Lymph Node Metastasis In Patients Research Articles

Inverse association between age and risk of lymph node metastasis in patients with early gastric cancer: a surveillance, epidemiology, and end results analysis.

Background: Recent studies have shown that young patients with gastric cancer are at a more advanced stage and have poor survival, but the cause is still unclear. The prognosis of gastric cancer is closely related to LNM, but the relationship between age and LNM in early gastric cancer (EGC) is currently unclear. Therefore, we aimed to study the relationship between age and the risk of LNM in EGC. Materials and Methods: We screened out patients with EGC who underwent surgery from the SEER research database from 1975 to 2016, and retrospectively analyzed the proportion of LNM in different age groups. We grouped age into 18-39, 40-49, 50-59, 60-69, 70-79, and ≥ 80 years old, and used univariate analysis and multivariate logistic regression to analyze the correlation between age and LNM. Results: We included 9231 patients with EGC, with LNM rates of 20.3%, 23.3%, 21.0%, 19.8%, 18.1%, and 13.2% in the age groups of 18-39 years old (2.3%), 40-49 (6.1%), 50-59 years old (15.7%), 60-69 years old (24.8%), 70-79 years old (27.2%) and ≥80 years old (23.9%), respectively. We found that when older than 39 years old, the risk of LNM and postoperative survival time of EGC patients decrease (p<0.001). Multivariate analysis results showed that age, tumor size, the number of retrieved lymph nodes (rN), tumor grade, and tumor location were related to LNM. Conclusions: This study found that age in patients with EGC was inversely related to the risk of LNM, and positively correlated with postoperative survival. For older patients with EGC, endoscopic treatment is more appropriate. For young patients with EGC, LNM should be considered when choosing endoscopic treatment.

Leptin as a predictive marker for lymph node metastasis in patients with papillary thyroid cancer

Background Leptin is a neuroendocrine hormone produced from adipose tissue with many vital functions. There is a well-known association between leptin and papillary thyroid cancer (PTC). Preoperative prediction of lymph node metastasis is one of the interesting topics in PTC. This study aimed to determine whether the serum leptin peptide is a significant predictor marker for node metastasis in cases with PTC. Patients and methods This is a prospective comparative study which was conducted at Ain Shams University Hospitals and El-Sheikh Zayed specialized hospital between January 2021 and January 2023. Forty patients with PTC were enrolled in this study and according to postoperative histopathology, we divided them into two groups: group A, 24 cases, those with pathological negative node and group B, 16 cases, those with pathological positive node. Results We had no statistically significant difference in the preoperative demographics data between both groups. Postoperatively, the median serum leptin level and percentage of leptin decrease were significantly higher in in group B. Multivariate logistic regression analysis showed that preoperative serum leptin level, thyroid imaging reporting and data system, multifocality, extra thyroid extension, and size of dominant nodule were significant predictors for node metastasis. There was a significant negative correlation between leptin level and age, while there was a significant positive correlation between leptin level and lymph node metastasis. Conclusion Preoperative serum leptin level is considered a significant predictor marker for lymph node metastasis in PTC.

Predictive factors of lymph node metastasis in patients with 2014 figo stage Ib1-IIa2 cervical squamous cell cancer: A multicentric study

Predictive factors of lymph node metastasis in patients with 2014 figo stage Ib1-IIa2 cervical squamous cell cancer: A multicentric study

Evaluating Mediastinal Lymph Node Metastasis of Non-Small Cell Lung Cancer Using Mono-exponential, Bi-exponential, and Stretched-exponential Models of Diffusion-weighted Imaging.

To explore and compare the diagnostic values of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) parameters of primary lesions and lymph nodes (LNs) to predict mediastinal LN metastasis in patients with non-small cell lung cancer. Sixty-one patients with non-small cell lung cancer underwent preoperative magnetic resonance imaging, including multiple b -value DWI. The DWI parameters, including apparent diffusion coefficient (ADC) from a mono-exponential model, true diffusion (D) coefficient, pseudo-diffusion (D*) coefficient, and perfusion fraction (f) from a bi-exponential model, distributed diffusion coefficient (DDC) and intravoxel diffusion heterogeneity index (α) from a stretched-exponential model of primary tumors and LNs and the size characteristics of LNs, were measured and compared. Multivariate logistic regression analysis was used to establish models for predicting mediastinal LN metastasis. Receiver operating characteristic analysis was applied to evaluate diagnostic performances. The DWI parameters of primary tumors showed no statistical significance between LN metastasis-positive and LN metastasis-negative groups. Nonmetastatic LNs had significantly higher ADC, D, DDC, and α values compared with metastatic LNs (all P < 0.05). The short-dimension, long-dimension, and short-long dimension ratio of metastatic LNs was significantly larger than those of nonmetastatic ones (all P < 0.05). The D value showed the best diagnostic performance among all DWI-derived single parameters, and the short dimension of LNs performed the same among all the size variables. Furthermore, the combination of DWI parameters (ADC and D) and the short dimension of LNs can significantly improve diagnostic efficiency. The ADC, D, DDC, and α from the mono-exponential, bi-exponential, and stretched-exponential models were demonstrated efficient in differentiating benign from metastatic LNs, and the combination of ADC, D, and short dimension of LNs may have a better diagnostic performance than DWI or size-derived parameters either in combination or individually.

2-[18F]FDG PET-based quantification of lymph node metabolic heterogeneity for predicting lymph node metastasis in patients with colorectal cancer.

The pre-surgical estimation of lymph node (LN) metastasis in colorectal cancer (CRC) poses a significant diagnostic predicament. The associations between LN morphology, density, and metabolic heterogeneity and LN metastasis status in CRCs have been seldomly examined through the lens of radiomics. This research aimed to assess 2-[18F]FDG PET-based quantification of intratumoral metabolic heterogeneity for predicting lymph node metastasis in patients with colorectal cancer. The construction of the model utilized data from 264 CRC patients, all of whom underwent preoperative 2-[18F]FDG PET/CT. Radiomic features were extracted from PET and CT images of LNs. Least absolute shrinkage and selection operator (LASSO) regression was implemented for selecting pertinent imaging features with a tenfold cross-validation. The predictive accuracy for LN metastasis status was juxtaposed against traditional methodologies (comprising CT-reported LN status and PET/CT-reported LN status) by deploying the receiver operating characteristic (ROC) curve analysis. The radiomics signature was evaluated based on discrimination, calibration, and clinical utility parameters. The model was further subjected to validation using an independent cohort of 132 patients from the period of January 2012 to June 2020. The radiomics model was composed of eight significant radiomic features (five from PET and three from CT), encapsulating metabolic and density heterogeneity. The radiomics signature (area under the curve (AUC), 0.908) showcased a significantly superior performance compared to CT-reported LN status (AUC, 0.563, P < 0.001) and PET/CT-reported LN status (AUC, 0.64, P < 0.001) for predicting LN-positive or LN-negative status. The radiomics signature (AUC, 0.885) also showcased a significantly superior performance compared to CT-reported LN status (AUC, 0.587, P < 0.001) and PET/CT-reported LN status (AUC, 0.621, P < 0.001) to identify N1 and N2. This signature maintained its independence from clinical risk factors and exhibited robustness in the validation test set. Decision curve analysis attested to the clinical utility of the radiomics signature. The radiomics signature based on 2-[18F]FDG PET/CT, which derived image features directly from LNs irrespective of clinical risk factors, displayed enhanced diagnostic performance compared to conventional CT or PET/CT-reported LN status. This allows for the identification of pre-surgical LN metastasis status and facilitates a patient-specific prediction of LN metastasis status in CRC patients.

Predictive factors of axillary lymph node involvement in Tunisian women with early breast cancer.

Axillary lymph node involvement (ALNI) is associated with an increased risk of local recurrence and poor prognosis in early breast cancer. The determination of the risk of positive axillary lymph node contributes to therapeutic decisions. The aim of this study was to identify clinicopathological predictive factors of axillary lymph node metastases in patients with early breast cancer. We included patients with clinical T0, T1 andT2 invasive breast carcinoma who underwent resection of the primary tumor and axillary staging by sentinel lymph node biopsy and/or axillar lymph node dissection between 2012 and 2018. Of the 135patients included, 41.5% had ALNI. Regarding univariate analysis, clinical factors correlated with positive ALNM were clinical tumour size>30mm, clinical tumour stage, clinical number of tumours, clinical axillary nodal status and nodal status on ultrasound. Pathologic factors associated with nodal involvement were pathologic tumour stage, tumour grade SBR, number of foci, lymphovascular invasion, perineural invasion and Ki67>20%.In multivariate logistic regression, clinical axillary nodal status, pathologic tumour stage and lymphovascular invasion (LVI) remained as independent predictors of ALNI. Based on these results, we suggest that clinical axillary nodal status, pathologic tumour stage and LVI are predictive factors for ALNM in Tunisian women with early breast cancer.

A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma.

In this study, we constructed and validated models based on deep learning and radiomics to facilitate preoperative diagnosis of cervical lymph node metastasis (LNM) using contrast-enhanced computed tomography (CECT). CECT scans of 100 patients with OSCC (217 metastatic and 1973 non-metastatic cervical lymph nodes: development set, 76 patients; internally independent test set, 24 patients) who received treatment at the Peking University School and Hospital of Stomatology between 2012 and 2016 were retrospectively collected. Clinical diagnoses and pathological findings were used to establish the gold standard for metastatic cervical LNs. A reader study with two clinicians was also performed to evaluate the lymph node status in the test set. The performance of the proposed models and the clinicians was evaluated and compared by measuring using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). A fusion model combining deep learning with radiomics showed the best performance (ACC, 89.2%; SEN, 92.0%; SPE, 88.9%; and AUC, 0.950 [95% confidence interval: 0.908-0.993, P < 0.001]) in the test set. In comparison with the clinicians, the fusion model showed higher sensitivity (92.0 vs. 72.0% and 60.0%) but lower specificity (88.9 vs. 97.5% and 98.8%). A fusion model combining radiomics and deep learning approaches outperformed other single-technique models and showed great potential to accurately predict cervical LNM in patients with OSCC. The fusion model can complement the preoperative identification of LNM of OSCC performed by the clinicians.

Selective adjuvant radiation therapy for distant lymph node metastasis in patients with stage 4B epithelial ovarian cancer: a case series

Although the efficacy of surgery followed by taxane- and platinum-based systemic chemotherapy has been clearly demonstrated in the standard first-line treatment of epithelial ovarian cancer, the role of radiation therapy for distant lymph node metastasis in patients with epithelial ovarian cancer is not well-established due to a lack of reported studies. We identified four patients who underwent selective adjuvant radiation therapy for neck and para-aortic lymph node lesions after primary debulking surgery between 2020 and 2022, followed by platinum-based chemotherapy for stage 4B high-grade serous ovarian cancer. Through a retrospective review of medical records, we analyzed patient clinicopathologic features, treatment course, and imaging findings. The median age was 49.25 years (range, 46–54 years). All patients had the International Federation of Gynecology and Obstetrics stage 4B disease. Following primary debulking surgery, all patients received weekly paclitaxel-carboplatin chemotherapy and maintenance treatment with poly(ADP-ribose) polymerase (PARP) inhibitors. All patients received selective adjuvant radiation therapy for neck and para-aortic lymph node metastasis before PARP inhibitor maintenance. The median follow-up time was 36.75 months (range, 19–45 months). All patients achieved a complete response. None of the patients experienced disease recurrence or died during the follow-up period. The management of distant lymph node metastasis in patients with epithelial ovarian cancer remains a matter of debate. Selective adjuvant radiation therapy in first-line treatment for ovarian cancer appears to be a feasible approach with maintenance therapy for stage 4B epithelial ovarian cancer.

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

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

A machine-learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with early-stage breast cancer.

It has been reported that patients with early breast cancer with 1-2 positive sentinel lymph nodes have a lower risk of non-sentinel lymph node (NSLN) metastasis and cannot benefit from axillary lymph node dissection. To develop the potential of machine learning based on multiparametric magnetic resonance imaging (MRI) and clinical factors for predicting the risk of NSLN metastasis in breast cancer. This retrospective study included 144 patients with 1-2 positive sentinel lymph node breast cancer. Multiparametric MRI morphologic findings and the detailed demographical characteristics of the primary tumor and axillary lymph node were extracted. The logistic regression, support vector classification, extreme gradient boosting, and random forest algorithm models were established to predict the risk of NSLN metastasis. The prediction efficiency of a machine-learning-based model was evaluated. Finally, the relative importance of each input variable was analyzed for the best model. Of the 144 patients, 80 (55.6%) developed NSLN metastasis. A total of 24 imaging features and 14 clinicopathological features were analyzed. The extreme gradient boosting algorithm had the strongest prediction efficiency with an area under curve of 0.881 and 0.781 in the training set and test set, respectively. Five main factors for the metastasis of NSLN were found, including histological grade, cortical thickness, fatty hilum, short axis of lymph node, and age. The machine-learning model incorporating multiparametric MRI features and clinical factors can predict NSLN metastasis with high accuracy for breast cancer and provide predictive information for clinical protocol.

Dynamic changes of serum miR-105-3p expression and prognostic value evaluation of postoperative thyroid cancer.

To explore the dynamic changes of serum miR-105-3p expression after thyroid cancer surgery and its correlation with clinicopathological manifestations and to evaluate its clinical value as a potential biomarker after surgery. A total of 100 thyroid cancer patients admitted to Shaanxi Provincial People's Hospital from November 2020 to August 2021 were selected as the research objects. The aim was to detect the expression of serum miR-105-3p in patients and its correlation with tumor pathological characteristics (pathological type, tumor differentiation, TNM stage, lymph node metastasis), and to detect the dynamic changes of postoperative serum miR-105-3p in patients to evaluate its prognostic value as a potential biomarker. The level of serum miR-105-3p increases in patients with well-differentiated thyroid cancer and lymph node metastasis; the level of serum miR-105-3p gradually decreases with the passage of time after surgery, and there is a significant difference between 4 d after surgery and before surgery; serum miR-105-3p level can significantly distinguish between patients with poor prognosis and good prognosis within 2 years after the operation, and it can predict the improvement of the prognosis of thyroid cancer after surgery. The level of serum miR-105-3p is closely related to the degree of differentiation and lymph node metastasis in patients with thyroid cancer. Its level gradually decreases with the passage of time after surgery. It has a good diagnostic value for the prognosis of thyroid cancer after surgery and when it is expected to become a thyroid cancer surgery. Potential biomarkers for post-diagnosis.

Dynamic changes of serum miR-105-3p expression and prognostic value evaluation of postoperative thyroid cancer

Objective. To explore the dynamic changes of serum miR-105-3p expression after thyroid cancer surgery and its correlation with clinicopathological manifestations and to evaluate its clinical value as a potential biomarker after surgery. Methods. A total of 100 thyroid cancer patients admitted to Shaanxi Provincial People’s Hospital from November 2020 to August 2021 were selected as the research objects. The aim was to detect the expression of serum miR-105-3p in patients and its correlation with tumor pathological characteristics (pathological type, tumor differentiation, TNM stage, lymph node metastasis), and to detect the dynamic changes of postoperative serum miR-105-3p in patients to evaluate its prognostic value as a potential biomarker. Results. The level of serum miR-105-3p increases in patients with well-differentiated thyroid cancer and lymph node metastasis; the level of serum miR-105-3p gradually decreases with the passage of time after surgery, and there is a significant difference between 4 d after surgery and before surgery; serum miR-105-3p level can significantly distinguish between patients with poor prognosis and good prognosis within 2 years after the operation, and it can predict the improvement of the prognosis of thyroid cancer after surgery. Conclusions. The level of serum miR-105-3p is closely related to the degree of differentiation and lymph node metastasis in patients with thyroid cancer. Its level gradually decreases with the passage of time after surgery. It has a good diagnostic value for the prognosis of thyroid cancer after surgery and when it is expected to become a thyroid cancer surgery. Potential biomarkers for post-diagnosis.

The ratio of lymphocyte/red blood cells and platelets/lymphocytes are predictive biomarkers for lymph node metastasis in patients with breast cancer.

Axillary lymph node metastasis (LNM) affects the progression of breast cancer. However, it is difficult to preoperatively diagnose axillary lymph node status with high sensitivity. Therefore, we hypothesized that platelets/lymphocytes ratio (PLR) and lymphocytes/ red blood cells ratio (LRR) might help in the prognosis of lymph node metastasis in T1-T2 breast cancer. 166 patients (Chang Ning Maternity & Infant Health Institute) were included in our study, and the associations of PLR and LPR with lymph node metastasis were investigated. Peripheral blood was collected one week before the surgery, and the patients were divided into different categories based on their PLR and LRR. The incidence of LNM was significantly increased in the high PLR group (p= 0.002) compared with the low PLR group; LNM was also significantly increased in the low LRR group (p= 0.036) compared with the high LPR group. Further, our study revealed that high PLR (p< 0.001, OR = 4.397, 95% CI = 2.005-9.645), low LRR (p= 0.017, OR = 0.336, 95%CI = 0.136-0.825) and high clinical T stage (p< 0.001, OR = 3.929, 95%CI = 1.913-8.071) are independent predictors of LNM. PLR and LRR could be identified as predictors of LNM in patients with T1/T2 breast cancer.

Thyroglobulin Antibody (TgAb) Positive is an Independent Risk Factor for Lymph Node Metastasis in Patients with Differentiated Thyroid Carcinoma.

To investigate the relationship between lymph node metastasis and the clinicopathologic features of differentiated thyroid carcinoma (DTC) patients with thyroglobulin antibody (TgAb) positive and negative. A total of 443 patients with DTC were included in this study. Clinicopathological data of the patients were collected, including tumor size, clinical stage, calcification, Hashimoto's thyroiditis, extra-membrane infiltration, BRAF V600E mutation status, and thyroid-related hormone and antibody levels. The relationship between of lymph node metastasis and clinicopathologic features was analyzed. There were 227(51.2%) TgAb negative and 216(48.8%) TgAb positive DTC patients. Compared with patients without lymph node metastasis, DTC patients with lymph node metastasis had a higher proportion of patients with <55 years of age, maximum tumor diameter >1cm, calcification, BRAF V600E mutation, and TgAb positive. Multivariate regression logistic analysis showed that <55 years old (odds ratio (OR): 2.744, 95% CI: 1.665-4.522, P<0.001), maximum tumor diameter >1cm (OR: 2.163, 95% CI: 1.431-3.271, P<0.001), BRAF V600E mutation (OR: 2.489, 95% CI: 1.397-4.434, P=0.002), and TgAb positive (OR: 1.540, 95% CI: 1.020-2.326, P=0.040) were risk factors for lymph node metastasis. Maximum tumor diameter >1cm and BRAF V600E increased the risk by more than one fold for lymph node metastasis in TgAb-negative and TgAb-positive DTC patients. Younger age (<55 years old), maximum tumor diameter >1cm, BRAF V600E mutation, and TgAb positive were independent risk factors for lymph node metastasis in DTC. And maximum tumor diameter >1cm and BRAF V600E mutation were risk factors for lymph node metastasis both in TgAb positive and negative DTC patients.

Growth characteristics of early-stage (IA) lung adenocarcinoma and its value in predicting lymph node metastasis

BackgroundWe aim to compare the differences in growth characteristics between part-solid and solid lung adenocarcinoma, and to investigate the value of volume doubling time (VDT) or mass doubling time (MDT) in predicting lymph node (LN) metastasis and preoperative evaluation in patients of early-stage (IA) non-small cell lung cancer (NSCLC).MethodWe reviewed 8,653 cases of surgically resected stage IA lung adenocarcinoma between 2018 and 2022, with two follow-up visits at least 3 months apart, comparing diameter, volume, and mass growth of pSN and SN. VDT and MDT calculations for nodules with a volume change of at least 25%. Univariable or multivariable analysis was used to identify the risk factors. The area under the curve (AUC) for the receiver operating characteristic (ROC) curves was used to evaluate the diagnostic value.ResultsA total of 144 patients were included 114 with solid nodules (SN) and 25 with part-solid nodules (pSN). During the follow-up period, the mean VDTt and MDTt of SN were shorter than those of pSN, 337 vs. 541 days (p = 0.005), 298 vs. 458 days (p = 0.018), respectively. Without considering the ground-glass component, the mean VDTc and MDTc of SN were shorter than the solid component of pSN, 337 vs. 498 days (p = 0.004) and 298 vs. 453 days (p = 0.003), respectively. 27 nodules were clinically and pathologically diagnosed as N1/N2. Logistic regression identified initial diameter (p < 0.001), consolidation increase (p = 0.019), volume increase (p = 0.020), mass increase (p = 0.021), VDTt (p = 0.002), and MDTt (p = 0.004) were independent factors for LN metastasis. The ROC curves showed that the AUC for VDTt was 0.860 (95% CI, 0.778–0.943; p < 0.001) and for MDTt was 0.848 (95% CI, 0.759–0.936; p < 0.001).ConclusionsOur study showed significant differences in the growth characteristics of pSN and SN, and the application of VDT and MDT could be a valid predictor LN metastasis in patients with early-stage NSCLC.

Prediction of lymph node metastasis of lung squamous cell carcinoma by machine learning algorithm classifiers.

Lymph node metastasis (LNM) is an essential factor affecting the prognosis of patients with lung squamous cell carcinoma (LUSC), as well as a critical consideration for the choice of treatment strategy. Exploring effective methods for predicting LNM in LUSC may benefit clinical decision making. We used data collected from the Surveillance, Epidemiology, and End Results (SEER) database to develop machine learning algorithm classifiers, including boosted trees (BTs), based on the primary clinical parameters of patients to predict LNM in LUSC. Training on a large-sample training cohort (n = 8,063) allowed for the construction of several concise classifiers for LNM prediction in LUSC, which were then validated using test and in-house cohorts (n = 2,017 and 57, respectively). The six classifiers established in this research enabled distinction between patients with and without LNM. Among these classifiers, the BT classifier was the top performer, with accuracy, F1 scores, precision, recall, sensitivity, and specificity values of 0.654, 0.621, 0.654, 0.592, 0.592, and 0.711, respectively. The precision recall (PR) and receiver operating characteristic (ROC) (with area under the curve = 0.714) curves also supported this result, which was validated by the in-house cohort. Notably, the tumor stage was a critical factor in determining LNM in patients with LUSC. The use of classifiers, especially the BT classifier, may serve as a useful tool for improving clinical precision and individualized treatment of patients with LUSC.

Prediction of central lymph node metastasis in patients with papillary thyroid microcarcinoma by gradient-boosting decision tree model based on ultrasound radiomics and clinical features.

In recent years, the study of radiomics in thyroid diseases has developed rapidly. This study aimed to establish a preoperative radiomics prediction model for central compartment lymph node metastases (CLNMs) in papillary thyroid microcarcinoma (PTMC) patients using gradient-boosting decision tree (GBDT) model and evaluate the performance of the model. A total of 274 patients with PTMC admitted for thyroid surgery at China-Japan Union Hospital of Jilin University from January 2020 to July 2022 were retrospectively analyzed. Patients were randomized into training and validation cohorts according to a ratio of 8:2. Radiomics features were extracted from the ultrasound (US) images of PTMC lesions. The open-source software Pyradiomics was used to extract radiomics features, and WEKA software was used to select CLNM-related radiomics features. Clinical risk factors for CLNM were screened by statistical methods. The GBDT model was constructed by combining radiomics features and clinical risk factors, and compared with the diagnostic efficacy of US-reported cervical lymph node status. Shapley Additive exPlanations (SHAP) was applied to visualize and analyze the GBDT model globally and locally. A total of seven radiomics features were significantly correlated with central lymph node status in the training and validation cohorts. The predictors in the GBDT model included the radiomics features, sex, age, and body mass index (BMI). The area under the curve (AUC) values of the GBDT model in the training and validation cohorts were 0.946 [95% confidence interval (CI): 0.920-0.972] and 0.845 (95% CI: 0.714-0.976), respectively, compared with 0.583 (95% CI: 0.508-0.659) and 0.582 (95% CI: 0.430-0.736) for US-reported lymph node status alone. The Delong test showed a significant difference between AUS in the training and validation cohorts (P<0.001, respectively). SHAP visual analysis showed the effect of each parameter on the GBDT model globally and locally. Decision curve analysis demonstrated the clinical utility of the GBDT model. The prediction of CLNM by the GBDT model, based on US radiomics features and clinical factors, can be better than that by using US alone in patients with PTMC. Furthermore, the GBDT model may serve a guidance of clinical decision for patient's treatment strategy.

Deep learning-based clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in patients with rectal cancer: a two-center study.

Precise preoperative evaluation of lymph node metastasis (LNM) is crucial for ensuring effective treatment for rectal cancer (RC). This research aims to develop a clinical-radiomics nomogram based on deep learning techniques, preoperative magnetic resonance imaging (MRI) and clinical characteristics, enabling the accurate prediction of LNM in RC. Between January 2017 and May 2023, a total of 519 rectal cancer cases confirmed by pathological examination were retrospectively recruited from two tertiary hospitals. A total of 253 consecutive individuals were selected from Center I to create an automated MRI segmentation technique utilizing deep learning algorithms. The performance of the model was evaluated using the dice similarity coefficient (DSC), the 95th percentile Hausdorff distance (HD95), and the average surface distance (ASD). Subsequently, two external validation cohorts were established: one comprising 178 patients from center I (EVC1) and another consisting of 88 patients from center II (EVC2). The automatic segmentation provided radiomics features, which were then used to create a Radscore. A predictive nomogram integrating the Radscore and clinical parameters was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were employed to evaluate the discrimination capabilities of the Radscore, nomogram, and subjective evaluation model, respectively. The mean DSC, HD95 and ASD were 0.857 ± 0.041, 2.186 ± 0.956, and 0.562 ± 0.194 mm, respectively. The nomogram, which incorporates MR T-stage, CEA, CA19-9, and Radscore, exhibited a higher area under the ROC curve (AUC) compared to the Radscore and subjective evaluation in the training set (0.921 vs. 0.903 vs. 0.662). Similarly, in both external validation sets, the nomogram demonstrated a higher AUC than the Radscore and subjective evaluation (0.908 vs. 0.735 vs. 0.640, and 0.884 vs. 0.802 vs. 0.734). The application of the deep learning method enables efficient automatic segmentation. The clinical-radiomics nomogram, utilizing preoperative MRI and automatic segmentation, proves to be an accurate method for assessing LNM in RC. This approach has the potential to enhance clinical decision-making and improve patient care. Research registry, identifier 9158, https://www.researchregistry.com/browse-the-registry#home/registrationdetails/648e813efffa4e0028022796/.

Ultrasound and demographic predictors of papillary thyroid cancer local metastasis at the preoperative stage: a predictive model

Background. Due to the low sensitivity and specificity of neck ultrasound in the evaluation of the central lymph nodes, it is necessary to find alternative ways to predict central lymph node metastases in patients with papillary thyroid cancer (PTC). The purpose of the study is to develop a prognostic model for assessing the risk of local metastasis of papillary thyroid cancer based on preoperative ultrasound and demographic predictors. Materials and methods. A retrospective monocenter cohort study was conducted. The data of 401 patients who underwent surgery for PTC were processed. The main group included 179 patients in whom central lymph node metastases were detected during histopathological examination. The comparison group consisted of 222 patients without metastases according to the results of histopathological examination. When conducting the analysis, the following signs were considered as risk factors: subcapsular location of the tumor; size; blurred boundaries; the presence of calcification areas (microcalcifications); age of a patient. Inclusion criteria: PTC that was cytologically confirmed at the preoperative stage; surgeries (thyroidectomy/hemithyroidectomy and central neck lymph node dissection). Exclusion criteria: history of neck surgery; multifocal lesions of the thyroid gland. Results. The following logistic equation was obtained using the StatPlus program: y = –1.839 – 0.037 × X1 + 0.097 × X2 + 1.123 × X3 + 1.198 × X4 + + 0.692 × X5, where X1, X2, X3, X4, X5 are age (years), size (mm), subcapsular location (presence), blurred boundaries (presence), calcifications (presence), respectively. The obtained prognostic model provided the following operational characteristics when specifying the threshold value for P, which is equal to 0.44 (the decisive rule X &gt; T): sensitivity — 75.4 %, specificity — 79.3 %, diagnostic efficiency — 75.1 %. To assess the quality of the model, the receiver operator characteristic (ROC) analysis was performed. The following data were obtained: area under the ROC curve = 0.797 (95% confidence interval: 0.753–0.841), which corresponds to the good quality of the model, and Youden index J = 0.5155. Checking the quality of the model on a control group of 100 people gave the following results: sensitivity — 72.2 %, specificity — 76.1 %, diagnostic efficiency — 75.8 %. Conclusions. The application of a prognostic model based on ultrasound data and the patient’s age makes it possible to predict the presence of local metastases of the PTC at the preoperative stage with a diagnostic efficiency of 75.8 %. No prognostic model gives 100% prediction accuracy. When choosing the treatment, additional influencing factors (history of radiation exposure, burdened family history, etc.) for a specific patient should be considered.

Radiomics and Clinicopathological Characteristics for Predicting Lymph Node Metastasis in Testicular Cancer

Accurate prediction of lymph node metastasis (LNM) in patients with testicular cancer is highly relevant for treatment decision-making and prognostic evaluation. Our study aimed to develop and validate clinical radiomics models for individual preoperative prediction of LNM in patients with testicular cancer. We enrolled 91 patients with clinicopathologically confirmed early-stage testicular cancer, with disease confined to the testes. We included five significant clinical risk factors (age, preoperative serum tumour markers AFP and B-HCG, histotype and BMI) to build the clinical model. After segmenting 273 retroperitoneal lymph nodes, we then combined the clinical risk factors and lymph node radiomics features to establish combined predictive models using Random Forest (RF), Light Gradient Boosting Machine (LGBM), Support Vector Machine Classifier (SVC), and K-Nearest Neighbours (KNN). Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Finally, the decision curve analysis (DCA) was used to evaluate the clinical usefulness. The Random Forest combined clinical lymph node radiomics model with the highest AUC of 0.95 (±0.03 SD; 95% CI) was considered the candidate model with decision curve analysis, demonstrating its usefulness for preoperative prediction in the clinical setting. Our study has identified reliable and predictive machine learning techniques for predicting lymph node metastasis in early-stage testicular cancer. Identifying the most effective machine learning approaches for predictive analysis based on radiomics integrating clinical risk factors can expand the applicability of radiomics in precision oncology and cancer treatment.