Prediction Of Lymph Node Metastasis Research Articles

Evaluation of the diagnostic value of Sentinel Lymph Node in patients with gastric adenocarcinoma.

Sentinel lymph node biopsy (SLNB) has been proven as a safe and efficient procedure in some cancers like breast cancer and melanoma with a reduction of complications and side effects of unnecessary lymphadenectomy in many patients. However, the diagnostic value of SLNB in gastric cancer is a point of debate. This study evaluated the diagnostic value of SLNB using radiotracer and isosulphan blue dye injection in patients with Gastric Adenocarcinomas (GA). This descriptive study was performed at Imam-Reza HOSPITAL on 39 patients diagnosed with GA with no lymphatic metastasis using two methods: the combination of radionuclide with isosulphan together (R&I) method compared with the isosulphan alone method. Lymphatic dissection was performed in all patients. The pathological results were compared between the sentinel lymph nodes (SLN) and other lymph nodes and their accordance rate was calculated. In the T1 group, the sentinel lymph node biopsy detection rate was 100% for the combination of the R&I method and 60% for the isosulphan method and the false negative rate was zero. These values respectively were 88.8% and 88.8% in the T2 group with a false negative rate of 75%. In the T3 group, the values were 100% for the combination of the R&I method and 93.7% for the isosulphan method with a false negative rate of 40%. In the combination of the R&I method, the sensitivity, specificity, and positive and negative predictive values were 57.9, 100, 100, and 69.2 percent respectively. Based on the false negative rate (47.4%), SLNB by injection of isosulphan blue dye alone is not a diagnostic enough value for predicting lymph node metastasis in GA. Although, SLNB by combination of the R&I had better accuracy compared to the isosulphan alone, more studies with larger samples are needed to prove this result.

Immunohistochemical staining of versican as a potential marker for predicting lymph node metastasis in gastric cancer

Gastric cancer is one of the most common cancers and has a high mortality rate. Lymph node metastasis is a key determinant of prognosis, and an essential mechanism involved in metastasis is the epithelial-mesenchymal transition. In this study, we aimed to assess the diagnostic role of versican (VCAN), a molecule participating in the epithelial-mesenchymal transition, on the detection of metastatic cancer. The expression of VCAN was evaluated using immunohistochemistry, and its biological activity was examined using gastric cancer cell lines. In patients with lymph node metastasis, VCAN expression was more prominent at primary tumor sites. In addition, VCAN was found to promote cell migration in vitro, thus potentially facilitating the distribution of metastases. Overall, increased expression of VCAN at the primary site may signify the development of metastases in lymph nodes because this protein is recognized as contributing to the migration of cancer cells into lymph nodes.

A metabolic-inflammatory-nutritional score (MINS) is associated with lymph node metastasis and prognostic stratification for endometrial cancer patients.

Objective: This study aims to propose a personalized cancer prediction model based on the metabolic-inflammatory-nutritional score (MINS) for predicting lymph node metastasis (LNM) in endometrial cancer (EC) and validated prediction of survival probability in patients with a family history of Lynch syndrome-associated cancers (LSAC). Methods: A total of 676 patients diagnosed with EC were enrolled in this study. We calculated the optimal cutoff value using restricted cubic splines (RCS) analysis or the mean value. Our feature selection process for constructing the MINS involved using the LASSO regression model. MINS were evaluated for LNM using logistic regression analysis. To assess the prognostic value of the MINS, we generated survival curves using the Kaplan-Meier method with a log-rank test. Furthermore, we constructed a nomogram to validate the prognostic significance of the MINS. The predictive accuracy of nomogram was evaluated using the concordance index (C-index) and calibration plot. Results: LNM risk was associated with family history of LSAC and MINS group (all adjusted p<0.05). Patients in the high-risk MINS group or patients with a family history of LSAC exhibited poorer overall survival (p=0.038, p=0.001, respectively). Additionally, a nomogram was demonstrated effective predictive performance with a C-index of 0.778 (95% CI: 0.725-0.832). Conclusion: Preoperative MINS has been determined to be associated with the risk of LNM in EC patients. Utilizing MINS as a basis, the development of a prognostic nomogram holds promise as an effective tool for risk stratification in clinical settings among EC patients with a family history of LSAC.

Predictive models for lymph node metastasis in endometrial cancer: A systematic review and bibliometric analysis.

Lymph node metastasis is associated with a poorer prognosis in endometrial cancer. The objective was to synthesize and critically appraise existing predictive models for lymph node metastasis risk stratification in endometrial cancer. This study is a systematic review. We searched the Web of Science for articles reporting models predicting lymph node metastasis in endometrial cancer, with a systematic review and bibliometric analysis conducted based upon which. Risk of bias was assessed by the Prediction model Risk Of BiAS assessment Tool (PROBAST). A total of 64 articles were included in the systematic review, published between 2010 and 2023. The most common articles were "development only." Traditional clinicopathological parameters remained the mainstream in models, for example, serum tumor marker, myometrial invasion and tumor grade. Also, models based upon gene-signatures, radiomics and digital histopathological images exhibited an acceptable self-reported performance. The most frequently validated models were the Mayo criteria, which reached a negative predictive value of 97.1%-98.2%. Substantial variability and inconsistency were observed through PROBAST, indicating significant between-study heterogeneity. A further bibliometric analysis revealed a relatively weak link between authors and organizations on models predicting lymph node metastasis in endometrial cancer. A number of predictive models for lymph node metastasis in endometrial cancer have been developed. Although some exhibited promising performance as they demonstrated adequate to good discrimination, few models can currently be recommended for clinical practice due to lack of independent validation, high risk of bias and low consistency in measured predictors. Collaborations between authors, organizations and countries were weak. Model updating, external validation and collaborative research are urgently needed. None.

Prevalence of Tumor Budding and its Significance for Predicting Lymph Node Metastasis in Carcinomas at Tertiary Care Center

Background: “Tumor budding” basically means the presence of clusters of less than 5 malignant cells in the tumor stroma, located ahead of the invasive front of tumor. Various studies have been carried out regarding tumor budding, its significance in tumor metastasis involving head and neck, breast, gastrointestinal tract, pancreas and cervix and is now evolving in cancer reporting templates. We conducted this study in regards to growing importance of tumor budding in cancer metastasis with objectives to find out the prevalence of tumor budding in carcinoma and to know the association of tumor budding with lymph node, perineural and lymphovascular invasion. Methods: All the resected specimen with regional lymph node were examined for presence or absence of tumor buds. It was graded as high/ low grade based on the number of buds. Association of the tumor buds with lymph node metastasis, lymphovascular and perineural invasion was calculated. The study was conducted from January 2019 to January 2020. Purposive sampling was used. Ethical clearance was ensured from the institutional review board before the start of study (IRC 1322/ 018). All surgically resected specimen of carcinomas with regional lymph nodes were included in the study whereas autolysed specimen and patient who refused to give consent were excluded from the study. Result: Total 37 cases of resected specimens of gastrointestinal tract, breast, head and neck were evaluated. The prevalence of tumor budding was 91.9%. In every instance where tumor budding existed, there was an observation of high-grade tumor bud. Tumor budding was strongly associated with lymph node invasion (p = 0.005). Significant value was not observed in the correlation of tumor budding with lymphovascular and perineural invasion. Conclusion: Tumor budding holds an important role in predicting lymph node metastasis in carcinomas, highlighting its importance in the histopathological reports of resected specimen.

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.

An invited commentary on "preoperative ca19.9 level predicts lymph node metastasis in resectable adenocarcinoma of the head of the pancreas: a further plea for biological resectability criteria".

An invited commentary on "preoperative ca19.9 level predicts lymph node metastasis in resectable adenocarcinoma of the head of the pancreas: a further plea for biological resectability criteria".

Sphericity of lymph nodes using 3D-CT predicts metastasis in lung cancer patients

BackgroundThe presence of mediastinal lymph node metastasis is important because it is related to the treatment and prognosis of lung cancer. Although prevalently used, evaluation of lymph nodes is not always reliable. We introduced sphericity as a criterion for evaluating morphologic differences between metastatic and nonmetastatic nodes.MethodsWe reviewed the cases of 66 patients with N2 disease and of 68 patients with N0-1 disease who underwent lobectomy with mediastinal dissection between January 2012 and December 2021. The sphericity of the dissected station lymph nodes, which represents how close the node is to being a true sphere, was evaluated along with the diameter and volume. Each parameter was obtained and evaluated for ability to predict metastasis.ResultsMetastatic lymph nodes had a larger short-axis diameter (average: 8.2 mm vs. 5.4 mm, p < 0.001) and sphericity (average: 0.72 vs. 0.60, p < 0.001) than those of nonmetastatic lymph nodes. Short-axis diameter ≥ 6 mm and sphericity ≥ 0.60 identified metastasis with 76.2% sensitivity and 70.2% specificity (AUC = 0.78, p < 0.001) and 92.1% sensitivity and 53.9% specificity (AUC = 0.78, p < 0.001), respectively. For lymph nodes with a short-axis diameter ≥ 5 mm, sphericity ≥ 0.60 identified metastasis with 84.1% sensitivity and 89.3% specificity.ConclusionBy using 3D-CT analysis to examine sphericity, we showed that metastatic lymph nodes became spherical. Our method for predicting lymph node metastasis based on sphericity of lymph nodes with a short-axis diameter ≥ 5 mm could do so with higher sensitivity than the conventional method, and with acceptable specificity.

Exploring the significance of tumor volume in endometrial cancer: Clinical pathological features, prognosis, and adjuvant therapies.

To assist clinicians in formulating treatment strategies for endometrial cancer (EC), this retrospective study explores the relationship between tumor volume and clinical pathological features, as well as prognosis, in patients undergoing staging surgery. Preoperative pelvic MRI examinations were conducted on 234 histologically confirmed EC patients. The ITK-SNAP software was employed to manually delineate the region of interest in the MRI images and calculate the tumor volume (MRI-TV). The analysis focused on investigating the relationship between MRI-TV and the clinical pathological features and prognosis of EC patients. Larger MRI-TV was found to be associated with various adverse prognostic factors (G3, deep myometrial invasion, cervical stromal invasion, lymphovascular space invasion, lymph node metastasis, advanced international federation of gynecology and obstetrics staging, and receipt of adjuvant therapy). The receiver operating characteristic curve indicated that MRI-TV ≥ 8 cm3 predicted deep myometrial invasion, and MRI-TV ≥ 12 cm3 predicted lymph node metastasis. Penalized spline (P-spline) regression analysis identified 14 cm3 of MRI-TV as the optimal prognostic cutoff value. MRI-TV ≥ 14 cm3 was an independent prognostic factor for overall survival and disease-free survival. For patients with MRI-TV ≥ 14 cm3, the disease-free survival rate with adjuvant therapy was superior to that of the sole staging surgery group. This study demonstrates a significant correlation between MRI-TV and clinical pathological features and prognosis in EC. For patients with MRI-TV ≥ 14 cm3, staging surgery followed by adjuvant therapy was superior to sole staging surgery.

Amide proton transfer weighted combined with diffusion kurtosis imaging for predicting lymph node metastasis in cervical cancer

ObjectiveTo investigate the value of amide proton transfer weighted (APTw) combined with diffusion kurtosis imaging (DKI) in quantitative prediction of lymph node metastasis (LNM) in cervical carcinoma (CC). MethodsData of 19 LNM(+) and 50 LNM(−) patients with CC were retrospectively analyzed. 3.0 T MRI scan was performed before the operation, including APTw and DKI. After post-processing, quantitative magnetization transfer ratio asymmetric at 3.5 ppm [MTRasym (3.5 ppm)], mean kurtosis (MK), and mean diffusivity (MD) maps were obtained. The MTRasym(3.5 ppm), MK, and MD values were respectively measured by two observers, and intra-class correlation coefficients (ICC) were used to test the consistency of the results. The independent samples t-test or Mann-Whitney U test was used to compare the differences in the values of each parameter. The ROC curve was used to analyze the predictive performance of parameters with significant differences and their combination parameter. ResultsThe two observers had good agreement in the measurement of each data (ICC > 0.75). The MTRasym(3.5 ppm) and MK values of the LNM(+) group(3.260 ± 0.538% and 0.531 ± 0.202) were higher than those of the LNM(−) group(2.698 ± 0.597% and 0.401 ± 0.148) (P < 0.05), while there was no significant difference in MD values between the two groups(P > 0.05). The area under the curves (AUCs) of MTRasym(3.5 ppm), MK value, and MTRasym(3.5 ppm) + MK value were 0.763, 0.716, and 0.813, respectively, when predicting LNM status of CC. ConclusionAPTw and DKI can quantitatively predict LNM status of CC, which is of importance in clinical diagnosis and treatment.

Risk factors associated with lymph node metastasis in early-stage distal gastric cancer.

This study was designed to explore the risk factors of lymph node metastasis (LNM) in distal gastric cancer with early stage, and to provide reference for the choice of treatment protocols. In this retrospective observational study, 824 early distal gastric cancer (EDGC) cases who treated at our unit from 2010 to 2020 were selected as research objects. Subsequently, univariate and multivariate logistic regression analyses were conducted to investigate the associations between LNM and clinicopathological features. Of these 824 EDGC cases, 140 (17.0%) developed LNM, including 72 N1 stage and 68 N2-3 stage LNM. Multivariate logistic regression analysis identified the tumor diameter ≥1.75cm (odds ratio (OR)=2.361, p<0.001), tumor location (OR=1.552, p=0.046), histological classification (p=0.004), tumor infiltration depth (OR=2.154, p=0.001), and vascular infiltration (OR=4.354, p<0.001) as independent predictors for LNM. Logistic regression analyses based on 756 N0-1 LNM cases identified the smoking history (OR=0.507, p=0.043), tumor diameter ≥1.75cm (OR=2.265, p=0.010), tumor location (OR=1.834, p=0.036), histological classification (p=0.018), tumor infiltration depth (OR=1.939, p=0.034), and vascular infiltration (OR=3.225, p<0.001) as independent predictors for N1 LNM. Moreover, preoperative hypoalbuminemia (OR=7.087, p=0.015), significant preoperative weight loss (OR=2.724, p=0.023), tumor diameter ≥1.75cm (OR=5.484, p=0.001), multiple tumors (OR=9.986, p=0.038), histological classification (p=0.029), and vascular infiltration (OR=33.704, p<0.001) were proved to be associated with LNM for T1a tumors. The tumor diameter, location and infiltration depth, histological classification, and vascular infiltration were expected to be used as predictors of LNM in EDGC, and preoperative hypoalbuminemia, significant weight loss, tumor diameter and number, histological classification, and vascular infiltration were associated with LNM for T1a tumors.

A nomogram based on multimodal ultrasound and clinical features for the prediction of central lymph node metastasis in unifocal papillary thyroid carcinoma.

To build a predictive model for central lymph node metastasis (CLNM) in unifocal papillary thyroid carcinoma (UPTC) using a combination of clinical features and multimodal ultrasound (MUS). This retrospective study, included 390 UPTC patients who underwent MUS between January 2017 and October 2022 and were divided into a training cohort (n = 300) and a validation cohort (n = 90) based on a cut-off date of June 2022. Independent indicators for constructing the predictive nomogram models were identified using multivariate regression analysis. The diagnostic yield of the 3 predictive models was also assessed using the area under the receiver operating characteristic curve (AUC). Both clinical factors (age, diameter) and MUS findings (microcalcification, virtual touch imaging score, maximal value of virtual touch tissue imaging and quantification) were significantly associated with the presence of CLNM in the training cohort (all P < .05). A predictive model (MUS + Clin), incorporating both clinical and MUS characteristics, demonstrated favourable diagnostic accuracy in both the training cohort (AUC = 0.80) and the validation cohort (AUC = 0.77). The MUS + Clin model exhibited superior predictive performance in terms of AUCs over the other models (training cohort 0.80 vs 0.72, validation cohort 0.77 vs 0.65, P < .01). In the validation cohort, the MUS + Clin model exhibited higher sensitivity compared to the CLNM model for ultrasound diagnosis (81.2% vs 21.6%, P < .001), while maintaining comparable specificity to the Clin model alone (62.3% vs 47.2%, P = .06). The MUS + Clin model demonstrated good calibration and clinical utility across both cohorts. Our nomogram combining non-invasive features, including MUS and clinical characteristics, could be a reliable preoperative tool to predict CLNM treatment of UPTC. Our study established a nomogram based on MUS and clinical features for predicting CLNM in UPTC, facilitating informed preoperative clinical management and diagnosis.

Development and Validation of a Computed Tomography-Based Radiomics Nomogram for the Preoperative Prediction of Central Lymph Node Metastasis in Papillary Thyroid Microcarcinoma

This study aims to develop and validate a computed tomography (CT)-based radiomics nomogram for pre-operatively predicting central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC) and explore the underlying biological basis by using RNA sequencing data. This study trained 452 PTMC patients across two hospitals from January 2012 to December 2020. The sets were randomly divided into the training (n = 339), internal test (n = 86), external test (n = 27) sets. Radiomics features were extracted from primary lesion's pre-operative CT images for each patient. After screening for features, five algorithms such as K-nearest neighbor, logistics regression, linear-support vector machine (SVM), Gaussian SVM, and polynomial SVM were used to establish the radiomics models. The performance of these five algorithms was evaluated and compared directly to radiologist's interpretation (CT-reported lymph node status). The radiomics signature score (Rad-score) was generated using a linear combination of the selected features. By combining the clinical risk factors and Rad score, a radiomics nomogram was established and compared with Rad-score and clinical model. The performance of the nomogram was evaluated based on the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve analysis (DCA). The potential biological basis of nomogram was revealed by performing genetic analysis based on the RNA sequencing data. A total of 25 radiomic features were ultimately selected to train the machine learning models, and the five machine learning models outperformed the radiologists' interpretation by achieving area under the ROC curves (AUCs) ranging from 0.606 to 0.730 in the internal test set. By incorporating the Rad score and clinical risk factors (sex, age, tumor-diameter, and CT-reported lymph node status), this nomogram achieved AUCs of 0.800 and 0.803 in the internal and external test set, which were higher than that of the Rad-score and clinical model, respectively. Calibration curves and DCA also showed that the nomogram had good performance. As for the biological basis exploration, in patients predicted by nomogram to be PTC patients with CLMN, 109 genes were dysregulated, and some of them were associated with pathways and biological processes such as tumor angiogenesis. This radiomics nomogram successfully identified CLNM on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists and had the potential to be integrated into clinical decision making as a non-invasive pre-operative tool.

Pancreatic ductal adenocarcinoma staging: A narrative review of radiologic techniques and advances

Radiology plays an important role in the initial diagnosis and staging of patients with pancreatic ductal adenocarcinoma (PDAC). CT is the preferred modality over MRI, due to wider availability, greater consistency in image quality, and lower cost. MRI and PET/CT are usually reserved as problem-solving tools in select patients. The National Comprehensive Cancer Network (NCCN) guidelines define resectability criteria based on tumor involvement of the arteries and veins, and triage patients into resectable, borderline resectable, locally advanced, and metastatic categories. Patients with resectable disease are eligible for upfront surgical resection, while patients with high-stage disease are treated with neoadjuvant chemotherapy and/or radiation therapy with hopes of downstaging the disease. The accuracy of staging critically depends on imaging technique and the experience of the radiologists. Several challenges in accurate preoperative staging include prediction of lymph node metastases, detection of subtle liver and peritoneal metastases, and disease restaging following neoadjuvant therapy. Artificial intelligence (AI) has the potential to function as “second readers” to improve upon the radiologists’ detection of small early-stage tumors, which can shift more patients toward surgical resection of potentially curable cancer. AI may also provide imaging biomarkers that can predict disease recurrence and patient survival after pancreatic resection and assist in selection of patients most likely to benefit from surgery thus improving patient outcomes.

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.

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/.

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.

Lymph node metastasis-related lncRNA GAS6-AS1 facilitates the progression of esophageal squamous cell carcinoma.

Lymph node metastasis is the main type of metastasis in esophageal squamous cell carcinoma (ESCC), especially when the primary tumor invasion depth reaches above the adventitia layer (T3 stage), the incidence of lymph node metastasis increases sharply. Abnormal expression of long non-coding RNAs (lncRNAs) has been confirmed in ESCC, but there are still many unknown connections between lncRNAs and lymph node metastasis. We used transcriptome sequencing (RNA-seq) to analyze 10 pairs of ESCC tissues with primary tumor stage T3 and their paired normal epithelium. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to further verify the sequencing results, and survival curve analysis, logistic regression analysis, and receiver operating characteristic (ROC) curve analysis were used to investigate its clinical application value. We investigated the growth and metastasis effects of lncRNA GAS6-AS1 on ESCC cell lines TE-1 and KYSE410 in vitro and in vivo. Other functional experiments included cell apoptosis and cell cycle experiments. Based on our RNA-seq data, lncRNA GAS6-AS1 is highly expressed in ESCC tissues, especially in cancer tissues with lymph node metastasis. The qRT-PCR experiment analysis showed that high expression of GAS6-AS1 was related to poor tumor differentiation and tumor stage. Logistic regression analysis showed that it was an independent risk factor for lymph node metastasis, and ROC analysis validated that it could predict lymph node metastasis. Further survival analysis suggested that high expression of GAS6-AS1 was associated with patients' poor prognosis. In vitro experiments, knocking down GAS6-AS1 inhibited the growth and metastasis of ESCC cells and inhibited tumor growth in vivo. In addition, knocking down GAS6-AS1 can inhibit cell cycle and promote cell apoptosis. Our results revealed that lncRNA GAS6-AS1 obtained from RNA-seq can be used as an independent risk factor for ESCC lymph node metastasis and an effective biomarker to predict, and that it was related to the growth and metastasis of ESCC. It may represent a new biomarker to aid in the assessment of the lymph node metastasis of ESCC.

A model for predicting lymph node metastasis of thyroid carcinoma: a multimodality convolutional neural network study.

Early preoperative evaluation of cervical lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) is critical for further surgical treatment. However, insufficient accuracy in predicting LNM status for PTC based on ultrasound images is a problem that needs to be urgently resolved. This study aimed to clarify the role of convolutional neural networks (CNNs) in predicting LNM for PTC based on multimodality ultrasound. In this study, the data of 308 patients who were clinically diagnosed with PTC and had confirmed LNM status via postoperative pathology at Beijing Tiantan Hospital, Capital Medical University, from August 2018 to April 2022 were incorporated into CNN algorithm development and evaluation. Of these patients, 80% were randomly included into the training set and 20% into the test set. The ultrasound examination of cervical LNM was performed to assess possible metastasis. Residual network 50 (Resnet50) was employed for feature extraction from the B-mode and contrast-enhanced ultrasound (CEUS) images. For each case, all of features were extracted from B-mode ultrasound images and CEUS images separately, and the ultrasound examination data of cervical LNM information were concatenated together to produce a final multimodality LNM prediction. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the predictive model. Heatmaps were further developed for visualizing the attention region of the images of the best-working model. Of the 308 patients with PTC included in the analysis, 158 (51.3%) were diagnosed as LNM and 150 (48.7%) as non-LNM. In the test set, when a triple-modality method (i.e., B-mode image, CEUS image, and ultrasound examination of cervical LNM) was used, accuracy was maximized at 80.65% (AUC =0.831; sensitivity =80.65%; specificity =82.26%), which showed an expected increased performance over B-mode alone (accuracy =69.00%; AUC =0.720; sensitivity =70.00%; specificity =73.00%) and a dual-modality method (B-mode image plus CEUS image: accuracy =75.81%; AUC =0.742; sensitivity =74.19%; specificity =77.42%). The heatmaps of our triple-modality model demonstrated a possible focus area and revealed the model's flaws. The PTC lymph node prediction model based on the triple-modality features significantly outperformed all the other feature configurations. This deep learning model mimics the workflow of a human expert and leverages multimodal data from patients with PTC, thus further supporting clinical decision-making.

Machine-learning methods based on the texture and non-texture features of MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer.

The establishment of an accurate, stable, and non-invasive prediction model of sentinel lymph node (SLN) metastasis in breast cancer is difficult nowadays. The aim of this work is to identify the optimal machine learning model based on the three-dimensional (3D) image features of magnetic resonance imaging (MRI) for the preoperative prediction of SLN metastasis in breast cancer patients. A total of 172 patients with histologically proven breast cancer were enrolled retrospectively, including 74 SLN metastasis patients and 98 non-SLN metastasis patients. All of them underwent diffusion-weighted imaging (DWI) magnetic resonance imaging (MRI) scan. Firstly, a total of 10,320 texture and four non-texture features were extracted from the region of interests (ROIs) of image. Twenty-four feature selection methods and 11 classification methods were then evaluated by using 10-fold cross-validation to identify the optimal machine learning model in terms of the mean area under the curve (AUC), accuracy (ACC), and stability. The result showed that the model based on the combination of minimum redundancy maximum relevance (MRMR) + random forest (RF) exhibited the optimal predictive performance (AUC: 0.97±0.03; ACC: 0.89±0.05; stability: 2.94). Moreover, we independently investigated the performance of feature selection methods and classification methods, and observed that L1-support vector machine (L1-SVM) (AUC: 0.80±0.08; ACC: 0.76±0.07) and sequential forward floating selection (SFFS) (stability: 3.04) presented the best average predictive performance and stability among all feature selection methods, respectively. RF (AUC: 0.85±0.11; ACC: 0.80±0.09) and SVM (stability: 8.43) showed the best average predictive performance and stability among all classification methods, respectively. The identified model based on the 3D image features of MRI provides a non-invasive way for the preoperative prediction of SLN metastasis in breast cancer patients.