scholarly journals A Machine Learning Approach to Predict an Early Biochemical Recurrence after a Radical Prostatectomy

2020 ◽  
Vol 10 (11) ◽  
pp. 3854
Author(s):  
Seongkeun Park ◽  
Jieun Byun ◽  
Ji young Woo
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Radical Prostatectomy ◽  
Prediction Model ◽  
Cancer Patients ◽  
Biochemical Recurrence ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Operating Characteristics ◽  
Prediction Ability

Background: Approximately 20–50% of prostate cancer patients experience biochemical recurrence (BCR) after radical prostatectomy (RP). Among them, cancer recurrence occurs in about 20–30%. Thus, we aim to reveal the utility of machine learning algorithms for the prediction of early BCR after RP. Methods: A total of 104 prostate cancer patients who underwent magnetic resonance imaging and RP were evaluated. Four well-known machine learning algorithms (i.e., k-nearest neighbors (KNN), multilayer perceptron (MLP), decision tree (DT), and auto-encoder) were applied to build a prediction model for early BCR using preoperative clinical and imaging and postoperative pathologic data. The sensitivity, specificity, and accuracy for detection of early BCR of each algorithm were evaluated. Area under the receiver operating characteristics (AUROC) analyses were conducted. Results: A prediction model using an auto-encoder showed the highest prediction ability of early BCR after RP using all data as input (AUC = 0.638) and only preoperative clinical and imaging data (AUC = 0.656), followed by MLP (AUC = 0.607 and 0.598), KNN (AUC = 0.596 and 0.571), and DT (AUC = 0.534 and 0.495). Conclusion: The auto-encoder-based prediction system has the potential for accurate detection of early BCR and could be useful for long-term follow-up planning in prostate cancer patients after RP.

scholarly journals Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy

2021 ◽  
Vol 20 ◽  
pp. 153303382110246
Author(s):  
Jihwan Park ◽  
Mi Jung Rho ◽  
Hyong Woo Moon ◽  
Jaewon Kim ◽  
Chanjung Lee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Random Forest ◽  
Radical Prostatectomy ◽  
Prediction Model ◽  
Biochemical Recurrence ◽  
Clinical Decision ◽  
Outcome Variable ◽  
Tertiary Hospitals ◽  
Input Variables

Objectives: To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. Patients and Methods: This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. Results: We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. Conclusion: We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system.

scholarly journals RHCG and TCAF1 promoter hypermethylation predicts biochemical recurrence in prostate cancer patients treated by radical prostatectomy

Oncotarget ◽  
2016 ◽  
Vol 8 (4) ◽  
pp. 5774-5788 ◽  
Author(s):  
Siri H. Strand ◽  
Michal Switnicki ◽  
Mia Moller ◽  
Christa Haldrup ◽  
Tine M. Storebjerg ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Cancer Patients ◽  
Biochemical Recurrence ◽  
Promoter Hypermethylation

scholarly journals Oligometastatic Disease Detection with 68Ga-PSMA-11 PET/CT in Hormone-Sensitive Prostate Cancer Patients (HSPC) with Biochemical Recurrence after Radical Prostatectomy: Predictive Factors and Clinical Impact

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4982
Author(s):  
Carlos Artigas ◽  
Romain Diamand ◽  
Qaid Ahmed Shagera ◽  
Nicolas Plouznikoff ◽  
Fabrice Fokoue ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Cancer Patients ◽  
Predictive Factors ◽  
Clinical Management ◽  
Biochemical Recurrence ◽  
Oligometastatic Disease ◽  
Psma Pet ◽  
Pet Ct ◽  
Hormone Sensitive

Metastasis-directed therapy (MDT) in oligometastatic prostate cancer has the potential of delaying the start of androgen deprivation therapy (ADT) and disease progression. We aimed to analyze the efficacy of PSMA-PET/CT in detecting oligometastatic disease (OMD), to look for predictive factors of OMD, and to evaluate the impact of PSMA-PET/CT findings on clinical management. We retrospectively analyzed a homogeneous population of 196 hormone-sensitive prostate cancer patients (HSPC), considered potential candidates for MDT, with a PSMA-PET/CT performed at biochemical recurrence (BCR) after radical prostatectomy (RP). Multivariable logistic regression analysis was performed based on several clinico-pathological factors. Changes in clinical management before and after PSMA-PET/CT were analyzed. The OMD detection rate was 44% for a total positivity rate of 60%. PSMA-PET/CT positivity was independently related to PSA (OR (95%CI), p) (1.7 (1.3–2.3), p < 0.0001) and PSAdt (0.4 (0.2–0.8), p = 0.013), and OMD detection was independently related to PSA (1.6 (1.2–2.2), p = 0.001) and no previous salvage therapy (0.3 (0.1–0.9), p = 0.038). A treatment change was observed in 58% of patients, mostly to perform MDT after OMD detection (60% of changes). This study showed that PSMA-PET/CT is an excellent imaging technique to detect OMD early in HSPC patients with BCR after RP, changing therapeutic management mostly into MDT.

Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia

2021 ◽  
pp. 1-29
Author(s):  
Fikrewold H. Bitew ◽  
Corey S. Sparks ◽  
Samuel H. Nyarko
Keyword(s):  
Machine Learning ◽  
Linear Models ◽  
Learning Algorithms ◽  
Public Health Problem ◽  
Water Source ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Global Public Health ◽  
Prediction Ability ◽  
Extreme Gradient Boosting

Abstract Objective: Child undernutrition is a global public health problem with serious implications. In this study, estimate predictive algorithms for the determinants of childhood stunting by using various machine learning (ML) algorithms. Design: This study draws on data from the Ethiopian Demographic and Health Survey of 2016. Five machine learning algorithms including eXtreme gradient boosting (xgbTree), k-nearest neighbors (K-NN), random forest (RF), neural network (NNet), and the generalized linear models (GLM) were considered to predict the socio-demographic risk factors for undernutrition in Ethiopia. Setting: Households in Ethiopia. Participants: A total of 9,471 children below five years of age. Results: The descriptive results show substantial regional variations in child stunting, wasting, and underweight in Ethiopia. Also, among the five ML algorithms, xgbTree algorithm shows a better prediction ability than the generalized linear mixed algorithm. The best predicting algorithm (xgbTree) shows diverse important predictors of undernutrition across the three outcomes which include time to water source, anemia history, child age greater than 30 months, small birth size, and maternal underweight, among others. Conclusions: The xgbTree algorithm was a reasonably superior ML algorithm for predicting childhood undernutrition in Ethiopia compared to other ML algorithms considered in this study. The findings support improvement in access to water supply, food security, and fertility regulation among others in the quest to considerably improve childhood nutrition in Ethiopia.

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