Decreased functional outcome after partial nephrectomy is associated with overall mortality.
To create a model that predicts ≥25% reduction from baseline estimated glomerular filtration rate (eGFR) ...in patients undergoing robot-assisted partial nephrectomy (RAPN) and to investigate the role of acute kidney injury (AKI) in this patient population.
A total of 999 patients were identified from a multi-institutional database. Renal function was defined according to the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines for chronic kidney disease (CKD). AKI was defined as >25% reduction in eGFR from pre-RAPN period to discharge.
A nomogram to predict significant eGFR reduction (≥25% from baseline) in the time-frame between 3 and 15mo after RAPN was built based on the coefficients of Cox survival function that ultimately included age, sex, Charlson comorbidity index, baseline eGFR, RENAL nephrometry score, AKI in patients with normal baseline renal function, and AKI on CKD. Such landmark analysis was chosen in order to account for eGFR fluctuations occurring within the first 3mo of RAPN. The proportional hazard assumption was evaluated through the Schönfeld test. Internal validation was performed using the leave-one-out cross validation. Calibration was graphically investigated. The decision curve analysis (DCA) was used to evaluate the net clinical benefit.
Median (interquartile range IQR) age at surgery was 61yr (51, 68). Overall, 146 patients experienced significant eGFR reduction; median follow-up for survivors was 12.4mo. The 15-mo probability of significant eGFR reduction was 19%. All variables fitted into the model, including AKI in patients with normal renal function (hazard ratio HR: 4.51; 95% confidence interval CI: 3.12, 6.60; p<0.001) and AKI on CKD (HR: 4.90; 95% CI: 2.17, 11.1; p<0.001), emerged as predictors of significant eGFR reduction (all p≤0.048) and were considered to build a nomogram. The internally validated c index was 73%. The model demonstrated excellent calibration and a net benefit at the DCA with probabilities ≥4%.
We developed a nomogram that accurately predicts significant eGFR reduction after RAPN. This model may serve as a tool for early identification of patients at high risk for significant renal function decline after surgery.
We have developed a model for the prediction of renal function loss after partial nephrectomy for renal cancer.
We have developed a model for the prediction of chronic kidney disease upstaging after partial nephrectomy for renal cancer.
PURPOSEWe sought to 1) assess the association of radiomics features based on multiparametric magnetic resonance imaging with histopathological Gleason score, gene signatures and gene expression ...levels in prostate cancer and 2) build machine learning models based on radiomics features to predict adverse histopathological scores and the Decipher® genomics metastasis risk score. MATERIALS AND METHODSWe retrospectively analyzed the records of 64 patients with prostate cancer with a mean age of 64 years (range 41 to 76) who underwent magnetic resonance imaging between January 2016 and January 2017 before radical prostatectomy. A total of 226 magnetic resonance imaging radiomics features, including histogram and texture features in addition to lesion size and the PI-RADS™ (Prostate Imaging Reporting and Data System) score, were extracted from T2-weighted, apparent diffusion coefficient and diffusion kurtosis imaging maps. Radiomics features were correlated with the pathological Gleason score, 40 gene expression signatures, including Decipher, and 698 prostate cancer related gene expression levels. Cross-validated, lasso regularized, logistic regression machine learning models based on radiomics features were built and evaluated for the prediction of Gleason score 8 or greater and Decipher score 0.6 or greater. RESULTSA total of 14 radiomics features significantly correlated with the Gleason score (highest correlation r = 0.39, p = 0.001). A total of 31 texture and histogram features significantly correlated with 19 gene signatures, particularly with the PORTOS (Post-Operative Radiation Therapy Outcomes Score) signature (strongest correlation r = -0.481, p = 0.002). A total of 40 diffusion-weighted imaging features correlated significantly with 132 gene expression levels. Machine learning prediction models showed fair performance to predict a Gleason score of 8 or greater (AUC 0.72) and excellent performance to predict a Decipher score of 0.6 or greater (AUC 0.84). CONCLUSIONSMagnetic resonance imaging radiomics features are promising markers of prostate cancer aggressiveness on the histopathological and genomics levels.
Purpose
To assess the diagnostic value of magnetic resonance imaging (MRI)-based radiomics features using machine learning (ML) models in characterizing solid renal neoplasms, in ...comparison/combination with qualitative radiologic evaluation.
Methods
Retrospective analysis of 125 patients (mean age 59 years, 67% males) with solid renal neoplasms that underwent MRI before surgery. Qualitative (signal and enhancement characteristics) and quantitative radiomics analyses (histogram and texture features) were performed on T2-weighted imaging (WI), T1-WI pre- and post-contrast, and DWI. Mann–Whitney
U
test and receiver-operating characteristic analysis were used in a training set (
n
= 88) to evaluate diagnostic performance of qualitative and radiomics features for differentiation of renal cell carcinomas (RCCs) from benign lesions, and characterization of RCC subtypes (clear cell RCC ccRCC and papillary RCC pRCC). Random forest ML models were developed for discrimination between tumor types on the training set, and validated on an independent set (
n
= 37).
Results
We assessed 104 RCCs (51 ccRCC, 29 pRCC, and 24 other subtypes) and 21 benign lesions in 125 patients. Significant qualitative and quantitative radiomics features (area under the curve AUC between 0.62 and 0.90) were included for ML analysis. Models with best diagnostic performance on validation sets showed AUC of 0.73 (confidence interval CI 0.5–0.96) for differentiating RCC from benign lesions (using combination of qualitative and radiomics features); AUC of 0.77 (CI 0.62–0.92) for diagnosing ccRCC (using radiomics features), and AUC of 0.74 (CI 0.53–0.95) for diagnosing pRCC (using qualitative features).
Conclusion
ML models incorporating MRI-based radiomics features and qualitative radiologic assessment can help characterize renal masses.
Background
Biochemical recurrence (BCR) affects a significant proportion of patients who undergo robotic‐assisted laparoscopic prostatectomy (RALP).
Purpose
To evaluate the performance of a routine ...clinical prostate multiparametric magnetic resonance imaging (mpMRI) and Decipher genomic classifier score for prediction of biochemical recurrence in patients who underwent RALP.
Study Type
Retrospective cohort study.
Subjects
Ninety‐one patients who underwent RALP performed by a single surgeon, had mpMRI before RALP, Decipher taken from RALP samples, and prostate specific antigen (PSA) follow‐up for >3 years or BCR within 3 years, defined as PSA >0.2 mg/ml.
Field Strength/Sequence
mpMRI was performed at 27 different institutions using 1.5T (n = 10) or 3T scanners and included T2w, diffusion‐weighted imaging (DWI), or dynamic contrast‐enhanced (DCE) MRI.
Assessment
All mpMRI studies were reported by one reader using Prostate Imaging Reporting and Data System v. 2.1 (PI‐RADsv2.1) without knowledge of other findings. Eighteen (20%) randomly selected cases were re‐reported by reader B to evaluate interreader variability.
Statistical Tests
Univariate and multivariate analysis using greedy feature selection and tournament leave‐pair‐out cross‐validation (TLPOCV) were used to evaluate the performance of various variables for prediction of BCR, which included clinical (three), systematic biopsy (three), surgical (six: RALP Gleason Grade Group GGG, extracapsular extension, seminal vesicle invasion, intraoperative surgical margins PSM, final PSM, pTNM), Decipher (two: Decipher score, Decipher risk category), and mpMRI (eight: prostate volume, PSA density, PI‐RADv2.1 score, MRI largest lesion size, summed MRI lesions' volume and relative volume MRI‐lesion‐percentage, mpMRI ECE, mpMRI seminal vesicle invasion SVI) variables. The evaluation metric was the area under the curve (AUC).
Results
Forty‐eight (53%) patients developed BCR. The best‐performing individual features with TLPOCV AUC of 0.73 (95% confidence interval CI 0.64–0.82) were RALP GGG, MRI‐lesion‐percentage followed by biopsy GGG (0.72, 0.62–0.82), and Decipher score (0.71, 0.60–0.82). The best performance was achieved by feature selection of Decipher+Surgery and MRI + Surgery variables with TLPOCV AUC of 0.82 and 0.81, respectively
Data Conclusion
Relative lesion volume measured on a routine clinical mpMRI failed to outperform Decipher score in BCR prediction.
Level of Evidence: 3
Technical Efficacy: Stage 2
J. Magn. Reson. Imaging 2020;51:1075–1085.
Heterogeneity in renal cell carcinoma Beksac, Alp Tuna, M.D; Paulucci, David J., B.A; Blum, Kyle A., M.D ...
Urologic oncology,
08/2017, Letnik:
35, Številka:
8
Journal Article
Recenzirano
Abstract Introduction In recent years, molecular characterization of renal cell carcinoma has facilitated the identification of driver genes, specific molecular pathways, and characterization of the ...tumor microenvironment, which has led to a better understanding of the disease. This comprehension has revolutionized the treatment for patients with metastatic disease, but despite these advancements many patients will develop resistance leading to treatment failure. A primary cause of this resistance and subsequent treatment failure is tumor heterogeneity. We reviewed the literature on the mechanisms of tumor heterogeneity and its clinical implications. Methods A comprehensive literature search was performed using the MEDLINE/PubMed Index. Results Intertumor and intratumor heterogeneity is possibly a reason for treatment failure and development of resistance. Specifically, the genetic profile of a renal tumor differs spatially within a tumor as well as among patients. Genomic mutations can change temporally with resistant subclones becoming dominant over time. Conclusions Accounting for intratumor and intertumor heterogeneity with better sampling of cancer tissue is needed. This will hopefully lead to improved identification of driver mutations and actionable targets. Only then, we can move past the one-size-fits-all approach toward personalized treatment based on each individual׳s molecular profile.
The aim of this study is to evaluate the significance of renal pelvis aspiration (RPA) in the management of antenatal hydronephrosis (AHN). This study enrolled 15 AHN cases (one twin pregnancy) that ...necessitated RPA for AHN. Chromosomal abnormalities, gene disorders, and additional life‐threatening congenital abnormalities were eliminated prior to intrauterine interventions. Urine analysis were performed for the evaluation of renal function. Normal renal function was observed in six neonates/infants (40%) (group 1), whereas impaired renal function and various type of urinary system anomalies were observed in 9 neonates/infants (60%) (group 2) during the short‐term and longitudinal follow‐up periods. There were statistically significant differences in the oligohydroamniosis rate, mean fetal urine sodium value, mean fetal urine β2‐microglobulin, mean gestational week at birth, and mean birthweight values between the groups (P = 0.007, P < 0.001, P = 0.035, P < 0.001, and P = 0.001, respectively). Renal pelvis aspiration and urine analysis were substantial for the management of AHN in necessary cases. β2‐microglobulin and sodium are clinically useful markers to detect the presence of severe renal damage due to obstructive uropathy and thus, important adjuvants in the proper selection of fetuses for further antenatal interventions.
To find an association between genomic features of connective tissue and pejorative clinical outcomes on radical prostatectomy specimens. We performed a retrospective analysis of patients who ...underwent radical prostatectomy and underwent a Decipher transcriptomic test for localized prostate cancer in our institution (
= 695). The expression results of selected connective tissue genes were analyzed after multiple
tests, revealing significant differences in the transcriptomic expression (over- or under-expression). We investigated the association between transcript results and clinical features such as extra-capsular extension (ECE), clinically significant cancer, lymph node (LN) invasion and early biochemical recurrence (eBCR), defined as earlier than 3 years after surgery). The Cancer Genome Atlas (TCGA) was used to evaluate the prognostic role of genes on progression-free survival (PFS) and overall survival (OS). Out of 528 patients, we found that 189 had ECE and 27 had LN invasion. The Decipher score was higher in patients with ECE, LN invasion, and eBCR. Our gene selection microarray analysis showed an overexpression in both ECE and LN invasion, and in clinically significant cancer for
,
,
,
,
,
,
,
,
,
,
, and underexpression in
and
. In the TCGA population, overexpression of these genes was correlated with worse PFS. Significant co-occurrence of these genes was observed. When presenting overexpression of our gene selection, the 5-year PFS rate was 53% vs. 68% (
= 0.0315). Transcriptomic overexpression of connective tissue genes correlated to worse clinical features, such as ECE, clinically significant cancer and BCR, identifying the potential prognostic value of the gene signature of the connective tissue in prostate cancer. TCGAp cohort analysis showed a worse PFS in case of overexpression of the connective tissue genes.
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