Localized renal cell carcinoma management: an update

Validity of a multiphase CT-based radiomics model in predicting the Leibovich risk groups for localized clear cell renal cell carcinoma: an exploratory study

OBJECTIVE

To develop and validate a multiphase CT-based radiomics model for preoperative risk stratification of patients with localized clear cell renal cell carcinoma (ccRCC).

METHODS

A total of 425 patients with localized ccRCC were enrolled and divided into training, validation, and external testing cohorts. Radiomics features were extracted from three-phase CT images (unenhanced, arterial, and venous), and radiomics signatures were constructed by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The radiomics score (Rad-score) for each patient was calculated. The radiomics model was established and visualized as a nomogram by incorporating significant clinical factors and Rad-score. The predictive performance of the radiomics model was evaluated by the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA).

RESULTS

The AUC of the triphasic radiomics signature reached 0.862 (95% CI: 0.809-0.914), 0.853 (95% CI: 0.785-0.921), and 0.837 (95% CI: 0.714-0.959) in three cohorts, respectively, which were higher than arterial, venous, and unenhanced radiomics signatures. Multivariate logistic regression analysis showed that Rad-score (OR: 4.066, 95% CI: 3.495-8.790) and renal vein invasion (OR: 12.914, 95% CI: 1.118-149.112) were independent predictors and used to develop the radiomics model. The radiomics model showed good calibration and discrimination and yielded an AUC of 0.872 (95% CI: 0.821-0.923), 0.865 (95% CI: 0.800-0.930), and 0.848 (95% CI: 0.728-0.967) in three cohorts, respectively. DCA showed the clinical usefulness of the radiomics model in predicting the Leibovich risk groups.

CONCLUSIONS

The radiomics model can be used as a non-invasive and useful tool to predict the Leibovich risk groups for localized ccRCC patients.

CRITICAL RELEVANCE STATEMENT

The triphasic CT-based radiomics model achieved favorable performance in preoperatively predicting the Leibovich risk groups in patients with localized ccRCC. Therefore, it can be used as a non-invasive and effective tool for preoperative risk stratification of patients with localized ccRCC.

KEY POINTS

• The triphasic CT-based radiomics signature achieves better performance than the single-phase radiomics signature. • Radiomics holds prospects in preoperatively predicting the Leibovich risk groups for ccRCC. • This study provides a non-invasive method to stratify patients with localized ccRCC.

CD34+ Stem Cell Treatment for Knee Osteoarthritis: A Treatment and Rehabilitation Algorithm

Osteoarthritis is a group of multiple overlapping pathological conditions that cause destruction of articular cartilage and other structures of the joint. It is a progressive disease that leads to limitations of physical activity. New forms of treatment are therefore sought to alleviate the clinical symptoms of osteoarthritis and avoid surgery. Stem cell based therapy is an emerging field in orthopaedics. This study describes the treatment of knee osteoarthritis with CD34+ stem cells at the Medical Magnus Outpatient Clinic in Lodz, Poland, together with the treatment and rehabilitation algorithm developed for maximum effectiveness of this procedure. The algorithm includes 3 rehabilitation stages: preoperative, hospitalization and outpatient periods.

The percentage of resected and ischemic volume determined by a geometric model is a significant predictor of renal functional change after partial nephrectomy

Purpose

The percentage of parenchyma preserved plays a predominant role in predicting renal function after partial nephrectomy (PN). Currently there is no standard method to estimate preserved renal parenchyma. In this study we propose a formula of the percentage of resected and ischemic volume (PRAIV) determined by a geometric model and evaluate the relationships between renal functional change and PRAIV as well as other clinical parameters.

Materials and Methods

We identified 71 patients who underwent open PN between January 2004 and April 2014. Assuming the kidney to be an ellipsoid with bilaterally equal volume and tumor to be a sphere, we calculated PRAIV by integral calculus. Nadir estimated glomerular filtration rate (eGFR) between postoperative 3 and 12 months were recorded. The correlation between percent eGFR reduction, PRAIV, and other clinical parameters were examined.

Results

On univariate analysis, age (p=0.03), depth of tumor invasion (p=0.004), C index (p=0.003), RAIV (p=0.04), and PRAIV (p<0.001) were correlated with percent reduction of eGFR. However, only age (p=0.007) and PRAIV (p<0.001) were significantly correlated with percent reduction of eGFR on multivariate analysis. Depicting these values along the regression line, we found R² was 0.194 and 0.073 for PRAIV and age, respectively.

Conclusions

PRAIV determined by a geometric model is a significant predictor of renal functional change after PN. Using PRAIV, we can estimate percent eGFR reduction preoperatively for better patient consultation and surgical planning.

Stem cells in clinical practice: applications and warnings

Stem cells are a relevant source of information about cellular differentiation, molecular processes and tissue homeostasis, but also one of the most putative biological tools to treat degenerative diseases. This review focuses on human stem cells clinical and experimental applications. Our aim is to take a correct view of the available stem cell subtypes and their rational use in the medical area, with a specific focus on their therapeutic benefits and side effects. We have reviewed the main clinical trials dividing them basing on their clinical applications, and taking into account the ethical issue associated with the stem cell therapy.