Clinical Pharmacist Counselling Improves Long-term Medication Safety and Patient-reported Outcomes in Anti-TNF-treated Patients With Inflammatory Bowel Diseases: The Prospective, Randomized AdPhaNCED Trial.

BACKGROUND: Antitumor necrosis factor (anti-TNF) antibody treatment has led to marked improvements in the management of patients with inflammatory bowel diseases (IBDs). Nevertheless, anti-TNF therapy is associated with potential adverse drug reactions (ADRs). Our prospective, randomized trial investigated the effect of intensified clinical pharmacist counselling in a multidisciplinary team on medication safety in anti-TNF-treated IBD patients. METHODS: Patients with IBD with ongoing anti-TNF treatment were enrolled in our tertiary center AdPhaNCED trial and randomized to either receive conventional standard of care (control group) or additional clinical pharmacist counselling (intervention group) over 12 months. The primary end point consisted of the number and severity of ADRs associated with anti-TNF therapy. Secondary end points included patient satisfaction with medication information and medication safety. RESULTS: One hundred twenty-seven IBD patients were included in this study. Anti-TNF-related ADRs were significantly lower in the intervention compared with the control group (0.20 vs 0.32 [mean] ADR/patient/month, P = .006) after 12 months. The risk of more severe ADRs (Common Terminology Criteria for Adverse Events [CTCAE] grade ≥2) was significantly higher in the control compared with the intervention group (hazard ratio, 0.34; P = .001). The probability of ADR resolution (hazard ratio, 2.02; P < .001) and patient satisfaction with medication information (14.82 vs 11.60; P < .001) were significantly higher in the intervention group compared with the control group. CONCLUSIONS: Our study results demonstrate that intensified pharmacist counselling significantly reduces the occurrence and severity of therapy-related ADRs and improves patient satisfaction. Clinical pharmacists should therefore be part of a holistic approach to IBD care delivered by a multidisciplinary team.

The prospective, randomized AdPhaNCED trial demonstrated that anti-TNF-treated IBD patients had diminished and less severe drug-related adverse reactions and higher patient satisfaction when they received intensified pharmacist counselling in comparison with conventional standard of care over 12 months.

Predictive uncertainty in deep learning-based MR image reconstruction using deep ensembles: Evaluation on the fastMRI data set.

PURPOSE: To estimate pixel-wise predictive uncertainty for deep learning-based MR image reconstruction and to examine the impact of domain shifts and architecture robustness. METHODS: Uncertainty prediction could provide a measure for robustness of deep learning (DL)-based MR image reconstruction from undersampled data. DL methods bear the risk of inducing reconstruction errors like in-painting of unrealistic structures or missing pathologies. These errors may be obscured by visual realism of DL reconstruction and thus remain undiscovered. Furthermore, most methods are task-agnostic and not well calibrated to domain shifts. We propose a strategy that estimates aleatoric (data) and epistemic (model) uncertainty, which entails training a deep ensemble (epistemic) with nonnegative log-likelihood (aleatoric) loss in addition to the conventional applied losses terms. The proposed procedure can be paired with any DL reconstruction, enabling investigations of their predictive uncertainties on a pixel level. Five different architectures were investigated on the fastMRI database. The impact on the examined uncertainty of in-distributional and out-of-distributional data with changes to undersampling pattern, imaging contrast, imaging orientation, anatomy, and pathology were explored. RESULTS: Predictive uncertainty could be captured and showed good correlation to normalized mean squared error. Uncertainty was primarily focused along the aliased anatomies and on hyperintense and hypointense regions. The proposed uncertainty measure was able to detect disease prevalence shifts. Distinct predictive uncertainty patterns were observed for changing network architectures. CONCLUSION: The proposed approach enables aleatoric and epistemic uncertainty prediction for DL-based MR reconstruction with an interpretable examination on a pixel level.

Deep learning-based age estimation from clinical Computed Tomography image data of the thorax and abdomen in the adult population.

Aging is an important risk factor for disease, leading to morphological change that can be assessed on Computed Tomography (CT) scans. We propose a deep learning model for automated age estimation based on CT- scans of the thorax and abdomen generated in a clinical routine setting. These predictions could serve as imaging biomarkers to estimate a "biological" age, that better reflects a patient's true physical condition. A pre-trained ResNet-18 model was modified to predict chronological age as well as to quantify its aleatoric uncertainty. The model was trained using 1653 non-pathological CT-scans of the thorax and abdomen of subjects aged between 20 and 85 years in a 5-fold cross-validation scheme. Generalization performance as well as robustness and reliability was assessed on a publicly available test dataset consisting of thorax-abdomen CT-scans of 421 subjects. Score-CAM saliency maps were generated for interpretation of model outputs. We achieved a mean absolute error of 5.76 ± 5.17 years with a mean uncertainty of 5.01 ± 1.44 years after 5-fold cross-validation. A mean absolute error of 6.50 ± 5.17 years with a mean uncertainty of 6.39 ± 1.46 years was obtained on the test dataset. CT-based age estimation accuracy was largely uniform across all age groups and between male and female subjects. The generated saliency maps highlighted especially the lumbar spine and abdominal aorta. This study demonstrates, that accurate and generalizable deep learning-based automated age estimation is feasible using clinical CT image data. The trained model proved to be robust and reliable. Methods of uncertainty estimation and saliency analysis improved the interpretability.

Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities.

BACKGROUND: Machine learning (ML) is considered an important technology for future data analysis in health care. METHODS: The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers. RESULTS AND CONCLUSION: In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future. KEY POINTS: · ML provides a viable clinical solution for the reconstruction, processing, and analysis of hybrid imaging obtained from MRI, CT, and PET..

Quantification of Facial Fat Compartment Variations: A Three-Dimensional Morphometric Analysis of the Cheek.

BACKGROUND: Cheek contour is a main determinant of facial attractiveness. The aim of this study was to evaluate the relationship between age, sex, and body mass index (BMI) and cheek fat volume in a large cohort to better understand and treat facial aging. METHODS: The authors performed a retrospective review of the archives of the Department of Diagnostic and Interventional Radiology, University Hospital of Tübingen. Epidemiologic data and medical history were assessed. Cheek superficial and deep fat compartment volumes were measured on magnetic resonance images. Statistical analyses were performed using SPSS and SAS statistical software. RESULTS: A total of 87 patients with a mean age of 46 years (range, 18 to 81 years) were included. The superficial and deep fat compartment volumes of the cheek increased with BMI ( P < 0.001 and P = 0.005), but there was no significant relationship between age and volume. The ratio of superficial versus deep fat did not change with age. No significant difference of the superficial or deep fat compartments was found between men and women in a regression analysis ( P = 0.931 and P = 0.057). CONCLUSIONS: Cheek fat volume measurements on magnetic resonance imaging scans using reconstruction software suggest that the fat volume of the cheek increases with BMI but does not change significantly with age. Further studies are needed to elucidate the role of age-related changes of bone structures or sagging of the fat compartments.

Estimating uncertainty in read-out patterns: Application to controls-based denoising and voxel-based morphometry patterns in neurodegenerative and neuropsychiatric diseases.

Quantifying pathology-related patterns in patient data with pattern expression score (PES) is a standard approach in medical image analysis. In order to estimate the PES error, we here propose to express the uncertainty contained in read-out patterns in terms of the expected squared Euclidean distance between the read-out pattern and the unknown "true" pattern (squared standard error of the read-out pattern, SE2 ). Using SE2 , we predicted and optimized the net benefit (NBe) of the recently suggested method controls-based denoising (CODE) by weighting patterns of nonpathological variance (NPV). Multi-center MRI (1192 patients with various neurodegenerative and neuropsychiatric diseases, 1832 healthy controls) were analysed with voxel-based morphometry. For each pathology, accounting for SE2 , NBe correctly predicted classification improvement and allowed to optimize NPV pattern weights. Using these weights, CODE improved classification performances in all but one analyses, for example, for prediction of conversion to Alzheimer's disease (AUC 0.81 vs. 0.75, p = .01), diagnosis of autism (AUC 0.66 vs. 0.60, p < .001), and of major depressive disorder (AUC 0.62 vs. 0.50, p = .03). We conclude that the degree of uncertainty in a read-out pattern should generally be reported in PES-based analyses and suggest using weighted CODE as a complement to PES-based analyses.

Evaluating the Use of Neonatal Colonization Screening for Empiric Antibiotic Therapy of Sepsis and Pneumonia.

(1) Background: Since 2013, weekly screening for multidrug-resistant Gram-negative (MDRGN) bacteria has been performed in German neonatal intensive care units (NICU). National guidelines recommend considering these colonization analyses for antibiotic treatment regimens. Our retrospective single center study provides insight into the clinical dichotomy of bacterial colonization and infection rates in neonates. (2) Methods: We analyzed microbiological data of neonates admitted to our tertiary level NICU over nine years. Colonization with MDRGN/Serratia marcescens (SERMA) was compared to microbiological findings in sepsis and pneumonia. (3) Results: We analyzed 917 blood and 1799 tracheal aspirate samples. After applying criteria from the Nosocomial Infection Surveillance for Neonates (NEO-KISS), we included 52 and 55 cases of sepsis and pneumonia, respectively; 19.2% of sepsis patients and 34.5% of pneumonia patients had a prior colonization with MDRGN bacteria or SERMA. In these patients, sepsis was not attributable to MDRGN bacteria yet one SERMA, while in pneumonias, ten MDRGN bacteria and one SERMA were identified. We identified late-onset pneumonia and cesarean section as risk factors for MDRGN/SERMA acquisition. (4) Conclusions: Colonization screening is a useful tool for hygiene surveillance. However, our data suggest that consideration of colonization with MDRGN/SERMA might promote extensive use of last resort antibiotics in neonates.

Self-supervised contrastive learning with random walks for medical image segmentation with limited annotations.

Medical image segmentation has seen significant progress through the use of supervised deep learning. Hereby, large annotated datasets were employed to reliably segment anatomical structures. To reduce the requirement for annotated training data, self-supervised pre-training strategies on non-annotated data were designed. Especially contrastive learning schemes operating on dense pixel-wise representations have been introduced as an effective tool. In this work, we expand on this strategy and leverage inherent anatomical similarities in medical imaging data. We apply our approach to the task of semantic segmentation in a semi-supervised setting with limited amounts of annotated volumes. Trained alongside a segmentation loss in one single training stage, a contrastive loss aids to differentiate between salient anatomical regions that conform to the available annotations. Our approach builds upon the work of Jabri et al. (2020), who proposed cyclical contrastive random walks (CCRW) for self-supervision on palindromes of video frames. We adapt this scheme to operate on entries of paired embedded image slices. Using paths of cyclical random walks bypasses the need for negative samples, as commonly used in contrastive approaches, enabling the algorithm to discriminate among relevant salient (anatomical) regions implicitly. Further, a multi-level supervision strategy is employed, ensuring adequate representations of local and global characteristics of anatomical structures. The effectiveness of reducing the amount of required annotations is shown on three MRI datasets. A median increase of 8.01 and 5.90 pp in the Dice Similarity Coefficient (DSC) compared to our baseline could be achieved across all three datasets in the case of one and two available annotated examples per dataset.

Diagnostic Utility of Interleukin-6 in Early-Onset Sepsis among Term Newborns: Impact of Maternal Risk Factors and CRP Evaluation.

(1) Background: Interleukin-6 (IL-6) levels act as an early infection marker preceding C-reactive protein (CRP) elevation. This study seeks to analyze IL-6 behavior in suspected early-onset sepsis (EOS) cases among term newborns, comparing it to that of CRP and evaluating IL-6's diagnostic utility. We also aim to assess the impact of maternal risk factors on EOS in term newborns, quantifying their influence for informed decision making. (2) Methods: The retrospective data analysis included 533 term newborns who were admitted to our hospital because of suspected EOS. IL-6, CRP, and the impact of maternal risk factors were analyzed in the context of EOS using binomial test, Chi-squared test, logistic and linear regression. (3) Results: In the cases of EOS, both IL-6 and CRP were elevated. The increase in CRP can be predicted by the initial increase in IL-6 levels. Among the assessed risk factors, intrapartum maternal fever (adjusted odds ratio 18.1; 95% CI (1.7-4.1)) was identified as the only risk factor significantly associated with EOS. (4) Conclusions: Employing IL-6 as an early infection marker enhanced EOS diagnostic precision due to its detectable early rise. However, caution is required, as elevations in IL-6 and CRP levels do not exclusively indicate EOS. Increased CRP levels in healthy newborns with maternal risk factors may be attributed to dynamics of vaginal labor.

Women-only versus mixed-gender groups in multimodal, day clinic treatment of trauma-related disorders.

Background: In the German healthcare system benefits include inpatient and day clinic psychotherapy of trauma-related disorders, which are often provided in gender specific groups. Despite this widespread practice, the actual impact and potential outcome have been studied relatively scarcely and are still open to debate. The present study therefore examines whether the therapeutic relationships in the group vary depending on the group composition and whether this has an effect on therapy outcome. Methods: 66 patients (N = 55 women) were treated in our multimodal, day clinic groupbased treatment program and filled out symptom questionnaires (ETI, BDI-II, PHQ-15, Questionnaire on social support) at admission and discharge and the Group Questionnaire (GQ; including subscales positive bonding, positive working and negative relationships) at the end of each of the eight weeks of treatment. Patients took part in five women-only and six mixed-gender groups and underwent therapy in a closed group format, so that they began and ended therapy within the same group. All patients were diagnosed with trauma-related disorders, which included PTSD, cPTSD, depression, somatoform disorders and further mental diseases. Outcome data were examined using functional regression analysis and linear mixed models. Results: In women-only groups, positive bonding and positive working relationship were significantly higher than in mixed-gender groups. Negative relationship ratings were significantly lower in women-only groups, especially in the middle phase of treatment. However, no effect of an exclusively female group composition on treatment outcome was found, except for the higher increase of perceived social support for patients treated in women-only groups. Conclusions: Group relationships were considered more pleasant in women-only groups than in mixed-gender groups, but this had no effect on the symptom-based therapy outcome. To enhance feelings of safety and comfort, traumatized women should have the possibility to choose whether to be treated in a women-only or a mixed-gender group.

[The Economic Evaluation of the GLICEMIA 2.0 Study as an Example of a Complex Intervention]. / Die gesundheitsökonomische Evaluation der GLICEMIA 2.0-Studie als Beispiel für eine komplexe Intervention.

BACKGROUND: A piggyback approach was used to evaluate the cost-effectiveness of the prevention program delivered at the point of care pharmacy in the GLICEMIA 2.0 study that sought to guide participants in the intervention group to improved glycemic control in type 2 diabetes with sustained incentivization of lifestyle changes, therapeutic compliance, and adherence. The control group received passive medication management and monitoring. METHODS: Primary endpoint of the GLICEMIA 2.0 study was the stabilization of HbA1c values. For health economic evaluation, incremental differences in output changes were examined, defined as the difference in the distribution of the HbA1c values between both groups over time. Direct program costs and anticipated indirect costs of service utilization were used as cost parameters. A net monetary benefit approach was used to validate cost-effectiveness thresholds via the formation of ICER values. RESULTS: The intervention group had significantly higher reductions in HbA1c-values. Risk stratification of initial HbA1c showed (short-term) cost effectiveness for initially higher HbA1c values. Due to the limited study period, no long-term differences in medical resource utilization could be assessed. CONCLUSION: The GLICEMIA program indicates considerable effectiveness potentials, especially for high-risk patients. The study design seems to have assisted the intervention group's adherence in contrast to the control group. Detailed impacts within the complex intervention could not be validated due to restrictions of the study design as a complex intervention. Overall, statements about effect sustainability and further utilization rates progressions are limited due to a lack of follow-up.

A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions.

We describe a publicly available dataset of annotated Positron Emission Tomography/Computed Tomography (PET/CT) studies. 1014 whole body Fluorodeoxyglucose (FDG)-PET/CT datasets (501 studies of patients with malignant lymphoma, melanoma and non small cell lung cancer (NSCLC) and 513 studies without PET-positive malignant lesions (negative controls)) acquired between 2014 and 2018 were included. All examinations were acquired on a single, state-of-the-art PET/CT scanner. The imaging protocol consisted of a whole-body FDG-PET acquisition and a corresponding diagnostic CT scan. All FDG-avid lesions identified as malignant based on the clinical PET/CT report were manually segmented on PET images in a slice-per-slice (3D) manner. We provide the anonymized original DICOM files of all studies as well as the corresponding DICOM segmentation masks. In addition, we provide scripts for image processing and conversion to different file formats (NIfTI, mha, hdf5). Primary diagnosis, age and sex are provided as non-imaging information. We demonstrate how this dataset can be used for deep learning-based automated analysis of PET/CT data and provide the trained deep learning model.

Development of a Hybrid-Imaging-Based Prognostic Index for Metastasized-Melanoma Patients in Whole-Body 18F-FDG PET/CT and PET/MRI Data.

Besides tremendous treatment success in advanced melanoma patients, the rapid development of oncologic treatment options comes with increasingly high costs and can cause severe life-threatening side effects. For this purpose, predictive baseline biomarkers are becoming increasingly important for risk stratification and personalized treatment planning. Thus, the aim of this pilot study was the development of a prognostic tool for the risk stratification of the treatment response and mortality based on PET/MRI and PET/CT, including a convolutional neural network (CNN) for metastasized-melanoma patients before systemic-treatment initiation. The evaluation was based on 37 patients (19 f, 62 ± 13 y/o) with unresectable metastasized melanomas who underwent whole-body 18F-FDG PET/MRI and PET/CT scans on the same day before the initiation of therapy with checkpoint inhibitors and/or BRAF/MEK inhibitors. The overall survival (OS), therapy response, metastatically involved organs, number of lesions, total lesion glycolysis, total metabolic tumor volume (TMTV), peak standardized uptake value (SULpeak), diameter (Dmlesion) and mean apparent diffusion coefficient (ADCmean) were assessed. For each marker, a Kaplan−Meier analysis and the statistical significance (Wilcoxon test, paired t-test and Bonferroni correction) were assessed. Patients were divided into high- and low-risk groups depending on the OS and treatment response. The CNN segmentation and prediction utilized multimodality imaging data for a complementary in-depth risk analysis per patient. The following parameters correlated with longer OS: a TMTV < 50 mL; no metastases in the brain, bone, liver, spleen or pleura; ≤4 affected organ regions; no metastases; a Dmlesion > 37 mm or SULpeak < 1.3; a range of the ADCmean < 600 mm2/s. However, none of the parameters correlated significantly with the stratification of the patients into the high- or low-risk groups. For the CNN, the sensitivity, specificity, PPV and accuracy were 92%, 96%, 92% and 95%, respectively. Imaging biomarkers such as the metastatic involvement of specific organs, a high tumor burden, the presence of at least one large lesion or a high range of intermetastatic diffusivity were negative predictors for the OS, but the identification of high-risk patients was not feasible with the handcrafted parameters. In contrast, the proposed CNN supplied risk stratification with high specificity and sensitivity.

Apparent Diffusion Coefficient (ADC) Histogram Analysis in Parotid Gland Tumors: Evaluating a Novel Approach for Differentiation between Benign and Malignant Parotid Lesions Based on Full Histogram Distributions.

The aim of this study was to assess the diagnostic value of ADC distribution curves for differentiation between benign and malignant parotid gland tumors and to compare with mean ADC values. 73 patients with parotid gland tumors underwent head-and-neck MRI on a 1.5 Tesla scanner prior to surgery and histograms of ADC values were extracted. Histopathological results served as a reference standard for further analysis. ADC histograms were evaluated by comparing their similarity to a reference distribution using Chi2-test-statistics. The assumed reference distribution for benign and malignant parotid gland lesions was calculated after pooling the entire ADC data. In addition, mean ADC values were determined. For both methods, we calculated and compared the sensitivity and specificity between benign and malignant parotid gland tumors and three subgroups (pleomorphic adenoma, Warthin tumor, and malignant lesions), respectively. Moreover, we performed cross-validation (CV) techniques to estimate the predictive performance between ADC distributions and mean values. Histopathological results revealed 30 pleomorphic adenomas, 22 Warthin tumors, and 21 malignant tumors. ADC histogram distribution yielded a better specificity for detection of benign parotid gland lesions (ADChistogram: 75.0% vs. ADCmean: 71.2%), but mean ADC values provided a higher sensitivity (ADCmean: 71.4% vs. ADChistogram: 61.9%). The discrepancies are most pronounced in the differentiation between malignant and Warthin tumors (sensitivity ADCmean: 76.2% vs. ADChistogram: 61.9%; specificity ADChistogram: 81.8% vs. ADCmean: 68.2%). Using CV techniques, ADC distribution revealed consistently better accuracy to differentiate benign from malignant lesions ("leave-one-out CV" accuracy ADChistogram: 71.2% vs. ADCmean: 67.1%). ADC histogram analysis using full distribution curves is a promising new approach for differentiation between primary benign and malignant parotid gland tumors, especially with respect to the advantage in predictive performance based on CV techniques.

Head-to-head comparison of biparametric versus multiparametric MRI of the prostate before robot-assisted transperineal fusion prostate biopsy.

PURPOSE: Prostate biparametric magnetic resonance imaging (bpMRI) including T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) might be an alternative to multiparametric MRI (mpMRI, including dynamic contrast imaging, DCE) to detect and guide targeted biopsy in patients with suspected prostate cancer (PCa). However, there is no upgrading peripheral zone PI-RADS 3 to PI-RADS 4 without DCE in bpMRI. The aim of this study was to evaluate bpMRI against mpMRI in biopsy-naïve men with elevated prostate-specific antigen (PSA) scheduled for robot-assisted-transperineal fusion-prostate biopsy (RA-TB). METHODS: Retrospective single-center-study of 563 biopsy-naïve men (from 01/2015 to 09/2018, mean PSA 9.7 ± 6.5 ng/mL) with PI-RADSv2.1 conform mpMRI at 3 T before RA-TB. Clinically significant prostate cancer (csPCa) was defined as ISUP grade ≥ 2 in any core. Two experienced readers independently evaluated images according to PI-RADSv2.1 criteria (separate readings for bpMRI and mpMRI sequences, 6-month interval). Reference standard was histology from RA-TB. RESULTS: PI-RADS 2 was scored in 5.1% of cases (3.4% cancer/3.4% csPCa), PI-RADS 3 in 16.9% (32.6%/3.2%), PI-RADS 4 in 57.6% (66.1%/58.3%) and PI-RADS 5 in 20.4% of cases (79.1%/74.8%). For mpMRI/bpMRI test comparison, sensitivity was 99.0%/97.1% (p < 0.001), specificity 47.5%/61.2% (p < 0.001), PPV 69.5%/75.1% (p < 0.001) and NPV 97.6%/94.6% (n.s.). csPCa was considered gold standard. 35 cases without cancer were upgraded to PI-RADS 4 (mpMRI) and six PI-RADS 3 cases with csPCa were not upgraded (bpMRI). CONCLUSION: In patients planned for RA-TB with elevated PSA and clinical suspicion for PCa, specificity was higher in bpMRI vs. mpMRI, which could solve constrains regarding time and contrast agent.

Mixture density networks for the indirect estimation of reference intervals.

BACKGROUND: Reference intervals represent the expected range of physiological test results in a healthy population and are essential to support medical decision making. Particularly in the context of pediatric reference intervals, where recruitment regulations make prospective studies challenging to conduct, indirect estimation strategies are becoming increasingly important. Established indirect methods enable robust identification of the distribution of "healthy" samples from laboratory databases, which include unlabeled pathologic cases, but are currently severely limited when adjusting for essential patient characteristics such as age. Here, we propose the use of mixture density networks (MDN) to overcome this problem and model all parameters of the mixture distribution in a single step. RESULTS: Estimated reference intervals from varying settings with simulated data demonstrate the ability to accurately estimate latent distributions from unlabeled data using different implementations of MDNs. Comparing the performance with alternative estimation approaches further highlights the importance of modeling the mixture component weights as a function of the input in order to avoid biased estimates for all other parameters and the resulting reference intervals. We also provide a strategy to generate partially customized starting weights to improve proper identification of the latent components. Finally, the application on real-world hemoglobin samples provides results in line with current gold standard approaches, but also suggests further investigations with respect to adequate regularization strategies in order to prevent overfitting the data. CONCLUSIONS: Mixture density networks provide a promising approach capable of extracting the distribution of healthy samples from unlabeled laboratory databases while simultaneously and explicitly estimating all parameters and component weights as non-linear functions of the covariate(s), thereby allowing the estimation of age-dependent reference intervals in a single step. Further studies on model regularization and asymmetric component distributions are warranted to consolidate our findings and expand the scope of applications.

T2 mapping for the characterization of prostate lesions.

PURPOSE: Purpose of this study is to evaluate the diagnostic accuracy of quantitative T2/ADC values in differentiating between PCa and lesions showing non-specific inflammatory infiltrates and atrophy, features of chronic prostatitis, as the most common histologically proven differential diagnosis. METHODS: In this retrospective, single-center cohort study, we analyzed 55 patients suspected of PCa, who underwent mpMRI (3T) including quantitative T2 maps before robot-assisted mpMRI-TRUS fusion prostate biopsy. All prostate lesions were scored according to PI-RADS v2.1. Regions of interest (ROIs) were annotated in focal lesions and normal prostate tissue. Quantitative mpMRI values from T2 mapping and ADC were compared using two-tailed t tests. Receiver operating characteristic curves (ROCs) and cutoff were calculated to differentiate between PCa and chronic prostatitis. RESULTS: Focal lesions showed significantly lower ADC and T2 mapping values than normal prostate tissue (p < 0.001). PCa showed significantly lower ADC and T2 values than chronic prostatitis (p < 0.001). ROC analysis revealed areas under the receiver operating characteristic curves (AUCs) of 0.85 (95% CI 0.74-0.97) for quantitative ADC values and 0.84 (95% CI 0.73-0.96) for T2 mapping. A significant correlation between ADC and T2 values was observed (r = 0.70; p < 0.001). CONCLUSION: T2 mapping showed high diagnostic accuracy for differentiating between PCa and chronic prostatitis, comparable to the performance of ADC values.

Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities.

BACKGROUND: Machine learning (ML) is considered an important technology for future data analysis in health care. METHODS: The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers. RESULTS AND CONCLUSION: In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future. KEY POINTS: · ML provides a viable clinical solution for the reconstruction, processing, and analysis of hybrid imaging obtained from MRI, CT, and PET.. CITATION FORMAT: · Küstner T, Hepp T, Seith F. Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities. Fortschr Röntgenstr 2022; 194: 605 - 612.

Efficacy and tolerability of chemosaturation in patients with hepatic metastases from uveal melanoma.

BACKGROUND: Patients with hepatic metastatic uveal melanoma still have a poor outcome. PURPOSE: To evaluate overall survival (OS), progression-free survival (PFS), and response predictors in these patients treated with chemosaturation by percutaneous hepatic perfusion with melphalan (CS-PHP). MATERIAL AND METHODS: Between June 2015 and March 2020, a total of 29 patients (median age 69.7 years; age range 30-81 years; 60% women; median BMI 25.7 kg/m2; range 18.7-35.3kg/m2; 1-6 procedures per patient) were treated with 53 CS-PHPs. All patients received cross-sectional imaging for initial and follow-up examinations. Baseline tumor load, extrahepatic tumor load, tumor response, PFS, and OS were assessed. Non-parametric statistics were used. RESULTS: After the initial CS-PHP, a partial response was observed in 11 patients (41%), stable disease in 12 patients (44%) and progressive disease in 4 patients (15%); two patients died before the response was evaluated. After initial CS-PHP, median OS was 12.9 ± 7.4 months and median PFS was 7.1 ± 7.4 months. OS after one year was 50%. After the second CS-PHP, median PFS was 7.9 ± 5.7 months. Seven patients had a liver tumor burden >25%, associated with a significantly shorter OS (6.0 ± 2.4 vs. 14.1 ± 12.7 months; P = 0.008). At the time of first CS-PHP, 41% (12/29) of the patients had extrahepatic metastases that did not affect OS (11.1 ± 8.4 months vs. 12.9 ± 13.6 months; P = 0.66). CONCLUSION: CS-PHP is a safe and effective treatment for the hepatic metastatic uveal melanoma, especially for patients with a hepatic tumor burden <25%.

Diagnostic benefit of multiparametric MRI over contrast-enhanced CT in patients with bladder cancer: A single-center 1-year experience.

PURPOSE: To assess the clinical applicability of local tumor staging in urinary bladder cancer (BC) with preoperative multiparametric MRI (mpMRI) using the five-point Vesical Imaging-Reporting and Data System (VI-RADS) scoring system and to compare it to dual-phase contrast-enhanced computed tomography (CECT). METHODS: 33 patients with primary untreated bladder cancer underwent CECT followed by preoperative multiparametric 3.0 T MRI between July 2019 and August 2020 and were enrolled in this retrospective study. Two radiologists initially performed staging on the CECT image data sets and - blinded to CT results - on subsequent mpMRI. BCs were staged according to the VI-RADS scoring system. Postoperative pathology was correlated to the VI-RADS score and the CECT results. The performance of VI-RADS in determining detrusor muscle invasion was analyzed using a receiver operating characteristic curve. Based on the histopathology, sensitivity, specificity and accuracy for muscle invasiveness between both image modalities were compared using the Chi square test. RESULTS: A total of 33 patients (29 male, median age 70 years, IQR: 59-81 years) were included. 10 tumors were categorized as non-muscle invasive (30%) and 23 as muscle invasive BC (70%) in final histology. Tumor stages were correctly assigned as being either muscle invasive or non-muscle invasive on both CECT and mpMRI with regard to both early and late stages of BC (Ta-Tis and T3a-T4b). T-stages bordering the histopathologic limits of muscle invasiveness (T1-T2a-b) resulted in overestimation of muscle invasion in 43% of cases (VI-RADS 3-4) for the mpMRI image data sets and in an underestimation of muscle invasion in up to 55.5% of cases analysing the CECT data. Sensitivity and specificity for the determination of muscle invasion in CECT and mpMRI were 80%/80% and 74%/61% for Radiologist#1 and 70%/90% and 83%/70% for Radiologist#2, respectively. CONCLUSIONS: There are advantages and disadvantages of both CECT and mpMRI when used in the clinical assessment of BC muscular tumor invasion. In borderline cases, only the combination of cross-sectional imaging and histopathological staging may help in making the optimal treatment decisions.