Radiomics in esophageal 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 [1]. 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].

As an example, data from the OEO2 and MAGIC trials for esophageal and esophago-gastric cancer have shown a 6% [3] and 13% [4] improvement in 5-year overall-survival, respectively. Trimodality therapy for esophageal and esophago-gastric cancer combining neoadjuvant chemo- and radiation-therapy in addition to surgery may also be superior to neoadjuvant chemotherapy alone in a selected patient population [8]. The CROSS trial [6] comparing neoadjuvant chemoradiotherapy plus surgery with surgery alone in patients with esophageal and esophago-gastric cancer showed a superior overall-survival of 49 vs. 24 months, hazard ratio 0.657, p = 0.003, and a pathological complete response rate of 29%, for patients with multi-modality treatment with no increase in surgical mortality (4% in surgery and trimodality groups, respectively).

A recent systematic review and meta-analysis of neoadjuvant chemotherapy in patients with gastric cancer has also found improved 3-year survival rates (relative risk 1.30; 95% CI 1.06–1.59, p < 0.01) [9]. Typical management pathways are shown in Figs. 1 and 2 for esophageal, esophago-gastric, and gastric cancer, respectively. Nevertheless overall-survival remains poor despite these improvements in patient care.

Recent genomic analyses have highlighted the genetic heterogeneity present in esophageal, esophago-gastric [10], and gastric cancer [11, 12] as an underlying cause for the differences in outcome and heterogeneity of response to therapy. Quality of life also remains poor for many patients post-surgery, taking up to 3 years to return to pre-therapy levels in patients undergoing esophageal resection [13]. Better patient stratification remains a key challenge for patients with upper gastrointestinal tract cancers.

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.

For gastric cancer, initial staging is again by contrast-enhanced CT. If curative treatment is being considered, the use of endoscopic ultrasound (EUS) is helpful in determining the proximal and distal extent of the tumor, whereas 18F-FDG PET/CT has been shown to improve staging by detecting involved lymph nodes and metastatic disease, although it can be less accurate in mucinous and diffuse tumors [20].

In esophageal cancer, PET has the potential to change management in up to a third of patients [21, 22], and is often incorporated into radiotherapy planning pathway [23, 24]. The American College of Surgeons Oncology Group reported sensitivity and specificity of 18F-FDG-PET/CT scans to be 79% and 95%, respectively [18].

Magnetic resonance imaging (MRI) is currently not recommended for the routine imaging of esophageal or gastric cancer. However, with the recent advent of hybrid PET/MRI systems in clinical practice, there has been growing interest in MRI’s ability as an assessment tool. MRI provides excellent soft-tissue contrast, and may demonstrate the esophageal wall layers and adjacent nodes. Physiological sequences (e.g., diffusion-weighted MRI) may also be included as part of the protocol. An initial staging 18F-FDG PET/MRI study with a pathology gold standard has been promising for nodal assessment with reported accuracy of 83% compared to 75% and 50% for EUS and CT, respectively [25].

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.

Radiomic signatures provide additional information predictive of underlying tumor biology and behavior. These signatures can be used alone or with other patient-related data (e.g., pathological data; genomic data) to improve tumor phenotyping, treatment response prediction and prognosis. Radiomic signatures may be obtained for all cross-sectional imaging modalities, including CT, PET, and MRI. Figure 3 illustrates a typical radiomics pipeline.Figure 4 demonstrates the process of tumor segmentation for a 18F-FDG PET image with a corresponding plot of standardized uptake value for the tumor. Initial studies in esophageal, esophago-gastric, and gastric cancer have shown promise for patient care.

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.

In greater detail, five studies have investigated the prediction of response to therapy alone (n = 2); prognosis alone (n = 1) and the prediction of response to therapy and prognosis (n = 2) from pre-therapy imaging. One of the earliest studies by Tixier et al. showed in 41 patients that gray-level co-occurrence matrix (GLCM) homogeneity, GLCM entropy, gray-level size-zone matrix (GLSZM) size-zone variability and run length matrix (RLM) intensity variability differentiated non-responders, partial-responders, and complete-responders with sensitivities of 76%–92% [26]. Beukinga et al. showed in 97 patients that a clinical model including PET-derived gray-level run length (GLRL) long run low gray level emphasis and CT-derived run percentage had a higher area under the receiver operator curve (AUROC) compared to maximum standardized uptake value (SUV_max) in predicting therapy response [27].

In a study of 52 patients with squamous cancers, Nakajo et al. found that 18F-FDG PET/CT GLSZM intensity variability, and GLSZM size-zone variability, as well as standard volumetric parameters, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG), were predictors of tumor response but not of progression free or overall-survival [28]. Non-responders showed significantly higher intensity variability and size-zone variability. Similarly, in a study of 65 patients Paul et al. found that a model incorporating GLCM homogeneity was a predictor of response (with an AUROC value of 0.823) but not of survival [29]. However, in a larger study with 403 patients, Foley et al. found that total lesion glycolysis, histogram energy and kurtosis were independently associated with overall-survival [30].

Four studies have assessed pre- and post-therapy 18F-FDG PET imaging. An initial study by Tan et al. found in 20 patients that 2 SUV_mean parameters, SUV_mean decline and SUV_mean skewness, and 3 texture features GLCM inertia, GLCM correlation, and GLCM cluster prominence, were significant predictors of complete response with an AUROC of 0.76 [31]. In 217 patients with adenocarcinoma, Van Rossum et al. developed a prediction model which included change in run length matrix (RLM) run percentage, change in GLCM entropy, and post–chemoradiation roundness, and increased the corrected c-index (concordance-index, comparable to AUROC) from 0.67 to 0.77, compared to the clinical model alone [32]. Yip et al. found that a change in run length and size-zone matrix differentiated responders from non-responders [33]. More recently, Beukinga et al. found that clinical T-staging combined with post-chemoradiotherapy ¹⁸F-FDG PET orderliness provided high discriminatory accuracy in predicting pathologic complete response compared to clinical variables or SUVmax alone [34].

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.

The largest study in 49 patients found that histogram skewness, histogram kurtosis, GLSZM long-zone emphasis, and 2 Gabor transformed parameters MSA-54 and MSE-54, discriminated non-responders from responders using an artificial neural network-derived prediction model [35]. The two remaining studies have assessed prognostication. Ganeshan et al. found that lower histogram uniformity (with Gaussian filtration) from unenhanced CT images before start of treatment was an independent predictor for poorer overall-survival [36].Yip et al. analyzed contrast-enhanced images of 36 patients before and after treatment and found a significant decrease in histogram entropy and increase in uniformity (with Gaussian filtration) between the two time points. Higher post treatment entropy was associated with poorer overall-survival [37].

For gastric cancer (Table 4), three studies have assessed the potential of radiomic approaches for classification. Studies have found that first and second-order analysis in the contrast-enhanced images may help in differentiation of lymphoma from gastrointestinal stromal cancer [38] or adenocarcinoma [39]. Another study in 107 patients found that arterial phase standard deviation and entropy were correlated with poorer differentiation [40]. For prognostication, Yoon et al. investigated 26 HER2 + gastric cancer patients before trastuzumab-treatment. In their analysis, they found GLCM contrast, variance, correlation and angular second moment (also known as energy or uniformity) were associated with a poorer survival [41]. In another study, Giganti et al. showed in 56 patients, that first-order energy, entropy, and skewness were significantly associated with a negative prognosis [42]. Giganti et al. also assessed pre chemotherapy texture features derived from the late arterial phase of 34 patients. They found entropy and compactness were higher and uniformity lower in responders [43]. No studies have assessed prognostication or response to therapy in gastric cancer.

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.