Prediction Of Lymph Node Metastasis Research Articles

Multi-omics prediction of lymph node metastasis status in breast cancer

Lymph node metastasis status stands as a pivotal prognostic indicator in forecasting the outlook for breast cancer patients. Consequently, precise evaluation of this status holds paramount importance in the staging, treatment, and prognosis of breast cancer. The utilization of radiomics, genomics, proteomics, transcriptomics, and histopathology methodologies has notably enhanced the precision of lymph node metastasis status prediction in breast cancer. This review provides an overview of recent advancements in omics-based lymph node metastasis prediction for breast cancer, elucidating the significance of various omics prediction models and integrated multi-omics models in this predictive endeavor. The overarching goal is to augment the accuracy of preoperative lymph node metastasis status prediction in breast cancer, thereby aiding clinicians in the selection of efficacious personalized treatment strategies, while concurrently averting undertreatment of patients with a heightened risk of metastasis.

A clinical-radiomics nomogram based on spectral CT multi-parameter images for preoperative prediction of lymph node metastasis in colorectal cancer.

To develop a clinical-radiomics nomogram based on spectral CT multi-parameter images for predicting lymph node metastasis in colorectal cancer. A total of 76 patients with colorectal cancer and 156 lymph nodes were included. The clinical data of the patients were collected, including gender, age, tumor location and size, preoperative tumor markers, etc. Three sets of conventional images in the arterial, venous, and delayed phases were obtained, and six sets of spectral images were reconstructed using the arterial phase spectral data, including virtual monoenergetic images (40keV, 70keV, 100keV), iodine density maps, iodine no water maps, and virtual non-contrast images. Radiomics features of lymph nodes were extracted from the above images, respectively. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select features. A clinical model was constructed based on age and carcinoembryonic antigen (CEA) levels. The radiomics featuresselected were used to generate a composedradiomics signature (Com-RS). A nomogram was developed using age, CEA, and theCom-RS. The models' prediction efficiency, calibration, and clinical application value were evaluated by the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis, respectively. The nomogram outperforms the clinical model and the Com-RS (AUC = 0.879, 0.824). It is well calibrated and has great clinical application value. This study developed a clinical-radiomics nomogram based on spectral CT multi-parameter images, which can be used as an effective tool for preoperative personalized prediction of lymph node metastasis in colorectal cancer.

Development and validation of a nomogram to predict lymph node metastasis in patients with progressive muscle-invasive bladder cancer

PurposeTo develop and validate a nomogram for preoperative prediction of lymph node metastasis in patients with progressive muscle-invasive bladder cancer.Materials and methodsWe retrospectively recruited patients, divided them into training and validation cohorts, and gathered patient demographics, pathology data of transurethral bladder tumor resection specimens, imaging findings, and laboratory information. We performed logistic regression analyses, both single-variable and multi-variable, to investigate independent preoperative risk variables and develop a nomogram. Both internal and external validations were conducted to evaluate the predictive performance of this nomogram.ResultsThe training cohort consisted of 144 patients with advanced muscle-invasive bladder cancer, while the validation cohort included 62 individuals. The independent preoperative risk factors identified were tumor pathology grade, platelet count, tumor size on imaging, and lymph node size, which were utilized to develop the nomogram. The model demonstrated high predictive accuracy, as evidenced by the area under the receiver operating characteristic curve values of 0.898 and 0.843 for the primary and external validation cohorts, respectively. Calibration curves and decision curve analysis showed a good performance of the nomogram in both cohorts, indicating its high clinical applicability.ConclusionA nomogram for preoperative prediction of lymph node metastasis in patients with advanced muscle-invasive bladder cancer was successfully developed; its accuracy, reliability, and clinical value were demonstrated. This new tool would facilitate better clinical decisions regarding whether to perform complete lymph node dissection in cases of radical cystectomy.

Preoperatively predicting thelymphnodemetastasis and prognosis for gastric cancer patients.

The preoperative distinguishment of lymph nodes (LN) with metastasis plays a pivotal role in guiding the surgical extension for gastric cancer (GC). We aim to identify the preparative risk factors for LN metastasis in GC patients. We retrospectively reviewed 424 patients who underwent radical GC resection in our medical center between Jan 2011 and Dec 2018. Multivariate logistic regression was employed to identify risk factors for LN metastasis, while multivariate COX regression was utilized to evaluate prognostic factors. The median overall survival of patients with or without LN metastases was 31 and 58months, respectively. In multivariate analysis, lower albumin (OR = 0.512; P = 0.004) and prealbumin (OR = 0.367, P = 0.001) and higher CEA (OR = 3.178, P < 0.001), CA199 (OR = 2.278, P = 0.002) and platelets (OR = 1.697, P = 0.017) were found to be significantly associated with LN metastasis. In survival analysis, older age (HR = 1.712), larger tumors (HR = 1.082), higher D-dimer (HR = 1.561) and CA199 (HR = 1.553), advanced staging (stage II, HR = 3.446; stage III-IV, HR = 11.089), lower prealbumin levels (lower level for reference, HR = 0.63), and absence of adjuvant chemotherapy (HR = 0.396) was discovered to be associated with poorer overall survival (all P < 0.05). In conclusion, our results demonstrated that preoperative prealbumin-bound tumor markers can effectively predict LN metastasis. Additionally, prealbumin was found to possess prognostic value as well.

MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study.

The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20%-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions. To construct and evaluate a preoperative diagnostic method to predict occult lymph node metastasis (OCLNM) in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features. A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA) and survival analysis. Seventeen prediction models were constructed. The Resnet50 deep learning (DL) model based on the combination of radiomics and DL features achieves the optimal performance, with AUC values of 0.928 (95% CI: 0.881-0.975), 0.878 (95% CI: 0.766-0.990), 0.796 (95% CI: 0.666-0.927) and 0.834 (95% CI: 0.721-0.947) in the training, test, external validation set1 and external validation set2, respectively. Moreover, the Resnet50 model has great prediction value of prognosis in patients with early-stage OC and OP SCC. The proposed MRI-based Resnet50 deep learning model demonstrated high capability in diagnosis of OCLNM and prognosis prediction in the early-stage OC and OP SCC. The Resnet50 model could help refine the clinical diagnosis and treatment of the early-stage OC and OP SCC.

R2* mapping and reduced field-of-view diffusion-weighted imaging for preoperative assessment of nonenlarged lymph node metastasis in rectal cancer.

The aim of the current study is to investigate the diagnostic value of R2* mapping versus reduced field-of-view diffusion-weighted imaging (rDWI) of the primary lesion of rectal cancer for preoperative prediction of nonenlarged lymph node metastasis (NLNM). Eighty-one patients with pathologically confirmed rectal cancer underwent preoperative R2* mapping and rDWI sequences before total mesorectal excisions and accompanying regional lymph node dissections. Two radiologists independently performed whole-tumor measurements of R2* and apparent diffusion coefficient (ADC) parameters on primary lesions of rectal cancer. Patients were divided into positive (NLNM+) and negative (NLNM-) groups based on their pathological analysis. The tumor location, maximum diameter of the tumor, and maximum short diameter of the lymph node were assessed. R2* and ADC, pT stage, tumor grade, status of mesorectal fascia, and extramural vascular invasion were also studied for their potential relationships with NLNM using multivariate logistic regression analysis. The NLNM+ group had significantly higher R2* (43.56 ± 8.43 vs. 33.87 ± 9.57, p < 0.001) and lower ADC (1.00 ± 0.13 vs. 1.06 ± 0.22, p = 0.036) than the NLNM- group. R2* and ADC were correlated to lymph node metastasis (r = 0.510, p < 0.001 for R2*; r = -0.235, p = 0.035 for ADC). R2* and ADC showed good and moderate diagnostic abilities in the assessment of NLNM status with corresponding area-under-the-curve values of 0.795 and 0.636. R2* provided a significantly better diagnostic performance compared with ADC for the prediction of NLNM status (z = 1.962, p = 0.0498). The multivariate logistic regression analysis demonstrated that R2* was a compelling factor of lymph node metastasis (odds ratio = 56.485, 95% confidence interval: 5.759-554.013; p = 0.001). R2* mapping had significantly higher diagnostic performance than rDWI from the primary tumor of rectal cancer in the prediction of NLNM status.

Abstract PO5-15-03: Neutrophil-lymphocyte ratio and absolute lymphocyte count, inflammatory markers, may predict lymph node metastasis in elderly patients with operable breast cancer

Abstract Background: Blood-derived systemic inflammatory markers such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR) and absolute lymphocyte count (ALC) have been demonstrated as predictive markers of recurrence and prognosis in several malignancies. Increased chronic inflammation with aging has been reported to be involved in cancer cells and their microenvironment. Several studies have also reported a close relationship between inflammatory markers and cancer grade and prognosis in the elderly. In this study, we retrospectively examined the relationship between lymphocyte-related inflammatory markers and prognosis in elderly breast cancer patients. Method: We included 602 patients diagnosed with primary invasive breast cancer at 8 institutions between 2008 and 2013, all of whom were older than 70 years old and had undergone breast cancer surgery. Preoperative blood collection data were used to analyze the relationship between NLR, PLR, LMR, ALC and clinicopathological data including recurrence. Results: The pathological stages of the 602 patients were Stage I: 275, Stage II: 276, and Stage III: 51, with median NLR, PLR, LMR, and ALC values of 2.21, 134.51, 6.76, and 1840, respectively. The mean age of the patients was 75.3 years. The median follow-up was 72.8 months. 76 patients relapsed, and the mean time to relapse was 42.7 months. Lymphocyte-associated inflammatory markers and Li Sheng pathological characteristics showed no significant differences in stage, presence or absence of recurrence, or form of recurrence, but lymph node metastasis on surgical pathology showed significant differences between cases with and without lymph node metastasis in NLR and ALC (P&amp;lt; 0.05). Conclusions: LMR and ALC were considered useful biomarkers that can be easily measured to predict lymph node metastasis in operable elderly breast cancer Citation Format: Emi Tokuda, Midori Morita, Akihiko Shimomura, Yoshiya Horimoto, Yukino Kawamura, Yumiko Ishizuka, Katsutoshi Sekine, Sayaka Obayashi, Yuki Kojima, Toru Higuchi. Neutrophil-lymphocyte ratio and absolute lymphocyte count, inflammatory markers, may predict lymph node metastasis in elderly patients with operable breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-15-03.

Predictors for Lymph Node Metastasis and Survival of Patients with T1b Esophageal Adenocarcinoma Treated with Surgery and Endoscopic Therapy: An Analysis of Surveillance, Epidemiology, and End Results Database

Limited data exists regarding the long-term outcomes of endoscopic therapy (ET) with or without chemoradiation therapy (CRT) for T1b esophageal adenocarcinoma (EAC). Our aim was to identify the risk factors for lymph node metastasis (LNM) in T1b EAC and assess how the chosen treatment modality affects overall survival (OS) and cancer-specific survival (CSS). We analyzed histologically confirmed T1b EAC patients diagnosed between 2004 and 2018 using Surveillance Epidemiology and End Results database. Focusing on T1bN0M0 staging, we divided the patients into two groups: ET (n=174) and surgery (n=769), and calculated OS and CSS rates. Out of 1418 patients with T1b EAC, 228 cases (16.1%) exhibited LNM at diagnosis. Notable risk factors for LNM included poorly differentiated tumor and lesion size ≥20 mm. For T1bN0M0 cases, ET was commonly performed from 2009 to 2018 (OR 4.3), especially for patients aged ≥ 65 years (OR 3.1) with tumor size <20mm (OR 2.3). During 50 months median follow-up, age ≥ 65 years (HR 1.9), ET (HR 1.5), and CRT (HR 1.4) were associated with poorer OS. Factors linked to decreased CSS were age ≥ 65 years (sub hazard ratio (SHR) 1.6), poorly differentiated tumors (SHR 1.5), and CRT (SHR 1.5). In T1b EAC, tumor size ≥20mm and poor differentiation are notable risk factors for LNM. ET showed comparable CSS outcomes to surgery for carefully selected T1bN0M0 lesions. CRT did not provide additional survival benefit for these lesions; however, large scale studies are required to validate this finding.

Tumor budding as a risk factor for lymph node metastases in cutaneous squamous cell carcinoma: a systematic review and meta-analysis.

Current staging systems have limitations in stratifying high-risk cases of cutaneous squamous cell carcinoma (cSCC). Tumor budding (TB) has emerged as a potential prognostic factor in various cancers. This systematic review and meta-analysis aimed to evaluate the prognostic significance of TB in predicting lymph node metastases (NM) in cSCC. A comprehensive search of the PubMed, Web of Science, EMBASE, and Cochrane databases was conducted. Studies investigating the association of TB using a 5-bud cut-off and NM in cSCC were included. A meta-analysis was performed using odds ratios (OR) to evaluate the association between TB and NM. Six retrospective studies comprising 793 cSCC patients were included. The random-effects analysis showed a significant association between high TB (≥5 buds) and NM (OR = 13.29, 95% CI = 5.55; 31.86). Tumor budding is a promising histopathologic feature for predicting NM in cSCC. The results show a strong association between high TB and NM, supporting its utility as a risk factor for NM in cSCC. Its inclusion in clinical practice and cSCC staging might be helpful in the stratification of high-risk cases and guide optimal management strategies for each patient. However, further investigation is needed to determine standardized reporting guidelines for TB in cSCC.

A liquid biopsy assay for the noninvasive detection of lymph node metastases in T1 lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a common pathological type of lung cancer. The presence of lymph node metastasis plays a crucial role in determining the overall treatment approach and long-term prognosis for early LUAD, therefore accurate prediction of lymph node metastasis is essential to guide treatment decisions and ultimately improve patient outcomes. We performed transcriptome sequencing on T1 LUAD patients with positive or negative lymph node metastases and combined this data with The Cancer Genome Atlas Program cohort to identify potential risk molecules at the tissue level. Subsequently, by detecting the expression of these risk molecules by real-time quantitative PCR in serum samples, we developed a model to predict the risk of lymph node metastasis from a training cohort of 96 patients and a validation cohort of 158 patients. Through transcriptome sequencing analysis of tissue samples, we identified 11 RNA (miR-412, miR-219, miR-371, FOXC1, ID1, MMP13, COL11A1, PODXL2, CXCL13, SPOCK1 and MECOM) associated with positive lymph node metastases in T1 LUAD. As the expression of FOXC1 and COL11A1 was not detected in serum, we constructed a predictive model that accurately identifies patients with positive lymph node metastases using the remaining nine RNA molecules in the serum of T1 LUAD patients. In the training set, the model achieved an area under the curve (AUC) of 0.89, and in the validation set, the AUC was 0.91. We have established a new risk prediction model using serum samples from T1 LUAD patients, enabling noninvasive identification of those with positive lymph node metastases.

Development and validation of a diagnostic model for predicting cervical lymph node metastasis in laryngeal and hypopharyngeal carcinoma

ObjectivesThe lymph node status is crucial for guiding the surgical approach for patients with laryngeal and hypopharyngeal carcinoma (LHC). Nonetheless, occult lymph node metastasis presents challenges to assessment and treatment planning. This study seeks to develop and validate a diagnostic model for evaluating cervical lymph node status in LHC patients.Materials and methodsThis study retrospectively analyzed a total of 285 LHC patients who were treated at the Department of Otolaryngology Head and Neck Surgery, Daping Hospital, Army Medical University, from January 2015 to December 2020. Univariate and multivariate logistic regression analyses were employed to construct the predictive model. Discrimination and calibration were used to assess the predictive performance of the model. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the model, and validation was conducted using 10-fold cross-validation, Leave-One-Out Cross Validation, and bootstrap methods.ResultsThis study identified significant predictors of lymph node metastasis in LHC. A diagnostic predictive model was developed and visualized using a nomogram. The model demonstrated excellent discrimination, with a C-index of 0.887 (95% CI: 0.835-0.933). DCA analysis indicated its practical applicability, and multiple validation methods confirmed its fitting and generalization ability.ConclusionThis study successfully established and validated a diagnostic predictive model for cervical lymph node metastasis in LHC. The visualized nomogram provides a convenient tool for personalized prediction of cervical lymph node status in patients, particularly in the context of occult cervical lymph node metastasis, offering valuable guidance for clinical treatment decisions.

A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer.

Occult lymph node metastasis (OLNM) is an essential prognostic factor for early-stage tongue cancer (cT1-2N0M0) and a determinant of treatment decisions. Therefore, accurate prediction of OLNM can significantly impact the clinical management and outcomes of patients with tongue cancer. The aim of this study was to develop and validate a multiomics-based model to predict OLNM in patients with early-stage tongue cancer. The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach. Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870-1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors. The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.

The diameter of cutaneous melanoma serves as a prognostic indicator for survival among acral-melanoma predominant East Asian patients.

Tumor staging plays a pivotal role in melanoma management, where the depth of tumor invasion has been traditionally used as the cornerstone of staging. Paradoxically, the tumor diameter has not been integrated into the staging system. The aim of this study is to elucidate the clinical implications and prognostic value of tumor diameter in cutaneous melanoma, with a particular emphasis on the acral-melanoma predominant East Asian population, thus potentially enriching the clinical evaluation and treatment strategies for cutaneous melanoma. From January 1st, 2006 to December 31st, 2022, a total of 352 patients were diagnosed with melanoma in our center. Among them, there were 135 patients diagnosed as cutaneous melanoma who received complete surgical wide excision and regional lymph nodes assessment. The diameter of the tumor, the depth of tumor invasion, lymph node status and patient survival were all collected and analyzed. The diameter of cutaneous melanoma had a weak positive correlation with tumor thickness (r=0.26), however, it still had a significant predictive value for patients' overall survival (p=0.005) and disease free survival (p=0.023). As for lymph node metastasis prediction, the Breslow thickness had a better predictive value than tumor diameter (p=0.002 vs. p=0.565). In this study, though with only weak positive correlation to tumor thickness, the tumor diameter of melanoma showed a statistically significant correlation with the patients' overall survival and disease free survival. However, the larger tumor diameter cannot be used as an indicator of high risk of lymph node metastasis.

CT radiomics based on the peritumoral adipose region of gastric adenocarcinoma for preoperative prediction of lymph node metastasis

PurposeTo construct and validate CT radiomics model based on the peritumoral adipose region of gastric adenocarcinoma to preoperatively predict lymph node metastasis (LNM). Methods and Methods293 consecutive gastric adenocarcinoma patients receiving radical gastrectomy with lymph node dissection in two medical institutions were stratified into a development set (from Institution A, n = 237), and an external validation set (from Institution B, n = 56). Volume of interest of peritumoral adipose region was segmented on preoperative portal-phase CT images. The least absolute shrinkage and selection operator method and stepwise logistic regression were used to select features and build radiomics models. Manual classification was performed according to routine CT characteristics. A classifier incorporating the radiomics score and CT characteristics was developed for predicting LNM. Area under the receiver operating characteristic curve (AUC) was used to show discrimination between tumors with and without LNM, and the calibration curves and Brier score were used to evaluate the predictive accuracy. Violin plots were used to show the distribution of radiomics score. ResultsAUC values of radiomics model to predict LNM were 0.938, 0.905, and 0.872 in the training, internal test, and external validation sets, respectively, higher than that of manual classification (0.674, all P values < 0.01). The radiomics score of the positive LNM group were higher than that of the negative group in all sets (both P-values < 0.001). The classifier showed no improved predictive power compared with the radiomics signature alone with AUC values of 0.916 and 0.872 in the development and external validation sets, respectively. Multivariate analysis showed that radiomics score was an independent predictor. ConclusionsRadiomics model based on peritumoral adipose region could be a useful approach for preoperative LNM prediction in gastric adenocarcinoma.

Interpretable machine learning-based clinical prediction model for predicting lymph node metastasis in patients with intrahepatic cholangiocarcinoma

ObjectivePrediction of lymph node metastasis (LNM) for intrahepatic cholangiocarcinoma (ICC) is critical for the treatment regimen and prognosis. We aim to develop and validate machine learning (ML)-based predictive models for LNM in patients with ICC.MethodsA total of 345 patients with clinicopathological characteristics confirmed ICC from Jan 2007 to Jan 2019 were enrolled. The predictors of LNM were identified by the least absolute shrinkage and selection operator (LASSO) and logistic analysis. The selected variables were used for developing prediction models for LNM by six ML algorithms, including Logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision tree (DT), Multilayer perceptron (MLP). We applied 10-fold cross validation as internal validation and calculated the average of the areas under the receiver operating characteristic (ROC) curve to measure the performance of all models. A feature selection approach was applied to identify importance of predictors in each model. The heat map was used to investigate the correlation of features. Finally, we established a web calculator using the best-performing model.ResultsIn multivariate logistic regression analysis, factors including alcoholic liver disease (ALD), smoking, boundary, diameter, and white blood cell (WBC) were identified as independent predictors for LNM in patients with ICC. In internal validation, the average values of AUC of six models ranged from 0.820 to 0.908. The XGB model was identified as the best model, the average AUC was 0.908. Finally, we established a web calculator by XGB model, which was useful for clinicians to calculate the likelihood of LNM.ConclusionThe proposed ML-based predicted models had a good performance to predict LNM of patients with ICC. XGB performed best. A web calculator based on the ML algorithm showed promise in assisting clinicians to predict LNM and developed individualized medical plans.

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.

Prediction of lymph node metastasis in patients with papillary thyroid cancer based on radiomics analysis and intraoperative frozen section analysis: A retrospective study.

To evaluate the diagnostic efficiency among the clinical model, the radiomics model and the nomogram that combined radiomics features, frozen section (FS) analysis and clinical characteristics for the prediction of lymph node (LN) metastasis in patients with papillary thyroid cancer (PTC). A total of 208 patients were randomly divided into two groups randomly with a proportion of 7:3 for the training groups (n = 146) and the validation groups (n = 62). The Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for the selection of radiomics features extracted from ultrasound (US) images. Univariate and multivariate logistic analyses were used to select predictors associated with the status of LN. The clinical model, radiomics model and nomogram were subsequently established by logistic regression machine learning. The area under the curve (AUC), sensitivity and specificity were used to evaluate the diagnostic performance of the different models. The Delong test was used to compare the AUC of the three models. Multivariate analysis indicated that age, size group, Adler grade, ACR score and the psammoma body group were independent predictors of lymph node metastasis (LNM). The results showed that in both the training and validation groups, the nomogram showed better performance than the clinical model, albeit not statistically significant (p > .05), and significantly outperformed the radiomics model (p < .05). However, the nomogram exhibits a slight improvement in sensitivity that could reduce the incidence of false negatives. We propose that the nomogram holds substantial promise as an effective tool for predicting LNM in patients with PTC.

Platelet-lymphocyte ratio as a predictor of lymph node metastasis in small bowel cancer.

The purpose of this research was to investigate the potential predictive value of preoperative systemic inflammatory indexes in identifying lymph node metastasis among patients diagnosed with small bowel cancer. A retrospective analysis of clinical data was conducted on small bowel cancer patients who underwent surgical treatment at the gastrointestinal surgery department of our hospital between January 2010 and June 2021. Patients were divided into groups based on the presence or absence of lymph node metastasis as confirmed by postoperative pathological results. The study compared the differences in preoperative inflammatory indexes and clinical data between the two groups using single factor analysis and multifactorial Logistic regression analysis. Furthermore, a nomogram model for predicting lymph node metastasis in colorectal cancer was constructed using R software and internally validated. The study sample consisted of 140 small bowel cancer patients,postoperative pathology confirmed lymph node metastasis in 72 cases. Univariate analysis results indicated associations between preoperative inflammatory indexes and clinical data with lymph node metastasis in small bowel cancer. Multifactorial logistic regression analysis revealed that gender, PLR, number of lymph node dissection, and lymphovascular invasion independently influenced lymph node metastasis in small bowel cancer patients. The developed nomogram model demonstrated a C-index of 0.855 (95% CI 0.792-0.917), with a calibrated prediction curve closely resembling the ideal curve. An elevated PLR is an independent risk factor for LNM in patients with small bowel cancer.

CD146 Promotes EMT-Mediated Migration and Invasion of NSCLC via PI3K/Akt Signaling Pathway.

Recurrence and metastasis are the main causes of non-small cell lung cancer (NSCLC)-related death. CD146 has been identified as a potential risk factor for poor prognosis, closely related to the distant metastasis and drug resistance in various cancers. However, the clinical significance of CD146 in NSCLC requires further investigation. This study explored the correlation between CD146 expression and clinical variables using tumor tissue samples collected from our hospital. CD146 expression levels in NSCLC cell lines and tissues were assessed and compared using immunohistochemistry, real-time polymerase chain reaction (RT-qPCR), flow cytometry, and western blot analysis. The invasion and migration capabilities of tumor cells were determined using transwell and wound healing assays. The levels of proteins related to epithelial-mesenchymal transition (EMT) as well as the underlying PI3K/Akt signaling pathway was measured by western blotting. We discovered that CD146 expression is significantly associated with the EMT signaling pathway. High CD146 expression predicted lymph node metastasis, metastasis to distant organs, advanced Tumor, Node, Metastasis (TNM) staging, and poor survival in NSCLC patients. Wound healing and transwell assays showed that knocking down CD146 significantly suppressed cell migration along with cell invasion in NSCLC, whereas overexpressing CD146 notably enhanced these processes. Western blot analysis revealed significantly reduced levels of N-cadherin, vimentin, snail, twist, PI3K, and AKT phosphorylation in shCD146 H460 cells compared to vector control cells. Treatment with PI3K inhibitor PI3K-IN-1 increased E-cadherin expression levels but reduced N-cadherin, Twist, Vimentin, PI3K, and AKT phosphorylation levels in pcDNA3.1-CD146 A549 cells compared with the vector control cells. CD146 expression acts as a prognostic risk factor for adverse outcomes in NSCLC, promoting invasion and metastasis by activating the EMT through the PI3K/Akt signaling pathway. These findings underscore the potential therapeutic strategies targeting CD146, offering new treatment options for NSCLC patients, especially those at risk of metastasis.

The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction.

Lymph node metastasis (LNM) is considered an essential prognosis factor for adenocarcinoma of the esophagogastric junction (AEG), which also affects the treatment strategies of AEG. We aimed to evaluate automated machine learning (AutoML) algorithms for predicting LNM in Siewert type II T1 AEG. A total of 878 patients with Siewert type II T1 AEG were selected from the Surveillance, Epidemiology, and End Results (SEER) database to develop the LNM predictive models. The patients from two hospitals in Suzhou were collected as the test set. We applied five machine learning algorithms to develop the LNM prediction models. The performance of predictive models was assessed using various metrics including accuracy, sensitivity, specificity, the area under the curve (AUC), and receiver operating characteristic (ROC) curve. Patients with LNM exhibited a higher proportion of male individuals, a poor degree of differentiation, and submucosal infiltration, with statistical differences. The deep learning (DL) model demonstrated relatively good accuracy (0.713) and sensitivity (0.868) among the five models. Moreover, the DL model achieved the highest AUC (0.781) and sensitivity (1.000) in the test set. The DL model showed good predictive performance among five AutoML models, indicating the advantage of AutoML in modeling LNM prediction in patients with Siewert type II T1 AEG.