Search

Basal cell carcinoma (BCC) is the most commonly diagnosed skin cancer and is treated by surgical resection. Incomplete tumor removal requires surgical revision, leading to significant healthcare costs and impaired cosmesis. We investigated the clinical feasibility of a surgical navigation system for BCC surgery, based on molecular tissue characterization using rapid evaporative ionization mass spectrometry (REIMS). REIMS enables direct tissue characterization by analysis of cell-specific molecules present within surgical smoke, produced during electrocautery tissue resection. A tissue characterization model was built by acquiring REIMS spectra of BCC, healthy skin and fat from ex vivo skin cancer specimens. This model was used for tissue characterization during navigated skin cancer surgery. Navigation was enabled by optical tracking and real-time visualization of the cautery relative to a contoured resection volume. The surgical smoke was aspirated into a mass spectrometer and directly analyzed with REIMS. Classified BCC was annotated at the real-time position of the cautery. Feasibility of the navigation system, and tissue classification accuracy for ex vivo and intraoperative surgery were evaluated. Fifty-four fresh excision specimens were used to build the ex vivo model of BCC, normal skin and fat, with 92% accuracy. While 3 surgeries were successfully navigated without breach of sterility, the intraoperative performance of the ex vivo model was low (

Basal cell carcinoma (BCC) is the most commonly diagnosed cancer and the number of diagnosis is growing worldwide due to increased exposure to solar radiation and the aging population. Reduction of positive margin rates when removing BCC leads to fewer revision surgeries and consequently lower health care costs, improved cosmetic outcomes and better patient care. In this study, we propose the first use of a perioperative mass spectrometry technology (iKnife) along with a deep learning framework for detection of BCC signatures from tissue burns. Resected surgical specimen were collected and inspected by a pathologist. With their guidance, data were collected by burning regions of the specimen labeled as BCC or normal, with the iKnife. Data included 190 scans of which 127 were normal and 63 were BCC. A data augmentation approach was proposed by modifying the location and intensity of the peaks of the original spectra, through noise addition in the time and frequency domains. A symmetric autoencoder was built by simultaneously optimizing the spectral reconstruction error and the classification accuracy. Using t-SNE, the latent space was visualized. The autoencoder achieved an accuracy (standard deviation) of 96.62 (1.35%) when classifying BCC and normal scans, a statistically significant improvement over the baseline state-of-the-art approach used in the literature. The t-SNE plot of the latent space distinctly showed the separability between BCC and normal data, not visible with the original data. Augmented data resulted in significant improvements to the classification accuracy of the baseline model. We demonstrate the utility of a deep learning framework applied to mass spectrometry data for surgical margin detection. We apply the proposed framework to an application with light surgical overhead and high incidence, the removal of BCC. The learnt models can accurately separate BCC from normal tissue.

Background: To analyze the feasibility and accuracy of micro–computed tomography (micro-CT) for surgical margin assessment in breast excision specimen. Materials and methods: Two data sets of 30 micro-CT scans were retrospectively evaluated for positive resection margins by four observers in two phases, using pathology as a gold standard. Results of phase 1 were evaluated to define micro-CT evaluation guidelines for phase 2. Interobserver agreement was also assessed (kappa). In addition, a prospective study was conducted in which 40 micro-CT scans were directly acquired, reconstructed, and evaluated for positive resection margins by one observer. A suspect positive resection margin on micro-CT was annotated onto the specimen with ink, enabling local validation by pathology. Main outcome measures were accuracy, sensitivity, specificity, and positive predictive value (PPV). Results: Average accuracy, sensitivity, specificity, and PPV for the four observers were 63%, 38%, 70%, and 22%, respectively, in phase 1 and 72%, 40%, 78%, and 26%, respectively, in phase 2. The interobserver agreement was fair [kappa (range), 0.31 (0.12-0.80) in phase 1 and 0.23 (0-0.43) in phase 2]. In the prospective study 70% of the surgical resection margins were correctly evaluated. Ten specimens were annotated for positive resection margins, which correlated with three positive and three close (

Breast conserving surgery is a standard cancer treatment to resect breast tumors while preserving healthy tissue. The reoperation rate can be as high as 35% due to the difficulties associated with detection of remaining cancer in surgical margins. REIMS is a mass spectrometry method that can address this challenge through real-time measurement of molecular signature of tissue. However, the collection of breast spectra to train a cancer detection model is time consuming and large samples sizes are not practical. We propose an application of self-supervised learning to improve the performance of cancer detection at surgical margins using a limited number of labelled data samples. A deep model is trained for the intermediate task of capturing latent features of REIMS data without the use of cancer labels. The model compensates for the small data size by dividing the spectra into smaller patches and shuffling their order, generating new instances. By interrogating the shuffled data and learning the order of its patches, the model captures the characteristics of the data. The learnt weights from the model are then transferred to a subsequent network and fine-tuned for cancer detection. The proposed method achieved the accuracy, sensitivity and specificity to 97%, 91% and 100%, respectively, in data from 144 cancer and normal REIMS samples.

One in five women who undergo breast conserving surgery will need a second revision surgery due to remaining tumor. The iKnife is a mass spectrometry modality that produces real-time margin information based on the metabolite signatures in surgical smoke. Using this modality and real-time tissue classification, surgeons could remove all cancerous tissue during the initial surgery, improving many facets of patient outcomes. An obstacle in developing a iKnife breast cancer recognition model is the destructive, time-consuming and sensitive nature of the data collection that limits the size of the datasets. We address these challenges by first, building a self-supervised learning model from limited, weakly labeled data. By doing so, the model can learn to contextualize the general features of iKnife data from a more accessible cancer type. Second, the trained model can then be applied to a cancer classification task on breast data. This domain adaptation allows for the transfer of learnt weights from models of one tissue type to another. Our datasets contained 320 skin burns (129 tumor burns, 191 normal burns) from 51 patients and 144 breast tissue burns (41 tumor and 103 normal) from 11 patients. We investigate the effect of different hyper-parameters on the performance of the final classifier. The proposed two-step method performed statistically significantly better than a baseline model (p-value

PURPOSE: Basal Cell Carcinoma (BCC) is the most common cancer in the world. Surgery is the standard treatment and margin assessment is used to evaluate the outcome. The presence of cancerous cells at the edge of resected tissue i.e., positive margin, can negatively impact patient outcomes and increase the probability of cancer recurrence. Novel mass spectrometry technologies paired with machine learning can provide surgeons with real-time feedback about margins to eliminate the need for resurgery. To our knowledge, this is the first study to report the performance of cancer detection using Graph Convolutional Networks (GCN) on mass spectrometry data from resected BCC samples. METHODS: The dataset used in this study is a subset of an ongoing clinical data acquired by our group and annotated with the help of a trained pathologist. There is a total number of 190 spectra in this dataset, including 127 normal and 63 BCC samples. We propose single-layer and multi-layer conversion methods to represent each mass spectrum as a structured graph. The graph classifier is developed based on the deep GCN structure to distinguish between cancer and normal spectra. The results are compared with the state of the art in mass spectra analysis. RESULTS: The classification performance of GCN with multi-layer representation without any data augmentation is comparable to the previous studies that have used augmentation. CONCLUSION: The results indicate the capability of the proposed graph-based analysis of mass spectrometry data for tissue characterization or real-time margin assessment during cancer surgery.

PURPOSE: Current lumbar puncture simulators lack visual feedback of the needle path. We propose a lumbar puncture simulator that introduces a visual virtual reality feedback to enhance the learning experience. This method incorporates virtual reality and a position tracking system. We aim to assess the advantages of the stereoscopy of virtual reality (VR) on needle insertion skills learning. METHODS: We scanned and rendered spine models into three-dimensional (3D) virtual models to be used in the lumbar puncture simulator. The motion of the needle was tracked relative to the spine model in real-time using electromagnetic tracking, which allows accurate replay of the needle insertion path. Using 3D Slicer and SlicerVR, we created a virtual environment with the tracked needle and spine. In this study, 23 medical students performed a traditional lumbar puncture procedure using the augmented simulator. The participants’ insertions were tracked and recorded, allowing them to review their procedure afterwards. Twelve students were randomized into a VR group; they reviewed their procedure in VR, while the Control group reviewed their procedures on computer monitor. Students completed a standard System Usability Survey (SUS) about the system, and a self-reported confidence scale (1-5) in performing lumbar puncture. RESULTS: We integrated VR visual feedback in a traditional lumbar puncture simulator. The VR group gave an average 70.4 on the System Usability Survey (SUS) vs. 66.8 of the Control group. The only negative feedback on VR was that students felt they required technical assistance to set it up (SUS4). The results show a general affinity for VR and its easeof- use. Furthermore, the mean confidence level rose from 1.6 to 3.2 in the VR group, vs. 1.8 to 3.1 in the Control group (1.6 vs. 1.3 improvement). CONCLUSION: The VR-augmented lumbar puncture simulator workflow incorporates visual feedback capabilities and accurate tracking of the needle relative to the spine model. Moreover, VR feedback allow for a more comprehensive spatial awareness of the target anatomy for improved learning.

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. 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. 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. 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.

To evaluate a novel surgical navigation system for breast conserving surgery (BCS), based on real-time tumor tracking using the Calypso $$\circledR $$ 4D Localization System (Varian Medical Systems Inc., USA). Navigation-guided breast conserving surgery (Nav-BCS) was compared to conventional iodine seed-guided BCS ( $$^{125}$$ I-BCS). Two breast phantom types were produced, containing spherical and complex tumors in which wireless transponders (Nav-BCS) or a iodine seed ( $$^{125}$$ 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. $$^{125}$$ 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 $$^{125}$$ 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. The rate of incomplete tumor resections was 6.7% for Nav-BCS and 20% for $$^{125}$$ 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 $$^{125}$$ I-BCS (p = 0.63). For the complex tumors, these were 2.2 ± 1.1 mm (Nav-BCS) and 0.9 ± 2.4 mm ( $$^{125}$$ 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 ( $$^{125}$$ I-BCS, both (p

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

Background Breast cancer screening, improved imaging and neoadjuvant systemic therapy (NST) have led to increased numbers of non-palpable tumours suitable for breast-conserving surgery (BCS). Accurate tumour localization is essential to achieve a complete resection in these patients. This study evaluated the role of radioactive seed localization (RSL) in improving breast- and axilla-conserving surgery in patients with breast cancer with or without NST. Methods Patients who underwent RSL between 2007 and 2014 were included. Learning curves were analysed by the rates of minimally involved (in situ/invasive tumour cells on a length of 0–4 mm on ink) and positive resection margins (over 4 mm on ink) after BCS, and the median resection volume over time. Results A total of 367 patients with in situ carcinomas and 199 with non-palpable invasive breast cancer underwent RSL before primary surgery. A further 697 patients had RSL before NST, of whom 206 also underwent RSL of a histologically verified axillary lymph node metastasis. BCS was performed in 93·2 and 87·9 per cent of patients undergoing primary surgery for in situ and invasive tumours respectively, and 57·5 per cent of those in the NST group. The rate of BCS with positive resection margins was low and stable over time in the three groups (9·1, 9·7 and 11·2 per cent respectively). The median resection volume decreased significantly with time in the invasive cancer and NST groups. Conclusion In the present study of more than 1200 patients and 7 years of experience, RSL was shown to facilitate breast- and axilla-conserving surgery in a diverse patient population. There was a significant reduction in resection volume while maintaining low positive resection margin rates after BCS.

Background and objectives Breast conserving surgery (BCS) can be challenging for large regions of ductal carcinoma in situ (DCIS), resulting in high rates of positive resection margins. Radioactive seed localization (RSL) using multiple radioactive iodine ( 125 I) seeds can be used to bracket extensive DCIS (eDCIS). The goal of this study was to retrospectively compare the use of a single or multiple 125 I seeds in RSL to enable BCS in patients with eDCIS. Methods All patients with eDCIS (area of ≥3.0 cm) who underwent either single or multiple-seed RSL between January 2008 and December 2016 were included. Patient, tumor and surgery characteristics were compared between both groups. Primary outcome measures were positive resection margin and re-operation rates. Results Respectively 48 and 58 patients with eDCIS underwent single- and multiple-seed RSL and subsequent BCS. The rate of positive resection margin (focal and more than focal) with single-seed RSL was 47.9%, compared to 29.3% with multiple-seed RSL ( p = 0.06 ). The re-operation rate was 39.6% with single-seed RSL and 20.7% in the multiple-seed RSL group ( p = 0.05 ). Conclusion Multiple-seed RSL enables bracketing of large areas of DCIS, with the potential to decrease the high rate of positive resection margins in this patient group.

CT Brain Perfusion imaging (CTP) is a diagnostic tool for initial evaluation of acute ischemic stroke patients. Head movement of the patients during acquisition limits its applicability. CTP data with excessive head movement must be excluded or corrected for accurate CTP analysis. Instead of manual selection by visual inspection, this study provides an automatic method to select unsuitable CTP data subject to excessive head movement. We propose a 3D image-registration based movement measurement that provides 6 rigid transformation parameters: 3 rotation angles and 3 translations. This method is based on the registration of CTP datasets with a non contrast CT image with a larger volume of interest as reference, which is always available as part of a standard protocol for stroke patients. All parameters from the 3D registration are compared to a set of threshold value to objectively decide whether the CTP dataset suitable for accurate CTP analysis or not. Thresholds for unacceptable head movement were derived using controlled movement experiments with CTP phantom data. Validation was done by comparing the automatic selection of unsuitable data with radiologists' manual selection using binary classification analysis. The accuracy of the method was 77% with a high sensitivity (95%) and fair specificity (56%). Since all these processes are carried automatically, it assures that clinical decision are not based upon faulty CTP analysis of data with head movement and it saves time in acute time-critically situations for acute ischemic stroke patients.

Deformable image registration is typically formulated as an optimization problem involving a linearly weighted combination of terms that correspond to objectives of interest (e.g. similarity, deformation magnitude). The weights, along with multiple other parameters, need to be manually tuned for each application, a task currently addressed mainly via trial-and-error approaches. Such approaches can only be successful if there is a sensible interplay between parameters, objectives, and desired registration outcome. This, however, is not well established. To study this interplay, we use multi-objective optimization, where multiple solutions exist that represent the optimal trade-offs between the objectives, forming a so-called Pareto front. Here, we focus on weight tuning. To study the space a user has to navigate during manual weight tuning, we randomly sample multiple linear combinations. To understand how these combinations relate to desirability of registration outcome, we associate with each outcome a mean target registration error (TRE) based on expert-defined anatomical landmarks. Further, we employ a multi-objective evolutionary algorithm that optimizes the weight combinations, yielding a Pareto front of solutions, which can be directly navigated by the user. To study how the complexity of manual weight tuning changes depending on the registration problem, we consider an easy problem, prone-to-prone breast MR image registration, and a hard problem, prone-to-supine breast MR image registration. Lastly, we investigate how guidance information as an additional objective influences the prone-to-supine registration outcome. Results show that the interplay between weights, objectives, and registration outcome makes manual weight tuning feasible for the prone-to-prone problem, but very challenging for the harder prone-to-supine problem. Here, patient-specific, multi-objective weight optimization is needed, obtaining a mean TRE of 13.6 mm without guidance information reduced to 7.3 mm with guidance information, but also providing a Pareto front that exhibits an intuitively sensible interplay between weights, objectives, and registration outcome, allowing outcome selection.

Objective Computed Tomography Perfusion (CTP) is a promising tool to support treatment decision for acute ischemic stroke patients. However, head movement during acquisition may limit its applicability. Information of the extent of head motion is currently lacking. Our purpose is to qualitatively and quantitatively assess the extent of head movement during acquisition. Methods From 103 consecutive patients admitted with suspicion of acute ischemic stroke, head movement in 220 CTP datasets was qualitatively categorized by experts as none, minimal, moderate, or severe. The movement was quantified using 3D registration of CTP volume data with non-contrast CT of the same patient; yielding 6 movement parameters for each time frame. The movement categorization was correlated with National Institutes of Health Stroke Scale (NIHSS) score and baseline characteristic using multinomial logistic regression and student's t -test respectively. Results Moderate and severe head movement occurred in almost 25% (25/103) of all patients with acute ischemic stroke. The registration technique quantified head movement with mean rotation angle up to 3.6° and 14°, and mean translation up to 9.1 mm and 22.6 mm for datasets classified as moderate and severe respectively. The rotation was predominantly in the axial plane (yaw) and the main translation was in the scan direction. There was no statistically significant association between movement classification and NIHSS score and baseline characteristics. Conclusions Moderate or severe head movement during CTP acquisition of acute stroke patients is quite common. The presented registration technique can be used to automatically quantify the movement during acquisition, which can assist identification of CTP datasets with excessive head movement.

Head movement during brain Computed Tomography Perfusion (CTP) can deteriorate perfusion analysis quality in acute ischemic stroke patients. We developed a method for automatic detection of CTP datasets with excessive head movement, based on 3D image-registration of CTP, with non-contrast CT providing transformation parameters. For parameter values exceeding predefined thresholds, the dataset was classified as `severely moved'. Threshold values were determined by digital CTP phantom experiments. The automated selection was compared to manual screening by 2 experienced radiologists for 114 brain CTP datasets. Based on receiver operator characteristics, optimal thresholds were found of respectively 1.0 degrees, 2.8 degrees and 6.9 degrees for pitch, roll and yaw, and 2.8 mm for z-axis translation. The proposed method had a sensitivity of 91.4% and a specificity of 82.3%. This method allows accurate automated detection of brain CTP datasets that are unsuitable for perfusion analysis

Head movement is common during CT brain perfusion (CTP) acquisition of patients with acute ischemic stroke. The effects of this movement on the accuracy of CTP analysis has not been studied previously. The purpose of this study was to quantify the effects of head movement on CTP analysis summary maps using simulated phantom data. A dynamic digital CTP phantom dataset of 25 time frames with a simulated infarct volume was generated. Head movement was simulated by specific translations and rotations of the phantom data. Summary maps from this transformed phantom data were compared to the original data using the volumetric dice similarity coefficient (DSC). DSC for both penumbra and core strongly decreased for rotation angles larger than approximately 1°, 2°, and 7° for, respectively, pitch, roll, and yaw. The accuracy is also sensitive for small translations in the z-direction only. Sudden movements introduced larger errors than gradual movement. These results indicate that CTP summary maps are sensitive to head movement, even for small rotations and translations. CTP scans with head movement larger than the presented values should be interpreted with extra care.