Browsing by Subject "renal cancer"

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    The Combination of Low Skeletal Muscle Mass and High Tumor Interleukin-6 Associates with Decreased Survival in Clear Cell Renal Cell Carcinoma
    (MDPI, 2020-06-17) Kays, Joshua K.; Koniaris, Leonidas G.; Cooper, Caleb A.; Pili, Roberto; Jiang, Guanglong; Liu, Yunlong; Zimmers, Teresa A.; Medical and Molecular Genetics, School of Medicine
    Clear cell renal carcinoma (ccRCC) is frequently associated with cachexia which is itself associated with decreased survival and quality of life. We examined relationships among body phenotype, tumor gene expression, and survival. Demographic, clinical, computed tomography (CT) scans and tumor RNASeq for 217 ccRCC patients were acquired from the Cancer Imaging Archive and The Cancer Genome Atlas (TCGA). Skeletal muscle and fat masses measured from CT scans and tumor cytokine gene expression were compared with survival by univariate and multivariate analysis. Patients in the lowest skeletal muscle mass (SKM) quartile had significantly shorter overall survival versus the top three SKM quartiles. Patients who fell into the lowest quartiles for visceral adipose mass (VAT) and subcutaneous adipose mass (SCAT) also demonstrated significantly shorter overall survival. Multiple tumor cytokines correlated with mortality, most strongly interleukin-6 (IL-6); high IL-6 expression was associated with significantly decreased survival. The combination of low SKM/high IL-6 was associated with significantly lower overall survival compared to high SKM/low IL-6 expression (26.1 months vs. not reached; p < 0.001) and an increased risk of mortality (HR = 5.95; 95% CI = 2.86–12.38). In conclusion, tumor cytokine expression, body composition, and survival are closely related, with low SKM/high IL-6 expression portending worse prognosis in ccRCC.
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    Ordinal Multi-modal Feature Selection for Survival Analysis of Early-Stage Renal Cancer
    (Springer, 2018) Shao, Wei; Cheng, Jun; Sun, Liang; Han, Zhi; Feng, Qianjin; Zhang, Daoqiang; Huang, Kun; Medicine, School of Medicine
    Existing studies have demonstrated that combining genomic data and histopathological images can better stratify cancer patients with distinct prognosis than using single biomarker, for different biomarkers may provide complementary information. However, these multi-modal data, most high-dimensional, may contain redundant features that will deteriorate the performance of the prognosis model, and therefore it has become a challenging problem to select the informative features for survival analysis from the redundant and heterogeneous feature groups. Existing feature selection methods assume that the survival information of one patient is independent to another, and thus miss the ordinal relationship among the survival time of different patients. To solve this issue, we make use of the important ordinal survival information among different patients and propose an ordinal sparse canonical correlation analysis (i.e., OSCCA) framework to simultaneously identify important image features and eigengenes for survival analysis. Specifically, we formulate our framework basing on sparse canonical correlation analysis model, which aims at finding the best linear projections so that the highest correlation between the selected image features and eigengenes can be achieved. In addition, we also add constrains to ensure that the ordinal survival information of different patients is preserved after projection. We evaluate the effectiveness of our method on an early-stage renal cell carcinoma dataset. Experimental results demonstrate that the selected features correlated strongly with survival, by which we can achieve better patient stratification than the comparing methods.
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    Robotic Partial Nephrectomy for a Peripheral Renal Tumor
    (Liebert, 2018-05) Cooper, Caleb A.; Shum, Cheuk Fan; Sundaram, Chandru P.; Urology, School of Medicine
    Partial nephrectomy (PN) is the preferred surgical treatment for T1 renal tumors whenever technically feasible. When properly performed, it allows preservation of nephron mass without compromising oncologic outcomes. This reduces the postoperative risk of renal insufficiency, which translates into better overall survival for the patients. PN can be technically challenging, because it requires the surgeon to complete the tasks of tumor excision, hemostasis and renorrhaphy, all within an ischemic time of preferably below 30 minutes. The surgeon needs to avoid violating the tumor margins while leaving behind the maximal parenchymal volume at the same time. Variations such as zero ischemia, early unclamping, and selective clamping have been developed in an attempt to reduce the negative impact of renal ischemia, but inevitably add to the steep learning curves for any surgeon. Being able to appreciate the fine details of each surgical step in PN is the fundamental basis to the success of this surgery. The use of the robotic assistance allows a good combination of the minimally invasive nature of laparoscopic surgery and the surgical exposure and dexterity of open surgery. It also allows the use of adjuncts such as concurrent ultrasound assessment of the renal mass and intraoperative fluorescence to aid the identification of tumor margins, all with a simple hand switch at the console. Robot-assisted laparoscopic PN is now the most commonly performed type of PN in the United States and is gaining acceptance on the global scale. In this video, we illustrate the steps of robot-assisted laparoscopic PN and highlight the technical key points for success.
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    Temporal study of renal volume losses in patients with robotic partial nephrectomies
    (Liebert, 2022) Patel, Rushi S; Sundaram, Chandru P; Kondo, Tsunenori; Bahler, Clinton D; Urology, School of Medicine
    Purpose: Robotic partial nephrectomies by their nature are associated with renal volume loss. Our goal from this study is to examine renal volume loss over time post partial nephrectomy. Materials and Methods: Fifty patients were followed for 1-year post robotic partial nephrectomy with two-layer renorrhaphy and the sliding clip technique. This was done with a preoperative computed tomography (CT) scan to assess renal mass and location. Post robotic partial nephrectomy patients were imaged at time points 3-days, 6-months, and 12-months. Results: Patient demographics were 82% male with a median (IQR) age of 57 (45-67)and all were of Japanese descent. The medians (IQR) for warm ischemia time: 18 minutes (14-22), total operative time: 181.5 minutes (169.3-218.5), and estimated blood loss: 20 mL (10-50). The tumor characteristics had a median (IQR) diameter of 2.8 cm (2.5-3.4)with a RENAL score of 7 (6-8). The renal CT volumes showed median (IQR) volume losses at 3-days: -1% (-7.1, 1.8), 6-months: -15.3% (-20.6, -11.2), and 12-months: -16.3% (-19.0, -12.8). Significance was seen at the 3-days to 6-months comparison for volume loss (p<0.0001). Mean (SD) eGFR losses were as follows: at discharge 0.5% (12.9), 1-month -6.4% (11.8), 6-months -4.6% (9.8), and 12-months -3.6% (11.9). Statistical analysis showed significance for GFR loss at the comparison between discharge to 1-month and 6-months (p=0.01, p=0.04).Conclusion: The initial volume loss seen post-surgery from resected healthy tissue was not significant and only became relevant atlonger time points suggesting that loss could be from atrophy. Volume loss over time supports the hypothesis that suture renorrhaphy is a primary cause of volume loss when warm ischemia time is <25minutes.