High Risk Of Lymph Node Metastasis Research Articles

Lymph node metastasis in early invasive lung adenocarcinoma: Prediction model establishment and validation based on genomic profiling and clinicopathologic characteristics.

The presence of lymph node (LN) metastasis directly affects the treatment strategy for lung adenocarcinoma (LUAD). Next-generation sequencing (NGS) has been widely used in patients with advanced LUAD to identify targeted genes, while early detection of pathologic LN metastasis using NGS has not been assessed. Clinicopathologic features and molecular characteristics of 224 patients from Ruijin Hospital were analyzed to detect factors associated with LN metastases. Another 140 patients from Huashan Hospital were set as a test cohort. Twenty-four out of 224 patients were found to have lymph node metastases (10.7%). Pathologic LN-positive tumors showed higher mutant allele tumor heterogeneity (p < 0.05), higher tumor mutation burden (p < 0.001), as well as more frequent KEAP1 (p = 0.001), STK11 (p = 0.004), KRAS (p = 0.007), CTNNB1 (p = 0.017), TP53, and ARID2 mutations (both p = 0.02); whereas low frequency of EGFR mutation (p = 0.005). A predictive nomogram involving male sex, solid tumor morphology, higher T stage, EGFR wild-type, and TP53, STK11, CDKN2A, KEAP1, ARID2, KRAS, SDHA, SPEN, CTNNB1, DICER1 mutations showed outstanding efficiency in both the training cohort (AUC = 0.819) and the test cohort (AUC = 0.780). This study suggests that the integration of genomic profiling and clinical features identifies early-invasive LUAD patients at higher risk of LN metastasis. Improved identification of LN metastasis is beneficial for the optimization of the patient's therapy decisions.

Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer.

The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in LNM of invasive breast cancer. It also analyzed the biological significance of DLR phenotype based on genomics. Two cohorts from the Cancer Imaging Archive project were used, one as the training cohort (TCGA-Breast, n = 88) and one as the validation cohort (Breast-MRI-NACT Pilot, n = 57). Radiomics and deep learning features were extracted from preoperative DCE-MRI. After dual selection by principal components analysis (PCA) and relief methods, radiomics and deep learning models for predicting LNM were constructed by the random forest (RF) method. A post-fusion strategy was used to construct the DLR nomograms (DLRNs) for predicting LNM. The performance of the models was evaluated using the receiver operating characteristic (ROC) curve and Delong test. In the training cohort, transcriptome data were downloaded from the UCSC Xena online database, and biological pathways related to the DLR phenotypes were identified. Finally, hub genes were identified to obtain DLR gene expression (RadDeepGene) scores. DLRNs were based on area under curve (AUC) evaluation (training cohort, AUC = 0.98; validation cohort, AUC = 0.87), which were higher than single radiomics models or GoogLeNet models. The Delong test (radiomics model, P = 0.04; GoogLeNet model, P = 0.01) also validated the above results in the training cohorts, but they were not statistically significant in the validation cohort. The GoogLeNet phenotypes were related to multiple classical tumor signaling pathways, characterizing the biological significance of immune response, signal transduction, and cell death. In all, 20 genes related to GoogLeNet phenotypes were identified, and the RadDeepGene score represented a high risk of LNM (odd ratio = 164.00, P < 0.001). DLRNs combining radiomics and deep learning features of DCE-MRI images improved the preoperative prediction of LNM in breast cancer, and the potential biological characteristics of DLRN were identified through genomics.

Clinicopathological characteristics of endometrial carcinoma with different molecular subtypes and their correlation with lymph node metastasis.

Endometrial carcinoma (EC) is one of the three major malignancies of the female reproductive organs. With intense research of tumor molecular mechanisms and development of precision medicine in recent years, the traditional pathomorphological classification fails to meet the needs of clinical diagnosis and treatment for EC. This study aims to analyze the correlation of different Proactive Molecular Risk Classifier for Endometrial Cancer molecular subtypes with lymph node metastasis (LNM) and other clinical features in EC. 120 treatment-naive EC patients with surgery were enrolled in this study. The molecular subtypes of these patients were classified as follows by Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) molecular subtyping: mismatch repair deficiency (MMRd) in 22 cases (18.33%), polymerase epsilon exonuclease domain mutation (POLE EDM) in 2 cases (1.67%), p53 wild-type (p53-wt) in 64 cases (53.33%), and p53 abnormal (p53-abn) in 32 cases (26.67%). The clinicopathological features of 120 patients were retrospectively analyzed. Statistical significance was identified among the four molecular subtypes in terms of histological classification, International Federation of Gynecology and Obstetrics (FIGO) staging, pathological grading, and LNM. Among the enrolled cases, 26 had LNM and 94 had no lymph node involvement. According to the multivariate Logistic regression analysis, p53 wt (P=0.008, OR=0.078, 95% CI: 0.012-0.510) was a protective factor for LNM in EC patients, while poorly differentiated histology (P=0.001, OR=15.137, 95% CI: 3.013-76.044) was a risk factor. ProMisE classification system, being more objective and reproducible, can provide an important reference for preoperative decision-making. The patients with p53 wt by ProMisE had a low risk of LNM in preoperative diagnostic curettage specimens, while there was a higher risk of LNM among the patients with poorly differentiated EC.

Risk factors and a predictive nomogram for lymph node metastasis in superficial esophageal squamous cell carcinoma.

Superficial esophageal squamous cell carcinoma (ESCC) is defined as cancer infiltrating the mucosa and submucosa, regardless of regional lymph node metastasis (LNM). Endoscopic resection of superficial ESCC is suitable for lesions that have no or low risk of LNM. Patients with a high risk of LNM always need further treatment after endoscopic resection. Therefore, accurately assessing the risk of LNM is critical for additional treatment options. To analyze risk factors for LNM and develop a nomogram to predict LNM risk in superficial ESCC patients. Clinical and pathological data of superficial ESCC patients undergoing esophagectomy from January 1, 2009 to January 31, 2016 were collected. Logistic regression analysis was used to predict LNM risk factors, and a nomogram was developed based on risk factors derived from multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve was used to obtain the accuracy of the nomogram model. A total of 4660 patients with esophageal cancer underwent esophagectomy. Of these, 474 superficial ESCC patients were enrolled in the final analysis, with 322 patients in the training set and 142 patients in the validation set. The prevalence of LNM was 3.29% (5/152) for intramucosal cancer and increased to 26.40% (85/322) for submucosal cancer. Multivariate logistic analysis showed that tumor size, invasive depth, tumor differentiation, infiltrative growth pattern, tumor budding, and lymphovascular invasion were significantly correlated with LNM. A nomogram using these six variables showed good discrimination with an area under the ROC curve of 0.789 (95%CI: 0.737-0.841) in the training set and 0.827 (95%CI: 0.755-0.899) in the validation set. We developed a useful nomogram model to predict LNM risk for superficial ESCC patients which will facilitate additional decision-making in treating patients who undergo endoscopic resection.

The relationship between the expression of lncRNA MALAT1 and clinical features and prognosis in bladder cancer: A meta-analysis.

The objective of this meta-analysis is to evaluate the association between LncRNA MALAT1 and the clinicopathological characteristics and prognosis in patients with bladder cancer. Related studies were searched from Pubmed, Embase, Cochrane Library, CNKI and Web of Science up to Sep 1, 2021. Basic characteristic and prognostic data were extracted from the included studies. We synthesized and compared primary outcomes such as overall survival. Based on the cut-off value, sample size, and follow-up time, subgroup analysis was conducted. We calculated the combined hazard ratio (HR), odds ratio (OR), and 95% confidence interval (CI) to assess the relationship between LncRNA MALAT1 and prognosis clinicopathological features of bladder cancer patients. Seven studies with 822 patients were included in this meta-analysis. The results showed that the high lncRNA MALAT1 was significantly related to poor overall survival (HR = 2.34, 95% CI:1.61-3.38; P<0.001) in bladder cancer patients. Furthermore, a high level of LNCRNA MALAT1 is associated with lymph node metastasis (LNM) (OR = 1.82, 95% CI 1.32-2.52, P <0.001) in bladder cancer. The results of sensitivity analysis showed the stabilization and reliability of results in this Meta-analysis. In conclusion, elevated LncRNA MALAT1 is associated with a poor prognosis and a higher risk of LNM in patients with bladder cancer. LncRNA MALAT1 could be identified as a biomarker with a potential prognostic value in bladder cancer.

Correlation between the risk of lymph node metastasis and the expression of GBP1 in breast cancer patients.

To explore the prognostic value and correlation between the risk of lymph node metastasis (LNM) and Guanylate-binding Protein 1 (GBP1) in breast cancer (BC) patients. In this retrospective study, the clinical data of 150 patients with BC who were surgically resected in The Affiliated Qingdao Central Hospital of Qingdao University from January 2019 to December 2021 were included. Patients were divided into metastasis group (n=110) or non-metastasis group (n=40) according to whether there was LNM post-surgery. Logistic regression was used to analyze the risk factors for LNM in BC, and Kaplan-Meier was used to assess the risk of disease progression 12 months post-operation in both groups. Patients were divided into a GBP1 low expression-group (n=75) or a GBP1 high expression-group (n=75). The risk of disease progression, one-year post-surgery was analyzed, and the predictive value of GBP1 in BC tissue was assessed by the receiver operating characteristics (ROC) curve. Independent risk factors for BC with LNM were GBP1, CEA and TNM stage (P<0.05). There is a linear relationship between GBP1 expression and LNM risk in BC (χ2=0.88, P<0.05). Patients with high expression of GBP1 had a higher risk of LNM (χ2=3.204, P<0.001) and early postoperative progression (χ2=7.412, P<0.05). The AUC of GBP1 in predicting the risk of LNM was 0.840. Patients with BC and a higher expression of GBP1 could be at an increased risk of LNM. Elevations in GBP1 expression can also suggest a poor prognosis for patients with BC.

Single-cell transcriptomics reveals the drivers and therapeutic targets of lymph node metastasis in lung adenocarcinoma.

Lymph node metastasis (LNM) is usually the most common metastatic pathway in lung adenocarcinoma (LUAD) and is associated with a poorer prognosis and higher possibility of recurrence. Therefore, discovering the drivers and therapeutic targets of LNM is important for early and non-invasive detection of patients with a high risk of LNM and guiding individualized therapy. Various cell constitutions of the primary tumor and lymph node microenvironment was characterized based on scRNA-seq data. The copy number variation (CNV) analysis was performed to probe clonal structures and origins of metastatic lymph nodes, and found 6q loss and 20q gain may drive LNM in LUAD. Then a LNM-related cell subset, named Scissor+ cells, was identified using the Scissor algorithm. And cell-cell communication network among Scissor+ cells and microenvironment was further analyzed. Besides, a pro-LNM signature was subsequently constructed based on 27 genes using pseudotime trajectory analysis and gene set variation analysis. The pro-LNM signature showed a significant correlation with N stage and a good predictive ability of LUAD survival. At last, we identified that erastin and gefitinib could potentially inhibit LNM by targeting Scissor+ cells based on the drug sensitivity data of the cancer cell lines, which provided new insights for LUAD therapy.

Endoscopic submucosal dissection in early gastric cancer with papillary histological type.

412 Background: Gastric papillary adenocarcinoma has been regarded as differentiated-type adenocarcinomas along with well- or moderately-differentiated tubular adenocarcinomas in current endoscopic submucosal dissection (ESD) indication. We aimed to investigate lymph node metastasis (LNM) and to verify the safety of ESD in patients with papillary early gastric carcinoma (P-EGC). Methods: 4,264 patients with early gastric cancer (EGC) underwent curative gastrectomy between October 2000 and December 2017 at National Cancer Center, Korea. Among them, 45 patients were pathologically confirmed with P-EGC. We compared clinicopathological factors among differentiated-type EGC except P-EGC (n=2,106), undifferentiated-type EGC (n=2,113), and P-EGC. Logistic regression model was used to investigate risk factors of LNM. Results: P-EGCs were frequently located in lower third of the stomach (71.1%, P &lt; 0.001), and about half of them (46.7%, P &lt; 0.001) had elevated morphology. Mucosal cancer of P-EGC (37.9%) was significantly lower than differentiated-type (48.8%) and undifferentiated-type EGC (22.2%, P &lt; 0.001). Angiolymphatic invasion and LNM occurred most frequently in P-EGC (46.9% and 20.0%, respectively) compared to differentiated-type (18.9% and 9.2%, respectively) and undifferentiated-type EGCs (10.9% and 11.7%, respectively; P &lt; 0.001 and P = 0.004, respectively). P-EGC that was not mixed with other pathological type tended to have increased risk of LNM referent to differentiated EGC. (Odd ratio [OR] 2.53, 95% confidence interval [CI], 0.99-6.50). Among 45 patients with P-EGC, 13 patients met the current ESD indication; 3 patients corresponded to absolute indication and 10 patients did expanded indication. One patient had LNM and met expanded criteria with submucosal invasion less than 500 μm. In multivariate analysis, OR for LNM meeting expanded indication of P-EGC was 40.67 (95% CI, 2.54-702.76) referent to that of absolute indication compared to LNM meeting expanded indication of non-papillary EGC (10.28, 95% CI, 1.37-77.04). Conclusions: When ESD was applied to P-EGC, it should be carefully performed because of high rate of submucosal invasion and high risk of LNM even in with submucosal invasion less than 500 μm.

Development and validation of nomogram to predict lymph node metastasis preoperatively in patients with pancreatic neuroendocrine tumor

BackgroundRoutine lymphadenectomy in pancreatic neuroendocrine tumors (pNETs) is debated. There lacks accurate model to predict lymph node metastasis (LNM) preoperatively in pNETs. Therefore, this study aimed at developing a nomogram in predicting LNM in pNETs preoperatively. MethodsPatients undergoing surgery from Surveillance, Epidemiology, and End Results (SEER) database (design cohort, n = 2742) and First Affiliated Hospital of Sun Yat-sen University (validation cohort, n = 136) were enrolled. Nomogram was developed based on risk factors determined by logistic regression analyses. The performance of nomogram was evaluated by area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis. ResultsIn design cohort, 915 of 2742 patients had LNM. Tumor in the pancreatic head, T stage, and tumor size were significantly associated with LNM (all p < 0.05). Prediction of nomogram was accurate with AUC of 0.776 in design cohort and 0.622 in validation cohort. The nomogram showed good agreement between prediction and observation in the design and validation cohort. Based on nomogram-predicted risk, patients with higher risk of LNM had worse overall survival over patients with lower risk of LNM (log-rank p < 0.001). ConclusionsThe novel nomogram could accurately predict LNM in pNET preoperatively. For patients with high risk of LNM, lymphadenectomy was recommended.

Establishment and validation of nomograms for predicting mesorectal lymph node staging and restaging.

Preoperative determination of lymph node (LN) status is crucial in treatment planning for rectal cancer. This study prospectively evaluated the risk factors for lymph node metastasis (LNM) at staging and restaging based on a node-by-node pairing between MRI imaging findings and histopathology and constructed nomograms to evaluate its diagnostic value. From July 2021 to July 2022, patients with histopathologically verified rectal cancer who underwent MRI before surgery were prospectively enrolled. Histological examination of each LN status in the surgical specimens and anatomical matching with preoperative imaging. Taking histopathological results as the gold standard, federating clinical features from patients and LN imaging features on MRI-T2WI. Risk factors for LN metastasis were identified by multivariate logistic regression analysis and used to create a nomogram. The performance of the nomograms was assessed with calibration plots and bootstrapped-concordance index and validated using validation cohorts. A total of 500 target LNs in 120 patients were successfully matched with node-by-node comparisons. A total of 353 LNs did not receive neoadjuvant therapy and 147 LNs received neoadjuvant chemoradiotherapy (neoCRT). Characterization of LNs not receiving neoadjuvant therapy and multivariate regression showed that the short diameter, preoperative CEA level, mrT-stage, border contour, and signal intensity were associated with a high risk of LN metastasis (P < 0.05). The nomogram predicted that the area under the curve was 0.855 (95% CI, 0.794-0.916) and 0.854 (95% CI, 0.727-0.980) in the training and validation cohorts, respectively. In the neoadjuvant therapy group, short diameter, ymrT-stage, internal signal, and MRI-EMVI were associated with LN positivity (P < 0.05), and the area under the curves using the nomogram was 0.912 (95% CI, 0.856-0.968) and 0.915 (95% CI, 0.817-1.000) in two cohorts. The calibration curves demonstrate good agreement between the predicted and actual probabilities for both the training and validation cohorts. Our nomograms combined with preoperative clinical and imaging biomarkers have the potential to improve the prediction of nodal involvement, which can be used as an essential reference for preoperative N staging and restaging of rectal cancer.

Platelet-to-lymphocyte ratio associated with the clinicopathological features and prognostic value of breast cancer: A meta-analysis.

The association of platelet-to-lymphocyte ratio (PLR) with the clinicopathological features and prognosis in patients with breast cancer was evaluated. Related studies were searched from PubMed, Embase, Cochrane Library, and Web of Science up to July 1, 2021. Then, basic characteristic and prognostic data were extracted from the included studies. We synthesized and compared primary outcomes such as overall survival. Subgroups analyses in pathology, geographical area, follow-up time, and sample size were conducted. The pooled hazard ratio (HR), odds ratio (OR), and 95% confidence interval (CI) served as measures to assess the relationship of PLR with prognosis and clinicopathological features of breast cancer patients. After literature retrieval and selection, 20 studies with 7484 patients were included in this meta-analysis. High PLR was significantly related to poor overall survival (HR = 1.88; 95% CI 1.61, 2.19; P < 0.001) in breast cancer patients. Also, high PLR was associated with lymph node metastasis (LNM) (OR = 1.82; 95% CI 1.32, 2.52; P < 0.001), advanced tumor-node-metastasis (TNM) stage (OR = 1.89; 95% CI 1.25, 2.87; P = 0.003), and distant metastasis (OR = 1.76; 95% CI 1.14, 2.72; P = 0.01) in breast cancer. The stability and reliability of results in this meta-analysis were confirmed by sensitivity analysis. Elevated PLR is related to a poor prognosis and a higher risk of LNM, advanced TNM stage, and distant metastasis in breast cancer patients. Therefore, PLR can be identified as a biomarker with potential prognostic value in breast cancer.

Radiomics-Based Preoperative Prediction of Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma Using Contrast-Enhanced Computed Tomography

Lymph node (LN) metastasis is significantly associated with worse prognosis for patients with intrahepatic cholangiocarcinoma (ICC). Improvement in preoperative assessment on LN metastasis helps in treatment decision-making. We aimed to investigate the role of radiomics-based method in predicting LN metastasis for patients with ICC. A total of 296 patients with ICC who underwent curative-intent hepatectomy and lymphadenectomy at two centers in China were analyzed. Radiomic features, including histogram- and wavelet-based features, shape and size features, and texture features were extracted from four-phase computerized tomography (CT) images. The clinical and conventional radiological variables which were independently associated with LN metastasis were also identified. A combined nomogram predicting LN metastasis was developed, and its performance was determined by discrimination, calibration, and stratification of long-term prognosis. The results were validated by the internal and external validation cohorts. Twenty-four radiomic features were selected into the nomogram. The established nomogram demonstrated good discrimination and calibration, with areas under the curve (AUCs) of 0.98 [95% confidence interval (CI) 0.96-0.99], 0.93 (0.88-0.98), and 0.89 (0.81-0.96) in the training and two validation cohorts, respectively. The 5-year overall survival (OS) and recurrence-free survival (RFS) rates of patients with high risk of LN metastasis as grouped by nomogram were poorer than those of patients with low risk in the training cohort (OS 28.8% versus 53.9%, p < 0.001; RFS 26.3% versus 44.2%, p = 0.001). Similar results were observed in the two validation cohorts. Radiomics-based method provided accurate prediction of LN metastasis and prognostic assessment for ICC patients, and might aid the preoperative surgical decision.

Comparison of Prognosis and Lymph Node Metastasis in T1-Stage Colonic and Rectal Carcinoma: A Retrospective Study.

BackgroundLimited evidence and contradictory results have been reported regarding the impact of tumor site on lymph node metastasis (LNM) and prognosis in T1 stage adenocarcinoma (AC). We aimed to compare two anatomic locations in terms of LNM and prognosis using a comprehensive statistical analysis of a large population.MethodsThe Surveillance, Epidemiology, and End Results (SEER) database and our center (First Affiliated Hospital of Nanchang University) were used to extract patient information. Univariate and multivariate logistic or Cox regression and propensity score matching were used to explore the association between LNM/survival and tumor site.ResultsInformation for 12,404 patients, including 9655 colonic AC and 2749 rectal AC patients, was extracted from the SEER database. The 516 AC patients included 184 colonic and 332 rectal AC patients from our center. Multivariate logistic regression analysis revealed a correlation between LNM and tumor site (colon vs rectum, odds ratio [OR] =1.52, 95% CI, 1.349–1.714, P<0.001). Additionally, we found that younger age, T1b stage, poor differentiation, and lymphatic invasion were risk factors for LNM. After adjusting for confounding factors by PSM, we found that the location of the rectum remained a higher risk factor for LNM. However, we found that patients diagnosed with rectal AC had a prognosis similar to that of patients diagnosed with colonic AC, which was demonstrated by the analysis of SEER data and data from our center.ConclusionT1-stage rectal AC may have a higher risk of LNM than colonic AC, while rectal AC has a prognosis similar to that of colonic AC.

Risk factors for lymph node metastasis of soft tissue sarcomas of the head, neck, and extremities, and the clinical significance of negative lymph node dissection

BackgroundThis study sought to define the risk factors for lymph node metastasis (LNM) of soft tissue sarcomas (STS) of the head, neck, and extremities, and the clinical significance of negative lymph node dissection (NLND).MethodsSTS patient data in the Surveillance, Epidemiology, and End Results (SEER) database from 1988 to 2015 were extracted and pooled. Logistics regression analysis was used to identify risk factors for LNM, Cox proportional hazards and Fine–Grey’s models were used for survival analysis, and Propensity score matching analysis (PSM) was used to assess the impact of NLND on patient prognosis.ResultsA total of 3276 patients were enrolled in the study, of whom 283 (8.6%) developed LNM. Rhabdomyosarcoma had the highest rate of LNM (25.3%), followed by clear cell sarcoma (16.8%) and epithelioid sarcoma (12.4%), while leiomyosarcoma had the lowest rate of LNM (1.3%). Sex, tumor size, grade, histology, and site were significantly associated with LNM. For specific histologic subtypes of STS, NLND significantly improves overall survival (HR: 0.718, 95%CI 0.535–0.962; P = 0.026) and cancer-specific survival (HR: 0.699, 95%CI 0.506–0.967; P = 0.031) and reduces cancer-specific mortality (Gray’s test, P = 0.017). However, NLND did not improve overall survival (P = 0.46) or reduce cancer-specific mortality (Gray’s test, P = 0.772) of patients with leiomyosarcoma.ConclusionsHistology is an independent risk factor for LNM in STS of the head, neck, and extremities. Prophylactic NLND treatment was necessary and had a clinical benefit for patients with STS who were at high risk for LNM but had no significant impact on the prognosis of patients with leiomyosarcoma.

Oncological outcomes after piecemeal endoscopic mucosal resection of large non-pedunculated colorectal polyps with covert submucosal invasive cancer

ObjectiveManagement of covert submucosal invasive cancer (SMIC) discovered after piecemeal endoscopic mucosal resection (pEMR) of large (>20 mm) non-pedunculated colorectal polyps is challenging. The residual cancer risk is largely unknown....

Using a new diagnostic tool to predict lymph node metastasis in advanced epithelial ovarian cancer leads to simple lymphadenectomy decision rules: A multicentre study from the FRANCOGYN group.

ObjectiveThe aim of this study was to develop a new diagnostic tool to predict lymph node metastasis (LNM) in patients with advanced epithelial ovarian cancer undergoing primary cytoreductive surgery.Materials and methodThe FRANCOGYN group’s multicenter retrospective ovarian cancer cohort furnished the patient population on which we developed a logistic regression model. The prediction model equation enabled us to create LNM risk groups with simple lymphadenectomy decision rules associated with a user-friendly free interactive web application called shinyLNM.Results277 patients from the FRANCOGYN cohort were included; 115 with no LNM and 162 with LNM. Three variables were independently and significantly (p<0.05) associated with LNM in multivariate analysis: pelvic and/or para-aortic LNM on CT and/or PET/CT (p<0.00), initial PCI ≥ 10 and/or diaphragmatic carcinosis (p = 0.02), and initial CA125 ≥ 500 (p = 0.02). The ROC-AUC of this prediction model after leave-one-out cross-validation was 0.72. There was no difference between the predicted and the observed probabilities of LNM (p = 0.09). Specificity for the group at high risk of LNM was 83.5%, the LR+ was 2.73, and the observed probability of LNM was 79.3%; sensitivity for the group at low-risk of LNM was 92.0%, the LR- was 0.24, and the observed probability of LNM was 25.0%.ConclusionThis new tool may prove useful for improving surgical planning and provide useful information for patients.

Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma.

Diagnosis of lymph node metastasis (LNM) is critical for patients with pancreatic ductal adenocarcinoma (PDAC). We aimed to build deep learning radiomics (DLR) models of dual-energy computed tomography (DECT) to classify LNM status of PDAC and to stratify the overall survival before treatment. From August 2016 to October 2020, 148 PDAC patients underwent regional lymph node dissection and scanned preoperatively DECT were enrolled. The virtual monoenergetic image at 40keV was reconstructed from 100 and 150keV of DECT. By setting January 1, 2021, as the cut-off date, 113 patients were assigned into the primary set, and 35 were in the test set. DLR models using VMI 40keV, 100keV, 150keV, and 100 + 150keV images were developed and compared. The best model was integrated with key clinical features selected by multivariate Cox regression analysis to achieve the most accurate prediction. DLR based on 100 + 150keV DECT yields the best performance in predicting LNM status with the AUC of 0.87 (95% confidence interval [CI]: 0.85-0.89) in the test cohort. After integrating key clinical features (CT-reported T stage, LN status, glutamyl transpeptadase, and glucose), the AUC was improved to 0.92 (95% CI: 0.91-0.94). Patients at high risk of LNM portended significantly worse overall survival than those at low risk after surgery (P = 0.012). The DLR model showed outstanding performance for predicting LNM in PADC and hold promise of improving clinical decision-making.

Analysis of Prostate Cancer Tumor Microenvironment Identifies Reduced Stromal CD4 Effector T-cell Infiltration in Tumors with Pelvic Nodal Metastasis

BackgroundPelvic nodal metastasis in prostate cancer impacts patient outcome negatively. ObjectiveTo explore tumor-infiltrating immune cells as a potential predictive tool for regional lymph node (LN) metastasis. Design, setting, and participantsWe applied multiplex immunofluorescence and targeted transcriptomic analysis on 94 radical prostatectomy specimens in patients with (LN+) or without (LN–) pelvic nodal metastases. Both intraepithelial and stromal infiltrations of immune cells and differentially expressed genes (mRNA and protein levels) were correlated with the nodal status. Outcome measurements and statistical analysisThe identified CD4 effector cell signature of nodal metastasis was validated in a comparable independent patient cohort of 184 informative cases. Patient outcome analysis and decision curve analysis were performed with the CD4 effector cell density–based signature. Results and limitationsIn the discovery cohort, both tumor epithelium and stroma from patients with nodal metastasis had significantly lower infiltration of multiple immune cell types, with stromal CD4 effector cells highlighted as the top candidate marker. Targeted gene expression analysis and confirmatory protein analysis revealed key alteration of extracellular matrix components in tumors with nodal metastasis. Of note, stromal CD4 immune cell density was a significant independent predictor of LN metastasis (odds ratio [OR] = 0.15, p = 0.004), and was further validated as a significant predictor of nodal metastasis in the validation cohort (OR = 0.26, p < 0.001). ConclusionsDecreased T-cell infiltrates in the primary tumor (particularly CD4 effector cells) are associated with a higher risk of LN metastasis. Future evaluation of CD4-based assays on prostate cancer diagnostic biopsy materials may improve selection of at-risk patients for the treatment of LN metastasis. Patient summaryIn this report, we found that cancer showing evidence of cancer metastasis to the lymph nodes tends to have less immune cells present within the tumor. We conclude that the extent of immune cells present within a prostate tumor can help doctors determine the most appropriate treatment plan for individual patients.

High GFPT1 expression predicts unfavorable outcomes in patients with resectable pancreatic ductal adenocarcinoma

BackgroundGlutamine-fructose-6-phosphate transaminase 1 (GFPT1) is the first rate-limiting enzyme of the hexosamine biosynthesis pathway (HBP), which plays a pivotal role in the progression of pancreatic ductal adenocarcinoma (PDAC). Therefore, we investigated the prognostic significance of GFPT1 expression in patients with resectable PDAC.MethodsWe analyzed public datasets to compare GFPT1 expression in tumor tissues and normal/adjacent pancreatic tissues. We measured the relative GFPT1 expression of 134 resected PDAC specimens in our institution, using real-time polymerase chain reaction (PCR). Survival was compared between high and low GFPT1 expression groups using Kaplan-Meier curves and log-rank tests. Multivariate analyses were estimated using Cox regression and logistic regression models.ResultsGFPT1 is generally upregulated in PDAC tissues, according to the analysis of public datasets. The data from our institution shows that high GFPT1 expression was correlated with a high rate of lymph node (LN) metastasis (p = 0.038) and was an independent risk factor for LN metastasis (odds ratio (OR) = 3.14, 95% confidence interval (CI) = 1.42 to 6.90, P = 0.005). High GFPT1 expression was significantly associated with poor overall survival (OS; P = 0.019) in patients with resected PDAC. The multivariable-adjusted hazard ratio (HR) for mortality when comparing patients with high and low GFPT1 expression was 2.54 (95% CI = 1.35 to 4.79, P = 0.004).ConclusionsGFPT1 is generally upregulated in PDAC tissue and is associated with a high risk of LN metastasis and an unfavorable outcome in patients with resectable PDAC, suggesting its crucial role in PDAC progression.

Corpora Cavernos invasion vs. Corpus Spongiosum invasion in Penile Cancer: A systematic review and meta-analysis

Objective: Changes were made in the 8th edition of the American Joint Committee on Cancer (AJCC) staging system according to cavernosum invasion for penile squamous cell carcinoma. This study aimed to determine the difference of prognostic validity between corpora cavernosa (CC) invasion and corpus spongiosum (CS) invasion.Methods: In this study, we searched PubMed, Cochrane CENTRAL, and Embase to select English-language articles until July 15, 2020. Pooled analyses of hazard ratios (HRs) and odds ratios (ORs) were performed.Results: Eleven studies including 3692 cases were included in the final ananlysis (1431 cases with CC and 1360 cases with CS). According to the anatomical structure, the pooled results demonstrated that patients with CC invasion had a similar rate of LNM to those with CS invasion (OR 1.34, 95% confidence interval (CI) 0.97-1.86; P=0.076). However, patients with CC invasion had a higher rate of lymph node metastasis (LNM) than those with CS invasion according to the 8th edition tumor stage (OR 1.58, 95% CI 1.14-2.21; P<0.001). Regarding survival, patients with CS invasion obtained a significantly better cancer-specific survival (CSS) (HR, 0.67; 95% CI, 0.46-0.96; P=0.030), but not in overall survival (OS) (HR: 1.30; 95% CI, 0.52-3.20; P=0.585) than those with CC invasion. No a significant publication bias was observed by Begg's and Egger's tests.Conclusions: The systematic comparison suggests that patients with CS invasion had better CSS than those with CC invasion. CC invasion was associated with a high risk of LNM. The conclusions should be validated by large-scale studies.