Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study

Wei, Liwei; Huang, Yongdi; Chen, Zheng; Lei, Hongyu; Qin, Xiaoping; Cui, Lihong; Zhuo, Yumin

doi:10.3389/fonc.2021.763381

Cited by 11 publications

(8 citation statements)

References 45 publications

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“…The DCA plot also indicated that the XGBoost algorithm had the highest net benefit across the entire range of threshold probabilities compared with the other three predictive models. 27 This algorithm has also been reported to be effective in predicting the prognosis of patients with esophageal cancer, 25 non‐small‐cell lung cancers experiencing bone metastasis, 28 and osteosarcoma. 29 While the above studies used data from the SEER database, a multi‐institutional retrospective study was designed to construct and compare different ML models including LR, SVM, DT, RF, XGBoost, and LightGBM to predict survival in stage I–II CC patients who underwent complete resection in a real‐world setting.…”

Section: Discussionmentioning

confidence: 98%

A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms

Yu¹,

Shou

et al. 2022

Cancer Medicine

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Background Prediction models with high accuracy rates for nonmetastatic cervical cancer (CC) patients are limited. This study aimed to construct and compare predictive models on the basis of machine learning (ML) algorithms for predicting the 5‐year survival status of CC patients through using the Surveillance, Epidemiology, and End Results public database of the National Cancer Institute. Methods The data registered from 2004 to 2016 were extracted and randomly divided into training and validation cohorts (8:2). The least absolute shrinkage and selection operator (LASSO) regression was employed to identify significant factors. Then, four predictive models were constructed, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). The predictive models were evaluated and compared using Receiver‐operating characteristics with areas under the curves (AUCs) and decision curve analysis (DCA), respectively. Results A total of 13,802 patients were involved and classified into training ( N = 11,041) and validation ( N = 2761) cohorts. By using the LASSO regression method, seven factors were identified. In the training cohort, the XGBoost model showed the best performance (AUC = 0.8400) compared to the other three models (all p < 0.05 by Delong's test). In the validation cohort, the XGBoost model also demonstrated a superior prediction ability (AUC = 0.8365) than LR and SVM models (both p < 0.05 by Delong's test), although the difference was not statistically significant between the XGBoost and the RF models ( p = 0.4251 by Delong's test). Based on the DCA results, the XGBoost model was also superior, and feature importance analysis indicated that the tumor stage was the most important variable among the seven factors. Conclusions The XGBoost model proved to be an effective algorithm with better prediction abilities. This model is proposed to support better decision‐making for nonmetastatic CC patients in the future.

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Section: Discussionmentioning

confidence: 98%

A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms

Yu¹,

Shou

et al. 2022

Cancer Medicine

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“…The clinical application of ML algorithms may facilitate a paradigm shift in the medical field, as these algorithms are efficient, objective, and reproducible when it comes to large amounts of nonlinear data ( 24 , 29 – 32 ). They also have the potential to improve the quality of early diagnosis, identify disease progression, and increase the likelihood of predicting patient-specific outcomes ( 25 , 33 , 34 ). These advantages can facilitate the sharing of information for decision-making between clinicians and patients and promote efficient planning and visualization of the use of healthcare services.…”

Section: Discussionmentioning

confidence: 99%

“…Machine learning (ML), as an important branch of artificial intelligence, can continuously optimize the performance of predictive or diagnostic models by learning and analyzing data, and can handle non-linear relationships better than traditional statistical scores. As a result, ML-based models have great potential for the diagnosis and prognosis of diseases ( 23 – 25 ). Therefore, our goal was to develop a new decision-support ML model based on real-world data for diagnosing PCa in patients with PSA levels ≤20 ng/mL.…”

Section: Introductionmentioning

confidence: 99%

Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis

Deng

et al. 2022

Front. Oncol.

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ObjectiveThe aim of this study was to develop a predictive model to improve the accuracy of prostate cancer (PCa) detection in patients with prostate specific antigen (PSA) levels ≤20 ng/mL at the initial puncture biopsy.MethodsA total of 146 patients (46 with Pca, 31.5%) with PSA ≤20 ng/mL who had undergone transrectal ultrasound-guided 12+X prostate puncture biopsy with clear pathological results at the First Affiliated Hospital of Guangxi Medical University (November 2015 to December 2021) were retrospectively evaluated. The validation group was 116 patients drawn from Changhai Hospital(52 with Pca, 44.8%). Age, body mass index (BMI), serum PSA, PSA-derived indices, several peripheral blood biomarkers, and ultrasound findings were considered as predictive factors and were analyzed by logistic regression. Significant predictors (P < 0.05) were included in five machine learning algorithm models. The performance of the models was evaluated by receiver operating characteristic curves. Decision curve analysis (DCA) was performed to estimate the clinical utility of the models. Ten-fold cross-validation was applied in the training process.ResultsProstate-specific antigen density, alanine transaminase-to-aspartate transaminase ratio, BMI, and urine red blood cell levels were identified as independent predictors for the differential diagnosis of PCa according to multivariate logistic regression analysis. The RandomForest model exhibited the best predictive performance and had the highest net benefit when compared with the other algorithms, with an area under the curve of 0.871. In addition, DCA had the highest net benefit across the whole range of cut-off points examined.ConclusionThe RandomForest-based model generated showed good prediction ability for the risk of PCa. Thus, this model could help urologists in the treatment decision-making process.

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“…As one of the components of artificial intelligence, ML has been widely used in clinical practice, such as for epidemic prediction (23, 24), and survival analysis (25, 26). ML models have also been proposed for the prediction of lymph node metastasis in a variety of malignancies (27,28). Based on these promising clinical applications of ML models, the present study aimed to develop and validate a novel ML-based model for predicting the risk of early-stage ILNM in patients with SCCP.…”

Section: Introductionmentioning

confidence: 99%

A machine learning-based model for predicting the risk of early-stage inguinal lymph node metastases in patients with squamous cell carcinoma of the penis

et al. 2023

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ObjectiveInguinal lymph node metastasis (ILNM) is significantly associated with poor prognosis in patients with squamous cell carcinoma of the penis (SCCP). Patient prognosis could be improved if the probability of ILNM incidence could be accurately predicted at an early stage. We developed a predictive model based on machine learning combined with big data to achieve this.MethodsData of patients diagnosed with SCCP were obtained from the Surveillance, Epidemiology, and End Results Program Research Data. By combing variables that represented the patients' clinical characteristics, we applied five machine learning algorithms to create predictive models based on logistic regression, eXtreme Gradient Boosting, Random Forest, Support Vector Machine, and k-Nearest Neighbor. Model performance was evaluated by ten-fold cross-validation receiver operating characteristic curves, which were used to calculate the area under the curve of the five models for predictive accuracy. Decision curve analysis was conducted to estimate the clinical utility of the models. An external validation cohort of 74 SCCP patients was selected from the Affiliated Hospital of Xuzhou Medical University (February 2008 to March 2021).ResultsA total of 1,056 patients with SCCP from the SEER database were enrolled as the training cohort, of which 164 (15.5%) developed early-stage ILNM. In the external validation cohort, 16.2% of patients developed early-stage ILNM. Multivariate logistic regression showed that tumor grade, inguinal lymph node dissection, radiotherapy, and chemotherapy were independent predictors of early-stage ILNM risk. The model based on the eXtreme Gradient Boosting algorithm showed stable and efficient prediction performance in both the training and external validation groups.ConclusionThe ML model based on the XGB algorithm has high predictive effectiveness and may be used to predict early-stage ILNM risk in SCCP patients. Therefore, it may show promise in clinical decision-making.

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Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study

Cited by 11 publications

References 45 publications

A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms

A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms

Machine learning model for the prediction of prostate cancer in patients with low prostate-specific antigen levels: A multicenter retrospective analysis

A machine learning-based model for predicting the risk of early-stage inguinal lymph node metastases in patients with squamous cell carcinoma of the penis

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