Distributed radiomics as a signature validation study using the Personal Health Train infrastructure

Shi, Zhenwei; Zhovannik, Ivan; Traverso, Alberto; Dankers, Frank J. W. M.; Deist, Timo M.; Kalendralis, Petros; Monshouwer, R.; Bussink, Johan; Fijten, Rianne; Aerts, Hugo J.W.L.; Dekker, André; Wee, Leonard

doi:10.1038/s41597-019-0241-0

Cited by 53 publications

(65 citation statements)

References 30 publications

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Section: Discussionmentioning

confidence: 93%

“…As has been shown in other publications, the proposed methodology here can be used prospectively for exchanging radiomics prediction models for training or validation, in accordance with a paradigm known as distributed (or equivalently, federated) machine learning. [41][42][43] We have provided examples of SPARQL queries, primarily as a form of guidance notes on how to use this data submission. We would encourage the academic community to adjust them according to their own questions and potentially utilize this methodology for multicenter studies.…”

Section: B Potential Applicationsmentioning

confidence: 99%

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FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections

et al. 2020

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Purpose One of the most frequently cited radiomics investigations showed that features automatically extracted from routine clinical images could be used in prognostic modeling. These images have been made publicly accessible via The Cancer Imaging Archive (TCIA). There have been numerous requests for additional explanatory metadata on the following datasets — RIDER, Interobserver, Lung1, and Head–Neck1. To support repeatability, reproducibility, generalizability, and transparency in radiomics research, we publish the subjects’ clinical data, extracted radiomics features, and digital imaging and communications in medicine (DICOM) headers of these four datasets with descriptive metadata, in order to be more compliant with findable, accessible, interoperable, and reusable (FAIR) data management principles. Acquisition and validation methods Overall survival time intervals were updated using a national citizens registry after internal ethics board approval. Spatial offsets of the primary gross tumor volume (GTV) regions of interest (ROIs) associated with the Lung1 CT series were improved on the TCIA. GTV radiomics features were extracted using the open‐source Ontology‐Guided Radiomics Analysis Workflow (O‐RAW). We reshaped the output of O‐RAW to map features and extraction settings to the latest version of Radiomics Ontology, so as to be consistent with the Image Biomarker Standardization Initiative (IBSI). Digital imaging and communications in medicine metadata was extracted using a research version of Semantic DICOM (SOHARD, GmbH, Fuerth; Germany). Subjects’ clinical data were described with metadata using the Radiation Oncology Ontology. All of the above were published in Resource Descriptor Format (RDF), that is, triples. Example SPARQL queries are shared with the reader to use on the online triples archive, which are intended to illustrate how to exploit this data submission. Data format The accumulated RDF data are publicly accessible through a SPARQL endpoint where the triples are archived. The endpoint is remotely queried through a graph database web application at http://sparql.cancerdata.org. SPARQL queries are intrinsically federated, such that we can efficiently cross‐reference clinical, DICOM, and radiomics data within a single query, while being agnostic to the original data format and coding system. The federated queries work in the same way even if the RDF data were partitioned across multiple servers and dispersed physical locations. Potential applications The public availability of these data resources is intended to support radiomics features replication, repeatability, and reproducibility studies by the academic community. The example SPARQL queries may be freely used and modified by readers depending on their research question. Data interoperability and reusability are supported by referencing existing public ontologies. The RDF data are readily findable and accessible through the aforementioned link. Scripts used to create the RDF are made available at a code repository linked to this submissio...

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Section: Discussionmentioning

confidence: 93%

Section: B Potential Applicationsmentioning

confidence: 99%

FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections

et al. 2020

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“…Security and privacy settings within VLP blocked transfer and exposure of patientlevel records from the validation site to the investigator. Previous studies (21)(22)(23) have proven that the algorithm converges to the same result as if all of the patient data was locally processed on site by an investigator. The workflow of the distributed learning approach is shown in Figure 1.…”

Section: Distributed Learningmentioning

confidence: 90%

External Validation of Radiation-Induced Dyspnea Models on Esophageal Cancer Radiotherapy Patients

et al. 2019

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Purpose: Radiation-induced lung disease (RILD), defined as dyspnea in this study, is a risk for patients receiving high-dose thoracic irradiation. This study is a TRIPOD (Transparent Reporting of A Multivariable Prediction Model for Individual Prognosis or Diagnosis) Type 4 validation of previously-published dyspnea models via secondary analysis of esophageal cancer SCOPE1 trial data. We quantify the predictive performance of these two models for predicting the maximal dyspnea grade ≥ 2 within 6 months after the end of high-dose chemo-radiotherapy for primary esophageal cancer. Materials and methods: We tested the performance of two previously published dyspnea risk models using baseline, treatment and follow-up data on 258 esophageal cancer patients in the UK enrolled into the SCOPE1 multi-center trial. The tested models were developed from lung cancer patients treated at MAASTRO Clinic (The Netherlands) from the period 2002 to 2011. The adverse event of interest was dyspnea ≥ Grade 2 (CTCAE v3) within 6 months after the end of radiotherapy. As some variables were missing randomly and cannot be imputed, 212 patients in the SCOPE1 were used for validation of model 1 and 255 patients were used for validation of model 2. The model parameter Forced Expiratory Volume in 1 s (FEV 1), as a predictor to both validated models, was imputed using the WHO performance status. External validation was performed using an automated, decentralized approach, without exchange of individual patient data. Results: Out of 258 patients with esophageal cancer in SCOPE1 trial data, 38 patients (14.7%) developed radiation-induced dyspnea (≥ Grade 2) within 6 months after chemo-radiotherapy. The discrimination performance of the models in esophageal cancer patients treated with high-dose external beam radiotherapy was moderate, area under curve (AUC) of 0.68 (95% CI 0.55-0.76) and 0.70 (95% CI 0.58-0.77), respectively. The curves and AUCs derived by distributed learning were identical to the results from validation on a local host. Shi et al. Dyspnea Model for EC Patients Conclusion: We have externally validated previously published dyspnea models using an esophageal cancer dataset. FEV 1 that is not routinely measured for esophageal cancer was imputed using WHO performance status. Prediction performance was not statistically different from previous training and validation sets. Risk estimates were dominated by WHO score in Model 1 and baseline dyspnea in Model 2. The distributed learning approach gave the same answer as local processing, and could be performed without accessing a validation site's individual patients-level data.

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“…A recent study [ 58 ] proposed deep RL of marked temporal point processes to characterize actions from agents and feedback from the environment seen as asynchronous stochastic discrete events. This has potential utility in radiotherapy where operations run continuously and induce periodic progress reports, incremental results, or state changes, but also for applications in distributed radiomics (see the multi-center study in [ 59 ] centered on a radiomic signature developed at one site and validated in its performance at another site).…”

Section: Learning Approaches and Significance For Radiomicsmentioning

confidence: 99%

Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

Capobianco

Deng

2020

Cancers

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Processing and modeling medical images have traditionally represented complex tasks requiring multidisciplinary collaboration. The advent of radiomics has assigned a central role to quantitative data analytics targeting medical image features algorithmically extracted from large volumes of images. Apart from the ultimate goal of supporting diagnostic, prognostic, and therapeutic decisions, radiomics is computationally attractive due to specific strengths: scalability, efficiency, and precision. Optimization is achieved by highly sophisticated statistical and machine learning algorithms, but it is especially deep learning that stands out as the leading inference approach. Various types of hybrid learning can be considered when building complex integrative approaches aimed to deliver gains in accuracy for both classification and prediction tasks. This perspective reviews some selected learning methods by focusing on both their significance for radiomics and their unveiled potential.

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Distributed radiomics as a signature validation study using the Personal Health Train infrastructure

Cited by 53 publications

References 30 publications

FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections

FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections

External Validation of Radiation-Induced Dyspnea Models on Esophageal Cancer Radiotherapy Patients

Radiomics at a Glance: A Few Lessons Learned from Learning Approaches

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