Deep Learning-based Radiomics Framework for Multi-Modality PET-CT Images

Multimodal positron emission tomography - computed tomography (PET-CT) imaging is widely regarded as the imaging modality of choice for cancer management. This is because PET-CT combines the high sensitivity of PET in detecting regions of abnormal functions and the specificity of CT in depicting the underlying anatomy of where the abnormal functions are occurring. Radiomics is an emerging research field that enables the extraction and analysis of quantitative features from medical images, providing valuable insights into the underlying pathophysiology that cannot be discerned by the naked eyes. This information is capable of assisting decision-making in clinical practice, leading to better personalised treatment planning, patient outcome prediction, and therapy response assessment. The aim of this thesis is to propose a new deep learning-based radiomics framework for multimodal PET-CT images. The proposed framework comprises of three methods: 1) a tumour segmentation method via a self-supervision enabled false positive and false negative reduction network; 2) a constrained hierarchical multi-modality feature learning is constructed to predict the patient outcome with multimodal PET-CT images; 3) an automatic neural architecture search method to automatically find the optimal network architecture for both patient outcome prediction and tumour segmentation. Extensive experiments have been conducted on three datasets, including one public soft-tissue sarcomas dataset, one public challenge dataset, and one in-house lung cancer data. The results demonstrated that the proposed methods obtained better performance in all tasks when compared to the state-of-the-art methods

DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT

Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from the nasopharynx. Survival prediction is a major concern for NPC patients, as it provides early prognostic information to plan treatments. Recently, deep survival models based on deep learning have demonstrated the potential to outperform traditional radiomics-based survival prediction models. Deep survival models usually use image patches covering the whole target regions (e.g., nasopharynx for NPC) or containing only segmented tumor regions as the input. However, the models using the whole target regions will also include non-relevant background information, while the models using segmented tumor regions will disregard potentially prognostic information existing out of primary tumors (e.g., local lymph node metastasis and adjacent tissue invasion). In this study, we propose a 3D end-to-end Deep Multi-Task Survival model (DeepMTS) for joint survival prediction and tumor segmentation in advanced NPC from pretreatment PET/CT. Our novelty is the introduction of a hard-sharing segmentation backbone to guide the extraction of local features related to the primary tumors, which reduces the interference from non-relevant background information. In addition, we also introduce a cascaded survival network to capture the prognostic information existing out of primary tumors and further leverage the global tumor information (e.g., tumor size, shape, and locations) derived from the segmentation backbone. Our experiments with two clinical datasets demonstrate that our DeepMTS can consistently outperform traditional radiomics-based survival prediction models and existing deep survival models.Comment: Accepted at IEEE Journal of Biomedical and Health Informatics (JBHI

The HBP1 tumor suppressor is a negative epigenetic regulator of MYCN driven neuroblastoma through interaction with the PRC2 complex

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Assessment of Healthy Tissue Metabolism to Predict Outcomes in Oncologic [18F]FDG PET/CT

Background: The use of 2-[18F]Fluoro-2-deoxy-d-glucose ([18F]FDG) in positron emission tomography/computed tomography (PET/CT) imaging is not specific to oncologic applications but reflects various pathologic processes with high metabolic activity. Thus, evaluating healthy tissue metabolism (HTM) based on [18F]FDG in cancer patients receiving cytotoxic anti-cancer treatment may provide prognostic information which could potentially assist in identifying patients at high risk of developing treatment-related adverse events (AEs) and those who may have poor outcome. However, unlike cancer imaging HTM assessment with [18F]FDG is lacking standardization in the research setting.Purpose: The main aim of this thesis was to review the applications of [18F]FDG PET/CT in the assessment of anti-cancer treatment-related AEs and to assess methods used in the literature to measure HTM. Further, to evaluate the repeatability and interobserver variation of HTM in lung cancer patients. Finally, HTM based on [18F]FDG uptake was assessed as an imaging biomarker to predicts AEs and outcomes in Hodgkin lymphoma (HL) patients. Methods: A comprehensive literature search was conducted in PubMed, Embase and Web of Science databases for published data on [18F]FDG uptake in different HT for assessment of AEs in cancer patients. Different common and modified methods of assessment were applied to measure [18F]FDG uptake in liver, spleen and other HT. Retrospective test-retest repeatability and interobserver analyses of HTM were also performed on 22 patients with non-small cell lung cancer who underwent [18F]FDG PET/CT of the thorax 2 days apart without intervening treatment (from a prospective study) to measure the maximum, mean and peak standardised uptake values (SUVmax, SUVmean and SUVpeak). Moreover, [18F]FDG uptake in 200 patients with advanced HL from the RATHL trial was retrospectively measured in bone marrow (BM), mediastinal blood pool (MBP), liver and spleen at baseline (PET0) and after 2 cycles of chemotherapy (PET2). Results: Out of the reviewed studies, (n = 80, 94%) reported an association between [18F]FDG uptake in HT and treatment-related AEs. Quantitative assessment using SUVmean was mainly applied in those studies to assess changes in HTM at multiple timepoint. Further evaluation of the liver, spleen and other HT showed that using SUVmean reduces bias across different methods. Further, applying fixed volume of interest (VOI) was comparable to more sophisticated approaches. In comparison to other PET metrics, SUVmean also showed better repeatability as expressed with the within-subject coefficient of variation (wCV) of 20% and high interobserver agreement of ≤10% in HT in the thorax; however, left ventricle uptake was highly variable in a test-retest analysis. In HL, HT uptake changed significantly during treatment. BM uptake at PET0 was associated with baseline haematological parameters, higher risk of neutropenia at cycles 1-2 and failure of early response. Non-responding patients with high BM uptake at PET2 had inferior progression-free survivor (PFS). Conclusion: Most of the studies reviewed from the literature reported an association between HTM and treatment-related AEs among different cancer types and treatment modalities. SUVmean was mainly used in those studies to correlate changes in HTM with treatment-related AEs which was shown to be more stable than SUVmax and SUVpeak. [18F]FDG uptake in uninvolved BM has a prognostic value in HL