The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the most common primary hepatic neoplasm. Thanks to recent advances in computed tomography (CT) and magnetic resonance imaging (MRI), there is potential to improve detection, segmentation, discrimination from HCC mimics, and monitoring of therapeutic response. Radiomics, artificial intelligence (AI), and derived tools have already been applied in other areas of diagnostic imaging with promising results. In this review, we briefly discuss the current clinical applications of radiomics and AI in the detection, segmentation, and management of HCC. Moreover, we investigate their potential to reach a more accurate diagnosis of HCC and to guide proper treatment planning

Abdominal Tumor Characterization and Recognition Using Superior-Order Cooccurrence Matrices, Based on Ultrasound Images

The noninvasive diagnosis of the malignant tumors is an important issue in research nowadays. Our purpose is to elaborate computerized, texture-based methods for performing computer-aided characterization and automatic diagnosis of these tumors, using only the information from ultrasound images. In this paper, we considered some of the most frequent abdominal malignant tumors: the hepatocellular carcinoma and the colonic tumors. We compared these structures with the benign tumors and with other visually similar diseases. Besides the textural features that proved in our previous research to be useful in the characterization and recognition of the malignant tumors, we improved our method by using the grey level cooccurrence matrix and the edge orientation cooccurrence matrix of superior order. As resulted from our experiments, the new textural features increased the malignant tumor classification performance, also revealing visual and physical properties of these structures that emphasized the complex, chaotic structure of the corresponding tissue

APPLICATION OF FUZZY-MLP MODEL TO ULTRASONIC LIVER IMAGE CLASSIFICATION

In this paper, we propose the application of fuzzy-MLP in theclassification of ultrasonic liver images. The four sets of ultrasonic liverimages used in the experiment are: normal, liver cysts, alcoholic cirrhosisand carcinoma.To deal with the sample images efficiently, we extract textural features fromthe Pathology Bearing Regions (PBRs) of the ultrasound liver images. Theselected features for the classification are entropy, energy and maximumprobability-based texture features extracted using gray level co-occurrencematrix second-order statistics. The fuzzy-MLP model is constructed for theselected features classify various categories of ultrasonic liver images.The efficacy of Fuzzy-MLP model and conventional artificial neural network(ANN) has been compared on the basis of the same feature vector. A testwith 82 training data and 110 test data for all the four classes shows 92.73%classification accuracy for the proposed fuzzy-MLP model. It is comparedwith the 81.82% counterpart provided by conventional ANN method

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor

Medical Image Analytics (Radiomics) with Machine/Deeping Learning for Outcome Modeling in Radiation Oncology

Image-based quantitative analysis (radiomics) has gained great attention recently. Radiomics possesses promising potentials to be applied in the clinical practice of radiotherapy and to provide personalized healthcare for cancer patients. However, there are several challenges along the way that this thesis will attempt to address. Specifically, this thesis focuses on the investigation of repeatability and reproducibility of radiomics features, the development of new machine/deep learning models, and combining these for robust outcomes modeling and their applications in radiotherapy. Radiomics features suffer from robustness issues when applied to outcome modeling problems, especially in head and neck computed tomography (CT) images. These images tend to contain streak artifacts due to patients’ dental implants. To investigate the influence of artifacts for radiomics modeling performance, we firstly developed an automatic artifact detection algorithm using gradient-based hand-crafted features. Then, comparing the radiomics models trained on ‘clean’ and ‘contaminated’ datasets. The second project focused on using hand-crafted radiomics features and conventional machine learning methods for the prediction of overall response and progression-free survival for Y90 treated liver cancer patients. By identifying robust features and embedding prior knowledge in the engineered radiomics features and using bootstrapped LASSO to select robust features, we trained imaging and dose based models for the desired clinical endpoints, highlighting the complementary nature of this information in Y90 outcomes prediction. Combining hand-crafted and machine learnt features can take advantage of both expert domain knowledge and advanced data-driven approaches (e.g., deep learning). Thus, we proposed a new variational autoencoder network framework that modeled radiomics features, clinical factors, and raw CT images for the prediction of intrahepatic recurrence-free and overall survival for hepatocellular carcinoma (HCC) patients in this third project. The proposed approach was compared with widely used Cox proportional hazard model for survival analysis. Our proposed methods achieved significant improvement in terms of the prediction using the c-index metric highlighting the value of advanced modeling techniques in learning from limited and heterogeneous information in actuarial prediction of outcomes. Advances in stereotactic radiation therapy (SBRT) has led to excellent local tumor control with limited toxicities for HCC patients, but intrahepatic recurrence still remains prevalent. As an extension of the third project, we not only hope to predict the time to intrahepatic recurrence, but also the location where the tumor might recur. This will be clinically beneficial for better intervention and optimizing decision making during the process of radiotherapy treatment planning. To address this challenging task, firstly, we proposed an unsupervised registration neural network to register atlas CT to patient simulation CT and obtain the liver’s Couinaud segments for the entire patient cohort. Secondly, a new attention convolutional neural network has been applied to utilize multimodality images (CT, MR and 3D dose distribution) for the prediction of high-risk segments. The results showed much improved efficiency for obtaining segments compared with conventional registration methods and the prediction performance showed promising accuracy for anticipating the recurrence location as well. Overall, this thesis contributed new methods and techniques to improve the utilization of radiomics for personalized radiotherapy. These contributions included new algorithm for detecting artifacts, a joint model of dose with image heterogeneity, combining hand-crafted features with machine learnt features for actuarial radiomics modeling, and a novel approach for predicting location of treatment failure.PHDApplied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163092/1/liswei_1.pd

Dynamic And Quantitative Radiomics Analysis In Interventional Radiology

Interventional Radiology (IR) is a subspecialty of radiology that performs invasive procedures driven by diagnostic imaging for predictive and therapeutic purpose. The development of artificial intelligence (AI) has revolutionized the industry of IR. Researchers have created sophisticated models backed by machine learning algorithms and optimization methodologies for image registration, cellular structure detection and computer-aided disease diagnosis and prognosis predictions. However, due to the incapacity of the human eye to detect tiny structural characteristics and inter-radiologist heterogeneity, conventional experience-based IR visual evaluations may have drawbacks. Radiomics, a technique that utilizes machine learning, offers a practical and quantifiable solution to this issue. This technology has been used to evaluate the heterogeneity of malignancies that are difficult to detect by the human eye by creating an automated pipeline for the extraction and analysis of high throughput computational imaging characteristics from radiological medical pictures. However, it is a demanding task to directly put radiomics into applications in IR because of the heterogeneity and complexity of medical imaging data. Furthermore, recent radiomics studies are based on static images, while many clinical applications (such as detecting the occurrence and development of tumors and assessing patient response to chemotherapy and immunotherapy) is a dynamic process. Merely incorporating static features cannot comprehensively reflect the metabolic characteristics and dynamic processes of tumors or soft tissues. To address these issues, we proposed a robust feature selection framework to manage the high-dimensional small-size data. Apart from that, we explore and propose a descriptor in the view of computer vision and physiology by integrating static radiomics features with time-varying information in tumor dynamics. The major contributions to this study include: Firstly, we construct a result-driven feature selection framework, which could efficiently reduce the dimension of the original feature set. The framework integrates different feature selection techniques to ensure the distinctiveness, uniqueness, and generalization ability of the output feature set. In the task of classification hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) in primary liver cancer, only three radiomics features (chosen from more than 1, 800 features of the proposed framework) can obtain an AUC of 0.83 in the independent dataset. Besides, we also analyze features’ pattern and contributions to the results, enhancing clinical interpretability of radiomics biomarkers. Secondly, we explore and build a pulmonary perfusion descriptor based on 18F-FDG whole-body dynamic PET images. Our major novelties include: 1) propose a physiology-and-computer-vision-interpretable descriptor construction framework by the decomposition of spatiotemporal information into three dimensions: shades of grey levels, textures, and dynamics. 2) The spatio-temporal comparison of pulmonary descriptor intra and inter patients is feasible, making it possible to be an auxiliary diagnostic tool in pulmonary function assessment. 3) Compared with traditional PET metabolic biomarker analysis, the proposed descriptor incorporates image’s temporal information, which enables a better understanding of the time-various mechanisms and detection of visual perfusion abnormalities among different patients. 4) The proposed descriptor eliminates the impact of vascular branching structure and gravity effect by utilizing time warping algorithms. Our experimental results showed that our proposed framework and descriptor are promising tools to medical imaging analysis

Prediction of Chemotherapy Response of Liver Metastases from Baseline CT-Images Using Deep Neural Networks

Dans les pays développés, le cancer colorectal est reconnu comme étant la deuxième cause la plus importante de mortalité liée au cancer. La chimiothérapie est considérée comme un traitement standard pour les métastases colorectales du foie (MCF). Parmi les patients qui développent des MCF, l’évaluation de la réponse du patient au traitement de chimiothérapie est souvent requise pour déterminer le besoin d’une chimiothérapie de seconde ligne, ainsi qu’une éligibilité à la chirurgie. Toutefois, tandis que les régimes basés sur un régime dénommé FOLFOX sont typiquement utilisés pour le traitement de la MCF, l’identification de la sensibilité du patient reste difficile. Les systèmes de diagnostic assistés par ordinateur peuvent fournir de l’information supplémentaire sur la classification des métastases du foie identifiées au niveau des images de diagnostic. Du aux quelques difficultés que rencontrent les radiologues pour distinguer, à l’oeil nu, les lésions traitées des lésions non-traitées, nous proposons dans cette étude un système automatisé basé sur les réseaux profonds convolutifs (RPC). Dans un premier lieu, ces réseaux profonds différencient les lésions traitées des lésions non-traitées, pour ensuite identifier les nouvelles lésions apparaissant sur les tomodensitométries. Ensuite, un réseau de neurones dense émet une prédiction, à partir des lésions non traitées visibles sur les tomodensitométries de prétraitement pour les patients à MCF sous chimiothérapie, sur leur réponse au régime spécifique de chimiothérapie. Dans ce contexte, la référence pour l’évaluation de la réponse au traitement pour le régime approprié de chimiothérapie était le degré de régression de la tumeur en histopathologie. La méthode adoptée dans cette étude nous a aidé à adresser les trois grands objectifs de ce projet de recherche. La première étape était de développer un système automatique de classification des tumeurs traitées et non-traitées à partir des tomodensitométries de patients. La deuxième étape a été de concevoir une nouvelle approche pour prédire la réponse au traitement FOLFOX qui utilise le médicament Bevacizumab en tant que traitement de première ligne. La troisième étape était de prédire le pourcentage de changement volumétrique de la tumeur suivant deux moments temporels consécutifs. Les algorithmes d’intelligence artificielle (IA), et les approches d’apprentissage profond en particulier, ont montré des progrès prometteurs en vision par ordinateur ainsi qu’en traitement d’images. Il existe de nombreuses applications en analyse d’images médicales qui utilisent des réseaux profonds convolutifs pour propulser ces progrès en avant le plus rapidement possible. En pratique, les radiologues et lesmédecins évaluent les images médicales visuellement pour le diagnostic, la détection, la récurrence, le suivi et la caractérisation des maladies. Les méthodes d’apprentissage profond sont souvent supérieures pour la reconnaissance automatique de structures complexes à partir d’images, et quantifier l’évaluation de propriétés et caractéristiques radiographiques.----------ABSTRACT: In developed countries, colorectal cancer is known as the second cause of cancer-related mortality. Chemotherapy is considered a standard treatment for colorectal liver metastases (CLM). Among patients who develop CLM, the assessment of patient response to chemotherapy is often required to determine the need for second-line chemotherapy, and eligibility for surgery. However, a drug regimen known as FOLFOX are typically used for CLM treatment, the identification of responsive patients remains elusive. Computer-aided diagnosis systems may provide insight into the classification of liver metastases identified on diagnostic images. Due to some difficulties for radiologists to distinguish between treated and untreated lesions from the naked eyes, in this study, we propose a fully automated framework based on deep convolutional neural networks (DCNN) which first differentiates treated and untreated lesions to identify new lesions appearing on CT scans, followed by a fully connected neural network to predict from untreated lesions in pre-treatment computed tomography (CT) for patients with CLMundergoing chemotherapy, their response to the specific chemotherapy regimen. In this respect, the ground truth for assessment of treatment response for proper chemotherapy regimen was histopathology to determine the tumor regression grade (TRG). The adopted method in this study helped us to address the three main research objectives. The first step is to develop an automated framework for the classification of treated and untreated tumors based on the patient’s CT images. The second step is to design a new approach for the prediction of response to FOLFOX regimens with Bevacizumab agents as the first-line of treatment. The third step is to predict the percentage of the tumor volume change following two consecutive exams. Artificial intelligence (AI) algorithms, particularly deep learning approach, have shown very astonishing progress in computer vision and image processing tasks. There are several applications in the medical image analysis area which use DCNN to propel these methods forward as quickly as possible. In practice, radiologists and physicians attempt to assess visually medical images for diagnosis, detection, recurrence, monitoring, and characterization of diseases. Deep learning methods surpass at the recognition of complicated patterns from imaging data automatically and quantify the assessment of radiographic features and characteristics