Novel Computer-Aided Diagnosis Schemes for Radiological Image Analysis

The computer-aided diagnosis (CAD) scheme is a powerful tool in assisting clinicians (e.g., radiologists) to interpret medical images more accurately and efficiently. In developing high-performing CAD schemes, classic machine learning (ML) and deep learning (DL) algorithms play an essential role because of their advantages in capturing meaningful patterns that are important for disease (e.g., cancer) diagnosis and prognosis from complex datasets. This dissertation, organized into four studies, investigates the feasibility of developing several novel ML-based and DL-based CAD schemes for different cancer research purposes. The first study aims to develop and test a unique radiomics-based CT image marker that can be used to detect lymph node (LN) metastasis for cervical cancer patients. A total of 1,763 radiomics features were first computed from the segmented primary cervical tumor depicted on one CT image with the maximal tumor region. Next, a principal component analysis algorithm was applied on the initial feature pool to determine an optimal feature cluster. Then, based on this optimal cluster, machine learning models (e.g., support vector machine (SVM)) were trained and optimized to generate an image marker to detect LN metastasis. The SVM based imaging marker achieved an AUC (area under the ROC curve) value of 0.841 ± 0.035. This study initially verifies the feasibility of combining CT images and the radiomics technology to develop a low-cost image marker for LN metastasis detection among cervical cancer patients. In the second study, the purpose is to develop and evaluate a unique global mammographic image feature analysis scheme to identify case malignancy for breast cancer. From the entire breast area depicted on the mammograms, 59 features were initially computed to characterize the breast tissue properties in both the spatial and frequency domain. Given that each case consists of two cranio-caudal and two medio-lateral oblique view images of left and right breasts, two feature pools were built, which contain the computed features from either two positive images of one breast or all the four images of two breasts. For each feature pool, a particle swarm optimization (PSO) method was applied to determine the optimal feature cluster followed by training an SVM classifier to generate a final score for predicting likelihood of the case being malignant. The classification performances measured by AUC were 0.79±0.07 and 0.75±0.08 when applying the SVM classifiers trained using image features computed from two-view and four-view images, respectively. This study demonstrates the potential of developing a global mammographic image feature analysis-based scheme to predict case malignancy without including an arduous segmentation of breast lesions. In the third study, given that the performance of DL-based models in the medical imaging field is generally bottlenecked by a lack of sufficient labeled images, we specifically investigate the effectiveness of applying the latest transferring generative adversarial networks (GAN) technology to augment limited data for performance boost in the task of breast mass classification. This transferring GAN model was first pre-trained on a dataset of 25,000 mammogram patches (without labels). Then its generator and the discriminator were fine-tuned on a much smaller dataset containing 1024 labeled breast mass images. A supervised loss was integrated with the discriminator, such that it can be used to directly classify the benign/malignant masses. Our proposed approach improved the classification accuracy by 6.002%, when compared with the classifiers trained without traditional data augmentation. This investigation may provide a new perspective for researchers to effectively train the GAN models on a medical imaging task with only limited datasets. Like the third study, our last study also aims to alleviate DL models’ reliance on large amounts of annotations but uses a totally different approach. We propose employing a semi-supervised method, i.e., virtual adversarial training (VAT), to learn and leverage useful information underlying in unlabeled data for better classification of breast masses. Accordingly, our VAT-based models have two types of losses, namely supervised and virtual adversarial losses. The former loss acts as in supervised classification, while the latter loss works towards enhancing the model’s robustness against virtual adversarial perturbation, thus improving model generalizability. A large CNN and a small CNN were used in this investigation, and both were trained with and without the adversarial loss. When the labeled ratios were 40% and 80%, VAT-based CNNs delivered the highest classification accuracy of 0.740±0.015 and 0.760±0.015, respectively. The experimental results suggest that the VAT-based CAD scheme can effectively utilize meaningful knowledge from unlabeled data to better classify mammographic breast mass images. In summary, several innovative approaches have been investigated and evaluated in this dissertation to develop ML-based and DL-based CAD schemes for the diagnosis of cervical cancer and breast cancer. The promising results demonstrate the potential of these CAD schemes in assisting radiologists to achieve a more accurate interpretation of radiological images

Can machine learning methods contribute as a decision support system in sequential oligometastatic radioablation therapy?

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsCancer treatment is among the major medical challenges of this century. Sequential oligometastatic radio-ablation (SOMA) is a novel treatment method that aims at ablating reoccurring metastasis in a single session with a targeted high dose of radiation. To know if SOMA is the best possible treatment method for a patient, the benefits of each available therapy need to be understood and evaluated. The ability to model complex systems, such as cancer treatment, is the strength of machine learning techniques. These techniques have improved the understanding of numerous medical therapies already. In some cases, they can serve as medical support systems if they deliver reliable results that doctors can trust and understand. The results obtained from applying numerous machine learning techniques to the data of SOMA-treated patients show that there are favorable techniques in some cases. It was observed that the Random Forest algorithm proved superior at different classification tasks. Additionally, regression problems opposed a great challenge, as the amount of data is very limited. Finally, SHAP values - a novel machine learning interpretation technique – provided valuable insights into understanding the rationale of each algorithm. They proved that the machine learning algorithms could learn patterns aligned with the human intuition in the problems presented. SHAP values show great potential in bridging the gap between complex machine learning algorithms and their interpretability. They display how an algorithm learns from the data and derives results. This opens up exciting possibilities for applying machine learning algorithms in the real world

Advanced Statistical Learning Techniques for High-Dimensional Imaging Data

With the rapid development of neuroimaging techniques, scientists are interested in identifying imaging biomarkers that are related to different subtypes or transitional stages of various cancers, neuropsychiatric diseases, and neurodegenerative diseases. Scalar-on-image models have been proven to demonstrate good performance in such tasks. However, due to their high dimensionality, traditional methods may not work well in the estimation of such models. Some existing penalization methods may improve the performance but fail to take the complex spatial structure of the neuroimaging data into account. In the past decade, the spatially regularized methods have been popular due to their good performance in terms of both estimation and prediction. Despite the progress, many challenges still remain. In particular, most existing image classification methods focus on binary classification and consequently may underperform for the tasks of classifying diseases with multiple subtypes or transitional stages. Moreover, neuroimaging data usually present significant heterogeneity across subjects. As a result, existing methods for homogeneous data may fail. In this dissertation, we investigate several new statistical learning techniques and propose a Spatial Multi-category Angle based Classifier (SMAC), a Subject Variant Scalar-on-Image Regression (SVSIR) model and a Masking Convolutional Neural Network (MCNN) model to address the above issues. Extensive simulation studies and practical applications in neuroscience are presented to demonstrate the effectiveness of our proposed methods.Doctor of Philosoph

Radiomics analyses for outcome prediction in patients with locally advanced rectal cancer and glioblastoma multiforme using multimodal imaging data

Personalized treatment strategies for oncological patient management can improve outcomes of patient populations with heterogeneous treatment response. The implementation of such a concept requires the identification of biomarkers that can precisely predict treatment outcome. In the context of this thesis, we develop and validate biomarkers from multimodal imaging data for the outcome prediction after treatment in patients with locally advanced rectal cancer (LARC) and in patients with newly diagnosed glioblastoma multiforme (GBM), using conventional feature-based radiomics and deep-learning (DL) based radiomics. For LARC patients, we identify promising radiomics signatures combining computed tomography (CT) and T2-weighted (T2-w) magnetic resonance imaging (MRI) with clinical parameters to predict tumour response to neoadjuvant chemoradiotherapy (nCRT). Further, the analyses of externally available radiomics models for LARC reveal a lack of reproducibility and the need for standardization of the radiomics process. For patients with GBM, we use postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w MRI for the detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS). We show that DL models built on MET-PET have an improved diagnostic and prognostic value as compared to MRI

Pancreatic cancer : prevention through improved diagnostics in individuals at risk

With the intent of timely detecting malignant precursors or pancreatic cancer (PC) in an early stage, current guidelines recommend surveillance for patients with pancreatic cystic neoplasms (PCNs), such as intraductal papillary mucinous neoplasms (IPMN), and individuals at risk (IAR) for familiar/hereditary pancreatic cancer (PC). This surveillance is mainly performed with magnetic resonance imaging (MRI). However, despite the associated examination time and costs, MRI is still suboptimal regarding its accuracy in predicting malignancy. This thesis aims to contribute towards improved secondary prevention of PC through more efficient and accurate diagnostic methods in patients with IPMN and familiar/hereditary PC through (i) a shorter MRI protocol, (ii) new imaging features and radiomics models for the prediction of malignancy, (iii) a better understanding of the surveillance program for IAR. In Study I, we compared a short (SP) and a comprehensive pancreatic MRI protocol (CP), with an acquisition time of approximately 8 and 35 minutes, respectively, in a cohort of 154 patients with PCNs. Our results showed that the SP provided equivalent clinical information in evaluating mural nodules, as well as cystic and main pancreatic duct diameters, compared to the more time-consuming and expensive CP in the surveillance of PCNs. In Study II, we assessed whether two novel features, such as volumetry and elongation value (EV) and other routinely used resection criteria, could predict malignancy in a cohort of 106 patients operated for BD- and mixed-type IPMN. Cases with mass-forming PC were excluded as a possible cause of main pancreatic duct (MPD) dilatation. Our results showed that volumetry and EV were not predictive for malignancy. Only elevated serum levels of CA19-9, mural nodules and dilated MPD (in the absence of stricturing masses) were associated with malignancy. In Study III, we evaluated the performance of MRI-based radiomics models in the preoperative prediction of malignancy in 130 patients operated for BD and mixed-type IPMN after exclusion of mass-forming PC. The radiomics models were internally cross-validated. Our results showed that a “pure” radiomics model outperformed a model including standard clinical and imaging features, suggesting that it might effectively predict malignancy in BD-IPMN even without standard clinical/imaging information. In Study IV, we described the imaging findings and the performance of a mainly MRI-based surveillance program in a cohort of 278 individuals at risk (IAR) for familial/hereditary PC. Our results showed that focal pancreatic lesions were identified in over half of IAR, the vast majority being small cysts. No lesions with high-grade dysplasia were detected. Five patients had PC, with the same prevalence of stage I and stage IV. The sensitivity and positive predictive values for detecting PC and its precursors were low (60% and 37%, respectively). In conclusion, a short pancreatic MRI protocol can be safely used in the surveillance of PCNs without losing relevant clinical information. Although novel imaging features cannot predict malignancy in IPMN, radiomics have shown its potentiality. In the context of individuals at risk for familial or hereditary PC undergoing surveillance, the early diagnosis of PC is still challenging with “traditional” cross-imaging methods such as MRI

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.

Novel Deep Learning Models for Medical Imaging Analysis

abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page