testing-cochlear-implant-electrode-insertion-training-system-for

Testing a Cochlear Implant Electrode Insertion Training System for Optimal Electrode Array Placement in Different Inner Ear Anatomies

Successful intracochlear placement of the cochlear implant (CI) electrode array is a key surgical step in cochlear implantation. Without it, ...

LMU Munich

Bladder cancer (BC) treatment algorithms depend on accurate tumor staging. To date, computed tomography (CT) is recommended for assessment of lymph node (LN) metastatic spread in muscle-invasive and high-risk BC. However, the diagnostic efficacy of radiologist-evaluated CT imaging studies is limited.

Radiomics Signature Using Manual Versus Automated Segmentation for Lymph Node Staging of Bladder Cancer.

(1) Background: To evaluate radiomics features as well as a combined model with clinical parameters for predicting overall survival in patients with bladder cancer (BCa). (2) Methods: This retrospective study included 301 BCa patients who received radical cystectomy (RC) and pelvic lymphadenectomy. Radiomics features were extracted from the regions of the primary tumor and pelvic lymph nodes as well as the peritumoral regions in preoperative CT scans. Cross-validation was performed in the training cohort, and a Cox regression model with an elastic net penalty was trained using radiomics features and clinical parameters. The models were evaluated with the time-dependent area under the ROC curve (AUC), Brier score and calibration curves. (3) Results: The median follow-up time was 56 months (95% CI: 48−74 months). In the follow-up period from 1 to 7 years after RC, radiomics models achieved comparable predictive performance to validated clinical parameters with an integrated AUC of 0.771 (95% CI: 0.657−0.869) compared to an integrated AUC of 0.761 (95% CI: 0.617−0.874) for the prediction of overall survival (p = 0.98). A combined clinical and radiomics model stratified patients into high-risk and low-risk groups with significantly different overall survival (p < 0.001). (4) Conclusions: Radiomics features based on preoperative CT scans have prognostic value in predicting overall survival before RC. Therefore, radiomics may guide early clinical decision-making.

Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy.

Radiomics promises to enhance the discriminative performance for clinically significant prostate cancer (csPCa), but still lacks validation in real-life scenarios. This study investigates the classification performance and robustness of machine learning radiomics models in heterogeneous MRI datasets to characterize suspicious prostate lesions for non-invasive prediction of prostate cancer (PCa) aggressiveness compared to conventional imaging biomarkers.

Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets.

Primary stapes surgery is considered a challenging intervention in ear surgery. Despite an risk of deafness in 0.5-1 percent, this procedure has still a good benefit-risk ratio due to the improvement in hearing and quality of life that is usually achieved. However, revision after prior stapes surgery is considered even more challenging. Revisions after stapes surgery are very heterogeneous procedures, both in terms of the indication and the surgical strategy and are generally considered to be significantly more demanding. Reasons for complications after prior stapes surgery as well as strategies for successful revisions are not well described in the literature.

Revisions after prior stapes surgery: aspects on indication, intraoperative findings and surgical strategies.

Despite the widespread research application of radiomics, there is a knowledge gap regarding the optimal voxel intensity normalization strategy for FDG-PET radiomics. We investigated the impact of 3 normalization strategies on the prognostic utility of individual radiomic features and machine learning models in patients with oropharyngeal squamous cell carcinoma (OPSCC).

FDG-PET Intensity Normalization Improves Radiomics-Based Survival Prediction in Patients with Oropharyngeal Cancer: A Comparison of the Standardized Uptake Value with Alternative Normalization Techniques.

Article	Year
Radiomics Signature Using Manual Versus Automated Segmentation for Lymph Node Staging of Bladder Cancer. European urology focus\| PubMed ID: 36115774	2023
Radiomics for the Prediction of Overall Survival in Patients with Bladder Cancer Prior to Radical Cystectomy. Cancers\| PubMed ID: 36139609	2022
Performance variability of radiomics machine learning models for the detection of clinically significant prostate cancer in heterogeneous MRI datasets. Quantitative imaging in medicine and surgery\| PubMed ID: 36330197	2022
Revisions after prior stapes surgery: aspects on indication, intraoperative findings and surgical strategies. European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery\| PubMed ID: 39589410	2025
FDG-PET Intensity Normalization Improves Radiomics-Based Survival Prediction in Patients with Oropharyngeal Cancer: A Comparison of the Standardized Uptake Value with Alternative Normalizati... AJNR. American journal of neuroradiology\| PubMed ID: 40379456	2025

Andrea Schreier

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