Lymph Node Metastasis Group Research Articles

Preoperative Role of Superb Microvascular Imaging and Shear-Wave Elastography for Prediction of Axillary Lymph Node Metastasis in Patients With Breast Cancer.

This study aims to evaluate the role of shearwave elastography (SWE) and superb microvascular imaging (SMI) for preoperative prediction of axillary lymph node metastasis (ALNM) in patients with breast cancer. In a cohort of 214 women with breast cancer, B-Mode ultrasonography (US), SMIvascular-index (SMIvi), and SWE (E-mean, E-ratio) values were recorded before tru-cut biopsy. Axillary fine-needle aspiration biopsy (FNAB) and sentinel lymph node sampling results were collected. Imaging findings and histopathological data were statistically compared. Receiver operating characteristic curve analysis was used to evaluate diagnostic performance. Reverse stepwise logistical regression analysis was conducted. Although ALNM was negative in 111 cases, it was positive in 103 patients. Axillary lymph node metastasis (+) group had larger size ( P < 0.001), higher vascularization (SMIvi: 8.0 ± 6.0 versus 5.0 ± 4.3, P < 0.001), and higher elasticity value (E-mean: 129 ± 31 kPa versus 117.3 ± 40 kPa, P = 0.014). Axillary lymph node metastasis was observed statistically more frequently in Her-2 positive cases ( P = 0.005). There was no significant difference between other B-mode US findings ( P > 0.05), SMI Adler ( P = 0.878), and E-ratio ( P = 0.212). The most appropriate cutoff value for the prediction of ALNM was 23.5 mm for size, 3.8 for SMIvi, and 138.5 kPa for E-mean. The most sensitive (77%) method was the SMIvi measurement, while the most specific (86%) finding was Her-2 positivity. The combined model (being Her-2 positive, >23.5 cm, and >3.8 SMIvi) increased the specificity (78%), PPV (71%), and accuracy (68%). Although the increased size is a previously studied parameter in predicting the risk of ALNM, Her-2 and data obtained by SWE, and SMI can be used to assist conventional US.

Risk factor analysis and prediction model for papillary thyroid carcinoma with lymph node metastasis.

We aimed to identify the clinical factors associated with lymph node metastasis (LNM) based on ultrasound characteristics and clinical data, and develop a nomogram for personalized clinical decision-making. A retrospective analysis was performed on 252 patients with papillary thyroid carcinoma (PTC). The patient's information was subjected to univariate and multivariate logistic regression analyses to identify risk factors. A nomogram to predict LNM was established combining the risk factors. The performance of the nomogram was evaluated using receiver operating characteristic (ROC) curve, calibration curve, cross-validation, decision curve analysis (DCA), and clinical impact curve. There are significant differences between LNM and non-LNM groups in terms of age, sex, tumor size, hypoechoic halo around the nodule, thyroid capsule invasion, lymph node microcalcification, lymph node hyperechoic area, peak intensity of contrast (PI), and area under the curve (AUC) of the time intensity curve of contrast (P<0.05). Age, sex, thyroid capsule invasion, lymph node microcalcification were independent predictors of LNM and were used to establish the predictive nomogram. The ROC was 0.800, with excellent discrimination and calibration. The predictive accuracy of 0.757 and the Kappa value was 0.508. The calibration curve, DCA and calibration curve demonstrated that the prediction model had excellent net benefits and clinical practicability. Age, sex, thyroid capsule invasion, and lymph node microcalcification were identified as significant risk factors for predicting LNM in patients with PTC. The visualized nomogram model may assist clinicians in predicting the likelihood of LNM in patients with PTC prior to surgery.

Construction of a comprehensive predictive model for axillary lymph node metastasis in breast cancer: a retrospective study

PurposeThe accurate assessment of axillary lymph node metastasis (LNM) in early-stage breast cancer (BC) is of great importance. This study aimed to construct an integrated model based on clinicopathology, ultrasound, PET/CT, and PET radiomics for predicting axillary LNM in early stage of BC.Materials and methods124 BC patients who underwent 18 F-fluorodeoxyglucose (18 F-FDG) PET/CT and whose diagnosis were confirmed by surgical pathology were retrospectively analyzed and included in this study. Ultrasound, PET and clinicopathological features of all patients were analyzed, and PET radiomics features were extracted to establish an ultrasound model (clinicopathology and ultrasound; model 1), a PET model (clinicopathology, ultrasound, and PET; model 2), and a comprehensive model (clinicopathology, ultrasound, PET, and radiomics; model 3), and the diagnostic efficacy of each model was evaluated and compared.ResultsThe T stage, US_BIRADS, US_LNM, and PET_LNM in the positive axillary LNM group was significantly higher than that of in the negative LNM group (P = 0.013, P = 0.049, P < 0.001, P < 0.001, respectively). Radiomics score for predicting LNM (RS_LNM) for the negative LNM and positive LNM were statistically significant difference (-1.090 ± 0.448 vs. -0.693 ± 0.344, t = -4.720, P < 0.001), and the AUC was 0.767 (95% CI: 0.674–0.861). The ROC curves showed that model 3 outperformed model 1 for the sensitivity (model 3 vs. model 1, 82.86% vs. 48.57%), and outperformed model 2 for the specificity (model 3 vs. model 2, 82.02% vs. 68.54%) in the prediction of LNM. The AUC of mode 1, model 2 and model 3 was 0.687, 0.826 and 0.874, and the Delong test showed the AUC of model 3 was significantly higher than that of model 1 and model 2 (P < 0.05). Decision curve analysis showed that model 3 resulted in a higher degree of net benefit for all the patients than model 1 and model 2.ConclusionThe use of a comprehensive model based on clinicopathology, ultrasound, PET/CT, and PET radiomics can effectively improve the diagnostic efficacy of axillary LNM in BC. Trial registration: This study was registered at ClinicalTrials Gov (number NCT05826197) on 7th, May 2023.

An artificial intelligence prediction model outperforms conventional guidelines in predicting lymph node metastasis of T1 colorectal cancer.

According to guidelines, a lot of patients with T1 colorectal cancers (CRCs) undergo additional surgery with lymph node dissection after being treated by endoscopic resection (ER) despite the low incidence of lymph node metastasis (LNM). The aim of this study was to develop an artificial intelligence (AI) model to more effectively identify T1 CRCs at risk for LNM and reduce the rate of unnecessary additional surgery. We retrospectively analyzed 651 patients with T1 CRCs. The patient cohort was randomly divided into a training set (546 patients) and a test set (105 patients) (ratio 5:1), and a classification and regression tree (CART) algorithm was trained on the training set to develop a predictive AI model for LNM. The model used 12 clinicopathological factors to predict positivity or negativity for LNM. To compare the performance of the AI model with the conventional guidelines, the test set was evaluated according to the Japanese Society for Cancer of the Colon and Rectum (JSCCR) and National Comprehensive Cancer Network (NCCN) guidelines. Finally, we tested the performance of the AI model using the test set and compared it with the JSCCR and NCCN guidelines. The AI model had better predictive performance (AUC=0.960) than the JSCCR (AUC=0.588) and NCCN guidelines (AUC=0.850). The specificity (85.8% vs. 17.5%, p<0.001), balanced accuracy (92.9% vs. 58.7%, p=0.001), and the positive predictive value (36.3% vs. 9.0%, p=0.001) of the AI model were significantly better than those of the JSCCR guidelines and reduced the percentage of the high-risk group for LNM from 83.8% (JSCCR) to 20.9%. The specificity of the AI model was higher than that of the NCCN guidelines (85.8% vs. 82.4%, p=0.557), but there was no significant difference between the two. The sensitivity of the NCCN guidelines was lower than that of our AI model (87.5% vs. 100%, p=0.301), and according to the NCCN guidelines, 1.2% of the 105 test set patients had missed diagnoses. The AI model has better performance than conventional guidelines for predicting LNM in T1 CRCs and therefore could significantly reduce unnecessary additional surgery.

Role of lymphatic invasion in predicting biochemical recurrence after radical prostatectomy.

Lymphatic invasion in prostate cancer is associated with poor prognosis. However, there is no consensus regarding the clinical and prognostic value of lymphatic invasion. This study aimed to investigate the prognostic value of lymphatic invasion in biochemical recurrence (BCR) and compare the recurrence rates between patients with lymphatic invasion and lymph node metastasis. We retrospectively analyzed 2,207 patients who underwent radical prostatectomy (RP) without pelvic lymph node dissection (PLND) and 742 patients who underwent RP with PLND for clinically localized or locally advanced prostate cancer, between 1993 and 2020, at Seoul National University Hospital. Kaplan-Meier analysis was performed to estimate BCR-free survival (BCRFS) using the log-rank test. The Cox proportional hazards model was used to identify the significant factors for BCR. Propensity score matching was performed with a 1:2 ratio to match age, initial PSA level, pathological T stage, and Gleason score to exclude confounding effects. Of the 2,207 patients who underwent RP without PLND, lymphatic invasion (L1Nx) was observed in 79 (3.5%) individuals. Among the 742 patients who underwent RP with PLND, lymph node metastases were found in 105 patients (14.2%). In patients with lymph node metastasis, lymphatic invasion was observed in 50 patients (47.6%), whereas lymphatic invasion was observed in 53 patients (8.3%) among those without lymph node metastasis. In patients who underwent RP without PLND, Kaplan-Meier analysis showed significantly poorer BCR-free survival in the L1Nx group than in the L0Nx group (p < 0.001). In patients who underwent RP with PLND, the L1N0, L0N1, and L1N1 groups showed significantly worse prognoses than the L0N0 group (p < 0.001). However, there was no significant difference in BCRFS between the L1N0 and lymph node metastasis groups, including the L0N1 and L1N1 groups. After propensity score matching at a 1:2 ratio, the L1Nx group showed significantly poorer outcomes in terms of BCRFS than the L0Nx group (p = 0.05). In addition, the L1N0 group showed a significantly worse prognosis than the L0N0 group after propensity score matching. Lymphatic invasion in radical prostatectomy specimens is an independent prognostic factor, which can complement lymph node status for predicting biochemical recurrence. Considering lymphatic invasion as an adverse pathological finding, similar to lymph node metastasis, adjuvant therapy could be considered in patients with lymphatic invasion.

Prognostic significance of common hepatic artery lymph node metastasis in gastric cancer.

The number of lymph nodes is used to determine the prognosis in patients with gastric cancer undergoing D2 lymph node dissection. However, a group of extraperigastric lymph nodes, including lymph node 8a, are also considered to be effective in prognosis. In our clinical experience, in most patients during D2 lymph node dissection, the lymph nodes are removed en-bloc with the specimen and are not marked separately. The aim was to analyze the importance and prognostic impact of 8a lymph node metastasis in patients with gastric cancer. Patients who underwent gastrectomy and D2 lymph node dissection for gastric cancer between 2015 and 2022 were included in the study. Patients were divided into two groups based on metastasis to the 8a lymph node: metastatic and nonmetastatic. The effect of clinicopathologic features and the prevalence of lymph node metastasis on the prognosis of the two groups were analyzed. The present study included 78 patients. The mean number of dissected lymph nodes was 27 (IQR, 15-62). There were 22 (28.2%) patients in the 8a lymph node metastatic group. Patients with 8a lymph node metastatic disease had shorter overall survival and shorter disease-free survival. Those with metastatic 8a lymph nodes among pathologic N2/3 patients had shorter overall and disease-free survival rates (p < 0.05). In conclusion, we believe that anterior common hepatic artery (8a) LN metastasis is a key factor that negatively affects both disease-free and overall survival in patients with locally advanced gastric cancer.

Expression of PI3K-AKT-mTOR signaling pathway in gastric cancer

Background: The PI3K/AKT/m TOR pathway is activated in gastric cancer (GC). This pathway may be an appropriate target for GC therapy. Such therapy may involve inhibiting cell proliferation, enhancing apoptosis, and restoring the sensitivity of cancer cells to chemotherapy. Aim: To study the expression of the PI3K/AKT/mTOR pathway in Egyptian patients with GC. Methods: Enrolled Patients were divided into 2 groups, group 1 included 23 patients with lymph node metastatic (LNM) gastric cancer (GC) and group 2 included 10 patients with non-metastatic GC. Liver and renal biochemical tests, CBC, H. pylori testing, CEA, CA19-9 &amp; metastatic radiological work up were done. Upper Gastrointestinal endoscopy was done to diagnose the site, size and morphology of GC and to take biopsy from the suspicious lesion, adjacent mucosa and all parts of the stomach. Enrolled patients had received surgical resection when appropriate. The harvested tumor tissue was processed for histopathology as well as Immunohistochemical staining (IHC) for detection of expression of PI3K/AKT/mTOR. IHC score for the expression of mTOR, AKT &amp; PI3K (ranging from 0 to 12) was calculated as follows: Grade of stain intensity × Grade of coloration rate. A score ≥4 was considered positive. Results: The mTOR expression was positive in 54.5%, AKT expression was positive in 72.7% &amp; PI3K expression was positive in 36.4% of cases. There was significantly higher expression of AKT in metastatic (87%) than non-metastatic GC (40%), p=0.010. AKT expression was significantly higher in LNM GC (median score was 12) than non-metastatic GC (median score was 2.5), p=0.028. AKT score at cutoff value of more than 3 was a statistically significant discriminator between LNM and non-metastatic GC, AUC=0.743, p=.014, sensitivity, 87% &amp; specificity, 60%. Conclusion: The mTOR expression was positive in 54.5%, AKT expression was positive in 72.7% &amp; PI3K expression was positive in 36.4% of GC cases. AKT expression score may discriminate between LN metastatic and non-metastatic gastric cancer

Lymph node metastasis-related gene signature shows good performance in predicting prognosis and immune infiltration in cervical cancer.

This study aimed to construct a lymph node metastasis-related gene signature to predict prognosis and immune infiltration in patients with cervical cancer. Clinical and RNA sequencing data of 193 patients with cervical cancer, which were divided into lymph node metastasis (N1) and non-lymph node metastasis (N0) groups, were acquired from TCGA. Differentially expressed genes (DEGs) between the N1 and N0 groups were detected, and protein-protein interaction combined with LASSO analysis was conducted to further screen lymph node metastasis-related genes. Univariate and multivariate Cox regression analyses were performed to establish a predictive signature. The genetic features, potential biological behavior, and immune infiltration characteristics of the predictive signature were explored. Furthermore, the sensitivity of patients to chemotherapy drugs was estimated based on the predictive signature and the expression of TEKT2 and RPGR was investigated in the cervical cancer tissue samples. A total of 271 lymph node metastasis-related DEGs, including 100 upregulated and 171 downregulated genes, were identified. Two genes, TEKT2 and RPGR, were associated with lymph node metastasis and prognosis in cervical cancer, and were used to construct a lymph node metastasis-related predictive signature. Based on the predictive signature, patients with cervical cancer were divided into high- and low-risk groups. The high-risk group, characterized by a higher tumor mutation burden and somatic mutation rate, indicated a poor overall survival. The activation of immune infiltration and increased expression of checkpoint genes were observed in the high-risk group, indicating that they might benefit from immunotherapy. Cytarabine, FH535, and procaspase-activating compound-1 were estimated as reasonable chemotherapy options for patients in the high-risk group, whereas two taxanes and five tyrosine kinase inhibitors, including etoposide and vinorelbine, had therapeutic significance for patients in the low-risk group. The expression of TEKT2 and RPGR was significantly downregulated in cervical cancer tissues, especially in metastatic lymph node tissues. The lymph node metastasis-related predictive signature based on TEKT2 and RPGR showed good performance in predicting the survival outcomes of patients with cervical cancer. The risk score of the predictive signature was related to genetic variation and immune infiltration, which could guide immunotherapy and chemotherapy strategies.

Wolf in sheep’s clothing: Effect of primary lung tumor cells equipped with elevated neuron-like properties on brain metastases formation.

e14009 Background: It is clinically significant to decipher the mechanisms underlying lung cancer-associated brain metastases. However, comparative studies on primary tumors between patients with and without brain metastases failed to unveil such mechanisms. Moreover, the clinical observation that the incidence of brain metastases after resection of the primary tumor was as high in lymph node-negative (non-LNM) patients as in those with lymph node metastases (LNM) challenges our established knowledge that lymph node staging correlates with distant metastases. Here, we hypothesized that tumor cells in non-LNM patients might acquire a unique mechanism to develop brain metastases. Methods: We ran whole-transcriptome sequencing and methylation sequencing on paired primary tumor and brain metastases samples from 15 NSCLC patients. We compared the differentially expressed genes (DEGs) between patients with LNM and non-LNM patients. Results: Pathway enrichment analysis showed that GO and KEGG pathways associated with neuronal transmission and synaptic signaling were upregulated in primary tumors of the non-LNM group vs. that of the LNM group. In contrast, brain metastases revealed an inversed trend of differential expression for these pathways between non-LNM and LNM groups. Exploratory validation at methylation level revealed that high expression of the neuronal transmission-associated gene GABRG3 correlated with hypomethylation of this gene (R=-0.39, p=0. 042), responsive to increased expression of GABRG3 in brain metastases. Brain metastases expressed higher neuronal transmission-related signatures than paired primary tumors. Intriguingly, this upregulation of tumor-neuron interactions was more pronounced in patients with lymph node metastases, suggesting that i) lymph node metastases might aid primary tumor to develop brain metastases by promoting the adaptation of tumor cells to the brain microenvironment, while ii) non-LNM patients already acquired neuron-like traits at primary sites, heralding the potential of brain metastases. Conclusions: The hypothetical mechanism of lymph node-negative lung cancer developing brain metastases might be that these tumor cells acquired properties of brain cells as early as in primary sites, masking themselves as ‘sheep’ to communicate with the host’s brain cells, and eventually unmasked as ‘wolf’: brain metastases. Further functional studies are warranted to validate this hypothesis.

Study of endometrial carcinoma on correlation between molecular typing and lymph node metastasis and construction of prediction model.

e17621 Background: TCGA molecular typing was proposed by The Cancer Genome Atlas (TCGA) in 2013 and recommended by the fifth edition of WHO, which has a strong prognositic value and is included in the risk stratification system, clinical diagnosis and treatment decision-making system of endometrial cancer (EC) patients. Meanwhile, Lymph node management in patients with EC is still controversial. Lymph node dissection (LND) and sentinel lymph node biopsy (SLNB) have their own advantages and limitations. This study attempts to establish a predictive model for lymph node metastasis (LNM) in patients with EC based on molecular typing and other clinical features and establish risk stratification, in order to provide a feasible method for predicting LNM in EC. Methods: In this study, SPSS19, R 4.2.1 and python 3.6 were used to establish a nomogram and evaluate it through a number of indicators, and the model was repeatedly verified by machine learning algorithms. With LNM as the outcome, risk stratification was set through the cut-off value of nomogram score to guide clinical treatment. Finally, an online LNM prediction website was designed to help patients. Results: A total of 325 EC patients were included, 35 (10.8%) with LNM and 290 (89.2%) without LNM. CA125 and cervical involvement were significantly correlated with LNM (p &lt; 0.001), molecular typing, neutrophil to lymphocyte ratio (NLR), menstrual status were associated with LNM (p &lt; 0.05). Lasso analysis screened out four independent factors: molecular typing, CA125, cervical involvement and menstrual status. The nomogram model was established by logistics regression (LR), with the prediction accuracy of the training set and validation set of 0.890 and 0.886, respectively. Machine learning algorithms in the training set also validates the model. Meanwhile, a risk stratification is established by the cut-off value of nomogram. We define high-risk group of LNM with more than the cut-off value and LND is recommended, while below the value is low-risk group and SLNB is recommended. Conclusions: We have established a nomogram clinical prediction model incorporating four factors, practicing stratified medical treatment about LND and SLNB, and formed a website for LNM prediction, genuinely providing patients recommendations.

Clinical and endoscopic features of metachronous gastric cancer with possible lymph node metastasis after endoscopic submucosal dissection and Helicobacter pylori eradication.

Several studies have reported the metachronous gastric cancers (MGCs) with possible lymph node metastasis (LNM) after endoscopic submucosal dissection (ESD) and Helicobacter pylori (H. pylori) eradication in which a curative ESD had not been achieved. There have been no published reports of evaluations of the features of patients with MGC with possible LNM after ESD and H. pylori eradication. We identified 264 patients with 369 MGCs after H. pylori eradication among the 4354 patients with 5059 early gastric cancers (EGCs) who underwent ESD between 1999 and 2017 and divided them into two groups: patients with MGCs with possible LNM (Group I) and patients with MGCsundergone curative ESD (Group II). We retrospectively compared the features of patients with MGCs and patients with EGCs at index ESD in the two groups. Group I consisted of 20 patients with 21 MGCs, and Group II consisted of 244 patients with 348 MGCs. Group I lesions were significantly more common in the posterior wall than in the lesser curvature (odds ratio [OR] = 3.97; 95% confidence intervals [CI] 1.20-13.10). Development of Group I was significantly more common in patients with a body mass index (BMI) < 19.0kg/m2 than in patients with a BMI ≥ 19.0kg/m2 at index ESD (OR = 4.44; 95% CI 1.30-15.20). During surveillance endoscopy after gastric ESD and H. pylori eradication, the posterior wall should be carefully examined to detect MGCs early. Lower BMI may be associated with the development of MGCs with possible LNM.

Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis for Assessing Normal-Sized Lymph Node Metastasis in Cervical Cancer: Comparison Between Readout-Segmented and Single-Shot Echo-Planar Diffusion-Weighted Imaging.

To compare the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis derived from readout-segmented echo-planar imaging (RS-EPI) and single-shot echo-planar imaging (SS-EPI) diffusion-weighted imaging (DWI) in evaluating normal-sized lymph node metastasis (LNM) in cervical cancer. Seventy-six pathologically confirmed cervical cancer patients (stages IB and IIA) were enrolled, including 61 patients with non-LNM (group A) and 15 patients with normal-sized LNM (group B). The recorded tumor volume on T2-weighted imaging was the reference against which both DWIs were evaluated. Each ADC histogram parameter (including ADC max , ADC 90 , ADC median , ADC mean , ADC 10 , ADC min , ADC skewness , ADC kurtosis , and ADC entropy ) was compared between SS-EPI and RS-EPI and between the 2 groups. There was no significant difference in tumor volume between the 2 DWIs and T2-weighted imaging (both P > 0.05). Higher ADC max and ADC entropy but lower ADC 10 , ADC min and ADC skewness were found in SS-EPI than those in RS-EPI (all P < 0.05). For SS-EPI, lower ADC 90 and higher ADC kurtosis were found in group B than those in group A (both P < 0.05). For RS-EPI, lower ADC 90 and higher ADC kurtosis and ADC entropy were found in group B than those in group A (all P < 0.05). Readout-segmented echo-planar imaging ADC kurtosis showed the highest area under the curve of 0.792 in the differentiation of the 2 groups (sensitivity, 80%; specificity, 73.77%). Compared with SS-EPI, the ADC histogram parameters derived from RS-EPI were more accurate, and ADC kurtosis held great potential in differentiating normal-sized LNM in cervical cancer.

Amide Proton Transfer-Weighted Imaging Combined with ZOOMit Diffusion Kurtosis Imaging in Predicting Lymph Node Metastasis of Cervical Cancer

The aim of this study is to investigate the feasibility of amide proton transfer-weighted (APTw) imaging combined with ZOOMit diffusion kurtosis imaging (DKI) in predicting lymph node metastasis (LNM) in cervical cancer (CC). Sixty-one participants with pathologically confirmed CC were included in this retrospective study. The APTw MRI and ZOOMit diffusion-weighted imaging (DWI) were acquired. The mean values of APTw and DKI parameters including mean kurtosis (MK) and mean diffusivity (MD) of the primary tumors were calculated. The parameters were compared between the LNM and non-LNM groups using the Student's t-test or Mann-Whitney U test. Binary logistic regression analysis was performed to determine the association between the LNM status and the risk factors. The diagnostic performance of these quantitative parameters and their combinations for predicting the LNM was assessed with receiver operating characteristic (ROC) curve analysis. Patients were divided into the LNM group (n = 17) and the non-LNM group (n = 44). The LNM group presented significantly higher APTw (3.7 ± 1.1% vs. 2.4 ± 1.0%, p < 0.001), MK (1.065 ± 0.185 vs. 0.909 ± 0.189, p = 0.005) and lower MD (0.989 ± 0.195 × 10-3 mm2/s vs. 1.193 ± 0.337 ×10-3 mm2/s, p = 0.035) than the non-LNM group. APTw was an independent predictor (OR = 3.115, p = 0.039) for evaluating the lymph node status through multivariate analysis. The area under the curve (AUC) of APTw (0.807) was higher than those of MK (AUC, 0.715) and MD (AUC, 0.675) for discriminating LNM from non-LNM, but the differences were not significant (all p > 0.05). Moreover, the combination of APTw, MK, and MD yielded the highest AUC (0.864), with the corresponding sensitivity of 76.5% and specificity of 88.6%. APTw and ZOOMit DKI parameters may serve as potential noninvasive biomarkers in predicting LNM of CC.

Abstract P3-04-08: Comparison Of Cone Beam Breast CT And Breast MRI In Preoperative Assessment Of Primary Breast Cancer Tumor Size

Abstract Comparison Of Cone Beam Breast CT And Breast MRI In Preoperative Assessment Of Primary Breast Cancer Tumor Size Yinan Ji＊1, Geyi Liao＊1, Ningbing Luo2, Yi Jiang1, Xiaoming Liao1, Wei Tang1, Huawei Yang 1. 1Department of Breast Surgery, 2Department of Radiology, Guangxi Medical University Tumor Hospital, Nanning, China Correspondence to: Dr. Huawei Yang, Department of Breast Surgery, Guangxi Medical University Cancer Hospital, 71 Hedi Road, Nanning, Guangxi Zhuang Autonomous Region 530021, P.R. China Email: Lordyhw@163.com ＊Contributed equally Background: Breast cancer is the malignant tumor with the highest incidence among women in China, seriously endangering women’s health. After breast cancer is diagnosed, surgery is one of the primary treatments. Assessing the extent of breast cancer is vitally important to formulate the best surgical option, obtain clear margins while minimizing the volume of removed tissue, and improve the treatment outcome and patient’s quality of life. Currently, breast surgery relies heavily on Breast MRI (bMRI) for preoperative assessment. However, bMRI may overestimate the tumor sizes. bMRI is contraindicated in patients with claustrophobia, metal implants, and allergy to gadolinium contrast medium. In addition, bMRI is generally expensive and time-consuming. Some patients find bMRI less favorable for cost and time concerns. Cone Beam Breast CT (CBBCT) is a new modality using Cone Beam CT technology and iodinated contrast media for true 3D high-resolution imaging of the breast. Our previous studies have proved the feasibility of CBBCT in the preoperative assessment of breast cancer. A large-scale study has been done to further examine the clinical significance of CBBCT in pre-operative assessment compared with bMRI. Methods: 273 female primary breast cancer patients were included in this study. The maximal diameters of the tumors were measured in CBBCT and bMRI images by an experienced radiologist. The differences between the image-measured size and surgical pathology size (gold standard) were calculated as ΔDCT and ΔDMR. ΔD ˃ 5mm was considered an overestimation and ΔD ˂-5mm was considered an underestimation. The absolute values of ΔDCT and ΔDMR were also calculated as ΔDCT_abs and ΔDMR_abs. A Student t-test was used to compare the statistical differences between ΔDCT_abs and ΔDMR_abs. Among ΔDCT_abs and ΔDMR_abs, a 5mm cut-off value was used to determine the concordance between image and pathology. The concordance rates of CBBCT-Pathology and bMRI-Pathology were tested with the χ2 method. Factors that may affect the accuracy of tumor size measurement were analyzed. Results: Overall there were no statistically significant differences between ΔDCT_abs and ΔDMR_abs. (0.58±0.68cm vs. 0.65±0.68cm; t=1.653, P=0.100). Using 5mm as the cut-off value of concordance, there was no significant difference in the CBBCT-Pathology and bMRI-Pathology concordance rates (64.8% vs. 60.1%, χ2 =1.320, P=0.251). The overestimation rate of CBBCT was significantly lower than that of bMRI (11.4% vs. 19.4%, χ2=6.810, P=0.009). But there was no significant difference in the underestimation rates (23.8% vs. 20.5%, χ2=0.860, P=0.354). ΔDCT_abs is significantly smaller than ΔDMR_abs among the invasive ductal carcinoma group (P=0.017), the premenopausal group (P=0.004), and the small tumor size (≤2cm) group (P=0.003). ΔDCT_abs and ΔDMR_abs.have no significant differences among the lymph node metastasis group, multi-lesion group, and enhancement type group (P &amp;gt; 0.05). Conclusions: CBBCT is concordant with surgical pathology in breast tumor size measurement and provides more accurate tumor size estimation compared with bMRI. CBBCT can be used as a valuable modality for pre-operative assessment. Citation Format: YINAN JI, Geyi Liao, Ningbing Luo, Yi Jiang, Xiaoming Liao, Wei Tang, Huawei Yang. Comparison Of Cone Beam Breast CT And Breast MRI In Preoperative Assessment Of Primary Breast Cancer Tumor Size [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-04-08.

LncRNA-BC069792 suppresses tumor progression by targeting KCNQ4 in breast cancer

BackgroundBreast cancer is the most common malignant tumor that threatens women's health. Attention has been paid on the study of long- non-coding RNA (lncRNA) in breast cancer. However, the specific mechanism remains not clear.MethodsIn this study, we explored the role of lncRNA BC069792 in breast cancer. In vitro and in vivo functional experiments were carried out in cell culture and mouse models. High-throughput next-generation sequencing technology and real-time fluorescence quantitative PCR technology were used to evaluate differentially expressed genes and mRNA expression, Western blot and immunohistochemical staining were used to detect protein expression. RNA immunoprecipitation assay and dual-luciferase activity assay were used to evaluate the competing endogenous RNAs (ceRNA), and rescue and mutation experiments were used for verification.ResultsWe found that lncRNA BC069792 was expressed at a low level in breast cancer tissues, and significantly decreased in breast cancer with high pathological grade, lymph node metastasis and high Ki-67 index groups. Moreover, BC069792 inhibited the proliferation, invasion and metastasis of breast cancer cells in vitro and in vivo. Mechanically, BC069792 acts as a molecular sponge to adsorb hsa-miR-658 and hsa-miR-4739, to up-regulate the protein expression of Potassium Voltage-Gated Channel Q4 (KCNQ4), inhibits the activities of JAK2 and p-AKT, and plays a role in inhibiting breast cancer growth.ConclusionsLncRNA BC069792 plays the role of tumor suppressor gene in breast cancer and is a new diagnostic index and therapeutic target in breast cancer.

CT-measured body composition radiomics predict lymph node metastasis in localized pancreatic ductal adenocarcinoma

BackgroundTo explored the value of CT-measured body composition radiomics in preoperative evaluation of lymph node metastasis (LNM) in localized pancreatic ductal adenocarcinoma (LPDAC).MethodsWe retrospectively collected patients with LPDAC who underwent surgical resection from January 2016 to June 2022. According to whether there was LNM after operation, the patients were divided into LNM group and non-LNM group in both male and female patients. The patient’s body composition was measured by CT images at the level of the L3 vertebral body before surgery, and the radiomics features of adipose tissue and muscle were extracted. Multivariate logistic regression (forward LR) analyses were used to determine the predictors of LNM from male and female patient, respectively. Sexual dimorphism prediction signature using adipose tissue radiomics features, muscle tissue radiomics features and combined signature of both were developed and compared. The model performance is evaluated on discrimination and validated through a leave-one-out cross-validation method.ResultsA total of 196 patients (mean age, 60 years ± 9 [SD]; 117 men) were enrolled, including 59 LNM in male and 36 LNM in female. Both male and female CT-measured body composition radiomics signatures have a certain predictive power on LNM of LPDAC. Among them, the female adipose tissue signature showed the highest performance (area under the ROC curve (AUC), 0.895), and leave one out cross validation (LOOCV) indicated that the signature could accurately classify 83.5% of cases; The prediction efficiency of the signature can be further improved after adding the muscle radiomics features (AUC, 0.924, and the accuracy of the LOOCV was 87.3%); The abilities of male adipose tissue and muscle tissue radiomics signatures in predicting LNM of LPDAC was similar, AUC was 0.735 and 0.773, respectively, and the accuracy of LOOCV was 62.4% and 68.4%, respectively.ConclusionsCT-measured body composition Radiomics strategy showed good performance for predicting LNM in LPDAC, and has sexual dimorphism. It may provide a reference for individual treatment of LPDAC and related research about body composition in the future.

Novel, alternative splicing signature to detect lymph node metastasis in prostate adenocarcinoma with machine learning.

The presence of lymph node metastasis leads to a poor prognosis for prostate cancer (Pca). Recently, many studies have indicated that gene signatures may be able to predict the status of lymph nodes. The purpose of this study is to probe and validate a new tool to predict lymph node metastasis (LNM) based on alternative splicing (AS). Gene expression profiles and clinical information of prostate adenocarcinoma cohort were retrieved from The Cancer Genome Atlas (TCGA) database, and the corresponding RNA-seq splicing events profiles were obtained from the TCGA SpliceSeq. Limma package was used to identify the differentially expressed alternative splicing (DEAS) events between LNM and non-LNM groups. Eight machine learning classifiers were built to train with stratified five-fold cross-validation. SHAP values was used to explain the model. 333 differentially expressed alternative splicing (DEAS) events were identified. Using correlation filter and the least absolute shrinkage and selection operator (LASSO) method, a 96 AS signature was identified that had favorable discrimination in the training set and validated in the validation set. The linear discriminant analysis (LDA) was the best classifier after 100 iterations of training. The LDA classifier was able to distinguish between LNM and non-LNM with an area under the receiver operating curve of 0.962 ± 0.026 in the training set (D1 = 351) and 0.953 in the validation set (D2 = 62). The decision curve analysis plot proved the clinical application of the AS-based model. Machine learning combined with AS data could robustly distinguish between LNM and non-LNM in Pca.

The ratio of lymph node eluate/serum cytokeratin 21-1 is a potential predictor of lymph node metastasis in lung adenocarcinoma.

Lymph node metastasis (LNM) plays an important role in prognosis of lung cancer, either in preoperative TNM staging or postoperative disease recurrence or progress. This study aimed to explore the diagnostic performances of biomarkers from lymph node eluate for LNM in lung adenocarcinoma (LUAD). A prospective analysis was conducted based on lymph node eluate specimens collected via ultrasound-guided lymph node biopsy from 48 LUAD patients with suspected LNM in the neck. According to cytopathological results, the patients were categorized into two groups: one with LNM and the other with non-LNM (NLNM). Carcinoembryonic antigen (CEA), cytokeratin 21-1 fragment (CYFRA21-1), neuron-specific enolase (NSE) and gastrin precursor releasing peptide (ProGRP) in lymph node eluate were detected by immunoassay analyzers, and tumor marker was simultaneously collected in serum of patients. The serum levels of CEA, CYFRA21-1, NSE, and the ratios of CYFRA21-1 and NSE in the lymph node eluate to serum were significantly higher in the LNM group, compared to NLNM group. The areas under curves (AUCs) for CEA, CYFRA21-1, NSE, and ratios of CYFRA21-1 and NSE were 0.79, 0.91, 0.85, 0.93, and 0.89, respectively. The ratio of CYFRA21-1 in lymph node eluate to serum (rCYFRA21-1) performed best in diagnosing, with a sensitivity of 92.3%, a specificity of 92.9%, and an AUC of 0.93. The rCYFRA21-1 to that in the serum might serve as a potential predictor for LNM in LUAD.

A Nomogram for Predicting Extraperigastric Lymph Node Metastasis in Patients With Early Gastric Cancer.

There are no clear guidelines to determine whether to perform D1 or D1+ lymph node dissection in early gastric cancer (EGC). This study aimed to develop a nomogram for estimating the risk of extraperigastric lymph node metastasis (LNM). Between 2009 and 2019, a total of 4,482 patients with pathologically confirmed T1 disease at 6 affiliated hospitals were included in this study. The basic clinicopathological characteristics of the positive and negative extraperigastric LNM groups were compared. The possible risk factors were evaluated using univariate and multivariate analyses. Based on these results, a risk prediction model was developed. A nomogram predicting extraperigastric LNM was used for internal validation. Multivariate analyses showed that tumor size (cut-off value 3.0 cm, odds ratio [OR]=1.886, P=0.030), tumor depth (OR=1.853 for tumors with sm2 and sm3 invasion, P=0.010), cross-sectional location (OR=0.490 for tumors located on the greater curvature, P=0.0303), differentiation (OR=0.584 for differentiated tumors, P=0.0070), and lymphovascular invasion (OR=11.125, P<0.001) are possible risk factors for extraperigastric LNM. An equation for estimating the risk of extraperigastric LNM was derived from these risk factors. The equation was internally validated by comparing the actual metastatic rate with the predicted rate, which showed good agreement. A nomogram for estimating the risk of extraperigastric LNM in EGC was successfully developed. Although there are some limitations to applying this model because it was developed based on pathological data, it can be optimally adapted for patients who require curative gastrectomy after endoscopic submucosal dissection.

A UPLC Q-Exactive Orbitrap Mass Spectrometry-Based Metabolomic Study of Serum and Tumor Tissue in Patients with Papillary Thyroid Cancer

To find the metabolomic characteristics of tumor or para-tumor tissues, and the differences in serums from papillary thyroid cancer (PTC) patients with or without lymph node metastasis. We collected serums of PTC patients with/without lymph node metastasis (SN1/SN0), tumor and adjacent tumor tissues of PTC patients with lymph node metastasis (TN1 and PN1), and without lymph node metastasis (TN0 and PN0). Metabolite detection was performed by ultra-high performance liquid chromatography combined with Q-Exactive orbitrap mass spectrometry (UPLC Q-Exactive). There were 31, 15, differential metabolites in the comparisons of TN1 and PN1, TN0 and PN0, respectively. Seven uniquely increased metabolites and fourteen uniquely decreased metabolites appeared in the lymph node metastasis (TN1 and PN1) group. Meanwhile, the results indicated that four pathways were co-owned pathways in two comparisons (TN1 and PN1, TN0 and PN0), and four unique pathways presented in the lymph node metastasis (TN1 and PN1) group. Common or differential metabolites and metabolic pathways were detected in the lymph node metastasis and non-metastatic group, which might provide novel ways for the diagnosis and treatment of PTC.