Improving CT detection sensitivity for nodal metastases in oesophageal cancer with combination of smaller size and lymph node axial ratio

CT-based radiomics for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis

Objective

We aimed to evaluate the diagnostic effectiveness of computed tomography (CT)-based radiomics for predicting lymph node metastasis (LNM) in patients diagnosed with esophageal cancer (EC).

Methods

The present study conducted a comprehensive search by accessing the following databases: PubMed, Embase, Cochrane Library, and Web of Science, with the aim of identifying relevant studies published until July 10th, 2023. The diagnostic accuracy was summarized using the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC). The researchers utilized Spearman's correlation coefficient for assessing the threshold effect, besides performing meta-regression and subgroup analysis for the exploration of possible heterogeneity sources. The quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies-2 and the Radiomics Quality Score (RQS).

Results

The meta-analysis included six studies conducted from 2018 to 2022, with 483 patients enrolled and LNM rates ranging from 27.2% to 59.4%. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC, along with their corresponding 95% CI, were 0.73 (0.67, 0.79), 0.76 (0.69, 0.83), 3.1 (2.3, 4.2), 0.35 (0.28, 0.44), 9 (6, 14), and 0.78 (0.74, 0.81), respectively. The results demonstrated the absence of significant heterogeneity in sensitivity, while significant heterogeneity was observed in specificity; no threshold effect was detected. The observed heterogeneity in the specificity was attributed to the sample size and CT-scan phases (P < 0.05). The included studies exhibited suboptimal quality, with RQS ranging from 14 to 16 out of 36. However, most of the enrolled studies exhibited a low-risk bias and minimal concerns relating to applicability.

Conclusion

The present meta-analysis indicated that CT-based radiomics demonstrated a favorable diagnostic performance in predicting LNM in EC. Nevertheless, additional high-quality, large-scale, and multicenter trials are warranted to corroborate these findings.

Systematic Review Registration

Open Science Framework platform at https://osf.io/5zcnd.

Preoperative CT radiomics of esophageal squamous cell carcinoma and lymph node to predict nodal disease with a high diagnostic capability

PURPOSE

To develop CT radiomics models of resectable esophageal squamous cell carcinoma (ESCC) and lymph node (LN) to preoperatively identify LN+.

MATERIALS AND METHODS

299 consecutive patients with ESCC were enrolled in the study, 140 of whom were LN+ and 159 were LN-. Of the 299 patients, 249 (from the same hospital) were randomly divided into a training cohort (n = 174) and a test cohort (n = 75). The remaining 50 patients, from a second hospital, were assigned to an external validation cohort. In the training cohort, preoperative contrast-enhanced CT radiomics features of ESCC and LN were extracted, then integrated with clinical features to develop three models: ESCC, LN and combined. The performance of these models was assessed using area under receiver operating characteristic curve (AUC), and F-1 score, which were validated in both the test cohort and external validation cohort.

RESULTS

An ESCC model was developed for the training cohort utilizing the 8 tumor radiomics features, and an LN model was constructed using 9 nodal radiomics features. A combined model was constructed using both ESCC and LN extracted features, in addition to cT stage and LN+ distribution. This combined model had the highest predictive ability among the three models in the training cohort (AUC = 0.948, F1-score = 0.878). The predictive ability was validated in both the test and external validation cohorts (AUC = 0.885 and 0.867, F1-score = 0.816 and 0.773, respectively).

CONCLUSION

To preoperatively determine LN+, the combined model is superior to models of ESCC and LN alone.

Development and Validation of PET/CT-Based Nomogram for Preoperative Prediction of Lymph Node Status in Esophageal Squamous Cell Carcinoma

PURPOSE

This study was conducted to predict the lymph node status and survival of esophageal squamous cell carcinoma before treatment by PET-CT-related parameters.

METHODS

From January 2013 to July 2018, patients with pathologically diagnosed ESCC at our hospital were retrospectively enrolled. Completed esophagectomy and two- or three-field lymph node dissections were conducted. Those with neoadjuvant therapy were excluded. The first 65% of patients in each year were regarded as the training set and the last 35% as the test set. Nomogram was constructed by the "rms" package. Five-year, overall survival was analyzed based on the best cutoff value of risk score determined by the "survivalROC" package.

RESULTS

Ultimately, 311 patients were included with 209 in the training set and 102 in the test set. The positive rate of the lymph node in the training set was 36.8% and that in the test set was 32.4%. The C-index of the training set was 0.763 and the test set was 0.766. The decision curve analysis showed that it was superior to the previous methods based on lymph node uptake or long/short axis diameter or axial ratio. Risk score > 0.20 was significantly associated with 5-year, overall survival (p = 0.0015) in all patients.

CONCLUSIONS

The nomogram constructed from PET-CT parameters including primary tumor metabolic length and thickness can accurately predict the risk of lymph node metastasis in ESCC. The risk score calculated by our model accurately predicts the patient's 5-year overall survival.

Progress of magnetic resonance imaging radiomics in preoperative lymph node diagnosis of esophageal cancer

Esophageal cancer, also referred to as esophagus cancer, is a prevalent disease in the cardiothoracic field and is a leading cause of cancer-related mortality in China. Accurately determining the status of lymph nodes is crucial for developing treatment plans, defining the scope of intraoperative lymph node dissection, and ascertaining the prognosis of patients with esophageal cancer. Recent advances in diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging (MRI) have improved the effectiveness of MRI for assessing lymph node involvement, making it a beneficial tool for guiding personalized treatment plans for patients with esophageal cancer in a clinical setting. Radiomics is a recently developed imaging technique that transforms radiological image data from regions of interest into high-dimensional feature data that can be analyzed. The features, such as shape, texture, and waveform, are associated with the cancer phenotype and tumor microenvironment. When these features correlate with the clinical disease outcomes, they form the basis for specific and reliable clinical evidence. This study aimed to review the potential clinical applications of MRI-based radiomics in studying the lymph nodes affected by esophageal cancer. The combination of MRI and radiomics is a powerful tool for diagnosing and treating esophageal cancer, enabling a more personalized and effectual approach.

A radiomics nomogram based on contrast-enhanced CT for preoperative prediction of Lymphovascular invasion in esophageal squamous cell carcinoma

Background and purpose

To develop a radiomics nomogram based on contrast-enhanced computed tomography (CECT) for preoperative prediction of lymphovascular invasion (LVI) status of esophageal squamous cell carcinoma (ESCC).

Materials and methods

The clinical and imaging data of 258 patients with ESCC who underwent surgical resection and were confirmed by pathology from June 2017 to December 2021 were retrospectively analyzed.The clinical imaging features and radiomic features were extracted from arterial-phase CECT. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature selection and signature construction. Multivariate logistic regression analysis was used to develop a radiomics nomogram prediction model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance and clinical effectiveness of the model in preoperative prediction of LVI status.

Results

We constructed a radiomics signature based on eight radiomics features after dimensionality reduction. In the training cohort, the area under the curve (AUC) of radiomics signature was 0.805 (95% CI: 0.740-0.860), and in the validation cohort it was 0.836 (95% CI: 0.735-0.911). There were four predictive factors that made up the individualized nomogram prediction model: radiomic signatures, TNRs, tumor lengths, and tumor thicknesses.The accuracy of the nomogram for LVI prediction in the training and validation cohorts was 0.790 and 0.768, respectively, the specificity was 0.800 and 0.618, and the sensitivity was 0.786 and 0.917, respectively. The Delong test results showed that the AUC value of the nomogram model was significantly higher than that of the clinical model and radiomics model in the training and validation cohort(P<0.05). DCA results showed that the radiomics nomogram model had higher overall benefits than the clinical model and the radiomics model.

Conclusions

This study proposes a radiomics nomogram based on CECT radiomics signature and clinical image features, which is helpful for preoperative individualized prediction of LVI status in ESCC.

Recurrent metastasis risk factors in esophageal cancer after salvage endoscopic resection for local failure following chemoradiotherapy

OBJECTIVES

Although salvage endoscopic resection is an optimal treatment for local failure after chemoradiotherapy for esophageal squamous cell carcinoma, recurrent metastasis (lymph node and/or distant metastasis) after salvage endoscopic resection may occur with a certain degree of unavoidable frequency and is associated with a poor prognosis. However, the risk factors for recurrent metastasis are unclear. This study aimed to evaluate the risk factors for recurrent metastasis after salvage endoscopic resection.

METHODS

Patients who underwent salvage endoscopic resection for local failure after chemoradiotherapy/radiotherapy were analyzed in this single-center, retrospective study. We evaluated the cumulative incidence rates of recurrent metastases, overall survival, and the risk factors for recurrent metastasis after salvage endoscopic resection.

RESULTS

We analyzed 132 patients. The 5-year cumulative incidence rate of recurrent metastases after salvage endoscopic resection was 25.7%. The 5-year overall survival rates in all patients and in patients with recurrent metastasis were 66.8% and 22.5%, respectively. Local failure pattern with a residual lesion after chemoradiotherapy/radiotherapy (subdistribution hazard ratio 2.34; P = 0.012) and the presence of lymphatic invasion in salvage endoscopic resection specimen (subdistribution hazard ratio 3.20; P = 0.002) were significant risk factors for recurrent metastasis.

CONCLUSIONS

Patients with local failure pattern with a residual lesion after chemoradiotherapy/radiotherapy and presence of lymphatic invasion have a high risk for recurrent metastasis. Thus, appropriate surveillance for these patients should be considered.

Radiomics Analysis of Lymph Nodes with Esophageal Squamous Cell Carcinoma Based on Deep Learning

Purpose

To assess the role of multiple radiomic features of lymph nodes in the preoperative prediction of lymph node metastasis (LNM) in patients with esophageal squamous cell carcinoma (ESCC).

Methods

Three hundred eight patients with pathologically confirmed ESCC were retrospectively enrolled (training cohort, n = 216; test cohort, n = 92). We extracted 207 handcrafted radiomic features and 1000 deep radiomic features of lymph nodes from their computed tomography (CT) images. The t-test and least absolute shrinkage and selection operator (LASSO) were used to reduce the dimensions and select key features. Handcrafted radiomics, deep radiomics, and clinical features were combined to construct models. Models I (handcrafted radiomic features), II (Model I plus deep radiomic features), and III (Model II plus clinical features) were built using three machine learning methods: support vector machine (SVM), adaptive boosting (AdaBoost), and random forest (RF). The best model was compared with the results of two radiologists, and its performance was evaluated in terms of sensitivity, specificity, accuracy, area under the curve (AUC), and receiver operating characteristic (ROC) curve analysis.

Results

No significant differences were observed between cohorts. Ten handcrafted and 12 deep radiomic features were selected from the extracted features (p < 0.05). Model III could discriminate between patients with and without LNM better than the diagnostic results of the two radiologists.

Conclusion

The combination of handcrafted radiomic features, deep radiomic features, and clinical features could be used clinically to assess lymph node status in patients with ESCC.

A novel web-based dynamic nomogram for recurrent laryngeal nerve lymph node metastasis in esophageal squamous cell carcinoma

Background

Patients with esophageal squamous cell carcinoma (ESCC) are liable to develop recurrent laryngeal nerve (RLN) lymph node metastasis (LNM). We aimed to assess the predictive value of the long diameter (LD) and short diameter (SD) of RLN lymph node (LN) and construct a web-based dynamic nomogram for RLN LNM prediction.

Methods

We reviewed 186 ESCC patients who underwent RLN LN dissection from January 2016 to December 2018 in the Affiliated Hospital of North Sichuan Medical College. Risk factors for left and right RLN LNM were determined by univariate and multivariate analyses. A web-based dynamic nomogram was constructed by using logistic regression. The performance was assessed by the area under the curve (AUC) and Brier score. Models were internally validated by performing five-fold cross-validation.

Results

Patients who underwent left and right RLN LN dissection were categorized as left cohort (n = 132) and right cohort (n = 159), with RLN LNM rates of 15.9% (21/132) and 21.4% (34/159), respectively. The AUCs of the LD (SD) of RLN LN were 0.663 (0.688) in the left cohort and 0.696 (0.705) in the right cohort. The multivariate analysis showed that age, the SD of RLN LN, and clinical T stage were significant risk factors for left RLN LNM (all P < 0.05), while tumor location, the SD of RLN LN, and clinical T stage were significant risk factors for right RLN LNM (all P < 0.05). The dynamic nomograms showed reliable performance after five-fold cross-validation [(left (right), mean AUC: 0.814, range: 0.614–0.891 (0.775, range: 0.084–0.126); mean Brier score: 0.103, range: 0.084–0.126 (0.145, range: 0.105–0.206)], available at https://mpthtw.shinyapps.io/leftnomo/ and https://mpthtw.shinyapps.io/rightnomo/.

Conclusion

The LD and SD of RLN LN are inadequate to predict RLN LNM accurately, but online dynamic nomograms by combined risk factors show better prediction performance and convenient clinical application.

Optimal settings of near-infrared fluorescence imaging with indocyanine green for intraoperative detection of lymph node metastasis in esophageal cancer

Lymphatic flow mapping using near-infrared fluorescence (NIR) imaging with indocyanine green (ICG) has been used for intraoperative diagnosis of lymph node metastasis (LNM) in various cancers. Accurate prediction of LNM intraoperatively may allow minimization of the extent of lymphadenectomy. However, a consistent method and diagnostic ability, allowing application of NIR-guided lymphatic flow mapping to esophageal cancer (EC), have not been established due to the multidirectional and complex characteristics of lymphatic flow in the esophagus. Herein, we present a novel NIR-guided surgical technique for predicting lymph node stations potentially containing LNM in EC with high diagnostic accuracy derived from appropriately adjusting the ICG injection setting.

A Narrative Review of Preventive Central Lymph Node Dissection in Patients With Papillary Thyroid Cancer - A Necessity or an Excess

Objective

This review article summarises the latest evidence for preventive central lymph node dissection in patients with papillary thyroid cancer taking into account the possible complications and risk of recurrence.

Background

Papillary thyroid cancer is the most frequent histological variant of malignant neoplasms of the thyroid gland. It accounts for about 80-85% of all cases of thyroid cancer. Despite good postoperative results and an excellent survival rate in comparison with many other malignant diseases, tumor metastases to the cervical lymph nodes are frequent. Most researchers agree that the presence of obvious metastases in the lymph nodes requires careful lymph node dissection. It was suggested to perform preventive routine lymphadenectomy in all patients with malignant thyroid diseases referred to surgery.

Methods

It was performed the literature review using the “papillary thyroid cancer”, “central lymph node dissection”, “hypocalcemia”, “recurrent laryngeal nerve paresis”, “metastasis”, “cancer recurrence” along with the MESH terms. The reference list of the articles was carefully reviewed as a potential source of information. The search was based on Medline, Scopus, Google Scholar, eLibrary engines. Selected publications were analyzed and their synthesis was used to write the review and analyse the role of preventive central lymph node dissection in patients with papillary thyroid cancer.

Conclusions

The necessity of preventive central lymph node dissection in patients with differentiated papillary thyroid carcinoma is still controversial. There is much evidence that it increases the frequency of transient hypocalcemia. Due to the fact that this complication is temporary, its significance in clinical practice is debatable. It can also be assumed that an extant of surgery in the neck area is associated with an increased risk of recurrent laryngeal nerve injury. However, most studies indicate that this injury is associated more with thyroidectomy itself than with lymph node dissection. Recurrent laryngeal nerve dysfunction is also a temporary complication in the vast majority of cases. At the same time, a large amount of data shows that central lymph node dissection reduces the risk of thyroid cancer recurrence in two times.

Optimal criteria for predicting lymph node metastasis in esophageal squamous cell carcinoma by anatomical location using preoperative computed tomography: a retrospective cohort study

PURPOSE

Predicting lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) is critical for selecting appropriate treatments despite the low accuracy of computed tomography (CT) for detecting LNM. Variation in potential nodal sizes among locations or patients' clinicopathological background factors may impact the diagnostic quality. This study explored the optimal criteria and diagnostic ability of CT by location.

METHODS

We retrospectively reviewed preoperative CT scans of 229 patients undergoing curative esophagectomy. We classified nodal stations into six groups: Cervical (C), Right-upper mediastinal (U_R), Left-upper mediastinal (U_L), Middle mediastinal (M), Lower mediastinal (L), and Abdominal (A). We then measured the short-axial diameter (SAD) of the largest lymph node in each area. We used receiver operating characteristics analyses to evaluate the CT diagnostic ability and determined the cut-off values for the SAD in all groups.

RESULTS

Optimal cut-offs were 6.5 mm (M), 6 mm (C, L, and A), and 5 mm (U_R and U_L). Diagnostic abilities differed among locations, and U_R had the highest sensitivity. A multivariate analysis showed poor differentiation to be an independent risk factor for a false-negative diagnosis (p = 0.044).

CONCLUSIONS

Optimal criteria and diagnostic abilities for predicting LNM in ESCC varied among locations, and poor differentiation might contribute to failure to detect LNM.

Can Lymphovascular Invasion be Predicted by Preoperative Contrast-Enhanced CT in Esophageal Squamous Cell Carcinoma?

Objective: To explore whether preoperative contrast-enhanced computed tomogrpahy (CT) can predict lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC), and provide a reliable reference for the formulation of clinical individualized treatment plans. Methods: This retrospective study enrolled 228 patients with surgically resected and pathologically confirmed ESCC, including 36 patients with LVI and 192 patients without LVI. All patients underwent contrast-enhanced CT (CECT) scan within 2 weeks before the operation. Tumor size (including tumor length and maximum tumor thickness), tumor-to-normal wall enhancement ratio (TNR), and gross tumor volume (GTV) were obtained. All clinical features and CECT-derived parameters associated with LVI were analyzed by univariate and multivariate analysis. The independent predictors for LVI were identified, and their combination was built by multivariate logistic regression analysis, using the significant variables from the univariate analysis as inputs. Results: Univariate analysis of clinical features and CECT-derived parameters revealed that age, TNR, and clinical N stage (cN stage) were significantly associated with LVI. The multivariable analysis results demonstrated that age (odds ratio [OR]: 5.32, 95% confidence interval [CI]: 2.224-12.743, P<.001), TNR (OR: 5.399, 95% CI: 1.609-18.110, P  =  .006), and cN stage (cN1: OR: 2.874, 95% CI: 1.182-6.989, P  =  .02; cN2: OR: 6.876, 95% CI: 2.222-21.227) were identified to be independent predictors for LVI. The combination of age, TNR, and cN stage achieved a relatively higher area under the curve (AUC) (0.798), accuracy (ACC) (65.4%), sensitivity (SEN) (69.4%), specificity (SPE) (79.7%), positive predictive value (PPV) (77.4%), and negative predictive value (NPV) (71.6%). Conclusions: The combination of clinical features and CECT-derived parameters may be effective in predicting LVI status preoperatively in ESCC.

Imaging features of sentinel lymph node mapped by multidetector-row computed tomography lymphography in predicting axillary lymph node metastasis

INTRODUCTION

Accurately assessing axillary lymph node (ALN) status in breast cancer is vital for clinical decision making and prognosis. The purpose of this study was to evaluate the predictive value of sentinel lymph node (SLN) mapped by multidetector-row computed tomography lymphography (MDCT-LG) for ALN metastasis in breast cancer patients.

METHODS

112 patients with breast cancer who underwent preoperative MDCT-LG examination were included in the study. Long-axis diameter, short-axis diameter, ratio of long-/short-axis and cortical thickness were measured. Logistic regression analysis was performed to evaluate independent predictors associated with ALN metastasis. The prediction of ALN metastasis was determined with related variables of SLN using receiver operating characteristic (ROC) curve analysis.

RESULTS

Among the 112 cases, 35 (30.8%) cases had ALN metastasis. The cortical thickness in metastatic ALN group was significantly thicker than that in non-metastatic ALN group (4.0 ± 1.2 mm vs. 2.4 ± 0.7 mm, P < 0.001). Multi-logistic regression analysis indicated that cortical thickness of > 3.3 mm (OR 24.53, 95% CI 6.58-91.48, P < 0.001) had higher risk for ALN metastasis. The best sensitivity, specificity, negative predictive value(NPV) and AUC of MDCT-LG for ALN metastasis prediction based on the single variable of cortical thickness were 76.2%, 88.5%, 90.2% and 0.872 (95% CI 0.773-0.939, P < 0.001), respectively.

CONCLUSION

ALN status can be predicted using the imaging features of SLN which was mapped on MDCT-LG in breast cancer patients. Besides, it may be helpful to select true negative lymph nodes in patients with early breast cancer, and SLN biopsy can be avoided in clinically and radiographically negative axilla.

Impact of postoperative lymph node status on the prognosis of esophageal squamous cell carcinoma after esophagectomy following neoadjuvant chemoradiotherapy: a retrospective study

BACKGROUND

Neoadjuvant chemoradiotherapy (nCRT) and surgery are widely used treatments for locally advanced esophageal squamous cell carcinoma (ESCC). Thus, it is critically important to investigate risk factors that affect patient prognosis after preoperative chemoradiotherapy and surgery.

METHODS

We conducted a retrospective analysis of 77 patients with ESCC who received nCRT and underwent surgery at our center from January 2015 to December 2019. We analyzed the primary clinical data, postoperative pathological results, recurrence, and death results.

RESULTS

Among the 77 ESCC patients who received nCRT and surgery, 19 achieved a postoperative pathologic complete response (pCR), and the overall pCR rate was 24.68%. The univariate analysis indicated that postoperative post-neoadjuvant treatment N stage (ypN) metastasis [hazards ratio (HR): 2.908; 95% confidence interval (CI): 0.874-9.676; P=0.082], a high lymph-node ratio [(LNR) >0.1] (HR: 7.149, 95% CI: 1.740-29.369; P=0.006), post-neoadjuvant treatment T3-4 (ypT3-4) (HR: 3.626, 95% CI: 0.824-15.956; P=0.088) affected disease-specific survival (DSS). The multivariate analysis indicated that a high LNR (>0.1) (HR: 6.170; 95% CI: 1.472-25.856; P=0.013) was a significant independent predictor of DSS. The univariate analysis indicated that postoperative ypN metastasis (HR: 2.283; 95% CI: 1.047-4.979; P=0.038) and a high LNR (>0.1) (HR: 4.210; 95% CI: 1.547-11.458; P=0.005) were associated with recurrence-free survival (RFS). The multivariate survival analysis showed that a high LNR (>0.1) (HR: 4.289; 95% CI: 1.538-11.965; P=0.005) was also a significant independent predictor of RFS. In this study, 57 positive lymph nodes were found in 30 of the 77 patients, including 16 left gastric lymph nodes, 9 pericardial lymph nodes, and 7 left supraclavicular lymph nodes.

CONCLUSIONS

A high LNR (>0.1) in ESCC patients after nCRT is a risk factor of DSS and RFS. ypN metastasis is also an independent predictor of RFS. Left gastric-arterial lymph nodes, para-cardiac lymph nodes, and left supraclavicular lymph nodes are the most common sites of metastasis in ESCC after nCRT.

Optimal Size Criterion for Malignant Lymph Nodes and a Novel Lymph Node Clinical Staging System for Unresectable Esophageal Squamous Cell Carcinoma: Evaluation by Multislice Spiral Computed Tomography

Objectives: The current Chinese draft nodal clinical staging system for unresectable esophageal cancer is controversial. Our study aimed to propose a new diagnostic criterion for lymph node metastasis (LNM) detected by multislice spiral computed tomography (MSCT) in nonsurgically treated esophageal squamous cell carcinoma (ESCC) patients and then develop a novel lymph node (LN) clinical staging system for better individual prognostic prediction. Methods: The short-axis diameters of regional LNs were measured in 393 nonsurgical patients. Regional nodes were considered positive for malignancy if the nodal size exceeded the optimal size, which was determined by Kaplan-Meier survival analysis. The novel LN clinical staging system was then constructed using the LASSO model based on the relative prognostic importance of different LN stations. Validation cohort was included to confirm the prognostic performance. Results: Regional nodes were considered positive for malignancy if they were larger than 10 mm in the low cervical and upper thoracic segments, 7 mm in the middle thoracic segment, and 8 mm in the lower thoracic and celiac segments. Using the LASSO model, stations 2R, 3A, 7 and 16 were qualified in the model. Further analysis showed that our LN clinical staging system had better homogeneity, discriminatory ability and clinical value than the draft nodal staging system. Conclusions: Our results show that the new diagnostic criterion may improve the diagnostic value of MSCT in metastatic LNs. The novel LN clinical staging system can stratify nonsurgically treated ESCC patients into different risk groups, providing valuable information for decision making and outcome prediction.

Unresected small lymph node assessment predicts prognosis for patients with pT3N0M0 thoracic esophageal squamous cell carcinoma

Background

The role of unresected small lymph nodes (LNs) which may contain metastases for thoracic esophageal squamous cell carcinoma (TESCC) has not been addressed. The aim of the study was to investigate the role of unresected small LNs assessment using computed tomography (CT) in prognostic estimates of pT3N0M0 TESCC patients.

Methods

Between January 2009 and December 2017, 294 patients who underwent esophagectomy with R0 resection at Sichuan Cancer Hospital were retrospectively examined, and the last follow-up time was July 2018. Patients were classified into CT-suspect and CT-negative groups according to the shortest diameter and the shape (axial ratio) of the unresected small LNs on preoperative CT. The Kaplan–Meier method was used to compare survival differences in prognostic factors. Univariate and multivariate analyses were performed to identify prognostic factors for survival and recurrence.

Results

Eighty-four patients (28.6%) were classified as CT-suspect group according to the diagnostic criteria; survival analysis suggested that CT-suspect group of patients had a relatively poorer prognosis (P<0.05). Cox regression analysis indicated that unresected small LNs status, tumor grade, and postoperative adjuvant therapy were independent prognostic factors for patients with pT3N0M0 TESCC (P<0.05). Further analysis shown the rates of total recurrence (TR) and locoregional recurrence (LR) in the CT-suspect group were significantly higher than that in the CT-negative group (TR, P<0.001; LR, P<0.001). Among the LRs, the rate of supraclavicular lymph node recurrence in the CT-suspect group was significantly higher than that in the CT-negative group (P<0.001).

Conclusions

Unresected small lymph node assessment is critically important and predict prognosis for pT3N0M0 TESCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02412-1.

Clinical TNM staging for esophageal, gastric, and colorectal cancers in the era of neoadjuvant therapy: A systematic review of the literature

AIM

Clinical staging is vital for selecting appropriate candidates and designing neoadjuvant treatment strategies for advanced tumors. The aim of this review was to evaluate diagnostic abilities of clinical TNM staging for gastrointestinal, gastrointestinal cancers.

METHODS

We conducted a systematic review of recent publications to evaluate the accuracy of diagnostic modalities on gastrointestinal cancers. A systematic literature search was performed in PubMed/MEDLINE using the keywords "TNM staging," "T4 staging," "distant metastases," "esophageal cancer," "gastric cancer," and "colorectal cancer," and the search terms used in Cochrane Reviews between January 2005 to July 2020. Articles focusing on preoperative diagnosis of: (a) depth of invasion; (b) lymph node metastases; and (c) distant metastases were selected.

RESULTS

After a full-text search, a final set of 55 studies (17 esophageal cancer studies, 26 gastric cancer studies, and 12 colorectal cancer studies) were used to evaluate the accuracy of clinical TNM staging. Positron emission tomography-computed tomography (PET-CT) and/or magnetic resonance imaging (MRI) were the best modalities to assess distant metastases. Fat and fiber mode of CT may be useful for T4 staging of esophageal cancer, CT was a partially reliable modality for lymph node staging in gastric cancer, and CT combined with MRI was the most reliable modality for liver metastases from colorectal cancer.

CONCLUSION

The most reliable diagnostic modality differed among gastrointestinal cancers depending on the type of cancer. Therefore, we propose diagnostic algorithms for clinical staging for each type of cancer.

Interobserver variability in target volume delineation in definitive radiotherapy for thoracic esophageal cancer: a multi-center study from China

Purpose

To investigate the interobserver variability (IOV) in target volume delineation of definitive radiotherapy for thoracic esophageal cancer (TEC) among cancer centers in China, and ultimately improve contouring consistency as much as possible to lay the foundation for multi-center prospective studies.

Methods

Sixteen cancer centers throughout China participated in this study. In Phase 1, three suitable cases with upper, middle, and lower TEC were chosen, and participants were asked to contour a group of gross tumor volume (GTV-T), nodal gross tumor volume (GTV-N) and clinical target volume (CTV) for each case based on their routine experience. In Phase 2, the same clinicians were instructed to follow a contouring protocol to re-contour another group of target volume. The variation of the target volume was analyzed and quantified using dice similarity coefficient (DSC).

Results

Sixteen clinicians provided routine volumes, whereas ten provided both routine and protocol volumes for each case. The IOV of routine GTV-N was the most striking in all cases, with the smallest DSC of 0.37 (95% CI 0.32–0.42), followed by CTV, whereas GTV-T showed high consistency. After following the protocol, the smallest DSC of GTV-N was improved to 0.64 (95% CI 0.45–0.83, P = 0.005) but the DSC of GTV-T and CTV remained constant in most cases.

Conclusion

Variability in target volume delineation was observed, but it could be significantly reduced and controlled using mandatory interventions.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s13014-020-01691-4.

Development of a prediction model for the risk of recurrent laryngeal nerve lymph node metastasis in thoracolaparoscopic esophagectomy with cervical anastomosis

Background

There are no effective preoperative diagnostic measures to predict the probability of left and right recurrent laryngeal nerve (RLN) lymph node (LN) metastasis using preoperative clinical data in patients undergoing thoracolaparoscopic esophagectomy with cervical anastomosis.

Methods

We retrospectively reviewed the clinical data of 1,660 consecutive patients with thoracic esophageal cancer who underwent esophagectomy with cervical anastomosis at the Department of Thoracic Surgery at the First Affiliated Hospital of Zhengzhou University between January 2015 and December 2020.

Results

A total of 299 and 343 patients who underwent left (Cohort 1) and right (Cohort 2) RLN LN dissection were included in the final analyses. The analyses were conducted within each cohort. Among the 299 patients in Cohort 1, left RLN LN involvement was found in 41 patients (13.7%). A multivariable analysis showed that age, tumor location, and short axis were significantly associated with RLN LN metastasis (all P<0.05). Among the 343 patients in Cohort 2, right RLN LN involvement was found in 65 patients (19.0%). A multivariable analysis showed that computed tomography (CT) appearance, tumor location, long axis, and short axis were significantly associated with RLN LN metastasis (all P<0.05). Based on the results of the multivariable analyses, we constructed nomograms that could estimate the probability of RLN LN metastasis. Finally, we stratified the 2 cohorts into risk subgroups using a recursive partitioning analysis (RPA). The risk of left and right RLN LN metastasis was found to be 9.3% and 7.5%, 27.3% and 21.4%, and 52.4% and 47.3% for the low-risk, intermediate-risk, and high-risk groups, respectively.

Conclusions

Our nomograms and RPAs appear to be suitable for the risk stratification of left and right RLN LN metastasis in patients undergoing thoracolaparoscopic esophagectomy with cervical anastomosis. This tool could be used to help clinicians to select more effective locoregional treatments, such as surgical protocols and radiation area selection.

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.

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.

CT radiomics features to predict lymph node metastasis in advanced esophageal squamous cell carcinoma and to discriminate between regional and non-regional lymph node metastasis: a case control study

Background

Prediction of lymph node status in esophageal squamous cell carcinoma (ESCC) is critical for clinical decision making. In clinical practice, computed tomography (CT) has been frequently used to assist in the preoperative staging of ESCC. Texture analysis can provide more information to reflect potential biological heterogeneity based on CT. A nomogram for the preoperative diagnosis of lymph node metastasis in patients with resectable ESCC has been previously developed. However, to the best of our knowledge, no reports focus on developing CT radiomics features to discriminate ESCC patients with regional lymph node metastasis (RLNM) and non-regional lymph node metastasis (NRLNM). We, therefore, aimed to develop CT radiomics models to predict lymph node metastasis (LNM) in advanced ESCC and to discriminate ESCC between RLNM and NRLNM.

Methods

This study enrolled 334 patients with pathologically confirmed advanced ESCC, including 152 patients without LNM and 182 patients with LNM, and 103 patients with RLNM and 79 patients NRLNM. Radiomics features were extracted from CT data for each patient. The least absolute shrinkage and selection operator (LASSO) model and independent samples t-tests or Mann-Whitney U tests were exploited for dimension reduction and selection of radiomics features. Optimal radiomics features were chosen using multivariable logistic regression analysis. The discriminating performance was assessed by area under the receiver operating characteristic curve (AUC) and accuracy.

Results

The radiomics features were developed based on multivariable logistic regression and were significantly associated with LNM status in both the training and validation cohorts (P<0.001). The radiomics models could differentiate between patients with and without LNM (AUC =0.79 and 0.75, and accuracy =0.75 and 0.71 in the training and validation cohorts, respectively). In patients with LNM, the radiomics features could effectively differentiate between RLNM and NRLNM (AUC =0.98 and 0.95, and accuracy =0.94 and 0.83 in the training and validation cohorts, respectively).

Conclusions

CT radiomics features could help predict the LNM status of advanced ESCC patients and effectively discriminate ESCC between RLNM and NRLNM.

Enlarged Mediastinal Lymph Nodes in Computed Tomography are a Valuable Prognostic Factor in Non-Small Cell Lung Cancer Patients with Pathologically Negative Lymph Nodes

Background

Most non-small cell lung cancer patients with enlarged mediastinal lymph nodes (LN) in preoperative computer tomography (CT) images are diagnosed with N0 in the pathological examination after surgery. However, these patients seem to have worse survival than those without enlarged mediastinal LN in our clinical practice. This study aimed to investigate whether the size of mediastinal LN is correlated with the prognosis in pathological N0 patients, which could help us to predict the prognoses further.

Methods

The retrospective cohort study involved 758 N0 patients with a thin layer CT scan. We have measured the size of mediastinal LN, including long diameter, short diameter, and volume on CT image, and classified patients by X-tile. Next, we explored the risk factors of enlarged LN by univariate and multivariate logistic analysis. Then, we have compared the 5-year cancer-specific survival by Kaplan-Meier and log-rank method. Multivariate Cox analysis was utilized to further survival analysis. Finally, we have constructed the prediction model by nomogram.

Results

A total of 150 N0 patients (19.8%) had mediastinal LN enlargement in our study. After multivariate logistic analysis, we found the LN enlargement was significantly correlated with age (p=0.001), pathology (p < 0.001) and tumor recurrence (p < 0.001). The patients with LN enlargement had a worse 5-year cancer-specific survival (75.3% vs 92.8%, p < 0.001) after Kaplan-Meier analysis. Patients with a larger volume had increased risk of tumor-associated death when compared with the normal group (p < 0.001) by multivariate Cox analyses.

Conclusion

N0 patients with larger mediastinal LN had a worse 5-year cancer-specific survival and a higher risk of recurrence. The volume of LN was the most valuable prognostic factor in N0 patients.

Role of precision imaging in esophageal cancer

Esophageal cancer is a major cause of morbidity and mortality worldwide. Recent advancements in the management of esophageal cancer have allowed for earlier detection, improved ability to monitor progression, and superior treatment options. These innovations allow treatment teams to formulate more customized management plans and have led to an increase in patient survival rates. For example, in order for the most effective management plan to be constructed, accurate staging must be performed to determine tumor resectability. This article reviews the multimodality imaging approach involved in making a diagnosis, staging, evaluating treatment response and detecting recurrence in esophageal cancer.

Investigation of the value of ultrasound-guided core needle biopsy from pathologic lymph nodes to the diagnosis of lymphoma

Introduction:

In recent years, techniques with minimally invasive have been gradually developed and used in the diagnosis of lymphoma. Among minimally invasive techniques, core needle biopsy (CNB) has been widely accepted as an effective tool for the diagnosis of malignant lymphoma, carcinoma and deep tumors that are only accessible via CT or endoscopic-guided. This study was conducted to investigate of diagnostic value of ultrasound guided CNB in the diagnosis of lymphoma in all parts of the body compared to surgical excisional biopsy (SEB).

Materials and Methods:

This is an descriptive epidemiological study that was performed on patients with suspected lymphoma referred to the intervention ward of Golestan Hospital in Ahvaz in 2019. For all patients with suspected lymphoma, CNB of lymph nodes was performed by ultrasound-guided. Finally, the final diagnosis of CNB was compared with the results of surgical biopsy in the studied specimens.

Results:

In this study, 40 patients were evaluated with suspected lymphoma. At initial diagnosis with CNB, 12 (30%) had NHL, 19 (47.5%) had Hodgkin's lymphoma, and 2 had high-grade lymphoma. Of the 40 patients examined, 29 required IHC to confirm the diagnosis. In 8 cases, the final diagnosis was done using SEB. Final diagnosis in 9 (22.5%) patients was confirmed by CNB only. The CNB along with the IHC also led to the final diagnosis in 23 (57.5%) patients. However, another 8 patients required biopsy to confirm the diagnosis by SEB.

Conclusion:

The findings of this study indicated that US-CNB is a highly efficient method of diagnosis of lymphoma with high specificity, in the fastest possible mode and with the least complications.

Evaluation of metastatic lymph nodes in cN0 thoracic esophageal cancer patients with inconsistent pathological lymph node diagnosis

BACKGROUND

Preoperative clinical diagnosis of lymph node (LN) metastasis and subsequent pathological diagnosis are often not in agreement. Detection of false-negative LNs is essential in selecting an optimal treatment strategy, and most importantly, the presence of false-negative LN is itself a significant prognostic indicator. Therefore, at present, there is an urgent need to establish more accurate and individualized evaluation methods for LN metastasis.

METHODS

Of 213 cN0 patients who underwent curative esophagectomy without preoperative neoadjuvant treatment, 60 (28%) had LN metastasis diagnosed pathologically. There were 129 false-negative LNs, of which 85 were detectable by preoperative computed tomography (CT). We retrospectively investigated the distribution, frequency, and characteristics of pathologically positive nodes in patients with clinically N0 esophageal cancer.

RESULTS

The paracardial region was the most frequent region of false-negative LNs, accounting for 26% (22 LNs) of the total incidence. False-negative LNs distributed widely from the neck to the abdomen in patients with a primary tumor in the middle thoracic esophagus. In patients with a primary tumor in the lower thoracic esophagus, four false-negative LNs were detected in the superior mediastinum. When the short-axis diameter, shape, and attenuation patterns of the LNs were used as criteria for metastasis diagnosis, they were insufficient for an accurate diagnosis. However, false-negative LNs in the most frequently occurring sites are characterized by smaller short-axis, suggesting that accurate diagnosis cannot be made unless the diagnostic criteria for the short-axis are reduced in addition to shape and attenuation.

CONCLUSIONS

Although restrictive to the most frequent regions of false-negative LNs occur, reducing size criterion and consideration of their shape and attenuation may contribute to improved diagnosis.

Combined CT texture analysis and nodal axial ratio for detection of nodal metastasis in esophageal cancer

OBJECTIVE

To assess the accuracy of a combination of CT texture analysis (CTTA) and nodal axial ratio to detect metastatic lymph nodes (LNs) in esophageal squamous cell carcinoma (ESCC).

METHODS

The contrast-enhanced chest CT images of 78 LNs (40 metastasis, 38 benign) from 38 patients with ESCC were retrospectively analyzed. Nodal axial ratios (short-axis/long-axis diameter) were calculated. CCTA parameters (kurtosis, entropy, skewness) were extracted using commercial software (TexRAD) with fine, medium, and coarse spatial filters. Combinations of significant texture features and nodal axial ratios were entered as predictors in logistic regression models to differentiate metastatic from benign LNs, and the performance of the logistic regression models was analyzed using the area under the receiver operating characteristic curve (AUROC).

RESULTS

The mean axial ratio of metastatic LNs was significantly higher than that of benign LNs (0.81 ± 0.2 vs 0.71 ± 0.1, p = 0.005; sensitivity 82.5%, specificity 47.4%); namely, significantly more round than benign. The mean values of the entropy (all filters) and kurtosis (fine and medium) of metastatic LNs were significantly higher than those of benign LNs (all, p < 0.05). Medium entropy showed the best performance in the AUROC analysis with 0.802 (p < 0.001; sensitivity 85.0%, specificity 63.2%). A binary logistic regression analysis combining the nodal axial ratio, fine entropy, and fine kurtosis identified metastatic LNs with 87.5% sensitivity and 65.8% specificity (AUROC = 0.855, p < 0.001).

CONCLUSION

The combination of CTTA features and the axial ratio of LNs has the potential to differentiate metastatic from benign LNs and improves the sensitivity for detection of LN metastases in ESCC.

ADVANCES IN KNOWLEDGE

The combination of CTTA and nodal axial ratio has improved CT sensitivity (up to 87.5%) for the diagnosis of metastatic LNs in esophageal cancer.

Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images

OBJECTIVE

To investigate the feasibility of a noninvasive detection of lymph node metastasis (LNM) for early-stage cervical cancer (ECC) patients with radiomics methods based on the textural features from ultrasound images.

METHODS

One hundred seventy-two ECC patients between January 2014 and September 2018 with pathologically confirmed lymph node status (LNS) and preoperative ultrasound images were retrospectively reviewed. Regions of interest (ROIs) were delineated by a senior radiologist in the ultrasound images. LIFEx was applied to extract textural features for radiomics study. Least absolute shrinkage and selection operator (LASSO) regression was applied for dimension reduction and for selection of key features. A multivariable logistic regression analysis was adopted to build the radiomics signature. The Mann-Whitney U test was applied to investigate the correlation between radiomics and LNS for both training and validation cohorts. Receiver operating characteristic (ROC) curves were applied to evaluate the accuracy of the radiomics prediction models.

RESULTS

A total of 152 radiomics features were extracted from ultrasound images, in which 6 features were significantly associated with LNS (p < 0.05). The radiomics signatures demonstrated a good discrimination between patients with LNM and non-LNM groups. The best radiomics performance model achieved an area under the curve (AUC) of 0.79 (95% confidence interval (CI), 0.71-0.88) in the training cohort and 0.77 (95% CI, 0.65-0.88) in the validation cohort.

CONCLUSIONS

The feasibility of radiomics features from ultrasound images for the prediction of LNM in ECC was investigated. This noninvasive prediction method may be used to facilitate preoperative identification of LNS in patients with ECC.

KEY POINTS

• Few studied had investigated the feasibility of radiomics based on ultrasound images for cervical cancer, even though it is the most common practice for gynecological cancer diagnosis and treatment. • The radiomics signatures based on ultrasound images demonstrated a good discrimination between patients with and without lymph node metastasis with an area under the curve (AUC) of 0.79 and 0.77 in the training and validation cohorts, respectively. • The radiomics model based on preoperative ultrasound images has the potential ability to predict lymph node status noninvasively in patients with early-state cervical cancer, so as to reduce the impact of invasive examination and to optimize the treatment choices.

Predictive Value of Preoperative Multidetector-Row Computed Tomography for Axillary Lymph Nodes Metastasis in Patients With Breast Cancer

Introduction: Axillary lymph nodes (ALN) status is an essential component in tumor staging and treatment planning for patients with breast cancer. The aim of present study was to evaluate the predictive value of preoperative multidetector-row computed tomography (MDCT) for ALN metastasis in breast cancer patients.

Methods: A total of 148 cases underwent preoperative MDCT examination and ALN surgery were eligible for the study. Logistic regression analysis of MDCT variates was used to estimate independent predictive factors for ALN metastasis. The prediction of ALN metastasis was determined with MDCT variates through receiver operating characteristic (ROC) analysis.

Results: Among the 148 cases, 61 (41.2%) cases had ALN metastasis. The cortical thickness in metastatic ALN was significantly thicker than that in non-metastatic ALN (7.5 ± 5.0 mm vs. 2.6 ± 2.8 mm, P < 0.001). Multi-logistic regression analysis indicated that cortical thickness of >3 mm (OR: 12.32, 95% CI: 4.50–33.75, P < 0.001) and non-fatty hilum (OR: 5.38, 95% CI: 1.51–19.19, P = 0.009) were independent predictors for ALN metastasis. The sensitivity, specificity and AUC of MDCT for ALN metastasis prediction based on combined-variated analysis were 85.3%, 87.4%, and 0.893 (95% CI: 0.832–0.938, P < 0.001), respectively.

Conclusions: Cortical thickness (>3 mm) and non-fatty hilum of MDCT were independent predictors for ALN metastasis. MDCT is a potent imaging tool for predicting ALN metastasis in breast cancer. Future prospective study on the value of contrast enhanced MDCT in preoperative ALN evaluation is warranted.

Minimally Invasive Salvage Operations for Esophageal Cancer after Definitive Chemoradiotherapy

BACKGROUND/AIMS

Because salvage surgery after definitive chemoradiotherapy for esophageal cancer is associated with high postoperative mortality and morbidity, minimally invasive methods are desirable. We analyzed the validity of minimally invasive salvage operations (MISO).

METHODS

Twenty-five patients underwent salvage operation between 2010 and 2016 in our institution, 10 having undergone right transthoracic salvage esophagectomy (TTSE group), 6 transhiatal salvage esophagectomy (THSE), 6 salvage lymphadenectomy (SLA), and 3 salvage endoscopic submucosal dissection (SESD). Patients who had undergone THSE, SLA, or SESD were categorized as the MISO group. Short- and long-term outcomes were assessed.

RESULTS

The mean duration of surgery was significantly shorter in the SLA groups than in the TTSE group (p = 0.0248). Blood loss was significantly less in the SLA than the TTSE group (p = 0.0340). Intensive care unit stay was shorter in the THSE than the TTSE group (p = 0.0412). There was no significant difference in postoperative mortality between the MISO and THSE groups. Postoperative hospital stay was significantly shorter in the SLA than the TTSE group (p = 0.0061). Patients' survivals did not differ significantly between the MISO and TTSE groups (p = 0.752). Multivariate analysis revealed that residual disease (R0; HR 4.872, 95% CI 1.387-17.110, p = 0.013) was the only independent factor influencing overall survival.

CONCLUSION

MISO is preferable because short-term outcomes are better and long-term outcomes do not differ from those of TTSE.

The MR radiomic signature can predict preoperative lymph node metastasis in patients with esophageal cancer

PURPOSE

To assess the role of the MR radiomic signature in preoperative prediction of lymph node (LN) metastasis in patients with esophageal cancer (EC).

PATIENTS AND METHODS

A total of 181 EC patients were enrolled in this study between April 2015 and September 2017. Their LN metastases were pathologically confirmed. The first half of this cohort (90 patients) was set as the training cohort, and the second half (91 patients) was set as the validation cohort. A total of 1578 radiomic features were extracted from MR images (T2-TSE-BLADE and contrast-enhanced StarVIBE). The lasso and elastic net regression model was exploited for dimension reduction and selection of the feature space. The multivariable logistic regression analysis was adopted to identify the radiomic signature of pathologically involved LNs. The discriminating performance was assessed with the area under receiver-operating characteristic curve (AUC). The Mann-Whitney U test was adopted for testing the potential correlation of the radiomic signature and the LN status in both training and validation cohorts.

RESULTS

Nine radiomic features were selected to create the radiomic signature significantly associated with LN metastasis (p < 0.001). AUC of radiomic signature performance in the training cohort was 0.821 (95% CI: 0.7042-0.9376) and in the validation cohort was 0.762 (95% CI: 0.7127-0.812). This model showed good discrimination between metastatic and non-metastatic lymph nodes.

CONCLUSION

The present study showed MRI radiomic features that could potentially predict metastatic LN involvement in the preoperative evaluation of EC patients.

KEY POINTS

• The role of MRI in preoperative staging of esophageal cancer patients is increasing. • MRI radiomic features showed the ability to predict LN metastasis in EC patients. • ICCs showed excellent interreader agreement of the extracted MR features.