Development of a multivariable risk model integrating urinary cell DNA methylation and cell-free RNA data for the detection of significant prostate cancer

Background: Prostate cancer exhibits severe clinical heterogeneity and there is a critical need for clinically implementable tools able to precisely and noninvasively identify patients that can either be safely removed from treatment pathways or those requiring further follow up. Our objectives were to develop a multivariable risk prediction model through the integration of clinical, urine-derived cell-free messenger RNA (cf-RNA) and urine cell DNA methylation data capable of noninvasively detecting significant prostate cancer in biopsy naïve patients. Methods: Post-digital rectal examination urine samples previously analyzed separately for both cellular methylation and cf-RNA expression within the Movember GAP1 urine biomarker cohort were selected for a fully integrated analysis (n = 207). A robust feature selection framework, based on bootstrap resampling and permutation, was utilized to find the optimal combination of clinical and urinary markers in a random forest model, deemed ExoMeth. Out-of-bag predictions from ExoMeth were used for diagnostic evaluation in men with a clinical suspicion of prostate cancer (PSA ≥ 4 ng/mL, adverse digital rectal examination, age, or lower urinary tract symptoms). Results: As ExoMeth risk score (range, 0-1) increased, the likelihood of high-grade disease being detected on biopsy was significantly greater (odds ratio = 2.04 per 0.1 ExoMeth increase, 95% confidence interval [CI]: 1.78-2.35). On an initial TRUS biopsy, ExoMeth accurately predicted the presence of Gleason score ≥3 + 4, area under the receiver-operator characteristic curve (AUC) = 0.89 (95% CI: 0.84-0.93) and was additionally capable of detecting any cancer on biopsy, AUC = 0.91 (95% CI: 0.87-0.95). Application of ExoMeth provided a net benefit over current standards of care and has the potential to reduce unnecessary biopsies by 66% when a risk threshold of 0.25 is accepted. Conclusion: Integration of urinary biomarkers across multiple assay methods has greater diagnostic ability than either method in isolation, providing superior predictive ability of biopsy outcomes. ExoMeth represents a more holistic view of urinary biomarkers and has the potential to result in substantial changes to how patients suspected of harboring prostate cancer are diagnosed

Interpretable Machine Learning Model for Clinical Decision Making

Despite machine learning models being increasingly used in medical decision-making and meeting classification predictive accuracy standards, they remain untrusted black-boxes due to decision-makers\u27 lack of insight into their complex logic. Therefore, it is necessary to develop interpretable machine learning models that will engender trust in the knowledge they generate and contribute to clinical decision-makers intention to adopt them in the field. The goal of this dissertation was to systematically investigate the applicability of interpretable model-agnostic methods to explain predictions of black-box machine learning models for medical decision-making. As proof of concept, this study addressed the problem of predicting the risk of emergency readmissions within 30 days of being discharged for heart failure patients. Using a benchmark data set, supervised classification models of differing complexity were trained to perform the prediction task. More specifically, Logistic Regression (LR), Random Forests (RF), Decision Trees (DT), and Gradient Boosting Machines (GBM) models were constructed using the Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD). The precision, recall, area under the ROC curve for each model were used to measure predictive accuracy. Local Interpretable Model-Agnostic Explanations (LIME) was used to generate explanations from the underlying trained models. LIME explanations were empirically evaluated using explanation stability and local fit (R2). The results demonstrated that local explanations generated by LIME created better estimates for Decision Trees (DT) classifiers

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Prostate cancer radiogenomics—from imaging to molecular characterization

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radio-logical assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-de-signed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research

Radiomics in prostate cancer: an up-to-date review

: Prostate cancer (PCa) is the most common worldwide diagnosed malignancy in male population. The diagnosis, the identification of aggressive disease, and the post-treatment follow-up needs a more comprehensive and holistic approach. Radiomics is the extraction and interpretation of images phenotypes in a quantitative manner. Radiomics may give an advantage through advancements in imaging modalities and through the potential power of artificial intelligence techniques by translating those features into clinical outcome prediction. This article gives an overview on the current evidence of methodology and reviews the available literature on radiomics in PCa patients, highlighting its potential for personalized treatment and future applications

Deep learning for clinical decision support in oncology

In den letzten Jahrzehnten sind medizinische Bildgebungsverfahren wie die Computertomographie (CT) zu einem unersetzbaren Werkzeug moderner Medizin geworden, welche eine zeitnahe, nicht-invasive Begutachtung von Organen und Geweben ermöglichen. Die Menge an anfallenden Daten ist dabei rapide gestiegen, allein innerhalb der letzten Jahre um den Faktor 15, und aktuell verantwortlich für 30 % des weltweiten Datenvolumens. Die Anzahl ausgebildeter Radiologen ist weitestgehend stabil, wodurch die medizinische Bildanalyse, angesiedelt zwischen Medizin und Ingenieurwissenschaften, zu einem schnell wachsenden Feld geworden ist. Eine erfolgreiche Anwendung verspricht Zeitersparnisse, und kann zu einer höheren diagnostischen Qualität beitragen. Viele Arbeiten fokussieren sich auf „Radiomics“, die Extraktion und Analyse von manuell konstruierten Features. Diese sind jedoch anfällig gegenüber externen Faktoren wie dem Bildgebungsprotokoll, woraus Implikationen für Reproduzierbarkeit und klinische Anwendbarkeit resultieren. In jüngster Zeit sind Methoden des „Deep Learning“ zu einer häufig verwendeten Lösung algorithmischer Problemstellungen geworden. Durch Anwendungen in Bereichen wie Robotik, Physik, Mathematik und Wirtschaft, wurde die Forschung im Bereich maschinellen Lernens wesentlich verändert. Ein Kriterium für den Erfolg stellt die Verfügbarkeit großer Datenmengen dar. Diese sind im medizinischen Bereich rar, da die Bilddaten strengen Anforderungen bezüglich Datenschutz und Datensicherheit unterliegen, und oft heterogene Qualität, sowie ungleichmäßige oder fehlerhafte Annotationen aufweisen, wodurch ein bedeutender Teil der Methoden keine Anwendung finden kann. Angesiedelt im Bereich onkologischer Bildgebung zeigt diese Arbeit Wege zur erfolgreichen Nutzung von Deep Learning für medizinische Bilddaten auf. Mittels neuer Methoden für klinisch relevante Anwendungen wie die Schätzung von Läsionswachtum, Überleben, und Entscheidungkonfidenz, sowie Meta-Learning, Klassifikator-Ensembling, und Entscheidungsvisualisierung, werden Wege zur Verbesserungen gegenüber State-of-the-Art-Algorithmen aufgezeigt, welche ein breites Anwendungsfeld haben. Hierdurch leistet die Arbeit einen wesentlichen Beitrag in Richtung einer klinischen Anwendung von Deep Learning, zielt auf eine verbesserte Diagnose, und damit letztlich eine verbesserte Gesundheitsversorgung insgesamt.Over the last decades, medical imaging methods, such as computed tomography (CT), have become an indispensable tool of modern medicine, allowing for a fast, non-invasive inspection of organs and tissue. Thus, the amount of acquired healthcare data has rapidly grown, increased 15-fold within the last years, and accounts for more than 30 % of the world's generated data volume. In contrast, the number of trained radiologists remains largely stable. Thus, medical image analysis, settled between medicine and engineering, has become a rapidly growing research field. Its successful application may result in remarkable time savings and lead to a significantly improved diagnostic performance. Many of the work within medical image analysis focuses on radiomics, i. e. the extraction and analysis of hand-crafted imaging features. Radiomics, however, has been shown to be highly sensitive to external factors, such as the acquisition protocol, having major implications for reproducibility and clinical applicability. Lately, deep learning has become one of the most employed methods for solving computational problems. With successful applications in diverse fields, such as robotics, physics, mathematics, and economy, deep learning has revolutionized the process of machine learning research. Having large amounts of training data is a key criterion for its successful application. These data, however, are rare within medicine, as medical imaging is subject to a variety of data security and data privacy regulations. Moreover, medical imaging data often suffer from heterogeneous quality, label imbalance, and label noise, rendering a considerable fraction of deep learning-based algorithms inapplicable. Settled in the field of CT oncology, this work addresses these issues, showing up ways to successfully handle medical imaging data using deep learning. It proposes novel methods for clinically relevant tasks, such as lesion growth and patient survival prediction, confidence estimation, meta-learning and classifier ensembling, and finally deep decision explanation, yielding superior performance in comparison to state-of-the-art approaches, and being applicable to a wide variety of applications. With this, the work contributes towards a clinical translation of deep learning-based algorithms, aiming for an improved diagnosis, and ultimately overall improved patient healthcare

Automatic Craniomaxillofacial Landmark Digitization via Segmentation-Guided Partially-Joint Regression Forest Model and Multiscale Statistical Features

The goal of this paper is to automatically digitize craniomaxillofacial (CMF) landmarks efficiently and accurately from cone-beam computed tomography (CBCT) images, by addressing the challenge caused by large morphological variations across patients and image artifacts of CBCT images

Unmasking Bias and Inequities: A Systematic Review of Bias Detection and Mitigation in Healthcare Artificial Intelligence Using Electronic Health Records

Objectives: Artificial intelligence (AI) applications utilizing electronic health records (EHRs) have gained popularity, but they also introduce various types of bias. This study aims to systematically review the literature that address bias in AI research utilizing EHR data. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline. We retrieved articles published between January 1, 2010, and October 31, 2022, from PubMed, Web of Science, and the Institute of Electrical and Electronics Engineers. We defined six major types of bias and summarized the existing approaches in bias handling. Results: Out of the 252 retrieved articles, 20 met the inclusion criteria for the final review. Five out of six bias were covered in this review: eight studies analyzed selection bias; six on implicit bias; five on confounding bias; four on measurement bias; two on algorithmic bias. For bias handling approaches, ten studies identified bias during model development, while seventeen presented methods to mitigate the bias. Discussion: Bias may infiltrate the AI application development process at various stages. Although this review discusses methods for addressing bias at different development stages, there is room for implementing additional effective approaches. Conclusion: Despite growing attention to bias in healthcare AI, research using EHR data on this topic is still limited. Detecting and mitigating AI bias with EHR data continues to pose challenges. Further research is needed to raise a standardized method that is generalizable and interpretable to detect, mitigate and evaluate bias in medical AI.Comment: 29 pages, 2 figures, 2 tables, 2 supplementary files, 66 reference