Robotic-assisted harvest of latissimus dorsi muscle flap for breast reconstruction: review of the literature

Robotic-assisted surgery continues to gain ground over conventional surgical methods, due to reported better results regarding the aesthetic outcome and the decreased percentage of complications. Although latissimus dorsi flap harvesting for breast reconstruction has been already used for several years, a plethora of serious complications has been reported. Recently, minimally invasive surgical approaches, such as robotic-assisted technique, have been suggested with conflicting outcomes to overcome technical difficulties. Therefore, the literature review was conducted regarding robotic-assisted harvesting of the latissimus dorsi flap for breast reconstruction. A narrative review of the contemporary literature was performed in the PubMed database for the use of robotic-assisted surgery of latissimus dorsi muscle flap harvesting for breast reconstruction. Appropriate search terms were used, and specific inclusion and exclusion criteria were applied. Five studies met the inclusion criteria. A total of 32 cases of robotically assisted harvesting of pedicled latissimus dorsi muscle flap for implant-based breast reconstruction have been identified. All flaps were successfully harvested without converting in the traditional open procedure. There were no significant postoperative complications, expect from few cases of postoperative seromas, which were conservatively managed. Additionally, all patients were satisfied with their postoperative cosmetic outcome. The robotic-assisted harvesting technique of the latissimus dorsi flap for breast reconstruction is safe and comparable to the conventional methods. Reduced hospital stays and superior aesthetic outcome are the main advantages, while total cost and the difficulty of reaching the learning curve plateau are the main concerns regarding this modern and minimally invasive surgical approach

What scans we will read: imaging instrumentation trends in clinical oncology

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non- invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi- dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging