Prediction Of Lymph Node Metastasis Research Articles

Factors Determining Lymph Node Metastasis in Patients with Early Stage Non-Squamous Cell Cervical Carcinoma

Aim: To evaluate the clinical and pathological characteristics of early stage non-squamous cell cervical carcinoma (non-SCCC) patients and identify factors that predict lymph node metastasis.
 Materials and method: Patients who underwent radical hysterectomy plus lymphadenectomy for stage IB1-IIA2 non-SCCC between 1993 and 2022 in six gynecologic oncology centers were included in this retrospective study. The effects of the risk factors on lymph node metastasis were evaluated by using univariate and multivariate logistic regression analysis.
 Results: The study involved 126 patients with a median age of 48 years (range, 26-77 years). According to FIGO 2009 staging, 91 patients (72.2%) were at stage IB1, 24 (19%) were at stage IB2, 9 (7.1%) were at stage IIA1, and 2 (1.6%) were at stage IIA2. Tumor subtype were adenocarcinoma in 93 (73.8%) patients and adenosquamous carcinoma in 28 (22.2%) patients. In univariate analysis; age, tumor size, stromal invasion, parametrial invasion, uterine involvement, lymphovascular space invasion (LVSI), vaginal involvement, and surgical border involvement were associated with lymph node metastasis. In multivariate analysis, LVSI (Hazard Ratio: 19.63, 95% Confidence Interval: 3.499-110.166; p=0.001) and uterine involvement (Hazard Ratio: 4.36, 95% Confidence Interval: 1.178-16.165; p=0.027) were identified as independent prognostic factors for lymph node metastasis.
 Conclusion: LVSI and uterine involvement were independent predictors of lymph node metastasis in early-stage non-SCCC patients. These factors can be identified with high accuracy in biopsy specimens and imaging methods in the preoperative period.

Establishment and validation of a risk score model based on EUS: assessment of lymph node metastasis in early gastric cancer

Lymph node metastasis significantly affects the prognosis of early gastric cancer patients. Endoscopic ultrasonography (EUS) plays a crucial role in the preoperative assessment of early gastric cancer. This study evaluated the efficacy of EUS in identifying lymph node metastasis in early gastric cancer patients and developed a risk score model to aid in choosing the best treatment options. We retrospectively analyzed the effectiveness of EUS for detecting lymph node metastasis in early gastric cancer patients. A risk score model for predicting lymph node metastasis preoperatively was created using independent risk factors identified through binary logistic regression analysis and subsequently validated. Receiver operating characteristic (ROC) curves were generated for both the development and validation cohorts. The overall accuracy of EUS in identifying lymph node metastasis was 85.3%, although its sensitivity (29.2%) and positive predictive value (38.7%) were relatively low. Patients were categorized based on preoperative risk factors for lymph node metastasis, including tumor size ≥20 mm, lymph nodes ≥10 mm, BMI ≥24 kg/m2, and lymph node metastasis on CT scans. A 7-point risk score model was developed to assess the likelihood of lymph node metastasis. The areas under the ROC curve (AUCs) for the development and validation sets were 0.842 and 0.837, respectively, with sensitivities of 64% and 79%, respectively. We developed a practical risk score model based on preoperative factors to help EUS predict lymph node metastasis in early gastric cancer patients, guiding the selection of optimal treatment approaches for these patients.

Development and validation of a nomogram for preoperative prediction of ipsilateral cervical central lymph node metastasis in papillary thyroid cancer: a population-based study.

The incidence of papillary thyroid cancer (PTC) has increased dramatically, and it is susceptible to cervical lymph node metastasis (LNM), predominantly in the ipsilateral cervical central lymph node metastasis (CLNM). Ipsilateral cervical CLNM affects patients' surgical options and survival rates. In this study, we integrated multiple factors to establish a nomogram-based preoperative prediction model of ipsilateral cervical CLNM in PTC. Data were retrospectively collected from 609 patients with PTC admitted to Peking University International Hospital, all of whom underwent ipsilateral cervical lymph node dissection. They were randomly divided into a modeling set and validation set in the ratio of 7:3. Binary logistic regression was used to analyze independent risk factors for ipsilateral cervical CLNM in PTC and to construct a nomogram model. The performance of nomogram CLNM prediction was evaluated by the receiver operating characteristic (ROC) curve and calibration curve. Binary Logistic Regression showed that age, history of osteoporosis, complicated by Hashimoto's thyroiditis, enlarged lymph nodes in the central neck, and extrathyroidal extension were risk factors for ipsilateral cervical CLNM. Combining these five independent risk factors, a nomogram prediction model was developed. In the modeling set, the area under the curve (AUC) of the nomogram ROC was 0.782 [95% confidence interval (CI): 0.730-0.833], and the sensitivity and specificity of the model were 0.761 and 0.763, respectively, with a well-calibrated curve fit. Moreover, the model presented better discrimination than any of the independent risk factors. The nomogram performed well in the validation set (AUC 0.753; 95% CI: 0.648-0.858). A non-invasive, and accurate nomogram prediction model for ipsilateral cervical CLNM of PTC was established. It can help physicians identify patients with a high risk of ipsilateral cervical CLNM of PTC preoperative for individualized treatment.

A clinicopathologic feature-based nomogram for preoperative estimation of splenic hilar lymph node metastasis in advanced proximal gastric cancer without invasion of the greater curvature

BackgroundThe indications for splenic hilar lymph node dissection in advanced proximal gastric cancer without invasion of the greater curvature are controversial. We aimed to develop a preoperative nomogram for individualized prediction of splenic hilar lymph node metastasis in non–greater curvature advanced proximal gastric cancer. MethodsFrom January 2014 to December 2021, 558 patients with non–greater curvature advanced proximal gastric cancer who underwent D2 lymphadenectomy (including splenic hilar lymph node) were retrospectively analyzed and divided into a training cohort (n = 361) and validation cohort (n = 197), depending on the admission time. A preoperative predictive nomogram of splenic hilar lymph node metastasis was established based on independent predictors identified by multivariate analysis, and the performance and prognostic value were confirmed. ResultsIn the training and validation cohorts, 48 (13.3%) and 24 patients (12.2%) had pathologically confirmed splenic hilar lymph node metastasis, respectively. Tumor located in the posterior wall, tumor size ≥5 cm, Borrmann type IV, and splenic hilar lymph node lymphadenectasis on computed tomography were preoperative factors independently associated with splenic hilar lymph node metastasis. The nomogram developed based on these four parameters had a high concordance index of 0.850 (95% confidence interval, 0.793–0.907) and 0.825 (95% confidence interval, 0.743–0.908) in the training and validation cohorts, respectively, with well-fitting calibration plots and better net benefits in the decision curve analysis. In addition, disease-free survival and overall survival were significantly shorter in the high-risk group, with hazard ratios of 3.660 (95% confidence interval, 2.228–6.011; log-rank P < .0001) and 3.769 (95% confidence interval, 2.279–6.231; log-rank P < .0001), respectively. ConclusionThe nomogram has good performance in predicting the risk of splenic hilar lymph node metastasis in non–greater curvature advanced proximal gastric cancer preoperatively, which can help surgeons make rational clinical decisions.

A radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning and international multicohort study.

Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning and whole-transcriptome sequencing (WTS), to accurately evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients. In this study, conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort ( N =103), TCIA validation cohort ( N =51), Duke cohort ( N =138), Foshan cohort ( N =106), and TCGA cohort ( N =680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using technology. A support vector machine model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis. Risk stratification analysis of BC patients was performed by gene set enrichment analysis, differential comparison of immune checkpoint gene expression, and drug sensitivity testing. For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts ( P <0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort ( P <0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and the TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups ( P <0.05). Additionally, different risk groups may exhibit varying treatment responses ( P <0.05). Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic, and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients.

Development and validation of a nomogram for predicting lymph node metastasis in ductal carcinoma in situ with microinvasion: A SEER population-based study.

Ductal carcinoma in situ with microinvasion (DCIS-MI) is a special type of breast cancer. It is an invasive lesion less than 1.0 mm in size related to simple ductal carcinoma in situ (DCIS). Lymph node metastasis (LNM) in DCIS-MI often indicates a poor prognosis. Therefore, the management of lymph nodes plays a vital role in the treatment strategy of DCIS-MI. Since DCIS-MI is often diagnosed by postoperative paraffin section and immunohistochemical detection, to obtain the best clinical benefits for such patients, we aim to establish and verify a nomogram to predict the possibility of lymph node metastasis in DCIS-MI patients and help preoperative or intraoperative clinical decision-making. A retrospective analysis of patients with DCIS-MI in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2019 was performed. The study cohort was randomly divided into a training cohort and a validation cohort at a ratio of 7:3. The risk factors were determined by univariate and multivariate logistic regression analyses in the training cohort, and a nomogram was constructed. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram in the training set and validation set. An independent data cohort was obtained from the Shanghai Jiao Tong University Breast Cancer Database (SJTU-BCDB) for external validation. This study included 3951 female patients from SEER with DCIS-MI, including 244 patients with regional lymph node metastasis, accounting for 6.18% of the total. An independent test set of 323 patients from SJTU-BCDB was used for external validation. According to the multifactorial logistic regression analysis results, age at diagnosis, ethnicity, grade, and surgical modality were included in the prediction model. The areas under the ROC curves (AUCs) were 0.739 (95% CI: 0.702~0.775), 0.732 (95% CI: 0.675~0.788), and 0.707 (95%CI: 0.607-0.807) in the training, validation and external test groups, suggesting that the column line graphs had excellent differentiation. The calibration curves slope was close to 1, and the model's predicted values were in good agreement with the actual values. The DCA curves showed good clinical utility. In this study, we constructed accurate and practical columnar maps with some clinical benefit to predict the likelihood of lymph node metastasis in patients with postoperatively diagnosed DCIS-MI and provide a reference value for specifying treatment strategies.

CT-based intratumoral and peritumoral deep transfer learning features prediction of lymph node metastasis in non-small cell lung cancer.

The main metastatic route for lung cancer is lymph node metastasis, and studies have shown that non-small cell lung cancer (NSCLC) has a high risk of lymph node infiltration. This study aimed to compare the performance of handcrafted radiomics (HR) features and deep transfer learning (DTL) features in Computed Tomography (CT) of intratumoral and peritumoral regions in predicting the metastatic status of NSCLC lymph nodes in different machine learning classifier models. We retrospectively collected data of 199 patients with pathologically confirmed NSCLC. All patients were divided into training (n = 159) and validation (n = 40) cohorts, respectively. The best HR and DTL features in the intratumoral and peritumoral regions were extracted and selected, respectively. Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Light Gradient Boosting Machine (Light GBM), Multilayer Perceptron (MLP), and Logistic Regression (LR) models were constructed, and the performance of the models was evaluated. Among the five models in the training and validation cohorts, the LR classifier model performed best in terms of HR and DTL features. The AUCs of the training cohort were 0.841 (95% CI: 0.776-0.907) and 0.955 (95% CI: 0.926-0.983), and the AUCs of the validation cohort were 0.812 (95% CI: 0.677-0.948) and 0.893 (95% CI: 0.795-0.991), respectively. The DTL signature was superior to the handcrafted radiomics signature. Compared with the radiomics signature, the DTL signature constructed based on intratumoral and peritumoral areas in CT can better predict NSCLC lymph node metastasis.

Nomogram based on multimodal magnetic resonance combined with B7-H3mRNA for preoperative lymph node prediction in esophagus cancer.

Accurate preoperative prediction of lymph node metastasis (LNM) in esophageal cancer (EC) patients is of crucial clinical significance for treatment planning and prognosis. To develop a clinical radiomics nomogram that can predict the preoperative lymph node (LN) status in EC patients. A total of 32 EC patients confirmed by clinical pathology (who underwent surgical treatment) were included. Real-time fluorescent quantitative reverse transcription-polymerase chain reaction was used to detect the expression of B7-H3 mRNA in EC tissue obtained during preoperative gastroscopy, and its correlation with LNM was analyzed. Radiomics features were extracted from multi-modal magnetic resonance imaging of EC using Pyradiomics in Python. Feature extraction, data dimensionality reduction, and feature selection were performed using XGBoost model and leave-one-out cross-validation. Multivariable logistic regression analysis was used to establish the prediction model, which included radiomics features, LN status from computed tomography (CT) reports, and B7-H3 mRNA expression, represented by a radiomics nomogram. Receiver operating characteristic area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate the predictive performance and clinical application value of the model. The relative expression of B7-H3 mRNA in EC patients with LNM was higher than in those without metastasis, and the difference was statistically significant (P < 0.05). The AUC value in the receiver operating characteristic (ROC) curve was 0.718 (95%CI: 0.528-0.907), with a sensitivity of 0.733 and specificity of 0.706, indicating good diagnostic performance. The individualized clinical prediction nomogram included radiomics features, LN status from CT reports, and B7-H3 mRNA expression. The ROC curve demonstrated good diagnostic value, with an AUC value of 0.765 (95%CI: 0.598-0.931), sensitivity of 0.800, and specificity of 0.706. DCA indicated the practical value of the radiomics nomogram in clinical practice. This study developed a radiomics nomogram that includes radiomics features, LN status from CT reports, and B7-H3 mRNA expression, enabling convenient preoperative individualized prediction of LNM in EC patients.

Abstract 3795: Possible role of miR-375-3p in cervical lymph node metastasis of oral squamous cell carcinoma

Abstract Latent cervical lymph node metastasis (LNM) is the most important prognostic factor in early oral squamous cell carcinoma (OSCC). However, no clinically useful predictors of LNM in early OSCC are available. In this study, we focused on the microRNAs (miRNAs) involved in the expression of numerous genes and explored those associated with latent cervical LNM in OSCC. Microarray analysis of miRNA expression showed that the expression levels of miR-375-3p were significantly reduced in primary OSCC tissues with latent cervical LNM. Subsequently, we quantified the expression of miR-375-3p by RT-qPCR and digital PCR. Similar to the results of miRNA microarray analysis, miR-375-3p was significantly downregulated in primary OSCC tissues with latent cervical LNM. The results of digital PCR showed a more significant difference than those of RT-qPCR. When the reference value for predicting latent cervical LNM was set to less than 57.85 copies based on the receiver operating characteristic curve, the sensitivity, specificity, accuracy, and area under the curve were 80%, 100%, 90%, and 0.942, respectively. Next, we examined the effects of miR-375-3p mimics on the growth and migration of four human OSCC cell lines (SAS-L1, HSC2, HSC3, and Ca9-22) that do not express miR-375-3p. When an miR-375-3p mimic was introduced into these cells, cell proliferation and migration were significantly suppressed. Total RNA was extracted from these cells reverse-transfected with miR-375-3p mimic for 48 hours. Microarray analysis and Ingenuity Pathway Analysis (IPA) miRNA target filter showed that miR-375-3p overexpression reduced the expression of 266 genes, of which 37 genes had the target sequences of miR-375-3p in their 3’-untranslated region. Among these target genes, CEPT1 and TIMM8A knockdown significantly inhibited the migration of all human OSCC cells, similar to miR-375-3p mimics. Furthermore, IPA core analysis revealed a decrease in expression of genes involved in the activation of PI3K-AKT pathways by miR-375-3p overexpression. These results indicated that downregulating miR-375-3p expression could promote cervical LNM by enhancing cell proliferation and migration in OSCC. We also found that CEPT1 and TIMM8A as novel target genes of miR-375-3p regulated the migration of human OSCC cells. The expression levels of miR-375-3p in primary OSCC tissues appear to be a useful biomarker for predicting latent cervical LNM. Citation Format: Masato Saika, Koh-ichi Nakashiro, Norihiko Tokuzen, Hiroyuki Shirai, Daisuke Uchida. Possible role of miR-375-3p in cervical lymph node metastasis of oral squamous cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3795.

Predictive nomogram for lymph node metastasis and survival in gastric cancer using contrast-enhanced computed tomography-based radiomics: a retrospective study.

Lymph node involvement significantly impacts the survival of gastric cancer patients and is a crucial factor in determining the appropriate treatment. This study aimed to evaluate the potential of enhanced computed tomography (CT)-based radiomics in predicting lymph node metastasis (LNM) and survival in patients with gastric cancer before surgery. Retrospective analysis of clinical data from 192 patients diagnosed with gastric carcinoma was conducted. The patients were randomly divided into a training cohort (n=128) and a validation cohort (n=64). Radiomic features of CT images were extracted using the Pyradiomics software platform, and distinctive features were further selected using a Lasso Cox regression model. Features significantly associated with LNM were identified through univariate and multivariate analyses and combined with radiomic scores to create a nomogram model for predicting lymph node involvement before surgery. The predictive performance of radiomics features, CT-reported lymph node status, and the nomogram model for LNM were compared in the training and validation cohorts by plotting receiver operating characteristic (ROC) curves. High-risk and low-risk groups were identified in both cohorts based on the cut-off value of 0.582 within the radiomics evaluation scheme, and survival rates were compared. Seven radiomic features were identified and selected, and patients were stratified into high-risk and low-risk groups using a 0.582 cut-off radiomics score. Univariate and multivariate analyses revealed that radiomics features, diabetes mellitus, Nutrition Risk Screening (NRS) 2002 score, and CT-reported lymph node status were significant predictors of LNM in patients with gastric cancer. A predictive nomogram model was developed by combining these predictors with the radiomics score, which accurately predicted LNM in gastric cancer patients before surgery and outperformed other models in terms of accuracy and sensitivity. The AUC values for the training and validation cohorts were 0.82 and 0.722, respectively. The high-risk and low-risk groups in both the training and validation cohorts showed significant differences in survival rates. The radiomics nomogram, based on contrast-enhanced computed tomography (CECT ), is a promising non-invasive tool for preoperatively predicting LNM in gastric cancer patients and postoperative survival.

Prediction of bilateral thyroid carcinoma and lateral cervical lymph node metastasis in PTC patients with suspicious thyroid nodules.

This study aimed to evaluate the factors associated with bilateral papillary thyroid carcinoma (PTC) and lateral cervical lymph node metastasis (LLNM) in patients with suspicious unilateral PTC. This study analyzed patients with suspicious unilateral PTC who were enrolled in a university hospital between 2016 and 2019 in Zhejiang, China. Using logistic regression, the study examined the factors associated with bilateral PTC and LLNM in demographic data, anthropometric measurements, lifestyle factors, medical history, preoperative diagnostic tests, and histopathological factors. A total of 256 patients, with a mean age of 49 years, were enrolled. Bilateral PTC was associated with multifocality (aOR: 5.069, 95% CI: 2.440-10.529, P < 0.001), and contralateral nodule in the upper (aOR: 9.073, 95% CI: 2.111-38.985, P = 0.003) and middle (aOR: 9.926, 95% CI: 2.683-36.717, P < 0.001). LLNM was positively associated with bilateral PTC (aOR, 4.283, 95% CI: 1.378-13.308, p = 0.012), male (aOR, 3.377, 95% CI: 1.205-9.461, P = 0.021), upper location of carcinoma (aOR, 3.311, 95% CI: 1.091-10.053, p = 0.035), and punctate echogenic foci (aOR, 3.309, 95% CI: 1.165-9.394, P = 0.025). Contralateral maximal nodule in the upper (aOR: 0.098, 95% CI: 0.015-0.628, p = 0.014), middle (aOR: 0.114, 95% CI: 0.033-0.522, p < 0.001), and lower (aOR, 0.028, 95% CI: 0.003-0.276, P = 0.002) location were inversely associated with LLNM. Upper and middle location of contralateral nodule and tumor multifocality predicted the risk bilateral PTC. Bilateral PTC, male, upper tumor location, punctate echogenic foci and contralateral nodule location in the entire lobes were independent predictors for LLNM.

EMT Dynamics in Lymph Node Metastasis of Oral Squamous Cell Carcinoma.

Epithelial-mesenchymal transition (EMT) enables tumor cell invasion and metastasis. Many studies have demonstrated the critical role of EMT in lymph node metastasis in oral squamous cell carcinoma (OSCC). During EMT, epithelial cancer cells lose intercellular adhesion and apical-basal polarity and acquire mesenchymal properties such as motility and invasiveness. A significant feature of EMT is cadherin switching, involving the downregulation of E-cadherin and upregulation of N-cadherin. The TGF-β/SMAD pathway can also induce EMT. We aimed to evaluate EMT markers as predictors of lymph node metastasis in OSCC. We performed genetic profiling of 159 primary OSCCs from TCGA and analyzed the expression of EMT markers, including cadherin switch genes (CDH1, CDH2), and TGF-β/SMAD pathway genes. Samples were divided into advanced (stage III-IV) and early (stage I-II) stage groups. Differential expression analysis was performed, as well as an independent validation study containing fresh OSCC samples. TGF-β/SMAD pathway genes such as SMAD6 were upregulated in advanced stage tumors. N-cadherin and SNAIL2 were overexpressed in node-positive tumors. Keratins were downregulated in these groups. Our findings demonstrate that EMT marker expression correlates with lymph node metastasis in OSCC. Developing therapies targeting regulators such as N-cadherin may prevent metastasis and improve outcomes.

Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer

This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) for the preoperative evaluation of axillary lymph node (ALN) metastasis status in patients with a newly diagnosed unifocal breast cancer. A total of 883 eligible patients with breast cancer who underwent preoperative breast and axillary ultrasound were retrospectively enrolled between April 1, 2016, and June 30, 2022. The training cohort comprised 621 patients from Hospital I; the external validation cohorts comprised 112, 87, and 63 patients from Hospitals II, III, and IV, respectively. A DLR signature was created based on the deep learning and handcrafted features, and the DLRN was then developed based on the signature and four independent clinical parameters. The DLRN exhibited good performance, yielding areas under the receiver operating characteristic curve (AUC) of 0.914, 0.929, and 0.952 in the three external validation cohorts, respectively. Decision curve and calibration curve analyses demonstrated the favorable clinical value and calibration of the nomogram. In addition, the DLRN outperformed five experienced radiologists in all cohorts. This has the potential to guide appropriate management of the axilla in patients with breast cancer, including avoiding overtreatment.

Postoperative lymph node metastasis status and related factors in patients with early-stage, low-risk endometrial cancer assessed by the Mayo criteria

Objective: To investigate the postoperative lymph node metastasis status and related factors of patients with early-stage low-risk endometrial cancer evaluated by the Mayo criteria in order to make the preoperative evaluation more accurate. Methods: A total of 172 patients with early-stage low-risk endometrial cancer who underwent surgery in Sichuan Provincial Cancer Hospital from 2009 to 2018 and were evaluated as early low-risk according to the "Mayo criteria" were retrospectively enrolled, and were divided into lymph node metastasis group (7 cases) and non-metastasis group (165 cases) according to the results of postoperative pathological examination. The postoperative pathological examination results and prognosis of all patients were collected. The lymph node metastasis of the two groups was analyzed. The multiple logistic regression model was used to analyze the related factors of lymph node metastasis, and the area under the curve (AUC) of the relevant factors was calculated by using the receiver operating characteristic curve to predict the postoperative lymph node metastasis of patients. Results: The age of 172 patients was (53.0±8.6) years, and the follow-up [M(Q1,Q3)] was 64.1 (46.2, 91.4) months, among whom 2 relapsed and 1 died. Among the 7 patients with lymph node metastasis, 5 cases had pelvic lymph node metastasis, 1 case had pelvic lymph union and abdominal para-aortic lymph node metastasis, 1 case had isolated abdominal para-aortic lymph node metastasis, 3 cases had two lymph node metastases, and 4 cases had intrailiac lymph nodes being the most common metastasis site. Multivariate analysis showed that preoperative serum cancer antigen (CA) 125 was the relevant factor for postoperative lymph node metastasis, with an OR value (95%CI) of 1.022 (1.001-1.043) (P=0.042), with AUC (95%CI) of CA125 predicting lymph node metastasis of 0.850 (0.761-0.939) (P=0.002), and the optimal cut-off value of CA125 was 29.305 U/ml with the sensitivity of 85.7% and the specificity of 80.6%. Conclusions: When CA125>29.305 U/ml, patients with early-stage low-risk endometrial cancer assessed by the Mayo criteria, had an increased risk of lymph node metastasis. Therefore, adding preoperative serum CA125 level as an evaluation index on the basis of the classic "Mayo criteria" may help conduct more accurate preoperative evaluation and better identify early-stage low-risk EC patients.

Clinical development of MRI-based multi-sequence multi-regional radiomics model to predict lymph node metastasis in rectal cancer.

We aim to construct a magnetic resonance imaging (MRI)-based multi-sequence multi-regional radiomics model that will improve the preoperative prediction ability of lymph node metastasis (LNM) in T3 rectal cancer. Multi-sequence MRI data from 190 patients with T3 rectal cancer were retrospectively analyzed, with 94 patients in the LNM group and 96 patients in the non-LNM group. The clinical factors, subjective imaging features, and the radiomic features of tumor and peritumoral mesorectum region of patients were extracted from T2WI and ADC images. Spearman's rank correlation coefficient, Mann-Whitney's U test, and the least absolute shrinkage and selection operator were used for feature selection and dimensionality reduction. Logistic regression was used to construct six models. The predictive performance of each model was evaluated by the receiver operating characteristic curve (ROC). The differences of each model were characterized by area under the curve (AUC) via the DeLong test. The AUCs of T2WI, ADC single-sequence radiomics model and multi-sequence radiomics model were 0.73, 0.75, and 0.78, respectively. The multi-sequence multi-regional radiomics model with improved performance was created by combining the radiomics characteristics of the peritumoral mesorectum region with the multi-sequence radiomics model (AUC, 0.87; p < 0.01). The AUC of the clinical model was 0.68, and the MRI-clinical composite evaluation model was obtained by incorporating the clinical data with the multi-sequence multi-regional radiomics features, with an AUC of 0.89. The MRI-based multi-sequence multi-regional radiomics model significantly improved the prediction ability of LNM for T3 rectal cancer and could be applied to guide surgical decision-making in patients with T3 rectal cancer.

Accurate preoperative prediction of nodal metastasis in papillary thyroid microcarcinoma: Towards optimal management of patients.

To enhance the accuracy in predicting lymph node metastasis (LNM) preoperatively in patients with papillary thyroid microcarcinoma (PTMC), refining the "low-risk" classification for tailored treatment strategies. This study involves the development and validation of a predictive model using a cohort of 1004 patients with PTMC undergoing thyroidectomy along with central neck dissection. The data was divided into a training cohort (n = 702) and a validation cohort (n = 302). Multivariate logistic regression identified independent LNM predictors in PTMC, leading to the construction of a predictive nomogram model. The model's performance was assessed through ROC analysis, calibration curve analysis, and decision curve analysis. Identified LNM predictors in PTMC included age, tumor maximum diameter, nodule-capsule distance, capsular contact length, bilateral suspicious lesions, absence of the lymphatic hilum, microcalcification, and sex. Especially, tumors larger than 7 mm, nodules closer to the capsule (less than 3 mm), and longer capsular contact lengths (more than 1 mm) showed higher LNM rates. The model exhibited AUCs of 0.733 and 0.771 in the training and validation cohorts respectively, alongside superior calibration and clinical utility. This study proposes and substantiates a preoperative predictive model for LNM in patients with PTMC, honing the precision of "low-risk" categorization. This model furnishes clinicians with an invaluable tool for individualized treatment approach, ensuring better management of patients who might be proposed observation or ablative options in the absence of such predictive information.

Development and validation of a novel preoperative clinical model for predicting lymph node metastasis in perihilar cholangiocarcinoma

BackgroudWe aimed to develop a novel preoperative nomogram to predict lymph node metastasis (LNM) in perihilar cholangiocarcinoma (pCCA) patients.Methods160 pCCA patients were enrolled at Lihuili Hospital from July 2006 to May 2022. A novel nomogram model was established to predict LNM in pCCA patients based on the independent predictive factors selected by the multivariate logistic regression model. The precision of the nomogram model was evaluated through internal and external validation with calibration curve statistics and the concordance index (C-index). Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate and determine the clinical utility of the nomogram.ResultsMultivariate logistic regression demonstrated that age (OR = 0.963, 95% CI: 0.930–0.996, P = 0.030), CA19-9 level (> 559.8 U/mL vs. ≤559.8 U/mL: OR = 3.162, 95% CI: 1.519–6.582, P = 0.002) and tumour diameter (OR = 1.388, 95% CI: 1.083–1.778, P = 0.010) were independent predictive factors of LNM in pCCA patients. The C-index was 0.763 (95% CI: 0.667–0.860) and 0.677 (95% CI: 0.580–0.773) in training cohort and validation cohort, respectively. ROC curve analysis indicated the comparative stability and adequate discriminative ability of nomogram. The sensitivity and specificity were 0.820 and 0.652 in training cohort and 0.704 and 0.649 in validation cohort, respectively. DCA revealed that the nomogram model could augment net benefits in the prediction of LNM in pCCA patients.ConclusionsThe novel prediction model is useful for predicting LNM in pCCA patients and showed adequate discriminative ability and high predictive accuracy.

A comparison of machine learning methods for radiomics modeling in prediction of occult lymph node metastasis in clinical stage IA lung adenocarcinoma patients.

Accurate prediction of occult lymph node metastasis (ONM) is an important basis for determining whether lymph node (LN) dissection is necessary in clinical stage IA lung adenocarcinoma patients. The aim of this study is to determine the best machine learning algorithm for radiomics modeling and to compare the performances of the radiomics model, the clinical-radilogical model and the combined model incorporate both radiomics features and clinical-radilogical features in preoperatively predicting ONM in clinical stage IA lung adenocarcinoma patients. Patients with clinical stage IA lung adenocarcinoma undergoing curative surgery from one institution were retrospectively recruited and assigned to training and test cohorts. Radiomics features were extracted from the preoperative computed tomography (CT) images of the primary tumor. Seven machine learning algorithms were used to construct radiomics models, and the model with the best performance, evaluated using the area under the curve (AUC), was selected. Univariate and multivariate logistic regression analyses were performed on the clinical-radiological features to identify statistically significant features and to develop a clinical model. The optimal radiomics and clinical models were integrated to build a combined model, and its predictive performance was assessed using receiver operating characteristic curves, Brier score, and decision curve analysis (DCA). This study included 258 patients who underwent resection (training cohort, n=182; test cohort, n=76). Six radiomics features were identified. Among the seven machine learning algorithms, extreme gradient boosting (XGB) demonstrated the highest performance for radiomics modeling, with an AUC of 0.917. The combined model improved the AUC to 0.933 and achieved a Brier score of 0.092. DCA revealed that the combined model had optimal clinical efficacy. The superior performance of the combined model, based on XGB algorithm in predicting ONM in patients with clinical stage IA lung adenocarcinoma, might aid surgeons in deciding whether to conduct mediastinal LN dissection and contribute to improve patients' prognosis.

Expression of SOX4 Significantly Predicts the Risk of Lymph Node Metastasis for Patients With Early-Stage Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma stands as a notably aggressive malignancy within the digestive system. In cases of early esophageal cancer without lymph node metastasis, endoscopic surgical resection offers a viable alternative, often resulting in improved patient quality of life. However, the paucity of methods to preoperatively ascertain lymph node involvement complicates surgical planning. SOX4 gene was previously found to be highly associated with invasive metastasis in our work through single-cell RNA sequencing on 5 paired tumor/peritumor tissues. This research included the collection of 124 tissue samples from 106 patients (106 tumor and 18 lymph node specimens). Samples were methodically arranged into a tissue microarray and treated with immunohistochemical staining. Statistical analysis was conducted to assess the relationship between them. In the univariate analysis, 3 factors were identified as statistically significant in relation to lymph node metastasis: T category (P = .014), vascular invasion (P < .001), and SOX4 intensity (P = .001). Additionally, when evaluating SOX4 intensity alongside other clinical indicators, SOX4 was shown to independently influence lymph node metastasis. Further, the multivariate analysis revealed that vascular invasion (P < .001) and SOX4 intensity (P = .003) were significantly associated with lymph node metastasis, exhibiting hazard ratios of 10.174 and 7.142, respectively. The results of our study indicate that both SOX4 expression and vascular invasion serve as predictors of lymph node metastasis in patients diagnosed with category T1 esophageal squamous cell carcinoma, underscoring the potential utility of SOX4 in prognostic evaluations.

Prediction of lymph node metastasis in advanced gastric adenocarcinoma based on dual-energy CT radiomics: focus on the features of lymph nodes with a short axis diameter ≥6 mm.

To explore the value of the features of lymph nodes (LNs) with a short-axis diameter ≥6 mm in predicting lymph node metastasis (LNM) in advanced gastric adenocarcinoma (GAC) based on dual-energy CT (DECT) radiomics. Data of patients with GAC who underwent radical gastrectomy and LN dissection were retrospectively analyzed. To ensure the correspondence between imaging and pathology, metastatic LNs were only selected from patients with pN3, nonmetastatic LNs were selected from patients with pN0, and the short-axis diameters of the enrolled LNs were all ≥6 mm. The traditional features of LNs were recorded, including short-axis diameter, long-axis diameter, long-to-short-axis ratio, position, shape, density, edge, and the degree of enhancement; univariate and multivariate logistic regression analyses were used to establish a clinical model. Radiomics features at the maximum level of LNs were extracted in venous phase equivalent 120 kV linear fusion images and iodine maps. Intraclass correlation coefficients and the Boruta algorithm were used to screen significant features, and random forest was used to build a radiomics model. To construct a combined model, we included the traditional features with statistical significance in univariate analysis and radiomics scores (Rad-score) in multivariate logistic regression analysis. Receiver operating curve (ROC) curves and the DeLong test were used to evaluate and compare the diagnostic performance of the models. Decision curve analysis (DCA) was used to evaluate the clinical benefits of the models. This study included 114 metastatic LNs from 36 pN3 cases and 65 nonmetastatic LNs from 28 pN0 cases. The samples were divided into a training set (n=125) and a validation set (n=54) at a ratio of 7:3. Long-axis diameter and LN shape were independent predictors of LNM and were used to establish the clinical model; 27 screened radiomics features were used to build the radiomics model. LN shape and Rad-score were independent predictors of LNM and were used to construct the combined model. Both the radiomics model (area under the curve [AUC] of 0.986 and 0.984) and the combined model (AUC of 0.970 and 0.977) outperformed the clinical model (AUC of 0.772 and 0.820) in predicting LNM in both the training and validation sets. DCA showed superior clinical benefits from radiomics and combined models. The models based on DECT LN radiomics features or combined traditional features have high diagnostic performance in determining the nature of each LN with a short-axis diameter of ≥6 mm in advanced GAC.