scholarly journals Machine Learning Approach to Predict the Probability of Recurrence after Surgery for Renal Cell Carcinoma: Prediction Model Development Study (Preprint)

2020 ◽  
Author(s):  
HyungMin Kim ◽  
Sun Jung Lee ◽  
So Jin Park ◽  
In Young Choi ◽  
Sung-Hoo Hong
Keyword(s):  
Machine Learning ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Large Scale ◽  
Tertiary Care ◽  
Analytical Data ◽  
Machine Learning Algorithms ◽  
High Recurrence Rate ◽  
A Value

BACKGROUND Renal cell carcinoma (RCC) has a high recurrence rate of 20–30 % after nephrectomy for clinically localized disease, and more than 40 % of patients eventually die of the disease, making regular monitoring and constant management of utmost importance. OBJECTIVE The objective of this study was to develop an algorithm that predicts the probability of recurrence within 5 and 10 years of RCC. METHODS Data from 6,849 Korean RCC patients were collected from 8 tertiary care hospitals listed in the KOrean Renal Cell Carcinoma (KORCC) web-based database (DB). To predict RCC recurrence, 2,814 analytical data were extracted from the DB. Eight machine learning algorithms were used to predict the probability of RCC recurrence, and the results were compared. RESULTS Within five years of surgery, the highest area under the receiver operating characteristic curve (AUROC) was obtained from the naive Bayes (NB) model, with a value of 0.836. Within 10 years of surgery, the highest AUROC was obtained from the NB model, with a value of 0.784. CONCLUSIONS An algorithm was developed that predicts the probability of RCC recurrence within 5 and 10 years using the KORCC DB, a large-scale RCC cohort in Korea. It is expected that the developed algorithm will help clinicians manage prognosis and establish customized treatment strategies for patients with RCC after surgery.

scholarly journals Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

2020 ◽  
Vol 20 (1) ◽  
pp. 841-857
Author(s):  
Malena Manzi ◽  
Martín Palazzo ◽  
María Elena Knott ◽  
Pierre Beauseroy ◽  
Patricio Yankilevich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Renal Cell Carcinoma ◽  
Early Detection ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Learning Approach ◽  
Cell Renal Cell Carcinoma ◽  
Machine Learning Approach

Exploiting Synthetic Lethal Network for Precision Treatment of Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
Zhicheng Liu ◽  
Dongxu Lin ◽  
Linmeng Zhang ◽  
Chen Yang ◽  
Bin Guo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Large Scale ◽  
Target Genes ◽  
Clear Cell ◽  
Therapeutic Potential ◽  
Synthetic Lethality ◽  
Treatment Modalities ◽  
Cell Renal Cell Carcinoma

Abstract Background The emerging of targeted therapies has revolutionized the treatment modalities of advanced clear cell renal cell carcinoma (ccRCC) over the past fifteen years. However, lack of personalized treatment limits the development of effective clinical guidelines and improvement of patient prognosis. In this study, large-scale genomic profiles of ccRCC cohorts were exploited for conducting an integrative analysis. Method Based on synthetic lethality (SL), a concept that simultaneous losses of two genes cause cell death while a single loss does not, we sought to develop a computational pipeline to infer potential SL partners of ccRCC. Drug response prediction were received from three pharmacological databases to select agents which are likely to be effective in precisely treating patients with target gene mutation. Results We developed a credible method to identify SL pairs and determined a list of 72 candidate pairs which might be utilized to selectively eliminate tumors with genetic aberrations through SL partners of specific mutations. Further analysis identified BRD4 and PRKDC as novel medicine targets for patients with BAP1 mutations. After mapping these target genes to comprehensive drug datasets, two agents (BI-2536 and PI-103) were found to have considerable therapeutic potential in BAP1 mutant tumors. Conclusion Overall, our findings provide insight into the overview of ccRCC mutation patterns and offer novel opportunities for improving individualized cancer treatment.

A Radiomic-based Machine Learning Algorithm to Reliably Differentiate Benign Renal Masses from Renal Cell Carcinoma

2021 ◽  
Author(s):  
Nima Nassiri ◽  
Marissa Maas ◽  
Giovanni Cacciamani ◽  
Bino Varghese ◽  
Darryl Hwang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Renal Masses

scholarly journals Decadal Experience of Renal Cell Carcinoma from a Tertiary Care Teaching Institute in North India

2018 ◽  
Vol 9 (4) ◽  
pp. 558-564
Author(s):  
Singh Kawaljit ◽  
Sinha Rahul Janak ◽  
Gupta Ashok ◽  
Singh Vishwajeet
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Tertiary Care ◽  
North India ◽  
Care Teaching

scholarly journals Renal Cell Carcinoma Metastatic to the Sinonasal Cavity: A Review and Report of 8 Cases

2018 ◽  
Vol 97 (9) ◽  
pp. E6-E12 ◽  
Author(s):  
Pierre-Louis Bastier ◽  
Dorothée Dunion ◽  
Guillaume de Bonnecaze ◽  
Elie Serrano ◽  
Ludovic de Gabory
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Nasal Obstruction ◽  
Survival Data ◽  
Renal Cell ◽  
Tertiary Care ◽  
Local Treatment ◽  
Initial Presentation ◽  
Local Response

Renal cell carcinoma (RCC) metastatic in the sinonasal cavity is rare. In many cases, it represents the initial presentation of RCC. We conducted a retrospective chart review to report the clinical presentation, imaging, and treatment of RCC metastases in the sinonasal cavity at two tertiary care referral centers. Our population was made up of 8 patients—6 men and 2 women, aged 55 to 86 years (mean: 66.9; median: 63.5)—who had been diagnosed with cancer in the sinonasal cavity. The most common complaints were epistaxis, nasal obstruction, and diplopia. Cancers were located in the ethmoid sinus (n = 3), nasal cavity (n = 2), sphenoid sinus (n = 2), and maxillary sinus (n = 1). Local treatment involved resection and adjuvant radiotherapy in 4 patients, surgery alone in 2 patients, and radiotherapy alone in the other 2. The lesion was embolized before surgery in 4 cases. We also performed a critical review of similar published cases. Our literature review covered 53 cases of RCC metastatic to the sinonasal cavity, including ours. Metastases were the first presentation of RCC in 24 of these cases (45.3%); in our series, the metastases led to the diagnosis of the primary RCC in 3 cases (37.5%). In the 53 reported cases, metastatic resection was performed on 35 patients (66.0%). Survival data were available for 22 of these operated patients, and 17 of them achieved a complete local response. Adjunctive radiotherapy was not associated with a better local response. Overall survival was significantly better in patients who had an isolated metastasis rather than multiple metastases (p = 0.013). There was no difference in overall survival between patients whose metastasis represented the initial presentation of RCC and those whose metastasis did not (p = 0.95). We recommend that sinonasal metastasis be suspected in the event of unilateral nasal bleeding or nasal obstruction in patients diagnosed with RCC. Embolization may prevent abundant bleeding during removal. Surgery may improve the quality of life of these patients while decreasing nasal obstruction and bleeding.

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