A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

In vivo MRI based prostate cancer localization with random forests and auto-context model

Prostate cancer is one of the major causes of cancer death for men. Magnetic resonance (MR) imaging is being increasingly used as an important modality to localize prostate cancer. Therefore, localizing prostate cancer in MRI with automated detection methods has become an active area of research. Many methods have been proposed for this task. However, most of previous methods focused on identifying cancer only in the peripheral zone (PZ), or classifying suspicious cancer ROIs into benign tissue and cancer tissue. Few works have been done on developing a fully automatic method for cancer localization in the entire prostate region, including central gland (CG) and transition zone (TZ). In this paper, we propose a novel learning-based multi-source integration framework to directly localize prostate cancer regions from in vivo MRI. We employ random forests to effectively integrate features from multi-source images together for cancer localization. Here, multi-source images include initially the multi-parametric MRIs (i.e., T2, DWI, and dADC) and later also the iteratively-estimated and refined tissue probability map of prostate cancer. Experimental results on 26 real patient data show that our method can accurately localize cancerous sections. The higher section-based evaluation (SBE), combined with the ROC analysis result of individual patients, shows that the proposed method is promising for in vivo MRI based prostate cancer localization, which can be used for guiding prostate biopsy, targeting the tumor in focal therapy planning, triage and follow-up of patients with active surveillance, as well as the decision making in treatment selection. The common ROC analysis with the AUC value of 0.832 and also the ROI-based ROC analysis with the AUC value of 0.883 both illustrate the effectiveness of our proposed method

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

A survey on artificial intelligence in histopathology image analysis

The increasing adoption of the whole slide image (WSI) technology in histopathology has dramatically transformed pathologists' workflow and allowed the use of computer systems in histopathology analysis. Extensive research in Artificial Intelligence (AI) with a huge progress has been conducted resulting in efficient, effective, and robust algorithms for several applications including cancer diagnosis, prognosis, and treatment. These algorithms offer highly accurate predictions but lack transparency, understandability, and actionability. Thus, explainable artificial intelligence (XAI) techniques are needed not only to understand the mechanism behind the decisions made by AI methods and increase user trust but also to broaden the use of AI algorithms in the clinical setting. From the survey of over 150 papers, we explore different AI algorithms that have been applied and contributed to the histopathology image analysis workflow. We first address the workflow of the histopathological process. We present an overview of various learning-based, XAI, and actionable techniques relevant to deep learning methods in histopathological imaging. We also address the evaluation of XAI methods and the need to ensure their reliability on the field

Intelligent Analysis of Cerebral Magnetic Resonance Images: Extracting Relevant Information from Small Datasets

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 21-09-2017Los metodos de machine learning aplicados a imagenes medicas se estan convirtiendo en potentes herramientas para el analisis y diagnostico de pacientes. La alta disponibilidad de repositorios de im agenes de diferentes modalidades ha favorecido el desarrollo de sistemas que aprenden a extraer caracteristicas relevantes y construyen modelos predictivos a partir de grandes cantidades de informacion, por ejemplo, los metodos de deep learning. Sin embargo, el analisis de conjuntos de imagenes provenientes de un menor numero de sujetos, como es el caso de las imagenes adquiridas en entornos de investigacion cl nica y pre-cl nica, ha recibido considerablemente menos atencion. El objetivo de esta tesis es implementar un conjunto de herramientas avanzadas para resolver este problema, permitiendo el analisis robusto de Im agenes de Resonancia Magn etica (MRI por sus siglas en ingl es) cuando se dispone de pocos sujetos de estudio. En este contexto, las herramientas propuestas se emplean para analizar de manera autom atica conjuntos de datos obtenidos de imagenes funcionales de MR del cerebro en estudios de regulacion del apetito en roedores y humanos, y de im agenes funcionales y estructurales de MR de desarrollos tumorales en modelos animales y humanos. Los metodos propuestos se derivan de la idea de considerar cada voxel del conjunto de im agenes como un patron, en lugar de la nocion convencional de considerar cada imagen como un patr on. El Cap tulo 1 describe la motivaci on de esta tesis, incluyendo los objetivos propuestos, la estructura general del documento y las contribuciones de esta investigaci on. El Capitulo 2 contiene una introduccion actualizada del estado del arte en MRI, los procedimientos mas usados en el pre-procesamiento de imagenes, y los algoritmos de machine learning m as utiles y sus aplicaciones en MRI. El Cap tulo 3 presenta el dise~no experimental y los pasos de pre-procesamiento aplicados a los conjuntos de datos de regulaci on de apetito y desarrollo tumoral. El Capitulo 4 implementa nuevos metodos de aprendizaje supervisados para el analisis de conjuntos de datos de MRI obtenidos de un conjunto peque~no de sujetos. Se ilustra este enfoque presentando primero la metodolog a Fisher Maps, que permite la visualizaci on cuantitativa y no invasiva de la circuiter a cerebral del apetito, mediante el an alisis autom atico de Im agenes Ponderadas en Difusi on (DWI por sus siglas en ingl es). Esta metodolog a se extiende a la clasi caci on de im agenes completas combinando las predicciones obtenidas de cada p xel. El Cap tulo 5 propone un nuevo algoritmo de aprendizaje no supervisado, ilustrando su desempe~no sobre datos sint eticos y datos provenientes de estudios de tumores cerebrales y crecimiento tumoral. Por ultimo, en el Cap tulo 6 se resumen las principales conclusiones de este trabajo y se plantean amplias v as para su desarrollo futuro. En resumen, esta tesis presenta un nuevo enfoque capaz de trabajar en contextos con baja disponibilidad de sujetos de estudio, proponiendo algoritmos de aprendizaje supervisado y no supervisado. Estos metodos pueden ser facilmente generalizados a otros paradigmas o patologias, e incluso, a distintas modalidades de imagenes

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computa-tional as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles

PREDICTION OF RECURRENCE AND MORTALITY OF ORAL TONGUE CANCER USING ARTIFICIAL NEURAL NETWORK (A case study of 5 hospitals in Finland and 1 hospital from Sao Paulo, Brazil)

Cancer is a dreadful disease that had caused the death of millions of people. It is characterized by an uncontrollable growth of cell to form lumps or masses of tissue that are known as tumour. Therefore, it is a concern to all and sundry as these tumours mostly release hormones which have negative impact on the body system. Data mining approaches, statistical methods and machine learning algorithms have been proposed for effective cancer data classification. Artificial Neural Networks (ANN) have been used in this thesis for the prediction of recurrence and mortality of oral tongue cancer in patients. Similarly, ANN was also used to examine the diagnostic and prognostic factors. This was aimed at determining which of these diagnostic and prognostics factors had influence on the prediction of recurrence and mortality of oral tongue cancer in patients. Three different ANN have been applied for the learning and testing phases. The aim was to find the most effective technique. They are Elman, Feedforward, and Layer Recurrent neural networks techniques. Elman neural network was not able to make acceptable prediction of the recurrence or the mortality of tongue cancer based on the data. In contrast, Feedforward neural network captured the relationship between the prognostic factors and correctly predicted recurrence. However, it failed to predict the mortality based on the patient's data. Layer Recurrence neural network has been very effective and successfully predicted the recurrence and the mortality of oral tongue cancer in patients. The constructed layered recurrence neural network has been used to investigate the correlation between the prognostic factors. It was found that out of 11 prognostic factors in the data sheet, it was only 5 of them that had considerable impact on the recurrence and mortality. These are grade, depth, budding, modified stage, and gender. Time in months and disease free months were also used to train the network.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Urological Cancer 2020

This Urological Cancer 2020 collection contains a set of multidisciplinary contributions to the extraordinary heterogeneity of tumor mechanisms, diagnostic approaches, and therapies of the renal, urinary tract, and prostate cancers, with the intention of offering to interested readers a representative snapshot of the status of urological research