Integrating Biological and Radiological Data in a Structured Repository: a Data Model Applied to the COSMOS Case Study

Abstract

Integrating the information coming from biological samples with digital data, such as medical images, has gained prominence with the advent of precision medicine. Research in this field faces an ever-increasing amount of data to manage and, as a consequence, the need to structure these data in a functional and standardized fashion to promote and facilitate cooperation among institutions. Inspired by the Minimum Information About BIobank data Sharing (MIABIS), we propose an extended data model which aims to standardize data collections where both biological and digital samples are involved. In the proposed model, strong emphasis is given to the cause-effect relationships among factors as these are frequently encountered in clinical workflows. To test the data model in a realistic context, we consider the Continuous Observation of SMOking Subjects (COSMOS) dataset as case study, consisting of 10 consecutive years of lung cancer screening and follow-up on more than 5000 subjects. The structure of the COSMOS database, implemented to facilitate the process of data retrieval, is therefore presented along with a description of data that we hope to share in a public repository for lung cancer screening research.

Keywords: Lung cancer screening; Radiology workflow; Standardization; Structured reporting.

Fig. 1

Illustration of the extended MIABIS data model components and relation structure. As per the diagram defined by Eklund et al. (2020), relations between Sample, Sample Donor and Event are maintained, while the Implication and Sub-event components are here introduced. In example A, the reduction of core characteristics of the samples permits generalization of sample types, with the type specific characteristics being deferred to the sub-collections of the biobank. Apart from relying implicitly on the chronology of events, the implication component (Example B) captures the causal relationship between events. Symbols used for connecting components represent the cardinality of the relation; refer to Supplementary Materials (section C) for more details about their meaning

Fig. 2

Simplified Schematic Relational Diagram describing the main components identified in the COSMOS database along with the main relationships among them. Relationships with the Sample Donor are explicitly shown only for the Biological Sample and the DICOM Series Sample, but the same connections exist between Sample Donor and the various Events contained in the diagram. Symbols used for connecting components represent the cardinality of the relation for the case study considered; refer to Supplementary Materials (section C) for more details about their meaning

Fig. 3

Distribution of acquisition and reconstruction parameters among the LDCT DICOM series in the COSMOS dataset. Panel (a) shows distributions of X-ray Tube current and voltage, Reconstruction Convolution Kernel and Slice Thickness for the entire set of LDCT scan collected during the ten years of study. The number of studies versus the number of reconstructed LDCT series; the number of series for standard and lung reconstruction kernels, and the used slice thickness (“2.5” versus “1.25” mm), by the year of the study, are shown in panels b, c, and d, respectively

Fig. 4

Summary of the main radiologic features regarding nodule characteristics annotated in the Pulmonary Nodule Identification Sub-event. On panel (a), the number of documented nodules over the course of the ten years of the COSMOS study is reported, subdividing them in four groups according to Diameter size (mm). Panel (b) shows lesion distribution according to Type – texture related attribute, whereas panel (c) lesion distribution according to Lobe attribute (RUL = Right Upper Lung, RML = Right Middle Lung, RLL = Right Low Lung, LUL = Left Upper Lung, LLL = Left Low Lung)

Fig. 5

The distributions of a) Biological Sampling Events by year. b) sample collection procedure attribute, included in the Biological Sample Event (Table S2), and c) the Pathological result attribute of the Biological Sample Analysis Event (Table S8) for the subjects over the course of the COSMOS study