Treatment Stratification of Patients with Metastatic Castration-Resistant Prostate Cancer by Machine Learning

Harnessing machine learning to predict prostate cancer survival: a review

The prediction of survival outcomes is a key factor in making decisions for prostate cancer (PCa) treatment. Advances in computer-based technologies have increased the role of machine learning (ML) methods in predicting cancer prognosis. Due to the various effective treatments available for each non-linear landscape of PCa, the integration of ML can help offer tailored treatment strategies and precision medicine approaches, thus improving survival in patients with PCa. There has been an upsurge of studies utilizing ML to predict the survival of these patients using complex datasets, including patient and tumor features, radiographic data, and population-based databases. This review aims to explore the evolving role of ML in predicting survival outcomes associated with PCa. Specifically, we will focus on the applications of ML in forecasting biochemical recurrence-free, progression to castration-resistance-free, metastasis-free, and overall survivals. Additionally, we will suggest areas in need of further research in the future to enhance the utility of ML for a more clinically-utilizable PCa prognosis prediction and treatment optimization.

The Impact of the SMOTE Method on Machine Learning and Ensemble Learning Performance Results in Addressing Class Imbalance in Data Used for Predicting Total Testosterone Deficiency in Type 2 Diabetes Patients

BACKGROUND AND OBJECTIVE

Diabetes Mellitus is a long-term, multifaceted metabolic condition that necessitates ongoing medical management. Hypogonadism is a syndrome that is a clinical and/or biochemical indicator of testosterone deficiency. Cross-sectional studies have reported that 20-80.4% of all men with Type 2 diabetes have hypogonadism, and Type 2 diabetes is related to low testosterone. This study presents an analysis of the use of ML and EL classifiers in predicting testosterone deficiency. In our study, we compared optimized traditional ML classifiers and three EL classifiers using grid search and stratified k-fold cross-validation. We used the SMOTE method for the class imbalance problem.

METHODS

This database contains 3397 patients for the assessment of testosterone deficiency. Among these patients, 1886 patients with Type 2 diabetes were included in the study. In the data preprocessing stage, firstly, outlier/excessive observation analyses were performed with LOF and missing value analyses were performed with random forest. The SMOTE is a method for generating synthetic samples of the minority class. Four basic classifiers, namely MLP, RF, ELM and LR, were used as first-level classifiers. Tree ensemble classifiers, namely ADA, XGBoost and SGB, were used as second-level classifiers.

RESULTS

After the SMOTE, while the diagnostic accuracy decreased in all base classifiers except ELM, sensitivity values increased in all classifiers. Similarly, while the specificity values decreased in all classifiers, F1 score increased. The RF classifier gave more successful results on the base-training dataset. The most successful ensemble classifier in the training dataset was the ADA classifier in the original data and in the SMOTE data. In terms of the testing data, XGBoost is the most suitable model for your intended use in evaluating model performance. XGBoost, which exhibits a balanced performance especially when the SMOTE is used, can be preferred to correct class imbalance.

CONCLUSIONS

The SMOTE is used to correct the class imbalance in the original data. However, as seen in this study, when the SMOTE was applied, the diagnostic accuracy decreased in some models but the sensitivity increased significantly. This shows the positive effects of the SMOTE in terms of better predicting the minority class.

Applications of artificial intelligence in urologic oncology

PURPOSE

With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology.

MATERIALS AND METHODS

We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both "urology" and "artificial intelligence" with one of the following: "machine learning," "deep learning," "neural network," "renal cell carcinoma," "kidney cancer," "urothelial carcinoma," "bladder cancer," "prostate cancer," and "robotic surgery."

RESULTS

A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods.

CONCLUSIONS

AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.

Artificial Intelligence in Urooncology: What We Have and What We Expect

INTRODUCTION

Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers.

METHODOLOGY

We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors.

RESULTS

Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors.

CONCLUSIONS

AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.

Brainmarker-I Differentially Predicts Remission to Various Attention-Deficit/Hyperactivity Disorder Treatments: A Discovery, Transfer, and Blinded Validation Study

BACKGROUND

Attention-deficit/hyperactivity disorder is characterized by neurobiological heterogeneity, possibly explaining why not all patients benefit from a given treatment. As a means to select the right treatment (stratification), biomarkers may aid in personalizing treatment prescription, thereby increasing remission rates.

METHODS

The biomarker in this study was developed in a heterogeneous clinical sample (N = 4249) and first applied to two large transfer datasets, a priori stratifying young males (<18 years) with a higher individual alpha peak frequency (iAPF) to methylphenidate (N = 336) and those with a lower iAPF to multimodal neurofeedback complemented with sleep coaching (N = 136). Blinded, out-of-sample validations were conducted in two independent samples. In addition, the association between iAPF and response to guanfacine and atomoxetine was explored.

RESULTS

Retrospective stratification in the transfer datasets resulted in a predicted gain in normalized remission of 17% to 30%. Blinded out-of-sample validations for methylphenidate (n = 41) and multimodal neurofeedback (n = 71) corroborated these findings, yielding a predicted gain in stratified normalized remission of 36% and 29%, respectively.

CONCLUSIONS

This study introduces a clinically interpretable and actionable biomarker based on the iAPF assessed during resting-state electroencephalography. Our findings suggest that acknowledging neurobiological heterogeneity can inform stratification of patients to their individual best treatment and enhance remission rates.

Artificial intelligence empowered digital health technologies in cancer survivorship care: A scoping review

Objective

The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility in the application process.

Methods

Search for literatures published from 2010 to 2022 on sites MEDLINE, IEEE-Xplor, PubMed, Embase, Cochrane Central Register of Controlled Trials and Scopus systematically. The types of literatures include original research, descriptive study, randomized controlled trial, pilot study, and feasible or acceptable study. The literatures above described current status and effectiveness of digital medical technologies based on AI and used in cancer survivorship care services. Additionally, we use QuADS quality assessment tool to evaluate the quality of literatures included in this review.

Results

43 studies that met the inclusion criteria were analyzed and qualitatively synthesized. The current status and results related to the application of AI-driven DHTs in cancer survivorship care were reviewed. Most of these studies were designed specifically for breast cancer survivors' care and focused on the areas of recurrence or secondary cancer prediction, clinical decision support, cancer survivability prediction, population or treatment stratified, anti-cancer treatment-induced adverse reaction prediction, and so on. Applying AI-based DHTs to cancer survivors actually has shown some positive outcomes, including increased motivation of patient-reported outcomes (PROs), reduce fatigue and pain levels, improved quality of life, and physical function. However, current research mostly explored the technology development and formation (testing) phases, with limited-scale population, and single-center trial. Therefore, it is not suitable to draw conclusions that the effectiveness of AI-based DHTs in supportive cancer care, as most of applications are still in the early stage of development and feasibility testing.

Conclusions

While digital therapies are promising in the care of cancer patients, more high-quality studies are still needed in the future to demonstrate the effectiveness of digital therapies in cancer care. Studies should explore how to develop uniform standards for measuring patient-related outcomes, ensure the scientific validity of research methods, and emphasize patient and health practitioner involvement in the development and use of technology.

Natural Product-Based Studies for the Management of Castration-Resistant Prostate Cancer: Computational to Clinical Studies

Background: With prostate cancer being the fifth-greatest cause of cancer mortality in 2020, there is a dire need to expand the available treatment options. Castration-resistant prostate cancer (CRPC) progresses despite androgen depletion therapy. The mechanisms of resistance are yet to be fully discovered. However, it is hypothesized that androgens depletion enables androgen-independent cells to proliferate and recolonize the tumor. Objectives: Natural bioactive compounds from edible plants and herbal remedies might potentially address this need. This review compiles the available cheminformatics-based studies and the translational studies regarding the use of natural products to manage CRPC. Methods: PubMed and Google Scholar searches for preclinical studies were performed, while ClinicalTrials.gov and PubMed were searched for clinical updates. Studies that were not in English and not available as full text were excluded. The period of literature covered was from 1985 to the present. Results and Conclusion: Our analysis suggested that natural compounds exert beneficial effects due to their broad-spectrum molecular disease-associated targets. In vitro and in vivo studies revealed several bioactive compounds, including rutaecarpine, berberine, curcumin, other flavonoids, pentacyclic triterpenoids, and steroid-based phytochemicals. Molecular modeling tools, including machine and deep learning, have made the analysis more comprehensive. Preclinical and clinical studies on resveratrol, soy isoflavone, lycopene, quercetin, and gossypol have further validated the translational potential of the natural products in the management of prostate cancer.

A Machine Learning Predictive Model for Post-Ureteroscopy Urosepsis Needing Intensive Care Unit Admission: A Case-Control YAU Endourology Study from Nine European Centres

Introduction: With the rise in the use of ureteroscopy and laser stone lithotripsy (URSL), a proportionate increase in the risk of post-procedural urosepsis has also been observed. The aims of our paper were to analyse the predictors for severe urosepsis using a machine learning model (ML) in patients that needed intensive care unit (ICU) admission and to make comparisons with a matched cohort. Methods: A retrospective study was conducted across nine high-volume endourology European centres for all patients who underwent URSL and subsequently needed ICU admission for urosepsis (Group A). This was matched by patients with URSL without urosepsis (Group B). Statistical analysis was performed with ‘R statistical software’ using the ‘randomforests’ package. The data were segregated at random into a 70% training set and a 30% test set using the ‘sample’ command. A random forests ML model was then built with n = 300 trees, with the test set used for internal validation. Diagnostic accuracy statistics were generated using the ‘caret’ package. Results: A total of 114 patients were included (57 in each group) with a mean age of 60 ± 16 years and a male:female ratio of 1:1.19. The ML model correctly predicted risk of sepsis in 14/17 (82%) cases (Group A) and predicted those without urosepsis for 12/15 (80%) controls (Group B), whilst overall it also discriminated between the two groups predicting both those with and without sepsis. Our model accuracy was 81.3% (95%, CI: 63.7–92.8%), sensitivity = 0.80, specificity = 0.82 and area under the curve = 0.89. Predictive values most commonly accounting for nodal points in the trees were a large proximal stone location, long stent time, large stone size and long operative time. Conclusion: Urosepsis after endourological procedures remains one of the main reasons for ICU admission. Risk factors for urosepsis are reasonably accurately predicted by our innovative ML model. Focusing on these risk factors can allow one to create predictive strategies to minimise post-operative morbidity.

MRI and Targeted Biopsy Essential Tools for an Accurate Diagnosis and Treatment Decision Making in Prostate Cancer

Prostate cancer (PCa) is one of the most frequent causes of cancer death worldwide. Historically, diagnosis was based on physical examination, transrectal (TRUS) images, and TRUS biopsy resulting in overdiagnosis and overtreatment. Recently magnetic resonance imaging (MRI) has been identified as an evolving tool in terms of diagnosis, staging, treatment decision, and follow-up. In this review we provide the key studies and concepts of MRI as a promising tool in the diagnosis and management of prostate cancer in the general population and in challenging scenarios, such as anteriorly located lesions, enlarged prostates determining extracapsular extension and seminal vesicle invasion, and prior negative biopsy and the future role of MRI in association with artificial intelligence (AI).

Particle Swarm Optimized Gaussian Process Classifier for Treatment Discontinuation Prediction in Multicohort Metastatic Castration-Resistant Prostate Cancer Patients

Prostate cancer is the second leading cancer in men, according to the WHO world cancer report. Its prevention and treatment demand proper attention. Despite numerous attempts for disease prevention, prostate tumours can still become metastatic by blood circulation to other organs. Several treatments have been adopted. However, findings show that the docetaxel treatment induces adverse reactions in patients. Particle Swarm Optimized Gaussian Process Classifier (PSO-GPC) is proposed to determine when to discontinue treatment. Based on three cohorts of prostate cancer patients, we propose and compare several classifiers for the best performance in determining treatment discontinuation. Given the data skewness and class imbalance, the models are evaluated based on both the area under receiver operating characteristics curve (AUC) and area under precision recall curve (AUPRC). With the AUCs ranging between 0.6717 - 0.8499, and AUPRCs ranging between 0.1392 - 0.5423, PSO-GPC performs better than the state-of-the-art. We have carried out statistical analysis for ranking methods and analyzed independent cohort data with PSO-GPC, demonstrating its unbiased performance. A proper determination of treatment discontinuation in metastatic castration-resistant prostate cancer patients will reduce the mortality rate in cancer patients.

Contemporary application of artificial intelligence in prostate cancer: an i-TRUE study

Artificial intelligence (AI) involves technology that is able to emulate tasks previously carried out by humans. The growing incidence, novel diagnostic strategies and newer available therapeutic options have had resource and economic impacts on the healthcare organizations providing prostate cancer care. AI has the potential to be an adjunct to and, in certain cases, a replacement for human input in prostate cancer care delivery. Automation can also address issues such as inter- and intra-observer variability and has the ability to deliver analysis of large volume datasets quickly and accurately. The continuous training and testing of AI algorithms will facilitate development of futuristic AI models that will have integral roles to play in diagnostics, enhanced training and surgical outcomes and developments of prostate cancer predictive tools. These AI related innovations will enable clinicians to provide individualized care. Despite its potential benefits, it is vital that governance with AI related care is maintained and responsible adoption is achieved.

Artificial intelligence (AI) in urology-Current use and future directions: An iTRUE study

OBJECTIVE

Artificial intelligence (AI) is used in various urological conditions such as urolithiasis, pediatric urology, urogynecology, benign prostate hyperplasia (BPH), renal transplant, and uro-oncology. The various models of AI and its application in urology subspecialties are reviewed and discussed.

MATERIAL AND METHODS

Search strategy was adapted to identify and review the literature pertaining to the application of AI in urology using the keywords "urology," "artificial intelligence," "machine learning," "deep learning," "artificial neural networks," "computer vision," and "natural language processing" were included and categorized. Review articles, editorial comments, and non-urologic studies were excluded.

RESULTS

The article reviewed 47 articles that reported characteristics and implementation of AI in urological cancer. In all cases with benign conditions, artificial intelligence was used to predict outcomes of the surgical procedure. In urolithiasis, it was used to predict stone composition, whereas in pediatric urology and BPH, it was applied to predict the severity of condition. In cases with malignant conditions, it was applied to predict the treatment response, survival, prognosis, and recurrence on the basis of the genomic and biomarker studies. These results were also found to be statistically better than routine approaches. Application of radiomics in classification and nuclear grading of renal masses, cystoscopic diagnosis of bladder cancers, predicting Gleason score, and magnetic resonance imaging with computer-assisted diagnosis for prostate cancers are few applications of AI that have been studied extensively.

CONCLUSIONS

In the near future, we will see a shift in the clinical paradigm as AI applications will find their place in the guidelines and revolutionize the decision-making process.