A Novel Machine Learning Algorithm Combined With Multivariate Analysis for the Prognosis of Renal Collecting Duct Carcinoma

Wei, Liwei; Huang, Yongdi; Chen, Zheng; Li, Jinhua; Huang, Guangyi; Qin, Xiaoping; Cui, Lihong; Zhuo, Yumin

doi:10.3389/fonc.2021.777735

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…Furthermore, the XGBoost model was applied to predict the overall survival of genitourinary cancer patients in recent studies [38,39]. Wei et al designed the XGBoost model based on clinical data to predict the prognosis of renal collecting duct carcinoma, and the results implied that the models had the highest predictive accuracy and helped clinicians to make clinical decisions for patients [40]. Consistent with our study, clinical data should be considered a very important basis for diagnosing diseases when building models.…”

Section: Performance Compared Between the Ensemble Model And Urologistssupporting

confidence: 79%

Differentiate Xp11.2 Translocation Renal Cell Carcinoma from Computed Tomography Images and Clinical Data with ResNet-18 CNN and XGBoost

Lu¹,

Ma²,

Dong³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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This study aims to apply ResNet-18 convolutional neural network (CNN) and XGBoost to preoperative computed tomography (CT) images and clinical data for distinguishing Xp11.2 translocation renal cell carcinoma (Xp11.2 tRCC) from common subtypes of renal cell carcinoma (RCC) in order to provide patients with individualized treatment plans. Data from 45 patients with Xp11.2 tRCC from January 2007 to December 2021 are collected. Clear cell RCC (ccRCC), papillary RCC (pRCC), or chromophobe RCC (chRCC) can be detected from each patient. CT images are acquired in the following three phases: unenhanced, corticomedullary, and nephrographic. A unified framework is proposed for the classification of renal masses. In this framework, ResNet-18 CNN is employed to classify renal cancers with CT images, while XGBoost is adopted with clinical data. Experiments demonstrate that, if applying ResNet-18 CNN or XGBoost singly, the latter outperforms the former, while the framework integrating both technologies performs similarly or better than urologists. Especially, the possibility of misclassifying Xp11.2 tRCC, pRCC, and chRCC as ccRCC by the proposed framework is much lower than urologists. KEYWORDSResNet-18 CNN; XGBoost; computed tomography; TFE3; renal cell carcinoma Materials and Methods Patient EnrollmentThe complete CT imaging data and postoperative pathological data of patients with Xp11.2 tRCC diagnosed in the Nanjing Drum Tower Hospital were retrospectively analyzed from January 2007 to December 2021. 45 cases of Xp11.2 tRCC with complete clinicopathological data were enrolled in the study, including complete medical histories, clinical information, and pathological data. According to both tumor size and pathological stage, the matched patients included 45 with ccRCC, pRCC, or chRCC, respectively. The study was approved by the Institutional Review Board of Nanjing Drum Tower Hospital. Inclusion criteria:1. Patients underwent radical or partial nephrectomy. 2. Preoperative CT examination of renal tumors and complete examination records. 3. The reviewed pathological diagnosis was consistent with the initial postoperative diagnosis and the pathological diagnosis of Xp11.2 tRCC was validated by the next generation sequence. 4. These patients were without cardiovascular and cerebrovascular diseases and chronic kidney disease that affected the blood supply of the kidney. 5. These patients had a unilateral single renal tumor.

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Section: Performance Compared Between the Ensemble Model And Urologistssupporting

confidence: 79%

Differentiate Xp11.2 Translocation Renal Cell Carcinoma from Computed Tomography Images and Clinical Data with ResNet-18 CNN and XGBoost

Lu¹,

Ma²,

Dong³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

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“…The nomogram was found to be an advanced approach capable of predicting individual oncologic prognosis based on comprehensive characteristics ( 8 ). Moreover, as an emerging intersectional method, ML is adept at relating multiple variables and accurately predicting outcomes ( 9 ). Therefore, multiple ML predictive models have recently been used in disease diagnosis, prognostic prediction, and clinical decision-making ( 10 , 11 ).…”

Section: Introductionmentioning

confidence: 99%

Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

Zhang

Wei

et al. 2022

Front. Public Health

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PurposeThe purpose of this study was to investigate the clinical and non-clinical characteristics that may affect the early death rate of patients with metastatic colorectal carcinoma (mCRC) and develop accurate prognostic predictive models for mCRC.MethodMedical records of 35,639 patients with mCRC diagnosed from 2010 to 2019 were obtained from the SEER database. All the patients were randomly divided into a training cohort and a validation cohort in a ratio of 7:3. X-tile software was utilized to identify the optimal cutoff point for age and tumor size. Univariate and multivariate logistic regression models were used to determine the independent predictors associated with overall early death and cancer-specific early death caused by mCRC. Simultaneously, predictive and dynamic nomograms were constructed. Moreover, logistic regression, random forest, CatBoost, LightGBM, and XGBoost were used to establish machine learning (ML) models. In addition, receiver operating characteristic curves (ROCs) and calibration plots were obtained to estimate the accuracy of the models. Decision curve analysis (DCA) was employed to determine the clinical benefits of ML models.ResultsThe optimal cutoff points for age were 58 and 77 years and those for tumor size of 45 and 76. A total of 15 independent risk factors, namely, age, marital status, race, tumor localization, histologic type, grade, N-stage, tumor size, surgery, radiation, chemotherapy, bone metastasis, brain metastasis, liver metastasis, and lung metastasis, were significantly associated with the overall early death rate of patients with mCRC and the cancer-specific early death rate of patients with mCRC, following which nomograms were constructed. The ML models revealed that the random forest model accurately predicted outcomes, followed by logistic regression, CatBoost, XGBoost, and LightGBM models. Compared with other algorithms, the random forest model provided more clinical benefits than other models and can be used to make clinical decisions in overall early death and specific early death caused by mCRC.ConclusionML algorithms combined with nomograms may play an important role in distinguishing early deaths owing to mCRC and potentially help clinicians make clinical decisions and follow-up strategies.

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“…Unlike associated studies (Hou et al, 2020;Wei et al, 2021), categorical variables were included in the prediction model as "dummy variables", age: >76 years, sex: female, race: black, histological type: FTC, tumor size: >65 mm, surgery: no, radiation: no, chemotherapy: no, and RN_positive negative as a control. The XGB algorithm identifies the importance of features based on the magnitude of the gain value obtained for each variable (relative importance scores out of 100), with higher values indicating greater importance to the predicted target.…”

Section: Prognostic Model Constructionmentioning

confidence: 99%

A Predictive Model for the 10-year Overall Survival Status of Patients With Distant Metastases From Differentiated Thyroid Cancer Using XGBoost Algorithm-A Population-Based Analysis

et al. 2022

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Purpose: To explore clinical and non-clinical characteristics affecting the prognosis of patients with differentiated thyroid cancer with distant metastasis (DTCDM) and establish an accurate overall survival (OS) prognostic model.Patients and methods: Study subjects and related information were obtained from the National Cancer Institute’s surveillance, epidemiology, and results database (SEER). Kaplan‐Meier analysis, log-rank test, and univariate and multivariate Cox analysis were used to screen for factors influencing the OS of patients with DTCDM. Nine variables were introduced to build a machine learning (ML) model, receiver operating characteristic (ROC) was used to evaluate the recognition ability of the model, calibration plots were used to obtain prediction accuracy, and decision curve analysis (DCA) was used to estimate clinical benefit.Results: After applying the inclusion and exclusion criteria, a total of 3,060 patients with DTCDM were included in the survival analysis from 2004 to 2017. A machine learning prediction model was developed with nine variables: age at diagnosis, gender, race, tumor size, histology, regional lymph node metastasis, primary site surgery, radiotherapy, and chemotherapy. After excluding patients who survived <120 months, variables were sub-coded and machine learning was used to model OS prognosis in patients with DTCDM. Patients 6–50 years of age had the highest scores in the model. Other variables with high scores included small tumor size, male sex, and age 51–76. The AUC and calibration curves confirm that the XGBoost model has good performance. DCA shows that our model can be used to support clinical decision-making in a 10-years overall survival model.Conclusion: An artificial intelligence model was constructed using the XGBoost algorithms to predict the 10-years overall survival rate of patients with DTCDM. After model validation and evaluation, the model had good discriminative ability and high clinical value. This model could serve as a clinical tool to help inform treatment decisions for patients with DTCDM.

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A Novel Machine Learning Algorithm Combined With Multivariate Analysis for the Prognosis of Renal Collecting Duct Carcinoma

Cited by 4 publications

References 31 publications

Differentiate Xp11.2 Translocation Renal Cell Carcinoma from Computed Tomography Images and Clinical Data with ResNet-18 CNN and XGBoost

Differentiate Xp11.2 Translocation Renal Cell Carcinoma from Computed Tomography Images and Clinical Data with ResNet-18 CNN and XGBoost

Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer

A Predictive Model for the 10-year Overall Survival Status of Patients With Distant Metastases From Differentiated Thyroid Cancer Using XGBoost Algorithm-A Population-Based Analysis

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