Radiomics in [¹⁸F]FDG PET/CT:A leap in the dark?

Positron emission tomography (PET) imaging with the non-metabolisable glucose analogue 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG), combined with low dose computed tomography (CT) for anatomical reference, is an important tool to detect and stage cancer or active inflammations. Visual interpretation of PET/CT images consists of (qualitative) assessment of radiotracer uptake in different tissues and their density. Furthermore, the location, size, shape, and relation with surrounding tissues of these lesions provide important clues on their nature. Yet, medical images contain much more information about tissue biology hidden in the myriad of voxels of both lesions and healthy tissue than can be assessed visually. Quantification of radiotracer uptake heterogeneity and other tissue characteristics is studied in the field of radiomics. Radiomics is a form of medical image processing that aims to find stable and clinically relevant image-derived biomarkers for lesion characterisation, prognostic stratification, and response prediction, thereby contributing to precision medicine. Radiomics consists of the conversion of (parts of) medical images into a high-dimensional set of quantitative features and the subsequent mining of this dataset for potential information useful for the quantification or monitoring of tumour or disease characteristics in clinical practice. This thesis contributed to a deeper understanding of the methodological aspects of handcrafted radiomics in [18F]FDG PET/CT, specifically in small datasets. However, most radiomic papers present proof-of-concept studies and clinical implementation is still far away. At some point in the future, radiomic biomarkers may be used in clinical practice, but at the moment we should acknowledge the limitations of the field and try to overcome these. Only then, we will be able to cross the translational gap towards clinical readiness. Future research should focus on standardisation of feature selection, model building, and ideally a tool that implements these aspects. In such a way, radiomics may redeem the promise of bringing forth imaging biomarkers that contribute to precision medicine.<br/

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

EMT Network-based Lung Cancer Prognosis Prediction

Network-based feature selection methods on omics data have been developed in recent years. Their performance gain, however, is shown to be affected by the datasets, networks, and evaluation metrics. The reproducibility and robustness of biomarkers await to be improved. In this endeavor, one of the major challenges is the curse of dimensionality. To mitigate this issue, we proposed the Phenotype Relevant Network-based Feature Selection (PRNFS) framework. By employing a much smaller but phenotype relevant network, we could avoid irrelevant information and select robust molecular signatures. The advantages of PRNFS were demonstrated with the application of lung cancer prognosis prediction. Specifically, we constructed epithelial mesenchymal transition (EMT) networks and employed them for feature selection. We mapped multiple types of omics data on it alternatively to select single-omics signatures and further integrated them into multi-omics signatures. Then we introduced a multiplex network-based feature selection method to directly select multi-omics signatures. Both single-omics and multi-omics EMT signatures were evaluated on TCGA data as well as an independent multi-omics dataset. The results showed that EMT signatures achieved significant performance gain, although EMT networks covered less than 2.5% of the original data dimensions. Frequently selected EMT features achieved average AUC values of 0.83 on TCGA data. Employing EMT signatures on the independent dataset stratified the patients into significantly different prognostic groups. Multi-omics features showed superior performance over single-omics features on both TCGA data and the independent data. Additionally, we tested the performance of a few relational and non-relational databases for storing and retrieving omics data. Since biological data have large volume, high velocity, and wide varieties, it is necessary to have database systems that meet the need of integrative omics data analysis. Based on the results, we provided a few advices on building scalable omics data infrastructures

LITERATURE MINING SUSTAINS AND ENHANCES KNOWLEDGE DISCOVERY FROM OMIC STUDIES

Genomic, proteomic and other experimentally generated data from studies of biological systems aiming to discover disease biomarkers are currently analyzed without sufficient supporting evidence from the literature due to complexities associated with automated processing. Extracting prior knowledge about markers associated with biological sample types and disease states from the literature is tedious, and little research has been performed to understand how to use this knowledge to inform the generation of classification models from ‘omic’ data. Using pathway analysis methods to better understand the underlying biology of complex diseases such as breast and lung cancers is state-of-the-art. However, the problem of how to combine literature-mining evidence with pathway analysis evidence is an open problem in biomedical informatics research. This dissertation presents a novel semi-automated framework, named Knowledge Enhanced Data Analysis (KEDA), which incorporates the following components: 1) literature mining of text; 2) classification modeling; and 3) pathway analysis. This framework aids researchers in assigning literature-mining-based prior knowledge values to genes and proteins associated with disease biology. It incorporates prior knowledge into the modeling of experimental datasets, enriching the development process with current findings from the scientific community. New knowledge is presented in the form of lists of known disease-specific biomarkers and their accompanying scores obtained through literature mining of millions of lung and breast cancer abstracts. These scores can subsequently be used as prior knowledge values in Bayesian modeling and pathway analysis. Ranked, newly discovered biomarker-disease-biofluid relationships which identify biomarker specificity across biofluids are presented. A novel method of identifying biomarker relationships is discussed that examines the attributes from the best-performing models. Pathway analysis results from the addition of prior information, ultimately lead to more robust evidence for pathway involvement in diseases of interest based on statistically significant standard measures of impact factor and p-values. The outcome of implementing the KEDA framework is enhanced modeling and pathway analysis findings. Enhanced knowledge discovery analysis leads to new disease-specific entities and relationships that otherwise would not have been identified. Increased disease understanding, as well as identification of biomarkers for disease diagnosis, treatment, or therapy targets should ultimately lead to validation and clinical implementation

Modeling and prediction of advanced prostate cancer

Background: Prostate cancer (PCa) is the most commonly diagnosed cancer and second leading cause of cancer-related deaths for men in Western countries. The advanced form of the disease is life-threatening with few options for curative therapies. The development of novel therapeutic alternatives would greatly benefit from a more comprehensive and tailored mathematical and statistical methodology. In particular, statistical inference of treatment effects and the prediction of time-dependent effects in both preclinical and clinical studies remains a challenging yet interesting opportunity for applied mathematicians. Such methods are likely to improve the reproducibility and translatability of results and offer possibility for novel holistic insights into disease progression, diagnosis, and prognosis. Methods: Several novel statistical and mathematical techniques were developed over the course of this thesis work for the in vivo modeling of PCa treatment responses. A matching-based, blinded randomized allocation procedure for preclinical experiments was developed that provides assistance for the statistical design of animal intervention studies, e.g., through power analysis and accounting for the stratification of individuals. For the post-intervention testing of treatment effects, two novel mixed-effects models were developed that aim to address the characteristic challenges of preclinical longitudinal experiments, including the heterogeneous response profiles observed in animal studies. Subsequently, a Finnish clinical PCa hospital registry cohort was inspected with a strong emphasis on prostate-specific antigen (PSA), the most commonly used PCa marker. After exploring the PSA trends using penalized splines, a generalized mixed-effects prediction model was implemented with a focus on the ultra-sensitive range of the PSA assay. Finally, for metastatic, aggressive PCa, an ensemble Cox regression methodology was developed for overall survival prediction in the DREAM 9.5 mCRPC Challenge based on open datasets from controlled clinical trials. Results: The advantages of the improved experimental design and two proposed statistical models were demonstrated in terms of both increased statistical power and accuracy in simulated and real preclinical testing settings. Penalized regression models applied to the clinical patient datasets support the use of PSA in the ultra-sensitive range together with a model for relapse prediction. Furthermore, the novel ensemble-based Cox regression model that was developed for the overall survival prediction in advanced PCa outperformed the state-of-the-art benchmark and all other models submitted to the Challenge and provided novel predictors of disease progression and treatment responses. Conclusions: The methods and results provide preclinical researchers and clinicians with novel tools for comprehensive modeling and prediction of PCa. All methodology is available as open source R statistical software packages and/or web-based graphical user interfaces

Bioinformatics and Machine Learning for Cancer Biology

Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer

Stereotactic MRI-guided Adaptive Radiation Therapy for Non-metastatic Pancreatic Cancer; Outcomes and Toxicity Analysis for Patients Treated in an Underserved Urban Center

Background: Stereotactic MRI-guided Adaptive Radiation Therapy (SMART) is an emerging technology for treatment of pancreatic cancer patients. Initial results show favorable survival and toxicity. However, data is still sparse overall, and especially in underserved patient populations. The purpose of this study is to review SMART outcomes at our underserved urban academic cancer. Objectives: Stereotactic MRI-guided Adaptive Radiation Therapy (SMART) is an emerging technology for treatment of pancreatic cancer patients. Initial results show favorable survival and toxicity. However, data is still sparse overall, and especially in underserved patient populations. The purpose of this study is to review SMART outcomes at our underserved urban academic cancer. Methods: In this IRB approved retrospective chart review we reviewed 98 patients with non-metastatic pancreatic cancer, who completed SMART between November 2018-January 2021. All 98 patients were treated with 50 Gy in 5 daily fractions with adaptive technique as deemed appropriate by treating radiation oncologist. The primary endpoints were overall survival (OS), progression free survival (PFS), and both acute and late grade 3+ GI toxicity. OS, PFS, locoregional control and distant control were estimated by Kaplan-Meier method and compared using log-rank test. The effect of clinical features on OS was assessed using univariate and multivariate Cox proportional hazard models. OS and PFS were calculated from completion of radiation. Grade 3+ GI toxicity probably or definitively related to radiation was recorded. All incidences of GI bleeding, regardless of attribution, were also recorded. Results: Median follow up was 20.9 months from time of diagnosis and 14 months from radiation. 21 (21%) patients were borderline resectable, 42 (43%) locally advanced, 22 (22%) medically inoperable and 13 (13%) resectable. Neoadjuvant chemotherapy was given to 86 (88%) patients with a median of 3.5 months of chemotherapy (range 1-12), leaving 11 (12%) patients who did not have systemic chemotherapy. Median overall survival from radiation for the whole group was 15.7 months, and 1-year OS was 58%. There was a statistically significant worsening of overall survival from diagnosis between ECOG 2+ and ECOG 0/1 patients (HR 1.94, 1.05-3.57). 27 (27%) patients went on to have surgical resection with 23 (82%) having R0 resection, and 3 (11%) have an R1 resection. Improved OS was seen in patients with surgical resection (HR 0.06, 0.02-0.23). Acute grade 3+ GI toxicity from radiation was seen in 4 (4%) patients and late toxicity from radiation was seen in 6 (6%) patients. GI bleeding was seen in 16(16%) patients, 10 (62%) of which were on anticoagulation at the time of GI bleed and 5 (19%) of which had surgery. Portal vein complications occurred with 7 (7%) having portal vein thrombosis and 6 (6%) portal vein stenosis. Conclusions: SMART showed durable responses in pancreatic cancer patients with an acceptable toxicity profile. Attention needs to be paid to the moderate incident of GI bleeding, however further work is necessary to determine if bleeding was due to radiation, surgery, or disease progression. Surgical resection as well as performance status of ECOG 0-1 were associated with improved overall survival. Further follow up will be necessary to determine further durability of treatment response and long-term survival in these patients

Outcomes of MR-guided Stereotactic Body Radiotherapy (SBRT) or yttrium-90 Transarterial Radioembolization for Hepatocellular Carcinoma Treated at an Urban Liver Transplant Center

Background: There are overlapping indications for both stereotactic body radiotherapy (SBRT) and yttrium-90 (Y90) trans-arterial radioembolization as locoregional treatments for hepatocellular cancer, though most centers preferentially use one modality over the other. MR-guided radiation allows both effective on-table localization and integrated motion management as compared with many traditional linear accelerators, allowing SBRT to be done more easily. Y90 radioembolization has been a well-established modality to deliver highly conformal dose due to the localization of the microspheres to the vascular supply of a tumor. We looked at patient characteristics and treatment outcomes for patients receiving MR-guided SBRT or Y90 at an urban transplant center. Objectives: To compare patient characteristics and treatment outcomes of MR-guided SBRT with Y90 transarterial radioembolization in a liver transplant center. Methods: This retrospective single-institution study analyzed patients with HCC treated with SBRT or Y90 from August 2017 to September 2020. To select a patient population eligible for either treatment modality, any Y90 procedures for lesions \u3e 10 cm or for treatment volumes \u3e 1000 cc were omitted from the cohort. A total of 239 patients were included in the analysis, receiving a total of 98 courses of SBRT and 187 courses of Y90 treatment. Local control (LC), freedom from liver progression (FFLP), and overall survival (OS) rates were measured from treatment completion date to death date or last follow-up. All outcomes were censored at time of loss to follow-up; LC and FFLP were censored at time of liver transplant if applicable. Cox regression models were used for survival, with significant factors on the univariate analysis further analyzed with a multivariate model. Results: Median time to follow-up was 11 months (0-44 mo). The mean size of lesions treated with SBRT were smaller than those treated with Y90 (2.7 cm vs 4.3 cm, P\u3c0.01). The groups of patients differed in liver disease characteristics, with SBRT patients having fewer Child-Pugh A disease (62% vs 80%, P\u3c0.01), more having received locoregional treatments to the liver in the past (81% v 35%, P\u3c0.01), and more disease in previously treated liver (57% vs 25%, P\u3c0.01). Dose of radiation for SBRT was 45-50 Gy administered in 5 fractions; dose of Y90 radiation to tumor was prescribed to a median of 235.2 Gy (range 55.8-512.3 Gy). There was a higher rate of one year LC in the SBRT cohort (77% vs 57%, P\u3c0.01), while median FFLP (9 mo vs 8 mo, P=NS) and median OS were not significantly different (24 mo vs 21 mo, P=NS). Multivariate analysis revealed size of largest lesion (P\u3c0.01) was correlated with decreased local control; a 1 cm increase in tumor size was associated with a 25% increased risk of local failure. Subsequent transplant (P\u3c0.01) was the remaining significant factor. Treatment modality did not remain an independent predictor of LC. Predictors of OS in multivariate analysis included age (P=0.01), prior liver treatments (HR 2.86, P\u3c0.01), size of largest lesion (P\u3c0.01), Child-Pugh stage (P\u3c0.01), portal vein thrombosis (HR 1.6, P=0.04), and subsequent liver transplant (HR 0.08, P\u3c0.01). Conclusions: These findings support the effectiveness of both MR-guided SBRT and Y90 transarterial radioembolization in locoregional management of HCC at a single institution despite clear differences in the patient cohorts. Though survival outcomes were comparable, local control differences favored the cohort treated by SBRT, in large part due to differences in tumor size. This data supports further investigation in a randomized study between SBRT and Y90