Impact of radiogenomics in esophageal cancer on clinical outcomes: A pilot study

Brancato, Valentina; Garbino, Nunzia; Mannelli, Lorenzo; Aiello, Marco; Salvatore, Marco; Franzese, Monica; Cavaliere, Carlo

doi:10.3748/wjg.v27.i36.6110

Cited by 7 publications

(5 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interrogation of net relative differences between LH and RH identified a number of features of importance. Among the features identified by the Boruta algorithm, GLCM Joint entropy (randomness/variability in neighbourhood intensity values) has previously been identified as indicative of oesophageal tumour stage 33 . Overall, the identification of these features indicates that CT-features representative of heterogeneity may be a suitable marker for tumour presence in preclinical orthotopic GBM CE-CTs.…”

Section: Discussionmentioning

confidence: 99%

A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring

Connor,

Conroy,

White

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Here, we establish a CT-radiomics based method for application in invasive, orthotopic rodent brain tumour models. Twenty four NOD/SCID mice were implanted with U87R-Luc2 GBM cells and longitudinally imaged via contrast enhanced (CE-CT) imaging. Pyradiomics was employed to extract CT-radiomic features from the tumour-implanted hemisphere and non-tumour-implanted hemisphere of acquired CT-scans. Inter-correlated features were removed (Spearman correlation > 0.85) and remaining features underwent predictive analysis (recursive feature elimination or Boruta algorithm). An area under the curve of the receiver operating characteristic curve was implemented to evaluate radiomic features for their capacity to predict defined outcomes. Firstly, we identified a subset of radiomic features which distinguish the tumour-implanted hemisphere and non- tumour-implanted hemisphere (i.e, tumour presence from normal tissue). Secondly, we successfully translate preclinical CT-radiomic pipelines to GBM patient CT scans (n = 10), identifying similar trends in tumour-specific feature intensities (E.g. ‘glszm Zone Entropy’), thereby suggesting a mouse-to-human species conservation (a conservation of radiomic features across species). Thirdly, comparison of features across timepoints identify features which support preclinical tumour detection earlier than is possible by visual assessment of CT scans. This work establishes robust, preclinical CT-radiomic pipelines and describes the application of CE-CT for in-depth orthotopic brain tumour monitoring. Overall we provide evidence for the role of pre-clinical ‘discovery’ radiomics in the neuro-oncology space.

show abstract

Section: Discussionmentioning

confidence: 99%

A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring

Connor,

Conroy,

White

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…51 Radiogenomics Another example of precision medicine, radiogenomics is defined as the linking of medical imaging with molecular characteristics for the purpose of prognostication. 52 Radiogenomics can provide cost-effective, computer-aided diagnosis and treatment guidance and also curb invasive interventions. 53 The analysis of diverse largescale databases containing only quality imaging allows for such uses as tumor region assessment, a task previously performed by a radiologist using only functional and morphologic features.…”

Section: Recent Advances and Prospectsmentioning

confidence: 99%

“…Another example of precision medicine, radiogenomics is defined as the linking of medical imaging with molecular characteristics for the purpose of prognostication 52. Radiogenomics can provide cost-effective, computer-aided diagnosis and treatment guidance and also curb invasive interventions 53.…”

Section: Recent Advances and Prospectsmentioning

confidence: 99%

Esophageal cancer

Eisner

2024

Jaapa

View full text Add to dashboard Cite

Esophageal cancer is a challenging malignancy that often is diagnosed in advanced stages, resulting in a poor prognosis. This article provides a comprehensive review of the two main types of esophageal cancer, esophageal squamous cell carcinoma and esophageal adenocarcinoma, and reviews epidemiology, risk factors, pathogenesis, diagnostic modalities, staging systems, and established and emerging treatments. Recent advancements in treatment for resectable and unresectable esophageal cancer also are explored. These include immunotherapy, targeted therapy, sentinel lymph node mapping, radiogenomics, palliative measures, and screening measures.

show abstract

“…radiogenomic or radiopathomic investigation), and, in view of the recently faced emergency, a study aiming at improving the accuracy and efficiency of COVID-19 diagnosis through AI-based segmentation. Concerning the multi-omic oncology study, a researcher may be interested in exploring the correlation between radiomic features extracted from radiological images and genomic markers [ 83 ](or pathomic features [ 84 ]) associated with clinical/pathological outcomes in a specific cancer type and could leverage the CDB to access the required material (e.g. raw images, molecular data, image annotations) to obtain numerical descriptors and/or explore pre-extracted features to reproduce previously obtained results.…”

Section: Comprehensive Digital Biobanking Modelmentioning

confidence: 99%

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine

Brancato,

Esposito,

Coppola

et al. 2024

J Transl Med

Self Cite

View full text Add to dashboard Cite

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.

show abstract

Impact of radiogenomics in esophageal cancer on clinical outcomes: A pilot study

Cited by 7 publications

References 56 publications

A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring

A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring

Esophageal cancer

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine

Contact Info

Product

Resources

About