Esophageal cancer: Anatomic particularities, staging, and imaging techniques

Application Value of Gastroenterography Combined With CT in the Evaluation of Short-Term Efficacy and Prognosis in Patients With Esophageal Cancer Radiotherapy

Purpose

To observe the application value of gastroenterography combined with CT in the evaluation of short-term efficacy and prognosis in patients with esophageal cancer radiotherapy.

Methods

From January 2013 to December 2020, the clinical data of 207 patients with esophageal cancer treated by radiotherapy in our hospital were collected retrospectively. All patients received gastroenterography and CT examination before and after radiotherapy, and the patients were followed-up for 1 year, and the evaluation value of their short-term efficacy and prognosis was observed.

Results

After radiotherapy, the length diameter, short diameter, and volume of the lymph node were lower than those before radiotherapy (p < 0.05), but the maximum tube wall thickness had no significant difference (p > 0.05). The length diameter, short diameter, and volume of the lymph node, and the maximum tube wall thickness in the good efficacy group and the good prognosis group were lower, and the objective response rate in the good prognosis group was higher (p < 0.05). The area under the curve (AUC) of the length diameter, short diameter, and volume of the lymph node to evaluate the short-term efficacy of patients with esophageal cancer was 0.738, 0.705, and 0.748, respectively, and the AUC to evaluate the prognosis of patients with esophageal cancer was 0.751, 0.776, and 0.791, respectively.

Conclusion

Gastroenterography combined with CT has a good application value in the evaluation of short-term efficacy and prognosis in patients with esophageal cancer radiotherapy.

A Deep Learning Radiomics Analysis for Survival Prediction in Esophageal Cancer

The purpose of this study was to explore the deep learning radiomics (DLR) nomogram to predict the overall 3-year survival after chemoradiotherapy in patients with esophageal cancer. The 154 patients' data were used in this study, which was randomly split into training (116) and validation (38) data. Deep learning and handcrafted features were obtained via the preprocessing diagnostic computed tomography images. The selected features were used to construct radiomics signatures through the least absolute shrinkage and selection operator (LASSO) regression, maximizing relevance while minimizing redundancy. The DLR signature, handcrafted features' radiomics (HCR) signature, and clinical factors were incorporated to develop a DLR nomogram. The DLR nomogram was evaluated in terms of discrimination and calibration with comparison to the HCR signature-based radiomics model. The experimental results showed the outperforming discrimination ability of the proposed DLR over the HCR model in terms of Harrel's concordance index, 0.76 and 0.784, for training and validation sets, respectively. Also, the proposed DLR nomogram calibrates and classifies better than the HCR model in terms of AUC, 0.984 (vs. 0.797) and 0.942 (vs. 0.665) for training and validation sets, respectively. Furthermore, the nomogram-predicted Kaplan–Meier survival (KMS) curves differed significantly from the nonsurvival groups in the log-rank test (p value <0.05). The proposed DLR model based on conventional CT images showed the outperforming performance over the HCR signature model in noninvasively individualized prediction of the 3-year survival rate in esophageal cancer patients. The proposed model can potentially provide prognostic information that guides and helps the clinical decisions between observation and treatment.

Review of MR-Guided Radiotherapy for Esophageal Cancer

In this review, we outline the potential benefits and the future role of MRI and MR-guided radiotherapy (MRgRT) in the management of esophageal cancer. Although not currently used in most clinical practice settings, MRI is a useful non-invasive imaging modality that provides excellent soft tissue contrast and the ability to visualize cancer physiology. Chemoradiation therapy with or without surgery is essential for the management of locally advanced esophageal cancer. MRI can help stage esophageal cancer, delineate the gross tumor volume (GTV), and assess the response to chemoradiotherapy. Integrated MRgRT systems can help overcome the challenge of esophageal motion due to respiratory motion by using real-time imaging and tumor tracking with respiratory gating. With daily on-table MRI, shifts in tumor position and tumor regression can be taken into account for online-adaptation. The combination of accurate GTV visualization, respiratory gating, and online adaptive planning, allows for tighter treatment volumes and improved sparing of the surrounding normal organs. This could lead to a reduction in radiotherapy induced cardiac toxicity, pneumonitis and post-operative complications. Tumor physiology as seen on diffusion weighted imaging or dynamic contrast enhancement can help individualize treatments based on the response to chemoradiotherapy. Patients with a complete response on MRI can be considered for organ preservation while patients with no response can be offered an earlier resection. In patients with a partial response to chemoradiotherapy, areas of residual cancer can be targeted for dose escalation. The tighter and more accurate targeting enabled with MRgRT may enable hypofractionated treatment schedules.

Pneumo-PET-CT: Initial Results of This Novel Technique on the Evaluation of Esophageal and Gastric Tumors with Anatomic-Surgical Correlation

We present the initial results of a novel hybrid scanning-based technique that combines pneumo-computed tomography (PNCT) with positron emission tomography (PET) using 2-(fluorine-18) fluoro-2-deoxy-D-glucose (FDG). We denominate it pneumo-PET-CT. The focus of our discussion will be on the description of the pneumo-PET-CT technique and the interpretation criteria with emphasis on its benefits and applications in the presurgical and postneoadjuvant therapy evaluation of esophageal, esophagogastric junction (EGJ), and gastric tumors.