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  • 1
    Online Resource
    Online Resource
    Prospectively Accelerated T2-Weighted Imaging of the Prostate by Combining Compressed SENSE and Deep Learning in Patients with Histologically Proven Prostate Cancer
    MDPI AG ; 2022
    In:  Cancers Vol. 14, No. 23 ( 2022-11-22), p. 5741-
    Details
    In: Cancers, MDPI AG, Vol. 14, No. 23 ( 2022-11-22), p. 5741-
    Abstract: Background: To assess the performance of prospectively accelerated and deep learning (DL) reconstructed T2-weighted (T2w) imaging in volunteers and patients with histologically proven prostate cancer (PCa). Methods: Prospectively undersampled T2w datasets were acquired with acceleration factors of 1.7 (reference), 3.4 and 4.8 in 10 healthy volunteers and 23 patients with histologically proven PCa. Image reconstructions using compressed SENSE (C-SENSE) and a combination of C-SENSE and DL-based artificial intelligence (C-SENSE AI) were analyzed. Qualitative image comparison was performed using a 6-point Likert scale (overall image quality, noise, motion artifacts, lesion detection, diagnostic certainty); the T2 and PI-RADS scores were compared between the two reconstructions. Additionally, quantitative image parameters were assessed (apparent SNR, apparent CNR, lesion size, line profiles). Results: All C-SENSE AI-reconstructed images received a significantly higher qualitative rating compared to the C-SENSE standard images. Analysis of the quantitative parameters supported this finding, with significantly higher aSNR and aCNR. The line profiles demonstrated a significantly steeper signal change at the border of the prostatic lesion and the adjacent normal tissue in the C-SENSE AI-reconstructed images, whereas the T2 and PI-RADS scores as well as the lesion size did not differ. Conclusion: In this prospective study, we demonstrated the clinical feasibility of a novel C-SENSE AI reconstruction enabling a 58% acceleration in T2w imaging of the prostate while obtaining significantly better image quality.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers14235741
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2527080-1
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  • 2
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    Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging
    MDPI AG ; 2021
    In:  Cancers Vol. 13, No. 9 ( 2021-04-25), p. 2069-
    Details
    In: Cancers, MDPI AG, Vol. 13, No. 9 ( 2021-04-25), p. 2069-
    Abstract: Background: PDAC remains a tumor entity with poor prognosis and a 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response and patient survival. Non-invasive prediction of individual patient outcome however remains an unresolved task. Methods: Discrete cellularity regions of PDAC resection specimen (n = 43) were analyzed by routine histopathological work up. Regional tumor cellularity and CT-derived Hounsfield Units (HU, n = 66) as well as iodine concentrations were regionally matched. One-way ANOVA and pairwise t-tests were performed to assess the relationship between different cellularity level in conventional, virtual monoenergetic 40 keV (monoE 40 keV) and iodine map reconstructions. Results: A statistically significant negative correlation between regional tumor cellularity in histopathology and CT-derived HU from corresponding image regions was identified. Radiological differentiation was best possible in monoE 40 keV CT images. However, HU values differed significantly in conventional reconstructions as well, indicating the possibility of a broad clinical application of this finding. Conclusion: In this study we establish a novel method for CT-based prediction of tumor cellularity for in-vivo tumor characterization in PDAC patients.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers13092069
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
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  • 3
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    Online Resource
    Multimedication Guidelines: Assessment of the Size of the Target Group for Medication Review and Description of the Frequency of Their Potential Drug Safety Problems with Routine Data
    MDPI AG ; 2022
    In:  Pharmacoepidemiology Vol. 1, No. 1 ( 2022-04-15), p. 12-25
    Details
    In: Pharmacoepidemiology, MDPI AG, Vol. 1, No. 1 ( 2022-04-15), p. 12-25
    Abstract: (1) Background: About 10 years ago, several guidelines for the better management of patients with polypharmacy were issued. A central issue is the definition of the target group. The primary aim of this study is therefore to assess the size of the target group, applying the criteria of the German guidelines. A further aim is to describe the frequency of occurrence of medication safety issues for patients of the target group. (2) Methods: The study is based on administrative data of one large statutory health insurer in Germany (n = 9,012,523). (3) Results: The criteria of multimorbidity (at least three chronic diseases) and utilization of five or more concurrent drugs over at least 91 days is fulfilled by 14.1% of the insured patients, or almost 1.3 million persons. About 5% of this multimorbid and poly-medicated population fulfilled at least three of out of five additional occasion-related criteria. Medication safety issues occur frequently: treatment prevalence with potentially inadequate medication, QT-drugs, benzodiazepine or Z-drugs and proton pump inhibitors was 30.4%, 28.9%, 11.1% and 52.4%, respectively. (4) Conclusions: The analysis shows the scope of patients eligible for a structured medication review and demonstrates the relevance for counselling based on the high percentage potentially at risk due to medication therapy safety problems.
    Type of Medium: Online Resource
    ISSN: 2813-0618
    URL: Article
    DOI: 10.3390/pharma1010002
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 3136467-6
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  • 4
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    Online Resource
    Multimedication in Family Doctor Practices: The German Evidence-Based Guidelines on Multimedication
    MDPI AG ; 2022
    In:  Pharmacoepidemiology Vol. 1, No. 1 ( 2022-05-13), p. 35-48
    Details
    In: Pharmacoepidemiology, MDPI AG, Vol. 1, No. 1 ( 2022-05-13), p. 35-48
    Abstract: Patients with multimorbidity and multimedication require special attention from their treating physicians, as the risks of drug interactions and negative effects on adherence increase with the number of drugs. Most guidelines aim for the treatment of a single disease and do not take potential problems due to multimedication into account. In 2021, updates and evidence-based upgrades of the first version (2012) of the German Guidelines on Multimedication were issued. The aim of the article is to introduce the framework of these evidence-based guidelines, which follows the medication process in six steps: (1) inventory and medication assessment; (2) coordination with the patient; (3) prescription proposal and communication; (4) dispensing of medicines; (5) medication application and self-management; and (6) monitoring. For each step, recommendations and practice tips are presented. The central feature is a structured medication review. The target group is patients with multimorbidity and the concurrent use of five or more drugs. The Medication Appropriateness Index has been modified, and the guiding questions are recommended as guidance for the structured medication review. Overuse and undertreatment are taken into account. The guidelines were consented to in a formal process with 15 medical societies, a patient representative, and experts, as well as piloted in general practices.
    Type of Medium: Online Resource
    ISSN: 2813-0618
    URL: Article
    DOI: 10.3390/pharma1010005
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 5
    Online Resource
    Online Resource
    Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma
    MDPI AG ; 2020
    In:  Journal of Clinical Medicine Vol. 9, No. 3 ( 2020-03-07), p. 724-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 9, No. 3 ( 2020-03-07), p. 724-
    Abstract: To bridge the translational gap between recent discoveries of distinct molecular phenotypes of pancreatic cancer and tangible improvements in patient outcome, there is an urgent need to develop strategies and tools informing and improving the clinical decision process. Radiomics and machine learning approaches can offer non-invasive whole tumor analytics for clinical imaging data-based classification. The retrospective study assessed baseline computed tomography (CT) from 207 patients with proven pancreatic ductal adenocarcinoma (PDAC). Following expert level manual annotation, Pyradiomics was used for the extraction of 1474 radiomic features. The molecular tumor subtype was defined by immunohistochemical staining for KRT81 and HNF1a as quasi-mesenchymal (QM) vs. non-quasi-mesenchymal (non-QM). A Random Forest machine learning algorithm was developed to predict the molecular subtype from the radiomic features. The algorithm was then applied to an independent cohort of histopathologically unclassifiable tumors with distinct clinical outcomes. The classification algorithm achieved a sensitivity, specificity and ROC-AUC (area under the receiver operating characteristic curve) of 0.84 ± 0.05, 0.92 ± 0.01 and 0.93 ± 0.01, respectively. The median overall survival for predicted QM and non-QM tumors was 16.1 and 20.9 months, respectively, log-rank-test p = 0.02, harzard ratio (HR) 1.59. The application of the algorithm to histopathologically unclassifiable tumors revealed two groups with significantly different survival (8.9 and 39.8 months, log-rank-test p 〈 0.001, HR 4.33). The machine learning-based analysis of preoperative (CT) imaging allows the prediction of molecular PDAC subtypes highly relevant for patient survival, allowing advanced pre-operative patient stratification for precision medicine applications.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm9030724
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2662592-1
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  • 6
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    Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC) versus Autoimmune Pancreatitis (AIP)
    MDPI AG ; 2020
    In:  Journal of Clinical Medicine Vol. 9, No. 12 ( 2020-12-11), p. 4013-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 9, No. 12 ( 2020-12-11), p. 4013-
    Abstract: The differentiation of autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC) poses a relevant diagnostic challenge and can lead to misdiagnosis and consequently poor patient outcome. Recent studies have shown that radiomics-based models can achieve high sensitivity and specificity in predicting both entities. However, radiomic features can only capture low level representations of the input image. In contrast, convolutional neural networks (CNNs) can learn and extract more complex representations which have been used for image classification to great success. In our retrospective observational study, we performed a deep learning-based feature extraction using CT-scans of both entities and compared the predictive value against traditional radiomic features. In total, 86 patients, 44 with AIP and 42 with PDACs, were analyzed. Whole pancreas segmentation was automatically performed on CT-scans during the portal venous phase. The segmentation masks were manually checked and corrected if necessary. In total, 1411 radiomic features were extracted using PyRadiomics and 256 features (deep features) were extracted using an intermediate layer of a convolutional neural network (CNN). After feature selection and normalization, an extremely randomized trees algorithm was trained and tested using a two-fold shuffle-split cross-validation with a test sample of 20% (n = 18) to discriminate between AIP or PDAC. Feature maps were plotted and visual difference was noted. The machine learning (ML) model achieved a sensitivity, specificity, and ROC-AUC of 0.89 ± 0.11, 0.83 ± 0.06, and 0.90 ± 0.02 for the deep features and 0.72 ± 0.11, 0.78 ± 0.06, and 0.80 ± 0.01 for the radiomic features. Visualization of feature maps indicated different activation patterns for AIP and PDAC. We successfully trained a machine learning model using deep feature extraction from CT-images to differentiate between AIP and PDAC. In comparison to traditional radiomic features, deep features achieved a higher sensitivity, specificity, and ROC-AUC. Visualization of deep features could further improve the diagnostic accuracy of non-invasive differentiation of AIP and PDAC.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm9124013
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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  • 7
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    Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters
    MDPI AG ; 2020
    In:  Journal of Clinical Medicine Vol. 9, No. 5 ( 2020-04-25), p. 1250-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 9, No. 5 ( 2020-04-25), p. 1250-
    Abstract: Rationale: Pancreatic ductal adenocarcinoma (PDAC) remains a tumor entity of exceptionally poor prognosis, and several biomarkers are under current investigation for the prediction of patient prognosis. Many studies focus on promoting newly developed imaging biomarkers without a rigorous comparison to other established parameters. To assess the true value and leverage the potential of all efforts in this field, a multi-parametric evaluation of the available biomarkers for PDAC survival prediction is warranted. Here we present a multiparametric analysis to assess the predictive value of established parameters and the added contribution of newly developed imaging features such as biomarkers for overall PDAC patient survival. Methods: 103 patients with resectable PDAC were retrospectively enrolled. Clinical and histopathological data (age, sex, chemotherapy regimens, tumor size, lymph node status, grading and resection status), morpho-molecular and genetic data (tumor morphology, molecular subtype, tp53, kras, smad4 and p16 genetics), image-derived features and the combination of all parameters were tested for their prognostic strength based on the concordance index (CI) of multivariate Cox proportional hazards survival modelling after unsupervised machine learning preprocessing. Results: The average CIs of the out-of-sample data were: 0.63 for the clinical and histopathological features, 0.53 for the morpho-molecular and genetic features, 0.65 for the imaging features and 0.65 for the combined model including all parameters. Conclusions: Imaging-derived features represent an independent survival predictor in PDAC and enable the multiparametric, machine learning-assisted modelling of postoperative overall survival with a high performance compared to clinical and morpho-molecular/genetic parameters. We propose that future studies systematically include imaging-derived features to benchmark their additive value when evaluating biomarker-based model performance.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm9051250
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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  • 8
    Online Resource
    Online Resource
    Evidence-Based Decision Support for a Structured Care Program on Polypharmacy in Multimorbidity: A Guideline Upgrade Based on a Realist Synthesis
    MDPI AG ; 2022
    In:  Journal of Personalized Medicine Vol. 12, No. 1 ( 2022-01-07), p. 69-
    Details
    In: Journal of Personalized Medicine, MDPI AG, Vol. 12, No. 1 ( 2022-01-07), p. 69-
    Abstract: Evidence-based clinical guidelines generally consider single conditions, and rarely multimorbidity. We developed an evidence-based guideline for a structured care program to manage polypharmacy in multimorbidity by using a realist synthesis to update the German polypharmacy guideline including the following five methods: formal prioritization in focus groups; systematic guideline review of evidence-based multimorbidity/polypharmacy guidelines; evidence search/synthesis and recommendation development; multidisciplinary consent of recommendations; feasibility test of updated guideline. We identified the need for a better description of the target group, decision support, prioritization of medication, consideration of patient preferences and anticholinergic properties, and of healthcare interfaces. We conducted a systematic guideline review of eight guidelines and extracted and synthesized recommendations using the Ariadne principles. We also included 48 systematic reviews. We formulated and agreed upon 34 recommendations for the revised guideline. During the feasibility test, guideline use enabled 57% of GPs to identify problems, leading to medication changes in 49% and self-assessed improvement in 56% of patients. Although 58% of GPs felt that it was too long, 92% recommended it. Polypharmacy should be systematically reviewed at least annually. Patients, family members, and healthcare professionals should monitor and adjust it using prospective process validation, taking into account patient preferences and agreed treatment goals.
    Type of Medium: Online Resource
    ISSN: 2075-4426
    URL: Article
    DOI: 10.3390/jpm12010069
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662248-8
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  • 9
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    Online Resource
    High-Resolution, High b-Value Computed Diffusion-Weighted Imaging Improves Detection of Pancreatic Ductal Adenocarcinoma
    MDPI AG ; 2022
    In:  Cancers Vol. 14, No. 3 ( 2022-01-18), p. 470-
    Details
    In: Cancers, MDPI AG, Vol. 14, No. 3 ( 2022-01-18), p. 470-
    Abstract: Background: Our purpose was to investigate the potential of high-resolution, high b-value computed DWI (cDWI) in pancreatic ductal adenocarcinoma (PDAC) detection. Materials and Methods: We retrospectively enrolled 44 patients with confirmed PDAC. Respiratory-triggered, diffusion-weighted, single-shot echo-planar imaging (ss-EPI) with both conventional (i.e., full field-of-view, 3 × 3 × 4 mm voxel size, b = 0, 50, 300, 600 s/mm2) and high-resolution (i.e., reduced field-of-view, 2.5 × 2.5 × 3 mm voxel size, b = 0, 50, 300, 600, 1000 s/mm2) imaging was performed for suspected PDAC. cDWI datasets at b = 1000 s/mm2 were generated for the conventional and high-resolution datasets. Three radiologists were asked to subjectively rate (on a Likert scale of 1–4) the following metrics: image quality, lesion detection and delineation, and lesion-to-pancreas intensity relation. Furthermore, the following quantitative image parameters were assessed: apparent signal-to-noise ratio (aSNR), contrast-to-noise ratio (aCNR), and lesion-to-pancreas contrast ratio (CR). Results: High-resolution, high b-value computed DWI (r-cDWI1000) enabled significant improvement in lesion detection and a higher incidence of a high lesion-to-pancreas intensity relation (type 1, clear hyperintense) compared to conventional high b-value computed and high-resolution high b-value acquired DWI (f-cDWI1000 and r-aDWI1000, respectively). Image quality was rated inferior in the r-cDWI1000 datasets compared to r-aDWI1000. Furthermore, the aCNR and CR were higher in the r-cDWI1000 datasets than in f-cDWI1000 and r-aDWI1000. Conclusion: High-resolution, high b-value computed DWI provides significantly better visualization of PDAC compared to the conventional high b-value computed and high-resolution high b-value images acquired by DWI.
    Type of Medium: Online Resource
    ISSN: 2072-6694
    URL: Article
    DOI: 10.3390/cancers14030470
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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