Quantitative neuroimaging with handcrafted and deep radiomics in neurological diseases

The motivation behind this thesis is to explore the potential of "radiomics" in the field of neurology, where early diagnosis and accurate treatment selection are crucial for improving patient outcomes. Neurological diseases are a major cause of disability and death globally, and there is a pressing need for reliable imaging biomarkers to aid in disease detection and monitoring. While radiomics has shown promising results in oncology, its application in neurology remains relatively unexplored. Therefore, this work aims to investigate the feasibility and challenges of implementing radiomics in the neurological context, addressing various limitations and proposing potential solutions. The thesis begins with a demonstration of the predictive power of radiomics for identifying important diagnostic biomarkers in neuro-oncology. Building on this foundation, the research then delves into radiomics in non-oncological neurology, providing an overview of the pipeline steps, potential clinical applications, and existing challenges. Despite promising results in proof-of-concept studies, the field faces limitations, mostly data-related, such as small sample sizes, retrospective nature, and lack of external validation. To explore the predictive power of radiomics in non-oncological tasks, a radiomics approach was implemented to distinguish between multiple sclerosis patients and normal controls. Notably, radiomic features extracted from normal-appearing white matter were found to contain distinctive information for multiple sclerosis detection, confirming the hypothesis of the thesis. To overcome the data harmonization challenge, in this work quantitative mapping of the brain was used. Unlike traditional imaging methods, quantitative mapping involves measuring the physical properties of brain tissues, providing a more standardized and consistent data representation. By reconstructing the physical properties of each voxel based on multi-echo MRI acquisition, quantitative mapping produces data that is less susceptible to domain-specific biases and scanner variability. Additionally, the insights gained from quantitative mapping are building the bridge toward the physical and biological properties of brain tissues, providing a deeper understanding of the underlying pathology. Another crucial challenge in radiomics is robust and fast data labeling, particularly segmentation. A deep learning method was proposed to perform automated carotid artery segmentation in stroke at-risk patients, surpassing current state-of-the-art approaches. This novel method showcases the potential of automated segmentation to enhance radiomics pipeline implementation. In addition to addressing specific challenges, the thesis also proposes a community-driven open-source toolbox for radiomics, aimed at enhancing pipeline standardization and transparency. This software package would facilitate data curation and exploratory analysis, fostering collaboration and reproducibility in radiomics research. Through an in-depth exploration of radiomics in neuroimaging, this thesis demonstrates its potential to enhance neurological disease diagnosis and monitoring. By uncovering valuable information from seemingly normal brain tissues, radiomics holds promise for early disease detection. Furthermore, the development of innovative tools and methods, including deep learning and quantitative mapping, has the potential to address data labeling and harmonization challenges. Looking to the future, embracing larger, diverse datasets and longitudinal studies will further enhance the generalizability and predictive power of radiomics in neurology. By addressing the challenges identified in this thesis and fostering collaboration within the research community, radiomics can advance toward clinical implementation, revolutionizing precision medicine in neurology

Precision-medicine-toolbox: An open-source python package for facilitation of quantitative medical imaging and radiomics analysis

[en] Medical image analysis plays a key role in precision medicine as it allows the clinicians to identify anatomical abnormalities and it is routinely used in clinical assessment. Data curation and pre-processing of medical images are critical steps in the quantitative medical image analysis that can have a significant impact on the resulting model performance. In this paper, we introduce a precision-medicine-toolbox that allows researchers to perform data curation, image pre-processing and handcrafted radiomics extraction (via Pyradiomics) and feature exploration tasks with Python. With this open-source solution, we aim to address the data preparation and exploration problem, bridge the gap between the currently existing packages, and improve the reproducibility of quantitative medical imaging research

Automated detection and segmentation of non-small cell lung cancer computed tomography images.

peer reviewedDetection and segmentation of abnormalities on medical images is highly important for patient management including diagnosis, radiotherapy, response evaluation, as well as for quantitative image research. We present a fully automated pipeline for the detection and volumetric segmentation of non-small cell lung cancer (NSCLC) developed and validated on 1328 thoracic CT scans from 8 institutions. Along with quantitative performance detailed by image slice thickness, tumor size, image interpretation difficulty, and tumor location, we report an in-silico prospective clinical trial, where we show that the proposed method is faster and more reproducible compared to the experts. Moreover, we demonstrate that on average, radiologists & radiation oncologists preferred automatic segmentations in 56% of the cases. Additionally, we evaluate the prognostic power of the automatic contours by applying RECIST criteria and measuring the tumor volumes. Segmentations by our method stratified patients into low and high survival groups with higher significance compared to those methods based on manual contours

Development and external validation of a non-invasive molecular status predictor of chromosome 1p/19q co-deletion based on MRI radiomics analysis of Low Grade Glioma patients

Purpose The 1p/19q co-deletion status has been demonstrated to be a prognostic biomarker in lower grade glioma (LGG). The objective of this study was to build a magnetic resonance (MRI)-derived radiomics model to predict the 1p/19q co-deletion status. Method 209 pathology-confirmed LGG patients from 2 different datasets from The Cancer Imaging Archive were retrospectively reviewed; one dataset with 159 patients as the training and discovery dataset and the other one with 50 patients as validation dataset. Radiomics features were extracted from T2- and T1-weighted post-contrast MRI resampled data using linear and cubic interpolation methods. For each of the voxel resampling methods a three-step approach was used for feature selection and a random forest (RF) classifier was trained on the training dataset. Model performance was evaluated on training and validation datasets and clinical utility indexes (CUIs) were computed. The distributions and intercorrelation for selected features were analyzed. Results Seven radiomics features were selected from the cubic interpolated features and five from the linear interpolated features on the training dataset. The RF classifier showed similar performance for cubic and linear interpolation methods in the training dataset with accuracies of 0.81 (0.75−0.86) and 0.76 (0.71−0.82) respectively; in the validation dataset the accuracy dropped to 0.72 (0.6−0.82) using cubic interpolation and 0.72 (0.6−0.84) using linear resampling. CUIs showed the model achieved satisfactory negative values (0.605 using cubic interpolation and 0.569 for linear interpolation). Conclusions MRI has the potential for predicting the 1p/19q status in LGGs. Both cubic and linear interpolation methods showed similar performance in external validation

Precision-medicine-toolbox:An open-source python package for the quantitative medical image analysis

peer reviewedMedical image analysis plays a key role in precision medicine. Data curation and pre-processing are critical steps in quantitative medical image analysis that can have a significant impact on the resulting performance of machine learning models. In this work, we introduce the Precision-medicine-toolbox, allowing clinical and junior researchers to perform data curation, image pre-processing, radiomics extraction, and feature exploration tasks with a customizable Python package. With this open-source tool, we aim to facilitate the crucial data preparation and exploration steps, bridge the gap between the currently existing packages, and improve the reproducibility of quantitative medical imaging research

Open Source Repository and Online Calculator of Prediction Models for Diagnosis and Prognosis in Oncology

(1) Background: The main aim was to develop a prototype application that would serve as an open-source repository for a curated subset of predictive and prognostic models regarding oncology, and provide a user-friendly interface for the included models to allow online calculation. The focus of the application is on providing physicians and health professionals with patient-specific information regarding treatment plans, survival rates, and side effects for different expected treatments. (2) Methods: The primarily used models were the ones developed by our research group in the past. This selection was completed by a number of models, addressing the same cancer types but focusing on other outcomes that were selected based on a literature search in PubMed and Medline databases. All selected models were publicly available and had been validated TRIPOD (Transparent Reporting of studies on prediction models for Individual Prognosis Or Diagnosis) type 3 or 2b. (3) Results: The open source repository currently incorporates 18 models from different research groups, evaluated on datasets from different countries. Model types included logistic regression, Cox regression, and recursive partition analysis (decision trees). (4) Conclusions: An application was developed to enable physicians to complement their clinical judgment with user-friendly patient-specific predictions using models that have received internal/external validation. Additionally, this platform enables researchers to display their work, enhancing the use and exposure of their models

UR-CarA-Net: A Cascaded Framework with Uncertainty Regularization for Automated Segmentation of Carotid Arteries on Black Blood MR Images

We present a fully automated method for carotid artery (CA) outer wall segmentation in black blood MRI using partially annotated data and compare it to the state-of-the-art reference model. Our model was trained and tested on multicentric data of patients (106 and 23 patients, respectively) with a carotid plaque and was validated on different MR sequences (24 patients) as well as data that were acquired with MRI systems of a different vendor (34 patients). A 3D nnU-Net was trained on pre-contrast T1w turbo spin echo (TSE) MR images. A CA centerline sliding window approach was chosen to refine the nnU-Net segmentation using an additionally trained 2D U-Net to increase agreement with manual annotations. To improve segmentation performance in areas with semantically and visually challenging voxels, Monte-Carlo dropout was used. To increase generalizability, data were augmented with intensity transformations. Our method achieves state-of-the-art results yielding a Dice similarity coefficient (DSC) of 91.7% (interquartile range (IQR) 3.3%) and volumetric intraclass correlation (ICC) with ground truth of 0.90 on the development domain data and a DSC of 91.1% (IQR 7.2%) and volumetric ICC with ground truth of 0.83 on the external domain data outperforming top-ranked methods for open-source CA segmentation. The uncertainty-based approach increases the interpretability of the proposed method by providing an uncertainty map together with the segmentation

Multiparametric MRI as a Predictor of PSA Response in Patients Undergoing Stereotactic Body Radiation Therapy for Prostate Cancer

Purpose: To maximize the therapeutic ratio, it is important to identify adverse prognostic features in men with prostate cancer, especially among those with intermediate risk disease, which represents a heterogeneous group. These men may benefit from treatment intensification. Prior studies have shown pretreatment mpMRI may predict biochemical failure in patients with intermediate and/or high-risk prostate cancer undergoing conventionally fractionated external beam radiation therapy and/or brachytherapy. This study aims to evaluate pretreatment mpMRI findings as a marker for outcome in patients undergoing stereotactic body radiation therapy (SBRT). Methods and Materials: We identified all patients treated at our institution with linear accelerator based SBRT to 3625 cGy in 5 fractions, with or without androgen deprivation therapy (ADT) from November 2015 to March 2021. All patients underwent pretreatment Magnetic Resonance Imaging (MRI). Posttreatment Prostate Specific Imaging (PSA) measurements were typically obtained 4 months after SBRT, followed by every 3 to 6 months thereafter. A 2 sample t test was used to compare preoperative mpMRI features with clinical outcomes. Results: One hundred twenty-three men were included in the study. Pretreatment MRI variables including median diameter of the largest intraprostatic lesion, median number of prostate lesions, and median maximal PI-RADS score, were each predictive of PSA nadir and time to PSA nadir (P 0.30). With a median follow-up time of 15.9 months (IQR: 8.5-23.3), only 3 patients (2.4%) experienced biochemical recurrence as defined by the Phoenix criteria. Conclusions: Our experience shows the significant ability of mpMRI for predicting PSA outcome in prostate cancer patients treated with SBRT with or without ADT. Since PSA nadir has been shown to correlate with biochemical failure, this information may help radiation oncologists better counsel their patients regarding outcome after SBRT and can help inform future studies regarding who may benefit from treatment intensification with, for example, ADT and/or boosts to dominant intraprostatic lesions

Scorecards: Quantifying Dosimetric Plan Quality in Pancreatic Ductal Adenocarcinoma Stereotactic Body Radiation Therapy

Purpose: A scoring mechanism called the scorecard that objectively quantifies the dosimetric plan quality of pancreas stereotactic body radiation therapy treatment plans is introduced. Methods and Materials: A retrospective analysis of patients with pancreatic ductal adenocarcinoma receiving stereotactic body radiation therapy at our institution between November 2019 and November 2020 was performed. Ten patients were identified. All patients were treated to 36 Gy in 5 fractions, and organs at risk (OARs) were constrained based on Alliance A021501. The scorecard awarded points for OAR doses lower than those cited in Alliance A021501. A team of 3 treatment planners and 2 radiation oncologists, including a physician resident without plan optimization experience, discussed the relative importance of the goals of the treatment plan and added additional metrics for OARs and plan quality indexes to create a more rigorous scoring mechanism. The scorecard for this study consisted of 42 metrics, each with a unique piecewise linear scoring function which is summed to calculate the total score (maximum possible score of 365). The scorecard-guided plan, the planning and optimization for which were done exclusively by the physician resident with no prior plan optimization experience, was compared with the clinical plan, the planning and optimization for which were done by expert dosimetrists, using the Sign test. Results: Scorecard-guided plans had, on average, higher total scores than those clinically delivered for each patient, averaging 280.1 for plans clinically delivered and 311.7 for plans made using the scorecard (P = .003). Additionally, for most metrics, the average score of each metric across all 10 patients was higher for scorecard-guided plans than for clinically delivered plans. The scorecard guided the planner toward higher coverage, conformality, and OAR sparing. Conclusions: A scorecard tool can help clarify the goals of a treatment plan and provide an objective method for comparing the results of different plans. Our study suggests that a completely novice treatment planner can use a scorecard to create treatment plans with enhanced coverage, conformality, and improved OAR sparing, which may have significant effects on both tumor control and toxicity. These tools, including the scorecard used in this study, have been made freely available