Our Experience in Percutaneous Ablative Treatment of Renal T1a E T1b Lesions: Results from 90 Patients Treated with Microwave Ablation, Radiofrequency Ablation and Cryoablation

n/

Combined Trans-arterial Embolization and Microwave Ablation for the Treatment of >3 cm Liver Metastases

n/

Clinical Efficacy and Economic Impact of an Augmented Reality Navigation System: Experience On 498 Percutaneous Computed Tomogaphy-Guided Pulmonary Biopsies

n/

3T MRI-radiomic approach to predict for lymph node status in breast cancer patients

Simple SummaryBreast cancer is the most common cancer in women worldwide. The axillary lymph node status is one of the main prognostic factors. Currently, the methods to define the lymph node status are invasive and not without sequelae (from biopsy to lymphadenectomy). Radiomics is a new tool, and highly varied, but with high potential that has already shown excellent results in numerous fields of application. In our study, we have developed a classifier validated on a relatively large number of patients, which is able to predict lymph node status using a combination of patients clinical features, primary breast cancer histological features and radiomics features based on 3 Tesla post contrast-MR images. This approach can accurately select breast cancer patients who may avoid unnecessary biopsy and lymphadenectomy in a non-invasive way.Background: axillary lymph node (LN) status is one of the main breast cancer prognostic factors and it is currently defined by invasive procedures. The aim of this study is to predict LN metastasis combining MRI radiomics features with primary breast tumor histological features and patients' clinical data. Methods: 99 lesions on pre-treatment contrasted 3T-MRI (DCE). All patients had a histologically proven invasive breast cancer and defined LN status. Patients' clinical data and tumor histological analysis were previously collected. For each tumor lesion, a semi-automatic segmentation was performed, using the second phase of DCE-MRI. Each segmentation was optimized using a convex-hull algorithm. In addition to the 14 semantics features and a feature ROI volume/convex-hull volume, 242 other quantitative features were extracted. A wrapper selection method selected the 15 most prognostic features (14 quantitative, 1 semantic), used to train the final learning model. The classifier used was the Random Forest. Results: the AUC-classifier was 0.856 (label = positive or negative). The contribution of each feature group was lower performance than the full signature. Conclusions: the combination of patient clinical, histological and radiomics features of primary breast cancer can accurately predict LN status in a non-invasive way

Combined Antegrade and Retrograde Approach in Iatrogenic Ureteral Injuries: The Rendezvous Technique

n/

Safety and Feasibility of Steerable Radiofrequency Ablation in Combination with Cementoplasty for the Treatment of Large Extraspinal Bone Metastases

Background: Radiofrequency ablation (RFA) and cementoplasty, individually and in concert, has been adopted as palliative interventional strategies to reduce pain caused by bone metastases and prevent skeletal related events. We aim to evaluate the feasibility and safety of a steerable RFA device with an articulating bipolar extensible electrode for the treatment of extraspinal bone metastases. Methods: All data were retrospectively reviewed. All the ablation procedures were performed using a steerable RFA device (STAR, Merit Medical Systems, Inc., South Jordan, UT, USA). The pain was assessed with a VAS score before treatment and at 1-week and 3-, 6-, and 12-month follow-up. The Functional Mobility Scale (FMS) was recorded preoperatively and 1 month after the treatment through a four-point scale (4, bedridden; 3, use of wheelchair; 2, limited painful ambulation; 1, normal ambulation). Technical success was defined as successful intraoperative ablation and cementoplasty without major complications. Results: A statistically significant reduction of the median VAS score before treatment and 1 week after RFA and cementoplasty was observed (p < 0.001). A total of 6/7 patients who used a wheelchair reported normal ambulation 1 month after treatment. All patients with limited painful ambulation reported normal ambulation after the RFA and cementoplasty (p = 0.003). Technical success was achieved in all the combined procedures. Two cement leakages were reported. No local recurrences were observed after 1 year. Conclusions: The combined treatment of RFA with a steerable device and cementoplasty is a safe, feasible, and promising clinical option for the management of painful bone metastases, challenging for morphology and location, resulting in an improvement of the quality of life of patients

Different Treatments of Symptomatic Angiomyolipomas of the Kidney: Two Case Reports

Development of more sensitive imaging techniques has caused an increase in the number of diagnosed small renal tumors. Approximately 2–3% of these lesions are proved to be angiomyolipomas (AML), a rare benign tumor of the kidney sometimes causing pain and hematuria. The most required approach is observation, but in the case of recurrent symptoms or larger tumors, which may cause bleeding, a more active treatment is required. We present two cases of symptomatic AML tumors of different sizes in the kidney: one treated with transarterial embolization (TAE), and the other with percutaneous cryoablation (CRA). The lesions were diagnosed on the basis of contrast-enhanced computed tomography (CT) scan and magnetic resonance imaging (MRI). Both treatments proved to be effective and safe for treating renal AMLs. A follow-up carried out, based on contrast-enhanced CT scan, confirmed complete treatment of AML and decreased lesion size. There are myriad minimally invasive approaches for the treatment of renal AMLs, and the preservation of renal function remains a priority. The most popular treatment option is the selective renal artery embolization. Owing to its limited invasiveness, CRA could be an attractive option for the preventive treatment of AML

Efficacy of an Augmented Reality Navigation System (SIRIO) for Percutaneous Computed Tomographic-Guided Pulmonary Ground-Glass Opacity Biopsies

n/

CNN-Based Approaches with Different Tumor Bounding Options for Lymph Node Status Prediction in Breast DCE-MRI

Background: The axillary lymph node status (ALNS) is one of the most important prognostic factors in breast cancer (BC) patients, and it is currently evaluated by invasive procedures. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), highlights the physiological and morphological characteristics of primary tumor tissue. Deep learning approaches (DL), such as convolutional neural networks (CNNs), are able to autonomously learn the set of features directly from images for a specific task. Materials and Methods: A total of 155 malignant BC lesions evaluated via DCE-MRI were included in the study. For each patient’s clinical data, the tumor histological and MRI characteristics and axillary lymph node status (ALNS) were assessed. LNS was considered to be the final label and dichotomized (LN+ (27 patients) vs. LN− (128 patients)). Based on the concept that peritumoral tissue contains valuable information about tumor aggressiveness, in this work, we analyze the contributions of six different tumor bounding options to predict the LNS using a CNN. These bounding boxes include a single fixed-size box (SFB), a single variable-size box (SVB), a single isotropic-size box (SIB), a single lesion variable-size box (SLVB), a single lesion isotropic-size box (SLIB), and a two-dimensional slice (2DS) option. According to the characteristics of the volumes considered as inputs, three different CNNs were investigated: the SFB-NET (for the SFB), the VB-NET (for the SVB, SIB, SLVB, and SLIB), and the 2DS-NET (for the 2DS). All the experiments were run in 10-fold cross-validation. The performance of each CNN was evaluated in terms of accuracy, sensitivity, specificity, the area under the ROC curve (AUC), and Cohen’s kappa coefficient (K). Results: The best accuracy and AUC are obtained by the 2DS-NET (78.63% and 77.86%, respectively). The 2DS-NET also showed the highest specificity, whilst the highest sensibility was attained by the VB-NET based on the SVB and SIB as bounding options. Conclusion: We have demonstrated that a selective inclusion of the DCE-MRI’s peritumoral tissue increases accuracy in the lymph node status prediction in BC patients using CNNs as a DL approach

Accuracy of magnetic resonance imaging to identify pseudocapsule invasion in renal tumors

Purpose: To evaluate accuracy of MRI in detecting renal tumor pseudocapsule (PC) invasion and to propose a classification based on imaging of PC status in patients with renal cell carcinoma. Methods: From January 2017 to June 2018, 58 consecutive patients with localized renal cell carcinoma were prospectively enrolled. MRI was performed preoperatively and PC was classified, according to its features, as follows: MRI-Cap 0 (absence of PC), MRI-Cap 1 (presence of a clearly identifiable PC), MRI-Cap 2 (focally interrupted PC), and MRI-Cap 3 (clearly interrupted and infiltrated PC). A 3D image reconstruction showing MRI-Cap score was provided to both surgeon and pathologist to obtain complete preoperative evaluation and to compare imaging and pathology reports. All patients underwent laparoscopic partial nephrectomy. In surgical specimens, PC was classified according to the renal tumor capsule invasion scoring system (i-Cap). Results: A concordance between MRI-Cap and i-Cap was found in 50/58 (86%) cases. ρ coefficient for each MRI-cap and iCap categories was: MRI-Cap 0: 0.89 (p < 0.0001), MRI-Cap1: 0.75 (p < 0.0001), MRI-Cap 2: 0.76 (p < 0.0001), and MRI-Cap3: 0.87 (p < 0.0001). Sensitivity, specificity, positive predictive value, negative predictive value, and AUC were: MRI-Cap 0: Se 97.87% Spec 83.3%, PPV 95.8%, NPV 90.9%, and AUC 90.9; MRI-Cap 1: Se 77% Spec 95.5%, PPV 83.3%, NPV 93.5%, and AUC 0.86; MRI-Cap 2- iCap 2: Se 88% Spec 90%, PPV 79%, NPV 95%, and AUC 0.89; MRI-Cap 3: Se 94% Spec 95%, PPV 88%, NPV 97%, and AUC 0.94. Conclusions: MRI-Cap classification is accurate in evaluating renal tumor PC features. PC features can provide an imaging-guided landmark to figure out where a minimal margin could be preferable during nephron-sparing surgery