Clinical decision support system, a potential solution for diagnostic accuracy improvement in oral squamous cell carcinoma: A systematic review

BACKGROUND AND AIM: Oral squamous cell carcinoma (OSCC) is a rapidly progressive disease and despite the progress in the treatment of cancer, remains a life-threatening illness with a poor prognosis. Diagnostic techniques of the oral cavity are not painful, non-invasive, simple and inexpensive methods. Clinical decision support systems (CDSSs) are the most important diagnostic technologies used to help health professionals to analyze patients’ data and make decisions. This paper, by studying CDSS applications in the process of providing care for the cancer patients, has looked into the CDSS potentials in OSCC diagnosis. METHODS: We retrieved relevant articles indexed in MEDLINE/PubMed database using high-quality keywords. First, the title and then the abstract of the related articles were reviewed in the step of screening. Only research articles which had designed clinical decision support system in different stages of providing care for the cancer patient were retained in this study according to the input criteria. RESULTS: Various studies have been conducted about the important roles of CDSS in health processes related to different types of cancer. According to the aim of studies, we categorized them into several groups including treatment, diagnosis, risk assessment, screening, and survival estimation. CONCLUSION: Successful experiences in the field of CDSS applications in different types of cancer have indicated that machine learning methods have a high potential to manage the data and diagnostic improvement in OSCC intelligently and accurately. KEYWORDS: Squamous Cell Carcinoma; Clinical Decision Support System; Neoplasm; Dental Informatic

Machine learning application in cancer research: Mini Review

Nowadays, due to the significant growth of medical data production, utilization of interdisciplinary science, such as data mining, is also increasing. In order to discover the knowledge, form an enormous quantity of medical data, data mining would be helpful tools. One of the common data mining techniques is machine learning. This approach is the ability of learning without being explicitly programmed by computers through sets of algorithms. In the past few years, many researches have been carried out the machine learning algorithms utilization in cancer research. In this mini-review, besides defining the concepts of machine learning, the application of machine learning on cancer data also has been reviewed. The repeated studies are divided into four categories, including Identification of high-risk people, Prediction of cancer staging, Prediction of cancer clinical outcomes and Medical image analysis. Studies show that the use of machine learning in medical fields is increasing and there is a promising progress in this area

Machine learning in oral squamous cell carcinoma: current status, clinical concerns and prospects for future-A systematic review

Background: Oral cancer can show heterogenous patterns of behavior. For proper and effective management of oral cancer, early diagnosis and accurate prediction of prognosis are important. To achieve this, artificial intelligence (AI) or its subfield, machine learning, has been touted for its potential to revolutionize cancer management through improved diagnostic precision and prediction of outcomes. Yet, to date, it has made only few contributions to actual medical practice or patient care. Objectives: This study provides a systematic review of diagnostic and prognostic application of machine learning in oral squamous cell carcinoma (OSCC) and also highlights some of the limitations and concerns of clinicians towards the implementation of machine learning-based models for daily clinical practice. Data sources: We searched OvidMedline, PubMed, Scopus, Web of Science, and Institute of Electrical and Electronics Engineers (IEEE) databases from inception until February 2020 for articles that used machine learning for diagnostic or prognostic purposes of OSCC. Eligibility criteria: Only original studies that examined the application of machine learning models for prognostic and/or diagnostic purposes were considered. Data extraction: Independent extraction of articles was done by two researchers (A.R. & O.Y) using predefine study selection criteria. We used the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) in the searching and screening processes. We also used Prediction model Risk of Bias Assessment Tool (PROBAST) for assessing the risk of bias (ROB) and quality of included studies. Results: A total of 41 studies were published to have used machine learning to aid in the diagnosis/or prognosis of OSCC. The majority of these studies used the support vector machine (SVM) and artificial neural network (ANN) algorithms as machine learning techniques. Their specificity ranged from 0.57 to 1.00, sensitivity from 0.70 to 1.00, and accuracy from 63.4 % to 100.0 % in these studies. The main limitations and concerns can be grouped as either the challenges inherent to the science of machine learning or relating to the clinical implementations. Conclusion: Machine learning models have been reported to show promising performances for diagnostic and prognostic analyses in studies of oral cancer. These models should be developed to further enhance explainability, interpretability, and externally validated for generalizability in order to be safely integrated into daily clinical practices. Also, regulatory frameworks for the adoption of these models in clinical practices are necessary.Peer reviewe

Machine Learning na previsão de Cancro Colorretal em função de alterações metabólicas

No mundo atual, a quantidade de informação disponível nos mais variados setores é cada vez maior. É o caso da área da saúde, onde a recolha e tratamento de dados biomédicos procuram melhorar a tomada de decisão no tratamento a aplicar a um doente, recorrendo a ferramentas baseadas em Machine Learning. Machine Learning é uma área da Inteligência Artificial em que através da aplicação de algoritmos a um conjunto de dados é possível prever resultados ou até descobrir relações entre estes que seriam impercetíveis à primeira vista. Com este projeto pretende-se realizar um estudo em que o objetivo é investigar diversos algoritmos e técnicas de Machine Learning, de modo a identificar se o perfil de acilcarnitinas pode constituir um novo marcador bioquímico para a predição e prognóstico do Cancro Colorretal. No decurso do trabalho, foram testados diferentes algoritmos e técnicas de pré-processamento de dados. Foram realizadas três experiências distintas com o objetivo de validar as previsões dos modelos construídos para diferentes cenários, nomeadamente: prever se o paciente tem Cancro Colorretal, prever qual a doença que o paciente tem (Cancro Colorretal e outras doenças metabólicas) e prever se este tem ou não alguma doença. Numa primeira análise, os modelos desenvolvidos apresentam bons resultados na triagem de Cancro Colorretal. Os melhores resultados foram obtidos pelos algoritmos Random Forest e Gradient Boosting, em conjunto com técnicas de balanceamento dos dados e Feature Selection, nomeadamente Random Oversampling, Synthetic Oversampling e Recursive Feature SelectionIn today´s world, the amount of information available in various sectors is increasing. That is the case in the healthcare area, where the collection and treatment of biochemical data seek to improve the decision-making in the treatment to be applied to a patient, using Machine Learning-based tools. Machine learning is an area of Artificial Intelligence in which applying algorithms to a dataset makes it possible to predict results or even discover relationships that would be unnoticeable at first glance. This project’s main objective is to study several algorithms and techniques of Machine Learning to identify if the acylcarnitine profile may constitute a new biochemical marker for the prediction and prognosis of rectal cancer. In the course of the work, different algorithms and data preprocessing techniques were tested. Three different experiments were carried out to validate the predictions of the models built for different scenarios, namely: predicting whether the patient has Colorectal Cancer, predicting which disease the patient has (Colorectal Cancer and other metabolic diseases) and predicting whether he has any disease. As a first analysis, the developed models showed good results in Colorectal Cancer screening. The best results were obtained by the Random Forest and Gradient Boosting algorithms, together with data balancing and feature selection techniques, namely Random Oversampling, Synthetic Oversampling and Recursive Feature Selectio

Machine Learning Approaches to Predict Recurrence of Aggressive Tumors

Cancer recurrence is the major cause of cancer mortality. Despite tremendous research efforts, there is a dearth of biomarkers that reliably predict risk of cancer recurrence. Currently available biomarkers and tools in the clinic have limited usefulness to accurately identify patients with a higher risk of recurrence. Consequently, cancer patients suffer either from under- or over- treatment. Recent advances in machine learning and image analysis have facilitated development of techniques that translate digital images of tumors into rich source of new data. Leveraging these computational advances, my work addresses the unmet need to find risk-predictive biomarkers for Triple Negative Breast Cancer (TNBC), Ductal Carcinoma in-situ (DCIS), and Pancreatic Neuroendocrine Tumors (PanNETs). I have developed unique, clinically facile, models that determine the risk of recurrence, either local, invasive, or metastatic in these tumors. All models employ hematoxylin and eosin (H&E) stained digitized images of patient tumor samples as the primary source of data. The TNBC (n=322) models identified unique signatures from a panel of 133 protein biomarkers, relevant to breast cancer, to predict site of metastasis (brain, lung, liver, or bone) for TNBC patients. Even our least significant model (bone metastasis) offered superior prognostic value than clinopathological variables (Hazard Ratio [HR] of 5.123 vs. 1.397 p\u3c0.05). A second model predicted 10-year recurrence risk, in women with DCIS treated with breast conserving surgery, by identifying prognostically relevant features of tumor architecture from digitized H&E slides (n=344), using a novel two-step classification approach. In the validation cohort, our DCIS model provided a significantly higher HR (6.39) versus any clinopathological marker (p\u3c0.05). The third model is a deep-learning based, multi-label (annotation followed by metastasis association), whole slide image analysis pipeline (n=90) that identified a PanNET high risk group with over an 8x higher risk of metastasis (versus the low risk group p\u3c0.05), regardless of cofounding clinical variables. These machine-learning based models may guide treatment decisions and demonstrate proof-of-principle that computational pathology has tremendous clinical utility

MULTI-DIMENSIONAL INTERROGATION OF DNA MUTATIONS IN CANCER

Ph.DDOCTOR OF PHILOSOPH

Data mining applied to the Varicocele condition

O sistema de saúde guarda cada vez mais informação dos seus utentes o que dificulta ou até impossibilita a descoberta de novos conhecimentos só com as técnicas usualmente utilizadas, i.e., as tradicionais técnicas estatísticas. De facto, os investigadores clínicos têm sentido uma crescente necessidade em extrair novos conhecimentos para continuadamente contribuir para o melhoramento dos serviços de saúde prestados. Essa necessidade tem vindo a ser colmatada com a aplicação de um processo, chamado “data mining”, que auxilia, através da aplicação de diversas técnicas (i.e., classificação, clustering, associação, etc.), a descoberta de padrões de dados vistos como interessantes, mas ocultados com as tradicionais técnicas estatísticas. A área da infertilidade masculina já começou a aplicar o data mining, por exemplo, através da aplicação da técnica de classificação para prever o sucesso de uma técnica de Procriação Medicamente Assistida. Contudo, o varicocelo - um síndrome anatómico de varizes escrotais caracterizado pela dilatação das veias que drenam o sangue da região dos testículos que em certos casos dá origem à infertilidade - não foi ainda explorado com uma técnica de data mining. A sua prevalência atinge 40% dos homens tratados por infertilidade, sendo que a infertilidade masculina abrange 50% das causas da infertilidade de um casal. A correção do varicocelo pode ser alcançada com um tratamento radiológico chamado embolização, que tem por objetivo desvitalizar as veias dilatadas através da introdução de substâncias terapêuticas na circulação sanguínea. Neste contexto, este trabalho teve os seguintes principais objetivos: i) averiguar o sucesso da correção do varicocelo com a técnica da embolização através da identificação de algum melhoramento na média dos valores dos parâmetros seminais ou das categorias seminais com recurso a técnicas estatísticas inferenciais (i.e. ANOVA e Chi-quadrado); ii) predizer o sucesso da embolização com técnicas de classificação através da aplicação do decision tree do RapidMiner e do algoritmo W-J48; iii) identificar padrões que caracterizam os pacientes embolizados com a técnica de clustering através do algoritmo K-Means e eleger as relações de atributos que ocorrem mais frequentemente através da técnica de associação com o algoritmo FP-Growth. Este processo de análise de dados seguiu a metodologia Cross-Industry Standard Process for Data mining (CRISP-DM) aplicando-a à análise de uma amostra de 293 homens inférteis descritos com 64 atributos que foram submetidos à embolização no Centro Hospitalar e Universitário de Coimbra (CHUC) entre Janeiro de 2007 e Abril de 2016. Os resultados obtidos indicam que a embolização melhora significativamente a média das concentrações de espermatozoides até 12 meses e de suas morfologias até 6 meses depois da embolização (ANOVA p<0.05) o que permite fundamentar o interesse em prever o sucesso desta técnica terapêutica. Sua previsão computarizada com a árvore de decisão do RapidMiner permitiu prever com uma Accuracy e F-measure de 70.59% e uma AUC de 0.750 que a probabilidade condicional de engravidar tendo um homem com uma severidade baixa ou média do varicocelo e uma parceira entre os 24 e 33 anos inclusive é de 70.83%. Também se viu que a frequência relativa, de pacientes com uma concentração de espermatozoides normal 3 meses depois da embolização e uma motilidade progressiva normal destes antes do tratamento, é mais alta em grupos de pacientes que raramente trabalham em ambientes tóxicos. Estes resultados permitem contribuir para as investigações em curso no domínio da infertilidade, assim como nidentificação de medidas que permitem um maior auxílio na descoberta do conhecimento. Nomeadamente, vimos que a aplicação conjunta dos algoritmos de data mining com as técnicas estatísticas inferenciais, assim como a aplicação de diversas técnicas de data mining (i.e., classificação, clustering e associação), potencia a descoberta do conhecimento em dados clínicos