MI-Common Data Model: Extending Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) for Registering Medical Imaging Metadata and Subsequent Curation Processes.

PURPOSE: The explosion of big data and artificial intelligence has rapidly increased the need for integrated, homogenized, and harmonized health data. Many common data models (CDMs) and standard vocabularies have appeared in an attempt to offer harmonized access to the available information, with Observational Medical Outcomes Partnership (OMOP)-CDM being one of the most prominent ones, allowing the standardization and harmonization of health care information. However, despite its flexibility, still capturing imaging metadata along with the corresponding clinical data continues to pose a challenge. This challenge arises from the absence of a comprehensive standard representation for image-related information and subsequent image curation processes and their interlinkage with the respective clinical information. Successful resolution of this challenge holds the potential to enable imaging and clinical data to become harmonized, quality-checked, annotated, and ready to be used in conjunction, in the development of artificial intelligence models and other data-dependent use cases. METHODS: To address this challenge, we introduce medical imaging (MI)-CDM-an extension of the OMOP-CDM specifically designed for registering medical imaging data and curation-related processes. Our modeling choices were the result of iterative numerous discussions among clinical and AI experts to enable the integration of imaging and clinical data in the context of the ProCAncer-I project, for answering a set of clinical questions across the prostate cancer's continuum. RESULTS: Our MI-CDM extension has been successfully implemented for the use case of prostate cancer for integrating imaging and curation metadata along with clinical information by using the OMOP-CDM and its oncology extension. CONCLUSION: By using our proposed terminologies and standardized attributes, we demonstrate how diverse imaging modalities can be seamlessly integrated in the future.

Rheumatoid arthritis patients on persistent moderate disease activity on biologics have adverse 5-year outcome compared to persistent low-remission status and represent a heterogeneous group.

BACKGROUND: The long-term outcome of rheumatoid arthritis (RA) patients who in clinical practice exhibit persistent moderate disease activity (pMDA) despite treatment with biologics has not been adequately studied. Herein, we analyzed the 5-year outcome of the pMDA group and assessed for within-group heterogeneity. METHODS: We included longitudinally monitored RA patients from the Hellenic Registry of Biologic Therapies with persistent (cumulative time ≥ 50% of a 5-year period) moderate (pMDA, 3.2 < DAS28 ≤ 5.1) or remission/low (pRLDA, DAS28 ≤ 3.2) disease activity. The former was further classified into persistent lower-moderate (plMDA, DAS28 < 4.2) and higher-moderate (phMDA, DAS28 ≥ 4.2) subgroups. Five-year trajectories of functionality (HAQ) were the primary outcome in comparing pRLDA versus pMDA and assessing heterogeneity within the pMDA subgroups through multivariable mixed-effect regression. We further compared serious adverse events (SAEs) occurrence between the two groups. RESULTS: We identified 295 patients with pMDA and 90 patients with pRLDA, the former group comprising of plMDA (n = 133, 45%) and phMDA (n = 162, 55%). pMDA was associated with worse 5-year functionality trajectory than pRLDA (+ 0.27 HAQ units, CI 95% + 0.22 to + 0.33; p < 0.0001), while the phMDA subgroup had worse 5-year functionality than plMDA (+ 0.26 HAQ units, CI 95% 0.18 to 0.36; p < 0.0001). Importantly, higher persistent disease activity was associated with more SAEs [pRLDA: 0.2 ± 0.48 vs pMDA: 0.5 ± 0.96, p = 0.006; plMDA: 0.32 ± 0.6 vs phMDA: 0.64 ± 1.16, p = 0.038]. Male gender (p = 0.017), lower baseline DAS28 (p < 0.001), HAQ improvement > 0.22 (p = 0.029), and lower average DAS28 during the first trimester since treatment initiation (p = 0.001) independently predicted grouping into pRLDA. CONCLUSIONS: In clinical practice, RA patients with pMDA while on bDMARDs have adverse long-term outcomes compared to lower disease activity status, while heterogeneity exists within the pMDA group in terms of 5-year functionality and SAEs. Targeted studies to better characterize pMDA subgroups are needed, in order to assist clinicians in tailoring treatments.

Apparent diffusion coefficient measurements on a novel diffusion weighted MRI phantom utilizing EPI and HASTE sequences.

PURPOSE: The aim of this study is to introduce a novel DWI-MRI phantom and to compare Apparent Diffusion Coefficient (ADC) measurements, utilizing EPI-DWI and HASTE-DWI sequences and two different fitting algorithms. MATERIALS AND METHODS: 23 test tubes with different sucrose concentrations and polyacrylamide gels were used as a phantom for ADC measurements. The phantom was scanned on a clinical MRI system (1.5 T) over a two-month period utilizing an EPI-DWI and a HASTE-DWI sequence. ADC maps were calculated using a Weighted Linear (WL) and a Non Linear (NL) fitting algorithm. Measurements were performed with two sequences and two fitting algorithms. Geometric Distortions (GD), Ghosting Ratios (GR) and Signal to Structured Noise Ratios (SSNRs) were estimated using both sequences from the resultant ADC parametric maps. RESULTS: Polyacrylamide gels reveal lower coefficient of variation (CV%) as compared to sucrose solutions. ADC measurements performed with WL and NL algorithms reveal identical results with both sequences. WL and NL algorithms require approx. 3 s and 7 min respectively, for a single slice. EPI-DWI reveals a mean percent ADC value difference of (+4.5%) as compared to HASTE-DWI, regardless the type of fitting algorithm. CONCLUSION: Polyacrylamide gels can serve as a better means for simulating ADC values, compared with sucrose solutions used in this study. WL can be proposed as the method for ADC measurements in daily clinical practice. WL is significantly faster than NL fitting method and equally precise. SSNR measured directly on ADC maps is an excellent means for testing the precision of ADC measurements.

Exploring the competition between proliferative and invasive cancer phenotypes in a continuous spatial model.

Tumor is characterized by extensive heterogeneity with respect to its microenvironment and its genetic composition. We extend a previously developed monoclonal continuous spatial model of tumor growth to account for polyclonal cell populations and investigate the interplay between a more proliferative and a more invasive phenotype under different conditions. The model simulations demonstrate a transition from the dominance of the proliferative to the dominance of the invasive phenotype resembling malignant tumor progression and show a time period where both subpopulations are abundant. As the dominant phenotype switches from proliferative to invasive, the geometry of tumor changes from a compact and almost spherical shape to a more diffusive and fingered morphology with the proliferative phenotype to be restricted in the tumor bulk and the invasive to dominate at tumor edges. Different micro-environmental conditions and different phenotypic properties can promote or inhibit invasion demonstrating their mutual importance. The model provides a computational framework to investigate tumor heterogeneity and the constant interplay between the environment and the specific characteristics of phenotypes that should be taken into account for the prediction of tumor evolution, morphology and effective treatment.

Dealing with diversity in computational cancer modeling.

This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology.

Evaluation framework for the multilevel macroscopic models of solid tumor growth in the glioma case.

This paper investigates the applicability of multilevel macroscopic models for simulating solid tumor growth in the invasive glioblastoma multiforme (GBM) case. The continuum case approach tumor model based on the diffusion reaction equation is evaluated on a pre-segmented tomographic atlas where all tissue properties are known a priori. The atlas is further registered on a real clinical case where the tumor invasion status is gauged in two successive points in time. Based on the latter, the model attempts to fully replicate tumor growth taking into account tissue based properties as identified from the atlas template. The whole process is performed on a clinical platform specially designed to facilitate precise identification and delineation of tumors of large number of 3D tomographic datasets by an expert clinician. The promising results presented encourage the potential clinical applicability of the proposed model in the glioma case and identify crucial points and direction of further model refinement and research.

Spatially adaptive active contours: a semi-automatic tumor segmentation framework.

PURPOSE: Tumor segmentation constitutes a crucial step in simulating cancer growth and response to therapy. Incorporation of imaging data individualizes the simulation and assists clinical correlation with the predicted outcome. We adapted snakes to improve tumor segmentation including difficult cases with inherently inhomogeneous structure and poorly defined margins. METHODS: Snakes are flexible curves, based on the parameter-controlled deformation of an initial user-defined contour toward the boundary of the desired object, through the minimization of a suitable energy function. Although parameter-adjustment can yield fairly good results in homogeneous regions, traditional snakes often fail to provide an accurate segmentation result when both rigid and very elastic behavior is needed simultaneously to delineate the true outline of the tumor. We developed and tested a spatially adaptive active contour technique by introducing local snake bending, to improve traditional snakes performance for segmenting tumors. The key point of our method is the use of adaptable snake parameters, instead of constant ones, to adjust the bending of the curve according to the local edge characteristics. Our algorithm discriminates image regions according to underlying image features, such as gradient magnitude and corner strength. More specifically, it assigns each region a different "localized" set of parameters, one corresponding to a very flexible snake, and the other corresponding to a very rigid one, according to the local image characteristics. RESULTS: Qualitative results on more than 150 real MR images, as well as quantitative validation based on agreement with an expert clinician's annotations of the true tumor boundaries, demonstrate our approach is highly efficient compared to traditional active contours and region growing. Due to the use of adaptable parameters in the snake evolution process, our approach outperforms the other two methods, and consistently follows an expert's annotations. Statistical tests indicated significant difference between the results produced by our approach and two other algorithms traditional snakes and region growing, while multiple comparison showed that our method consistently outperformed those algorithms, with an average overlap of 89%, over the entire data set, while traditional snakes were at 82.5% and region growing at 59.2%. Furthermore, we performed several tests that demonstrate our method's stability to different initial contours, as well as, to lower resolution images. CONCLUSION: Our adaptive snake algorithm can spatially adapt to diverse image characteristics, producing outlines that mimic the true tumor boundaries. Results in MR datasets are very close to an expert clinician's intuition about the tumor boundaries.

DoctorEye: A clinically driven multifunctional platform, for accurate processing of tumors in medical images.

UNLABELLED: This paper presents a novel, open access interactive platform for 3D medical image analysis, simulation and visualization, focusing in oncology images. The platform was developed through constant interaction and feedback from expert clinicians integrating a thorough analysis of their requirements while having an ultimate goal of assisting in accurately delineating tumors. It allows clinicians not only to work with a large number of 3D tomographic datasets but also to efficiently annotate multiple regions of interest in the same session. Manual and semi-automatic segmentation techniques combined with integrated correction tools assist in the quick and refined delineation of tumors while different users can add different components related to oncology such as tumor growth and simulation algorithms for improving therapy planning. The platform has been tested by different users and over large number of heterogeneous tomographic datasets to ensure stability, usability, extensibility and robustness with promising results. AVAILABILITY: the platform, a manual and tutorial videos are available at: http://biomodeling.ics.forth.gr. it is free to use under the GNU General Public License.

DoctorEye: A multifunctional open platform for fast annotation and visualization of tumors in medical images.

This paper describes a flexible and easy-to-use annotation platform (GUI) for quick and precise identification and delineation of tumors in medical images. The design of the platform is clinically driven in order to ensure that the clinician can efficiently and intuitively annotate large number of 3D tomographic datasets. Both manual and well-known semiautomatic segmentation techniques are available in the platform allowing clinician to annotate multiple regions of interest at the same session. Additionally, it includes contour drawing, refinement and labeling tools that can effectively assist in the delineation of tumors. Furthermore, segmented tumor regions can be annotated, labeled, deleted, added and redefined. The platform has been tested over several MRI datasets to assess usability, extensibility and robustness with promising results.