Economic evaluation of flap fixation techniques after mastectomy: Results of a double-blind randomized controlled trial (SAM-trial).

Background: An economic evaluation was performed alongside an RCT investigating flap fixation in reducing seroma formation after mastectomy. The evaluation focused on the first year following mastectomy and assessed cost-effectiveness from a health care and societal perspective.

Methods: The economic evaluation was conducted between 2014 and 2018 in four Dutch breast clinics.

MSCDA: Multi-level semantic-guided contrast improves unsupervised domain adaptation for breast MRI segmentation in small datasets.

Deep learning (DL) applied to breast tissue segmentation in magnetic resonance imaging (MRI) has received increased attention in the last decade, however, the domain shift which arises from different vendors, acquisition protocols, and biological heterogeneity, remains an important but challenging obstacle on the path towards clinical implementation. In this paper, we propose a novel Multi-level Semantic-guided Contrastive Domain Adaptation (MSCDA) framework to address this issue in an unsupervised manner. Our approach incorporates self-training with contrastive learning to align feature representations between domains.

Automated detection and segmentation of non-small cell lung cancer computed tomography images.

Detection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts.

Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability.

Background: Radiomic features extracted from breast MRI have potential for diagnostic, prognostic, and predictive purposes. However, before they can be used as biomarkers in clinical decision support systems, features need to be repeatable and reproducible.

Objective: Identify repeatable radiomics features within breast tissue on prospectively collected MRI exams through multiple test-retest measurements.

Reply to Orlhac, F.; Buvat, I. Comment on "Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features' Stability with and without ComBat Harmonization. 2021, , 1848".

We would like to thank Orlhac and Buvat [...