Diagnostic performance of diffusion-weighted magnetic resonance imaging in assessing lymph node metastasis of esophageal cancer compared with PET

Preoperative dual-energy computed tomography and positron-emission tomography evaluation of lymph node metastasis in esophageal squamous cell carcinoma

PURPOSE

To investigate the detectability of lymph node metastasis in patients with esophageal squamous cell carcinoma using a combination of dual-energy computed tomography (CT) and positron-emission tomography (PET) parameters.

METHODS

We analyzed dual-energy CT and PET preoperative data in 27 consecutive patients with esophageal squamous cell carcinoma (23 men, 4 women; mean age, 73.7 years). We selected lymph nodes with a short-axis diameter of ≥5 mm and measured CT values, iodine concentrations, fat fractions, long- and short-axis diameters, and ratio of long- and short-axis diameters. We performed visual assessment of lymph node characteristics based on dual-energy CT and determined the maximum standardized uptake value via PET. The measured values were postoperatively compared between pathologically confirmed metastatic and nonmetastatic lymph nodes. Stepwise logistic regression analysis was performed to determine factors associated with lymph node metastasis. Diagnostic accuracy was assessed via receiver operating characteristic curve analysis.

RESULTS

Overall, 18 metastatic and 37 nonmetastatic lymph nodes were detected. CT values, iodine concentrations, fat fractions, and the maximum standardized uptake values differed significantly between metastatic and nonmetastatic lymph nodes (p < 0.05). Stepwise logistic regression showed that iodine concentration and the maximum standardized uptake value were significant predictors of metastatic lymph nodes. The areas under the curve of iodine concentrations and maximum standardized uptake values were 0.809 and 0.833, respectively. The area under the curve of the combined parameters was 0.884, with 83.3% sensitivity and 86.5% specificity.

CONCLUSION

Combined dual-energy CT and PET parameters improved the diagnosis of lymph node metastasis in patients with esophageal cancer.

Diffusion-Weighted Magnetic Resonance Imaging for the Diagnosis of Lymph Node Metastasis in Patients with Biliary Tract Cancer

Objective: The diagnostic efficacy of the apparent diffusion coefficient (ADC) in diffusion-weighted magnetic resonance imaging (DW-MRI) for lymph node metastasis in biliary tract cancer was investigated in the present study. Methods: In total, 112 surgically resected lymph nodes from 35 biliary tract cancer patients were examined in this study. The mean and minimum ADC values of the lymph nodes as well as the long-axis and short-axis diameters of the lymph nodes were assessed by computed tomography (CT). The relationship between these parameters and the presence of histological lymph node metastasis was evaluated. Results: Histological lymph node metastasis was detected in 31 (27.7%) out of 112 lymph nodes. Metastatic lymph nodes had a significantly larger short-axis diameter compared with non-metastatic lymph nodes (p = 0.002), but the long-axis diameter was not significantly different between metastatic and non-metastatic lymph nodes. The mean and minimum ADC values for metastatic lymph nodes were significantly reduced compared with those for non-metastatic lymph nodes (p < 0.001 for both). However, the minimum ADC value showed the highest accuracy for the diagnosis of histological lymph node metastasis, with an area under the curve of 0.877, sensitivity of 87.1%, specificity of 82.7%, and accuracy of 83.9%. Conclusions: The minimum ADC value in DW-MRI is highly effective for the diagnosis of lymph node metastasis in biliary tract cancer. Accurate preoperative diagnosis of lymph node metastasis in biliary tract cancer should enable the establishment of more appropriate treatment strategies.

Radiomics diagnostic performance for predicting lymph node metastasis in esophageal cancer: a systematic review and meta-analysis

BACKGROUND

Esophageal cancer, a global health concern, impacts predominantly men, particularly in Eastern Asia. Lymph node metastasis (LNM) significantly influences prognosis, and current imaging methods exhibit limitations in accurate detection. The integration of radiomics, an artificial intelligence (AI) driven approach in medical imaging, offers a transformative potential. This meta-analysis evaluates existing evidence on the accuracy of radiomics models for predicting LNM in esophageal cancer.

METHODS

We conducted a systematic review following PRISMA 2020 guidelines, searching Embase, PubMed, and Web of Science for English-language studies up to November 16, 2023. Inclusion criteria focused on preoperatively diagnosed esophageal cancer patients with radiomics predicting LNM before treatment. Exclusion criteria were applied, including non-English studies and those lacking sufficient data or separate validation cohorts. Data extraction encompassed study characteristics and radiomics technical details. Quality assessment employed modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) tools. Statistical analysis involved random-effects models for pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Heterogeneity and publication bias were assessed using Deek's test and funnel plots. Analysis was performed using Stata version 17.0 and meta-DiSc.

RESULTS

Out of 426 initially identified citations, nine studies met inclusion criteria, encompassing 719 patients. These retrospective studies utilized CT, PET, and MRI imaging modalities, predominantly conducted in China. Two studies employed deep learning-based radiomics. Quality assessment revealed acceptable QUADAS-2 scores. RQS scores ranged from 9 to 14, averaging 12.78. The diagnostic meta-analysis yielded a pooled sensitivity, specificity, and AUC of 0.72, 0.76, and 0.74, respectively, representing fair diagnostic performance. Meta-regression identified the use of combined models as a significant contributor to heterogeneity (p-value = 0.05). Other factors, such as sample size (> 75) and least absolute shrinkage and selection operator (LASSO) usage for feature extraction, showed potential influence but lacked statistical significance (0.05 < p-value < 0.10). Publication bias was not statistically significant.

CONCLUSION

Radiomics shows potential for predicting LNM in esophageal cancer, with a moderate diagnostic performance. Standardized approaches, ongoing research, and prospective validation studies are crucial for realizing its clinical applicability.

Radiomics-clinical nomogram for preoperative lymph node metastasis prediction in esophageal carcinoma

OBJECTIVES

This research aimed to develop a radiomics-clinical nomogram based on enhanced thin-section CT radiomics and clinical features for the purpose of predicting the presence or absence of metastasis in lymph nodes among patients with resectable esophageal squamous cell carcinoma (ESCC).

METHODS

This study examined the data of 256 patients with ESCC, including 140 cases with lymph node metastasis. Clinical information was gathered for each case, and radiomics features were derived from thin-section contrast-enhanced CT with the help of a 3D slicer. To validate risk factors that are independent of the clinical and radiomics models, least absolute shrinkage and selection operator logistic regression analysis was used. A nomogram pattern was constructed based on the radiomics features and clinical characteristics. The receiver operating characteristic curve and Brier Score were used to evaluate the model's discriminatory ability, the calibration plot to evaluate the model's calibration, and the decision curve analysis to evaluate the model's clinical utility. The confusion matrix was used to evaluate the applicability of the model. To evaluate the efficacy of the model, 1000 rounds of 5-fold cross-validation were conducted.

RESULTS

The clinical model identified esophageal wall thickness and clinical T (cT) stage as independent risk factors, whereas the radiomics pattern was built based on 4 radiomics features chosen at random. Area under the curve (AUC) values of 0.684 and 0.701 are observed for the radiomics approach and clinical model, respectively. The AUC of nomogram combining radiomics and clinical features was 0.711. The calibration plot showed good agreement between the incidence of lymph node metastasis predicted by the nomogram and the actual probability of occurrence. The nomogram model displayed acceptable levels of performance. After 1000 rounds of 5-fold cross-validation, the AUC and Brier score had median values of 0.702 (IQR: 0.65, 7.49) and 0.21 (IQR: 0.20, 0.23), respectively. High-risk patients (risk point >110) were found to have an increased risk of lymph node metastasis [odds ratio (OR) = 5.15, 95% CI, 2.95-8.99] based on the risk categorization.

CONCLUSION

A successful preoperative prediction performance for metastasis to the lymph nodes among patients with ESCC was demonstrated by the nomogram that incorporated CT radiomics, wall thickness, and cT stage.

ADVANCES IN KNOWLEDGE

This study demonstrates a novel radiomics-clinical nomogram for lymph node metastasis prediction in ESCC, which helps physicians determine lymph node status preoperatively.

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.

Low anti-CFL1 antibody with high anti-ACTB antibody is a poor prognostic factor in esophageal squamous cell carcinoma

BACKGROUND

Cofilin (CFL1, actin-binding protein) and β-actin (ACTB) are key molecules in the polymerization and depolymerization of actin microfilaments. The levels of these antibodies were analyzed, and the clinicopathological significance in patients with esophageal carcinoma were evaluated.

METHODS

The levels of anti-CFL1 and anti-ACTB antibodies were analyzed in serum samples of patients with esophageal carcinoma and of healthy donors. Eighty-seven cases underwent radical surgery and the clinicopathological characteristics and prognosis was examined.

RESULTS

Serum anti-CFL1 antibody (s-CFL1-Ab) levels and anti-ACTB antibody (s-ACTB-Ab) levels were significantly higher in patients with esophageal carcinoma than in healthy donors. Following the receiver operating characteristic curve analysis between healthy donors and esophageal carcinoma, the sensitivity and specificity for serum anti-CFL1 antibody (s-CFL1-Ab) were 53.3% and 68.8%. The sensitivity and specificity for serum anti-ACTB antibody (s-ACTB-Ab) were 54.9% and 67.7%, respectively. Univariate and multivariate analysis showed that s-CFL1-Ab and s-ACTB-Ab levels were not associated with sex, age, tumor depth, lymph node metastasis, or anti-p53-antibody levels. s-ACTB-Ab levels but not s-CFL1-Ab levels significantly correlated with squamous cell carcinoma antigen. Neither s-CFL1-Ab nor s-ACTB-Ab levels alone were obviously related to overall survival. However, patients with low s-CFL1-Ab levels and high s-ACTB-Ab levels exhibited significantly more unfavorable prognoses than those with high s-CFL1-Ab and low s-ACTB-Ab levels.

CONCLUSIONS

Serum levels of anti-CFL1 and anti-ACTB antibodies were significantly higher in patients with esophageal carcinoma than in healthy donors. A combination of low anti-CFL1 and high anti-ACTB antibodies is a poor prognostic factor in esophageal carcinoma.

Predicting Grade of Esophageal Squamous Carcinoma: Can Stretched Exponential Model-Based DWI Perform Better Than Bi-Exponential and Mono-Exponential Model?

Background

To evaluate and compare the potential performance of various diffusion parameters obtained from mono-exponential model (MEM)-, bi-exponential model (BEM)-, and stretched exponential model (SEM)-based diffusion-weighted imaging (DWI) in grading of esophageal squamous carcinoma (ESC).

Methods

Eighty-two patients with pathologically confirmed ESC without treatment underwent multi-b-value DWI scan with 13 b values (0~12,00 s/mm²). The apparent diffusion coefficient (ADC) deriving from the MEM; the pure molecular diffusion (ADC_slow), pseudo-diffusion coefficient (ADC_fast), perfusion, and fraction (f) deriving from the BEM; and the distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) deriving from the SEM were calculated and compared between poorly differentiated and well/moderately differentiated ESC, respectively. The prediction parameters and diagnostic efficiency were compared by drawing receiver operating characteristic (ROC) curves.

Results

The ADC, ADC_slow, ADC_fast, and DDC in poorly ESC were significantly lower than those in well/moderately differentiated ones. By using only one parameter, ADC_slow, DDC had the moderate diagnostic efficiency and the areas under the curve (AUC) were 0.758 and 0.813 in differentiating ESC. The DDC had the maximum AUC with sensitivity (88.00%) and specificity (68.42%). Combining ADC with ADC_fast, ADC_slow, and DDC and combining ADC_slow with ADC_fast can provide a higher diagnostic accuracy with AUC ranging from 0.756, 0.771, 0.816, and 0.793, respectively.

Conclusion

Various parameters derived from different DWI models including MEM, BEM, and SEM were potentially helpful in grading ESC. DDC obtained from SEM was the most promising diffusion parameter for predicting the grade of ESC.

Preoperative Diagnosis of Lymph Node Metastasis of Perihilar Cholangiocarcinoma Using Diffusion-Weighted Magnetic Resonance Imaging

INTRODUCTION

Although the prognosis of patients with resected perihilar cholangiocarcinoma (PHC) with histological lymph node metastasis (LNM) is poor, preoperative prediction of LNM is difficult. This study aimed to evaluate the diagnostic performance of diffusion-weighted magnetic resonance imaging (DWI) for LNM of PHC.

METHOD

Consecutive patients who underwent surgical resection of PHC between January 2012 and May 2020 were retrospectively reviewed. The lymph node (LN) area (mm²) and apparent diffusion coefficient (ADC) value ( × 10^-3 mm²/s) of pericholedochal LNs were measured by DWI. The characteristics of the patients and the LNs were evaluated according to the histological presence or absence of regional LNM. Univariate and multivariate analyses were performed to identify the predictors of LNM of PHC.

RESULTS

Of the 93 eligible patients, 49 (53%) were LNM positive and 44 (47%) were LNM negative. Although the characteristics of the patients were similar between the two groups, the mean ADC value was significantly lower in the LNM positive group than in the LNM negative group. On multivariate analysis, mean ADC value ≤1.80 × 10^-3 mm²/s was independently associated with LNM of PHC (risk ratio: 12.5, 95% confidence interval: 3.05-51.4; p = 0.0004). The sensitivity, specificity and accuracy of mean ADC values ≤ 1.80 × 10^-3 mm²/s for predicting LNM of PHC were 94%, 55% and 75%, respectively.

CONCLUSIONS

DWI might be useful for the preoperative diagnosis of LNM of PHC.

The Role of Magnetic Resonance Imaging in the Management of Esophageal Cancer

Esophageal cancer (EC) is the eighth more frequent cancer worldwide, with a poor prognosis. Initial staging is critical to decide on the best individual treatment approach. Current modalities for the assessment of EC are irradiating techniques, such as computed tomography (CT) and positron emission tomography/CT, or invasive techniques, such as digestive endoscopy and endoscopic ultrasound. Magnetic resonance imaging (MRI) is a non-invasive and non-irradiating imaging technique that provides high degrees of soft tissue contrast, with good depiction of the esophageal wall and the esophagogastric junction. Various sequences of MRI have shown good performance in initial tumor and lymph node staging in EC. Diffusion-weighted MRI has also demonstrated capabilities in the evaluation of tumor response to chemoradiotherapy. To date, there is not enough data to consider whole body MRI as a routine investigation for the detection of initial metastases or for prediction of distant recurrence. This narrative review summarizes the current knowledge on MRI for the management of EC.

Diffusion-weighted MRI and 18F-FDG PET/CT in assessing the response to neoadjuvant chemoradiotherapy in locally advanced esophageal squamous cell carcinoma

BACKGROUND

Neoadjuvant chemoradiotherapy (nCRT) followed by surgery is a currently widely used strategy for locally advanced esophageal cancer (EC). However, the conventional imaging methods have certain deficiencies in the evaluation and prediction of the efficacy of nCRT. This study aimed to explore the value of functional imaging in predicting the response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced esophageal squamous cell carcinoma (ESCC).

METHODS

Fifty-four patients diagnosed with locally advanced ESCC from August 2017 to September 2019 and treated with nCRT were retrospectively analyzed. DW-MRI scanning was performed before nCRT, at 10-15 fractions of radiotherapy, and 4-6 weeks after the completion of nCRT. ¹⁸F-FDG PET/CT scans were performed before nCRT and 4-6 weeks after the completion of nCRT. These ¹⁸F-FDG PET/CT and DW-MRI parameters and relative changes were compared between patients with pathological complete response (pCR) and non-pCR.

RESULTS

A total of 8 of 54 patients (14.8%) were evaluated as disease progression in the preoperative assessment. The remaining forty-six patients underwent operations, and the pathological assessments of the surgical resection specimens demonstrated pathological complete response (pCR) in 10 patients (21.7%) and complete response of primary tumor (pCR-T) in 16 patients (34.8%). The change of metabolic tumor volume (∆MTV) and change of total lesion glycolysis (∆TLG) were significantly different between patients with pCR and non-pCR. The SUVmax-T_post, MTV-T_post, and TLG-T_post of esophageal tumors in ¹⁸F-FDG PET/CT scans after neoadjuvant chemoradiotherapy and the ∆ SUVmax-T and ∆MTV-T were significantly different between pCR-T versus non-pCR-T patients. The esophageal tumor apparent diffusion coefficient (ADC) increased after nCRT; the ADC_during, ADC_post and ∆ADC_during were significantly different between pCR-T and non-pCR-T groups. ROC analyses showed that the model that combined ADC_during with TLG-T_post had the highest AUC (0.914) for pCR-T prediction, with 90.0% and 86.4% sensitivity and specificity, respectively.

CONCLUSION

¹⁸F-FDG PET/CT is useful for re-staging after nCRT and for surgical decision. Integrating parameters of ¹⁸F-FDG PET/CT and DW-MRI can identify pathological response of primary tumor to nCRT more accurately in ESCC.

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.

New frontiers in esophageal radiology

Esophageal cancer is the sixth most common cause of cancer related mortality worldwide. Advances in treatment have translated into steadily improving survival rates. Accurate preoperative staging of esophageal cancer is imperative in order to provide an accurate prognosis and direct patients to the most appropriate treatment. Current preoperative staging relies on imaging, most commonly endoscopic ultrasound (EUS), computed tomography (CT) and positron emission tomography (PET). A combination of these modalities should be used in preoperative staging, as each has advantages over another. Magnetic resonance imaging (MRI) has always shown promise in its ability to accurately stage esophageal cancer, though it has not been consistently adopted as a common tool for this purpose. Recent research has demonstrated that MRI can become an integral part of esophageal cancer clinical staging. Advances in MR technology that utilize radial sampling allow for shorter, free breathing techniques without degradation of image quality, resulting in improved capability for T and N staging of esophageal cancer. MRI enhanced with superparamagnetic iron oxide (SPIO) and ultrasmall SPIO (USPIO) nanoparticles has been shown to be useful for the detection of metastatic disease in lymph nodes. This article will review the current evidence in the role that imaging plays in staging esophageal cancer.

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.