Publications by authors named "Catharina S Lisson"
Background: Self-supervised pre-training of deep learning models with contrastive learning is a widely used technique in image analysis. Current findings indicate a strong potential for contrastive pre-training on medical images. However, further research is necessary to incorporate the particular characteristics of these images.
View Article and Find Full Text PDFHybrid positron emission tomography/magnetic resonance imaging (PET/MR) opens new possibilities in multimodal multiparametric (m2p) image analyses. But even the simultaneous acquisition of positron emission tomography (PET) and magnetic resonance imaging (MRI) does not guarantee perfect voxel-by-voxel co-registration due to organs and distortions, especially in diffusion-weighted imaging (DWI), which would be, however, crucial to derive biologically meaningful information. Thus, our aim was to optimize fusion and voxel-wise analyses of DWI and standardized uptake values (SUVs) using a novel software for m2p analyses.
View Article and Find Full Text PDFAccurate prediction of lymph node metastasis (LNM) in patients with testicular cancer is highly relevant for treatment decision-making and prognostic evaluation. Our study aimed to develop and validate clinical radiomics models for individual preoperative prediction of LNM in patients with testicular cancer. We enrolled 91 patients with clinicopathologically confirmed early-stage testicular cancer, with disease confined to the testes.
View Article and Find Full Text PDFArticle Synopsis
- Deep learning has the potential to improve medical imaging by reducing diagnostic errors and radiologist workload, but requires large annotated datasets for training, which are often scarce.
- Self-supervised learning methods allow models to be pre-trained on large unannotated datasets, making it feasible to fine-tune them with smaller annotated datasets for specific tasks.
- The study compares two self-supervised pre-training methods, finding that the masked autoencoder approach "SparK" is more effective and robust than contrastive methods when working with limited annotated data in medical imaging.
Article Synopsis
- - Mantle cell lymphoma (MCL) is a rare and aggressive type of cancer that often has a poor prognosis and is marked by frequent relapses, although some cases progress slowly and don't require immediate treatment.
- - The disease is linked to a specific genetic change (t(11;14)(q13;q32)) that leads to overexpression of Cyclin D1, which influences its clinical behavior and outcomes.
- - This study develops and tests various deep learning and machine learning models, finding that an optimized 3D CNN was the best at predicting MCL relapse from initial CT scans, achieving 70% accuracy, which could enhance clinical management if further validated in larger trials.
The study's primary aim is to evaluate the predictive performance of CT-derived 3D radiomics for MCL risk stratification. The secondary objective is to search for radiomic features associated with sustained remission. Included were 70 patients: 31 MCL patients and 39 control subjects with normal axillary lymph nodes followed over five years.
View Article and Find Full Text PDFBackground: Iterative reconstruction is well established for CT. Plain radiography also takes advantage of iterative algorithms to reduce scatter radiation and improve image quality. First applications have been described for bedside chest X-ray.
View Article and Find Full Text PDFObjective: MRI is the most important and sensitive imaging modality in the differentiation of unclear soft tissue tumors. A systematic approach helps to narrow down the large number of possible differential diagnoses.
Method: Our review systematically compares MRI characteristics of the major soft-tissue masses and aims to gain access to these often difficult tumor entities.
Background: Iterative scatter correction (ISC) is a new technique applicable to plain radiography; comparable to iterative reconstruction for computed tomography, it promises dose reduction and image quality improvement. ISC for bedside chest X-rays has been applied and evaluated for some time and has recently been commercially offered for plain skeletal radiography.
Purpose: To analyze the potential of ISC for plain skeletal radiography with regard to image quality improvement, dose reduction, and replacement for an antiscatter grid.
Objectives: To explore the diagnostic value of MRI-based 3D texture analysis to identify texture features that can be used for discrimination of low-grade chondrosarcoma from enchondroma.
Methods: Eleven patients with low-grade chondrosarcoma and 11 patients with enchondroma were retrospectively evaluated. Texture analysis was performed using mint Lesion: Kurtosis, entropy, skewness, mean of positive pixels (MPP) and uniformity of positive pixel distribution (UPP) were obtained in four MRI sequences and correlated with histopathology.