Date Published: June 01, 2019
Author(s): Bert-Ram Sah, Kasia Owczarczyk, Musib Siddique, Gary J. R. Cook, Vicky Goh.
Esophageal, esophago-gastric, and gastric cancers are major causes of cancer morbidity and cancer death. For patients with potentially resectable disease, multimodality treatment is recommended as it provides the best chance of survival. However, quality of life may be adversely affected by therapy, and with a wide variation in outcome despite multi-modality therapy, there is a clear need to improve patient stratification. Radiomic approaches provide an opportunity to improve tumor phenotyping. In this review we assess the evidence to date and discuss how these approaches could improve outcome in esophageal, esophago-gastric, and gastric cancer.
Esophageal or esophago-gastric cancer (456,000 new cases annually) and gastric cancer (952,000 new cases annually) are leading causes of cancer deaths worldwide . Above 50% of presenting patients are diagnosed with stage IV disease, precluding curative treatment. For patients with early stage disease, surgery, often combined with neoadjuvant chemotherapy or chemoradiotherapy, offers the best chance of cure [2–7].
For esophageal and esophago-gastric cancer, contrast-enhanced computed tomography (CT) remains the most commonly performed first step in staging due to the high prevalence of metastatic disease at presentation [14–16]. For patients being considered for a curative pathway, endoscopic ultrasound (EUS) and 18F-fluorodeoxyglucose positron emission tomography/CT (18F-FDG PET/CT) are performed due to the high sensitivity and specificity of EUS for local tumor and nodal staging; and 18F-FDG PET for distant metastases [17–19]. This aims to reduce the futile surgery rate.
Radiomic approaches are showing promise for patient stratification. Radiomics exploit the data performed as part of the clinical management pathway. In terms of imaging, a number of parameters may be extracted and combined including standard descriptors (e.g., size, morphology, TNM (tumor, node, metastasis) stage); qualitative, semi-quantitative, or quantitative physiological parameters (e.g., contrast enhancement, diffusion characteristics, tracer uptake); and additional agnostic features which are otherwise ‘invisible’, with bioinformatic approaches. Of these, texture-based features have been investigated most commonly to date. Table 1 highlights some features that have been investigated in studies.
Nine 18F-FDG PET studies have been performed in esophageal and esophago-gastric cancer and are summarized in Table 2. As yet no studies have been performed for gastric cancer. Studies to date have focused on the prediction of response or prognosis in comparison to standard practice. Studies have found that various first, second and high-order features have been contributory to the assessment of response, differentiating between responders and non-responders (with greater heterogeneity in non-responders), as well as being predictive of complete response. Performance has been better than conventional parameters alone. Prognostication data remain conflicting.
Nine studies have investigated the ability of CT-derived heterogeneity parameters for classification, prediction of response and overall-survival in patients with esophageal or gastric cancer. Three studies have been performed for esophageal cancer in terms of prediction of response or prognosis (Table 3). These have found that greater heterogeneity is present in non-responders and those with poorer outcome.
To date there have been little data for MRI in this tumor group as MRI is not performed routinely in the clinical pathway. There have been some exploratory data of pre-therapeutic ADC-maps of gastric cancer (Table 5). Liu et al. found that first-order statistics skewness may differ from node positive to node negative patients, and are associated with pathological characteristics including perineural and vascular invasion [44–47]. However, no studies so far have investigated prognostication or response assessment.
To date 22 imaging studies have been published investigating radiomic approaches in esophageal, esophagogastric, and gastric cancer, predominantly focused on texture analysis. Preliminary data for esophageal and esophago-gastric cancer suggest that there is potential for radiomic approaches in improving patient stratification for therapy. Eight 18F-FDG PET studies investigated the feasibility of heterogeneity analysis for response prediction (four studies with pre-therapy imaging only). Among the most often reported significant feature was GLCM entropy, reflecting the local randomness (irregularity) within the image, and where low GLCM entropy represents a more homogeneous texture. The reported accuracy for successful classification of therapy response ranged from 0.7 to 1.0 (AUC).