From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery

PURPOSE

Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.

METHODS

Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports.

RESULTS

The best-performing model using both quantitative evaluation and our visual ranking framework achieved a mean Dice score of 0.959. Quantitative metrics are positively associated with expert visual rankings. However, the predictive value of generated contours was limited with a sensitivity of 0.750 and a specificity of 0.433 when tested against pathology reports.

CONCLUSION

We present a clinical evaluation of deep learning models trained for intraoperative tumor segmentation in breast-conserving surgery. We demonstrate that automatic contouring is limited in predicting pathology margins despite achieving high performance on quantitative metrics.

3D magnetic seed localization for augmented reality in surgery

PURPOSE

For tumor resection, surgeons need to localize the tumor. For this purpose, a magnetic seed can be inserted into the tumor by a radiologist and, during surgery, a magnetic detection probe informs the distance to the seed for localization. In this case, the surgeon still needs to mentally reconstruct the position of the tumor from the probe's information. The purpose of this study is to develop and assess a method for 3D localization and visualization of the seed, facilitating the localization of the tumor.

METHODS

We propose a method for 3D localization of the magnetic seed by extending the magnetic detection probe with a tracking-based localization. We attach a position sensor (QR-code or optical marker) to the probe in order to track its 3D pose (respectively, using a head-mounted display with a camera or optical tracker). Following an acquisition protocol, the 3D probe tip and seed position are subsequently obtained by solving a system of equations based on the distances and the 3D probe poses.

RESULTS

The method was evaluated with an optical tracking system. An experimental setup using QR-code tracking (resp. using an optical marker) achieves an average of 1.6 mm (resp. 0.8 mm) 3D distance between the localized seed and the ground truth. Using a breast phantom setup, the average 3D distance is 4.7 mm with a QR-code and 2.1 mm with an optical marker.

CONCLUSION

Tracking the magnetic detection probe allows 3D localization of a magnetic seed, which opens doors for augmented reality target visualization during surgery. Such an approach should enhance the perception of the localized region of interest during the intervention, especially for breast tumor resection where magnetic seeds can already be used in the protocol.

Image-guided navigation system for minimally invasive total hip arthroplasty (MITHA) using an improved position-sensing marker

PURPOSE

Minimally invasive total hip arthroplasty (MITHA) is a treatment for hip arthritis, and it causes less tissue trauma, blood loss, and recovery time. However, the limited incision makes it difficult for surgeons to perceive the instruments' location and orientation. Computer-assisted navigation systems can help improve the medical outcome of MITHA. Directly applying existing navigation systems for MITHA, however, suffers from problems of bulky fiducial marker, severe feature-loss, multiple instruments tracking confusion, and radiation exposure. To tackle these problems, we propose an image-guided navigation system for MITHA using a novel position-sensing marker.

METHODS

A position-sensing marker is proposed to serve as the fiducial marker with high-density and multi-fold ID tags. It results in less feature span and enables the use of ID for each feature, overcoming the problem of bulky fiducial markers and multiple instruments tracking confusion. And the marker can be recognized even when a large part of locating features is obscured. As for the elimination of intraoperative radiation exposure, we propose a point-based method to achieve patient-image registration based on anatomical landmarks.

RESULTS

Quantitative experiments are conducted to evaluate the feasibility of our system. The accuracy of instrument positioning is achieved at 0.33 ± 0.18 mm, and that of patient-image registration is achieved at 0.79 ± 0.15 mm. And qualitative experiments are also performed, verifying that our system can be used in compact surgical spatial volume and can address severe feature-loss and tracking confusion problems. In addition, our system does not require any intraoperative medical scans.

CONCLUSION

Experimental results indicate that our proposed system can assist surgeons without larger space occupations, radiation exposure, and extra incision, showing its potential application value in MITHA.

Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters

Background

Breast cancer is a highly predominant destructive disease among women characterised with varied tumour biology, molecular subgroups and diverse clinicopathological specifications. The potentiality of machine learning to transform complex medical data into meaningful knowledge has led to its application in breast cancer detection and prognostic evaluation.

Objective

The emergence of data-driven diagnostic model for assisting clinicians in diagnostic decision making has gained an increasing curiosity in breast cancer identification and analysis. This motivated us to develop a breast cancer data-driven model for subtype classification more accurately.

Method

In this article, we proposed a firefly-support vector machine (SVM) breast cancer predictive model that uses clinicopathological and demographic data gathered from various tertiary care cancer hospitals or oncological centres to distinguish between patients with triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC).

Results

The results of the firefly-support vector machine (firefly-SVM) predictive model were distinguished from the traditional grid search-support vector machine (Grid-SVM) model, particle swarm optimisation-support vector machine (PSO-SVM) and genetic algorithm-support vector machine (GA-SVM) hybrid models through hyperparameter tuning. The findings show that the recommended firefly-SVM classification model outperformed other existing models in terms of prediction accuracy (93.4%, 86.6%, 69.6%) for automated SVM parameter selection. The effectiveness of the prediction model was also evaluated using well-known metrics, such as the F1-score, mean square error, area under the ROC curve, logarithmic loss and precision-recall curve.

Conclusion

Firefly-SVM predictive model may be treated as an alternate tool for breast cancer subgroup classification that would benefit the clinicians for managing the patient with proper treatment and diagnostic outcome.

Soft-tissue simulation of the breast for intraoperative navigation and fusion of preoperative planning

Computational preoperative planning offers the opportunity to reduce surgery time and patient risk. However, on soft tissues such as the breast, deviations between the preoperative and intraoperative settings largely limit the applicability of preoperative planning. In this work, we propose a high-performance accurate simulation model of the breast, to fuse preoperative information with the intraoperative deformation setting. Our simulation method encompasses three major elements: high-quality finite-element modeling (FEM), efficient handling of anatomical couplings for high-performance computation, and personalized parameter estimation from surface scans. We show the applicability of our method on two problems: 1) transforming high-quality preoperative scans to the intraoperative setting for fusion of preoperative planning data, and 2) real-time tracking of breast tumors for navigation during intraoperative radiotherapy. We have validated our methodology on a test cohort of nine patients who underwent tumor resection surgery and intraoperative radiotherapy, and we have quantitatively compared simulation results to intraoperative scans. The accuracy of our simulation results suggest clinical viability of the proposed methodology.

Bridging 3D Slicer and ROS2 for Image-Guided Robotic Interventions

Developing image-guided robotic systems requires access to flexible, open-source software. For image guidance, the open-source medical imaging platform 3D Slicer is one of the most adopted tools that can be used for research and prototyping. Similarly, for robotics, the open-source middleware suite robot operating system (ROS) is the standard development framework. In the past, there have been several "ad hoc" attempts made to bridge both tools; however, they are all reliant on middleware and custom interfaces. Additionally, none of these attempts have been successful in bridging access to the full suite of tools provided by ROS or 3D Slicer. Therefore, in this paper, we present the SlicerROS2 module, which was designed for the direct use of ROS2 packages and libraries within 3D Slicer. The module was developed to enable real-time visualization of robots, accommodate different robot configurations, and facilitate data transfer in both directions (between ROS and Slicer). We demonstrate the system on multiple robots with different configurations, evaluate the system performance and discuss an image-guided robotic intervention that can be prototyped with this module. This module can serve as a starting point for clinical system development that reduces the need for custom interfaces and time-intensive platform setup.

Application of Intraoperative Mass Spectrometry and Data Analytics for Oncological Margin Detection, A Review

OBJECTIVE

A common phase of early-stage oncological treatment is the surgical resection of cancerous tissue. The presence of cancer cells on the resection margin, referred to as positive margin, is correlated with the recurrence of cancer and may require re-operation, negatively impacting many facets of patient outcomes. There exists a significant gap in the surgeons ability to intraoperatively delineate between tissues. Mass spectrometry methods have shown considerable promise as intraoperative tissue profiling tools that can assist with the complete resection of cancer. To do so, the vastness of the information collected through these modalities must be digested, relying on robust and efficient extraction of insights through data analysis pipelines.

METHODS

We review clinical mass spectrometry literature and prioritize intraoperatively applied modalities. We also survey the data analysis methods employed in these studies.

RESULTS

Our review outlines the advantages and shortcomings of mass spectrometry imaging and point-based tissue probing methods. For each modality, we identify statistical, linear transformation and machine learning techniques that demonstrate high performance in classifying cancerous tissues across several organ systems. A limited number of studies presented results captured intraoperatively.

CONCLUSION

Through continued research of data centric techniques, like mass spectrometry, and the development of robust analysis approaches, intraoperative margin assessment is becoming feasible.

SIGNIFICANCE

By establishing the relatively short history of mass spectrometry techniques applied to surgical studies, we hope to inform future applications and aid in the selection of suitable data analysis frameworks for the development of intraoperative margin detection technologies.

Novel Multimodal, Multiscale Imaging System with Augmented Reality

A novel multimodal, multiscale imaging system with augmented reality capability were developed and characterized. The system offers 3D color reflectance imaging, 3D fluorescence imaging, and augmented reality in real time. Multiscale fluorescence imaging was enabled by developing and integrating an in vivo fiber-optic microscope. Real-time ultrasound-fluorescence multimodal imaging used optically tracked fiducial markers for registration. Tomographical data are also incorporated using optically tracked fiducial markers for registration. Furthermore, we characterized system performance and registration accuracy in a benchtop setting. The multiscale fluorescence imaging facilitated assessing the functional status of tissues, extending the minimal resolution of fluorescence imaging to ~17.5 µm. The system achieved a mean of Target Registration error of less than 2 mm for registering fluorescence images to ultrasound images and MRI-based 3D model, which is within clinically acceptable range. The low latency and high frame rate of the prototype system has shown the promise of applying the reported techniques in clinically relevant settings in the future.

Design and Workspace Analysis of a Differential Motion Rotary Style Breast Interventional Robot

Introduction

Magnetic Resonance Imaging has better resolution for soft tissue; at the same time, the robot can work in a stable manner for a long time. MRI image-guided breast interventional robots have attracted much attention due to their minimally invasive nature and accuracy. In this paper, a hydraulic-driven MRI-compatible breast interventional robot is proposed to perform breast interventional procedure.

Methods

First is the analysis of the design requirements of the hydraulic-driven MRI-compatible breast interventional robot, and then the design scheme is determined. Second, the three-dimensional model and the link frames are established. The workspace of the robot end point is solved by MATLAB/Simulink software. Then, the 3D printing technology is used to make a physical model of the MRI-compatible breast interventional robot. After assembly and debugging, the physical model is used for workspace verification, and the simulation result of the workspace shows that it is correct. Finally, the experimental research on the positioning error of the hydraulic drive is carried out, which established the theoretical foundation for the follow-up control research of the robot.

Results

The positioning error has nothing to do with the motion distance, speed, and length of the selected tubing. The errors are 0.564 mm, 0.534 mm, and 0.533 mm at different distances of 40 mm, 80 mm, and 120 mm, respectively. The errors are 0.552 mm, 0.564 mm, and 0.559 mm at different speeds of 3 mm/s, 5 mm/s, and 8 mm/s, respectively. The errors are 0.564 mm, 0.568 mm, and 0.548 mm for different lengths of 0.5 m, 1 m, and 1.6 m, respectively. Then, the robot's working space on the XOZ plane and the XOY plane meets the conditions.

Conclusion

The structure of a differential rotary breast interventional robot is determined, with the link frames assigned to the mechanism and the Denavit-Hartenberg parameters given. Workspace simulation of MRI-compatible breast interventional robot is done in MATLAB. The 3D printed MRI-compatible breast interventional robot is assembled and debugged to verify that its working space and positioning error meet the requirements.

An End-to-End Solution for Automatic Contouring of Tumor Region in Intraoperative Images of Breast Lumpectomy

Breast-conserving surgery, also known as lumpectomy, is an early stage breast cancer treatment that aims to spare as much healthy breast tissue as possible. A risk associated with lumpectomy is the presence of cancer positive margins post operation. Surgical navigation has been shown to reduce cancer positive margins but requires manual segmentation of the tumor intraoperatively. In this paper, we propose an end-to-end solution for automatic contouring of breast tumor from intraoperative ultrasound images using two convolutional neural network architectures, the U-Net and residual U-Net. The networks are trained on annotated intraoperative breast ultrasound images and evaluated on the quality of predicted segmentations. This work brings us one step closer to providing surgeons with an automated surgical navigation system that helps reduce cancer-positive margins during lumpectomy.

Electromagnetic (EM) catheter path tracking in ultrasound-guided brachytherapy of the breast

PURPOSE

To evaluate a novel navigation system for breast brachytherapy, based on ultrasound (US)-guided catheter needle implantations followed by electromagnetic (EM) tracking of catheter paths.

METHODS

Breast phantoms were produced, containing US-visible tumors. Ultrasound was used to localize the tumor pose and volume within the phantom, followed by planning an optimal catheter pattern through the tumor using navigation software. An electromagnetic (EM)-tracked catheter needle was used to insert the catheters in the desired pattern. The inserted catheters were visualized on a post-implant CT, serving as ground truth. Electromagnetic (EM) tracking and reconstruction of the inserted catheter paths were performed by pulling a flexible EM guidewire through each catheter, performed in two clinical brachytherapy suites. The accuracy of EM catheter tracking was evaluated by calculating the Hausdorff distance between the EM-tracked and CT-based catheter paths. The accuracy and clinical feasibility of EM catheter tracking were also evaluated in three breast cancer patients, performed in a separate experiment room.

RESULTS

A total of 71 catheter needles were implanted into 12 phantoms using US guidance and EM navigation, in an average ± SD time of 8.1 ± 2.9 min. The accuracy of EM catheter tracking was dependent on the brachytherapy suite: 2.0 ± 1.2 mm in suite 1 and 0.6 ± 0.2 mm in suite 2. EM catheter tracking was successfully performed in three breast brachytherapy patients. Catheter tracking typically took less than 5 min and had an average accuracy of 1.7 ± 0.3 mm.

CONCLUSION

Our preliminary results show a potential role for US guidance and EM needle navigation for implantation of catheters for breast brachytherapy. EM catheter tracking can accurately assess the implant geometry in breast brachytherapy patients. This methodology has the potential to evaluate catheter positions directly after the implantation and during the several fractions of the treatment.

A Digital Controlled Pulse Generator for a Possible Tumor Therapy Combining Irreversible Electroporation With Nanosecond Pulse Stimulation

The irreversible electroporation with microsecond electric pulses is a new ablation technique adopted in the tumor therapy worldwide. On the other hand, the nsPEF (nanosecond pulsed electric field) has been proved to provide a means to induce immunogenic cell death and elicits antitumor immunity, which is under intensive in-vitro and in-vivo studies and in clinical trials. Normally, one needs two different types of electric pulse generators for producing the pulses in the ranges of nanosecond and microsecond, respectively. In order to realize these two types of tumor treatments in complementary and optimize electrical pulse parameters, we have developed a compact high-voltage pulse generator with a wide pulse width tuning range, based on a capacitor discharging configuration digitally controlled by a silicon carbide MOSFET switching array through a pair of optic-coupler drivers. The developed digital pulse generator is capable of adjusting: pulse width over 100-100 μs, voltage over 0-2 kV and repetition rate up to 1.2 kHz. The pulse generator is designed in simulation, implemented and verified in experiments. The pulse generator is shown to deliver a complementary treatment on Murine melanoma B16 cell lines, i.e., triggering the cell early apoptosis under the 300 ns pulse stimulation while a complete killing under the 100 ns pulses. The pulse generator is further demonstrated to induce antitumor immunity in a preliminary in vivo study on the mice model.

OpenArm 2.0: Automated Segmentation of 3D Tissue Structures for Multi-Subject Study of Muscle Deformation Dynamics

We present a novel neural-network-based pipeline for segmentation of 3D muscle and bone structures from localized 2D ultrasound data of the human arm. Building from the U-Net [1] neural network framework, we examine various data augmentation techniques and training data sets to both optimize the network's performance on our data set and hypothesize strategies to better select training data, minimizing manual annotation time while maximizing performance. We then employ this pipeline to generate the OpenArm 2.0 data set, the first factorial set of multi-subject, multi-angle, multi-force scans of the arm with full volumetric annotation of the biceps and humerus. This data set has been made available on SimTK (https://simtk.org/projects/openarm) to enable future exploration of muscle force modeling, improved musculoskeletal graphics, and assistive device control.

Finite Element Breast Simulation for Sonography Image Registration

In case of female breast cancer, a breast conserving excision is often necessary. For this purpose, information from multiple medical imaging techniques have to be combined. Sonography imaging is essential for dense breast tissue and the only medical imaging technique available during surgery. During sonography of the outer breast quadrants the woman is usually in contralateral posterior oblique position, being in supine orientation while holding her ipsilateral arm over the head. Thus, these images cannot be directly registered with MRI or mammography images because these imaging technologies are performed in other patient positions with hands on the side of the body. Thus, we present a novel Finite Element approach how to enable a sonography image registration by showing the first time how to transfer the supine position with the arm straight on side into a supine position with the ipsilateral arm over the head which can be used to include information from MRI or mammography images. This approach is shown and validated with 3D scanner breast surface data as proof of concept. When comparing the simulation result with a 3D surface scan in supine orientation with the arm over the head, a mean surface distance error of 1.57 mm is achieved.

Passive Magnetic-Flux-Concentrator Based Electromagnetic Targeting System for Endobronchoscopy

In this paper, we demonstrate an innovative electromagnetic targeting system utilizing a passive magnetic-flux-concentrator for tracking endobronchoscope used in the diagnosis process of lung cancer tumors/lesions. The system consists of a magnetic-flux emitting coil, a magnetic-flux receiving electromagnets-array, and high permeability silicon-steel sheets rolled as a collar (as the passive magnetic-flux-concentrator) fixed in a guide sheath of an endobronchoscope. The emitting coil is used to produce AC magnetic-flux, which is consequently received by the receiving electromagnets-array. Due to the electromagnetic-induction, a voltage is induced in the receiving electromagnets-array. When the endobronchoscope’s guide sheath (with the silicon-steel collar) travels between the emitting coil and the receiving electromagnets-arrays, the magnetic flux is concentrated by the silicon-steel collar and thereby the induced voltage is changed. Through analyzing the voltage–pattern change, the location of the silicon–steel collar with the guide sheath is targeted. For testing, a bronchial-tree model for training medical doctors and operators is used to test our system. According to experimental results, the system is successfully verified to be able to target the endobronchoscope in the bronchial-tree model. The targeting errors on the x-, y- and z-axes are 9 mm, 10 mm, and 5 mm, respectively.

Accuracy assessment of target tracking using two 5-degrees-of-freedom wireless transponders

Purpose

Surgical navigation systems are generally only applied for targets in rigid areas. For non-rigid areas, real-time tumor tracking can be included to compensate for anatomical changes. The only clinically cleared system using a wireless electromagnetic tracking technique is the Calypso^® System (Varian Medical Systems Inc., USA), designed for radiotherapy. It is limited to tracking maximally three wireless 5-degrees-of-freedom (DOF) transponders, all used for tumor tracking. For surgical navigation, a surgical tool has to be tracked as well. In this study, we evaluated whether accurate 6DOF tumor tracking is possible using only two 5DOF transponders, leaving one transponder to track a tool.

Methods

Two methods were defined to derive 6DOF information out of two 5DOF transponders. The first method uses the vector information of both transponders (TTV), and the second method combines the vector information of one transponder with the distance vector between the transponders (OTV). The accuracy of tracking a rotating object was assessed for each method mimicking clinically relevant and worst-case configurations. Accuracy was compared to using all three transponders to derive 6DOF (Default method). An optical tracking system was used as a reference for accuracy.

Results

The TTV method performed best and was as accurate as the Default method for almost all transponder configurations (median errors < 0.5°, 95% confidence interval < 3°). Only when the angle between the transponders was less than 2°, the TTV method was inaccurate and the OTV method may be preferred. The accuracy of both methods was independent of the angle of rotation, and only the OTV method was sensitive to the plane of rotation.

Conclusion

These results indicate that accurate 6DOF tumor tracking is possible using only two 5DOF transponders. This encourages further development of a wireless EM surgical navigation approach using a readily available clinical system.

Real-time electromagnetic navigation for breast-conserving surgery using NaviKnife technology: A matched case-control study

Breast-conserving surgery (BCS) is a mainstay in breast cancer treatment. For nonpalpable breast cancers, current strategies have limited accuracy, contributing to high positive margin rates. We developed NaviKnife, a surgical navigation system based on real-time electromagnetic (EM) tracking. The goal of this study was to confirm the feasibility of intraoperative EM navigation in patients with nonpalpable breast cancer and to assess the potential value of surgical navigation. We recruited 40 patients with ultrasound visible, single, nonpalpable lesions, undergoing BCS. Feasibility was assessed by equipment functionality and sterility, acceptable duration of the operation, and surgeon feedback. Secondary outcomes included specimen volume, positive margin rate, and reoperation outcomes. Study patients were compared to a control group by a matched case-control analysis. There was no equipment failure or breach of sterility. The median operative time was 66 (44-119) minutes with NaviKnife vs 65 (34-158) minutes for the control (P = .64). NaviKnife contouring time was 3.2 (1.6-9) minutes. Surgeons rated navigation as easy to setup, easy to use, and useful in guiding nonpalpable tumor excision. The mean specimen volume was 95.4 ± 73.5 cm³ with NaviKnife and 140.7 ± 100.3 cm³ for the control (P = .01). The positive margin rate was 22.5% with NaviKnife and 28.7% for the control (P = .52). The re-excision specimen contained residual disease in 14.3% for NaviKnife and 50% for the control (P = .28). Our results demonstrate that real-time EM navigation is feasible in the operating room for BCS. Excisions performed with navigation result in the removal of less breast tissue without compromising postive margin rates.

Surgery Tutor for Computational Assessment of Technical Proficiency in Soft-Tissue Tumor Resection in a Simulated Setting

BACKGROUND

In competency-based medical education, progression between milestones requires reliable and valid methods of assessment. Surgery Tutor is an open-source motion tracking platform developed to objectively assess technical proficiency during open soft-tissue tumor resections in a simulated setting. The objective of our study was to provide evidence in support of construct validity of the scores obtained by Surgery Tutor. We hypothesized that Surgery Tutor would discriminate between novice, intermediate, and experienced operators.

METHODS

Thirty participants were assigned to novice, intermediate, or experienced groups, based on the number of prior soft-tissue resections performed. Each participant resected 2 palpable and 2 nonpalpable lesions from a soft-tissue phantom. Surgery Tutor was used to track hand and instrument motions, number of tumor breaches, and time to perform each resection. Mass of excised specimens and margin status were also recorded.

RESULTS

Surgery Tutor scores demonstrated "moderate" to "good" internal structure (test-retest reliability) for novice, intermediate, and experienced groups (interclass correlation coefficient = 0.596, 0.569, 0.737; p < 0.001). Evidence in support of construct validity (consequences) was demonstrated by comparing scores of novice, intermediate, and experienced participantsfor number of hand and instrument motions (690 ± 190, 597 ± 169, 469 ± 110; p < 0.001), number of tumor breaches (29 ± 34, 16 ± 11, 9 ± 6; p < 0.001), time per resection (677 ± 331 seconds, 561 ± 210 seconds, 449 ± 148 seconds; p < 0.001), mass of completely excised specimens (22 ± 7g, 21 ± 11g, 17 ± 6 g; p = 0.035), and rate of positive margin (68%, 50%, 28%; p < 0.001). There was "strong" and "moderate" relationships between motion scores and Objective Structured Assessment of Technical Skill scores, and time per resection and Objective Structured Assessment of Technical Skill scores respectively (r = -0.60, p < 0.001; r = -0.54, p < 0.001).

CONCLUSION

Surgery Tutor scores demonstrate evidenceof construct validity with regards to good internal structure, consequences, and relationship to other variables in the assessment of technical proficiency duringopen soft-tissue tumor resections in a simulated setting. Utilization of Surgery Tutor can provide formative feedback and objective assessment of surgical proficiency in a simulated setting.

A novel finite element model-based navigation system-supported workflow for breast tumor excision

In the case of female breast cancer, a breast-conserving excision is often desirable. This surgery is based on preoperatively gathered MRI, mammography, and sonography images. These images are recorded in multiple patient positions, e. g., 2D mammography images in standing position with a compressed breast and 3D MRI images in prone position. In contrast, the surgery happens in supine or beach chair position. Due to these different perspectives and the flexible, thus challenging, breast tissue, the excision puts high demands on the physician. Therefore, this publication presents a novel eight-step excision support workflow that can be used to include information captured preoperatively through medical imaging based on a finite element (FE) model. In addition, an indoor positioning system is integrated in the workflow in order to track surgical devices and the sonography transducer during surgery. The preoperative part of the navigation system-supported workflow is outlined exemplarily based on first experimental results including 3D scans of a patient in different patient positions and her MRI images. Graphical Abstract Finite Element model based navigation system supported workflow for breast tumor excision is based on eight steps and allows inclusion of information from medical images recorded in multiple patient positions.

Predicting Invasive Disease-Free Survival for Early-stage Breast Cancer Patients Using Follow-up Clinical Data

OBJECTIVE

Chinese women are seriously threatened by breast cancer with high morbidity and mortality. The lack of robust prognosis models results in difficulty for doctors to prepare an appropriate treatment plan that may prolong patient survival time. An alternative prognosis model framework to predict Invasive Disease-Free Survival (iDFS) for early-stage breast cancer patients, called MP4Ei, is proposed. MP4Ei framework gives an excellent performance to predict the relapse or metastasis breast cancer of Chinese patients in 5 years.

METHODS

MP4Ei is built based on statistical theory and gradient boosting decision tree framework. 5246 patients, derived from the Clinical Research Center for Breast (CRCB) in West China Hospital of Sichuan University, with early-stage (stage I-III) breast cancer are eligible for inclusion. Stratified feature selection, including statistical and ensemble methods, is adopted to select 23 out of the 89 patient features about the patient' demographics, diagnosis, pathology and therapy. Then 23 selected features as the input variables are imported into the XGBoost algorithm, with Bayesian parameter tuning and cross validation, to find out the optimum simplified model for 5-year iDFS prediction.

RESULTS

For eligible data, with 4196 patients (80%) for training, and with 1050 patients (20%) for testing, MP4Ei achieves comparable accuracy with AUC 0.8451, which has a significant advantage (p < 0.05).

CONCLUSION

This work demonstrates the complete iDFS prognosis model with very competitive performance.

SIGNIFICANCE

The proposed method in this paper could be used in clinical practice to predict patients' prognosis and future surviving state, which may help doctors make treatment plan.

Accuracy assessment of wireless transponder tracking in the operating room environment

PURPOSE

To evaluate the applicability of the Calypso^® wireless transponder tracking system (Varian Medical Systems Inc., USA) for real-time tumor motion tracking during surgical procedures on tumors in non-rigid target areas. An accuracy assessment was performed for an extended electromagnetic field of view (FoV) of 27.5 × 27.5 × 22.5 cm (which included the standard FoV of 14 × 14 × 19 cm) in which 5DOF wireless Beacon^® transponders can be tracked.

METHODS

Using a custom-made measurement setup, we assessed single transponder relative accuracy, absolute accuracy and jitter throughout the extended FoV at 1440 locations interspaced with 2.5 cm in each orthogonal direction. The NDI Polaris Spectra optical tracking system (OTS) was used as a reference. Measurements were taken in a room without surrounding distorting factors and repeated in an operating room (OR). In the OR, the influence of a carbon fiber and regular stainless steel OR tabletop was investigated.

RESULTS

The calibration of the OTS and transponder system resulted in an average root-mean-square error (RMSE) vector of 0.03 cm. For both the standard and extended FoV, all accuracy measures were dependent on transponder to tracking array (TA) distances and the absolute accuracy was also dependent on TA to OR tabletop distances. This latter influence was reproducible, and after calibrating this, the residual error was below 0.1 cm RMSE within the entire standard FoV. Within the extended FoV, this residual RMSE did not exceed 0.1 cm for transponder to TA distances up to 25 cm.

CONCLUSION

This study shows that transponder tracking is promising for accurate tumor tracking in the operating room. This applies when using the standard FoV, but also when using the extended FoV up to 25 cm above the TA, substantially increasing flexibility.

Real-time wireless tumor tracking during breast conserving surgery

PURPOSE

To evaluate a novel surgical navigation system for breast conserving surgery (BCS), based on real-time tumor tracking using the Calypso[Formula: see text] 4D Localization System (Varian Medical Systems Inc., USA). Navigation-guided breast conserving surgery (Nav-BCS) was compared to conventional iodine seed-guided BCS ([Formula: see text]I-BCS).

METHODS

Two breast phantom types were produced, containing spherical and complex tumors in which wireless transponders (Nav-BCS) or a iodine seed ([Formula: see text]I-BCS) were implanted. For navigation, orthogonal views and 3D volume renders of a CT of the phantom were shown, including a tumor segmentation and a predetermined resection margin. In the same views, a surgical pointer was tracked and visualized. [Formula: see text]I-BCS was performed according to standard protocol. Five surgical breast oncologists first performed a practice session with Nav-BCS, followed by two Nav-BCS and [Formula: see text]I-BCS sessions on spherical and complex tumors. Postoperative CT images of all resection specimens were registered to the preoperative CT. Main outcome measures were the minimum resection margin (in mm) and the excision times.

RESULTS

The rate of incomplete tumor resections was 6.7% for Nav-BCS and 20% for [Formula: see text]I-BCS. The minimum resection margins on the spherical tumors were 3.0 ± 1.4 mm for Nav-BCS and 2.5 ± 1.6 mm for [Formula: see text]I-BCS (p = 0.63). For the complex tumors, these were 2.2 ± 1.1 mm (Nav-BCS) and 0.9 ± 2.4 mm ([Formula: see text]I-BCS) (p = 0.32). Mean excision times on spherical and complex tumors were 9.5 ±  2.7 min and 9.4 ± 2.6 min (Nav-BCS), compared to 5.8 ± 2.2  min and 4.7 ± 3.4 min ([Formula: see text]I-BCS, both (p < 0.05).

CONCLUSIONS

The presented surgical navigation system improved the intra-operative awareness about tumor position and orientation, with the potential to improve surgical outcomes for non-palpable breast tumors. Results are positive, and participating surgeons were enthusiastic, but extended surgical experience on real breast tissue is required.

Supine Breast MRI Using Respiratory Triggering

RATIONALE AND OBJECTIVES

This study aims to evaluate if navigator-echo respiratory-triggered magnetic resonance acquisition can acquire supine high-quality breast magnetic resonance imaging (MRI).

MATERIALS AND METHODS

Supine respiratory-triggered magnetic resonance imaging (Trig-MRI) was compared to supine non-Trig-MRI to evaluate breathing-induced motion artifacts (group 1), and to conventional prone non-Trig-MRI (group 2, 16-channel breast coil), all at 3T. A 32-channel thorax coil was placed on top of a cover to prevent breast deformation. Ten volunteers were scanned in each group, including one patient. The acquisition time was recorded. Image quality was compared by visual examination and by calculation of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and image sharpness (IS).

RESULTS

Scan time increased from 56.5 seconds (non-Trig-MRI) to an average of 306 seconds with supine Trig-MRI (range: 120-540 seconds). In group 1, the median values (interquartile range) of SNR, CNR, and IS improved from 11.5 (6.0), 7.3 (3.1), and 0.23 (0.2) cm on supine non-Trig-MRI to 38.1 (29.1), 32.8 (29.7), and 0.12 (0) cm (all P < 0.01) on supine Trig-MRI. All qualitative image parameters in group 1 improved on supine Trig-MRI (all P < 0.01). In group 2, SNR and CNR improved from 14.7 (6.8) and 12.6 (5.6) on prone non-Trig-MRI to 36.2 (12.2) and 32.7 (12.1) (both P < 0.01) on supine Trig-MRI. IS was similar: 0.10 (0) cm vs 0.11 (0) cm (P = 0.88).

CONCLUSIONS

Acquisition of high-quality supine breast MRI is possible when respiratory triggering is applied, in a similar setup as during subsequent treatment. Image quality improved when compared to supine non-triggered breast MRI and prone breast MRI, but at the cost of increased acquisition time.

ConoSurf: Open-source 3D scanning system based on a conoscopic holography device for acquiring surgical surfaces

Background

A difficulty in computer‐assisted interventions is acquiring the patient's anatomy intraoperatively. Standard modalities have several limitations: low image quality (ultrasound), radiation exposure (computed tomography) or high costs (magnetic resonance imaging). An alternative approach uses a tracked pointer; however, the pointer causes tissue deformation and requires sterilizing. Recent proposals, utilizing a tracked conoscopic holography device, have shown promising results without the previously mentioned drawbacks.

Methods

We have developed an open‐source software system that enables real‐time surface scanning using a conoscopic holography device and a wide variety of tracking systems, integrated into pre‐existing and well‐supported software solutions.

Results

The mean target registration error of point measurements was 1.46 mm. For a quick guidance scan, surface reconstruction improved the surface registration error compared with point‐set registration.

Conclusions

We have presented a system enabling real‐time surface scanning using a tracked conoscopic holography device. Results show that it can be useful for acquiring the patient's anatomy during surgery.