Bilateral Single-Stage Nephrectomy for Synchronous Bilateral Renal Cell Carcinoma

Bilateral synchronous renal cell carcinoma (RCC) is uncommonly encountered. Debate exists among urologists in managing these cases in a single surgery versus staged surgeries. We aim to report our experience in managing encountered cases using single-stage surgeries. Retrospective collection of cases with pathologically confirmed RCC that had single-stage bilateral renal surgery over the past 2 years. Three cases were identified. Patients were managed using bilateral transverse lateral lumbotomy. All patients did not have intraoperative or postoperative complications. Kidney function stayed stable after surgery. Single-stage bilateral renal surgery is a safe procedure. Bilateral transverse lateral lumbotomy allows for a fast and safe surgery with minimal complications. There is a possible histological dis-concordance in bilateral synchronous RCC

Partial Nephrectomy for T1b/T2 Renal Mass: An Added Shift from Radical Nephrectomy

The aim of our study was to show our short-term experience in managing large renal masses (cT1b/T2) through partial nephrectomy (PN) over the last 3 years. Retrospective data collection for all patients managed by PN for renal masses larger than 4 cm over the last 3 years. Epidemiological data were collected. Surgical data including surgical and ischemic times as well as intra and postoperative complications were collected. Pre- and postoperative estimated glomerular filtration rate (eGFR) data were collected and correlated as well as postoperative complications and recurrence. We could identify 47 patients managed by PN for radiologically confirmed >4 cm renal masses. The mean age of the patients was 55.7 ± 13.4, including 29 males and 18 females. Masses were T1b and T2 in 40 and 7 patients, respectively. The mean tumor size was 6.2 ± 1.5 cm. Using renal nephrometry score; 8, 28, and 11 had low, moderate, and high complexity, respectively. Renal cell carcinoma (RCC) was identified in 42 patients. Five patients out of 42 cancerous cases (12%) had pathological T3 RCC. The mean preoperative and postoperative eGFR were 89.09 ± 12.41 and 88.50 ± 10.50, respectively (P 0.2). The median follow-up was 14 months and within that short time, no patient had evidence for cancer recurrence. PN for large renal masses is safe in experienced hands and should be attempted in a higher percentage of patients, regardless of the tumor complexity. No cancer recurrence or deterioration of renal function was observed within our short-term follow-up

COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

The world is still struggling in controlling and containing the spread of the COVID-19 pandemic caused by the SARS-CoV-2 virus. The medical conditions associated with SARS-CoV-2 infections have resulted in a surge in the number of patients at clinics and hospitals, leading to a significantly increased strain on healthcare resources. As such, an important part of managing and handling patients with SARS-CoV-2 infections within the clinical workflow is severity assessment, which is often conducted with the use of chest X-ray (CXR) images. In this work, we introduce COVID-Net CXR-S, a convolutional neural network for predicting the airspace severity of a SARS-CoV-2 positive patient based on a CXR image of the patient’s chest. More specifically, we leveraged transfer learning to transfer representational knowledge gained from over 16,000 CXR images from a multinational cohort of over 15,000 SARS-CoV-2 positive and negative patient cases into a custom network architecture for severity assessment. Experimental results using the RSNA RICORD dataset showed that the proposed COVID-Net CXR-S has potential to be a powerful tool for computer-aided severity assessment of CXR images of COVID-19 positive patients. Furthermore, radiologist validation on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, showed consistency between radiologist interpretation and critical factors leveraged by COVID-Net CXR-S for severity assessment. While not a production-ready solution, the ultimate goal for the open source release of COVID-Net CXR-S is to act as a catalyst for clinical scientists, machine learning researchers, as well as citizen scientists to develop innovative new clinical decision support solutions for helping clinicians around the world manage the continuing pandemic

Performance of the final model on the test set.

Performance of the final model on the test set.</p

Hyperparameters search space and the top five best-performing models.

Hyperparameters search space and the top five best-performing models.</p

TRIPOD checklist: Prediction model development.

(PDF)</p

ROC curves of single feature models.

ROC curves of single feature models.</p

Model coefficients for each feature.

A higher absolute value shows that a feature is more influential.</p

Model integration into the IT system to predict CT exams.

Model integration into the IT system to predict CT exams.</p

ROC and PR curve of the final model trained using the complete training set.

ROC and PR curve of the final model trained using the complete training set.</p