Publications by authors named "G J Van Leenders"
Background And Objective: A standardized intraoperative frozen section analysis of the prostate resection margin adjacent to the neurovascular bundle according to the NeuroSAFE technique is performed to maximize nerve sparing during radical prostatectomy (RP) for prostate cancer (PCa). The aim of this review was to analyze oncological and functional outcomes of NeuroSAFE.
Methods: A systematic search of the Medline, Embase, and Web of Science databases until July 2024 was performed.
Article Synopsis
- - The study aimed to assess how pathologists report tumor content in prostate cancer biopsies and evaluate consistency among them using 10 standardized cases.
- - A survey of 304 pathologists revealed that most report tumor extent as a percentage, but there is significant variability in how they calculate these percentages.
- - The findings indicate high interobserver variability, especially with percentage reporting, suggesting that using absolute measures of tumor content could provide more consistent results for patient prognosis.
Background: Segmentations are crucial in medical imaging for morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in clinical workflow, while automatic segmentation generally performs sub-par.
Purpose: To develop a minimally interactive deep learning-based segmentation method for soft-tissue tumors (STTs) on CT and MRI.
The Gleason score is the gold standard for grading of prostate cancer (PCa) and is assessed by assigning specific grades to different microscopical growth patterns. Aside from the Gleason grades, individual growth patterns such as cribriform architecture were recently shown to have independent prognostic value for disease outcome. PCa grading is performed on static tissue samples collected at one point in time, whereas in vivo epithelial tumour structures are dynamically invading, branching and expanding into the surrounding stroma.
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- A study validated a radiomics model that uses MRI imaging to differentiate between lipomas and atypical lipomatous tumors (ALTs), addressing challenges associated with traditional biopsy methods.
- Three cohorts were analyzed: two for external validation from the US and UK and one for prospective validation from the Netherlands, utilizing automatic and interactive segmentation methods for tumor imaging.
- The model demonstrated strong performance with area under the curve (AUC) scores ranging from 0.74 to 0.89, matching or exceeding the diagnostic abilities of expert radiologists.