Lymph Node Metastasis In Patients Research Articles

Development of a multi-modal learning-based lymph node metastasis prediction model for lung cancer

PurposeThis study proposed a three-dimensional (3D) multi-modal learning-based model for the automated prediction and classification of lymph node metastasis in patients with non-small cell lung cancer (NSCLC) using computed tomography (CT) images and clinical information. MethodsWe utilized clinical information and CT image data from 4239 patients with NSCLC across multiple institutions. Four deep learning algorithm-based multi-modal models were constructed and evaluated for lymph node classification. To further enhance classification performance, a soft-voting ensemble technique was applied to integrate the outcomes of multiple multi-modal models. ResultsA comparison of the classification performance revealed that the multi-modal model, which integrated CT images and clinical information, outperformed the single-modal models. Among the four multi-modal models, the Xception model demonstrated the highest classification performance, with an area under the curve (AUC) of 0.756 for the internal test dataset and 0.736 for the external validation dataset. The ensemble model (SEResNet50_DenseNet121_Xception) exhibited even better performance, with an AUC of 0.762 for the internal test dataset and 0.751 for the external validation dataset, surpassing the multi-modal model's performance. ConclusionsIntegrating CT images and clinical information improved the performance of the lymph node metastasis prediction models in patients with NSCLC. The proposed 3D multi-modal lymph node prediction model can serve as an auxiliary tool for evaluating lymph node metastasis in patients with non-pretreated NSCLC, aiding in patient screening and treatment planning.

A CT-based radiomics model for predicting lymph node metastasis in hepatic alveolar echinococcosis patients to support lymph node dissection

BackgroundHepatic alveolar echinococcosis (AE) is a severe zoonotic parasitic disease, and accurate preoperative prediction of lymph node (LN) metastasis in AE patients is crucial for disease management, but it remains an unresolved challenge. The aim of this study was to establish a radiomics model for the preoperative prediction of LN metastasis in hepatic AE patients.MethodsA total of 100 hepatic AE patients who underwent hepatectomy and hepatoduodenal ligament LN dissection at Qinghai Provincial People's Hospital between January 2016 and August 2023 were included in the study. The patients were randomly divided into a training set and a validation set at an 8:2 ratio. Radiomic features were extracted from three-dimensional images of the hepatoduodenal ligament LNs delineated on arterial phase computed tomography (CT) scans of hepatic AE patients. Least absolute shrinkage and selection operator (LASSO) regression was applied for data dimensionality reduction and feature selection. Multivariate logistic regression analysis was performed to develop a prediction model, and the predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).ResultsA total of 7 radiomics features associated with LN status were selected using LASSO regression. The classification performances of the training set and validation set were consistent, with area under the operating characteristic curve (AUC) values of 0.928 and 0.890, respectively. The model also demonstrated good stability in subsequent validation.ConclusionIn this study, we established and evaluated a radiomics-based prediction model for LN metastasis in patients with hepatic AE using CT imaging. Our findings may provide a valuable reference for clinicians to determine the occurrence of LN metastasis in hepatic AE patients preoperatively, and help guide the implementation of individualized surgical plans to improve patient prognosis.

Lymph Node Metastasis in Gastrointestinal Carcinomas: A View from a Proteomics Perspective.

Lymph node metastasis (LNM) is one of the major prognostic factors in human gastrointestinal carcinomas (GICs). The lymph node-positive patients have poorer survival than node-negative patients. LNM is directly associated with the recurrence and poor survival of patients with GICs. The early detection of LNM in patients and designing effective therapies to suppress LNM may significantly impact the survival of these patients. The rapid progress made in proteomic technologies could be successfully applied to identify molecular targets for cancers at high-throughput levels. LC-MS/MS analysis enables the identification of proteins involved in LN metastasis, which can be utilized for diagnostic and therapeutic applications. This review summarizes the studies on LN metastasis in GICs using proteomic approaches to date.

Analysis of risk factors for lymph node metastasis in patients with CN0 papillary thyroid carcinoma

Objective:To explore the risk factors of lymph node metastasis and multiple lymph node metastasis in patients with stage CN0 papillary thyroid carcinoma. Methods:The clinical case data of 3 099 patients with CN0 papillary thyroid cancer who underwent lymph node dissection at Xijing Hospital of Air Force Medical University from January 2013 to December 2022 were retrospectively analyzed, univariate and multivariate logistic regression were used to analyze the risk factors of lymph node metastasis and multiple lymph node metastasis. Results:Male gender, age<55 years, multifocal lesions, and lesion size ≥2 cm were independent risk factors for lymph node metastasis in CN0 patients（P<0.05）, while diabetes was an independent protective factor for lymph node metastasis（P<0.05）.Age<55 years, capsular invasion, and multifocal lesions were independent risk factors for the presence of ≥3 lymph nodes with metastasis （P<0.05）. Conclusion:In CN0 stage PTC patients, special attention should be given to the possibility of lymph node metastasis when they are male, aged <55 years, have multifocal lesions, or have lesion size >2 cm.

Dual-energy computed tomography for predicting cervical lymph node metastasis in laryngeal squamous cell carcinoma

Rationale and objectivesWe constructed a dual-energy computed tomography (DECT)-based model to assess cervical lymph node metastasis (LNM) in patients with laryngeal squamous cell carcinoma (LSCC). Materials and methodsWe retrospectively analysed 164 patients with LSCC who underwent preoperative DECT from May 2019 to May 2023. The patients were randomly divided into training (n = 115) and validation (n = 49) cohorts. Quantitative DECT parameters of the primary tumours and their clinical characteristics were collected. A logistic regression model was used to determine independent predictors of LNM, and a nomogram was constructed along with a corresponding online model. Model performance was assessed using the area under the curve (AUC) and the calibration curve, and the clinical value was evaluated using decision curve analysis (DCA). ResultsIn total, 64/164 (39.0 %) patients with LSCC had cervical LNM. Independent predictors of LNM included normalized iodine concentration in the arterial phase (odds ratio [OR]: 8.332, 95 % confidence interval [CI]: 2.813–24.678, P < 0.001), normalized effective atomic number in the arterial phase (OR: 5.518, 95 % CI: 1.095–27.818, P = 0.002), clinical T3-4 stage (OR: 5.684, 95 % CI: 1.701–18.989, P = 0.005), and poor histological grade (OR: 5.011, 95 % CI: 1.003–25.026, P = 0.049). These predictors were incorporated into the DECT-based nomogram and the corresponding online model, showing good calibration and favourable performance (training AUC: 0.910, validation AUC: 0.918). The DCA indicated a significant clinical benefit of the nomogram for estimating LNM. ConclusionsDECT parameters may be useful independent predictors of LNM in patients with LSCC, and a DECT-based nomogram may be helpful in clinical decision-making.

Effect of preoperative immune checkpoint inhibitors on reducing residual lymph node metastases in patients with gastric cancer: a retrospective study

Objective: To investigate the effect of immune checkpoint inhibitors on reducing residual lymph node metastasis in patients with gastric cancer. Methods: The cohort of this retrospective study comprised patients from Nanfang Hospital of Southern Medical University and the First Affiliated Hospital of Xiamen University who had undergone systemic treatment prior to gastrectomy with D2 lymphadenectomy and had achieved Grade 1 primary tumor regression (TRG1) from January 2014 to December 2023. After exclusion of patients who had undergone preoperative radiotherapy, data of 58 patients (Nanfang Hospital: 46; First Affiliated Hospital of Xiamen University: 12) were analyzed. These patients were allocated to preoperative chemotherapy (Chemotherapy group, N=36 cases) and preoperative immunotherapy plus chemotherapy groups (Immunotherapy group, N=22 cases). There were no significant differences between these groups in sex, age, body mass index, diabetes, tumor location, pathological type, Lauren classification, tumor differentiation, pretreatment depth of invasion by primary tumor, pretreatment lymph node stage, pretreatment clinical stage, mismatch repair protein status, number of preoperative treatment cycles, or duration of preoperative treatment (all P>0.05). The primary outcome measure was postoperative lymph node downstaging. Secondary outcomes included postoperative depth of invasion by tumor, number of lymph nodes examined, and factors affecting residual lymph node metastasis status. Results: Lymph node downstaging was achieved significantly more often in the Immunotherapy group than the Chemotherapy group (pN0: 90.9% [20/22] vs. 61.1% [22/36]; pN1: 4.5% [1/22] vs. 36.1% [13/36]; pN2: 4.5% [1/22) vs. 0; pN3: 0 vs. 2.8% [1/36], Z=-2.315, P=0.021). There were no significant difference between the two groups in number of lymph nodes examined (40.5±16.3 vs. 40.8±17.5, t=0.076, P=0.940) or postoperative depth of invasion by primary tumor (pT1a: 50.0% [11/22] vs. 30.6% [11/36]; pT1b: 13.6% [3/22] vs. 19.4% [7/36]; pT2: 13.6% [3/22] vs. 13.9% [5/36]; pT3: 13.6% [3/22] vs. 25.0% [9/36]; pT4a: 9.1% [2/22] vs. 11.1% [4/36], Z=-1.331, P=0.183). Univariate analysis revealed that both preoperative treatment regimens were associated with residual lymph node metastasis status in patients whose primary tumor regression was TRG1 (χ2=6.070, P=0.014). Multivariate analysis incorporated the following factors: pretreatment depth of invasion by primary tumor, pretreatment lymph node stage, pretreatment clinical stage, number of preoperative treatment cycles, and preoperative treatment duration. We found that a combination of immunotherapy and chemotherapy administered preoperatively was an independent protective factor for reducing residual lymph node metastases in study patients whose primary tumor regression was TRG1 (OR=0.147, 95%CI: 0.026-0.828, P=0.030). Conclusion: Compared with preoperative chemotherapy alone, a combination of preoperative immunotherapy and chemotherapy achieved greater reduction of residual lymph node metastases in the study patients who achieved TRG1 tumor regression in their primary lesions.

Analysis of risk factors for lymph node metastasis in 1096 patients with early gastric cancer and establishment of a predictive nomogram

Objective: To investigate the risk factors for lymph node metastasis in patients with early gastric cancer and establish a model for prediction of risk. Methods: The cohort of this retrospective observational study comprised 1096 patients who had undergone radical gastric cancer surgery combined with standard D1 lymphadenectomy and been diagnosed with early gastric cancer by postoperative pathology in Zhongshan Hospital affiliated with Fudan University from January 2016 to July 2022. The patients were allocated to groups with and without lymph node metastases. Clinicopathological characteristics were compared between the two groups and multi-factor logistic regression analysis used to identify independent risk factors for lymph node metastasis in patients with early gastric cancer. Indications for endoscopic resection in the Japanese Gastric Cancer Association (JGCA) guideline were also incorporated into construction of the model. The patient cohort was divided into training and validation sets in a 6:4 ratio. The identified independent risk factors were used to construct a predictive nomogram. Receiver operating characteristic curves were plotted separately and the difference between them in predictive efficacy was compared using the area under the curve (AUC). Results: A total of 1,096 patients with early gastric cancer were included, with 750 males and 346 females. Their average age was (61.4±10.9) years old, and the mean tumor diameter was (23.8±11.4) mm. Among them, 188 patients (17.2%) had positive lymph node metastasis, with 109 cases in N1 stage, 42 cases in N2 stage, and 37 cases in N3 stage. Additionally, 462 patients were in T1a stage, while 634 patients were in T1b stage. Univariate analysis showed that tumor diameter, location, Lauren classification, gross morphology, histological type, intravascular invasion, ulceration, differentiation type and tumor T stage were associated with lymph node metastasis after radical gastrectomy for early gastric cancer (all P<0.05). Multifactorial analysis showed that the presence of intravascular invasion (OR=14.822, 95%CI: 9.323-23.572, P<0.001), undifferentiated type (OR=3.095, 95%CI: 1.649-5.811, P<0.001), tumor T1b (OR=1.798, 95%CI: 1.053-3.079, P=0.032), and tumor diameter ≥2 cm (OR=1.229, 95%CI: 1.031-1.469, P=0.022) were independent risk factors for lymph node metastasis. The baseline data of the training set and validation set were consistent in terms of balance (all P>0.05). We used the above variables to establish a predictive nomogram for lymph node metastasis in patients with early gastric cancer. The AUC values obtained from the validation of the model in the training and validation sets were 0.880 (95%CI: 0.849-0.911) and 0.881 (95%CI: 0.841-0.921), respectively, and were significantly better than the predictive efficacy based on the JGCA guideline (AUC=0.777, 95%CI: 0.746-0.809, P<0.001). Conclusions: Patients with early gastric cancer and intravascular invasion, undifferentiated tumors, tumor T1b, and diameter ≥2 cm are at higher risk of postoperative lymph node metastasis than other patients. The predictive model developed in this study more accurately predicts lymph node metastasis in patients with early gastric cancer than previously proposed methods.

EBP1 promotes the malignant biological behaviors of kidney renal clear cell carcinoma through activation of p38/HIF-1α signaling pathway

BackgroundKidney Renal Clear Cell Carcinoma (KIRC) is a common malignant tumor of the urinary system, and its incidence is increasing. ERBB3 binding protein (EBP1) is upregulated in various cancers. However, the connection between EBP1 and KIRC has not been reported.MethodsThe expression of EBP1 in normal kidney tissue and KIRC tissue was analyzed through database and tissue microarray. EBP1 was knocked down in KIRC cell lines, and its impact on KIRC proliferation was assessed through CCK-8, soft agar assay, and flow cytometry. Scratch and transwell assays were used to evaluate the influence of EBP1 on KIRC invasion and migration. Nude mice tumor experiment were conducted to examine the effect of EBP1 on tumor tissue. Database analysis explored potential pathways involving EBP1, and validation was performed through Western blot experiments and p38 inhibitor.ResultsEBP1 is upregulated in KIRC and significantly correlates with clinical staging, pathological grading, and lymph node metastasis in patients. The mechanism research showed that knocking down EBP1 inhibited KIRC proliferation, invasion, and migration and inhibited p38 phosphorylation and the expression of hypoxia-inducible factor-1α (HIF-1α) in KIRC. p-38 inhibitor (SB203580) inhibits p38 phosphorylation and HIF-1α expression and suppresses cell viability in a concentration-dependent manner, but has no effect on EBP1 expression. HEK 293T cells overexpressing EBP1 showed increased expression of phosphorylated p38 and HIF-1α and enhanced cell viability, however, SB203580 inhibited this effect of EBP1.ConclusionEBP1 may promote the occurrence and development of KIRC by regulating the expression of p38/HIF-1α signaling pathway.Graphical

Differences in the Prognostic Impact between Single-zone and Multi-zone N2 Node Metastasis in Patients with Station-based Multiple N2 Non-Small Cell Lung Cancer.

The International Association for the Study of Lung Cancer suggest further subdivision of pathologic N (pN) stage in non-small-cell lung cancer (NSCLC) by incorporating the location and number of involved lymph node (LN) stations. We reclassified patients with the station-based N2b disease into single-zone and multi-zone N2b groups and compared survival outcomes between the groups. This retrospective study included patients with pN2 NSCLC who underwent lobectomy from 2006 to 2019. The N2 disease was subdivided into four categories: single-station N2 without N1 (N2a1), single-station N2 with N1 (N2a2), multiple-station N2 with single zone involvement (single-zone N2b), and multiple-station N2 with multiple zone involvement (multi-zone N2b). LN zones included in the subdivision of N2 disease were upper mediastinal, lower mediastinal, aortopulmonary, and subcarinal. Among 996 eligible patients, 211 (21.2%), 394 (39.6%) and 391 (39.4) were confirmed to have pN2a1, pN2a2, and pN2b disease, respectively. In multivariable analysis after adjustment for sex, age, pT stage, and adjuvant chemotherapy, overall survival was significantly better with single-zone N2b disease (n=125, 12.6%) than with multi-zone N2b disease (n=266, 26.7%) (hazard ratio 0.67, 95% confidence interval 0.49-0.90, p<0.009) and was comparable to that of N2a2 disease (1.12, 0.83-1.49, p=0.46). Prognosis of single-zone LN metastasis was better than that of multiple-zone LN metastasis in patients with N2b NSCLC. Along with the station-based N descriptors, zone-based descriptors might ensure optimal staging, enabling the most appropriate decision-making on adjuvant therapy for patients with pN2 NSCLC.

Preoperative Evaluation of Cervical Lymph Node Metastasis in Patients With Hashimoto's Thyroiditis Combined With Thyroid Papillary Carcinoma Using Machine Learning and Radiomics-Based Features: A Preliminary Study

To analyze the radiomic and clinical features extracted from 2D ultrasound images of thyroid tumors in patients with Hashimoto's thyroiditis (HT) combined with papillary thyroid carcinoma (PTC) using machine learning (ML) models, and to explore the diagnostic performance of the method in making preoperative noninvasive identification of cervical lymph node metastasis (LNM). A total of 528 patients with HT combined with PTC were enrolled and divided into two groups based on their pathological results of the presence or absence of LNM. The groups were subsequently designated the With LNM Group and the Without LNM Group. Three ultrasound doctors independently delineated the regions of interest and extracted radiomic features. Two modes, radiomic features and radiomics-clinical features, were used to construct random forest (RF), support vector machine (SVM), LightGBM, K-nearest neighbor (KNN), and XGBoost models. The performance of these five ML models in the two modes was evaluated by the receiver operating characteristic (ROC) curves on the test dataset, and SHapley Additive exPlanations (SHAP) was used for model visualization. All five ML models showed good performance, with area under the ROC curve (AUC) ranging from 0.798 to 0.921. LightGBM and XGBoost demonstrated the best performance, outperforming the other models (P<0.05). The ML models constructed with radiomics-clinical features performed better than those constructed using only radiomic features (P<0.05). The SHAP visualization of the best-performing models indicated that the anteroposterior diameter, superoinferior diameter, original_shape_VoxelVolume, age, wavelet-LHL_firstorder_10Percentile, and left-to-right diameter had the most significant effect on the LightGBM model. On the other hand, the superoinferior diameter, anteroposterior diameter, left-to-right diameter, original_shape_VoxelVolume, original_firstorder_InterquartileRange, and age had the most significant effect on the XGBoost model. ML models based on radiomics and clinical features can accurately evaluate the cervical lymph node status in patients with HT combined with PTC. Among the 5 ML models, LightGBM and XGBoost demonstrate the best evaluation performance.

DNA methylation biomarkers for predicting lymph node metastasis in colorectal cancer.

Colorectal cancer is one of the most common cancers worldwide. Lymph node metastasis is an important marker of colorectal cancer progression and plays a key role in the evaluation of patient prognosis. Accurate preoperative assessment of lymph node metastasis is crucial for devising appropriate treatment plans. However, current clinical imaging methods have limitations in many aspects. Therefore, the discovery of a method for accurately predicting lymph node metastasis is crucial clinical decision-making. DNA methylation is a common epigenetic modification that can regulate gene expression, which also has an important impact on the development of colorectal cancer. It is considered to be a promising biomarker with good specificity and stability and has promising application in predicting lymph node metastasis in patients with colorectal cancer. This article reviews the characteristics and limitations of currently available methods for predicting lymph node metastasis in patients with colorectal cancer and discusses the role of DNA methylation as a biomarker.

Clinical and pathological features of advanced rectal cancer with submesenteric root lymph node metastasis: Meta-analysis.

Advanced rectal cancer with submesenteric lymph node metastasis is a common complication of advanced rectal cancer, which has an important impact on the treatment and prognosis of patients. To investigate the clinical and pathological characteristics of inferior mesenteric artery (IMA) root lymph node metastases in patients with rectal cancer. The findings of this study provided us with fresh medical information that assisted us in determining the appropriate treatment for these patients. Our study searched PubMed, Google Scholar, and other databases and searched the relevant studies and reports on the risk factors of IMA root lymph node metastasis of rectal cancer published in the self-built database until December 31, 2023. After data extraction, the Newcastle-Ottawa scale was used to evaluate the quality of the included literature, and RevMan5.3 software was used for meta-analysis and heterogeneity testing. The fixed effect modules without heterogeneity were selected to combine the effect size, and the random effect modules with heterogeneity were selected to combine the effect size. The cause of heterogeneity was found through sensitivity analysis, and the data of various risk factors were combined to obtain the final effect size, odds ratio (OR) value, and 95% confidence interval (CI). Publication bias was tested by drawing funnel plots. A total of seven literature were included in this study. By combining the OR value of logistic multivariate regression and the 95%CI of various risk factors, we concluded that the risk factors for lymph node metastasis in the IMA region of rectal cancer were as follows: Preoperative carcinoembryonic antigen (CEA) > 5 ng/mL (OR = 0.32, 95%CI: 0.18-0.55, P < 0.05), tumor located above peritoneal reflexive (OR = 3.10, 95%CI: 1.78-5.42, P < 0.05), tumor size ≥ 5 cm (OR = 0.36, 95%CI: 0.22-0.57, P < 0.05), pathological type (mucinous adenocarcinoma/sig-ring cell carcinoma) (OR = 0.23, 95%CI: 0.13-0.41, P < 0.05), degree of tumor differentiation (low differentiation) (OR = 0.17, 95%CI: 0.10-0.31, P < 0.05), tumor stage (T3-4 stage) (OR = 0.11, 95%CI: 0.04-0.26, P < 0.05), gender and age were not risk factors for IMA root lymph node metastasis in rectal cancer (P > 0.05). Preoperative CEA level, tumor location, tumor size, tumor pathologic type, tumor differentiation, and T stage were correlated with IMA root lymph node metastasis.

A Preoperative Prediction Model for Lymph Node Metastasis in Patients with Gastric Cancer Using a Machine Learning-based Ultrasomics Approach.

This study aimed to develop an ultrasomics model for predicting lymph node metastasis preoperative in patients with gastric cancer (GC). This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was performed by an experienced radiologist to extract radiomics features using the Pyradiomics software. The Z-score algorithm was used for feature normalization, followed by the Wilcoxon test to identify the most informative features. Linear prediction models were constructed using the least absolute shrinkage and selection operator (LASSO). The performance of the ultrasomics model was evaluated using the area under curve (AUC), sensitivity, specificity, and the corresponding 95% confidence intervals (CIs). A total of 464 GC patients (mean age: 60.4 years ±11.3 [SD]; 328 men [70.7%]) were analyzed, of whom 291 had lymph node metastasis. The patients were randomly assigned to either the training (n=324) or test (n=140) sets, using a 7:3 ratio. An ultrasomics model that consisted of 19 radiomics features was developed using Wilcoxon and LASSO algorithms in the training set. Our ultrasomics model showed moderate performance for lymph node metastasis prediction in both the training (AUC: 0.802, 95%CI: 0.752-0.851, P<0.001) and test sets (AUC: 0.802, 95%CI: 0.724-0.879, P<0.001). The calibration curve analysis indicated good agreement between the predicted probabilities of ultrasomics and actual lymph node metastasis status. Our study highlights the potential of a machine learning-based ultrasomics model in predicting lymph node metastasis in GC patients, offering implications for personalized therapy approaches.

Predictors of lung cancer subtypes and lymph node status in non-small-cell lung cancer: intravoxel incoherent motion parameters and extracellular volume fraction

ObjectiveTo determine the performance of intravoxel incoherent motion (IVIM) parameters and the extracellular volume fraction (ECV) in distinguishing between different subtypes of lung cancer and predicting lymph node metastasis (LNM) status in patients with non-small-cell lung cancer (NSCLC).MethodsOne hundred sixteen patients with lung cancer were prospectively recruited. IVIM, native, and postcontrast T1 mapping examinations were performed, and the T1 values were measured to calculate the ECV. The differences in IVIM parameters and ECV were compared between NSCLC and small-cell lung cancer (SCLC), adenocarcinoma (Adeno-Ca) and squamous cell carcinoma (SCC), and NSCLC without and with LNM. The assessment of each parameter’s diagnostic performance was based on the area under the receiver operating characteristic curve (AUC).ResultsThe apparent diffusion coefficient (ADC), true diffusion coefficient (D), and ECV values in SCLC were considerably lower compared with NSCLC (all p < 0.001, AUC > 0.887). The D value in SCC was substantially lower compared with Adeno-Ca (p < 0.001, AUC = 0.735). The perfusion fraction (f) and ECV values in LNM patients were markedly higher compared with those without LNM patients (p < 0.01, < 0.001, AUC > 0.708).ConclusionIVIM parameters and ECV can serve as non-invasive biomarkers for assisting in the pathological classification and LNM status assessment of lung cancer patients.Critical relevance statementIVIM parameters and ECV demonstrated remarkable potential in distinguishing pulmonary carcinoma subtypes and predicting LNM status in NSCLC.Key PointsLung cancer is prevalent and differentiating subtype and invasiveness determine the treatment course.True diffusion coefficient and ECV showed promise for subtyping and determining lymph node status.These parameters could serve as non-invasive biomarkers to help determine personalized treatment strategies.Graphical

A clinical nomogram for predicting occult lymph node metastasis in patients with non-small-cell lung cancer ≤2 cm.

Sublobar resection has been shown to be feasible for non-small-cell lung cancers (NSCLC) <2 cm in size based on several prospective studies. However, the prognosis of clinical N0 patients who experience an N-stage upgrade after surgery [known as occult lymph node metastasis (OLM)] may be worse. The ability of predict OLM in patients eligible for sublobar resection remains a controversial issue. Patients with NSCLC ≤2 cm in diameter and containing a solid component who underwent surgical treatment at the Affiliated Hospital of Qingdao University were retrospectively enrolled, and 1:1 case matching was performed. The risk factors were identified through logistic regression analyses and theoretical criteria, followed by the development of a nomogram that was evaluated using 200 iterations of 10-fold cross-validation. After case matching, 130 pairs of patients were selected for modelling. According to the multivariable logistic regression analysis, the carcinoembryonic antigen level, consolidation tumour ratio, mean computed tomography number and tumour margin were included in the nomogram. The cross-validated average area under the receiver operating characteristic curve was found to be 0.86. Furthermore, calibration curve and decision curve analyses demonstrated the excellent predictive accuracy and clinical utility of the nomogram respectively. By utilizing accessible characteristics, we developed a nomogram that predicts the probability of OLM in patients with NSCLC ≤2 cm with a solid component. Risk stratification with this nomogram could aid in surgical method decision-making. Not applicable.

A nomogram for risk stratification of central cervical lymph node metastasis in patients with papillary thyroid carcinoma.

Whether to perform prophylactic central lymph node dissection for cN0 papillary thyroid carcinoma (PTC) patients is still controversial. This retrospective study aimed to develop and validate a nomogram based on ultrasound and dual-energy computed tomography (DECT) for the risk stratification of central lymph node metastasis (CLNM) in patients with PTC. A total of 525 patients from 2017 to 2019 [Tianjin First Central Hospital (Hospital A)] were retrospectively analyzed to form the training cohort and to conduct internal validation. Another group of 204 patients in 2020 (Hospital A) formed the temporal validation cohort. A total of 107 patients in 2020 [Binzhou Medical University Hospital (Hospital B)] formed the geographic validation cohort, which was a retrospective cohort study. The area under the curve (AUC), calibration curve, and decision curve were used to evaluate the performance of the nomogram. The locally weighted regression curve was used for risk stratification. Diameter, taller-than-wide, calcification, capsular invasion, and iodine concentration in the arterial and venous phases were independent risk predictors of CLNM. The AUC of the nomogram was 0.922 (95% confidence interval: 0.895-0.943) in the training cohort. Two external validation cohorts demonstrated the good performance of the nomogram in predicting CLNM, with AUCs of 0.912 and 0.861. The significantly improved net reclassification index and integrated discriminatory improvement index indicated that DECT was a powerful supplement to ultrasound for predicting CLNM. The risk stratification system divided all patients into low-risk (0-50 points), intermediate-risk (51-100 points), and high-risk groups (>100 points). The nomogram and risk stratification system estimated the utility of CLNM to guide individualized treatment of patients with PTC.

Increasing the accuracy and reproducibility of positron emission tomography radiomics for predicting pelvic lymph node metastasis in patients with cervical cancer using 3D local binary pattern-based texture features

BackgroundThe reproducibility of positron emission tomography (PET) radiomics features is affected by several factors, such as scanning equipment, drug metabolism time and reconstruction algorithm. We aimed to explore the role of 3D local binary pattern (LBP)-based texture in increasing the accuracy and reproducibility of PET radiomics for predicting pelvic lymph node metastasis (PLNM) in patients with cervical cancer. MethodsWe retrospectively analysed data from 177 patients with cervical squamous cell carcinoma. They underwent 18F-fluorodeoxyglucose (18F-FDG)whole-body PET/computed tomography (PET/CT), followed by pelvic 18F-FDG PET/magnetic resonance imaging (PET/MR). We selected reproducible and informative PET radiomics features using Lin's concordance correlation coefficient, least absolute shrinkage and selection operator algorithm, and established 4 models, PET/CT, PET/CT-fusion, PET/MR and PET/MR-fusion, using the logistic regression algorithm. We performed receiver operating characteristic (ROC) curve analysis to evaluate the models in the training data set (65 patients who underwent radical hysterectomy and pelvic lymph node dissection) and test data set (112 patients who received concurrent chemoradiotherapy or no treatment). The DeLong test was used for pairwise comparison of the ROC curves among the models. ResultsThe distribution of age, squamous cell carcinoma (SCC), International Federation of Gynaecology and Obstetrics stage and PLNM between the training and test data sets were different (P < 0.05). The LBP-transformed radiomics features (50/379) had higher reproducibility than the original radiomics features (9/107). Accuracy of each model in predicting PLNM was as follows: training data set: PET/CT = PET/CT-fusion = PET/MR-fusion (0.848) and test data set: PET/CT = PET/CT-fusion (0.985) > PET/MR = PET/MR-fusion (0.954). There was no statistical difference between the ROC curve of PET/CT and PET/MR models in both data sets (P > 0.05). ConclusionsThe LBP-transformed radiomics features based on PET images could increase the accuracy and reproducibility of PET radiomics in predicting pelvic lymph node metastasis in cervical cancer to allow the model to be generalised for clinical use across multiple centres.

The Importance of the Time Interval Between Preoperative 18F-FDG PET/CT Imaging and Neck Dissection for the Detection of Nodal Metastases in Patients with Head and Neck Squamous Cell Carcinoma.

Detection of nodal metastasis is critical for the treatment and prognosis of head and neck cancer (HNC). Positron emission tomography/computed tomography (PET/CT) is increasingly being used to detect cervical lymph node involvement. The purposes of this study were to (1) investigate the diagnostic accuracy of PET/CT for the detection of neck metastasis in patients with HNC and (2) determine the effect of the time interval between surgery and PET/CT. Fifty patients with head and neck squamous cell carcinoma who underwent PET/CT before surgery were included in this study. Preoperative PET/CT images that determined lymph node metastasis were compared with the histopathological analysis of neck dissection samples. Neck dissections were divided into three groups according to the time interval between surgery and PET/CT (0-2 weeks, >2-4 weeks, and >4 weeks). The concordance between PET/CT and histopathology was measured using the neck sides at different time intervals. The specificity, sensitivity, accuracy, negative predictive value (NPV), and positive predictive value (PPV) of PET/CT in detecting metastatic lymph nodes in the neck were calculated. A total of 79 neck dissections were included in the study as 29 (58%) of the patients underwent bilateral neck dissection. The overall accuracy of PET/CT in detecting nodal metastasis was highest for the 0-2 weeks interval (95.6%). During this time interval, the sensitivity, specificity, NPV, and PPV of PET/CT were 100%, 90.9%, 100%, and 92.3%, respectively. Although PET/CT is an important and reliable diagnostic method for detecting nodal metastases in patients with HNC, its reliability decreases as the time between surgeries increases. The optimal interval was 2 weeks; however, up to 4 weeks was acceptable.

The Association of Pretreatment Systemic Immune Inflammatory Response Index (SII) and Neutrophil-to-Lymphocyte Ratio (NLR) with Lymph Node Metastasis in Patients with Papillary Thyroid Carcinoma.

Immunoinflammatory response can participate in the development of cancer. To investigate the relationship between pretreatment systemic immune inflammatory response index (SII), systemic inflammatory response index (SIRI), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR) and lymph node metastasis in patients with papillary thyroid carcinoma (PTC). A retrospective analysis was performed on 547 PTC patients treated in Meizhou People's Hospital from January 2018 to December 2021. Clinicopathological data were collected, including gender, age, Hashimoto's thyroiditis, maximum tumor diameter, extra-membrane infiltration, disease stage, BRAF V600E mutation, pretreatment inflammatory index levels, and lymph node metastasis. The optimal cutoff values of SII, SIRI, NLR, PLR and LMR were calculated by receiver operating characteristic (ROC) curve, and the relationship between inflammatory indexes and other clinicopathological features and lymph node metastasis was analyzed. There were 303 (55.4%) PTC patients with lymph node metastasis. The levels of SII, SIRI, NLR, and PLR in patients with lymph node metastasis were significantly higher than those in patients without lymph node metastasis, while the levels of LMR were significantly lower than those in patients without lymph node metastasis (all p<0.05). When lymph node metastasis was taken as the endpoint, the critical value of SII was 625.375, the SIRI cutoff value was 0.705, the NLR cutoff value was 1.915 (all area under the ROC curve >0.6). The results of regression logistic analysis showed that age <55 years old (OR: 1.626, 95% CI: 1.009-2.623, p=0.046), maximum tumor diameter >1cm (OR: 2.681, 95% CI: 1.819-3.952, p<0.001), BRAF V600E mutation (OR: 2.709, 95% CI: 1.542-4.759, p=0.001), SII positive (≥625.375/<625.375, OR: 2.663, 95% CI: 1.560-4.546, p<0.001), and NLR positive (≥1.915/<1.915, OR: 1.808, 95% CI: 1.118-2.923, p=0.016) were independent risk factors for lymph node metastasis of PTC. Age <55 years old, maximum tumor diameter >1cm, BRAF V600E mutation, SII positive, and NLR positive were independent risk factors for lymph node metastasis in PTC.

The Oncologic Outcomes of Bilateral Central Lymph Node Dissection in Unilobar Papillary Thyroid Cancer and Its Risks: A Prospective Cohort Study.

Background Indications for performing a prophylactic central neck dissection (pCND) in papillary thyroid cancer (PTC) remain controversial. Thyroidectomy and central neck dissection (CND) are often recommended in all cases with proven differentiated thyroid cancer (DTC) and clinically positive lymph nodes (LNs), as well as in high risk for micro-metastasis patients with T3-T4 tumors or established metastatic nodes in the lateral compartments. Aims The aims of this study were to ascertain the role of performing bilateral central LN dissection in unilobar PTC in improving the oncological outcomes and outline the risks involved. Methods This was a department-based, prospective cohort study. We included all 20 patients who had unilobar PTC and underwent total thyroidectomy with bilateral CND. A postoperative histopathological analysis was used to identify metastatic central LNs. Results Twenty total thyroidectomies plus bilateral CNDs were performed, of which 10 were prophylactic bilaterally (those with N0), and all 20 were prophylactic on the contralateral side of PTC. Conventional risk factors (age, tumor size, and extrathyroidal extension)were notassociated with performing a pCND. The presence of unilobar PTC by preoperative FNAC was the only factor associated with performing bilateral CND. Positive ipsilateral LNs were retrieved in 55% of CNDs, while positive contralateral LNs were retrieved in only 15% of the patients. Conclusions The incidence of contralateral cervical LN metastasis in patients with unilateral PTC is low, while there is clear evidence of postoperative morbidity from routine contralateral CND in unilobar PTC. Contralateral CND in patients with unilobar PTC may be reserved for high-risk patients: males, those aged ≤45 years, tumors larger than 1.0 cm, and cases with extrathyroidal extension and micro-calcification on ultrasound.