1,785 research outputs found

    Radiomics and prostate MRI: Current role and future applications

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    Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer

    MRI-based prostate cancer detection with high-level representation and hierarchical classification

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    Extracting the high-level feature representation by using deep neural networks for detection of prostate cancer, and then based on high-level feature representation constructing hierarchical classification to refine the detection results

    Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives

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    Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention

    Reference tissue normalization of prostate MRI with automatic multi-organ deep learning pelvis segmentation

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    Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2018Prostate cancer is the most common cancer among male patients and second leading cause of death from cancer in men (excluding non-melanoma skin cancer). Magnetic Resonance Imaging (MRI) is currently becoming the modality of choice for clinical staging of localized prostate cancer. However, MRI lacks intensity quantification which hinders its diagnostic ability. The overall aim of this dissertation is to automate a novel normalization method that can potentially quantify general MR intensities, thus improving the diagnostic ability of MRI. Two Prostate multi-parametric MRI cohorts, of 2012 and 2016, were used in this retrospective study. To improve the diagnostic ability of T2-Weighted MRI, a novel multi-reference tissue normalization method was tested and automated. This method consists of computing the average intensity of the reference tissues and the corresponding normalized reference values to define a look-up-table through interpolation. Since the method requires delineation of multiple reference tissues, an MRI-specific Deep Learning model, Aniso-3DUNET, was trained on manual segmentations and tested to automate this segmentation step. The output of the Deep Learning model, that consisted of automatic segmentations, was validated and used in an automatic normalization approach. The effect of the manual and automatic normalization approaches on diagnostic accuracy of T2-weighted intensities was determined with Receiver Operating Characteristic (ROC) analyses. The Areas Under the Curve (AUC) were compared. The automatic segmentation of multiple reference-tissues was validated with an average DICE score higher than 0.8 in the test phase. Thereafter, the method developed demonstrated that the normalized intensities lead to an improved diagnostic accuracy over raw intensities using the manual approach, with an AUC going from 0.54 (raw) to 0.68 (normalized), and automatic approach, with an AUC going from 0.68 to 0.73. This study demonstrates that multi-reference tissue normalization improves quantification of T2-weighted images and diagnostic accuracy, possibly leading to a decrease in radiologist’s interpretation variability. It is also possible to conclude that this novel T2-weighted MRI normalization method can be automatized, becoming clinically applicable

    A review of artificial intelligence in prostate cancer detection on imaging

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    A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care

    Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification

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    Prostate cancer (PCa) is a severe disease among men globally. It is important to identify PCa early and make a precise diagnosis for effective treatment. For PCa diagnosis, Multi-parametric magnetic resonance imaging (mpMRI) emerged as an invaluable imaging modality that offers a precise anatomical view of the prostate gland and its tissue structure. Deep learning (DL) models can enhance existing clinical systems and improve patient care by locating regions of interest for physicians. Recently, DL techniques have been employed to develop a pipeline for segmenting and classifying different cancer types. These studies show that DL can be used to increase diagnostic precision and give objective results without variability. This work uses well-known DL models for the classification and segmentation of mpMRI images to detect PCa. Our implementation involves four pipelines; Semantic DeepSegNet with ResNet50, DeepSegNet with recurrent neural network (RNN), U-Net with RNN, and U-Net with a long short-term memory (LSTM). Each segmentation model is paired with a different classifier to evaluate the performance using different metrics. The results of our experiments show that the pipeline that uses the combination of U-Net and the LSTM model outperforms all other combinations, excelling in both segmentation and classification tasks.Comment: Accepted at CISCON-202
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