&lt;p&gt;Development and Validation of a Nomogram Predicting the Prognosis of Renal Cell Carcinoma After Nephrectomy&lt;/p&gt;

Xia, Mancheng; Yang, Hailan; Wang, Yusheng; Yin, Keqiang; Bian, Xiao-Dong; Chen, Jiawei; Shuang, Weibing

doi:10.2147/cmar.s250371

Cited by 3 publications

(3 citation statements)

References 43 publications

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“…For the same reason, the comparison with nomograms is not possible when the C-index is provided [ 5 , 86 , 87 ] but only when the AUC is present. In this case, a nomogram reached an AUC of 0.813 and 0.799 for the 3-year and 5-year survival, respectively [ 7 ]. Similar performance (0.801 AUC) is obtained by integrating expression data in predicting a high ISUP (International Society of Urological Pathology) grade of ccRCC [ 88 ].…”

Section: Discussionmentioning

confidence: 99%

“…In case of the metastatic RCC, the most effective prognostic factors are the histological subtype and the presence of the sarcomatoid component [ 4 ]. Nomograms, models based on different prognostic factors that affect survival, have been developed to improve the prediction of patient outcomes [ 5 , 6 , 7 ]. Among these factors, or variables, we mention sex, race, marital status, smoking history, type 2 diabetes mellitus, age at diagnosis, T stage, N stage, M stage, Fuhrman nuclear grade and surgical approach.…”

Section: Introductionmentioning

confidence: 99%

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

Giulietti¹,

Cecati²,

Sabanovic³

et al. 2021

Diagnostics

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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. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. 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). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Giulietti¹,

Cecati²,

Sabanovic³

et al. 2021

Diagnostics

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show abstract

“…Renal cell carcinoma (RCC) is one of the most frequent cancers in the genitourinary system, accounting for 3% of all adult cancers. 1 The global incidence of RCC has increased by approximately 2% each year throughout the past two decades. 2 Despite the increased use of medical imaging technology, approximately 30% of patients are still initially diagnosed with metastatic renal cell carcinoma (MRCC).…”

Section: Introductionmentioning

confidence: 99%

Construction and validation of a novel prognostic nomogram for patients with metastatic renal cell carcinoma: a SEER-based study

Zhou

et al. 2022

J Int Med Res

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Objective We aimed to establish and validate nomograms to evaluate overall survival (OS) and cancer-specific survival (CSS) in patients with metastatic renal cell carcinoma (MRCC). Methods Between 2010 and 2015, the clinical information of patients with MRCC was selected using the Surveillance, Epidemiology, and End Results database. Two nomograms were constructed based on Cox regression analysis, and their prediction accuracy was evaluated by concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Results After propensity score matching, there were 568 patients with MRCC in the training group and 568 in the validation group. Multivariate analyses revealed that age, residence, pathology, T stage, N stage, surgery, and metastatic sites were independent prognostic factors for the OS and CSS of MRCC. The C-index and ROC curves indicated that the two nomograms of OS and CSS showed satisfactory discriminative power. Furthermore, DCA displayed that the nomograms achieved more clinical net benefit than the American Joint Committee on Cancer staging system. Conclusion We constructed and validated two effective prognostic nomograms for patients with MRCC that accurately predicted the probabilities of 1-, 2-, and 3-year OS and CSS.

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A novel DNA methylation signature revealed GDF6 and RCC1 as potential prognostic biomarkers correlated with cell proliferation in clear cell renal cell carcinoma

Xu,

Wu,

Zhao

et al. 2023

Mol Biol Rep

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<p>Development and Validation of a Nomogram Predicting the Prognosis of Renal Cell Carcinoma After Nephrectomy</p>

Abstract: Objective: To develop and validate a nomogram for predicting the overall survival (OS) of renal cell carcinoma (RCC) patients after nephrectomy. Materials and Methods: In total, 488 patients with RCC who underwent nephrectomy at

Cited by 3 publications

References 43 publications

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

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

Construction and validation of a novel prognostic nomogram for patients with metastatic renal cell carcinoma: a SEER-based study

A novel DNA methylation signature revealed GDF6 and RCC1 as potential prognostic biomarkers correlated with cell proliferation in clear cell renal cell carcinoma

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