Outcome Prediction for SARS-CoV-2 Patients Using Machine Learning Modeling of Clinical, Radiological, and Radiomic Features Derived from Chest CT Images

Featured Application The present study demonstrates that semi-automatic segmentation enables the identification of regions of interest affected by SARS-CoV-2 infection for the extraction of prognostic features from chest CT scans without suffering from the inter-operator variability typical of segmentation, hence offering a valuable and informative second opinion. Machine Learning methods allow identification of the prognostic features potentially reusable for the early detection and management of other similar diseases. (1) Background: Chest Computed Tomography (CT) has been proposed as a non-invasive method for confirming the diagnosis of SARS-CoV-2 patients using radiomic features (RFs) and baseline clinical data. The performance of Machine Learning (ML) methods using RFs derived from semi-automatically segmented lungs in chest CT images was investigated regarding the ability to predict the mortality of SARS-CoV-2 patients. (2) Methods: A total of 179 RFs extracted from 436 chest CT images of SARS-CoV-2 patients, and 8 clinical and 6 radiological variables, were used to train and evaluate three ML methods (Least Absolute Shrinkage and Selection Operator [LASSO] regularized regression, Random Forest Classifier [RFC], and the Fully connected Neural Network [FcNN]) for their ability to predict mortality using the Area Under the Curve (AUC) of Receiver Operator characteristic (ROC) Curves. These three groups of variables were used separately and together as input for constructing and comparing the final performance of ML models. (3) Results: All the ML models using only RFs achieved an informative level regarding predictive ability, outperforming radiological assessment, without however reaching the performance obtained with ML based on clinical variables. The LASSO regularized regression and the FcNN performed equally, both being superior to the RFC. (4) Conclusions: Radiomic features based on semi-automatically segmented CT images and ML approaches can aid in identifying patients with a high risk of mortality, allowing a fast, objective, and generalizable method for improving prognostic assessment by providing a second expert opinion that outperforms human evaluation

Realistic 3D printed imaging tumor phantoms for validation of image processing algorithms

Medical imaging phantoms are widely used for validation and verification of imaging systems and algorithms in surgical guidance and radiation oncology procedures. Especially, for the performance evaluation of new algorithms in the field of medical imaging, manufactured phantoms need to replicate specific properties of the human body, e.g., tissue morphology and radiological properties. Additive manufacturing (AM) technology provides an inexpensive opportunity for accurate anatomical replication with customization capabilities. In this study, we proposed a simple and cheap protocol to manufacture realistic tumor phantoms based on the filament 3D printing technology. Tumor phantoms with both homogenous and heterogenous radiodensity were fabricated. The radiodensity similarity between the printed tumor models and real tumor data from CT images of lung cancer patients was evaluated. Additionally, it was investigated whether a heterogeneity in the 3D printed tumor phantoms as observed in the tumor patient data had an influence on the validation of image registration algorithms. A density range between -217 to 226 HUs was achieved for 3D printed phantoms; this range of radiation attenuation is also observed in the human lung tumor tissue. The resulted HU range could serve as a lookup-table for researchers and phantom manufactures to create realistic CT tumor phantoms with the desired range of radiodensities. The 3D printed tumor phantoms also precisely replicated real lung tumor patient data regarding morphology and could also include life-like heterogeneity of the radiodensity inside the tumor models. An influence of the heterogeneity on accuracy and robustness of the image registration algorithms was not found

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings

FIELDRT: an open-source platform for the assessment of target volume delineation in radiation therapy

Objectives: Target volume delineation (TVD) has been identified as a weakness in the accuracy of radiotherapy, both within and outside of clinical trials due to the intra/interobserver variations affecting the TVD quality. Sources of variations such as poor compliance or protocol violation may have adverse effect on treatment outcomes. In this paper, we present and describe the FIELDRT software developed for the ARENA project to improve the quality of TVD through qualitative and quantitative feedbacks and individual and personalized summary of trainee”s performance. Methods: For each site-specific clinical case included in the FIELDRT software, reference volumes, minimum and maximum “acceptable” volumes and organ at risk were derived by outlines of consultants and senior trainees. The software components currently developed include: (a) user-friendly importing interface (b) analysis toolbox to compute quantitative and qualitative (c) visualiser and (d) structured report generator for personalised feedback. The FIELDRT software was validated by comparing the performance of 63 trainees and by measuring performance over time. In addition, a trainee evaluation day was held in 2019 to collect feedback on FIELDRT. Results: Results show the trainees’ improvement when reoutlining a case after reviewing the feedback generated from the FIELDRT software. Comments and feedback received after evaluation day were positive and confirmed that FIELDRT can be a useful application for training purposes. Conclusion: We presented a new open-source software to support education in TVD and ongoing continuous professional development for clinical oncology trainees and consultants. ARENA in combination with FIELDRT implements site-specific modules with reference target and organs at risk volumes and automatically evaluates individual performance using several quantitative and qualitative feedbacks. Pilot results suggests this software could be used as an education tool to reduce variation in TVD so to guarantee high quality in radiotherapy

From mouse to man and back : closing the correlation gap between imaging and histopathology for lung diseases

Lung diseases such as fibrosis, asthma, cystic fibrosis, infection and cancer are life-threatening conditions that slowly deteriorate quality of life and for which our diagnostic power is high, but our knowledge on etiology and/or effective treatment options still contains important gaps. In the context of day-to-day practice, clinical and preclinical studies, clinicians and basic researchers team up and continuously strive to increase insights into lung disease progression, diagnostic and treatment options. To unravel disease processes and to test novel therapeutic approaches, investigators typically rely on end-stage procedures such as serum analysis, cyto-/chemokine profiles and selective tissue histology from animal models. These techniques are useful but provide only a snapshot of disease processes that are essentially dynamic in time and space. Technology allowing evaluation of live animals repeatedly is indispensable to gain a better insight into the dynamics of lung disease progression and treatment effects. Computed tomography (CT) is a clinical diagnostic imaging technique that can have enormous benefits in a research context too. Yet, the implementation of imaging techniques in laboratories lags behind. In this review we want to showcase the integrated approaches and novel developments in imaging, lung functional testing and pathological techniques that are used to assess, diagnose, quantify and treat lung disease and that may be employed in research on patients and animals. Imaging approaches result in often novel anatomical and functional biomarkers, resulting in many advantages, such as better insight in disease progression and a reduction in the numbers of animals necessary. We here showcase integrated assessment of lung disease with imaging and histopathological technologies, applied to the example of lung fibrosis. Better integration of clinical and preclinical imaging technologies with pathology will ultimately result in improved clinical translation of (therapy) study results

Bronchoscopic lung volume reduction for Emphysema: physiological and radiological correlations

Introduction: Patient selection in lung volume reduction (LVR) plays a pivotal role in achieving meaningful clinical outcomes. Currently, LVR patients are selected based on three established criteria: heterogeneity index, percentage of low attenuation area (%LAA), and fissure integrity score. Quantitative computed tomography (QCT) has been developed to quantify lung physiological indices at the lobar level and could potentially revolutionise patient selection in LVR procedures. We developed an in-house QCT software, LungSeg, and used its radiological indices for the purposes of this thesis. The aim of this thesis is to discover potential physiological and radiological indices that could serve as predictors for superior LVR outcomes for better patient selection. Methods: This thesis took two studies and analysed them using LungSeg. The first study was the long-term coil study, a randomised controlled study that had the control group crossing over to the treatment arm at 12 months. At 12 months post-procedure the baseline measurements were assessed against the 12-months post-procedural measurements. The second study was the short-term valve study which was another randomised controlled study that compared the primary and secondary endpoints between the control and the valve-treated group at three months post-procedure. Results: In the long-term coil study, we found that the best statistically significant combination of predictors for change in target lobar volume at inspiration was found to be the combination of baseline target LV at inspiration, -950HU EI at inspiration, and TLCabs with a model adjusted R2 of 0.407 (p = 0.0001). In a subsequent multivariate analysis using ≥45% LAA on the -950HU at Inspiration, the R2 of the same prediction model did improve to 0.493 (P-value = 0.002). Meanwhile, the best statistically significant combination of predictors for change in target lobar volume at inspiration following valve treatment was found to be the combination of baseline target LV at inspiration, target lobar fissure integrity and baseline FEV1abs with a model adjusted R2 of 0.193 (p = 0.105). Conclusion: Using QCT, we have improved the proposed patient selection algorithm for LVR procedures based on the best QCT and lung function predictors.Open Acces