scholarly journals A Genomic-Clinicopathologic Nomogram for the Prediction of Lymph Node Invasion in Prostate Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zongtai Zheng ◽  
Shiyu Mao ◽  
Zhuoran Gu ◽  
Ruiliang Wang ◽  
Yadong Guo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Lymph Node Status ◽  
Support Vector ◽  
Good Prediction ◽  
Gleason Grade ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Svm Model

Background. Lymph node status is important for treatment decision making in prostate cancer (PCa). We aimed to develop a genomic-clinicopathologic nomogram for the prediction of lymph node invasion (LNI) in PCa. Methods. Differentially expressed genes between LNI and non-LNI PCa samples were identified in the Cancer Genome Atlas database. Univariate Cox regression analysis and minimum redundancy maximum relevance were performed for gene selection. The synthetic minority oversampling technique (SMOTE) was conducted to balance the minority group (LNI group). Machine learning models were constructed in the training set and assessed in the validation set. Univariable logistic regression and multivariable logistic regression were applied to build a nomogram. Furthermore, the RNA-sequence data from our center were used to validate the expression levels of hub genes between five matched primary PCa and the corresponding LNI samples. Results. The 37-gene-based support vector machine (SVM) model had the optimal synthesized performance in the SMOTE-balanced training (area under the curve (AUC): 0.947) and validation (AUC: 0.901) sets. Incorporating the SVM-based risk score and the Gleason grade, the genomic-clinicopathologic nomogram demonstrated good prediction and calibration both in the SMOTE-balanced training (AUC: 0.946) and validation (AUC: 0.910) sets. The dysregulated expression of hub genes between PCa and LNI samples was also validated. Conclusion. The proposed nomogram combining the 37-gene-based SVM model with the Gleason grade had the potential to preoperatively predict LNI in PCa. Some of the hub genes should be prioritized for functional studies and mechanistic analyses.

scholarly journals Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-assisted Decision-support Models 

2020 ◽  
Author(s):  
Hailang Liu ◽  
Kun Tang ◽  
Ejun Peng ◽  
Liang Wang ◽  
Ding Xia ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Predictive Value ◽  
Support Vector ◽  
Gleason Grade ◽  
Good Discrimination ◽  
Grade Group ◽  
Testing Dataset

Abstract Background: This study aimed to develop a machine learning (ML)-assisted model capable of accurately predicting the probability of biopsy Gleason grade group upgrading before making treatment decisions.Methods: We retrospectively collected data from prostate cancer (PCa) patients who underwent systematic biopsy and radical prostatectomy from January 2015 to December 2019 at Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology. The study cohort was divided into training and testing datasets in a 70:30 ratio for further analysis. Four ML-assisted models were developed from 16 clinical features using logistic regression (LR), logistic regression optimized by least absolute shrinkage and selection operator (Lasso) regularization (Lasso-LR), random forest (RF) and support vector machine (SVM). The area under the curve (AUC) was applied to determine the model with the highest discrimination. Calibration plots were used to investigate the extent of over- or underestimation of predicted probabilities relative to the observed probabilities in models. Results: In total, 530 PCa patients were included, with 371 patients in the training dataset and 159 patients in the testing dataset. The Lasso-LR model showed good discrimination with an AUC, accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.776, 0.712, 0.679, 0.745, 0.730 and 0.695, respectively, followed by SVM (AUC 0.740, 95% confidence interval [CI]: 0.690–0.790), LR (AUC 0.725, 95% CI: 0.674–0.776) and RF (AUC 0.666, 95% CI: 0.618–0.714). Validation of the model showed that the Lasso-LR model had the best discriminative power (AUC 0.735, 95% CI: 0.656–0.813), followed by SVM (AUC 0.723, 95% CI: 0.644–0.802), LR (AUC 0.697, 95% CI: 0.615–0.778) and RF (AUC 0.607, 95% CI: 0.531–0.684) in the testing dataset. Both the Lasso-LR and SVM models were well-calibrated. Conclusion: The Lasso-LR model had good discrimination in the prediction of patients at high risk of harboring incorrect Gleason grade group assignment, and the use of this model may be greatly beneficial to urologists in treatment planning, patient selection, and the decision-making process for PCa patients.

scholarly journals Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-support Models

2020 ◽  
Author(s):  
Hailang Liu ◽  
Kun Tang ◽  
Ejun Peng ◽  
Liang Wang ◽  
Ding Xia ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Radical Prostatectomy ◽  
Predictive Value ◽  
Support Vector ◽  
Gleason Grade ◽  
Good Discrimination ◽  
Grade Group ◽  
Testing Dataset

Abstract Objective: To develop a machine learning (ML)-assisted model capable of accurately predicting the probability of biopsy Gleason grade group upgrading before making treatment decisions by integrating multiple clinical characteristics.Materials and Methods: We retrospectively collected data from PCa (prostate cancer) patients who underwent systematic biopsy and radical prostatectomy from January 2015 to December 2019 at Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology. The study cohort was divided into training and testing datasets in a 70:30 ratio for further analysis. Four ML-assisted models were developed from 16 clinical features using logistic regression (LR), logistic regression optimized by Lasso regularization (Lasso-LR), random forest (RF) and support vector machine (SVM). The area under the curve (AUC) was applied to determine the model with the highest discrimination. Calibration plots were used to investigate the extent of over- or underestimation of predicted probabilities relative to the observed probabilities in models. Results: In total, 530 PCa patients were included, with 371 patients in the training dataset and 159 patients in the testing dataset. The Lasso-LR model showed good discrimination with an AUC, accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 0.776, 0.712, 0.679, 0.745, 0.730 and 0.695, respectively, followed by SVM (AUC 0.740, 95%CI: 0.690–0.790), LR (AUC 0.725, 95%CI: 0.674–0.776) and RF (AUC 0.666, 95%CI: 0.618–0.714). Validation of the model showed that the Lasso-LR model had the best discriminative power (AUC 0.735, 95%CI: 0.656–0.813), followed by SVM (AUC 0.723, 95%CI: 0.644–0.802), LR (AUC 0.697, 95%CI: 0.615–0.778) and RF (AUC 0.607, 95%CI: 0.531–0.684) in the testing dataset. Both the Lasso-LR and SVM models were well-calibrated. Conclusions: The Lasso-LR model had good discrimination in the prediction of patients at high risk of harboring incorrect Gleason grade group assignment, and the use of this model may be greatly beneficial to urologists in treatment planning, patient selection, and the decision-making process for PCa patients.

Clinical significance of pelvic lymph node status in prostate cancer: review of 1690 cases

2016 ◽  
Vol 11 (3) ◽  
pp. 399-404 ◽  
Author(s):  
Livia Maccio ◽  
Valeria Barresi ◽  
Federica Domati ◽  
Eugenio Martorana ◽  
Anna Maria Cesinaro ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Lymph Node ◽  
Clinical Significance ◽  
Pelvic Lymph Node ◽  
Lymph Node Status ◽  
Node Status ◽  
Cancer Review

scholarly journals Intelligent Management of Sepsis in the Intensive Care Unit

2012 ◽  
pp. 1-16
Author(s):  
Vicent J. Ribas ◽  
Juan Carlos Ruiz-Rodríguez ◽  
Alfredo Vellido
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Support Vector ◽  
Clinical Settings ◽  
Svm Model ◽  
Intelligent Management ◽  
Alternative Techniques ◽  
Research Challenge ◽  
Mortality Predictors

Sepsis is a transversal pathology and one of the main causes of death in the Intensive Care Unit (ICU). It has in fact become the tenth most common cause of death in western societies. Its mortality rates can reach up to 60% for Septic Shock, its most acute manifestation. For these reasons, the prediction of the mortality caused by Sepsis is an open and relevant medical research challenge. This problem requires prediction methods that are robust and accurate, but also readily interpretable. This is paramount if they are to be used in the demanding context of real-time decision making at the ICU. In this brief contribution, three different methods are presented. One is based on a variant of the well-known support vector machine (SVM) model and provides and automated ranking of relevance of the mortality predictors while the other two are based on logistic-regression and logistic regression over latent Factors. The reported results show that the methods presented outperform in terms of accuracy alternative techniques currently in use in clinical settings, while simultaneously assessing the relative impact of individual pathology indicators.

scholarly journals Quantitative Analysis of Benign and Malignant Tumors in Histopathology: Predicting Prostate Cancer Grading Using SVM

2019 ◽  
Vol 9 (15) ◽  
pp. 2969 ◽  
Author(s):  
Bhattacharjee ◽  
Park ◽  
Kim ◽  
Prakash ◽  
Madusanka ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Features ◽  
Morphological Features ◽  
Support Vector ◽  
Epithelial Tissue ◽  
Gleason Grade ◽  
Svm Classification ◽  
Gleason Grading ◽  
Grade 5 ◽  
Grade 3

An adenocarcinoma is a type of malignant cancerous tissue that forms from a glandular structure in epithelial tissue. Analyzed stained microscopic biopsy images were used to perform image manipulation and extract significant features for support vector machine (SVM) classification, to predict the Gleason grading of prostate cancer (PCa) based on the morphological features of the cell nucleus and lumen. Histopathology biopsy tissue images were used and categorized into four Gleason grade groups, namely Grade 3, Grade 4, Grade 5, and benign. The first three grades are considered malignant. K-means and watershed algorithms were used for color-based segmentation and separation of overlapping cell nuclei, respectively. In total, 400 images, divided equally among the four groups, were collected for SVM classification. To classify the proposed morphological features, SVM classification based on binary learning was performed using linear and Gaussian classifiers. The prediction model yielded an accuracy of 88.7% for malignant vs. benign, 85.0% for Grade 3 vs. Grade 4, 5, and 92.5% for Grade 4 vs. Grade 5. The SVM, based on biopsy-derived image features, consistently and accurately classified the Gleason grading of prostate cancer. All results are comparatively better than those reported in the literature.

scholarly journals Preoperative Risk Assessment of Lymph Node Metastasis in cT1 Lung Cancer: A Retrospective Study from Eastern China

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Chengyan Zhang ◽  
Guanchao Pang ◽  
Chengxi Ma ◽  
Jingni Wu ◽  
Pingli Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Predictive Model ◽  
Smoking History ◽  
Lymph Node Status ◽  
Node Metastasis ◽  
Node Status

Background. Lymph node status of clinical T1 (diameter≤3 cm) lung cancer largely affects the treatment strategies in the clinic. In order to assess lymph node status before operation, we aim to develop a noninvasive predictive model using preoperative clinical information. Methods. We retrospectively reviewed 924 patients (development group) and 380 patients (validation group) of clinical T1 lung cancer. Univariate analysis followed by polytomous logistic regression was performed to estimate different risk factors of lymph node metastasis between N1 and N2 diseases. A predictive model of N2 metastasis was established with dichotomous logistic regression, externally validated and compared with previous models. Results. Consolidation size and clinical N stage based on CT were two common independent risk factors for both N1 and N2 metastases, with different odds ratios. For N2 metastasis, we identified five independent predictors by dichotomous logistic regression: peripheral location, larger consolidation size, lymph node enlargement on CT, no smoking history, and higher levels of serum CEA. The model showed good calibration and discrimination ability in the development data, with the reasonable Hosmer-Lemeshow test (p=0.839) and the area under the ROC being 0.931 (95% CI: 0.906-0.955). When externally validated, the model showed a great negative predictive value of 97.6% and the AUC of our model was better than other models. Conclusion. In this study, we analyzed risk factors for both N1 and N2 metastases and built a predictive model to evaluate possibilities of N2 metastasis of clinical T1 lung cancers before the surgery. Our model will help to select patients with low probability of N2 metastasis and assist in clinical decision to further management.

Prediction of side weir discharge coefficient by support vector machine technique

2016 ◽  
Vol 16 (4) ◽  
pp. 1002-1016 ◽  
Author(s):  
Hazi Mohammad Azamathulla ◽  
Amir Hamzeh Haghiabi ◽  
Abbas Parsaie
Keyword(s):  
Support Vector Machine ◽  
Discharge Coefficient ◽  
Sensitivity Analyses ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Good Prediction ◽  
Effective Parameters ◽  
Side Weir ◽  
Svm Model ◽  
Soft Computing Techniques

Side weirs have many possible applications in the field of hydraulic engineering. They are also considered an important structure in hydro systems. In this study, the support vector machine (SVM) technique was employed to predict the side weir discharge coefficient. The performance of SVM was compared with other types of soft computing techniques such as artificial neural networks (ANN) and adaptive neuro fuzzy inference systems (ANFIS). While ANN and ANFIS models provided a good prediction performance, the SVM model with a radial basis function kernel function outperforms them. The best SVM model was developed with a gamma coefficient and epsilon of 15 and 0.3, respectively. The SVM yielded a coefficient of determination (R2) equal to 0.96 and 0.93 for the training and testing data. Sensitivity analyses of the ANN, ANFIS and SVM models showed that the Froude number and ratio of weir length to the flow depth upstream of the weir are the most effective parameters for the prediction of the discharge coefficient.

Defining predictors of early, intermediate, and late biochemical recurrence in men with clinically localized prostate cancer treated with minimally invasive radical prostatectomy.

2017 ◽  
Vol 35 (6_suppl) ◽  
pp. 113-113
Author(s):  
Silvia Garcia Barreras ◽  
Igor Nunes-Silva ◽  
Rafael Sanchez-Salas ◽  
Fernando P. Secin ◽  
Victor Srougi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Laparoscopic Surgery ◽  
Radical Prostatectomy ◽  
Minimally Invasive ◽  
Biochemical Recurrence ◽  
Surgical Margins ◽  
Positive Surgical Margins

113 Background: Follow up after radical prostatectomy should be tailored to clinical and pathologic characteristics. To determine predictive factors for early, intermediate and late biochemical recurrence (BCR) after minimally invasive radical prostatectomy (MIRP: lap and robot) in patients with localized prostate cancer (PCa). Methods: Prospective clinical, pathologic, and outcome data were collected for 6195 patients with cT1-3N0M0 PCa treated with MIRP at our institution from 2000 to 2016. None of them received neoadjuvant therapy. BCR was defined as PSA level greater than 0.2 ng/ml. Time to BCR was divided in terciles to identify variables associated with early ( < 12 months), intermediate (12-36 months) and late BCR ( > 36 months). Comparisons among groups were performed using ANOVA or Chi square test. Logistic regression models were built to determine risk factors associated with BCR at each time interval. Results: We identified 1148 (19%) patients with BCR. Median time to BCR was 24 months. Statistically significant differences were found between the groups concerning PSA preoperative, D’Amico risk, type of surgery, pT stage, pathological Gleason, positive margins and extracapsular extension. Multivariable logistic regression analysis showed preoperative PSA, positive nodes, positive surgical margins and laparoscopic surgery were associated with early BCR. Laparoscopic surgery was the only risk factor associated with intermediate term BCR. Significant predictors of late BCR included Gleason ≥ 7, ≥ pT3, positive surgical margins, lymph node dissection performance and laparoscopic surgery. Conclusions: Patients with high risk features like Gleason ≥ 7, ≥ pT3 and or positive surgical margins may develop late recurrence and deserve long term follow up. Identify patients with higher PSA and lymph node invasion has an important predictive role due to the risk of BCR within the first year. The association between laparoscopic technique and late BCR deserves further evaluation.

scholarly journals Identification of Lymph Node Metastasis–Related Key Genes and Prognostic Risk Model in Bladder Cancer by Co-Expression Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Cheng Luo ◽  
Bin Huang ◽  
Yukun Wu ◽  
Yadong Xu ◽  
Wei Ou ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Gene Set Enrichment Analysis ◽  
Actin Binding ◽  
Prognostic Signature ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Node Metastasis ◽  
Key Genes

Background: Lymph node metastasis (LNM) is an important pathological characteristic of bladder cancer (BCa). However, the molecular mechanism underlying LNM was not thoroughly elaborated. Identification for LNM-related biomarkers may contribute to making suitable therapies. So, the current study was aimed to identify key genes and construct a prognostic signature.Methods: Based on the Cancer Genome Atlas (TCGA) database, gene expression and clinical information were obtained. Then, the weighted gene co-expression network analysis (WGCNA) was performed to identify the key modules and hub genes. A function analysis and a gene set enrichment analysis were applied to explore biological functions and pathways of interested genes. Furthermore, a prognostic model based on LNM-related genes was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis.Results: Finally, nine co-expression modules were constructed, and two modules (turquoise and green) were significantly associated with LNM. Three hub genes were identified as DACT3, TNS1, and MSRB3, which were annotated in actin binding, actin cytoskeleton, adaptive immune response, and cell adhesion molecular binding by the GSEA method. Further analysis demonstrated that three hub genes were associated with the overall survival of BCa patients. In addition, we built a prognostic signature based on the genes from LNM-related modules and evaluated the prognostic value of this signature.Conclusion: In general, this study revealed the key genes related to LNM and prognostic signature, which might provide new insights into therapeutic target of BCa.

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