Organ preservation in rectal cancer:Capita Selecta

The standard operative treatment for patients with rectal cancer has a high risk for morbidity and (perioperative) mortality. Patients with an extensive tumor undergo preoperative (chemo)radiation to reduce the tumor and potential lymph node metastases. In 15-25% of patients the tumor has completely disappeared, also called a 'complete response'. Patients with a complete response may undergo organ preservation as an alternative treatment. This thesis describes several topics regarding organ preservation in rectal cancer. Methods are described to optimize the patient selection. In addition, a new follow-up schedule is proposed which is potentially more efficient and less burdensome for patients. Also, the oncological outcomes of patients after organ preservation are compared to patients after surgery. Finally, this thesis guides future research with an emphasis on exploration of new techniques to enable more accurate response prediction and assessment in rectal cancer

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

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)

The impact of arterial input function determination variations on prostate dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic modeling: a multicenter data analysis challenge, part II

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study

Detection and staging of colonic lesions using computed tomography and magnetic resonance imaging

In Sweden, more than 6000 new patients were diagnosed with colorectal cancer in 2015 of which over 4000 patients had colon cancer and 1800 died from the disease. It is the second most common cancer after breast- and prostate cancer. In the last decade, significant improvement in the treatment of both rectal and colon cancer have been achieved. Diagnostic imaging, using CT, MRI and PET/CT, has become essential in the preoperative work-up. New neoadjuvant treatment strategies are under study in colon cancer. In the selection of patients for these treatments, pre- and post-treatment imaging has also become of interest. The overall aim of this thesis is to evaluate cross sectional imaging modalities for detection of colonic polyps and staging of patients with colon cancer using CT and MRI. The aim of paper 1 was to investigate the impact of radiation dose and spatial resolution in detecting colonic polyps in a phantom study simulating computed tomographic colonography (CTC). By using different scanning protocols with different slice -thickness, pitch and tube current we showed that the dose level could be substantially reduced by lowering the tube current without compromising the detection rate for polyps larger than 5 mm. The aim of paper 2 was to evaluate if high resolution MRI of colon cancer contributed to the standard staging procedure with CT with respect to assessment of local tumour extent, nodal staging and extramural venous invasion (EMVI). An advantage of MRI over CT due to its soft tissue discrimination to identify prognostic factors such as tumour stage and extramural venous invasion was found. The result of nodal staging for both modalities were equally moderate. The aim of paper 3 was to evaluate commonly used imaging CT criteria for lymph node metastases in predicting stage III disease. Of the different imaging criteria, morphological features performed best specifically internal heterogeneity and irregular outer border. None of the size criteria were predictive. The aim of paper 4 was to validate morphological CT criteria from paper 3 in a prospectively collected patient cohort using two observers. By using the criteria internal heterogeneity and a combination including irregular outer border, a moderate sensitivity and high specificity was achieved predicting stage III disease. CT and high resolution MRI can be used to classify colonic tumours into not locally advanced or locally advanced. The prediction of lymph node metastases with the most commonly used image modalities is however unsettled and challenged. In the setting of selecting patients to neoadjuvant chemotherapy, the ongoing trials so far have used inclusion criteria based on tumour T-stage only (T3cd-T4 as locally advanced). Patients with lower tumour T-stage but still have other adverse prognostic feature such as regional metastases will therefore potentially be undertreated and patients with no metastases will potentially be overtreated. Search for other prognostic factors identified on cross sectional imaging has to be performed