Evaluation of cancer outcome assessment using MRI: A review of deep-learning methods

Accurate evaluation of tumor response to treatment is critical to allow personalized treatment regimens according to the predicted response and to support clinical trials investigating new therapeutic agents by providing them with an accurate response indicator. Recent advances in medical imaging, computer hardware, and machine-learning algorithms have resulted in the increased use of these tools in the field of medicine as a whole and specifically in cancer imaging for detection and characterization of malignant lesions, prognosis, and assessment of treatment response. Among the currently available imaging techniques, magnetic resonance imaging (MRI) plays an important role in the evaluation of treatment assessment of many cancers, given its superior soft-tissue contrast and its ability to allow multiplanar imaging and functional evaluation. In recent years, deep learning (DL) has become an active area of research, paving the way for computer-assisted clinical and radiological decision support. DL can uncover associations between imaging features that cannot be visually identified by the naked eye and pertinent clinical outcomes. The aim of this review is to highlight the use of DL in the evaluation of tumor response assessed on MRI. In this review, we will first provide an overview of common DL architectures used in medical imaging research in general. Then, we will review the studies to date that have applied DL to magnetic resonance imaging for the task of treatment response assessment. Finally, we will discuss the challenges and opportunities of using DL within the clinical workflow

DEVELOPING NOVEL COMPUTER-AIDED DETECTION AND DIAGNOSIS SYSTEMS OF MEDICAL IMAGES

Reading medical images to detect and diagnose diseases is often difficult and has large inter-reader variability. To address this issue, developing computer-aided detection and diagnosis (CAD) schemes or systems of medical images has attracted broad research interest in the last several decades. Despite great effort and significant progress in previous studies, only limited CAD schemes have been used in clinical practice. Thus, developing new CAD schemes is still a hot research topic in medical imaging informatics field. In this dissertation, I investigate the feasibility of developing several new innovative CAD schemes for different application purposes. First, to predict breast tumor response to neoadjuvant chemotherapy and reduce unnecessary aggressive surgery, I developed two CAD schemes of breast magnetic resonance imaging (MRI) to generate quantitative image markers based on quantitative analysis of global kinetic features. Using the image marker computed from breast MRI acquired pre-chemotherapy, CAD scheme enables to predict radiographic complete response (CR) of breast tumors to neoadjuvant chemotherapy, while using the imaging marker based on the fusion of kinetic and texture features extracted from breast MRI performed after neoadjuvant chemotherapy, CAD scheme can better predict the pathologic complete response (pCR) of the patients. Second, to more accurately predict prognosis of stroke patients, quantifying brain hemorrhage and ventricular cerebrospinal fluid depicting on brain CT images can play an important role. For this purpose, I developed a new interactive CAD tool to segment hemorrhage regions and extract radiological imaging marker to quantitatively determine the severity of aneurysmal subarachnoid hemorrhage at presentation and correlate the estimation with various homeostatic/metabolic derangements and predict clinical outcome. Third, to improve the efficiency of primary antibody screening processes in new cancer drug development, I developed a CAD scheme to automatically identify the non-negative tissue slides, which indicate reactive antibodies in digital pathology images. Last, to improve operation efficiency and reliability of storing digital pathology image data, I developed a CAD scheme using optical character recognition algorithm to automatically extract metadata from tissue slide label images and reduce manual entry for slide tracking and archiving in the tissue pathology laboratories. In summary, in these studies, we developed and tested several innovative approaches to identify quantitative imaging markers with high discriminatory power. In all CAD schemes, the graphic user interface-based visual aid tools were also developed and implemented. Study results demonstrated feasibility of applying CAD technology to several new application fields, which has potential to assist radiologists, oncologists and pathologists improving accuracy and consistency in disease diagnosis and prognosis assessment of using medical image

Machine Learning Strategies to Analyze Quantitative Ultrasound Multi-Parametric Images for Prediction of Therapy Response in Breast Cancer Patients

In this thesis project, two novel machine learning strategies were investigated to predict tumor response to neoadjuvant chemotherapy (NAC) at pre-treatment using quantitative ultrasound (QUS) multi-parametric images. The ultrasound data for analytical development and evaluation of the methodologies investigated in this project were acquired from 181 patients diagnosed with locally advanced breast cancer (LABC) and planned for NAC followed by surgery. The QUS multi-parametric images were generated using spectral analyses on the raw ultrasound radiofrequency (RF) data acquired before starting the NAC. In the first machine learning approach investigated in this project, distinct intra-tumor regions were identified within the parametric maps using a hidden Markov random field (HMRF) and its expectation-maximization (EM) algorithm. Several hand-crafted features characterizing the tumor, intra-tumor regions, and the tumor margin were extracted from different parametric images. A multi-step feature selection procedure was applied to construct a QUS biomarker for response prediction. Evaluation results on an independent test set indicated that the developed biomarker using the characteristics of intra-tumor regions and tumor margin in conjunction with a decision tree model with adaptive boosting (AdaBoost) as the classifier could predict the treatment response of patients at pre-treatment with an accuracy of 85.4% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89. In the second machine learning approach investigated in this project, two deep convolutional neural network (DCNN) architectures including the residual network (ResNet) and residual attention network (RAN) were explored for extracting optimal feature maps from the parametric images, with a fully connected network for response prediction. Results demonstrated that the developed model with the RAN architecture to extract feature maps from the expanded parametric images of the tumor core and margin had a superior performance with an accuracy of 0.88 and an AUC of 0.86 on the independent test set. Also, survival analysis demonstrated a statistically significant difference between survival curves of the two response cohorts identified at pre-treatment based on both the conventional machine learning method and the deep learning model. Obtained results in this study demonstrated a great promise of QUS multi-parametric imaging integrated with both unsupervised learning methods in identifying distinct breast cancer intra-tumor regions and traditional classification techniques, as well as deep convolutional neural networks in predicting tumor response to NAC prior to start of treatment