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

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

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

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

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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

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

Artificial intelligence in bone metastases: an MRI and CT imaging review

Background: The purpose of this review is to study the role of radiomics as a supporting tool in predicting bone disease status, differentiating benign from malignant bone lesions, and characterizing malignant bone lesions. (2) Methods: Two reviewers conducted the literature search independently. Thirteen articles on radiomics as a decision support tool for bone lesions were selected. The quality of the methodology was evaluated according to the radiomics quality score (RQS). (3) Results: All studies were published between 2018 and 2021 and were retrospective in design. Eleven (85%) studies were MRI-based, and two (15%) were CT-based. The sample size was <200 patients for all studies. There is significant heterogeneity in the literature, as evidenced by the relatively low RQS value (average score = 22.6%). There is not a homogeneous protocol used for MRI sequences among the different studies, although the highest predictive ability was always obtained in T2W-FS. Six articles (46%) reported on the potential application of the model in a clinical setting with a decision curve analysis (DCA). (4) Conclusions: Despite the variability in the radiomics method application, the similarity of results and conclusions observed is encouraging. Substantial limits were found; prospective and multicentric studies are needed to affirm the role of radiomics as a supporting tool

The impact of tumor edema on T2-weighted 3T-MRI invasive breast cancer histological characterization: a pilot radiomics study

Background: to evaluate the contribution of edema associated with histological features to the prediction of breast cancer (BC) prognosis using T2-weighted MRI radiomics. Methods: 160 patients who underwent staging 3T-MRI from January 2015 to January 2019, with 164 histologically proven invasive BC lesions, were retrospectively reviewed. Patient data (age, menopausal status, family history, hormone therapy), tumor MRI-features (location, margins, enhancement) and histological features (histological type, grading, ER, PgR, HER2, Ki-67 index) were collected. Of the 160 MRI exams, 120 were considered eligible, corresponding to 127 lesions. T2-MRI were used to identify edema, which was classified in four groups: peritumoral, pre-pectoral, subcutaneous, or diffuse. A semi-automatic segmentation of the edema was performed for each lesion, using 3D Slicer open-source software. Main radiomics features were extracted and selected using a wrapper selection method. A Random Forest type classifier was trained to measure the performance of predicting histological factors using semantic features (patient data and MRI features) alone and semantic features associated with edema radiomics features. Results: edema was absent in 37 lesions and present in 127 (62 peritumoral, 26 pre-pectoral, 16 subcutaneous, 23 diffuse). The AUC-classifier obtained by associating edema radiomics with semantic features was always higher compared to the AUC-classifier obtained from semantic features alone, for all five histological classes prediction (0.645 vs. 0.520 for histological type, 0.789 vs. 0.590 for grading, 0.487 vs. 0.466 for ER, 0.659 vs. 0.546 for PgR, and 0.62 vs. 0.573 for Ki67). Conclusions: radiomic features extracted from tumor edema contribute significantly to predicting tumor histology, increasing the accuracy obtained from the combination of patient clinical characteristics and breast imaging data

Combined Trans-Arterial Embolization and Ablation for the Treatment of Large (>3 cm) Liver Metastases: Review of the Literature

Background: The aim of this review was to determine the state of clinical practice in the role of the combined approach of embolization and ablation in patients with secondary liver lesions greater than 3 cm who are not candidates for surgery, and to study its safety and efficacy. (2) Methods: Two reviewers conducted the literature search independently. Eight articles on the combined approach of embolization and ablation in secondary liver lesions were selected. (3) Results: The studies were published between 2009 and 2020. Two studies were prospective in design. The sample size was < 100 patients for all studies. All studies demonstrated the safety of the combined approach based on the low complication rate. Some studies lamented non-uniform systemic chemotherapy regimens and the variability in the sequence of embolization and ablation. (4) Conclusions: This review presents the combined approach of ablation and embolization in liver lesions greater than 3 cm as a safe therapeutic procedure with positive effects on patient survival. Prospective and multicentric studies are needed to further evaluate its efficacy