Value of two-phase dynamic multidetector computed tomography in differential diagnosis of post-inflammatory strictures from esophageal cancer

Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach

BACKGROUND

Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also for reducing the need for invasive procedures, consequently lowering complications and costs. Despite this, current techniques, which can be invasive and costly, struggle with achieving the necessary precision, highlighting a pressing need for more effective, non-invasive alternatives.

METHOD

We developed ResoLSTM-Depth, a deep learning model to distinguish ESCC stages T1-T2 from T3-T4. It integrates ResNet-18 and Long Short-Term Memory (LSTM) networks, leveraging their strengths in spatial and sequential data processing. This method uses arterial phase CT scans from ESCC patients. The dataset was meticulously segmented by an experienced radiologist for effective training and validation.

RESULTS

Upon performing five-fold cross-validation, the ResoLSTM-Depth model exhibited commendable performance with an accuracy of 0.857, an AUC of 0.901, a sensitivity of 0.884, and a specificity of 0.828. These results were superior to the ResNet-18 model alone, where the average accuracy is 0.824 and the AUC is 0.879. Attention maps further highlighted influential features for depth prediction, enhancing model interpretability.

CONCLUSION

ResoLSTM-Depth is a promising tool for ESCC invasion depth prediction. It offers potential for improvement in the staging and therapeutic planning of ESCC.

Prediction of distant metastasis in esophageal cancer using a radiomics-clinical model

BACKGROUND

Distant metastasis, which occurs at a rate of 25% in patients with esophageal cancer (EC), has a poor prognosis, with previous studies reporting an overall survival of only 3-10 months. However, few studies have been conducted to predict distant metastasis in EC, owing to a dearth of reliable biomarkers. The purpose of this study was to develop and validate an accurate model for predicting distant metastasis in patients with EC.

METHODS

A total of 299 EC patients were enrolled and randomly assigned to a training cohort (n = 207) and a validation cohort (n = 92). Logistic univariate and multivariate regression analyses were used to identify clinical independent predictors and create a clinical nomogram. Radiomic features were extracted from contrast-enhanced computed tomography (CT) images taken prior to treatment, and least absolute shrinkage and selection operator (Lasso) regression was used to screen the associated features, which were then used to develop a radiomic signature. Based on the screened features, four machine learning algorithms were used to build radiomics models. The joint nomogram with radiomic signature and clinically independent risk factors was developed using the logical regression algorithm. All models were validated and compared by discrimination, calibration, reclassification, and clinical benefit.

RESULTS

Multivariable analyses revealed that age, N stage, and degree of pathological differentiation were independent predictors of distant metastasis, and a clinical nomogram incorporating these factors was established. A radiomic signature was developed by a set of sixteen features chosen from 851 radiomic features. The joint nomogram incorporating clinical factors and radiomic signature performed better [AUC(95% CI) 0.827(0.742-0.912)] than the clinical nomogram [AUC(95% CI) 0.731(0.626-0.836)] and radiomics predictive models [AUC(95% CI) 0.754(0.652-0.855), LR algorithms]. Calibration and decision curve analyses revealed that the radiomics-clinical nomogram outperformed the other models. In comparison with the clinical nomogram, the joint nomogram's NRI was 0.114 (95% CI 0.075-0.345), and its IDI was 0.071 (95% CI 0.030-0.112), P = 0.001.

CONCLUSIONS

We developed and validated the first radiomics-clinical nomogram for distant metastasis in EC which may aid clinicians in identifying patients at high risk of distant metastasis.

Predicting Lymph Node Metastasis Using Computed Tomography Radiomics Analysis in Patients With Resectable Esophageal Squamous Cell Carcinoma

OBJECTIVES

We investigated the value of radiomics data, extracted from pretreatment computed tomography images of the primary tumor (PT) and lymph node (LN) for predicting LN metastasis in esophageal squamous cell carcinoma (ESCC) patients.

MATERIALS AND METHODS

A total 338 ESCC patients were retrospectively assessed. Primary tumor, the largest short-axis diameter LN (LSLN), and PT and LSLN interaction term (IT) radiomic features were calculated. Subsequently, the radiomic signature was combined with clinical risk factors in multivariable logistic regression analysis to build various clinical-radiomic models. Model performance was evaluated with respect to the fit, overall performance, differentiation, and calibration.

RESULTS

A clinical-radiomic model, which combined clinical and PT-LSLN-IT radiomic signature, showed favorable discrimination and calibration. The area under curve value was 0.865 and 0.841 in training and test set.

CONCLUSIONS

A venous computed tomography radiomic model based on the PT, LSLN, and IT radiomic features represents a novel noninvasive tool for prediction LN metastasis in ESCC.

Caustic ingestion: CT findings of esophageal injuries and thoracic complications

Ingestion of caustic substances, whether accidental or for the purpose of suicide, can cause severe lesions of the lips, oral cavity, pharynx, upper gastrointestinal system, and upper airways. In particular, caustic agents could be responsible for severe esophageal injuries resulting in short- and long-term complications. Because of these important clinical implications, timely diagnosis and appropriate management are crucial. In the evaluation of esophageal injuries, thoraco-abdominal computed tomography (CT) is preferable to endoscopy as it avoids the risk of esophageal perforation and allows the evaluation of esophageal injuries as well as of the surrounding tissue. In this review, we report CT findings of esophageal injuries and possible related thoracic complications caused by caustic ingestion.

Role of Multimodality Imaging in Gastroesophageal Reflux Disease and Its Complications, with Clinical and Pathologic Correlation

Gastroesophageal reflux disease (GERD) is a common condition and impairs the quality of life for millions of patients, accounts for considerable health care spending, and is a primary risk factor for esophageal adenocarcinoma. There have been substantial advances in understanding the pathogenesis of GERD and its complications and much progress in diagnosis and management of GERD; however, these have not been comprehensively discussed in the recent radiology literature. Understanding the role of imaging in GERD and its complications is important to aid in multidisciplinary treatment of GERD. GERD results from prolonged or recurrent reflux of gastric contents into the esophagus. Common symptoms include heartburn or regurgitation. Prolonged reflux of gastric contents into the esophagus can cause erosive esophagitis. Over time, the inflammatory response related to esophagitis can lead to deposition of fibrous tissue and development of strictures. Alternatively, the esophageal mucosa can undergo metaplasia (Barrett esophagus), a precursor to dysplasia (which can lead to adenocarcinoma). Conventional barium esophagography has long been considered the primary imaging modality for the esophagus, and the fluoroscopic findings for diagnosis of GERD have been well established. Multimodality imaging has a clear role in detection and assessment of the complications of GERD, specifically reflux esophagitis and Barrett esophagus; differentiation of benign and malignant strictures; and detection, staging, and posttreatment surveillance of esophageal adenocarcinoma. Given the dramatic changes in utilization of abdominal imaging during the past 2 decades, with significantly declining volume of fluoroscopic procedures and concomitant increase in CT and MRI studies, it is crucial that modern radiologists appreciate the value of barium esophagography in the workup of GERD and recognize the key imaging features of GERD and its complications at CT and MRI.

Analyzing the post-contrast attenuation of the esophageal wall on routine contrast-enhanced MDCT examination can improve the diagnostic accuracy in response evaluation of the squamous cell esophageal carcinoma to neoadjuvant chemoradiotherapy in comparison with the esophageal wall thickness

PURPOSE

To evaluate the accuracy of the multidetector computed tomography (MDCT) in the response evaluation of the esophageal squamous cell carcinoma (ESCC) to neoadjuvant chemoradiotherapy (nCRT) by analyzing the thickness and post-contrast attenuation of the esophageal wall after the nCRT.

METHODS

Contrast-enhanced (CE)-MDCT examinations in portal venous phase of one hundred patients with locally advanced ESCC who received nCRT and underwent esophageal resection and histopathology assessment of tumor regression grade (TRG) were retrospectively analyzed by measuring the maximal thickness and mean density of the esophageal wall in the segment involved by tumor and visually searching for hyperdense foci within it. Diagnostic performance was evaluated using the ROC analysis.

RESULTS

Average attenuation of the esophageal wall had stronger diagnostic performance for predicting pathologic complete regression (pCR) (AUC = 0.994; p < 0.001) in relation to maximal esophageal wall thickness (AUC = 0.731; p < 0.001). Maximal esophageal wall thickness ≤ 9 mm and average attenuation of the esophageal wall ≤ 64 HU predicted pCR with the sensitivity, specificity, and overall accuracy of 62.5%, 77.9%, and 73%, and 96.9%, 98.5%, and 98%, respectively. Combination of both cutoff values enabled correct assessment of pCR with the 100% accuracy. Visual detection of the hyperdense focus within the esophageal wall predicted pCR with the sensitivity, specificity, and overall accuracy values of 100%, 94.1%, and 96%, respectively.

CONCLUSION

Visual analysis and measurement of post-contrast attenuation of the esophageal wall after the nCRT can improve diagnostic accuracy of MDCT in the response evaluation of the ESCC to nCRT in comparison with measuring the esophageal wall thickness.

Use of the blue cotton screen method with endoscopy to detect occult esophageal foreign bodies

More than 20,000 cases of upper gastrointestinal foreign bodies (FBs) have been reported in the last 5 years in China. Early detection and treatment is vital in these patients. Differential diagnosis of esophageal injury and occult esophageal foreign bodies is challenging, particularly in the case of non-radio-opaque foreign bodies. A diagnostic technique with high accuracy and low risk is needed for clinical practice. We describe successful use of the "blue cotton screen method" to detect esophageal foreign bodies in 2 patients. The advantages and disadvantages of various diagnostic modalities in the management of patients with foreign body ingestion are presented. This technique is safer and more effective than traditional methods for foreign body impaction in the esophageal cavity. It could be applied for screening and in the differential diagnosis of esophageal injury and FBs in the esophageal lumen.