Breast cancer surgery with augmented reality

© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Introduction: Innovations in 3D spatial technology and augmented reality imaging driven by digital high-tech industrial science have accelerated experimental advances in breast cancer imaging and the development of medical procedures aimed to reduce invasiveness. Presentation of case: A 57-year-old post-menopausal woman presented with screen-detected left-sided breast cancer. After undergoing all staging and pre-operative studies the patient was proposed for conservative breast surgery with tumor localization. During surgery, an experimental digital and non-invasive intra-operative localization method with augmented reality was compared with the standard pre-operative localization with carbon tattooing (institutional protocol). The breast surgeon wearing an augmented reality headset (Hololens) was able to visualize the tumor location projection inside the patient's left breast in the usual supine position. Discussion: This work describes, to our knowledge, the first experimental test with a digital non-invasive method for intra-operative breast cancer localization using augmented reality to guide breast conservative surgery. In this case, a successful overlap of the previous standard pre-operative marks with carbon tattooing and tumor visualization inside the patient's breast with augmented reality was obtained. Conclusion: Breast cancer conservative guided surgery with augmented reality can pave the way for a digital non-invasive method for intra-operative tumor localization.info:eu-repo/semantics/publishedVersio

Evaluating the ability of an artificial-intelligence cloud-based platform designed to provide information prior to locoregional therapy for breast cancer in improving patient's satisfaction with therapy: the CINDERELLA trial

Background: Breast cancer therapy improved significantly, allowing for different surgical approaches for the same disease stage, therefore offering patients different aesthetic outcomes with similar locoregional control. The purpose of the CINDERELLA trial is to evaluate an artificial-intelligence (AI) cloud-based platform (CINDERELLA platform) vs the standard approach for patient education prior to therapy. Methods: A prospective randomized international multicentre trial comparing two methods for patient education prior to therapy. After institutional ethics approval and a written informed consent, patients planned for locoregional treatment will be randomized to the intervention (CINDERELLA platform) or controls. The patients in the intervention arm will use the newly designed web-application (CINDERELLA platform, CINDERELLA APProach) to access the information related to surgery and/or radiotherapy. Using an AI system, the platform will provide the patient with a picture of her own aesthetic outcome resulting from the surgical procedure she chooses, and an objective evaluation of this aesthetic outcome (e.g., good/fair). The control group will have access to the standard approach. The primary objectives of the trial will be i) to examine the differences between the treatment arms with regards to patients' pre-treatment expectations and the final aesthetic outcomes and ii) in the experimental arm only, the agreement of the pre-treatment AI-evaluation (output) and patient's post-therapy self-evaluation. Discussion: The project aims to develop an easy-to-use cost-effective AI-powered tool that improves shared decision-making processes. We assume that the CINDERELLA APProach will lead to higher satisfaction, better psychosocial status, and wellbeing of breast cancer patients, and reduce the need for additional surgeries to improve aesthetic outcome

Deep Aesthetic Assessment and Retrieval of Breast Cancer Treatment Outcomes

Treatments for breast cancer have continued to evolve and improve in recent years, resulting in a substantial increase in survival rates, with approximately 80\% of patients having a 10-year survival period. Given the serious impact that breast cancer treatments can have on a patient's body image, consequently affecting her self-confidence and sexual and intimate relationships, it is paramount to ensure that women receive the treatment that optimizes both survival and aesthetic outcomes. Currently, there is no gold standard for evaluating the aesthetic outcome of breast cancer treatment. In addition, there is no standard way to show patients the potential outcome of surgery. The presentation of similar cases from the past would be extremely important to manage women's expectations of the possible outcome. In this work, we propose a deep neural network to perform the aesthetic evaluation. As a proof-of-concept, we focus on a binary aesthetic evaluation. Besides its use for classification, this deep neural network can also be used to find the most similar past cases by searching for nearest neighbours in the highly semantic space before classification. We performed the experiments on a dataset consisting of 143 photos of women after conservative treatment for breast cancer. The results for accuracy and balanced accuracy showed the superior performance of our proposed model compared to the state of the art in aesthetic evaluation of breast cancer treatments. In addition, the model showed a good ability to retrieve similar previous cases, with the retrieved cases having the same or adjacent class (in the 4-class setting) and having similar types of asymmetry. Finally, a qualitative interpretability assessment was also performed to analyse the robustness and trustworthiness of the model

A 63-year-old woman presenting with a synovial sarcoma of the hand: a case report

Abstract Introduction Synovial sarcoma is a high-grade, soft-tissue sarcoma that most frequently is located in the vicinity of joints, tendons or bursae, although it can also be found in extra-articular locations. Most patients with synovial sarcoma of the hand are young and have a poor prognosis, as these tumors are locally aggressive and are associated with a relatively high metastasis rate. According to the literature, local recurrence and/or metastatic disease is found in nearly 80% of patients. Current therapy comprises surgery, systemic and limb perfusion chemotherapy, and radiotherapy. However, the 5-year survival rate is estimated to be only around 27% to 55%. Moreover, most authors agree that synovial sarcoma is one of the most commonly misdiagnosed malignancies of soft tissues because of their slow growing pattern, benign radiographic appearance, ability to change size, and the fact that they may elicit pain similar to that caused by common trauma. Case presentation We describe an unusual case of a large synovial sarcoma of the hand in a 63-year-old Caucasian woman followed for 12 years by a multidisciplinary team. In addition, a literature review of the most pertinent aspects of the epidemiology, diagnosis, treatment and prognosis of these patients is presented. Conclusion Awareness of this rare tumor by anyone dealing with hand pathology can hasten diagnosis, and this, in turn, can potentially increase survival. Therefore, a high index of suspicion for this disease should be kept in mind, particularly when evaluating young people, as they are the most commonly affected group.</p

Skin deformation analysis for pre-operative planning of DIEAP flap reconstruction surgery

© 2023 The Author(s). Published by Elsevier Ltd on behalf of IPEM. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Deep inferior epigastric artery perforator (DIEAP) flap reconstruction surgeries can potentially benefit from augmented reality (AR) in the context of surgery planning and outcomes improvement. Although three-dimensional (3D) models help visualize and map the perforators, the anchorage of the models to the patient's body during surgery does not consider eventual skin deformation from the moment of computed tomography angiography (CTA) data acquisition until the position of the patient while in surgery. In this work, we compared the 3D deformation registration from supine arms down (CTA position) to supine with arms at 90° degrees (surgical position), estimating the patient's skin deformation. We processed the data sets of 20 volunteers with a 3D rigid registration tool and performed a descriptive statistical analysis and statistical inference. With 2.45 mm of root mean square and 2.89 mm of standard deviation, results include 30% cases of deformation above 3 mm and 15% above 4 mm. Pose transformation deformation indicates that 3D surface data from the CTA scan position differs from data acquired in loco at the surgical table. Such results indicate that research should be conducted to construct accurate 3D models using CTA data to display on the patient, while considering projection errors when using AR technology.info:eu-repo/semantics/publishedVersio

3D breast volume estimation

© 2021 S. Karger AG, Basel.Introduction: Breast volume estimation is considered crucial for breast cancer surgery planning. A single, easy, and reproducible method to estimate breast volume is not available. This study aims to evaluate, in patients proposed for mastectomy, the accuracy of the calculation of breast volume from a low-cost 3D surface scan (Microsoft Kinect) compared to the breast MRI and water displacement technique. Material and methods: Patients with a Tis/T1-T3 breast cancer proposed for mastectomy between July 2015 and March 2017 were assessed for inclusion in the study. Breast volume calculations were performed using a 3D surface scan and the breast MRI and water displacement technique. Agreement between volumes obtained with both methods was assessed with the Spearman and Pearson correlation coefficients. Results: Eighteen patients with invasive breast cancer were included in the study and submitted to mastectomy. The level of agreement of the 3D breast volume compared to surgical specimens and breast MRI volumes was evaluated. For mastectomy specimen volume, an average (standard deviation) of 0.823 (0.027) and 0.875 (0.026) was obtained for the Pearson and Spearman correlations, respectively. With respect to MRI annotation, we obtained 0.828 (0.038) and 0.715 (0.018). Discussion: Although values obtained by both methodologies still differ, the strong linear correlation coefficient suggests that 3D breast volume measurement using a low-cost surface scan device is feasible and can approximate both the MRI breast volume and mastectomy specimen with sufficient accuracy. Conclusion: 3D breast volume measurement using a depth-sensor low-cost surface scan device is feasible and can parallel MRI breast and mastectomy specimen volumes with enough accuracy. Differences between methods need further development to reach clinical applicability. A possible approach could be the fusion of breast MRI and the 3D surface scan to harmonize anatomic limits and improve volume delimitation.info:eu-repo/semantics/publishedVersio