A5047580782- Prostate Cancer Diagnosis and Treatment
- Radiomics and Machine Learning in Medical Imaging
- Prostate Cancer Treatment and Research
- Urologic and reproductive health conditions
- AI in cancer detection
- Lung Cancer Research Studies
- MRI in cancer diagnosis
- Lung Cancer Treatments and Mutations
- Advanced MRI Techniques and Applications
- Statistical Methods in Clinical Trials
- Explainable Artificial Intelligence (XAI)
- Advanced X-ray and CT Imaging
- Pancreatic and Hepatic Oncology Research
- Urinary Bladder and Prostate Research
- Diagnosis and treatment of tuberculosis
- Peptidase Inhibition and Analysis
- Meta-analysis and systematic reviews
- Cardiac Imaging and Diagnostics
- Advanced Neural Network Applications
- Artificial Intelligence in Healthcare and Education
- Medical Imaging and Analysis
- Health Systems, Economic Evaluations, Quality of Life
University of Freiburg
2020-2025
University Medical Center Freiburg
2020-2025
Erasmus MC
2024
Klinikum Lippe
2024
Abstract Purpose To evaluate the diagnostic performance of a fully automated, commercially available AI algorithm for detecting prostate cancer and classifying lesions according to PI-RADS. Material methods In this retrospective single-center cohort study, we included consecutive patients with suspected who underwent 3T MRI between May 2017 2020. Histopathological ground truth was targeted transperineal ultrasound-fusion guided biopsy extensive systematic biopsy. We compared results those...
Abstract Automatic prostate tumor segmentation is often unable to identify the lesion even if multi-parametric MRI data used as input, and output difficult verify due lack of clinically established ground truth images. In this work we use an explainable deep learning model interpret predictions a convolutional neural network (CNN) for segmentation. The CNN uses U-Net architecture which was trained on from 122 patients automatically segment gland lesions. addition, co-registered whole mount...
The aim of this study was to compare image quality features and lesion characteristics between a faster deep learning (DL) reconstructed T2-weighted (T2-w) fast spin-echo (FSE) Dixon sequence with super-resolution (T2 This prospective conducted November 2022 April 2023 using 3T scanner. Both T2 Total 151 women were enrolled, 140 (mean age: 52 ± 14 years; 85 cysts 31 breast cancers) included in the final analysis. acquisition time 110 s 0 for
To establish and evaluate an ultra-fast MRI screening protocol for prostate cancer (PCa) in comparison to the standard multiparametric (mp) protocol, reducing scan time maintaining adequate diagnostic performance.
Abstract Objectives Achieving a consensus on definition for different aspects of radiomics workflows to support their translation into clinical usage. Furthermore, assess the perspective experts important challenges successful workflow implementation. Materials and methods The was achieved by multi-stage process. Stage 1 comprised screening, retrospective analysis with semantic mapping terms found in 22 definitions, compilation an initial baseline definition. Stages 2 3 consisted Delphi...
Multiparametric MRI (mpMRI) improves the detection of aggressive prostate cancer (PCa) subtypes. As cases active surveillance (AS) increase and tumor progression triggers definitive treatment, we evaluated whether an AI-driven algorithm can detect clinically significant PCa (csPCa) in patients under AS.Consecutive AS who received mpMRI (PI-RADSv2.1 protocol) subsequent MR-guided ultrasound fusion (targeted extensive systematic) biopsy between 2017 2020 were retrospectively analyzed....
The increase in multiparametric magnetic resonance imaging (mpMRI) examinations as a fundamental tool prostate cancer (PCa) diagnostics raises the need for supportive computer-aided analysis. Therefore, we evaluated performance of commercially available AI-based algorithm detection and classification multi-center setting. Representative patients with 3T mpMRI between 2017 2022 at three different university hospitals were selected. Exams read according to PI-RADSv2.1 protocol then assessed by...
Background: The aim of the study was to evaluate role different immunohistochemical and radiomics features in patients with small cell lung cancer (SCLC). Methods: Consecutive histologically proven SCLC limited (n = 47, 48%) or extensive disease 51, 52%) treated radiotherapy chemotherapy at our department were included analysis. expression markers from initial tissue biopsy, such as CD56, CD44, chromogranin A, synaptophysin, TTF-1, GLUT-1, Hif-1 a, PD-1, PD-L1, MIB-1/KI-67 well LDH und NSE...
Abstract Background In this work, we compare input level, feature level and decision data fusion techniques for automatic detection of clinically significant prostate lesions (csPCa). Methods Multiple deep learning CNN architectures were developed using the Unet as baseline. The CNNs use both multiparametric MRI images (T2W, ADC, High b-value) quantitative clinical (prostate specific antigen (PSA), PSA density (PSAD), gland volume & gross tumor (GTV)), only mp-MRI ( n = 118), input....
To investigate whether quantitative analysis of diffusion weighted images allows for improved risk stratification transition zone lesions in prostate magnetic resonance imaging (MRI) evaluated according to PI-RADSv2.1 [Prostate Imaging Reporting and Data System, target variable: clinically significant cancer (csPCa)].Consecutive patients with 3T MRI were enrolled the study. All on histopathologically verified by transperineal MRI-TRUS fusion biopsy. Two blinded radiologists re-evaluated all...
An accurate delineation of the intraprostatic gross tumor volume (GTV) is importance for focal treatment in patients with primary prostate cancer (PCa). Multiparametric MRI (mpMRI) standard care lesion detection but has been shown to underestimate GTV. This study investigated how far GTV be expanded order reach concordance histopathological reference and whether this strategy practicable clinical routine.Twenty-two planned prostatectomy preceded 3 Tesla mpMRI were prospectively examined....
The Prostate Imaging Reporting and Data System (PI-RADS) standardises reporting of prostate MRI for the detection clinically significant cancer. We provide protocol a planned living systematic review meta-analysis (1) diagnostic accuracy (sensitivity specificity), (2) cancer rates assessment categories (3) inter-reader agreement.Retrospective prospective studies on at least one outcomes interest are included. Each step that requires literature evaluation data extraction is performed by two...
Prostate magnetic resonance imaging has become the standard for prostate cancer in various clinical settings, with interpretation standardized according to Imaging Reporting and Data System (PI-RADS). Each year, hundreds of scientific studies that report on diagnostic performance PI-RADS are published. To keep up this ever-increasing evidence base, systematic reviews meta-analyses essential. As highly resource-intensive, we investigated whether a machine learning framework can reduce manual...
Multiparametric MRI of the prostate has become a fundamental tool in diagnostic pathway for cancer and is recommended before (or after negative) biopsy to guide increase accuracy, as staging examination (high-risk setting), prior inclusion into active surveillance. Despite this main field application, can be utilized obtain information variety benign disorders prostate.Systematic bibliographical research with extraction studies, national (German) well international guidelines (EAU, AUA),...
Abstract Automatic prostate tumor segmentation is often unable to identify the lesion even if in multi-parametric MRI data used as input, and output difficult verify due lack of clinically established ground truth images. In this work we use an explainable deep learning model interpret predictions a convolutional neural network (CNN) for segmentation. The CNN uses U-Net architecture which was trained on from 122 patients automatically segment gland lesions. addition, co-registered whole...
Zielsetzung Die multiparametrische MRT (MpMRI) der Prostata hat sich als unverzichtbares Instrument in diagnostischen Aufarbeitung von Prostatakrebs (PCa) etabliert und verbessert die Erkennung aggressiver Tumorsubtypen. Da Anzahl Patienten unter Active Surveillance (AS) deutlich steigt eine definitive Behandlung im Falle einer Tumorprogression klinisch nicht signifikantem zu Karzinom (csPCa) erforderlich ist, haben wir untersucht, ob ein automatisierter KI-gesteuerter Algorithmus...
A convolutional neural network was implemented to automatically segment tumors in multi-parametric MRI data. The influence of the variability ground truth data evaluated for automated prostate tumor segmentation. Therefore, agreement between predictions CNN measured with co-registered whole mount histopathology images and contours drawn by an expert radio-oncologist. results indicate that can discriminate from healthy tissue rather than mimicking radiologist.
Background/Aim: We examined the prognostic value of intraprostatic gross tumour volume (GTV) as measured by multiparametric MRI (mpMRI) in patients with prostate cancer following (primary) external beam radiation therapy (EBRT). Patients and Methods: In a retrospective monocentric study, we analysed (PCa) after EBRT. GTV was delineated pre-treatment mpMRI (GTV-MRI) using T2-weighted images. Cox-regression analyses were performed considering biochemical failure recurrence-free survival (BRFS)...