Role of Imaging in Esophageal Cancer Management in 2020: Update for Radiologists

Porphyromonas gingivalis lipopolysaccharide regulates cell proliferation, apoptosis, autophagy in esophageal squamous cell carcinoma via TLR4/MYD88/JNK pathway

The study aimed to explore the impact and potential mechanism of Porphyromonas gingivalis lipopolysaccharide (LPS-PG) on esophageal squamous cell carcinoma (ESCC) cell behavior. ESCC cells from the Shanghai Cell Bank were used, and TLR4, MYD88, and JNK interference vectors were constructed using adenovirus. The cells were divided into six groups: Control, Model, Model + radiotherapy + LPS-PG, Model + radiotherapy + 3-MA, Model + radiotherapy + LPS-PG + 3-MA, and Model + radiotherapy. Various radiation doses were applied to determine the optimal dose, and a radioresistant ESCC cell model was established and verified. CCK8 assay measured cell proliferation, flow cytometry and Hoechst 33258 assay assessed apoptosis, and acridine orange fluorescence staining tested autophagy. Western blot analyzed the expression of LC3II, ATG7, P62, and p-ULK1. Initially, CCK8 and acridine orange fluorescence staining identified optimal LPS-PG intervention conditions. Results revealed that 10 ng/ml LPS-PG for 12 h was optimal. LPS-PG increased autophagy activity, while 3-MA decreased it. LPS-PG + 3-MA group exhibited reduced autophagy. LPS-PG promoted proliferation and autophagy, inhibiting apoptosis in radioresistant ESCCs. LPS-PG regulated TLR4/MYD88/JNK pathway, enhancing ESCC autophagy, proliferation, and radioresistance. In conclusion, LPS-PG, through the TLR4/MYD88/JNK pathway, promotes ESCC proliferation, inhibits apoptosis, and enhances radioresistance by inducing autophagy.

Multifunctional Ablative Gastrointestinal Imaging Capsule (MAGIC) for Esophagus Surveillance and Interventions

Objective and Impact Statement: A clinically viable technology for comprehensive esophagus surveillance and potential treatment is lacking. Here, we report a novel multifunctional ablative gastrointestinal imaging capsule (MAGIC) technology platform to address this clinical need. The MAGIC technology could also facilitate the clinical translation and adoption of the tethered capsule endomicroscopy (TCE) technology. Introduction: Recently developed optical coherence tomography (OCT) TCE technologies have shown a promising potential for surveillance of Barrett's esophagus and esophageal cancer in awake patients without the need for sedation. However, it remains challenging with the current TCE technology for detecting early lesions and clinical adoption due to its suboptimal resolution, imaging contrast, and lack of visual guidance during imaging. Methods: Our technology reported here integrates dual-wavelength OCT imaging (operating at 800 and 1300 nm), an ultracompact endoscope camera, and an ablation laser, aiming to enable comprehensive surveillance, guidance, and potential ablative treatment of the esophagus. Results: The MAGIC has been successfully developed with its multimodality imaging and ablation capabilities demonstrated by imaging swine esophagus ex vivo and in vivo. The 800-nm OCT imaging offers exceptional resolution and contrast for the superficial layers, well suited for detecting subtle changes associated with early neoplasia. The 1300-nm OCT imaging provides deeper penetration, essential for assessing lesion invasion. The built-in miniature camera affords a conventional endoscopic view for assisting capsule deployment and laser ablation. Conclusion: By offering complementary and clinically viable functions in a single device, the reported technology represents an effective solution for endoscopic screening, diagnosis, and potential ablation treatment of the esophagus of a patient in an office setting.

Esophageal cancer: Treatment advances and need for screening

ABSTRACT

Esophageal cancer is a challenging malignancy that often is diagnosed in advanced stages, resulting in a poor prognosis. This article provides a comprehensive review of the two main types of esophageal cancer, esophageal squamous cell carcinoma and esophageal adenocarcinoma, and reviews epidemiology, risk factors, pathogenesis, diagnostic modalities, staging systems, and established and emerging treatments. Recent advancements in treatment for resectable and unresectable esophageal cancer also are explored. These include immunotherapy, targeted therapy, sentinel lymph node mapping, radiogenomics, palliative measures, and screening measures.

Decoding tumor stage by peritumoral and intratumoral radiomics in resectable esophageal squamous cell carcinoma

PURPOSE

To evaluate the potential application of radiomics in predicting Tumor-Node-Metastasis (TNM) stage in patients with resectable esophageal squamous cell carcinoma (ESCC).

METHODS

This retrospective study included 122 consecutive patients (mean age, 57 years; 27 women). Corresponding tumor of interest was identified on axial arterial-phase CT images with manual annotation. Radiomics features were extracted from intra- and peritumoral regions. Features were pruned to train LASSO regression model with 93 patients to construct a radiomics signature, whose performance was validated in a test set of 29 patients. Prognostic value of radiomics-predicted TNM stage was estimated by survival analysis in the entire cohort.

RESULTS

The radiomics signature incorporating one intratumoral and four peritumoral features was significantly associated with TNM stage. This signature discriminated tumor stage with an area under curve (AUC) of 0.823 in the training set, with similar performance in the test set (AUC 0.813). Recurrence-free survival (RFS) was significantly different between different radiomics-predicted TNM stage groups (Low-risk vs high-risk, log-rank P = 0.004). Univariate and multivariate Cox regression analyses revealed that radiomics-predicted TNM stage was an independent preoperative factor for RFS.

CONCLUSIONS

The proposed radiomics signature combing intratumoral and peritumoral features was predictive of TNM stage and associated with prognostication in ESCC.

MR imaging characteristics of different pathologic subtypes of esophageal carcinoma

OBJECTIVES

To describe the specific MRI characteristics of different pathologic subtypes of esophageal carcinoma (EC) METHODS: This prospective study included EC patients who underwent esophageal MRI and esophagectomy between April 2015 and October 2021. Pathomorphological characteristics of EC such as localized type (LT), ulcerative type (UT), protruding type (PT), and infiltrative type (IT) were assessed by two radiologists relying on the imaging characteristics of tumor, especially the specific imaging findings on the continuity of the mucosa overlying the tumor, the opposing mucosa, mucosa linear thickening, and transmural growth pattern. Intraclass correlation coefficients (ICC) were calculated for the consistency between two readers. The associations of imaging characteristics with different pathologic subtypes were assessed using multilogistic regression model (MLR).

RESULTS

A total of 201 patients were identified on histopathology with a high inter-reader agreement (ICC = 0.991). LT showed intact mucosa overlying the tumor. IT showed transmural growth pattern extending from the mucosa to the adventitia and a "sandwich" appearance. The remaining normal mucosa on the opposing side was linear and nodular in UT. PT showed correlation with T1 staging and grade 1; IT showed correlation with T3 staging and grades 2-3. Four MLR models showed high predictive performance on the test set with AUCs of 0.94 (LT), 0.87 (PT), 0.96 (IT), and 0.97 (UT), respectively, and the predictors that contributed most to the models matched the four specific characteristics.

CONCLUSIONS

Different pathologic subtypes of EC displayed specific MR imaging characteristics, which could help predict T staging and the degree of pathological differentiation.

CLINICAL RELEVANCE STATEMENT

Different pathologic subtypes of esophageal carcinoma displayed specific MR imaging characteristics, which correspond to differences in the degree of differentiation, T staging, and sensitivity to radiotherapy, and could also be one of the predictive factors of cause-specific survival and local progression-free rates.

KEY POINTS

Different types of EC had different characteristics on MR images. A total of 91/95 (96%) LTEC showed intact mucosa over the tumor, while masses or nodules are specific to PTEC; 21/27 (78%) ITEC showed a "sandwich" sign; and 33/35 (60%) UTEC showed linear and nodular opposing mucosa. In the association of tumor type with degree of differentiation and T staging, PTEC was predominantly associated with T1 and grade 1, and ITEC was associated with T3 and grades 2-3, while LTEC and UECT were likewise primarily linked with T2-3 and grades 2-3.

A radiomics nomogram for predicting postoperative recurrence in esophageal squamous cell carcinoma

Purpose

To establish and validate a radiomics nomogram for predicting recurrence of esophageal squamous cell carcinoma (ESCC) after esophagectomy with curative intent.

Materials and methods

The medical records of 155 patients who underwent surgical treatment for pathologically confirmed ESCC were collected. Patients were randomly divided into a training group (n=109) and a validation group (n=46) in a 7:3 ratio. ​Tumor regions are accurately segmented in computed tomography images of enrolled patients. Radiomic features were then extracted from the segmented tumors. We selected the features by Max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) methods. A radiomics signature was then built by logistic regression analysis. To improve predictive performance, a radiomics nomogram that incorporated the radiomics signature and independent clinical predictors was built. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses (DCA).

Results

We selected the five most relevant radiomics features to construct the radiomics signature. The radiomics model had general discrimination ability with an area under the ROC curve (AUC) of 0.79 in the training set that was verified by an AUC of 0.76 in the validation set. The radiomics nomogram consisted of the radiomics signature, and N stage showed excellent predictive performance in the training and validation sets with AUCs of 0.85 and 0.83, respectively. Furthermore, calibration curves and the DCA analysis demonstrated good fit and clinical utility of the radiomics nomogram.

Conclusion

We successfully established and validated a prediction model that combined radiomics features and N stage, which can be used to predict four-year recurrence risk in patients with ESCC who undergo surgery.

Effects of deep learning on radiologists' and radiology residents' performance in identifying esophageal cancer on CT

OBJECTIVE

To investigate the effectiveness of a deep learning model in helping radiologists or radiology residents detect esophageal cancer on contrast-enhanced CT images.

METHODS

This retrospective study included 250 and 25 patients with and without esophageal cancer, respectively, who underwent contrast-enhanced CT between December 2014 and May 2021 (mean age, 67.9 ± 10.3 years; 233 men). A deep learning model was developed using data from 200 and 25 patients with esophageal cancer as training and validation data sets, respectively. The model was then applied to the test data set, consisting of additional 25 and 25 patients with and without esophageal cancer, respectively. Four readers (one radiologist and three radiology residents) independently registered the likelihood of malignant lesions using a 3-point scale in the test data set. After the scorings were completed, the readers were allowed to reference to the deep learning model results and modify their scores, when necessary.

RESULTS

The area under the curve (AUC) of the deep learning model was 0.95 and 0.98 in the image- and patient-based analyses, respectively. By referencing to the deep learning model results, the AUCs for the readers were improved from 0.96/0.93/0.96/0.93 to 0.97/0.95/0.99/0.96 (p = 0.100/0.006/<0.001/<0.001, DeLong's test) in the image-based analysis, with statistically significant differences noted for the three less-experienced readers. Furthermore, the AUCs for the readers tended to improve from 0.98/0.96/0.98/0.94 to 1.00/1.00/1.00/1.00 (p = 0.317/0.149/0.317/0.073, DeLong's test) in the patient-based analysis.

CONCLUSION

The deep learning model mainly helped less-experienced readers improve their performance in detecting esophageal cancer on contrast-enhanced CT.

ADVANCES IN KNOWLEDGE

A deep learning model could mainly help less-experienced readers to detect esophageal cancer by improving their diagnostic confidence and diagnostic performance.

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.

Combination of clinical, radiomic, and "delta" radiomic features in survival prediction of metastatic gastroesophageal adenocarcinoma

Objectives

To identify combined clinical, radiomic, and delta-radiomic features in metastatic gastroesophageal adenocarcinomas (GEAs) that may predict survival outcomes.

Methods

A total of 166 patients with metastatic GEAs on palliative chemotherapy with baseline and treatment/follow-up (8-12 weeks) contrast-enhanced CT were retrospectively identified. Demographic and clinical data were collected. Three-dimensional whole-lesional radiomic analysis was performed on the treatment/follow-up scans. "Delta" radiomic features were calculated based on the change in radiomic parameters compared to the baseline. The univariable analysis (UVA) Cox proportional hazards model was used to select clinical variables predictive of overall survival (OS) and progression-free survival (PFS) (p-value <0.05). The radiomic and "delta" features were then assessed in a multivariable analysis (MVA) Cox model in combination with clinical features identified on UVA. Features with a p-value <0.01 in the MVA models were selected to assess their pairwise correlation. Only non-highly correlated features (Pearson's correlation coefficient <0.7) were included in the final model. Leave-one-out cross-validation method was used, and the 1-year area under the receiver operating characteristic curve (AUC) was calculated for PFS and OS.

Results

Of the 166 patients (median age of 59.8 years), 114 (69%) were male, 139 (84%) were non-Asian, and 147 (89%) had an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1. The median PFS and OS on treatment were 3.6 months (95% CI 2.86, 4.63) and 9 months (95% CI 7.49, 11.04), respectively. On UVA, the number of chemotherapy cycles and number of lesions at the end of treatment were associated with both PFS and OS (p < 0.001). ECOG status was associated with OS (p = 0.0063), but not PFS (p = 0.054). Of the delta-radiomic features, delta conventional HUmin, delta gray-level zone length matrix (GLZLM) GLNU, and delta GLZLM LGZE were incorporated into the model for PFS, and delta shape compacity was incorporated in the model for OS. Of the treatment/follow-up radiomic features, shape compacity and neighborhood gray-level dependence matrix (NGLDM) contrast were used in both models. The combined 1-year AUC (Kaplan-Meier estimator) was 0.82 and 0.81 for PFS and OS, respectively.

Conclusions

A combination of clinical, radiomics, and delta-radiomic features may predict PFS and OS in GEAs with reasonable accuracy.

CT radiomics features of meso-esophageal fat in predicting overall survival of patients with locally advanced esophageal squamous cell carcinoma treated by definitive chemoradiotherapy

OBJECTIVE

To investigate the value of CT radiomics features of meso-esophageal fat in the overall survival (OS) prediction of patients with locally advanced esophageal squamous cell carcinoma (ESCC).

METHODS

A total of 166 patients with locally advanced ESCC in two medical centers were retrospectively analyzed. The volume of interest (VOI) of meso-esophageal fat and tumor were manually delineated on enhanced chest CT using ITK-SNAP. Radiomics features were extracted from the VOIs by Pyradiomics and then selected using the t-test, the Cox regression analysis, and the least absolute shrinkage and selection operator. The radiomics scores of meso-esophageal fat and tumors for OS were constructed by a linear combination of the selected radiomic features. The performance of both models was evaluated and compared by the C-index. Time-dependent receiver operating characteristic (ROC) analysis was employed to analyze the prognostic value of the meso-esophageal fat-based model. A combined model for risk evaluation was constructed based on multivariate analysis.

RESULTS

The CT radiomic model of meso-esophageal fat showed valuable performance for survival analysis, with C-indexes of 0.688, 0.708, and 0.660 in the training, internal, and external validation cohorts, respectively. The 1-year, 2-year, and 3-year ROC curves showed AUCs of 0.640-0.793 in the cohorts. The model performed equivalently compared to the tumor-based radiomic model and performed better compared to the CT features-based model. Multivariate analysis showed that meso-rad-score was the only factor associated with OS.

CONCLUSIONS

A baseline CT radiomic model based on the meso-esophagus provide valuable prognostic information for ESCC patients treated with dCRT.

Imaging Recommendations for Diagnosis, Staging, and Management of Esophageal Cancer

Early staging and treatment initiation affect prognosis of patients with esophageal and esophagogastric junction cancer; hence, it is imperative to have knowledge of proper choice of imaging modality for staging of these patients, to effectively convey relevant imaging findings to the treating physician/surgeon. It is also essential to be aware of pertinent imaging findings that need to be conveyed to the treating physician/surgeon at staging, and after treatment, including post-therapy complications (if any), so as to provide timely management to such patients. In this article, we have provided imaging guidelines for diagnosis, staging, post-therapy response evaluation, follow-up, and assessment of post-therapy complications of esophageal and esophagogastric junction cancer in a systematic manner. Besides, risk factors and clinical workup have also been elucidated. We have also attached comprehensive staging and post-therapy contrast-enhanced computed tomography and fluorodeoxyglucose-positron emission tomography/computed tomography-based synoptic reporting formats “ECI-RADS” and “pECI-RADS,” respectively, for esophageal and esophagogastric junction cancer in the supplement, for effective communication of imaging findings between a radiologist and the treating physician/surgeon.

Role of Positron Emission Tomography/Computed Tomography in Gastrointestinal Malignancies: A Brief Review and Pictorial Essay

Positron emission tomography/computed tomography (PET/CT) is increasingly becoming a mainstay in diagnosis and management of many malignant disorders. However, its role in the assessment of gastro-intestinal lesions is still evolving. The aim of this review was to demonstrate the areas, where PET/CT is impactful and where it has limitations. This will allow for us to reduce unnecessary investigations and develop methods to overcome the limitations.

ImmunoPET: Antibody-Based PET Imaging in Solid Tumors

Immuno-positron emission tomography (immunoPET) is a molecular imaging modality combining the high sensitivity of PET with the specific targeting ability of monoclonal antibodies. Various radioimmunotracers have been successfully developed to target a broad spectrum of molecules expressed by malignant cells or tumor microenvironments. Only a few are translated into clinical studies and barely into clinical practices. Some drawbacks include slow radioimmunotracer kinetics, high physiologic uptake in lymphoid organs, and heterogeneous activity in tumoral lesions. Measures are taken to overcome the disadvantages, and new tracers are being developed. In this review, we aim to mention the fundamental components of immunoPET imaging, explore the groundbreaking success achieved using this new technique, and review different radioimmunotracers employed in various solid tumors to elaborate on this relatively new imaging modality.

Advances in the curative management of oesophageal cancer

The incidence of oesophageal cancer, in particular adenocarcinoma, has markedly increased over the last four decades with adenocarcinoma becoming the dominant subtype in the West, and mortality rates are high. Nevertheless, overall survival of patients with oesophageal cancer has doubled in the past 20 years, with earlier diagnosis and improved treatments benefiting those patients who can be treated with curative intent. Advances in endotherapy, surgical approaches, and multimodal and other combination therapies have been reported. New vistas have emerged in targeted therapies and immunotherapy, informed by new knowledge in genomics and molecular biology, which present opportunities for personalised cancer therapy and novel clinical trials. This review focuses exclusively on the curative intent treatment pathway, and highlights emerging advances.

The Role of the Carnitine/Organic Cation Transporter Novel 2 in the Clinical Outcome of Patients With Locally Advanced Esophageal Carcinoma Treated With Oxaliplatin

Esophageal cancer is the ninth most common malignancy worldwide, ranking sixth in mortality. Platinum-based chemotherapy is commonly used for treating locally advanced esophageal cancer, yet it is ineffective in a large portion of patients. There is a need for reliable molecular markers with direct clinical application for a prospective selection of patients who can benefit from chemotherapy and patients in whom toxicity is likely to outweigh the benefit. The cytotoxic activity of platinum derivatives largely depends on the uptake and accumulation into cells, primarily by organic cation transporters (OCTs). The aim of the study was to investigate the impact of OCT expression on the clinical outcome of patients with esophageal cancer treated with oxaliplatin. Twenty patients with esophageal squamous cell carcinoma (SCC) were prospectively enrolled and surgical specimens used for screening OCT expression level by western blotting and/or immunostaining, and for culture of cancer cells. Sixty-seven patients with SCC who received oxaliplatin and for whom follow-up was available were retrospectively assessed for organic cation/carnitine transporter 2 (OCTN2) expression by real time RT-PCR and immunostaining. OCTN2 staining was also performed in 22 esophageal adenocarcinomas. OCTN2 function in patient-derived cancer cells was evaluated by assessing L-carnitine uptake and sensitivity to oxaliplatin. The impact of OCTN2 on oxaliplatin activity was also assessed in HEK293 cells overexpressing OCTN2. OCTN2 expression was higher in tumor than in normal tissues. In patient-derived cancer cells and HEK293 cells, the expression of OCTN2 sensitized to oxaliplatin. Patients treated with oxaliplatin who had high OCTN2 level in the tumor tissue had a reduced risk of recurrence and a longer survival time than those with low expression of OCTN2 in tumor tissue. In conclusion, OCTN2 is expressed in esophageal cancer and it is likely to contribute to the accumulation and cytotoxic activity of oxaliplatin in patients with esophageal carcinoma treated with oxaliplatin.

Prediction of Locoregional Recurrence-Free Survival of Oesophageal Squamous Cell Carcinoma After Chemoradiotherapy Based on an Enhanced CT-Based Radiomics Model

BACKGROUND AND PURPOSE

Chemoradiotherapy is the standard treatment for moderate and advanced oesophageal cancer. The aim of this study was to establish a predictive model based on enhanced computed tomography examination, and to evaluate its clinical value for detecting locoregional recurrence-free survival (LRFS) in cases of oesophageal squamous cell carcinoma after radiotherapy.

MATERIALS AND METHODS

In total, 218 patients with pathologically diagnosed oesophageal squamous cell carcinoma who received radical chemoradiotherapy from July 2016 to December 2017 were collected in this study. Patients were randomly divided into either a training group (n=153) or a validation group (n=65) in a 7:3 ratio. Clinical patient information was then recorded. The enhanced computed tomography scan images of the patients were imported into 3D-slicer software (version 4.8.1), and the radiomic features were extracted by the Python programme package. In the training group, the dimensionality reduction of the radiomic features was implemented by Lasso regression, and then a radiological label, the model of predicting LRFS, was established and evaluated. To achieve a better prediction performance, the radiological label was combined with clinical risk factor information to construct a radiomics nomogram. A receiver operating characteristic curve was used to evaluate the efficacy of different models. Calibration curves were used to assess the consistency between the predicted and observed recurrence risk, and the Hosmer-Lemeshow method was used to test model fitness. The C-index evaluated the discriminating ability of the prediction model. Decision curve analysis was used to determine the clinical value of the constructed prediction model.

RESULTS

Of the 218 patients followed up in this study, 44 patients (28.8%) in the training group and 21 patients (32.3%) in the validation group experienced recurrence. There was no difference in LRFS between the two groups (χ2 = 0.525, P=0.405). Lasso regression was used in the training group to select six significant radiomic features. The radiological label established using these six features had a satisfactory prediction performance. The C-index was 0.716 (95% CI: 0.645-0.787) in the training group and 0.718 (95% CI: 0.612-0.825) in the validation group. The radiomics nomogram, which included the radiological label and clinical risk factors, achieved a better prediction than the radiological label alone. The C-index was 0.742 (95% CI: 0.674-0.810) in the training group and 0.715 (95% CI: 0.609-0.820) in the validation group. The results of the calibration curve and decision curve analyses indicated that the radiomics nomogram was superior in predicting LRFS of oesophageal carcinoma after radiotherapy.

CONCLUSIONS

A radiological label was successfully established to predict the LRFS of oesophageal squamous cell carcinoma after radiotherapy. The radiomics nomogram was complementary to the clinical prognostic features and could improve the prediction of the LRFS after radiotherapy for oesophageal cancer.

Contrast-Enhanced CT-Based Radiomics Analysis in Predicting Lymphovascular Invasion in Esophageal Squamous Cell Carcinoma

Objectives

To develop a radiomics model based on contrast-enhanced CT (CECT) to predict the lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC) and provide decision-making support for clinicians.

Patients and Methods

This retrospective study enrolled 334 patients with surgically resected and pathologically confirmed ESCC, including 96 patients with LVI and 238 patients without LVI. All enrolled patients were randomly divided into a training cohort and a testing cohort at a ratio of 7:3, with the training cohort containing 234 patients (68 patients with LVI and 166 without LVI) and the testing cohort containing 100 patients (28 patients with LVI and 72 without LVI). All patients underwent preoperative CECT scans within 2 weeks before operation. Quantitative radiomics features were extracted from CECT images, and the least absolute shrinkage and selection operator (LASSO) method was applied to select radiomics features. Logistic regression (Logistic), support vector machine (SVM), and decision tree (Tree) methods were separately used to establish radiomics models to predict the LVI status in ESCC, and the best model was selected to calculate Radscore, which combined with two clinical CT predictors to build a combined model. The clinical model was also developed by using logistic regression. The receiver characteristic curve (ROC) and decision curve (DCA) analysis were used to evaluate the model performance in predicting the LVI status in ESCC.

Results

In the radiomics model, Sphericity and gray-level non-uniformity (GLNU) were the most significant radiomics features for predicting LVI. In the clinical model, the maximum tumor thickness based on CECT (cThick) in patients with LVI was significantly greater than that in patients without LVI (P<0.001). Patients with LVI had higher clinical N stage based on CECT (cN stage) than patients without LVI (P<0.001). The ROC analysis showed that both the radiomics model (AUC values were 0.847 and 0.826 in the training and testing cohort, respectively) and the combined model (0.876 and 0.867, respectively) performed better than the clinical model (0.775 and 0.798, respectively), with the combined model exhibiting the best performance.

Conclusions

The combined model incorporating radiomics features and clinical CT predictors may potentially predict the LVI status in ESCC and provide support for clinical treatment decisions.

Human Gut Microbiota-associated Gastrointestinal Malignancies: A Comprehensive Review

The human gastrointestinal tract houses trillions of microbes. The gut and various types of microorganisms, including bacteria, viruses, fungi, and archaea, form a complex ecosystem known as the gut microbiota, and the whole genome of the gut microbiota is referred to as the gut microbiome. The gut microbiota is essential for homeostasis and the overall well-being of a person and is increasingly considered an adjunct "virtual organ," with a complexity level comparable to that of the other organ systems. The gut microbiota plays an essential role in nutrition, local mucosal homeostasis, inflammation, and the mucosal immune system. An imbalanced state of the gut microbiota, known as dysbiosis, can predispose to development of various gastrointestinal malignancies through three speculated pathogenic mechanisms: (a) direct cytotoxic effects with damage to the host DNA, (b) disproportionate proinflammatory signaling inducing inflammation, and (c) activation of tumorigenic pathways or suppression of tumor-suppressing pathways. Several microorganisms, including Helicobacter pylori, Epstein-Barr virus, human papillomavirus, Mycoplasma species, Escherichia coli, and Streptococcus bovis, are associated with gastrointestinal malignancies such as esophageal adenocarcinoma, gastric adenocarcinoma, gastric mucosa-associated lymphoid tissue lymphoma, colorectal adenocarcinoma, and anal squamous cell carcinoma. Imaging plays a pivotal role in diagnosis and management of microbiota-associated gastrointestinal malignancies. Appropriate use of probiotics, fecal microbiota transplantation, and overall promotion of the healthy gut are ongoing areas of research for prevention and treatment of malignancies. Online supplemental material is available for this article. ^©RSNA, 2021.

Interleukin-18 and -10 may be associated with lymph node metastasis in breast cancer

Reports on the expression of interleukin (IL)-10 in breast cancer are rare. The present study investigated the correlation between IL-18 and −10 in breast cancer, and assessed their clinical significance. Breast cancer (n=104) and breast fibroadenoma (n=31) tissues that were surgically removed and pathologically confirmed at Jinan Central Hospital Affiliated to Shandong University (Jinan, China) between November 2016 and January 2019 were collected. The expression of IL-18 and −10 was observed via immunohistochemistry. Breast cancer tissues were positive for IL-18 expression, which was primarily located in the cell membrane and cytoplasm. A significant difference in IL-18 expression was observed between breast cancer and fibroadenoma tissues (75.0 vs. 19.4%; P<0.001). IL-10 was expressed in breast cancer tissues and primarily located in the cytoplasm. Breast cancer tissues showed a significantly higher level of IL-10 expression compared with breast fibroadenoma tissues (78.8 vs. 22.6%; P<0.001). The regions of positive IL-18 and −10 expression were consistent. Tissues with positive expression of IL-18 and/or −10 had a significantly higher rate of lymph node metastasis than those with negative expression (IL-18: 67.9 vs. 42.3%; P=0.035; and IL-10: 67.1 vs. 40.9%; P=0.047). In conclusion, IL-18 is highly expressed in breast cancer and correlates positively with IL-10. Both IL-18 and −10 may correlate positively with lymph node metastasis in breast cancer.