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".

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MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study.

This retrospective study investigated the value of pretreatment contrast-enhanced Magnetic Resonance Imaging (MRI)-based radiomics for the prediction of pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients. A total of 292 breast cancer patients, with 320 tumors, who were treated with neo-adjuvant systemic therapy and underwent a pretreatment MRI exam were enrolled. As the data were collected in two different hospitals with five different MRI scanners and varying acquisition protocols, three different strategies to split training and validation datasets were used.

The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features' Stability with and without ComBat Harmonization.

While handcrafted radiomic features (HRFs) have shown promise in the field of personalized medicine, many hurdles hinder its incorporation into clinical practice, including but not limited to their sensitivity to differences in acquisition and reconstruction parameters. In this study, we evaluated the effects of differences in in-plane spatial resolution (IPR) on HRFs, using a phantom dataset (n = 14) acquired on two scanner models. Furthermore, we assessed the effects of interpolation methods (IMs), the choice of a new unified in-plane resolution (NUIR), and ComBat harmonization on the reproducibility of HRFs.

Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer.

Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.

Reducing Seroma Formation and Its Sequelae After Mastectomy by Closure of the Dead Space: A Multi-center, Double-Blind Randomized Controlled Trial (SAM-Trial).

Background: Seroma is a common complication after mastectomy, with an incidence of 3% to 85%. Seroma is associated with pain, delayed wound healing, and additional outpatient clinic visits, leading potentially to repeated seroma aspiration or even surgical interventions. This study aimed to assess the effect of flap fixation using sutures or tissue glue in preventing seroma formation and its sequelae.

MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability.

Radiomics is an emerging field using the extraction of quantitative features from medical images for tissue characterization. While MRI-based radiomics is still at an early stage, it showed some promising results in studies focusing on breast cancer patients in improving diagnoses and therapy response assessment. Nevertheless, the use of radiomics raises a number of issues regarding feature quantification and robustness.

Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework.

The advancement of artificial intelligence concurrent with the development of medical imaging techniques provided a unique opportunity to turn medical imaging from mostly qualitative, to further quantitative and mineable data that can be explored for the development of clinical decision support systems (cDSS). Radiomics, a method for the high throughput extraction of hand-crafted features from medical images, and deep learning -the data driven modeling techniques based on the principles of simplified brain neuron interactions, are the most researched quantitative imaging techniques. Many studies reported on the potential of such techniques in the context of cDSS.

Radiomics: from qualitative to quantitative imaging.

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes.

Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: A systematic review.

Purpose: MRI-based tumor response prediction to neoadjuvant systemic therapy (NST) in breast cancer patients is increasingly being studied using radiomics with outcomes that appear to be promising. The aim of this study is to systematically review the current literature and reflect on its quality.

Methods: PubMed and EMBASE databases were systematically searched for studies investigating MRI-based radiomics for tumor response prediction.

Risk of Positive Sentinel Lymph Node After Neoadjuvant Systemic Therapy in Clinically Node-Negative Breast Cancer: Implications for Postmastectomy Radiation Therapy and Immediate Breast Reconstruction.

Background: Residual axillary lymph node involvement after neoadjuvant systemic therapy (NST) is the determining factor for postmastectomy radiation therapy (PMRT). Preoperative identification of patients needing PMRT is essential to enable shared decision-making when choosing the optimal timing of breast reconstruction. We determined the risk of positive sentinel lymph node (SLN) after NST in clinically node-negative (cN0) breast cancer.

Reducing seroma formation and its sequelae after mastectomy by closure of the dead space: The interim analysis of a multi-center, double-blind randomized controlled trial (SAM trial).

Objective: The main objective of this double-blind randomized controlled trial (RCT) was to assess seroma formation and its sequelae in patients undergoing mastectomy. Patients were randomized into one of three groups in which different wound closure techniques were applied: 1) conventional wound closure without flap fixation (CON) 2) flap fixation using sutures (FF-S) and 3) flap fixation using an adhesive tissue glue (FF-G).

Background: Seroma formation is still a bothersome complication after mastectomy.

Analysis of TNF-α and interleukin-6 in seroma of patients undergoing mastectomy with or without flap fixation: is there a predictive value for seroma formation and its sequelae?

Background: Seroma formation is a common complication after mastectomy. Flap fixation has the potential to prevent seroma formation, but identifying patients that are at risk of developing seroma, remains challenging. The aim of this study was to assess the association between pro-inflammatory cytokines in seroma fluid one day after surgery and seroma formation and it sequelae.

A multi-center, double blind randomized controlled trial evaluating flap fixation after mastectomy using sutures or tissue glue versus conventional closure: protocol for the Seroma reduction After Mastectomy (SAM) trial.

Background: Seroma formation is a common complication after mastectomy and is associated with delayed wound healing, infection, skin flap necrosis, patient discomfort and repeated visits to the out patient clinic to deal with seroma and its sequelae. Closing the dead space after mastectomy seems to be key in reducing seroma and its complications. Various methods have been described to reduce the dead space after mastectomy: closed suction drainage, quilting of the skin flaps and application of adhesive tissue glues.

A systematic review of flap fixation techniques in reducing seroma formation and its sequelae after mastectomy.

Background: Seroma formation is a common complication after mastectomy. This review aims to elucidate which surgical techniques are most effective in reducing the dead space and therefore seroma formation in patients undergoing mastectomy.

Methods: A literature search was performed to identify clinical studies comparing any form of flap fixation to conventional closure technique in patients undergoing mastectomy with or without axillary clearance.