An External-Validated Prediction Model to Predict Lung Metastasis among Osteosarcoma: A Multicenter Analysis Based on Machine Learning

Li, Wenle; Liu, Wencai; Memon, Fida Hussain; Wang, Bing; Xu, Chan; Dong, Shengtao; Wang, Haosheng; Hu, Zhaohui; Quan, Xubin; Deng, Yizhuo; Liu, Qiang; Su, Shibin; Yin, Chengliang

doi:10.1155/2022/2220527

Cited by 22 publications

(18 citation statements)

References 35 publications

(42 reference statements)

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“…Six ML models, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGB), Random Forest (RF), Decision Tree (DT), and Naive Bayesian model (NBC), were used to build prediction models, the performance of which was compared by 10-fold cross-validation method [16][17][18][19]. The model with the greatest AUC value was regarded as the preferred prediction model, whose corresponding network calculator is designed to individually assess the risk of BM in patients with RCC [20][21][22][23].…”

Section: Establishment and Verification Of Prediction Modelsmentioning

confidence: 99%

Establishment and Validation of a Machine Learning Prediction Model Based on Big Data for Predicting the Risk of Bone Metastasis in Renal Cell Carcinoma Patients

Liu

Yin

et al. 2022

Computational and Mathematical Methods in Medicine

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Purpose. Since the prognosis of renal cell carcinoma (RCC) patients with bone metastasis (BM) is poor, this study is aimed at using big data to build a machine learning (ML) model to predict the risk of BM in RCC patients. Methods. A retrospective study was conducted on 40,355 RCC patients in the SEER database from 2010 to 2017. LASSO regression and multivariate logistic regression analysis was performed to determine independent risk factors of RCC-BM. Six ML algorithm models, including LR, GBM, XGB, RF, DT, and NBC, were used to establish risk models for predicting RCC-BM. The prediction performance of ML models was weighed by 10-fold cross-validation. Results. The study investigated 40,355 patients diagnosed with RCC in the SEER database, where 1,811 (4.5%) were BM patients. Independent risk factors for BM were tumor grade, T stage, N stage, liver metastasis, lung metastasis, and brain metastasis. Among the RCC-BM risk prediction models established by six ML algorithms, the XGB model showed the best prediction performance ( AUC = 0.891 ). Therefore, a network calculator based on the XGB model was established to individually assess the risk of BM in patients with RCC. Conclusion. The XGB risk prediction model based on the ML algorithm performed a good prediction effect on BM in RCC patients.

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Section: Establishment and Verification Of Prediction Modelsmentioning

confidence: 99%

Establishment and Validation of a Machine Learning Prediction Model Based on Big Data for Predicting the Risk of Bone Metastasis in Renal Cell Carcinoma Patients

Liu

Yin

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Beside, machine learning is also an important prognostic gene screening method, referring to previous research (4,5,11). Next, a support vector machine (SVM) and random forest (RF) were used to construct a specimen classification model to screen the most closely related prognostic genes for OS metastasis.…”

Section: Further Screening For Os Metastasisrelated Genes Based On Ma...mentioning

confidence: 99%

“…Even after long-term standardized chemotherapy, OS still has a 35% recurrence rate (3). Metastasis remains the leading cause of death in OS patients, with the major metastases being in the lungs, other bone tissue, and lymph (4,5). However, up to 80-90% of OS patients with metastatic cancer are difficult to detect clinically due to the small size of early metastases and the low sensitivity of diagnostic imaging (6).…”

Section: Introductionmentioning

confidence: 99%

Identification of risk model based on glycolysis-related genes in the metastasis of osteosarcoma

Huang¹,

Xiao²,

Wang³

et al. 2022

Front. Endocrinol.

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BackgroundGlycolytic metabolic pathway has been confirmed to play a vital role in the proliferation, survival, and migration of malignant tumors, but the relationship between glycolytic pathway-related genes and osteosarcoma (OS) metastasis and prognosis remain unclear.MethodsWe performed Gene set enrichment analysis (GSEA) on the osteosarcoma dataset in the TARGET database to explore differences in glycolysis-related pathway gene sets between primary osteosarcoma (without other organ metastases) and metastatic osteosarcoma patient samples, as well as glycolytic pathway gene set gene difference analysis. Then, we extracted OS data from the TCGA database and used Cox proportional risk regression to identify prognosis-associated glycolytic genes to establish a risk model. Further, the validity of the risk model was confirmed using the GEO database dataset. Finally, we further screened OS metastasis-related genes based on machine learning. We selected the genes with the highest clinical metastasis-related importance as representative genes for in vitro experimental validation.ResultsUsing the TARGET osteosarcoma dataset, we identified 5 glycolysis-related pathway gene sets that were significantly different in metastatic and non-metastatic osteosarcoma patient samples and identified 29 prognostically relevant genes. Next, we used multivariate Cox regression to determine the inclusion of 13 genes (ADH5, DCN, G6PD, etc.) to construct a prognostic risk score model to predict 1- (AUC=0.959), 3- (AUC=0.899), and 5-year (AUC=0.895) survival under the curve. Ultimately, the KM curves pooled into the datasets GSE21257 and GSE39055 also confirmed the validity of the prognostic risk model, with a statistically significant difference in overall survival between the low- and high-risk groups (P<0.05). In addition, machine learning identified INSR as the gene with the highest importance for OS metastasis, and the transwell assay verified that INSR significantly promoted OS cell metastasis.ConclusionsA risk model based on seven glycolytic genes (INSR, FAM162A, GLCE, ADH5, G6PD, SDC3, HS2ST1) can effectively evaluate the prognosis of osteosarcoma, and in vitro experiments also confirmed the important role of INSR in promoting OS migration.

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“…In addition, BRAF mutations are closely related to poor prognosis of tumors, and it is currently a hot topic of research on combining BRAF mutation testing and FNA to improve the accuracy of thyroid nodule diagnosis and effectively identify people at high risk of thyroid cancer ( Poller and Glaysher, 2017 ; Goldner et al, 2019 ). However, the cost of BRAF gene testing is high, and there is a lack of diagnostic models for BRAF mutations, especially the nomogram based on simple clinical features and ultrasound findings of patients ( Li et al, 2022b ).…”

Section: Introductionmentioning

confidence: 99%

Construction and validation of BRAF mutation diagnostic model based on ultrasound examination and clinical features of patients with thyroid nodules

Fang

et al. 2022

Front. Genet.

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Introduction: Fine Needle Aspiration (FNA) is currently the most popular method for identifying benign and malignant thyroid nodules. However, its diagnostic sensitivity is sometimes limited, which makes it necessary to apply genetic testing and other modalities as a secondary diagnostic method. The diagnostic accuracy of thyroid nodule can be improved by combining mutations in the B-Raf proto-oncogene serine/threonine kinase (BRAF) with FNA. Thus, this study was conducted to create a nomogram diagnostic model based on the clinical and ultrasonic characteristics of patients with BRAF mutations to aid in the identification of benign and malignant thyroid nodules using FNA.Methods: From April 2018 to December 2021, 275 patients with thyroid nodules who underwent ultrasonography and BRAF gene testing (137 positive and 138 negative) were included from Xianyang Central Hospital. The clinical and ultrasonic characteristics of the patients were used to develop a nomographic, diagnostic model of BRAF gene mutation, and to validate and evaluate the usefulness of the model.Results: Independent risk factors for BRAF mutations included: focal strong echogenicity (microcalcifications, OR = 3.04, 95%CI = 1.41–6.58, p = 0.005), hypoechogenicity (OR = 3.8, 95%CI = 1.14–12.61, p = 0.029), lymph node metastases (OR = 3.54, 95%CI = 1.43–8.75, p = 0.006), margin (lobulated, OR = 3.7, 95%CI = 1.66–8.23, p = 0.001; extrathyroidal invasion, OR = 2.81, 95%CI = 1.11–7.06, p = 0.029), and shape (vertical position, OR = 2.7, 95%CI = 1.11–6.59, p = 0.029). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the BRAF mutation diagnostic model constructed on these factors was 0.806 (95% CI = 0.754–0.851), and 39.5% was set as the threshold probability of making a clinical decision. The results of the validation and clinical utility evaluation showed that our model had good predictive performance and clinical application value.Conclusion: Our nomogram diagnostic model based on clinical and ultrasound features of patients accurately predicted the possibility of BRAF gene mutations in patients with thyroid nodules.

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An External-Validated Prediction Model to Predict Lung Metastasis among Osteosarcoma: A Multicenter Analysis Based on Machine Learning

Cited by 22 publications

References 35 publications

Establishment and Validation of a Machine Learning Prediction Model Based on Big Data for Predicting the Risk of Bone Metastasis in Renal Cell Carcinoma Patients

Establishment and Validation of a Machine Learning Prediction Model Based on Big Data for Predicting the Risk of Bone Metastasis in Renal Cell Carcinoma Patients

Identification of risk model based on glycolysis-related genes in the metastasis of osteosarcoma

Construction and validation of BRAF mutation diagnostic model based on ultrasound examination and clinical features of patients with thyroid nodules

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