New frontiers in esophageal radiology

Advances in the Imaging of Esophageal and Gastroesophageal Junction Malignancies

Accurate imaging is key for the diagnosis and treatment of esophageal and gastroesophageal junction cancers . Current imaging modalities, such as computed tomography (CT) and 18F-FDG (2-deoxy-2-[18F]fluoro-D-glucose) positron emission tomography (PET)/CT, have limitations in accurately staging these cancers. MRI shows promise for T staging and residual disease assessment. Novel PET tracers, like FAPI, FLT, and hypoxia markers, offer potential improvements in diagnostic accuracy. 18F-FDG PET/MRI combines metabolic and anatomic information, enhancing disease evaluation. Radiomics and artificial intelligence hold promise for early detection, treatment planning, and response assessment. Theranostic nanoparticles and personalized medicine approaches offer new avenues for cancer therapy.

Application and development of nanomaterials in the diagnosis and treatment of esophageal cancer

Esophageal cancer is a malignant tumor with a high incidence worldwide. Currently, there are a lack of effective early diagnosis and treatment methods for esophageal cancer. However, delivery systems based on nanoparticles (NPs) have shown ideal efficacy in real-time imaging and chemotherapy, radiotherapy, gene therapy, and phototherapy for tumors, which has led to their recent widespread design as novel treatment strategies. Compared to traditional drugs, nanomedicine has unique advantages, including strong targeting ability, high bioavailability, and minimal side effects. This article provides an overview of the application of NPs in the diagnosis and treatment of esophageal cancer and provides a reference for future research.

ESMO/ASCO Recommendations for a Global Curriculum in Medical Oncology Edition 2023

The European Society for Medical Oncology (ESMO) and ASCO are publishing a new edition of the ESMO/ASCO Global Curriculum (GC) with contributions from more than 150 authors. The purpose of the GC is to provide recommendations for the training of physicians in medical oncology and to establish a set of educational standards for trainees to qualify as medical oncologists. This edition builds on prior ones in 2004, 2010, and 2016 and incorporates scientific advances and input from an ESMO ASCO survey on GC adoption conducted in 2019, which revealed that GC has been adopted or adapted in as many as two thirds of the countries surveyed. To make GC even more useful and applicable, certain subchapters were rearranged into stand-alone chapters, that is, cancer epidemiology, diagnostics, and research. In line with recent progress in the field of multidisciplinary cancer care new (sub)chapters, such as image-guided therapy, cell-based therapy, and nutritional support, were added. Moreover, this edition includes an entirely new chapter dedicated to cancer control principles, aiming to ensure that medical oncologists are able to identify and implement sustainable and equitable cancer care, tailored to local needs and resources. Besides content renewal, modern didactic principles were introduced. GC content is presented using two chapter templates (cancer-specific and non-cancer-specific), with three didactic points (objectives, key concepts, and skills). The next step is promoting GC as a contemporary and comprehensive document applicable all over the world, particularly due to its capacity to harmonize education in medical oncology and, in so doing, help to reduce global disparities in cancer care.

Applications and Prospects of Artificial Intelligence-Assisted Endoscopic Ultrasound in Digestive System Diseases

Endoscopic ultrasound (EUS) has emerged as a widely utilized tool in the diagnosis of digestive diseases. In recent years, the potential of artificial intelligence (AI) in healthcare has been gradually recognized, and its superiority in the field of EUS is becoming apparent. Machine learning (ML) and deep learning (DL) are the two main AI algorithms. This paper aims to outline the applications and prospects of artificial intelligence-assisted endoscopic ultrasound (EUS-AI) in digestive diseases over the past decade. The results demonstrated that EUS-AI has shown superiority or at least equivalence to traditional methods in the diagnosis, prognosis, and quality control of subepithelial lesions, early esophageal cancer, early gastric cancer, and pancreatic diseases including pancreatic cystic lesions, autoimmune pancreatitis, and pancreatic cancer. The implementation of EUS-AI has opened up new avenues for individualized precision medicine and has introduced novel diagnostic and treatment approaches for digestive diseases.

Diagnostic value of conventional endoscopic ultrasound for lymph node metastasis in upper gastrointestinal neoplasia: A meta-analysis

BACKGROUND

Upper gastrointestinal neoplasia mainly includes esophageal cancer and gastric cancer, both of which have high morbidity and mortality. Lymph node metastasis (LNM), as the most common metastasis mode of both diseases, is an important factor affecting tumor stage, treatment strategy and clinical prognosis. As a new fusion technology, endoscopic ultrasound (EUS) is becoming increasingly used in the diagnosis and treatment of digestive system diseases, but its use in detecting LNM in clinical practice remains limited.

AIM

To evaluate the diagnostic value of conventional EUS for LNM in upper gastrointestinal neoplasia.

METHODS

Using the search mode of "MeSH + Entry Terms" and according to the predetermined inclusion and exclusion criteria, we conducted a comprehensive search and screening of the PubMed, EMBASE and Cochrane Library databases from January 1, 2000 to October 1, 2022. Study data were extracted according to the predetermined data extraction form. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies tool, and the results of the quality assessment were presented using Review Manager 5.3.5 software. Finally, Stata14.0 software was used for a series of statistical analyses.

RESULTS

A total of 22 studies were included in our study, including 2986 patients. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic score and diagnostic odds ratio of conventional EUS in the diagnosis of upper gastrointestinal neoplasia LNM were 0.62 [95% confidence interval (CI): 0.50-0.73], 0.80 (95%CI: 0.73-0.86), 3.15 (95%CI: 2.46-4.03), 0.47 (95%CI: 0.36-0.61), 1.90 (95%CI: 1.51-2.29) and 6.67 (95%CI: 4.52-9.84), respectively. The area under the summary receiver operating characteristic curve was 0.80 (95%CI: 0.76-0.83). Sensitivity analysis indicated that the results of the meta-analysis were stable. There was considerable heterogeneity among the included studies, and the threshold effect was an important source of heterogeneity. Univariable meta-regression and subgroup analysis showed that tumor type, sample size and EUS diagnostic criteria were significant sources of heterogeneity in specificity (P < 0.05). No significant publication bias was found.

CONCLUSION

Conventional EUS has certain clinical value and can assist in the detection of LNM in upper gastrointestinal neoplasia, but it cannot be used as a confirmatory or exclusionary test.

CIABNet: Category imbalance attention block network for the classification of multi-differentiated types of esophageal cancer

BACKGROUND

Esophageal cancer has become one of the important cancers that seriously threaten human life and health, and its incidence and mortality rate are still among the top malignant tumors. Histopathological image analysis is the gold standard for diagnosing different differentiation types of esophageal cancer.

PURPOSE

The grading accuracy and interpretability of the auxiliary diagnostic model for esophageal cancer are seriously affected by small interclass differences, imbalanced data distribution, and poor model interpretability. Therefore, we focused on developing the category imbalance attention block network (CIABNet) model to try to solve the previous problems.

METHODS

First, the quantitative metrics and model visualization results are integrated to transfer knowledge from the source domain images to better identify the regions of interest (ROI) in the target domain of esophageal cancer. Second, in order to pay attention to the subtle interclass differences, we propose the concatenate fusion attention block, which can focus on the contextual local feature relationships and the changes of channel attention weights among different regions simultaneously. Third, we proposed a category imbalance attention module, which treats each esophageal cancer differentiation class fairly based on aggregating different intensity information at multiple scales and explores more representative regional features for each class, which effectively mitigates the negative impact of category imbalance. Finally, we use feature map visualization to focus on interpreting whether the ROIs are the same or similar between the model and pathologists, thus better improving the interpretability of the model.

RESULTS

The experimental results show that the CIABNet model outperforms other state-of-the-art models, which achieves the most advanced results in classifying the differentiation types of esophageal cancer with an average classification accuracy of 92.24%, an average precision of 93.52%, an average recall of 90.31%, an average F1 value of 91.73%, and an average AUC value of 97.43%. In addition, the CIABNet model has essentially similar or identical to the ROI of pathologists in identifying histopathological images of esophageal cancer.

CONCLUSIONS

Our experimental results prove that our proposed computer-aided diagnostic algorithm shows great potential in histopathological images of multi-differentiated types of esophageal cancer.

The development of a nomogram model for predicting left recurrent laryngeal nerve lymph node metastasis in esophageal cancer based on radiomics and clinical factors

Background

The lymph node dissection for esophageal cancer is controversial. Some prediction models of lymph node metastasis (LNM) use the short diameter of lymph nodes measured by computed tomography (CT) examination as a predictor, but the size of that for judging metastasis is still controversial. However, radiomics can extract some features in tumors that cannot be obtained by naked eyes, which may have a higher value in predicting LNM. In this study, a nomogram was developed based on radiomics and clinical factors to predict left recurrent laryngeal nerve lymph node (RLNN) metastasis in patients with esophageal squamous cell carcinoma (ESCC).

Methods

There were 350 patients included in this retrospective study. And the postoperative pathological results determined whether there was left RLNN metastasis. A univariate analysis was conducted of the clinical data. The least absolute shrinkage and selection operator regression analysis was conducted to filter the radiomics features extracted from CT images. The multivariate logistic regression equation was used to construct a nomogram. The area under the curve (AUC) was used to evaluate the predictive ability. Due to the small sample size, we chose to perform internal validation after the model was established by 10-fold cross-validation, Harrell's concordance index (C-index), bootstrap validation and calibration.

Results

Ultimately, 3 indicators were screened out; that is, tumor location, surface volume ratio, and run-length non-uniformity. We then constructed the nomogram using these 3 indicators. The model had good accuracy and calibration performance. It has an AUC of 0.903 (95% confidence interval: 0.861-0.945), a sensitivity of 0.873, and a specificity of 0.756. Ten-fold cross-validation showed that the sensitivity and specificity of the training set were 88.08% and 75.81%, and the validation set had a sensitivity of 85.08% and a specificity of 75.49%. The Brier score was 0.074, and C-index was 0.904, which indicated good consistency between the actual and predicted results.

Conclusions

A nomogram constructed based on radiomics features and clinical factors can be used to predict the metastasis of left RLNN in patients with ESCC in a non-invasive way, which provided a reference for clinicians to formulate individualized lymph node dissection plans.