Materiały źródłowe

• #1 Prognosis and survival for kidney cancer | Canadian Cancer Society

  https://cancer.ca/en/cancer-information/cancer-types/kidney/prognosis-and-survival

  If you have kidney cancer, you may have questions about your prognosis. A prognosis is the doctors best estimate of how cancer will affect someone and how it will respond to treatment. Prognosis and survival depend on many factors. Only a doctor familiar with your medical history, the type and stage and other features of the cancer, the treatments chosen and the response to treatment can put all of this information together with survival statistics to arrive at a prognosis. […] The stage of kidney cancer is the most important prognostic factor. People who have tumours that are only in the kidney have a better prognosis than people with cancer that has spread outside the kidney. […] Low-grade tumours have a better prognosis than high-grade tumours. Low-grade tumours are less likely to spread because they grow slowly. High-grade tumours are more aggressive and tend to spread quickly.

• #2 Prognosis and survival for kidney cancer | Canadian Cancer Society

  https://cancer.ca/en/cancer-information/cancer-types/kidney/prognosis-and-survival

  Papillary and chromophobe types of renal cell carcinoma have a better prognosis because they are often low grade. […] Collecting duct carcinoma and renal medullary carcinoma have a poor prognosis because they are often very aggressive. […] The most common system used to predict prognosis for people with metastatic renal cell carcinoma is the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC). […] These predictors are combined to develop a level of risk: Favourable risk means the person has none of the predictors. Intermediate risk means the person has 1 or 2 predictors. Poor risk means the person has 3 or more predictors.

• #3 Prognostic factors of overall survival in renal cancer patients – single oncological center study

  https://pmc.ncbi.nlm.nih.gov/articles/PMC3974466/

  In this group of factors ECOG performance status most substantially influenced overall survival. […] Smoking history was a negative predictor of overall survival in the investigated group. […] Multivariate Cox model analysis revealed that several previously described factors had a statistically significant, independent influence on overall survival. The set of identified independent prognostic factors (IPFs) of overall survival (at p < 0.05) consisted of performance status, smoking history, hemoglobin concentration, AJCC anatomical staging, tumor grade, and presence of microvascular invasion. [...] Data regarding RCC prognostication in Polish literature are extremely poor. There are only a few Polish studies assessing RCC prognostic factors by the use of modern statistical tools like multivariate Cox regression analysis. The prognostic value of clinical variables (expressed as HR) varies in different studies even when they applied to similar group of patients. Their role is well established for some, but for others (hemoglobin concentration, smoking history) is still debatable. Smoking history seems to be new IPF with strong negative impact on survival in patients with RCC.

• #4 Survival Rates for Kidney Cancer | American Cancer Society

  https://www.cancer.org/cancer/types/kidney-cancer/detection-diagnosis-staging/survival-rates.html

  Survival rates can give you an idea of what percentage of people with the same type and stage of cancer are still alive a certain amount of time (usually 5 years) after they were diagnosed. […] Keep in mind that survival rates are estimates and are often based on previous outcomes of large numbers of people who had a specific cancer, but they cant predict what will happen in any particular persons case. […] A relative survival rate compares people with the same type and stage of kidney cancer to people in the overall population. […] The SEER database tracks 5-year relative survival rates for kidney cancer in the United States, based on how far the cancer has spread. […] Based on people diagnosed with cancers of the kidney (or renal pelvis) between 2014 and 2020. […] People now being diagnosed with kidney cancer may have a better outlook than these numbers show. Treatments improve over time, and these numbers are based on people who were diagnosed and treated at least 5 years earlier.

• #5 Kidney Cancer: Life Expectancy and Prognosis by Stage

  https://www.healthline.com/health/kidney-cancer/kidney-cancer-prognosis-stage

  The five-year survival rate for stage 1 kidney cancer is 81 percent. That means that out of 100 people, 81 people diagnosed with stage 1 kidney cancer are still alive five years after their original diagnosis. […] The five-year survival rate for stage 2 kidney cancer is 74 percent. That means out of 100 people, 74 people diagnosed with stage 2 kidney cancer are still alive five years after being diagnosed. […] The five-year survival rate for stage 3 kidney cancer is 53 percent. That means that out of 100 people, 53 people diagnosed with stage 3 kidney cancer will still be living five or more years after being diagnosed. […] The five-year survival rate in this stage drops to 8 percent. That means that out of 100 people, 8 people diagnosed with stage 4 cancer will still be living five years after receiving their diagnosis.

• #6 Kidney Cancer: Life Expectancy and Prognosis by Stage

  https://www.healthline.com/health/kidney-cancer/kidney-cancer-prognosis-stage

  Five-year survival rate statistics are determined by observing large numbers of people. Each cancer case is unique, however, and the numbers cant be used to predict outlooks for individuals. If you have kidney cancer and want to understand your life expectancy, speak with your doctor. […] Five-year survival rate by stage: 1 – 81%, 2 – 74%, 3 – 53%, 4 – 8%.

• #7

  https://link.springer.com/article/10.1007/s10278-021-00500-y

  According to cancer statistics, the 5-year relative survival rate of RCC patients depends on the cancer stage at the time of diagnosis. The survival rate after 5 years is 93%, 69%, and 12% for localized tumors, tumors with regional lymph nodes metastasis, and tumors with distant metastasis, respectively. […] The results of AFT Weibull shared-frailty model with bootstrapping are presented in Table 2. According to these results, tumor ISUP (International Society of Urologic Pathologists) grade, tumor malignancy, body mass index, and pathology t-stage were the most significant predictors of OS among the clinical features (p0.002,0.02,0.05, and0.02, respectively). […] The most significant predictors of OS between the selected radiomic features were flatness, area density (MVEE), and median (p0.02,0.02, and0.05, respectively).

• #8 Kidney Cancer: Stages and Prognosis

  https://www.webmd.com/cancer/kidney-cancer-stages-prognosis

  When youre looking for information on life with kidney cancer, youll often see a figure called the 5-year survival rate. This number compares people with the same stage of kidney cancer to people without cancer 5 years after diagnosis. The 5-year survival rate for all types of kidney cancer combined is 77.6%. That means youre 77.6% as likely to live at least 5 years as people who dont have cancer. […] How far the cancer has spread can also affect the survival rate for kidney cancer, which is: 93% when the cancer is localized. That means theres no sign of cancer outside your kidney. […] 74% when the cancer is regional, meaning it has spread to nearby areas like your lymph nodes or adrenal gland. […] 17% if the cancer is distant, which means it has spread to other body parts like your brain, bones, or lungs.

• #9 Survival for kidney cancer | Cancer Research UK

  https://www.cancerresearchuk.org/about-cancer/kidney-cancer/survival

  Around 15 out of 100 people (around 15%) with stage 4 kidney cancer will survive their cancer for 5 years or more after theyre diagnosed. […] Generally for people with kidney cancer in England: around 80 out of 100 people (around 80%) survive their cancer for 1 year or more, more than 65 out of 100 people (more than 65%) survive their cancer for 5 years or more, more than 50 out of 100 people (more than 50%) survive their cancer for 10 years or more. […] Your outlook depends on the stage of the kidney cancer when it was diagnosed. This means how big it is and whether it has spread. […] The type of cancer and the grade of the cancer cells can also affect your survival. […] It is important your doctor knows your performance status if you have kidney cancer. […] People who do not have these symptoms have a better outlook than people who do have these symptoms.

• #10 Prognostic factors of overall survival in renal cancer patients – single oncological center study

  https://pmc.ncbi.nlm.nih.gov/articles/PMC3974466/

  The course of renal cancer is highly unpredictable. Patients with small tumor may have distant metastasis with adverse prognosis, while patients with metastasis to lymph nodes, after nephrectomy may live more than five years. […] Among the variety of major scoring systems referring to renal cancer, it is remarkable how different sets of IPFs they may use, depending on aspects of a prognosis they are about to assess and groups of patients they apply to. […] One of the merits of the current prognostic tools is the fact that their efficacy is measurable. It is expressed by prediction accuracy (PA), a value that falls within the range from 100% (an ideal confidence of the prediction) to 50% (what represents the outcome probability assessment equal to a toss of a coin). […] The 1, 2, 3, 4, and 5-year cumulative survival probability in the entire group of patients was 58.8%, 38.2%, 32.7%, 29.1%, and 21.4%, respectively.

• #11 Deep learning can predict survival directly from histology in clear cell renal cell carcinoma | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0272656

  Survival prognosis as determined by current risk calculators, such as the IMDC risk calculator, is used to guide therapy decisions. So far, no models based on artificial intelligence are currently used in clinical practice. However, first studies were conducted to explore the potential of such models in the prediction of survival. In one study, a CNN was able to stratify risk into a high- and a low-risk group in patients with stage I clear cell RCC (ccRCC) using HE slides. Similar results were obtained in another study, where tumor and nuclei features were extracted from HE slides, which also allowed a significant risk stratification in terms of survival. These positive results, however, were achieved using the Kidney Renal Clear Cell Carcinoma (KIRC) cohort from The Cancer Genome Atlas (TCGA) only.

• #12 The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

  https://www.mdpi.com/2075-4418/11/2/206

  Nomograms, models based on different prognostic factors that affect survival, have been developed to improve the prediction of patient outcomes. […] The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). […] In this review, we investigated and reported the state of art of the application of AI for diagnosis and prognosis in renal cancer using only molecular data as input, the most commonly selected genes, and we analyzed the non-AI-based predictors making a comparison between the two approaches. […] Most of published studies are focused on the prediction of prognosis and diagnosis in clear cell RCC since this is the most dominant type of renal cancer; therefore, more data are available and, consequently, the algorithms can be better trained.

• #13 Kidney Cancer (Renal Cell Cancer) Recurrence Prediction Tools | Memorial Sloan Kettering Cancer Center

  https://www.mskcc.org/cancer-care/types/kidney/prediction-tools

  Our renal cell carcinoma nomogram is an online tool that can be used to predict the likelihood that a patients kidney cancer will not recur at five years after surgery, following a new diagnosis. […] Understanding the risk of renal cell carcinoma recurrence is important because it is a key factor in deciding whether a patient requires additional treatment. This nomogram can help doctors and patients to develop the best treatment plan following surgery.

• #14 The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

  https://www.mdpi.com/2075-4418/11/2/206

  The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. […] Detecting ccRCC in the early stage would significantly ameliorate the prognosis, even though localized ccRCC removal by nephrectomy does not eliminate the high risk of metastatic relapse. […] Therefore, also considering the increase in the number of RCC cases, development of efficient strategies for an early diagnosis and for the identification of tumors with a worse prognosis is very important. […] The currently adopted prognostic factors for RCC include the TNM staging system, the four-tiered WHO/ISUP (International Society of Urological Pathology) grading system, histologic subtype, presence of the sarcomatoid component, microvascular invasion, tumor necrosis and invasion of the collecting system.

• #15 A novel nine-microRNA-based model to improve prognosis prediction of renal cell carcinoma | BMC Cancer | Full Text

  https://bmccancer.biomedcentral.com/articles/10.1186/s12885-022-09322-9

  Nine miRNAs were strongly correlated with the prognosis (P0.01), and those with high expression levels had a poor prognosis. […] The established nine-miRNAs prognostic model has the potential to facilitate prognostic prediction. Moreover, this model was closely related to the immune microenvironment, immune infiltration, and immune checkpoint genes of RCC. […] Based on KM survival analysis, it can be found that the model can predict the prognosis of RCC. The survival time of the high-risk group was much shorter than that of the low-risk group. Moreover, the high expression of the nine miRNAs in the model all predicts a poor prognosis. […] The area under the ROC curve suggests that the model with moderate diagnostic ability. […] Univariate and multivariate analysis further proved that our index could be an independent prognostic index and had a higher predictive value than the traditional tumor pathological stage. […] In conclusion, we successfully constructed a nine-miRNAs prognostic model with good predictive power. Moreover, this model was closely related to the TME, immune infiltration and ICGs of RCC, and it is hoped that it can provide new ideas for RCC immunotherapy.

• #16 Better prediction of clinical outcome in clear cell renal cell carcinoma based on a 6 metabolism-related gene signature | Scientific Reports

  https://www.nature.com/articles/s41598-023-38380-7

  It has been reported that metabolic disorders participate in the formation and progression of clear cell renal cell carcinoma (ccRCC). However, the predictive value of metabolism-related genes (MRGs) in clinical outcome of ccRCC is still largely unknown. Herein, a novel metabolism-related signature was generated to assess the effect of MRGs on the prognosis of ccRCC patients. […] A new metabolism-related signature of 6 hub MRGs (PAFAH2, ACADSB, ACADM, HADH, PYCR1 and ITPKA) was constructed, with a good prognostic prediction ability in the TCGA cohort. […] Collectively, a 6-MRG prognostic risk signature is generated to estimate the prognostic status of ccRCC patients, providing a novel insight in the prognosis prediction and treatment of ccRCC. […] Herein, a 6-MRG prognostic risk signature was constructed to evaluate ccRCC prognosis. Patients with low-risk were shown to have a better overall survival (OS).

• #17 Better prediction of clinical outcome in clear cell renal cell carcinoma based on a 6 metabolism-related gene signature | Scientific Reports

  https://www.nature.com/articles/s41598-023-38380-7

  Therefore, our study demonstrates the potential prognostic value of the 6-MRG prognostic risk signature, which may provide a novel insight in ccRCC treatment and be promising biomarkers for ccRCC progression. […] Overall, the above results demonstrate that our signature may be useful for predicting prognosis in patients with ccRCC. […] Our signature indicated novel ccRCC therapeutic targets, providing candidate therapeutic strategies for ccRCC. […] In conclusion, we systematically explore the underlying regulation mechanism of MRGs and their roles in immune-relative pathways of ccRCC. Moreover, we reveal the potential prognostic value of this 6-MRG prognostic risk signature, which may provide novel insights in ccRCC treatment and be promising biomarkers for ccRCC progression.

• #18 Multimodal Deep Learning for Prognosis Prediction in Renal Cancer

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8651560/

  Clear-cell renal cell carcinoma (ccRCC) is common and associated with substantial mortality. TNM stage and histopathological grading have been the sole determinants of a patients prognosis for decades and there are few prognostic biomarkers used in clinical routine. […] In the study, we developed and evaluated a multimodal deep learning model (MMDLM) for prognosis prediction in ccRCC. […] The MMDLM showed great performance in predicting the prognosis of ccRCC patients with a mean C-index of 0.7791 and a mean accuracy of 83.43%. […] Multimodal deep learning can contribute to prognosis prediction in ccRCC and potentially help to improve the clinical management of this disease. […] An AI-based computer program can analyze various medical data (microscopic images, CT/MRI scans, and genomic data) simultaneously and thereby predict the survival time of patients with renal cancer.

• #19 Multimodal Deep Learning for Prognosis Prediction in Renal Cancer

  https://pmc.ncbi.nlm.nih.gov/articles/PMC8651560/

  When combining conventional histopathological input with CT and MRI images, the mean C-index increased to 0.7791 0.0278 with a maximum of 0.8123. […] Strikingly, only the MMDLM was significantly better than all independent prognostic factors. […] A total of 113 patients could be included in these analyses. Here accuracy reached 83.43% 11.62% with a maximum of 100% upon 12-fold cross validation. […] Dividing the cohort according to the MMDLMs prediction (Alive vs. Dead) into low- and high-risk patients showed a highly significant difference in the survival curves. […] Our integrative approach could be used to distinguish between low- and high-risk patients, who would be more suitable for intensified treatment and/or surveillance.

• #20 Deep learning can predict survival directly from histology in clear cell renal cell carcinoma | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0272656

  Deep learning can predict survival directly from histology in clear cell renal cell carcinoma. Artificial intelligence-based image analysis has the potential to improve outcome prediction and thereby risk stratification. Thus, we investigated whether a convolutional neural network (CNN) can extract relevant image features from a representative hematoxylin and eosin-stained slide to predict 5-year overall survival (5y-OS) in ccRCC. The CNN was trained to predict 5y-OS in a binary manner using slides from TCGA and validated using an independent in-house cohort. A mean balanced accuracy of 72.0% (standard deviation [SD] = 7.9%), sensitivity of 72.4% (SD = 10.6%), specificity of 71.7% (SD = 11.9%) and area under receiver operating characteristics curve (AUROC) of 0.75 (SD = 0.07) was achieved on the TCGA training set (n = 254 patients / WSIs) using 10-fold cross-validation. On the external validation cohort (n = 99 patients / WSIs), mean accuracy, sensitivity, specificity and AUROC were 65.5% (95%-confidence interval [CI]: 62.9-68.1%), 86.2% (95%-CI: 81.8-90.5%), 44.9% (95%-CI: 40.2-49.6%), and 0.70 (95%-CI: 0.69-0.71). A multivariable model including age, tumor stage and metastasis yielded an AUROC of 0.75 on the TCGA cohort. The inclusion of the CNN-based classification (Odds ratio = 4.86, 95%-CI: 2.70-8.75, p < 0.01) raised the AUROC to 0.81. On the validation cohort, both models showed an AUROC of 0.88. In univariable Cox regression, the CNN showed a hazard ratio of 3.69 (95%-CI: 2.60-5.23, p < 0.01) on TCGA and 2.13 (95%-CI: 0.92-4.94, p = 0.08) on external validation. The results demonstrate that the CNN's image-based prediction of survival is promising and thus this widely applicable technique should be further investigated with the aim of improving existing risk stratification in ccRCC.

• #21 Deep learning can predict survival directly from histology in clear cell renal cell carcinoma | PLOS One

  https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0272656

  In the present study, a pretrained ResNet18 CNN was used to predict 5-year OS in ccRCC directly from HE-stained diagnostic whole slide images. Good performances were seen in binary prediction on the training set. The results were validated on an independent external test set, demonstrating the generalizability of this method. Furthermore, the CNN-based classification was an independent predictor in a multivariable clinicopathological model. […] CNN-based prognostication of overall survival using HE-stained slides in ccRCC shows promising performance and generalizability and can be combined with existing clinicopathological parameters. This widely applicable technique shows the potential of artificial intelligence in image-based outcome prediction. Further research is needed to fine-tune this method and increase robustness. The inclusion of this method in existing risk stratification models or the development of new, combined models should be pursued in the future.

• #22 A three-feature prediction model for metastasis-free survival after surgery of localized clear cell renal cell carcinoma | Scientific Reports

  https://www.nature.com/articles/s41598-021-88177-9

  After surgery of localized renal cell carcinoma, over 20% of the patients will develop distant metastases. Our aim was to develop an easy-to-use prognostic model for predicting metastasis-free survival after radical or partial nephrectomy of localized clear cell RCC. The model was validated in an external cohort of 714 patients. Patients were stratified into clinically meaningful risk categories using only three features: tumor size, tumor grade and microvascular invasion. The presented model retains high accuracy while requiring only three features that are routinely collected and widely available. The median follow-up in our RCC patient cohorts was 76.1 months in the training cohort and 65.4 months in the validation cohort. 55 (28%) patients in the training cohort and 134 (19%) patients in the validation cohort developed distant metastases during postoperative follow-up. We employed regularized Cox regression using the LASSO L1-norm to discover a minimal set of clinicopathologic features for predicting RCC metastases after surgery. Only three features, tumor size, tumor grade (Fuhrman) and microvascular invasion, were found to be essential for an accurate prediction model. The novel proposed model achieved high prediction accuracy. The C-index and standard error was 0.755 in the training cohort and 0.836 in the validation cohort. Our model confirmed that routinely reported microvascular invasion increases the risk of disease recurrence. In conclusion, only three tumour features (tumor size, tumor grade and microvascular invasion) were sufficient for the prediction of metastasis-free survival after surgery of localized clear cell RCC with a similar prediction accuracy as the 2003 Leibovich model.

• #23

  https://link.springer.com/article/10.1007/s10278-021-00500-y

  Our findings suggest that the model based on radiomic features alone has better prognostic power compared with the clinical model and adding radiomic features to clinical features resulted in the best performance. […] In this work, we demonstrated that imaging biomarkers, including tumor flatness and area density which belong to morphological features and median from the statistical category, are predictive of overall survival after partial or radical nephrectomy. […] Among clinical features, tumor ISUP grade, malignancy, pathology t-stage, and body mass index are statistically significant predictors of OS.

• #24 Study improves prediction of therapy response in patients with metastatic renal cell carcinoma

  https://medicalxpress.com/news/2023-06-therapy-response-patients-metastatic-renal.html

  Study improves prediction of therapy response in patients with metastatic renal cell carcinoma. […] Reliable prediction of response to treatment is critical for optimal patient management. […] The research group led by Dr. Klümper has now been able to show that the investigation of two inexpensive and widely available inflammatory markers in the blood (C-reactive protein (CRP) and albumin) significantly improves the prediction of therapy response in patients with metastatic renal cell carcinoma, especially in the large group of patients with disease control in the first follow-up (>80%). […] Our study’s findings are derived from patient cohorts participating in two separate randomized trials focused on metastatic renal cell carcinoma. These results strongly advocate for the immediate implementation of the mGPS as a prognostic tool for predicting outcomes in individuals diagnosed with metastatic renal cell carcinoma. […] Improved prediction of treatment failure could better identify patients who could benefit from a change or intensification of therapy.

• #25 Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis

  https://www.mdpi.com/2227-9059/11/11/2875

  Renal cell carcinoma is a significant health burden worldwide, necessitating accurate and efficient diagnostic methods to guide treatment decisions. […] This review aims to provide a comprehensive overview of the current state and potential of digital pathology in the context of renal cell carcinoma. […] The integration of digital pathology tools with other diagnostic modalities, such as radiology and genomics, enables a novel multimodal characterization of different types of renal cell carcinoma. […] With continuous advancements and refinement, AI technologies are expected to play an integral role in diagnostics and clinical decision-making, improving patient outcomes. […] Morphological verification of the primary lesion and any metastases is essential before treatment and helps to identify the histological variant of the tumor. Additionally, post-surgical staging is important for evaluating the probability of recurrence and predicting prognosis.

• #26 Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis

  https://www.mdpi.com/2227-9059/11/11/2875

  The trained histological models can be further developed and used to assess prognosis. […] Machine learning technologies can contribute to the prognosis of ccRCC and potentially help improve the clinical management of this disease. […] The application of AI to investigate a series of molecular markers in each sample has predictive value and can be integrated with morphological features to improve risk stratification and personalized therapy. […] The relationship between histological features of ccRCC and genetic and epigenetic data, which are also successfully used as prognostic markers, has been extensively studied in the literature. […] The most important parameter associated with the prognosis of the disease is grading. […] The sensitivity and specificity were 84.6% and 81.3%, respectively.

• #27 Development and validation of a nomogram to predict overall survival for patients with metastatic renal cell carcinoma | BMC Cancer | Full Text

  https://bmccancer.biomedcentral.com/articles/10.1186/s12885-020-07586-7

  Heterogeneity of metastatic renal cell carcinoma (RCC) constraints accurate prognosis prediction of the tumor. We therefore aimed at developing a novel nomogram for accurate prediction of overall survival (OS) of patients with metastatic RCC. Overall, 2315 metastatic RCC patients in the SEER database who fulfilled our inclusion criteria were utilized in constructing a nomogram for predicting OS of newly diagnosed metastatic RCC patients. The nomogram incorporated eight clinical factors: Fuhrman grade, lymph node status, sarcomatoid feature, cancer-directed surgery and bone, brain, liver, and lung metastases, all significantly associated with OS. We developed and validated an accurate nomogram for individual OS prediction of metastatic RCC patients. Accurate prognostic models are invaluable in designing clinical trials, patient psychological management, and governing therapeutic modalities. The efficiency of the MSKCC model was validated in an independent study of 353 participants, previously untreated for metastatic RCC. The two models provide the median OS for each group after stratifying metastatic RCC patients based on the prognostic risk factors. Clinical and pathological characteristics of metastatic RCC patients are highly heterogeneous. The nomogram displayed superior predictive capability and higher clinical application than the conventional AJCC staging system.

• #28 The Kidney Failure Risk Equation

  https://kidneyfailurerisk.com/

  For patients with localized kidney cancer facing either a partial or radical nephrectomy, the Kidney Cancer Risk Equation (KCRE) can be used to predict the risk of kidney failure 5-years after kidney cancer surgery. Knowing your risk can help inform treatment decisions, such as surgery (partial versus radical nephrectomy), or watchful waiting. […] The kidney failure risk equations were developed in patients with CKD stages G3-G5 referred to nephrologists in Canada, and have now been validated in more than 700,000 individuals spanning 30 + countries worldwide. […] The four and eight variable equations accurately predict the 2 and 5 year probability of treated kidney failure (dialysis or transplantation) for a potential patient with CKD Stage 3 to 5. Predicted risks may differ from observed risks in clinical populations with lower and higher observed risks than the study population, and a calibration factor for non-North American cohorts has been added.

• #29 The Kidney Failure Risk Equation

  https://kidneyfailurerisk.com/

  Determining the probability of kidney failure may be useful for patient and provider communication, triage and management of nephrology referrals and timing of dialysis access placement and living related kidney transplant. Prospective trials evaluating the utility of this instrument for clinical decision making are in progress.

• #30 Kidney Cancer: Life Expectancy and Prognosis by Stage

  https://www.healthline.com/health/kidney-cancer/kidney-cancer-prognosis-stage

  Five-year survival rate statistics are determined by observing large numbers of people. Each cancer case is unique, however, and the numbers cant be used to predict outlooks for individuals. If you have kidney cancer and want to understand your life expectancy, speak with your doctor. […] Five-year survival rate by stage: 1 – 81%, 2 – 74%, 3 – 53%, 4 – 8%.

• #31 Kidney Cancer: Stages and Prognosis

  https://www.webmd.com/cancer/kidney-cancer-stages-prognosis

  But its important to keep in mind that none of these numbers reflects your particular illness. Every person is unique, and a number of things — like cancer type (renal vs transitional), specific cell type, the stage you were in at diagnosis, and your overall health — can play a role. […] Also, these numbers reflect what was happening in the past. Experts collect them every 5 years. Diagnosis and treatment continue to improve. Death rates have dropped steadily. Talk to your doctor about the best treatment for your particular type and stage of kidney cancer.

• #32

  https://winshipcancer.emory.edu/cancer-types-and-treatments/kidney-cancer/outcomes.php

  As a result, our kidney cancer outcomes our survival rates, rates of recurrence and management of long-term side effects are better than most other cancer centers in the U.S. […] Research shows survival rates are up to 25% higher when starting treatment at an NCI-designated Comprehensive Cancer Center. […] While many kidney cancer patients will never have a recurrence, some will. […] At Winship, were proud to offer some of the most long-lasting treatments and post-treatment follow-up care in the country. […] Well design your treatment plan in a way that not only provides you the best kidney cancer prognosis but also helps keep your cancer from returning. […] For patients whose kidney cancer is treated with surgery, we have quality measures in place that include testing the surrounding tissue to confirm theres no remaining cancer. […] And once youve completed treatment at Winship, we will continue to monitor your condition and follow you for at least five years to ensure that you remain cancer-free and assist you with the management of any long-term side effects of treatment.

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