Precise Navigation Research Articles

Magnetic-Acoustic Sequentially Actuated CAR T Cell Microrobots for Precision Navigation and In Situ Antitumor Immunoactivation.

Despite its clinical success, chimeric antigen receptor T (CAR T)-cell immunotherapy remains limited in solid tumors, owing to the harsh physical barriers and immunosuppressive microenvironment. Here a CAR-T-cell-based live microrobot (M-CAR T) is created by decorating CAR T with immunomagnetic beads using click conjugation. M-CAR Ts are capable of magnetic-acoustic actuation for precision targeting and in situ activation of antitumor immune responses. Sequential actuation endows M-CAR Ts with magnetically actuated anti-flow and obstacle avoidance as well as tissue penetration driven by acoustic propulsion, enabling efficient migration and accumulation in artificial tumor models. In vivo, sequentially actuated M-CAR Ts achieves long-distance targeting and accumulate at the peritumoural area under programmable magnetic guidance, and subsequently acoustic tweezers actuate M-CAR Ts to migrateinto deep tumor tissues, resulting in a 6.6-fold increase in accumulated exogenous CD8+ CAR T cells compared with that without actuation. Anti-CD3/CD28 immunomagnetic beads stimulate infiltrated CAR T proliferation and activation in situ, significantly enhancing their antitumor efficacy. Thus, thissequential-actuation-guided cell microrobot combines the merits of autonomous targeting and penetration of intelligent robots with in situ T-cell immunoactivation, and holds considerable promise for precision navigation and cancer immunotherapies.

Improving the precision and accuracy of wildlife monitoring with multi‐constellation, multi‐frequency GNSS collars

AbstractThe use of global navigation satellite systems (GNSS) technology within the fields of ecology and biology has increased over recent years. With Global Positioning System, GLObalnaya NAvigatsionnaya Sputnikovaya Sistema, Galileo, and BeiDou systems fully deployed, >140 navigation satellites are currently available for navigation and high precision positioning applications. The technological improvements in GNSS devices, mainly due to the multi‐frequency capability, reduce signal acquisition time and increase the reliability, the continuity, and the accuracy of the estimated position, especially in complex environments like forests or areas with a steep topography. This study aims to test experimentally the influence of multi‐constellation multi‐frequency modules on the performance of the GNSS collars used to monitor wildlife. We applied static and kinematic tests designed to assess and compare the performance of GNSS collars equipped with a single‐frequency versus a multi‐frequency chipset. We evaluated the availability and continuity of solutions, number of satellites used, dilution of precision, precision, accuracy, and repeatability of these quality parameters for GNSS devices in southeastern Spain from February 2021 to June 2022. The results confirmed that the multi‐constellation multi‐frequency GNSS collar showed a stable and good performance in terms of the number of satellites used (>10), horizontal dilution of precision (<1.3), fix success rate (100%), mean location error (<1.5 m), and circular error probability (CEP), which was better by approximately one order of magnitude than the single‐frequency collar (1–10 m). The use of reliable and accurate GNSS devices will expand our knowledge of animal behavior and the interactions between species. Multi‐frequency GNSS collars allow collection of accurate locations, providing fine‐scale information about animal behavior (e.g., feeding strategies, competition for resources), whereas the single‐frequency GNSS collars can used for broad‐scale studies (e.g., home ranges, habitat use).

Performance Analysis of Real-Time GPS/Galileo Precise Point Positioning Integrated with Inertial Navigation System

The integration of global navigation satellite system (GNSS) precise point positioning (PPP) and inertial navigation system (INS) is widely used in navigation for its robustness and resilience, especially in case of GNSS signal blockage. With GNSS modernization, a variety of PPP models have been developed and studied, which has also led to various PPP/INS integration methods. In this study, we investigated the performance of a real-time GPS/Galileo zero-difference ionosphere-free (IF) PPP/INS integration with the application of uncombined bias products. This uncombined bias correction was independent of PPP modeling on the user side and also enabled carrier phase ambiguity resolution (AR). CNES (Centre National d'Etudes Spatiales) real-time orbit, clock, and uncombined bias products were used. Six positioning modes were evaluated, including PPP, PPP/INS loosely coupled integration (LCI), PPP/INS tightly coupled integration (TCI), and three of these with uncombined bias correction through a train positioning test in an open sky environment and two van positioning tests at a complex road and city center. All of the tests used a tactical-grade inertial measurement unit (IMU). In the train test, we found that ambiguity-float PPP had almost identical performance with LCI and TCI, which reached an accuracy of 8.5, 5.7, and 4.9 cm in the north (N), east (E) and up (U) direction, respectively. After AR, significant improvements on the east error component were achieved, which were 47%, 40%, and 38% for PPP-AR, PPP-AR/INS LCI, and PPP-AR/INS TCI, respectively. In the van tests, frequent signal interruptions due to bridges, vegetation, and city canyons make the IF AR difficult. TCI achieved the highest accuracies, which were 32, 29, and 41 cm for the N/E/U component, respectively, and also effectively eliminated the solution re-convergence in PPP.

Optimization of Basepoint Configuration in Localization of Signal Interference Device

Precise navigation, as a method for guiding vehicles from one point to another, is an important subject these days especially in navigation of aircraft. Global navigation satellite systems (GNSSs) are capable tools for such a purpose. Any intentional or unintentional interference in satellite signals may cause risks of deadly accidents. Therefore, it is tremendously important to control airports or harbors and locate any existing radio frequency interference device. This localization can be done based on measuring time of arrival (TOA), angle of arrival (AOA), or time difference of arrival (TDOA) of signals from the device to sensors or receivers at some basepoints. In this article, a method is proposed based on these arrivals for optimizing the configuration created by these basepoints from a large grid of points covering a control area. Furthermore, a simulation test was performed to verify the theory, and after that a control network was designed and optimized for the international Landvetter Airport of Sweden. Our simulation studies show that when the AOA is used, our optimization is more robust with respect to the control grid resolution. In addition, optimization based on the TDOA improves the coverage over the control area with a significant reduction of error of control points, but because of the special geometric shape of the Landvetter Airport, such an optimization was not successful.

Low-latency GNSS multipath simulation and building wall detection in urban environments

Precise navigation for fully autonomous driving—especially in dense urban areas—requires periodic precise position estimates. Global Navigation Satellite System (GNSS) technology has the potential to provide absolute positioning accuracy at a centimeter level. However, buildings in urban environments cause signal distortions and signal reflections—the so-called multipath—which are the most challenging parts in the GNSS error budget. Hence, we developed a scalable real-time multipath simulator for mitigating potential multipath receptions. The simulator uses three-dimensional (3D) building information, satellite, and user positions. The key drivers of latency are the calculation of reflection, diffraction, and line-of-sight, as well as the response time of the 3D building model database. The memory manager of the graphic processing units (GPUs) in combination with a dedicated load balancer enables fast and efficient multipath analysis. Selected case studies demonstrate the simulator’s potential to significantly improve the position accuracy of the processing engine. The use of the multipath simulator reduces the error in 61% of the error measurements in a stress test scenario to less than half of the non-multipath processing. The scalability of the simulator is demonstrated by combining the multipath simulator with a traffic simulator. Furthermore, we present a novel methodology for the detection of walls using GNSS signals to better account for incomplete or erroneous 3D building information in GNSS signal processing.

Precision Navigation of Venous Thrombosis Guided by Viscosity-Activatable Near-Infrared Fluorescence.

Thrombus are blood clots formed by abnormal hemostasis in blood vessels and are closely associated with various diseases such as pulmonary embolism, myocardial infarction and stroke. Early diagnosis and treatment of thrombus is the key to reducing the high risk of thrombotic disease. Given that early thrombus is small in early size, free instability, wide regional distribution and fast formation, it is urgent to develop all-inclusive detection methods that combine high signal-to-noise ratio, in situ dynamic and rapid in-depth tissue imaging. Near-infrared (NIR) fluorescence imaging, with its excellent high spatiotemporal resolution and tissue penetration depth, is a powerful technique for direct visualization of thrombotic events in situ. Considering the fibrin highly expressed in the thrombus is a typical thrombotic target. Moreover, the viscosity of the thrombus is markedly higher than its surroundings. Therefore, we developed a fibrin-targeting and viscosity-activating thrombus NIR fluorescent probe (TIR-V) for high-resolution and high-sensitivity in situ lighten-up thrombus. TIR-V has the advantages of good thrombus targeting, significant "off-on" fluorescence specific response to viscosity, bright NIR fluorescence and good biocompatibility. The thrombus is clearly delineated by a high signal-to-noise ratio NIR fluorescence imaging, enabling imaging detection and precise navigation of thrombotic regions. This work demonstrates the potential of TIR-V as a bifunctional probe for definitive diagnostic imaging and direct navigation of thrombotic lesions in clinical applications.

Common Android Smartphones and Apps for Cost-Efficient GNSS Data Collection: An Overview

Global Navigation Satellite System (GNSS) sensors are increasingly considered as major sources of precise Position, Navigation, and Timing (PNT) information. Android-based smartphones contain inbuilt GNSS sensors, and they now contribute to a major global GNSS market segment. Various hardware and Android-based applications (App) developments are witnessed to best exploit the current multi-constellation, multi-frequency GNSS environment. Since Android version 7.0 (Nougat) and Location Application Programming Interface (API) 24, recording GNSS raw data in the smartphone became possible. This paper describes the potential of commercial Smartphones and reviews the capabilities of Android Apps for cost-efficient GNSS measurements for the geospatial community and a novel classification of the Apps based on their capabilities and usability. Results on the positioning capability of common smartphones using real-time data show the achievable modest solution precision, and these are suitable for use of the devices in geomatics applications, improved App development, and for IoT-based system and infrastructure development.

Equatorial Plasma Bubbles Identification with Single Frequency GNSS Receiver Data

Ionosphere is the highest contributor of positional error in Global Navigation Satellite System (GNSS) owing to the trans-ionospheric signal delay, distortion and possible outage. Equatorial and Low latitudinal ionosphere experiences frequent perturbations in plasma density most of which is due to the presence of Equatorial Plasma Bubbles (EPBs). In order to improve positional and navigational precision of GNSS signals, EPBs must be identified and corrected. EPBs begin to manifest post-sunset and continue to thrive past midnight. This paper proposes a Rate of TEC Index (ROTI) based EPB identifier using single frequency GNSS receiver data. GNSS data has been collected from the archives of Scripps Orbit and Permanent Array Center (SOPAC). The raw data has been downloaded from the global continuous network station, IISC, located at Bangalore (13.0219°N, 77.5671°E) in Receiver Independent Exchange (RINEX) format. Analysis of EPB occurrence has been carried out for six consecutive years in the second half of the 24th solar cycle (2014-2019). The results show a clear surge in the number of EPBs identified in the course of equinoxes.

TAKEN: A Traffic Knowledge-Based Navigation System for Connected and Autonomous Vehicles

Connected and autonomous vehicles (CAVs) have witnessed significant attention from industries, and academia for research and developments towards the on-road realisation of the technology. State-of-the-art CAVs utilise existing navigation systems for mobility and travel path planning. However, reliable connectivity to navigation systems is not guaranteed, particularly in urban road traffic environments with high-rise buildings, nearby roads and multi-level flyovers. In this connection, this paper presents TAKEN-Traffic Knowledge-based Navigation for enabling CAVs in urban road traffic environments. A traffic analysis model is proposed for mining the sensor-oriented traffic data to generate a precise navigation path for the vehicle. A knowledge-sharing method is developed for collecting and generating new traffic knowledge from on-road vehicles. CAVs navigation is executed using the information enabled by traffic knowledge and analysis. The experimental performance evaluation results attest to the benefits of TAKEN in the precise navigation of CAVs in urban traffic environments.

Visible CCD Camera-Guided Photoacoustic Imaging System for Precise Navigation during Functional Rat Brain Imaging

In photoacoustic (PA) imaging, tissue absorbs specific wavelengths of light. The absorbed energy results in thermal expansion that generates ultrasound waves that are reconstructed into images. Existing commercial PA imaging systems for preclinical brain imaging are limited by imprecise positioning capabilities and inflexible user interfaces. We introduce a new visible charge-coupled device (CCD) camera-guided photoacoustic imaging (ViCPAI) system that integrates an ultrasound (US) transducer and a data acquisition platform with a CCD camera for positioning. The CCD camera accurately positions the US probe at the measurement location. The programmable MATLAB-based platform has an intuitive user interface. In vitro carbon fiber and in vivo animal experiments were performed to investigate the precise positioning and imaging capabilities of the ViCPAI system. We demonstrated real-time capturing of bilateral cerebral hemodynamic changes during (1) forelimb electrical stimulation under normal conditions, (2) forelimb stimulation after right brain focal photothrombotic ischemia (PTI) stroke, and (3) progression of KCl-induced cortical spreading depression (CSD). The ViCPAI system accurately located target areas and achieved reproducible positioning, which is crucial in animal and clinical experiments. In animal experiments, the ViCPAI system was used to investigate bilateral cerebral cortex responses to left forelimb electrical stimulation before and after stroke, showing that the CBV and SO2 in the right primary somatosensory cortex of the forelimb (S1FL) region were significantly changed by left forelimb electrical stimulation before stroke. No CBV or SO2 changes were observed in the bilateral cortex in the S1FL area in response to left forelimb electrical stimulation after stroke. While monitoring CSD progression, the ViCPAI system accurately locates the S1FL area and returns to the same position after the probe moves, demonstrating reproducible positioning and reducing positioning errors. The ViCPAI system utilizes the real-time precise positioning capability of CCD cameras to overcome various challenges in preclinical and clinical studies.

Point Cloud-Based Spacecraft Self-Localization for Distant Small Body Exploration

Image-based terrain-relative localization is expected to play an important role in precision navigation in various small-body missions. In the Hayabusa2 mission, accurate navigation was achieved by manual matching between the asteroid image and a set of vertices of a shape model of the target body, called a “point cloud.” The manual process intentionally excludes invisible regions and adopts visible parts for matching. In this paper, an autonomous optical localization using the point cloud, which will take the place of the manually matching process, is proposed. The proposed method performs matching with the asteroid rims derived from the captured image and point cloud, respectively. Furthermore, the proposed method allows selecting the area used for matching by adding 1-bit information, representing the visible or invisible flag, to the point cloud. The proposed method has achieved the position estimation accuracy of 0.23 [px] (1σ) in simulations with various shading conditions. This paper applies the proposed method to the Hayabusa2 flight data and shows that the proposed method bears comparison with the Hayabusa2 team estimation results.

Vision Based Docking of Multiple Satellites with an Uncooperative Target

With the ever-growing number of space debris in orbit, the need to prevent further space pollution is becoming more and more apparent. Refueling, servicing, inspection and deorbiting of spacecraft are some example missions that require precise navigation and docking in space. Having multiple, collaborating robots handling these tasks can greatly increase the efficiency of the mission in terms of time and cost. This article will introduce a modern and efficient control architecture for satellites on collaborative docking missions. The proposed architecture uses a centralized scheme that combines state-of-the-art, ad-hoc implementations of algorithms and techniques to maximize robustness and flexibility. It is based on a Model Predictive Controller (MPC) for which, an efficient cost function and constraint sets are designed to ensure safe and accurate docking. A simulation environment is also presented to validate and test the proposed control scheme.

Hardware approach to brain tumour detection using AI concepts using real time embedded systems with Raspberry pi

Tumors have become the second leading cause of cancer nowadays, posing significant risks to numerous patients. The medical field is in urgent need of rapid, automated, efficient, and reliable techniques to detect tumors, especially brain tumors. Early and accurate detection plays a crucial role in successful treatment and keeping patients safe. To address this challenge, various image processing techniques are employed in the medical domain. These advancements have allowed doctors to administer appropriate treatments, leading to the successful recovery of many tumor patients. Tumors are characterized by the abnormal growth of cells, which proliferate uncontrollably. Brain tumors, in particular, can be devastating as they compete with healthy cells and tissues for essential nutrients, ultimately resulting in brain dysfunction. Traditionally, doctors have relied on manual examination of MR images to identify the location and extent of brain tumors. Unfortunately, this approach is prone to errors and can be extremely time-consuming. To overcome these limitations, we have implemented a cutting-edge deep learning architecture known as Convolution Neural Network (CNN), a type of Neural Network (NN) that utilizes Transfer Learning. This CNN-based model enables us to automatically detect the presence of brain tumors in medical images with high accuracy. If a tumor is detected, the model outputs a positive result; otherwise, it indicates the absence of a tumor. In our approach, we use K-means clustering in conjunction with Raspberry Pi to precisely pinpoint the location of the brain tumor. This ensures targeted and efficient treatment planning. Additionally, we have integrated an Arduino controller to facilitate the movement of robotic wheels, allowing for precise navigation to the exact location of the tumor during medical interventions. Overall, our system represents a significant advancement in tumor detection and localization, offering faster and more reliable results compared to manual methods. By leveraging state-of-the-art technology and innovative techniques, we strive to enhance patient outcomes, ultimately saving more lives in the fight against brain tumors.

Multi-Signal Approaches for Repeated Sampling Schemes in Inertial Sensor Calibration

Inertial sensor calibration plays a progressively important role in many areas of research among which navigation engineering. By performing this task accurately, it is possible to significantly increase general navigation performance by correctly filtering out the deterministic and stochastic measurement errors that characterize such devices. While different techniques are available to model and remove the deterministic errors, there has been considerable research over the past years with respect to modelling the stochastic errors which have complex structures. In order to do the latter, different replicates of these error signals are collected and a model is identified and estimated based on one of these replicates. While this procedure has allowed to improve navigation performance, it has not yet taken advantage of the information coming from all the other replicates collected on the same sensor. However, it has been observed that there is often a change of error behaviour between replicates which can also be explained by different (constant) external conditions under which each replicate was taken. Whatever the reason for the difference between replicates, it appears that the model structure remains the same between replicates but the parameter values vary. Assuming the model structure has been identified, in this work we therefore consider and study the properties of different approaches that allow to combine the information from all replicates considering this phenomenon, confirming their validity both in simulation settings and also when applied to real inertial sensor error signals. By taking into account parameter variation between replicates, this work highlights how these approaches can improve the average navigation precision as well as obtain reliable estimates of the uncertainty of the navigation solution.

Radiopaque Nanorobots as Magnetically Navigable Contrast Agents for Localized In Vivo Imaging of the Gastrointestinal Tract.

Magnetic nanorobots offer wireless navigation capability in hard-to-reach areas of the human body for targeted therapy and diagnosis. Though in vivo imaging is required for guidance of the magnetic nanorobots toward the target areas, most of the imaging techniques are inadequate to reveal the potential locomotion routes. This work proposes the use of radiopaque magnetic nanorobotsalong with microcomputed tomography (microCT) for localized in vivo imaging applications. The nanorobots consist of a contrast agent, barium sulfate (BaSO4 ), magnetized by the decoration of magnetite (Fe3 O4 ) particles. The magnetic features lead to actuation under rotating magnetic fields and enable precise navigation in a microfluidic channel used to simulate confined spaces of the body. In this channel, the intrinsic radiopacity of the nanorobots also provides the possibility to reveal the internal structures by X-ray contrast. Furthermore, in vitro analysis indicates nontoxicity of the nanorobots. In vivo experiments demonstrate localization of the nanorobots in a specific part of the gastrointestinal (GI) tract upon the influence of the magnetic field, indicating the efficient control even in the presence of natural peristaltic movements. The nanorobots reported here highlight that smart nanorobotic contrast agents can improve the current imaging-based diagnosis techniques by providing untethered controllability in vivo.

Superstable homogeneous lipiodol-ICG formulation: initial feasibility and first-in-human clinical application for ruptured hepatocellular carcinoma.

The most common treatment of spontaneous tumor rupture hemorrhage (STRH) is transcatheter arterial embolization (TAE) followed by liver resection, and surgical navigation using near-infrared fluorescence is effective method for detecting hidden lesions and ill-defined tumor boundaries. However, due to the blockage of the tumor-supplying artery after effective TAE treatment, it is difficult to deliver sufficient fluorescent probes to the tumor region. In this study, we report on the successful application of superstable homogeneous intermixed formulation technology (SHIFT) in precise conversion hepatectomy for ruptured hepatocellular carcinoma (HCC). A homogeneous lipiodol-ICG formulation obtained by SHIFT (SHIFT-ICG) was developed for clinical practice for STRH. A ruptured HCC patient received the combined protocol for embolization and fluorescence surgical navigation and exhibited excellent hemostatic effect. Lipiodol and ICG were both effectively deposited in the primary lesion, including a small metastatic lesion. In follow-up laparoscopic hepatectomy, SHIFT-ICG could clearly and precisely image the full tumor regions and boundaries in real time, and even indistinguishable satellite lesions still expressed a remarkable fluorescence intensity. In conclusion, the simple and green SHIFT-ICG formulation can be effectively used in emergency embolization hemostasis and later precise fluorescence navigation hepatectomy in patients with ruptured HCC bleeding and has high clinical application value.

Design and Evaluation of a Learning-Based Vascular Interventional Surgery Robot

Interventional therapy is one of the most effective methods for diagnosing and treating vascular-related diseases at present. It relies on achieving precise and safe navigation of intravascular tools within a patient’s vasculature. Vascular Interventional Surgical Robots (VISR) can reduce surgeons’ exposure to operational hazards including radiation. However, the absence of apt position control and force feedback remains a challenge. This study presents an isomorphic master–slave VISR for precise navigation of endovascular tools viz. catheters and guidewires. The master console aids operators in issuing manipulation commands and logs feedback from the force, rotation, and translation data. The slave manipulator uses the commands received from the master platform for actual tool navigation. However, precise master–slave position control and force feedback are precursors for optimal patient outcomes. This study utilized a fuzzy-PID controller for precise tool navigation and a neural network model for resistance force modulation with 50 mN precision. Furthermore, we evaluated the performance of using the learning-based models within our VISR and compared it with the performances from conventional methods. Results show that the models enhanced the proposed robotic system with better navigation precision, faster response speed, and improved force measurement capabilities.

Design and Installed Performance Analysis of a Miniaturized All-GNSS Bands Antenna Array for Robust Navigation on UAV Platforms.

Global navigation satellite systems (GNSS) are vital technologies of our age and serve a plethora of industries that rely on precise positioning for automation, efficiency, and safety. Emerging applications of unmanned aerial vehicles (UAV) in critical applications like security, surveillance, critical logistics and defense demand precise and robust navigation capabilities even in challenging environments with high multipath or (un-)intended interference. The design of robust GNSS receivers for UAV applications, capable of suppressing interfering signals, is challenging due to the need for multi-antenna systems and the stringent requirements on hardware to be lightweight and miniaturized enough to fit onto small mobile platforms. In order to overcome these limitations, the present article details a four-element wideband antenna array, fitting into a 100 mm diameter footprint. The array is capable to operate across all GNSS frequency bands while incorporating, if needed, a multipath mitigation solution. The antenna design relies on a modular concept with 3D printed Dielectric Resonator Antennas (DRAs) and vertical choke rings. The antenna performance is evaluated in terms of its radiation pattern via installed antenna simulations and measurements in an anechoic chamber. The effect of different installation heights on the antenna pattern is studied. Furthermore, GNSS measurements carried out with the array alone and mounted on the UAV are presented.

Performance of GPS and IMU sensor fusion using unscented Kalman filter for precise i-Boat navigation in infinite wide waters

The Unmanned Surface Vehicle (USV) navigation system needs an accurate, firm, and reliable performance to avoid obstacles, as well as carry out automatic movements during missions. The Global Positioning System (GPS) is often used in these systems to provide absolute position information. However, the GPS measurements are affected by external conditions such as atmospheric bias and multipath effects. This leads to the inability of the stand-alone GPS to provide accurate positioning for the USV systems. One of the solutions to correct the errors of this sensor is by conducting GPS and Inertial Measurement Unit (IMU) fusion. The IMU sensor is complementary to the GPS and not affected by external conditions. However, it accumulates noise as time elapses. Therefore, this study aims to determine the fusion of the GPS and IMU sensors for the i-Boat navigation system, which is a USV developed by Institut Teknologi Sepuluh Nopember (ITS) Surabaya. Using the Unscented Kalman filter (UKF), sensor fusion was carried out based on the state equation defined by the dynamic and kinematic mathematical model of ship motion in 6 degrees of freedom. Then the performance of this model was tested through several simulations using different combinations of attitude measurement data. Two scenarios were conducted in the simulations: attitude measurement inclusion and exclusion (Scenarios I and II, respectively). The results showed that the position estimation in Scenario II was better than in Scenario I, with the Root Mean Square Error (RMSE) value of 0.062 m. Further simulations showed that the presence of attitude measurement data caused a decrease in the fusion accuracy. The UKF simulation with eight measurement parameters (Scenarios A, B and C) and seven measurement parameters (Scenarios D, E and F), as well as analytical attitude movement, indicated that yaw data had the largest noise accumulation compared to roll and pitch.

Updates and Debate Concerning Margin Adequacy and Management following Breast-Conserving Surgery

Objective: Breast-conserving surgery (BCS) experienced a significant surge during the last decades due to the increase of early breast cancer detection. Central to the discussion is margin adequacy which represents one of the most significant predictive factors for local relapse. This paper aims to shed light on the problem of margins in breast surgery. Mechanism: We performed a systematic narrative review of the literature by conducting a search using Medline/PubMed, Scopus, and Embase. The following keywords were considered: “breast-conserving surgery” AND “margins”/“margin”. Findings in Brief: In the case of invasive breast cancer, “no ink on tumor” can be considered an adequate margin, while for ductal carcinoma in situ (DCIS), a distance of 2 mm from tumor should be obtained. Many novel tools have been developed based both on the latest radiological imaging techniques and on the tissue expression of certain markers, with the aim of precise navigation of tumor excision and intraoperative evaluation of cavity excision margins. Oncoplastic surgery can be considered oncologically safe while improving the cosmetic outcome and patients’ quality of life. The appropriate use of adjuvant treatments in the context of a multidisciplinary and personalized management of breast cancer is the only means to omit a second intervention in some carefully selected cases. Conclusions: Debate still exists concerning the definition of adequate clear margin following BCS for DCIS. Further studies are required to better assess multimodal treatment approaches in this condition.