Advanced Gastric Cancer: CT Radiomics Prediction of Lymph Modes Metastasis After Neoadjuvant Chemotherapy.

This study aims to create and assess machine learning models for predicting lymph node metastases following neoadjuvant treatment in advanced gastric cancer (AGC) using baseline and restaging computed tomography (CT). We evaluated CT images and pathological data from 158 patients with resected stomach cancer from two institutions in this retrospective analysis. Patients were eligible for inclusion if they had histologically proven gastric cancer. They had received neoadjuvant chemotherapy, with at least 15 lymph nodes removed. All patients received baseline and preoperative abdominal CT and had complete clinicopathological reports. They were divided into two cohorts: (a) the primary cohort (n = 125) for model creation and (b) the testing cohort (n = 33) for evaluating models' capacity to predict the existence of lymph node metastases. The diagnostic ability of the radiomics-model for lymph node metastasis was compared to traditional CT morphological diagnosis by radiologist. The radiomics model based on the baseline and preoperative CT images produced encouraging results in the training group (AUC 0.846) and testing cohort (AUC 0.843). In the training cohort, the sensitivity and specificity were 81.3% and 77.8%, respectively, whereas in the testing cohort, they were 84% and 75%. The diagnostic sensitivity and specificity of the radiologist were 70% and 42.2% (using baseline CT) and 46.3% and 62.2% (using preoperative CT). In particular, the specificity of radiomics model was higher than that of conventional CT in diagnosing N0 cases (no lymph node metastasis). The CT-based radiomics model could assess lymph node metastasis more accurately than traditional CT imaging in AGC patients following neoadjuvant chemotherapy.

Early Contrast-Enhanced MR for Diagnosing Complete Tumor Response of Locally Advanced Esophageal Squamous Cell Carcinoma After Neoadjuvant Therapy: A Retrospective Comparative Study.

BACKGROUND: This study assessed the performance of early contrast-enhanced magnetic resonance (ECE-MR) in the detecting of complete tumor response (ypT0) in patients with esophageal squamous cell carcinoma following neoadjuvant therapy. PATIENTS AND METHODS: Preoperative MR images of consecutive patients who underwent neoadjuvant therapy and surgical resection were reviewed retrospectively. The accuracy of ECE-MR and T2WI+DWI was evaluated by comparing the findings with pathological results. Receiver operating characteristic curve analysis was used to assess the diagnostic performance, and DeLong method was applied to compare the areas under the curves (AUC). Chi-squared analysis was conducted to explore the difference in pathological changes. RESULTS: A total of 198 patients (mean age 62.6 ± 7.8 years, 166 men) with 201 lesions were included. The AUC of ECE-MR was 0.85 (95% CI 0.79-0.90) for diagnosing ypT1-4, which was significantly higher than that of T2WI+DWI (AUC 0.69, 95% CI 0.63-0.76, p < 0.001). The diagnostic performance of both T2WI+DWI and ECE-MR improved with increasing tumor stage. The AUCs of ECE-MRI were higher in ypT1 and ypT2 tumors than T2WI+DWI. Degree 2-3 tumor-infiltrating lymphocytes and neutrophils were commonly seen in ypT0 tumors misdiagnosed by ECE-MR. CONCLUSIONS: Visual evaluation of ECE-MR is a promising diagnostic protocol for the detection of complete tumor response, especially for differentiation with early stage tumors. The accurate diagnosis of complete tumor response after neoadjuvant therapy using imaging modalities is of important significance for clinical decision-making for patients with esophageal squamous cell carcinoma. It is hoped that early contrast-enhanced MR will provide supportive advice for the development of individualized treatment options for patients.

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.

PURPOSE: Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed and evaluated segmentation potential exclusively on post-chemoradiation T2-weighted MRI using convolutional neural networks, with the aim of reducing the detection workload for radiologists and clinicians. METHODS: A total of 372 consecutive patients with LARC were retrospectively enrolled from October 2015 to December 2017. The standard-of-care neoadjuvant process included 22-fraction intensity-modulated radiation therapy and oral capecitabine. Further, 243 patients (3061 slices) were grouped into training and validation datasets with a random 80:20 split, and 41 patients (408 slices) were used as the test dataset. A symmetric eight-layer deep network was developed using the nnU-Net Framework, which outputs the segmentation result with the same size. The trained deep learning (DL) network was examined using fivefold cross-validation and tumor lesions with different TRGs. RESULTS: At the stage of testing, the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were applied to quantitatively evaluate the performance of generalization. Considering the test dataset (41 patients, 408 slices), the average DSC, HD95, and MSD were 0.700 (95% CI: 0.680-0.720), 17.73 mm (95% CI: 16.08-19.39), and 3.11 mm (95% CI: 2.67-3.56), respectively. Eighty-two percent of the MSD values were less than 5 mm, and fifty-five percent were less than 2 mm (median 1.62 mm, minimum 0.07 mm). CONCLUSIONS: The experimental results indicated that the constructed pipeline could achieve relatively high accuracy. Future work will focus on assessing the performances with multicentre external validation.

Resting state functional connectome in breast cancer patients with fear of cancer recurrence.

This study aimed to investigate network-level brain functional changes in breast cancer patients and their relationship with fear of cancer recurrence (FCR). Resting-state functional MRI was collected from 43 patients with breast cancer and 40 healthy controls (HCs). Graph theory analyses, whole-brain voxel-wise functional connectivity strength (FCS) analyses and seed-based functional connectivity (FC) analyses were performed to identify connection alterations in breast cancer patients. Correlations between brain functional connections (i.e. FCS and FC) and FCR level were assessed to further reveal the neural mechanisms of FCR in breast cancer patients. Graph theory analyses indicated a decreased clustering coefficient in breast cancer patients compared to HCs (P = 0.04). Patients with breast cancer exhibited significantly higher FCS in both higher-order function networks (frontoparietal, default mode, and dorsal attention systems) and primary somatomotor networks. Among the hyperconnected regions in breast cancer, the left inferior frontal operculum demonstrated a significant positive correlation with FCR. Our findings suggest that breast cancer patients exhibit less segregation of brain function, and the left inferior frontal operculum is a key region associated with FCR. This study offers insights into the neural mechanisms of FCR in breast cancer patients at the level of brain connectome.

Extranodal Extension at Pretreatment MRI and the Prognostic Value for Patients with Rectal Cancer.

Background Detection of extranodal extension (ENE) at pathology is a poor prognostic indicator for rectal cancer, but whether ENE can be identified at pretreatment MRI is, to the knowledge of the authors, unknown. Purpose To evaluate the performance of pretreatment MRI in detecting ENE using a matched pathologic reference standard and to assess its prognostic value in patients with rectal cancer. Materials and Methods This single-center study included a prospective development data set consisting of participants with rectal adenocarcinoma who underwent pretreatment MRI and radical surgery (December 2021 to January 2023). MRI characteristics were identified by their association with ENE-positive nodes (χ2 test and multivariable logistic regression) and the performance of these MRI features was assessed (area under the receiver operating characteristic curve [AUC]). Interobserver agreement was assessed by Cohen κ coefficient. The prognostic value of ENE detected with MRI for predicting 3-year disease-free survival was assessed by Cox regression analysis in a retrospective independent validation cohort of patients with locally advanced rectal cancer (December 2019 to July 2020). Results The development data set included 147 participants (mean age, 62 years ± 11 [SD]; 87 male participants). The retrospective cohort included 110 patients (mean age, 60 years ± 9; 79 male participants). Presence of vessel interruption and fusion (both P < .001), heterogeneous internal structure, and the broken-ring and tail signs (odds ratio range, 4.10-23.20; P value range, <.001 to .002) were predictors of ENE at MRI, and together achieved an AUC of 0.91 (95% CI: 0.88, 0.93) in detecting ENE. Interobserver agreement was moderate for the presence of vessel interruption and fusion (κ = 0.46 for both) and substantial for others (κ = 0.61-0.67). The presence of ENE at pretreatment MRI was independently associated with worse 3-year disease-free survival (hazard ratio, 3.00; P = .02). Conclusion ENE can be detected at pretreatment MRI, and its presence was associated with worse prognosis for patients with rectal cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Eberhardt in this issue.

A comparative study of mono-exponential and advanced diffusion-weighted imaging in differentiating stage IA endometrial carcinoma from benign endometrial lesions.

PURPOSE: The purpose of the current investigation is to compare the efficacy of different diffusion models and diffusion kurtosis imaging (DKI) in differentiating stage IA endometrial carcinoma (IAEC) from benign endometrial lesions (BELs). METHODS: Patients with IAEC, endometrial hyperplasia (EH), or a thickened endometrium confirmed between May 2016 and August 2022 were retrospectively enrolled. All of the patients underwent a preoperative pelvic magnetic resonance imaging (MRI) examination. The apparent diffusion coefficient (ADC) from the mono-exponential model, pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) from the bi-exponential model, distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index from the stretched-exponential model, diffusion coefficient (Dk) and diffusion kurtosis (K) from the DKI model were calculated. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficiency. RESULTS: A total of 90 patients with IAEC and 91 patients with BELs were enrolled. The values of ADC, D, DDC and Dk were significantly lower and D* and K were significantly higher in cases of IAEC (p < 0.05). Multivariate analysis showed that K was the only predictor. The area under the ROC curve of K was 0.864, significantly higher compared with the ADC (0.601), D (0.811), D* (0.638), DDC (0.743) and Dk (0.675). The sensitivity, specificity and accuracy of K were 78.89%, 85.71% and 80.66%, respectively. CONCLUSION: Advanced diffusion-weighted imaging models have good performance for differentiating IAEC from EH and endometrial thickening. Among all of the diffusion parameters, K showed the best performance and was the only independent predictor. Diffusion kurtosis imaging was defined as the most valuable model in the current context.

Total Neoadjuvant Therapy With PD-1 Blockade for High-Risk Proficient Mismatch Repair Rectal Cancer.

Importance: Total neoadjuvant therapy (TNT) is the standard treatment for locally advanced rectal cancer, especially for patients with high-risk factors. However, the efficacy of TNT combined with immunotherapy for patients with proficient mismatch repair (pMMR) rectal cancer is unknown. Objectives: To evaluate the safety and efficacy of TNT with induction chemoimmunotherapy followed by long-course chemoradiation in patients with high-risk, pMMR rectal cancer and to identify potential molecular biomarkers associated with treatment efficacy. Design, Setting, and Participants: This cohort study was a single-arm phase 2 trial conducted at Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, from June 2020 to October 2021. Biopsies and plasma were collected before treatment for whole-exome sequencing and cell-free DNA sequencing, respectively. Data were analyzed from May 2022 to September 2022. Interventions: Participants received 3 cycles of induction oxaliplatin and capecitabine combined with camrelizumab and radiotherapy (50.6 Gy in 22 fractions) with concurrent capecitabine. Patients without disease progression received 2 cycles of consolidation oxaliplatin/capecitabine. Main Outcomes and Measures: The primary end point was pathologic complete response rate. Results: Of 25 patients enrolled (19 men [76%]; 6 women [24%]; median [IQR] age, 58 [48-64] years), 22 patients (88%) completed the TNT schedule. The pathologic complete response rate was 33.3% (7/21). Twelve patients (48%) achieved clinical complete response, and 4 patients (16%) chose to watch and wait. R0 resection was achieved in 21 of 21 patients, and the major pathologic response rate was 38.1% (8/21). The most common adverse event was nausea (80%, 20/25); grade 3 toxic effects occurred in 9 of 25 patients (36%). Patients with tumor shrinkage of 50% or greater after induction oxaliplatin/capecitabine and camrelizumab or clinical complete response had higher percentages of LRP1B mutation. Mutation of LRP1B was associated with high tumor mutation burden and tumor neoantigen burden. Patients with high tumor mutation burden all benefited from therapy. Conclusions and Relevance: This study found that TNT with induction chemoimmunotherapy followed by long-course chemoradiation was safe and effective for patients with high-risk rectal cancer with pMMR status. Longer follow-up and larger clinical studies are needed to validate this innovative regimen. There is also an urgent need to further validate the predictive value of LRP1B and discover other novel biomarkers with potential predictive value for rectal cancer.

Peri-lesion regions in differentiating suspicious breast calcification-only lesions specifically on contrast enhanced mammography.

PURPOSE: The explore the added value of peri-calcification regions on contrast-enhanced mammography (CEM) in the differential diagnosis of breast lesions presenting as only calcification on routine mammogram. METHODS: Patients who underwent CEM because of suspicious calcification-only lesions were included. The test set included patients between March 2017 and March 2019, while the validation set was collected between April 2019 and October 2019. The calcifications were automatically detected and grouped by a machine learning-based computer-aided system. In addition to extracting radiomic features on both low-energy (LE) and recombined (RC) images from the calcification areas, the peri-calcification regions, which is generated by extending the annotation margin radially with gradients from 1 mm to 9 mm, were attempted. Machine learning (ML) models were built to classify calcifications into malignant and benign groups. The diagnostic matrices were also evaluated by combing ML models with subjective reading. RESULTS: Models for LE (significant features: wavelet-LLL_glcm_Imc2_MLO; wavelet-HLL_firstorder_Entropy_MLO; wavelet-LHH_glcm_DifferenceVariance_CC; wavelet-HLL_glcm_SumEntropy_MLO;wavelet-HLH_glrlm_ShortRunLowGray LevelEmphasis_MLO; original_firstorder_Entropy_MLO; original_shape_Elongation_MLO) and RC (significant features: wavelet-HLH_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_10Percentile_CC; original_firstorder_Maximum_MLO; wavelet-HHH_glcm_Autocorrelation_MLO; original_shape_Elongation_MLO; wavelet-LHL_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_RootMeanSquared_MLO) images were set up with 7 features. Areas under the curve (AUCs) of RC models are significantly better than those of LE models with compact and expanded boundary (RC v.s. LE, compact: 0.81 v.s. 0.73, p < 0.05; expanded: 0.89 v.s. 0.81, p < 0.05) and RC models with 3 mm boundary extension yielded the best performance compared to those with other sizes (AUC = 0.89). Combining with radiologists' reading, the 3mm-boundary RC model achieved a sensitivity of 0.871 and negative predictive value of 0.937 with similar accuracy of 0.843 in predicting malignancy. CONCLUSIONS: The machine learning model integrating intra- and peri-calcification regions on CEM has the potential to aid radiologists' performance in predicting malignancy of suspicious breast calcifications.

Value of multiple models of diffusion-weighted imaging to predict hepatic lymph node metastases in colorectal liver metastases patients.

BACKGROUND: About 10%-31% of colorectal liver metastases (CRLM) patients would concomitantly show hepatic lymph node metastases (LNM), which was considered as sign of poor biological behavior and a relative contraindication for liver resection. Up to now, there's still lack of reliable preoperative methods to assess the status of hepatic lymph nodes in patients with CRLM, except for pathology examination of lymph node after resection. AIM: To compare the ability of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) models in distinguishing between benign and malignant hepatic lymph nodes in patients with CRLM who received neoadjuvant chemotherapy prior to surgery. METHODS: In this retrospective study, 97 CRLM patients with pathologically confirmed hepatic lymph node status underwent magnetic resonance imaging, including DWI with ten b values before and after chemotherapy. Various parameters, such as the apparent diffusion coefficient from the mono-exponential model, and the true diffusion coefficient, the pseudo-diffusion coefficient, and the perfusion fraction derived from the intravoxel incoherent motion model, along with distributed diffusion coefficient (DDC) and α from the stretched-exponential model (SEM), were measured. The parameters before and after chemotherapy were compared between positive and negative hepatic lymph node groups. A nomogram was constructed to predict the hepatic lymph node status. The reliability and agreement of the measurements were assessed using the coefficient of variation and intraclass correlation coefficient. RESULTS: Multivariate analysis revealed that the pre-treatment DDC value and the short diameter of the largest lymph node after treatment were independent predictors of metastatic hepatic lymph nodes. A nomogram combining these two factors demonstrated excellent performance in distinguishing between benign and malignant lymph nodes in CRLM patients, with an area under the curve of 0.873. Furthermore, parameters from SEM showed substantial repeatability. CONCLUSION: The developed nomogram, incorporating the pre-treatment DDC and the short axis of the largest lymph node, can be used to predict the presence of hepatic LNM in CRLM patients undergoing chemotherapy before surgery. This nomogram was proven to be more valuable, exhibiting superior diagnostic performance compared to quantitative parameters derived from multiple b values of DWI. The nomogram can serve as a preoperative assessment tool for determining the status of hepatic lymph nodes and aiding in the decision-making process for surgical treatment in CRLM patients.

The differential diagnosis of lung precursor glandular lesions, micro-invasive adenocarcinoma, and invasive adenocarcinoma using low dose spectral computed tomography perfusion imaging.

Background: Few studies about the association between computed tomography (CT) perfusion imaging parameters and invasiveness in lung adenocarcinoma (LUAD) have been conducted using low dose spectral CT perfusion imaging. The purpose of this study was to investigate application of spectral revolution CT low-dose perfusion imaging in the differential diagnosis of different pathological subtypes of LUAD. Methods: This was a cross-sectional study based on historical data from January 2018 to May 2019 in Peking University Cancer Hospital & Institute. A total of 62 cases were enrolled, including 2 cases of atypical adenomatous hyperplasia (AAH), 3 cases of adenocarcinoma in situ (AIS), 4 cases of minimally invasive adenocarcinoma (MIA), and 53 cases of invasive adenocarcinoma (IAC), all confirmed with pathology. The inclusion and exclusion criteria were regulated. Using Revolution low-dose CT perfusion imaging (GE, USA), the CT perfusion parameters of hemodynamics were obtained: blood flow (BF), blood volume (BV), impulse residue function time of arrival (IRF TO), maximum slope of increase (MSI), mean transit time (MTT), permeability surface area product (PS), positive enhancement integral (PEI), and maximum enhancement time (Tmax). Univariate analysis of variance (ANOVA) or Kruskal-Wallis test was used to compare the differences of CT perfusion quantitative parameters among AAH, AIS, MIA, and IAC. Mann-Whitney test was used to compare the difference of CT perfusion imaging parameters between preinvasive lesions (AAH and AIS) and invasive lung cancer (MIA and IAC). Results: Statistically significant differences in IRF TO were observed in LUAD with different invasiveness, namely, among AIS, MIA, and IAC groups (0.56±0.74 vs. 0.54±1.08 vs. 4.39±2.19, P=0.004). Statistically significant differences in IRF TO were also observed between pre-invasive lesions group (AAH and AIS) and invasive lung cancer group (MIA and IAC) (1.12±1.27 vs. 3.75±2.79, P=0.031), and between AAH + AIS + MIA groups and IAC group (0.83±1.13 vs. 4.12±2.69, P<0.001). There were no statistically significant differences in other CT perfusion parameters of hemodynamics among different pathological subtypes of LUAD (P>0.05). Conclusions: The low-dose perfusion parameter IRF TO of revolution CT has the potential to be employed in the differential diagnosis of different pathological subtypes of LUAD.

Radiomics analysis from magnetic resonance imaging in predicting the grade of nonfunctioning pancreatic neuroendocrine tumors: a multicenter study.

OBJECTIVES: To explore the potential of radiomics features to predict the histologic grade of nonfunctioning pancreatic neuroendocrine tumor (NF-PNET) patients using non-contrast sequence based on MRI. METHODS: Two hundred twenty-eight patients with NF-PNETs undergoing MRI at 5 centers were retrospectively analyzed. Data from center 1 (n = 115) constituted the training cohort, and data from centers 2-5 (n = 113) constituted the testing cohort. Radiomics features were extracted from T2-weighted images and the apparent diffusion coefficient. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures. The area under receiver operating characteristic curve (AUC) was performed to assess models. RESULTS: Tumor boundary, enhancement homogeneity, and vascular invasion were used to construct the radiological model to stratify NF-PNET patients into grade 1 and 2/3 groups, which yielded AUC of 0.884 and 0.684 in the training and testing groups. A radiomics model including 4 features was constructed, with an AUC of 0.941 and 0.871 in the training and testing cohorts. The fusion model combining the radiomics signature and radiological characteristics showed good performance in the training set (AUC = 0.956) and in the testing set (AUC = 0.864), respectively. CONCLUSION: The developed model that integrates radiomics features with radiological characteristics could be used as a non-invasive, dependable, and accurate tool for the preoperative prediction of grade in NF-PNETs. CLINICAL RELEVANCE STATEMENT: Our study revealed that the fusion model based on a non-contrast MR sequence can be used to predict the histologic grade before operation. The radiomics model may be a new and effective biological marker in NF-PNETs. KEY POINTS: The diagnostic performance of the radiomics model and fusion model was better than that of the model based on clinical information and radiological features in predicting grade 1 and 2/3 of nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs). Good performance of the model in the four external testing cohorts indicated that the radiomics model and fusion model for predicting the grades of NF-PNETs were robust and reliable, indicating the two models could be used in the clinical setting and facilitate the surgeons' decision on risk stratification. The radiomics features were selected from non-contrast T2-weighted images (T2WI) and diffusion-weighted imaging (DWI) sequence, which means that the administration of contrast agent was not needed in grading the NF-PNETs.

Small arteriole sign: an imaging feature for staging T4a colon cancer.

OBJECTIVES: By analyzing the distribution of existing and newly proposed staging imaging features in pT1-3 and pT4a tumors, we searched for a salient feature and validated its diagnostic performance. METHODS: Preoperative multiphase contrast-enhanced CT images of the training cohort were retrospectively collected at three centers from January 2016 to December 2017. We used the chi-square test to analyze the distribution of several stage-related imaging features in pT1-3 and pT4a tumors, including small arteriole sign (SAS), outer edge of the intestine, tumor invasion range, and peritumoral adipose tissue. Preoperative multiphase contrast-enhanced CT images of the validation cohort were retrospectively collected at Beijing Cancer Hospital from January 2018 to December 2018. The diagnostic performance of the selected imaging feature, including accuracy, sensitivity, and specificity, was validated and compared with the conventional clinical tumor stage (cT) by the McNemar test. RESULTS: In the training cohort, a total of 268 patients were enrolled, and only SAS was significantly different between pT1-3 and pT4a tumors. The accuracy, sensitivity, and specificity of the SAS and conventional cT in differentiating T1-3 and T4a tumors were 94.4%, 81.6%, and 97.3% and 53.7%, 32.7%, and 58.4%, respectively (all p < 0.001). In the validation cohort, a total of 135 patients were collected. The accuracy, sensitivity, and specificity of the SAS and the conventional cT were 93.3%, 76.2%, and 96.5% and 62.2%, 38.1%, and 66.7%, respectively (p < 0.001, p = 0.021, p < 0.001). CONCLUSION: Small arteriole sign positivity, an indirect imaging feature of serosa invasion, may improve the accuracy of identifying T4a colon cancer. CLINICAL RELEVANCE STATEMENT: Small arteriole sign helps to distinguish T1-3 and T4a colon cancer and further improves the accuracy of preoperative CT staging of colon cancer. KEY POINTS: • The accuracy of preoperative CT staging of colon cancer is not ideal, especially for T4a tumors. • Small arteriole sign (SAS) is a newly defined imaging feature that shows the appearance of tumor-supplying arterioles at the site where they penetrate the intestine wall. • SAS is an indirect imaging marker of tumor invasion into the serosa with a great value in distinguishing between T1-3 and T4a colon cancer.

Effect of concomitant atrial septal defect on left ventricular function in adult patients with unrepaired Ebstein's anomaly: a cardiovascular magnetic resonance imaging study.

BACKGROUND: Due to the heterogeneity of anatomic anomalies in Ebstein's anomaly (EA), particularly in the subset of patients with atrial septal defect (ASD), hemodynamic changes, which ultimately cause left ventricular (LV) deterioration remain unclear. The current study aimed to investigate the effect of concomitant ASD on LV function using cardiovascular magnetic resonance (CMR) imaging in patients with EA. METHODS: This study included 31 EA patients with ASD, 76 EA patients without ASD, 35 patients with simple ASD and 40 healthy controls. Left/right ventricular (RV, the RV was defined as a summation of the functional RV and atrialized RV in EA patients) volumes and functional parameters, LV strain parameters, and echocardiogram indices were compared among the four groups. Associations between variables were evaluated via Spearman or Pearson correlation analyses. The association between risk factors and LV ejection fraction (EF) was determined via multivariate linear regression analysis. RESULTS: Both EA patients and ASD patients had a higher RV/LV end-diastolic volume (RVEDV/LVEDV) as well as lower LV and RV ejection fractions (LVEF/RVEF) compared to healthy controls (all p < 0.05). Moreover, the EA patients with ASD had a significantly higher RVEDV/LVEDV and a lower LVEF and RVEF than those without ASD (all p < 0.05). Multivariate linear regression analysis revealed that the presence of ASD was independently associated with LVEF (ß = - 0.337, p < 0.001). The RVEDV/LVEDV index was associated with LVEF (r = - 0.361, p < 0.001). Furthermore, the LV longitudinal peak diastolic strain rate (PDSR) was lower in EA patients with ASD than those without ASD, patients with simple ASD, and healthy controls (p < 0.05). CONCLUSION: Concomitant ASD is an important risk factor of LV dysfunction in patients with EA, and diastolic dysfunction is likely the predominate mechanism related to LV dysfunction.

CT radiomics in the identification of preoperative understaging in patients with clinical stage T1-2N0 esophageal squamous cell carcinoma.

Background: Predicting preoperative understaging in patients with clinical stage T1-2N0 (cT1-2N0) esophageal squamous cell carcinoma (ESCC) is critical to customizing patient treatment. Radiomics analysis can provide additional information that reflects potential biological heterogeneity based on computed tomography (CT) images. However, to the best of our knowledge, no studies have focused on identifying CT radiomics features to predict preoperative understaging in patients with cT1-2N0 ESCC. Thus, we sought to develop a CT-based radiomics model to predict preoperative understaging in patients with cT1-2N0 esophageal cancer, and to explore the value of the model in disease-free survival (DFS) prediction. Methods: A total of 196 patients who underwent radical surgery for cT1-2N0 ESCC were retrospectively recruited from two hospitals. Among the 196 patients, 134 from Peking University Cancer Hospital were included in the training cohort, and 62 from Henan Cancer Hospital were included in the external validation cohort. Radiomics features were extracted from patients' CT images. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and model construction. A clinical model was also built based on clinical characteristics, and the tumor size [the length, thickness and the thickness-to-length ratio (TLR)] was evaluated on the CT images. A radiomics nomogram was established based on multivariate logistic regression. The diagnostic performance of the models in predicting preoperative understaging was assessed by the area under the receiver operating characteristic curve (AUC). Kaplan-Meier curves with the log-rank test were employed to analyze the correlation between the nomogram and DFS. Results: Of the patients, 50.0% (67/134) and 51.6% (32/62) were understaged in the training and validation groups, respectively. The radiomics scores and the TLRs of the tumors were included in the nomogram. The AUCs of the nomogram for predicting preoperative understaging were 0.874 [95% confidence interval (CI): 0.815-0.933] in the training cohort and 0.812 (95% CI: 0.703-0.912) in the external validation cohort. The diagnostic performance of the nomogram was superior to that of the clinical model (P<0.05). The nomogram was an independent predictor of DFS in patients with cT1-2N0 ESCC. Conclusions: The proposed CT-based radiomics model could be used to predict preoperative understaging in patients with cT1-2N0 ESCC who have undergone radical surgery.

Analysis of enhanced CT imaging signs and clinicopathological prognostic factors in hepatoid adenocarcinoma of stomach patients with radical surgery: a retrospective study.

BACKGROUND: To investigate the association between CT signs and clinicopathological features and disease recurrence in patients with hepatoid adenocarcinoma of stomach (HAS). METHODS: Forty nine HAS patients undergoing radical surgery were retrospectively collected. Association between CT and clinicopathological features and disease recurrence was analyzed. Multivariate logistic model was constructed and evaluated for predicting recurrence by using receiver operating characteristic (ROC) curve. Survival curves between model-defined risk groups was compared using Kaplan-Meier method. RESULTS: 24(49.0%) patients developed disease recurrence. Multivariate logistic analysis results showed elevated serum CEA level, peritumoral fatty space invasion and positive pathological vascular tumor thrombus were independent factors for disease recurrence. Odds ratios were 10.87 (95%CI, 1.14-103.66), 6.83 (95%CI, 1.08-43.08) and 42.67 (95%CI, 3.66-496.85), respectively. The constructed model showed an area under ROC of 0.912 (95%CI,0.825-0.999). The model-defined high-risk group showed poorer overall survival and recurrence-free survival than the low-risk group (both P < 0.001). CONCLUSIONS: Preoperative CT appearance of peritumoral fatty space invasion, elevated serum CEA level, and pathological vascular tumor thrombus indicated poor prognosis of HAS patients.

Usefulness of attenuation value on computed tomography plain scan for diagnosing enlarged mediastinal lymph nodes metastases.

Background: To evaluate the diagnostic value of computed tomography (CT) attenuation in mediastinal lymph node metastases of malignant tumors. Methods: A retrospective review was conducted of a Chinese institutional database of consecutive patients with a history of malignant tumors. Those who had enlarged, necrotic, or hypermetabolic lymph nodes detected in the mediastinum during routine CT examination or positron emission tomography (PET)/CT imaging from January 2019 to December 2021 were collected for investigation. All patients underwent endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) and were followed up for at least 6 months to diagnose lymph node metastases. One-to-one correlation was attempted between the CT images of the lymph nodes and EBUS-TBNA area of the same lymph node groups and similar size. Radiologists measured size, as well as plain CT and contrast-enhanced CT (CECT) attenuation values of mediastinal lymph nodes, and evaluated the effectiveness of these variables in diagnosing lymph node metastasis. Results: A total of 135 lymph nodes of 114 patients were included in the study. In the univariate analysis, the long-axis diameter, short-axis diameter, short-axis/long-axis ratio, and plain CT attenuation values of lymph nodes were found to be statistically significantly different between the metastatic and non-metastatic lymph nodes. The areas under receiver operator characteristic (ROC) curves (AUCs) of long-axis diameter, short-axis diameter, short-axis/long-axis ratio, and plain CT attenuation value for diagnosing metastases were 0.711, 0.788, 0.671, and 0.827, respectively. The best value of the AUC for diagnosing lymph node metastases was 0.827 [95% confidence interval (CI): 0.749-0.890] using plain CT attenuation value ≤45 Hounsfield units (HU). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 92.8%, 69.2%, 86.5%, and 81.8%, respectively. Similar results were obtained from the 68 cases of lung cancer. Plain CT attenuation values reached the best AUC (0.860) for diagnosing lymph node metastases. Conclusions: Plain CT attenuation of lymph nodes is an effective method for diagnosing enlarged mediastinal lymph nodes with a history of multiple malignancies or lung cancer. Plain CT could be used as an additional test where there is no PET/CT available in cases of diagnostic dilemma.

Optimal selection of b-values for differential diagnosis of mediastinal lymph nodes using diffusion-weighted imaging.

This study proposed to investigate the optimal selection of b-values in diffusion-weighted imaging for distinguishing malignant from benign mediastinal lymph nodes. Diffusion-weighted imaging with six b-values was performed on 35 patients at 1.5 T. Image quality score, signal-to-noise ratio, and relative contrast ratio of lymph node to chest muscle were compared between the diffusion-weighted images with a b-value up to 800 and 1000 s/mm2. Using a lower and an upper b-value in the range of 0-1000 s/mm2, eight apparent diffusion coefficient maps were obtained from a mono-exponential model. Receiver operating characteristic analysis was employed to evaluate the performance of the apparent diffusion coefficients for distinguishing malignant from benign mediastinal lymph nodes by using the area under the curve as a criterion. The mean image quality score and the relative contrast ratio showed no difference between b-values of 800 and 1000 s/mm2. In the receiver operating characteristic analysis, the areas under the curve of apparent diffusion coefficient with b-value pairs of (0, 800), (0, 1000), and (50, 800) s/mm2 were significantly higher than those from the other b-value pairs. No significant difference was observed among the three b-value pairs. Apparent diffusion coefficient obtained from b-value pairs of (0, 800), (0, 1000), and (50, 800) s/mm2 showed superior diagnostic performance compared to the other b-value combinations. Based on several practical considerations, the b-value pair of (50, 800) s/mm2 is recommended for differential diagnosis of mediastinal lymph nodes.

Procedure-related pain during CT-guided percutaneous transthoracic needle biopsies of lung lesions: a prospective study.

BACKGROUND: The existing data on the degree of pain in patients during CT-guided percutaneous transthoracic needle biopsy (PTNB) of lung lesions are limited and the factors related to pain are unclear. In this study, we aimed to evaluate the prevalence and severity of pain reported during PTNB and to identify factors associated with increased reported pain. METHODS: Patients who underwent PTNB from April 2022 to November 2022 were prospectively evaluated using the numeric rating scale, which assesses subjective pain based on a 0-10 scoring system (0 = no pain; 10 = the worst pain imaginable). The scale divides the scores into three categories: mild pain (1-3 points), moderate pain (4-6 points), and severe pain (7-10 points). Pain scores from 4 to 10 were considered significant pain. Demographic data of patients, lesion characteristics, biopsy variables, complications, the patient's subjective feelings, and pathological result data were analyzed by multivariable logistic regression analysis to identify variables associated with significant pain. RESULTS: We enrolled 215 participants who underwent 215 biopsy procedures (mean age: 64.5 ± 9.3 years, 123 were men). The mean procedure-related pain score was 2 ± 2. Overall, 20% (43/215) of participants reported no pain (score of 0), 67.9% (146/215) reported pain scores of 1-3, 11.2% (24/215) reported scores of 4-6, and 0.9% (2/215) reported scores of 7 or higher. Furthermore, non-significant pain (scores of 0-3) was reported during 87.9% (189/215) of the procedures. In the adjusted model, significant pain was positively associated with lesions ≥ 34 mm (p = 0.001, odds ratio [OR] = 6.90; 95% confidence interval [CI]: 2.18, 21.85), a needle-pleural angle ≥ 77° (p = 0.047, OR = 2.44; 95% CI: 1.01, 5.89), and a procedure time ≥ 26.5 min (p = 0.031, OR = 3.11; 95% CI: 1.11, 8.73). CONCLUSIONS: Most participants reported no pain or mild pain from CT-guided percutaneous transthoracic needle biopsies of lung lesions. However, those with a larger lesion, a greater needle-pleural angle, and a longer procedure time reported greater pain.

Tumor attachment to Major intrahepatic vascular for Colorectal liver metastases.

BACKGROUND: Colorectal liver metastases attached major intrahepatic vessels has been considered to be a risk factor for survival outcome after liver resection. The present study aimed to clarify the outcomes of R1 surgery (margin < 1 mm) in CRLM patients, distinguishing parenchymal margin R1 and attached to major intrahepatic vessels R1. METHODS: In present study, 283 CRLM patients who were evaluated to be attached to major intrahepatic vessels initially and underwent liver resection following preoperative chemotherapy. They were assigned to two following groups: R0 (n = 167), R1 parenchymal (n = 58) and R1 vascular (n = 58). The survival outcomes and local recurrence rates were analyzed in each group. RESULTS: Overall, 3- and 5-year overall survival rates after liver resection were 53.0% and 38.2% (median overall survival 37 months). Five-year overall survival was higher in patients with R0 than parenchymal R1 (44.9%% vs. 26.3%, p = 0.009), whereas there was no significant difference from patients with vascular R1 (34.3%, p = 0.752). In the multivariable analysis, preoperative chemotherapy > 4 cycles, clinical risk score 3-5, RAS mutation, parenchymal R1 and CA199 > 100 IU/ml were identified as independent predictive factors of overall survival (p < 0.05). There was no significant difference for local recurrence among three groups. CONCLUSION: Parenchymal R1 resection was independent risk factor for CRLM. Vascular R1 surgery achieved survival outcomes equivalent to R0 resection. Non-anatomic liver resection for CRLM attached to intrahepatic vessels might be pursued to increase patient resectability by preoperative chemotherapy.

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

OBJECTIVE: To investigate the value of CT radiomics features of meso-esophageal fat in the overall survival (OS) prediction of patients with locally advanced esophageal squamous cell carcinoma (ESCC). METHODS: A total of 166 patients with locally advanced ESCC in two medical centers were retrospectively analyzed. The volume of interest (VOI) of meso-esophageal fat and tumor were manually delineated on enhanced chest CT using ITK-SNAP. Radiomics features were extracted from the VOIs by Pyradiomics and then selected using the t-test, the Cox regression analysis, and the least absolute shrinkage and selection operator. The radiomics scores of meso-esophageal fat and tumors for OS were constructed by a linear combination of the selected radiomic features. The performance of both models was evaluated and compared by the C-index. Time-dependent receiver operating characteristic (ROC) analysis was employed to analyze the prognostic value of the meso-esophageal fat-based model. A combined model for risk evaluation was constructed based on multivariate analysis. RESULTS: The CT radiomic model of meso-esophageal fat showed valuable performance for survival analysis, with C-indexes of 0.688, 0.708, and 0.660 in the training, internal, and external validation cohorts, respectively. The 1-year, 2-year, and 3-year ROC curves showed AUCs of 0.640-0.793 in the cohorts. The model performed equivalently compared to the tumor-based radiomic model and performed better compared to the CT features-based model. Multivariate analysis showed that meso-rad-score was the only factor associated with OS. CONCLUSIONS: A baseline CT radiomic model based on the meso-esophagus provide valuable prognostic information for ESCC patients treated with dCRT.