No code machine learning: validating the approach on use-case for classifying clavicle fractures.

PURPOSE: We created an infrastructure for no code machine learning (NML) platform for non-programming physicians to create NML model. We tested the platform by creating an NML model for classifying radiographs for the presence and absence of clavicle fractures. METHODS: Our IRB-approved retrospective study included 4135 clavicle radiographs from 2039 patients (mean age 52 ± 20 years, F:M 1022:1017) from 13 hospitals. Each patient had two-view clavicle radiographs with axial and anterior-posterior projections. The positive radiographs had either displaced or non-displaced clavicle fractures. We configured the NML platform to automatically retrieve the eligible exams using the series' unique identification from the hospital virtual network archive via web access to DICOM Objects. The platform trained a model until the validation loss plateaus. Once the testing was complete, the platform provided the receiver operating characteristics curve and confusion matrix for estimating sensitivity, specificity, and accuracy. RESULTS: The NML platform successfully retrieved 3917 radiographs (3917/4135, 94.7 %) and parsed them for creating a ML classifier with 2151 radiographs in the training, 100 radiographs for validation, and 1666 radiographs in testing datasets (772 radiographs with clavicle fracture, 894 without clavicle fracture). The network identified clavicle fracture with 90 % sensitivity, 87 % specificity, and 88 % accuracy with AUC of 0.95 (confidence interval 0.94-0.96). CONCLUSION: A NML platform can help physicians create and test machine learning models from multicenter imaging datasets such as the one in our study for classifying radiographs based on the presence of clavicle fracture.

Artificial Intelligence in Cataract Surgery: A Systematic Review.

Purpose: The purpose of this study was to assess the current use and reliability of artificial intelligence (AI)-based algorithms for analyzing cataract surgery videos. Methods: A systematic review of the literature about intra-operative analysis of cataract surgery videos with machine learning techniques was performed. Cataract diagnosis and detection algorithms were excluded. Resulting algorithms were compared, descriptively analyzed, and metrics summarized or visually reported. The reproducibility and reliability of the methods and results were assessed using a modified version of the Medical Image Computing and Computer-Assisted (MICCAI) checklist. Results: Thirty-eight of the 550 screened studies were included, 20 addressed the challenge of instrument detection or tracking, 9 focused on phase discrimination, and 8 predicted skill and complications. Instrument detection achieves an area under the receiver operator characteristic curve (ROC AUC) between 0.976 and 0.998, instrument tracking an mAP between 0.685 and 0.929, phase recognition an ROC AUC between 0.773 and 0.990, and complications or surgical skill performs with an ROC AUC between 0.570 and 0.970. Conclusions: The studies showed a wide variation in quality and pose a challenge regarding replication due to a small number of public datasets (none for manual small incision cataract surgery) and seldom published source code. There is no standard for reported outcome metrics and validation of the models on external datasets is rare making comparisons difficult. The data suggests that tracking of instruments and phase detection work well but surgical skill and complication recognition remains a challenge for deep learning. Translational Relevance: This overview of cataract surgery analysis with AI models provides translational value for improving training of the clinician by identifying successes and challenges.

Patient-Centric Quality Standards.

To ensure the quality, safety and efficacy of medicinal products, it is necessary to develop and execute appropriate manufacturing process and product control strategies. Traditionally, product control strategies have focused on testing known quality attributes with limits derived from levels administered in preclinical and clinical studies with an associated statistical analysis to account for variability. However, not all quality attributes have impact to the patient and those with the potential to impact safety and efficacy may not be significant when dosed at patient-centric levels. Therefore, achieving patient-centricity is understanding patient relevance, which is defined as the level of impact that a quality attribute could have on safety and efficacy within the potential exposure range. A patient-centric quality standard (PCQS) is therefore a set of patient relevant attributes and their associated acceptance ranges to which a drug product should conform within the expected patient exposure range. This manuscript describes historical perspectives details the way to create and leverage a PCQS in a variety of pharmaceutical product modalities.

Veteran influenza vaccination acceptance rates after completion of the COVID-19 vaccination series among historical influenza vaccine refusers.

BACKGROUND: Vaccine hesitancy remains an obstacle in disease prevention. The recent COVID-19 pandemic highlighted this issue and may influence acceptance of other recommended immunizations. The objective of this study was to determine the association between receiving the COVID-19 vaccination and the subsequent acceptance of the influenza vaccination in a Veteran population that historically declined influenza vaccination. METHODS: Influenza vaccination acceptance rates for the 2021-2022 influenza season were compared in patients who historically declined the influenza vaccine and either received or declined COVID-19 vaccinations. Logistic regression analysis was used to analyze factors associated with receiving influenza vaccination among vaccine hesitant individuals. RESULTS: A higher proportion of patients who had received the COVID-19 vaccination(s) subsequently accepted the influenza vaccination compared to the control group (37% vs. 11%, OR = 5.03; CI 3.15-8.26; p = 0.0001). CONCLUSION: Among previous influenza vaccine decliners, those who received COVID-19 vaccination had significantly higher odds of receiving subsequent influenza vaccination.

Accurate drusen segmentation in optical coherence tomography via order-constrained regression of retinal layer heights.

Drusen are an important biomarker for age-related macular degeneration (AMD). Their accurate segmentation based on optical coherence tomography (OCT) is therefore relevant to the detection, staging, and treatment of disease. Since manual OCT segmentation is resource-consuming and has low reproducibility, automatic techniques are required. In this work, we introduce a novel deep learning based architecture that directly predicts the position of layers in OCT and guarantees their correct order, achieving state-of-the-art results for retinal layer segmentation. In particular, the average absolute distance between our model's prediction and the ground truth layer segmentation in an AMD dataset is 0.63, 0.85, and 0.44 pixel for Bruch's membrane (BM), retinal pigment epithelium (RPE) and ellipsoid zone (EZ), respectively. Based on layer positions, we further quantify drusen load with excellent accuracy, achieving 0.994 and 0.988 Pearson correlation between drusen volumes estimated by our method and two human readers, and increasing the Dice score to 0.71 ± 0.16 (from 0.60 ± 0.23) and 0.62 ± 0.23 (from 0.53 ± 0.25), respectively, compared to a previous state-of-the-art method. Given its reproducible, accurate, and scalable results, our method can be used for the large-scale analysis of OCT data.

Spatially regularized low-rank tensor approximation for accurate and fast tractography.

Tractography based on diffusion Magnetic Resonance Imaging (dMRI) is the prevalent approach to the in vivo delineation of white matter tracts in the human brain. Many tractography methods rely on models of multiple fiber compartments, but the local dMRI information is not always sufficient to reliably estimate the directions of secondary fibers. Therefore, we introduce two novel approaches that use spatial regularization to make multi-fiber tractography more stable. Both represent the fiber Orientation Distribution Function (fODF) as a symmetric fourth-order tensor, and recover multiple fiber orientations via low-rank approximation. Our first approach computes a joint approximation over suitably weighted local neighborhoods with an efficient alternating optimization. The second approach integrates the low-rank approximation into a current state-of-the-art tractography algorithm based on the unscented Kalman filter (UKF). These methods were applied in three different scenarios. First, we demonstrate that they improve tractography even in high-quality data from the Human Connectome Project, and that they maintain useful results with a small fraction of the measurements. Second, on the 2015 ISMRM tractography challenge, they increase overlap, while reducing overreach, compared to low-rank approximation without joint optimization or the traditional UKF, respectively. Finally, our methods permit a more comprehensive reconstruction of tracts surrounding a tumor in a clinical dataset. Overall, both approaches improve reconstruction quality. At the same time, our modified UKF significantly reduces the computational effort compared to its traditional counterpart, and to our joint approximation. However, when used with ROI-based seeding, joint approximation more fully recovers fiber spread.

Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience.

The multitude of artificial intelligence (AI)-based solutions, vendors, and platforms poses a challenging proposition to an already complex clinical radiology practice. Apart from assessing and ensuring acceptable local performance and workflow fit to improve imaging services, AI tools require multiple stakeholders, including clinical, technical, and financial, who collaborate to move potential deployable applications to full clinical deployment in a structured and efficient manner. Postdeployment monitoring and surveillance of such tools require an infrastructure that ensures proper and safe use. Herein, the authors describe their experience and framework for implementing and supporting the use of AI applications in radiology workflow.

Distinct contributions of the innate immune receptors TLR2 and RP105 to formation and architecture of structured lung granulomas in mice infected with Mycobacterium tuberculosis.

Granulomas are key histopathological features of Mycobacterium tuberculosis (Mtb) infection, with complex roles in pathogen control and dissemination. Thus, understanding drivers and regulators of granuloma formation is important for improving tuberculosis diagnosis, treatment, and prevention. Yet, molecular mechanisms underpinning granuloma formation and dynamics remain poorly understood. Here we used low-dose Mtb infection of C57BL/6 mice, which elicits structured lung granulomas composed of central macrophage clusters encased by a lymphocyte mantle, alongside the disorganized lymphocyte and macrophage clusters commonly observed in Mtb-infected mice. Using gene-deficient mice, we observed that Toll-like receptor (TLR) 2 and the TLR-related Radioprotective 105 kDa protein (RP105) contributed to the extent and spatial positioning of pathology in infected lung tissues, consistent with functional cooperation between TLR2 and RP105 in the innate immune recognition of Mtb. In mice infected with the highly virulent Mtb clinical isolate HN878, TLR2, but not RP105, positively regulated the extent of central macrophage regions within structured granulomas. Moreover, RP105, but not TLR2, promoted the formation of structured lung granulomas, suggesting that the functions of RP105 as an innate immune sensor for Mtb reach beyond its roles as TLR2 co-receptor. TLR2 and RP105 contributions to lung pathology are governed by Mtb biology, as neither receptor affected the frequency or architecture of structured granulomas in mice infected with the reference strain Mtb H37Rv. Thus, by revealing distinctive as well as cooperative functions of TLR2 and RP105 in lung pathology, our data identify TLRs as molecular determinants of TB granuloma formation and architecture, and expand understanding of how interactions between innate immune receptors and Mtb shape TB disease manifestation.

Structure of benzene from mass-correlated rotational Raman spectroscopy.

We present high resolution rotational Raman spectra and derived geometry parameters for benzene. Rotational Raman spectra with sub-5 MHz resolution were obtained via high-resolution mass-correlated rotational alignment spectroscopy. Isotopologue spectra for C6H6, 13C-C5H6, C6D6, and 13C-C5D6 were distinguished through their correlated mass information. Spectra for 13C6H6 were obtained with lower resolution. Equilibrium and effective bond lengths were estimated from measured inertial moments, based on explicit assumptions and approximations. We discuss the origin of significant bias in previously published geometry parameters and the possibility to derive H,D isotope-specific bond lengths from purely experimental data.

Mass-Correlated High-Resolution Spectra and the Structure of Benzene.

Mass-correlated rotational alignment spectroscopy resolved the rotational Raman spectra for 5 benzene isotopologues with unprecedented resolution. 13-C isotopologues were characterized at natural abundance. Fitted rotational constants allowed the analysis of effective and equilibrium bond lengths for benzene with sub-mÅ uncertainties. We found that previously reported experimental structures were wrong by multiple mÅ, due to unrecognized H/D isotope effects. Our results also refute recent experimental and theoretical literature claims of identical effective C-H and C-D bond lengths in benzene and reveal an isotope effect similar to that in other small molecules.

Reducing instability of inter-subject covariance of FDG uptake networks using structure-weighted sparse estimation approach.

PURPOSE: Sparse inverse covariance estimation (SICE) is increasingly utilized to estimate inter-subject covariance of FDG uptake (FDGcov) as proxy of metabolic brain connectivity. However, this statistical method suffers from the lack of robustness in the connectivity estimation. Patterns of FDGcov were observed to be spatially similar with patterns of structural connectivity as obtained from DTI imaging. Based on this similarity, we propose to regularize the sparse estimation of FDGcov using the structural connectivity. METHODS: We retrospectively analyzed the FDG-PET and DTI data of 26 healthy controls, 41 patients with Alzheimer's disease (AD), and 30 patients with frontotemporal lobar degeneration (FTLD). Structural connectivity matrix derived from DTI data was introduced as a regularization parameter to assign individual penalties to each potential metabolic connectivity. Leave-one-out cross validation experiments were performed to assess the differential diagnosis ability of structure weighted SICE approach. A few approaches of structure weighted were compared with the standard SICE. RESULTS: Compared to the standard SICE, structural weighting has shown more stable performance in the supervised classification, especially in the differentiation AD vs. FTLD (accuracy of 89-90%, while unweighted SICE only 85%). There was a significant positive relationship between the minimum number of metabolic connection and the robustness of the classification accuracy (r = 0.57, P < 0.001). Shuffling experiments showed significant differences between classification score derived with true structural weighting and those obtained by randomized structure (P < 0.05). CONCLUSION: The structure-weighted sparse estimation can enhance the robustness of metabolic connectivity, which may consequently improve the differentiation of pathological phenotypes.

Engaging the Wisdom of Older Veterans to Enhance VA Healthcare, Research, and Services.

BACKGROUND: Stakeholder engagement helps ensure that research is relevant, clinical innovations are responsive, and healthcare services are patient-centered. OBJECTIVE: Establish and sustain a Veteran engagement board involving older Veterans and caregivers to provide input on aging-related research and clinical demonstration projects. DESIGN AND PARTICIPANTS: The Older Veteran Engagement Team (OVET)-a group of eight Veterans and one caregiver who range in age from 62 to 92-was formed in November 2017 and has met monthly since January 2018. The OVET provides feedback on topics that reflect the foci of the VA Eastern Colorado Geriatric Research Education and Clinical Center (GRECC) (e.g., physical functioning, hearing health, and emotional wellness/mental health). Ongoing evaluation documents the return on investment of Veteran engagement. MAIN MEASURES: The OVET member and provider/investigator meeting evaluations with longitudinal follow-up at 6 and 12 months. RESULTS: Return on investment of Veteran engagement is multi-faceted. For OVET, ROI ranges from grant support to improved healthcare quality/efficiency to social-emotional benefits. To date, funding awards total over $2.3 M for NIH and VA-funded projects to which OVET provided substantive feedback. Documented impacts on healthcare services include reductions in patient wait times, more appropriate utilization of services and increased patient satisfaction. Social-emotional benefits include generativity, as OVET members contribute to improving clinical and community-based supports for other Veterans. The OVET provides an opportunity for older Veterans to share their lived experience with trainees and early career investigators who are preparing for careers serving Veterans. CONCLUSION: The OVET is similar to other established stakeholder engagement groups; team members offer their individual viewpoints at any stage of research, clinical demonstration, or quality improvement projects. The OVET provides a mechanism for the voice of older Veterans and caregivers to shape aspects of individual projects. Importantly, these projects support patient-centered care and promote the characteristics of an age-friendly healthcare system.

[Experiences with digital care of patients with chronic and acute lung diseases during the SARS-CoV-2 pandemic]. / Erfahrungen mit der digitalen Versorgung von Patienten mit chronischen und akuten Lungenerkrankungen während der SARS-CoV-2-Pandemie.

BACKGROUND: Management of patients with respiratory disorders, such as asthma or chronic obstructive pulmonary disease (COPD), became challenging during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic due to infection prevention measures. To maintain care, a remote monitoring program was initiated, comprising a smartphone app and a Bluetooth spirometry device. OBJECTIVE: To assess patient- and physician-related experience with remote monitoring. MATERIAL AND METHODS: Structured questionnaires were developed to rate experiences from the patient or physician perspective on six-level Likert scales. Interactions between patients and physicians via the digital platform and overall utilization was analyzed. RESULTS: A total of 745 patients with asthma, COPD, post-coronavirus disease 2019 (COVID-19) and other respiratory diseases were enrolled from 31 centers in Germany. Mean follow-up was 49.4 ± 12.6 weeks. Each participant submitted on average 289 measurements. Patient-reported experience with the remote monitoring program was positive, with the highest satisfaction reported for "Experience with home measurement" (1.4 ± 0.5; 99% positive), followed by "Communication/interaction" (1.8 ± 0.9; 83% positive) and "Overall satisfaction with program" (1.8 ± 0.8; 87% positive). In all, 70% reported subjective quality of life improvements related to participation in the program. Physician satisfaction with the program was also high with a mean rating of 2.2 ± 1.2. DISCUSSION: App-based remote monitoring was successfully implemented in routine care during the SARS-CoV­2 pandemic and demonstrated potential for improvements in care. Patient-relevant experience was positive in all dimensions and remote monitoring was well accepted. Physicians who participated in the program also expressed positive experiences, as demonstrated by a high level of interaction with the platform and positive evaluations of effects from the program.

Multiple instance learning detects peripheral arterial disease from high-resolution color fundus photography.

Peripheral arterial disease (PAD) is caused by atherosclerosis and is a common disease of the elderly leading to excess morbidity and mortality. Early PAD diagnosis is important, as the only available causal therapy is addressing risk factors like smoking, hypercholesterolemia or hypertension. However, current diagnostic techniques often do not detect early stages of PAD. We theorize that PAD's underlying cause atherosclerosis can be detected on color fundus photography (CFP) images with a convolutional neural network architecture, which might aid earlier PAD diagnosis and improve disease monitoring. In this explorative study a deep attention-based Multiple Instance Learning (MIL) architecture is used to capture retinal imaging biomarkers on CFP images of 135 examinations. To capture subtle variations in vascular structures, higher image resolution can be utilized by partitioning the CFP into patches. Our architecture converts each patch into a feature vector, and determines its relative importance via an automatically computed attention weight. Our best model achieves an ROC AUC score of 0.890. Visualizing these attention weights provides insights about the network's decision and suggests ocular involvement in PAD. Statistical analysis confirms that the optic disc and the temporal arcades are weighted significantly higher (p < 0.001) than retinal background. Our results support the feasibility of detecting the presence of PAD with a modern deep learning approach.

German regional variation of acute and high oral corticosteroid use for asthma.

OBJECTIVE: To improve understanding of real-world asthma treatment and inform physician education, we evaluated regional variation in asthma prevalence and oral corticosteroid (OCS) use across Germany. METHODS: We developed a machine learning gradient-boosted tree model with IMS® Disease Analyzer electronic medical records, which cover 3% of German patients. This model had a 91% accuracy in predicting the presence of asthma and chronic obstructive pulmonary disease. We applied the model to the IMS® Longitudinal Prescription database, with 82% national coverage, to classify patients receiving treatment for airflow obstruction from October 2017-September 2018 in 63 regions in Germany. RESULTS: Of 2.4 million individuals under statutory health insurance predicted to have asthma, 13.7%, 18.7%, 36.5%, 29.4%, and 1.7% received treatment classified as Global Initiative for Asthma (GINA) Steps 1, 2, 3, 4, and 5, respectively. Approximately 7-15% of those at GINA Steps 1-4 and 35% at Step 5 treatment received ≥1 acute OCS prescription (duration <10 days). Of patients receiving GINA Steps 1-4 and Step 5 treatments, 1-3% and 86%, respectively, received ≥1 high-dosage OCS prescription. Cumulative OCS dosage and percentages of patients receiving OCS differed substantially across regions, and regions with lower OCS use had greater use of biologic therapies. CONCLUSIONS: Both acute and high OCS use varied regionally across Germany, with overall use suggesting patients are considerable risk of adverse effects and long-term health consequences.Supplemental data for this article can be accessed at publisher's website.

Parcellation-Free prediction of task fMRI activations from dMRI tractography.

The relationship between brain structure and function plays a crucial role in cognitive and clinical neuroscience. We present a supervised machine learning based approach that captures this relationship by predicting the spatial extent of activations that are observed with task based functional Magnetic Resonance Imaging (fMRI) from the local white matter connectivity, as reflected in diffusion MRI (dMRI) tractography. In particular, we explore three different feature representations of local connectivity patterns that do not require a pre-defined parcellation of cortical and subcortical structures. Instead, they employ cluster-based Bag of Features, Gaussian Mixture Models, and Fisher vectors. We demonstrate that our framework can be used to test the statistical significance of structure-function relationships, compare it to parcellation-based and group-average benchmarks, and propose an algorithm for visualizing our chosen feature representations that permits a neuroanatomical interpretation of our results.

[Smartphone-based fundus imaging: applications and adapters]. / Smartphone-basierte Fundusfotografie: Anwendungen und Adapter.

BACKGROUND: Smartphone-based fundus imaging (SBFI) is an innovative and low-cost alternative for color fundus photography. Since the first reports on this topic more than 10 years ago a large number of studies on different adapters and clinical applications have been published. OBJECTIVE: The aim of this review article is to provide an overview on the development of SBFI and adapters and clinical applications published so far. MATERIAL AND METHODS: A literature search was performed using the MEDLINE and Science Citation Index Expanded databases without time restrictions. RESULTS: Overall, 11 adapters were included and compared in terms of exemplary image material, field of view, acquisition costs, weight, software, application range, smartphone compatibility and certification. Previously published SBFI applications are screening for diabetic retinopathy, glaucoma and retinopathy of prematurity as well as the application in emergency medicine, pediatrics and medical education/teaching. Image quality of conventional retinal cameras is in general superior to SBFI. First approaches on automatic detection of diabetic retinopathy through SBFI are promising and the use of automatic image processing algorithms enables the generation of wide-field image montages. CONCLUSION: SBFI is a versatile, mobile, low-cost alternative to conventional equipment for color fundus photography. In addition, it facilitates the delegation of ophthalmological examinations to assistance personnel in telemedical settings, could simplify retinal documentation, improve teaching, and improve ophthalmological care, particularly in countries with low and middle incomes.

Weekly vs. 3-weekly paclitaxel, carboplatin, and cetuximab (PCC) in recurrent/metastatic head and neck cancer.

The combination of paclitaxel, carboplatin and cetuximab (PCC) is efficacious in patients with recurrent/metastatic (R/M) squamous cell carcinoma of the head and neck (SCCHN). The current study assessed the incidence of grade 3/4 (G3/4) toxicity for patients receiving weekly or 3-weekly PCC for R/M SCCHN. The present single-institution, retrospective analysis included 74 patients who received weekly [paclitaxel 45 mg/m2 and carboplatin area under the curve (AUC), 1.5] or 3-weekly (paclitaxel 175 mg/m2 and carboplatin AUC, 5) PCC. For each regimen, cetuximab was administered at 400 mg/m2 for the first week, after which the dosage was reduced to 250 mg/m2 weekly until disease progression occurred. Toxicity was assessed according to the Common Terminology Criteria for Adverse Events v4.03, and response to therapy was determined using computed tomography every 12 weeks. The results revealed that 26 patients (35%) received weekly PCC and 48 patients (65%) received PCC every 3 weeks. A total of 6 (25%) patients receiving weekly PCC experienced G3/4 toxicity compared with 30 (66%) patients that received PCC every 3 weeks (odds ratio, 0.18; 95% confidence interval, 0.05-0.64; P=0.01). The most common G3/4 side effects were neutropenia (8 vs. 53%), anemia (15 vs. 32%) and fatigue (3 vs. 10%). The incidence of G3/4 toxicity or any grade toxicity requiring dose modification or discontinuation was 74 vs. 77%, respectively. The overall response rate was 39% with weekly PCC compared with 27% in those receiving PCC every 3 weeks. The 1-year progression-free and overall survival rates were 27 and 46% for patients receiving weekly PCC, and 13 and 44% for patients receiving PCC every 3 weeks. Weekly PCC had a reduced risk of G3/4 toxicity when compared with PCC administered every 3 weeks. Considering the improved tolerance of weekly PCC, this regimen should be considered for older patients and patients being treated with second-line chemotherapy.

Rab6b localizes to the Golgi complex in murine macrophages and promotes tumor necrosis factor release in response to mycobacterial infection.

The proinflammatory cytokine tumor necrosis factor (TNF) plays a central role in the host control of mycobacterial infections. Expression and release of TNF are tightly regulated, yet the molecular mechanisms that control the release of TNF by mycobacteria-infected host cells, in particular macrophages, are incompletely understood. Rab GTPases direct the transport of intracellular membrane-enclosed vesicles and are important regulators of macrophage cytokine secretion. Rab6b is known to be predominantly expressed in the brain where it functions in retrograde transport and anterograde vesicle transport for exocytosis. Whether it executes similar functions in the context of immune responses is unknown. Here we show that Rab6b is expressed by primary mouse macrophages, where it localized to the Golgi complex. Infection with Mycobacterium bovis bacille Calmette-Guérin (BCG) resulted in dynamic changes in Rab6b expression in primary mouse macrophages in vitro as well as in organs from infected mice in vivo. We further show that Rab6b facilitated TNF release by M. bovis BCG-infected macrophages, in the absence of discernible impact on Tnf messenger RNA and intracellular TNF protein expression. Our observations identify Rab6b as a positive regulator of M. bovis BCG-induced TNF trafficking and secretion by macrophages and positions Rab6b among the molecular machinery that orchestrates inflammatory cytokine responses by macrophages.

Evaluating a Machine Learning Tool for the Classification of Pathological Uptake in Whole-Body PSMA-PET-CT Scans.

The importance of machine learning (ML) in the clinical environment increases constantly. Differentiation of pathological from physiological tracer-uptake in positron emission tomography/computed tomography (PET/CT) images is considered time-consuming and attention intensive, hence crucial for diagnosis and treatment planning. This study aimed at comparing and validating supervised ML algorithms to classify pathological uptake in prostate cancer (PC) patients based on prostate-specific membrane antigen (PSMA)-PET/CT. Retrospective analysis of 68Ga-PSMA-PET/CTs of 72 PC patients resulted in a total of 77 radiomics features from 2452 manually delineated hotspots for training and labeled pathological (1629) or physiological (823) as ground truth (GT). As the held-out test dataset, 331 hotspots (path.:128, phys.: 203) were delineated in 15 other patients. Three ML classifiers were trained and ranked to assess classification performance. As a result, a high overall average performance (area under the curve (AUC) of 0.98) was achieved, especially to detect pathological uptake (0.97 mean sensitivity). However, there is still room for improvement to detect physiological uptake (0.82 mean specificity), especially for glands. The ML algorithm applied to manually delineated lesions predicts hotspot labels with high accuracy on unseen data and may be an important tool to assist in clinical diagnosis.