Small renal carcinoma : the "when" and "how" of operation, active surveillance, and ablation

Small, locally restricted renal cell carcinoma less than 4 cm in size should ideally be removed operatively by nephron-sparing tumour enucleation (partial kidney resection). In an increasingly elderly population, there is a growing trend toward parallel incidence of renal cell carcinoma and chronic renal insufficiency, with the latter's associated general comorbidities. Thus, for some patients, the risks of the anaesthesia and operation increase, while the advantage in terms of survival decreases. Transcutaneous radio-frequency ablation under local anaesthesia, transcutaneous afterloading high-dose-rate brachytherapy under local anaesthesia, and percutaneous stereotactic ablative radiotherapy may offer a less invasive alternative therapy. Active surveillance is to be regarded as no more than a controlled bridging up to definitive treatment (operation or ablation), while watchful waiting, on account of the lack of prognostic relevance and the symptomatology of renal cell carcinoma, with its comorbidity-related, clearly reduced life expectancy, does not involve any further diagnostic or therapeutic measures

[Letter to editor] Shedding of bevacizumab in tumour cells derived extracellular vesicles as a new therapeutic escape mechanism in glioblastoma

Glioblastoma (GBM) is the most aggressive type of primary brain tumours. Anti-angiogenic therapies (AAT), such as bevacizumab, have been developed to target the tumour blood supply. However, GBM presents mechanisms of escape from AAT activity, including a speculated direct effect of AAT on GBM cells. Furthermore, bevacizumab can alter the intercellular communication of GBM cells with their direct microenvironment. Extracellular vesicles (EVs) have been recently described as main acts in the GBM microenvironment, allowing tumour and stromal cells to exchange genetic and proteomic material. Herein, we examined and described the alterations in the EVs produced by GBM cells following bevacizumab treatment. Interestingly, bevacizumab that is able to neutralise GBM cells-derived VEGF-A, was found to be directly captured by GBM cells and eventually sorted at the surface of the respective EVs. We also identified early endosomes as potential pathways involved in the bevacizumab internalisation by GBM cells. Via MS analysis, we observed that treatment with bevacizumab induces changes in the EVs proteomic content, which are associated with tumour progression and therapeutic resistance. Accordingly, inhibition of EVs production by GBM cells improved the anti-tumour effect of bevacizumab. Together, this data suggests of a potential new mechanism of GBM escape from bevacizumab activity

Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback

Ultrasound (US) imaging is widely employed for diagnosis and staging of peripheral vascular diseases (PVD), mainly due to its high availability and the fact it does not emit radiation. However, high inter-operator variability and a lack of repeatability of US image acquisition hinder the implementation of extensive screening programs. To address this challenge, we propose an end-to-end workflow for automatic robotic US screening of tubular structures using only the real-time US imaging feedback. We first train a U-Net for real-time segmentation of the vascular structure from cross-sectional US images. Then, we represent the detected vascular structure as a 3D point cloud and use it to estimate the longitudinal axis of the target tubular structure and its mean radius by solving a constrained non-linear optimization problem. Iterating the previous processes, the US probe is automatically aligned to the orientation normal to the target tubular tissue and adjusted online to center the tracked tissue based on the spatial calibration. The real-time segmentation result is evaluated both on a phantom and in-vivo on brachial arteries of volunteers. In addition, the whole process is validated both in simulation and physical phantoms. The mean absolute radius error and orientation error ($\pm$ SD) in the simulation are $1.16\pm0.1~mm$ and $2.7\pm3.3^{\circ}$, respectively. On a gel phantom, these errors are $1.95\pm2.02~mm$ and $3.3\pm2.4^{\circ}$. This shows that the method is able to automatically screen tubular tissues with an optimal probe orientation (i.e. normal to the vessel) and at the same to accurately estimate the mean radius, both in real-time.Comment: Accepted for publication in IEEE Transactions on Industrial Electronics Video: https://www.youtube.com/watch?v=VAaNZL0I5i

Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans

Consistent segmentation of COVID-19 patient's CT scans across multiple time points is essential to assess disease progression and response to therapy accurately. Existing automatic and interactive segmentation models for medical images only use data from a single time point (static). However, valuable segmentation information from previous time points is often not used to aid the segmentation of a patient's follow-up scans. Also, fully automatic segmentation techniques frequently produce results that would need further editing for clinical use. In this work, we propose a new single network model for interactive segmentation that fully utilizes all available past information to refine the segmentation of follow-up scans. In the first segmentation round, our model takes 3D volumes of medical images from two-time points (target and reference) as concatenated slices with the additional reference time point segmentation as a guide to segment the target scan. In subsequent segmentation refinement rounds, user feedback in the form of scribbles that correct the segmentation and the target's previous segmentation results are additionally fed into the model. This ensures that the segmentation information from previous refinement rounds is retained. Experimental results on our in-house multiclass longitudinal COVID-19 dataset show that the proposed model outperforms its static version and can assist in localizing COVID-19 infections in patient's follow-up scans.Comment: 10 pages, 11 figures, 4 table

Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs

Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation. Image segmentation methods have proven to help quantify the disease burden and even help predict the outcome. The availability of longitudinal CT series may also result in an efficient and effective method to reliably assess the progression of COVID-19, monitor the healing process and the response to different therapeutic strategies. In this paper, we propose a new framework to identify infection at a voxel level (identification of healthy lung, consolidation, and ground-glass opacity) and visualize the progression of COVID-19 using sequential low-dose non-contrast CT scans. In particular, we devise a longitudinal segmentation network that utilizes the reference scan information to improve the performance of disease identification. Experimental results on a clinical longitudinal dataset collected in our institution show the effectiveness of the proposed method compared to the static deep neural networks for disease quantification.Comment: MICCAI 202

Position-based Dynamics Simulator of Brain Deformations for Path Planning and Intra-Operative Control in Keyhole Neurosurgery

Many tasks in robot-assisted surgery require planning and controlling manipulators' motions that interact with highly deformable objects. This study proposes a realistic, time-bounded simulator based on Position-based Dynamics (PBD) simulation that mocks brain deformations due to catheter insertion for pre-operative path planning and intra-operative guidance in keyhole surgical procedures. It maximizes the probability of success by accounting for uncertainty in deformation models, noisy sensing, and unpredictable actuation. The PBD deformation parameters were initialized on a parallelepiped-shaped simulated phantom to obtain a reasonable starting guess for the brain white matter. They were calibrated by comparing the obtained displacements with deformation data for catheter insertion in a composite hydrogel phantom. Knowing the gray matter brain structures' different behaviors, the parameters were fine-tuned to obtain a generalized human brain model. The brain structures' average displacement was compared with values in the literature. The simulator's numerical model uses a novel approach with respect to the literature, and it has proved to be a close match with real brain deformations through validation using recorded deformation data of in-vivo animal trials with a mean mismatch of 4.73$\pm$2.15%. The stability, accuracy, and real-time performance make this model suitable for creating a dynamic environment for KN path planning, pre-operative path planning, and intra-operative guidance.Comment: 8 pages, 8 figures. This article has been accepted for publication in a future issue of IEEE Robotics and Automation Letters, but has not been fully edited. Content may change prior to final publication. 2377-3766 (c) 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. A. Segato and C. Di Vece equally contribute