis.

CT arthrography and MR arthrography are accurate methods for the study of surface cartilage lesions and cartilage loss. They also provide information on subchondral bone and marrow changes, and ligaments and meniscal lesions that can be associated with osteoarthritis. Nuclear medicine also offers new insights in the assessment of the disease. This article discusses the strengths and limitations of CT arthrography and MR arthrography. It also highlights nuclear medicine methods that may be relevant to the study of osteoarthritis in research and clinical practice.