Application of spectral CT imaging in evaluating lymph node metastasis in patients with gastric cancers: initial findings

Combination of clinical and spectral-CT iodine concentration for predicting liver metastasis in gastric cancer: a preliminary study

PURPOSE

This study aimed to determine the diagnostic efficacy of various indicators and models for the prediction of gastric cancer with liver metastasis.

METHODS

Clinical and spectral computed tomography (CT) data from 80 patients with gastric adenocarcinoma who underwent surgical resection were retrospectively analyzed. Patients were divided into metastatic and non-metastatic groups based on whether or not to occur liver metastasis, and the region of interest (ROI) was measured manually on each phase iodine map at the largest level of the tumor. Iodine concentration (IC), normalized iodine concentration (nIC), and clinical data of the primary gastric lesions were analyzed. Logistic regression analysis was used to construct the clinical indicator (CI) and clinical indicator-spectral CT iodine concentration (CI-Spectral CT-IC) Models, which contained all of the parameters with statistically significant differences between the groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the accuracy of the models.

RESULTS

The metastatic group showed significantly higher levels of Cancer antigen125 (CA125), carcinoembryonic antigen (CEA), IC, and nIC in the arterial phase, venous phase, and delayed phase than the non-metastatic group (all p < 0.05). Normalized iodine concentration Venous Phase (nICVP) exhibited a favorable performance among all IC and nIC parameters for forecasting gastric cancer with liver metastasis (area under the curve (AUC), 0.846). The combination model of clinical data with significant differences and nICVP showed the best diagnostic accuracy for predicting liver metastasis from gastric cancer, with an AUC of 0.897.

CONCLUSION

nICVP showed the best diagnostic efficacy for predicting gastric cancer with liver metastasis. Clinical Indicators-normalized ICVP model can improve the prediction accuracy for this condition.

Dual-energy CT improves differentiation of non-hypervascular pancreatic neuroendocrine neoplasms from CA 19-9-negative pancreatic ductal adenocarcinomas

PURPOSE

To evaluate the utility of dual-energy CT (DECT) in differentiating non-hypervascular pancreatic neuroendocrine neoplasms (PNENs) from pancreatic ductal adenocarcinomas (PDACs) with negative carbohydrate antigen 19-9 (CA 19-9).

METHODS

This retrospective study included 26 and 39 patients with pathologically confirmed non-hypervascular PNENs and CA 19-9-negative PDACs, respectively, who underwent contrast-enhanced DECT before treatment between June 2019 and December 2021. The clinical, conventional CT qualitative, conventional CT quantitative, and DECT quantitative parameters of the two groups were compared using univariate analysis and selected by least absolute shrinkage and selection operator regression (LASSO) analysis. Multivariate logistic regression analyses were performed to build qualitative, conventional CT quantitative, DECT quantitative, and comprehensive models. The areas under the receiver operating characteristic curve (AUCs) of the models were compared using DeLong's test.

RESULTS

The AUCs of the DECT quantitative (based on normalized iodine concentrations [nICs] in the arterial and portal venous phases: 0.918; 95% confidence interval [CI] 0.852-0.985) and comprehensive (based on tumour location and nICs in the arterial and portal venous phases: 0.966; 95% CI 0.889-0.995) models were higher than those of the qualitative (based on tumour location: 0.782; 95% CI 0.665-0.899) and conventional CT quantitative (based on normalized conventional CT attenuation in the arterial phase: 0.665; 95% CI 0.533-0.797; all P < 0.05) models. The DECT quantitative and comprehensive models had comparable performances (P = 0.076).

CONCLUSIONS

Higher nICs in the arterial and portal venous phases were associated with higher blood supply improving the identification of non-hypervascular PNENs.

Preoperative diagnosis of metastatic lymph nodes by CT-histopathologic matching analysis in gastric adenocarcinoma using dual-layer spectral detector CT

OBJECTIVES

There still remain challenges to accurate diagnosis of lymph node (LN) involvement in gastric cancer (GC) on conventional CT. This study evaluated the quantitative data derived from dual-layer spectral detector CT (DLCT) for preoperative diagnosis of metastatic LNs compared to conventional CT images.

METHODS

Patients with adenocarcinoma scheduled for gastrectomy were enrolled in this prospective study from July, 2021, to February, 2022. Regional LNs were labeled on preoperative DLCT. The LNs were located and matched using carbon nanoparticle solution during surgery according to their locations and anatomic landmarks on preoperative images. The matched LNs were randomly split into training and validation cohorts in a ratio of 2:1. The DLCT quantitative parameters in the training cohort were investigated using logistic regression models to identify independent predictors of metastatic LNs, and these predictors were subsequently applied to the validation cohort. Receiver operating characteristic curves were compared between the DLCT parameters and conventional CT images.

RESULTS

Fifty-five patients were included in the study, with 267 successfully matched LNs (90 metastatic, 177 nonmetastatic). Independent predictors included arterial phase CT attenuation on 70-keV images, venous phase electron density, and clustered feature. These combination predictors had areas under the curve (AUC) of 0.855 and 0.907 in the training and validation cohorts, respectively. Compared to conventional CT criteria alone, the model had higher AUC and accuracy (0.741 vs. 0.907, 75.28% vs. 87.64%; p < 0.01) for LN diagnosis.

CONCLUSION

Incorporating DLCT parameters improved preoperative diagnosis of LN metastasis in GC, increasing the accuracy of clinical N stage.

CLINICAL RELEVANCE STATEMENT

Compared to conventional CT criteria, quantitative parameters from dual-layer spectral detector CT showed higher diagnostic efficacy for the preoperative diagnosis of lymph node metastases in gastric cancer, increasing the accuracy of clinical N stage.

KEY POINTS

• Quantitative parameters from dual-layer spectral detector CT are useful for the preoperative diagnosis of lymph node metastases in gastric adenocarcinoma, increasing the accuracy of clinical N stage. • The values for metastatic lymph nodes are higher than those of nonmetastatic ones. The arterial phase of CT attenuation on 70-keV images, venous phase of electron density, and clustered feature independently predicted lymph node metastases. • Prediction model had area under the curve of 0.907, sensitivity of 81.82%, specificity of 91.07%, and accuracy of 87.64% for the preoperative diagnosis of lymph node metastasis.

The diagnostic value of dual-layer CT in the assessment of lymph nodes in lymphoma patients with PET/CT as a reference standard

This study aimed to evaluate the diagnostic performances of dual-layer CT (DLCT) for the identification of positive lymph nodes (LNs) in patients with lymphoma and retrospectively included 1165 LNs obtained by biopsy from 78 patients with histologically proven lymphoma, who underwent both pretreatment DLCT and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). According to 18F-FDG PET/CT findings as a reference standard, cases were categorized into the LN-negative and LN-positive groups. LNs were then randomly divided at a ratio of 7:3 into the training (n = 809) and validation (n = 356) cohorts. The patients' clinical characteristics and quantitative parameters including spectral curve slope (λHU), iodine concentration (IC) on arterial phase (AP) and venous phase (VP) images were compared between the LN-negative and LN-positive groups using Chi-square test, t-test or Mann-Whitney U test for categorical variables or quantitative parameters. Multivariate logistic regression analysis with tenfold cross-validation was performed to establish the most efficient predictive model in the training cohort. The area under the curve (AUC) was used to evaluate the diagnostic value of the predictive model, and differences in AUC were determined by the DeLong test. Moreover, the predictive model was validated in the validation cohort. Repeatability analysis was performed for LNs using intraclass correlation coefficients (ICCs). In the training cohort, long diameter (LD) had the highest AUC as an independent factors compared to other parameter in differentiating LN positivity from LN negativity (p = 0.006 to p < 0.001), and the AUC of predictive model jointly involving LD and λHU-AP was significantly elevated (AUC of 0.816, p < 0.001). While the AUC of predictive model in the validation cohort was 0.786. Good to excellent repeatability was observed for all parameters (ICC > 0.75). The combination of DLCT with morphological and functional parameters may represent a potential imaging biomarker for detecting LN positivity in lymphoma.

Detecting lymph node metastasis of esophageal cancer on dual-energy computed tomography

Background

Using conventional computed tomography (CT), the accurate diagnosis of lymph node (LN) metastasis of esophageal cancer is difficult.

Purpose

To examine dual-energy CT parameters to predict LN metastasis preoperatively in patients with esophageal cancer.

Material and Methods

Twenty-six consecutive patients who underwent dual-energy CT before an esophageal cancer surgery (19 patients with LN metastases) were analyzed. The included LNs had a short-axis diameter of ≥4 mm and were confirmed to be resected on postoperative CT. Their short-axis diameter, CT value, iodine concentration (IC), and fat fraction were measured on early- and late-phase contrast-enhanced dual-energy CT images and compared between pathologically confirmed metastatic and non-metastatic LNs.

Results

In total, 51 LNs (34 metastatic and 17 non-metastatic) were included. In the early phase, IC and fat fraction were significantly lower in the metastatic than in the non-metastatic LNs (IC = 1.6 mg/mL vs. 2.2 mg/mL; fat fraction = 20.3% vs. 32.5%; both P < 0.05). Furthermore, in the late phase, IC and fat fraction were significantly lower in the metastatic than in the non-metastatic LNs (IC = 2.0 mg/mL vs. 3.0 mg/mL; fat fraction = 20.4% vs. 33.0%; both P < 0.05). Fat fraction exhibited accuracies of 82.4% and 78.4% on early- and late-phase images, respectively. Conversely, short-axis diameter and CT value on both early- and late-phase images were not significantly different between the metastatic and non-metastatic LNs (P > 0.05).

Conclusion

Using dual-energy CT images, IC and fat fraction are useful for diagnosing LN metastasis in patients with esophageal cancer.

Spectral CT of the abdomen: Where are we now?

Spectral CT adds a new dimension to radiological evaluation, beyond assessment of anatomical abnormalities. Spectral data allows for detection of specific materials, improves image quality while at the same time reducing radiation doses and contrast media doses, and decreases the need for follow up evaluation of indeterminate lesions. We review the different acquisition techniques of spectral images, mainly dual-source, rapid kV switching and dual-layer detector, and discuss the main spectral results available. We also discuss the use of spectral imaging in abdominal pathologies, emphasizing the strengths and pitfalls of the technique and its main applications in general and in specific organs.

Grossing of Gastrointestinal Specimens: Best Practices and Current Controversies

The proper handling of the gross specimen is imperative, as it is the most important first step in providing excellent patient care. Our diagnoses depend on the correct description and submission of tissue sections for histologic analysis. A logical and problem-solving approach to handling the gross specimen is presented.

Application of Multi-Slice Spiral CT in the Evaluation of Diffuse Lung Diseases

This article analyzes the manifestations, characteristics, and significance of multi-slice spiral CT for diffuse lung disease, and evaluates the diagnostic value of multi-slice CT multi-directional reconstruction for diffuse lung disease. After performing multi-slice spiral CT examination on the patient and collecting relevant data, the characteristic multi-slice CT imaging findings of diffuse lung disease were determined by statistical analysis. Diffuse lung disease is representative in multi-slice spiral CT image imaging manifestations of the disease include multiple disseminated small nodules, multiple voids, ground glass shadows, and lung consolidation. And analyze the correlation of image performance, and then use statistical methods to analyze and evaluate the value of multi-slice spiral CT characteristic images in the diagnosis of diffuse lung disease, and analyze the characteristics of these characteristic multi-slice CT image appearances. The use of high-resolution CT to screen the characteristic CT imaging findings of the same research object, and then to perform a statistical analysis of the diagnostic differences with multi-slice spiral CT, further confirmed the importance of multi-slice CT for diffuse lung disease Diagnostic value. Studies have shown that multi-slice CT imaging technology is of great significance in the evaluation of diffuse lung diseases.

Preoperative Prediction of Cervical Nodal Metastasis in Papillary Thyroid Carcinoma: Value of Quantitative Dual-Energy CT Parameters and Qualitative Morphologic Features

OBJECTIVE. The purpose of our study was to assess the value of combining quantitative dual-energy CT (DECT) parameters with qualitative morphologic parameters for the preoperative prediction of cervical nodal metastasis from papillary thyroid carcinoma (PTC). MATERIALS AND METHODS. Thirty-five patients with pathologically proven PTC underwent single-phase contrast-enhanced DECT before thyroidectomy and cervical lymphadenectomy. Analyses of quantitative DECT parameters and qualitative morphologic features of metastatic and benign lymph nodes (LNs) were independently performed. The diagnostic performances of using only quantitative parameters, only morphologic features, and their combination for predicting cervical nodal metastasis were statistically calculated with ROC curves and logistic regression models. RESULTS. A total of 206 LNs, 80 metastatic and 126 benign, were included. The best single performer in DECT was the normalized iodine concentration in the venous phase, which had low sensitivity (62.5%) but high specificity (85.7%), for diagnosing metastatic cervical LNs. On the other hand, the best single performer in qualitative morphologic parameters was using the criterion of shortest diameter of greater than 5 mm, which had low specificity (69.8%) but high sensitivity (86.3%). Combining these two parameters improved the AUC, sensitivity, and specificity to 0.846, 86.3%, and 72.2%, respectively. The combination of multiple quantitative DECT parameters and all morphologic data further improved AUC, sensitivity, and specificity to 0.878, 87.5%, and 73.8%, respectively, which was significant compared with the use of any single parameter. CONCLUSION. The combination of quantitative DECT parameters with morphologic data improves performance in the preoperative diagnosis of metastatic cervical LNs in patients with PTC.

Lysyl oxidase and hypoxia-inducible factor 1α: biomarkers of gastric cancer

BACKGROUND

Gastric cancer (GC) is one of the main causes of cancer mortality worldwide. Recent studies on tumor microenvironments have shown that tumor metabolism exerts a vital role in cancer progression.

AIM

To investigate whether lysyl oxidase (LOX) and hypoxia-inducible factor 1α (HIF1α) are prognostic and predictive biomarkers in GC.

METHODS

A total of 80 tissue and blood samples were collected from 140 patients admitted to our hospital between August 2008 and March 2012. Immunohistochemical staining was performed to measure the expression of LOX and HIF1α in tumor and adjacent tissues collected from patients with GC. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis was used to detect the mRNA expression levels of LOX and HIF1α in patients with GC. In addition, single-factor analysis was applied to analyze the relationship between LOX, HIF1α and prognosis of GC.

RESULTS

Immunohistochemical staining suggested that the expression levels of LOX and HIF1α increased in tumor tissues from patients with GC. QRT-PCR analysis indicated that mRNA expression of LOX and HIF1α was also upregulated in tumor tissues, which was in accordance with the above results. We also detected expression of these two genes in blood samples. The expression level of LOX and HIF1α was higher in patients with GC than in healthy controls. Additional analysis showed that the expression level of LOX and HIF1α was related to the clinicopathological characteristics of GC. Expression of LOX and HIF1α increased with the number of lymph node metastases, deeper infiltration depth and later tumor–node–metastasis stages. Single-factor analysis showed that high expression of LOX and HIF1α led to poor prognosis of patients with GC.

CONCLUSION

LOX and HIF1α can be used as prognostic and predictive biomarkers for GC.