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

Body Composition and Clinical Outcomes in Esophageal Cancer Patients Treated with Immune Checkpoint Inhibitors

BACKGROUND

Obesity is associated with increased mortality in various cancers, but the relationship between obesity and clinical outcomes in unresectable or recurrent esophageal cancer who receive immune checkpoint inhibitors (ICIs) remains unknown. This study investigated the association between body composition and clinical outcomes in patients with unresectable or recurrent esophageal cancer who received ICIs.

METHODS

Utilizing an unbiased database of 111 unresectable or recurrent esophageal cancers, we evaluated the relationships between body composition (body mass index, waist circumference, psoas major muscle volume, and subcutaneous and visceral fat areas) at the initiation of ICI treatment and clinical outcomes including the disease control rate and progression-free survival (PFS).

RESULTS

Waist circumference was significantly associated with the disease control rate at the first assessment (P = 0.0008). A high waist circumference was significantly associated with favorable PFS in patients treated with nivolumab. In an univariable model, for 5-cm increase of waist circumference in the outcome category of PFS, univariable hazard ratio (HR) was 0.73 (95% confidence interval [CI], 0.61-0.87; P = 0.0002). A multivariable model controlling for potential confounders yielded a similar finding (multivariable HR, 0.56; 95% CI, 0.33-0.94; P = 0.027). We observed the similar finding in esophageal cancer patients treated with pembrolizumab+CDDP+5-FU (P = 0.048). In addition, waist circumference was significantly associated with the prognostic nutritional index (P = 0.0073).

CONCLUSIONS

A high waist circumference was associated with favorable clinical outcomes in ICI-treated patients with unresectable or recurrent esophageal cancer, providing a platform for further investigations on the relationships among body composition, nutrition, and the immune status.

Esophageal cancer: Treatment advances and need for screening

ABSTRACT

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.

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

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.

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

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.

The diverse role of RNA methylation in esophageal cancer

Esophageal cancer is one of the major life-threatening diseases in the world. RNA methylation is the most common post-transcriptional modification and a wide-ranging regulatory system controlling gene expression. Numerous studies have revealed that dysregulation of RNA methylation is critical for cancer development and progression. However, the diverse role of RNA methylation and its regulators in esophageal cancer remains to be elucidated and summarized. In this review, we focus on the regulation of major RNA methylation, including m ⁶A, m ⁵C, and m ⁷G, as well as the expression patterns and clinical implications of its regulators in esophageal cancer. We systematically summarize how these RNA modifications affect the "life cycle" of target RNAs, including mRNA, microRNA, long non-coding RNA, and tRNA. The downstream signaling pathways associated with RNA methylation during the development and treatment of esophageal cancer are also discussed in detail. Further studies on how these modifications function together in the microenvironment of esophageal cancer will draw a clearer picture of the clinical application of novel and specific therapeutic strategies.

The application of radiomics in esophageal cancer: Predicting the response after neoadjuvant therapy

Esophageal cancer (EC) is one of the fatal malignant neoplasms worldwide. Neoadjuvant therapy (NAT) combined with surgery has become the standard treatment for locally advanced EC. However, the treatment efficacy for patients with EC who received NAT varies from patient to patient. Currently, the evaluation of efficacy after NAT for EC lacks accurate and uniform criteria. Radiomics is a multi-parameter quantitative approach for developing medical imaging in the era of precision medicine and has provided a novel view of medical images. As a non-invasive image analysis method, radiomics is an inevitable trend in NAT efficacy prediction and prognosis classification of EC by analyzing the high-throughput imaging features of lesions extracted from medical images. In this literature review, we discuss the definition and workflow of radiomics, the advances in efficacy prediction after NAT, and the current application of radiomics for predicting efficacy after NAT.