Prediction Of Lymph Node Metastasis Research Articles

Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning

ObjectiveTo develop and validate an interpretable machine learning (ML) model for the preoperative prediction of central lymph node metastasis (CLNM) in papillary thyroid microcarcinoma (PTMC).MethodsFrom December 2016 to December 2023, we retrospectively analyzed 710 PTMC patients who underwent thyroidectomies. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) regression method, alongside the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm in conjunction with multivariate logistic regression. Eight ML algorithms, namely Decision Tree, Random Forest (RF), K-nearest neighbors, Support vector machine, Extreme Gradient Boosting, Naive Bayes, Logistic regression, and Light Gradient Boosting machine, were developed for the prediction of CLNM. The performance of these models was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 scores. Additionally, the Shapley Additive Explanation (SHAP) algorithm was utilized to clarify the results of the optimal ML model.ResultsThe results indicated that 32.95% of the patients (234/710) presented with CLNM. Tumor diameter, multifocality, lymph nodes identified via ultrasound (US-LN), and extrathyroidal extension (ETE) were identified as independent predictors of CLNM. The RF model achieved the highest performance in the validation set with an AUC of 0.893(95%CI: 0.846-0.940), accuracy of 0.832, sensitivity of 0.764, specificity of 0.866, PPV of 0.743, NPV of 0.879, and F1-score of 0.753. Furthermore, the DCA demonstrated that the RF model exhibited a superior clinical net benefit.ConclusionOur model predicted the risk of CLNM in PTMC patients with high accuracy preoperatively.

External validation of the eCura system and comparison with the W-eCura score for predicting lymph node metastasis after non-curative endoscopic submucosal dissection for early gastric cancer: a multicenter retrospective cohort study.

The eCura system is a widely used risk-scoring model for predicting lymph node metastasis (LNM) after non-curative endoscopic submucosal dissection (ESD) for early gastric cancer (EGC), but its external validation is limited. Recently, the W-eCura score, a modified version, was proposed. We aimed to validate the eCura system and compare its discriminatory performance with the W-eCura score. A multicenter retrospective study was conducted using data from 19 Japanese institutions. The patients who underwent ESD for EGC followed by gastrectomy with lymph node dissection were included. The predictive performance of the eCura system, including calibration and discrimination, was evaluated and its discrimination was compared with the W-eCura score. Among 901 eligible patients, 65 cases (7.2%) showed LNM. The eCura system demonstrated good calibration, with a calibration-in-the-large of -0.008 (95% confidence interval [CI] -0.024-0.010), an observed-to-expected ratio of 0.905 (95% CI 0.707-1.121), and a calibration slope of 0.975 (95% CI 0.692-1.257). Discrimination was also good, with a C-statistic of 0.741 (95% CI 0.676-0.806). In patients evaluable for both systems, the C-statistics for the eCura system and W-eCura score were 0.745 (95% CI 0.675-0.816) and 0.750 (95% CI 0.684-0.817), respectively, showing no significant difference (P=0.547). The eCura system was validated as a reliable tool for predicting LNM following ESD in real-world clinical settings.

Multimodal AI model for preoperative prediction of axillary lymph node metastasis in breast cancer using whole slide images

In breast cancer management, predicting axillary lymph node (ALN) metastasis using whole-slide images (WSIs) of primary tumor biopsies is a challenging and underexplored task for pathologists. We developed METACANS, an multimodal artificial intelligence (AI) model that integrates WSIs with clinicopathological features to predict ALN metastasis. METACANS was trained on 1991 cases and externally validated across five cohorts with a total of 2166 cases. Across all validation cohorts, METACANS achieved an area under the curve (AUC) of 0.733 (95% CI, 0.711–0.755), with an overall negative predictive value of 0.846, sensitivity of 0.820, specificity of 0.504, and balanced accuracy of 0.662. Without additional annotations, METACANS identified pathological imaging patterns linked to metastatic status, such as micropapillary growth, infiltrative patterns, and necrosis. While its predictive performance may not yet support immediate clinical application, METACANS addresses the task of predicting ALN metastasis using WSIs and clinicopathological features, and demonstrates the feasibility of multimodal AI approaches for preoperative axillary staging in breast cancer.

Stacking classifiers based on integrated machine learning model: fusion of CT radiomics and clinical biomarkers to predict lymph node metastasis in locally advanced gastric cancer patients after neoadjuvant chemotherapy

BackgroundThe early prediction of lymph node positivity (LN+) after neoadjuvant chemotherapy (NAC) is crucial for optimizing individualized treatment strategies. This study aimed to integrate radiomic features and clinical biomarkers through machine learning (ML) approaches to enhance prediction accuracy by focusing on patients with locally advanced gastric cancer (LAGC).MethodsWe retrospectively enrolled 277 patients with LAGC and randomly divided them into training (n = 193) and validation (n = 84) sets at a 7:3 ratio. In total, 1,130 radiomics features were extracted from pre-treatment portal venous phase computed tomography scans. These features were linearly combined to develop a radiomics score (rad score) through feature engineering. Then, using the rad score and clinical biomarkers as input features, we applied simple statistical strategies (relying on a single ML model) and integrated statistical strategies (including classification model integration techniques, such as hard voting, soft voting, and stacking) to predict LN+ post-NAC. The diagnostic performance of the model was assessed using receiver operating characteristic curves with corresponding areas under the curve (AUC).ResultsOf all ML models, the stacking classifier, an integrated statistical strategy, exhibited the best performance, achieving an AUC of 0.859 for predicting LN+ in patients with LAGC. This predictive model was transformed into a publicly available online risk calculator.ConclusionsWe developed a stacking classifier that integrates radiomics and clinical biomarkers to predict LN+ in patients with LAGC undergoing surgical resection, providing personalized treatment insights.

A Nomogram Based on Fibrinogen-to-Neutrophil Ratio as a Novel Predictor of Lymph Node Metastasis in Patients with Cervical Cancer and Type 2 Diabetes Mellitus

A Nomogram Based on Fibrinogen-to-Neutrophil Ratio as a Novel Predictor of Lymph Node Metastasis in Patients with Cervical Cancer and Type 2 Diabetes Mellitus

Machine learning method based on enhanced CT to predict lymph node metastasis of rectal cancer

Machine learning method based on enhanced CT to predict lymph node metastasis of rectal cancer

Multi-task Deep Learning Based on Longitudinal CT Images Facilitates Prediction of Lymph Node Metastasis and Survival in Chemotherapy-Treated Gastric Cancer.

Accurate preoperative assessment of lymph node metastasis (LNM) and overall survival (OS) status is essential for patients with locally advanced gastric cancer (LAGC) receiving neoadjuvant chemotherapy (NAC), providing timely guidance for clinical decision-making. However, current approaches to evaluate LNM and OS have limited accuracy. In this study, we used longitudinal CT images from 1,021 LAGC patients to develop and validate a multi-task deep learning model named co-attention tri-oriented spatial Mamba (CTSMamba) to simultaneously predict LNM and OS. CTSMamba was trained and validated on 398 patients, and the performance was further validated on 623 patients at two additional centers. Notably, CTSMamba exhibited significantly more robust performance than a clinical model in predicting LNM across all of the cohorts. Additionally, integrating CTSMamba survival scores with clinical predictors further improved personalized OS prediction. These results support the potential of CTSMamba to accurately predict LNM and OS from longitudinal images, potentially providing clinicians with a tool to inform individualized treatment approaches and optimized prognostic strategies.

A nomogram model for predicting lymph node metastasis of rectal cancer by combining preoperative magnetic resonance imaging signs and tumour markers.

This study aimed to explore the diagnostic value of high-resolution magnetic resonance images and tumour markers in predicting lymph node metastasis of rectal cancer. The clinical, imaging, and pathological data of patients with suspected rectal cancer were collected. The baseline data, and surgical and pathological characteristics were compared between the lymph node metastasis group and no metastasis group. Univariate and multivariate logistic regression were used to analyse the clinical and pathological factors, and preoperative magnetic resonance imaging (MRI) signs of extramural vascular invasion and rectal cancer lymph node metastasis. A nomogram model was established with statistically significant factors. 150 patients were included. Among them, 50 (33.3%) presented with vascular tumour thrombus, and 72 (48.0%) had lymph node metastasis. The detection of regional lymph nodes (DWI-LN) was an independent risk factor for lymph node metastasis. The area under curve of the nomogram model was 0.804. Preoperative serum CA19.9, and the relationship between tumour and peritoneal reflection in preoperative MRI and DWI-LN have clinical value in predicting lymph node metastasis in patients with rectal cancer.

Habitat radiomics assists radiologists in accurately diagnosing lymph node metastasis of adenocarcinoma of the esophagogastric junction

ObjectivesThis study aimed to develop a habitat radiomics (HR) model capable of preoperatively predicting lymph node metastasis (LNM) in adenocarcinoma of the esophagogastric junction (AEG) and to implement its use in clinical practice.MethodsIn this retrospective analysis, 337 patients from three centers were enrolled and divided into three cohorts: training, validation, and test (208, 52, and 77 patients, respectively). We constructed HR models, conventional radiomics models, and combined models to identify LNM in AEG. The area under the curve (AUC) was employed to identify the optimal model, which was then evaluated for assisting radiologists in the empirical and RADS groups in diagnosing LNM. Finally, the prediction process of the optimal model was visualized using SHAP plots.ResultsThe HR model demonstrated superior performance, achieving the highest AUC values of 0.876, 0.869, and 0.795 in the training, validation, and test cohorts, respectively. Regardless of seniority, the empirical group of radiologists showed a significant improvement in the AUC and accuracy when using the HR model, compared to working alone (p < 0.05). Furthermore, the RADS group radiologists exhibited strong reclassification ability, effectively reevaluating patients with false-negative LN initially classified as Node-RADS score 1 or 2 by themselves.ConclusionThe HR model facilitates the accurate prediction of LNM in AEG and holds potential as a valuable tool to augment radiologists’ diagnostic capabilities in daily clinical practice.Critical relevance statementThe habitat radiomics model could accurately predict the lymph node status of adenocarcinoma in the esophagogastric junction and assist radiologists in improving diagnostic efficacy, which lays the foundation for accurate staging and effective treatment.Key PointsAccurate lymph node diagnosis in esophagogastric junction adenocarcinoma is beneficial for prognosis.Habitat radiomics model accurately predicted and assisted physicians in diagnosing lymph nodes.The habitat model effectively reclassified false-negative lymph nodes at Node-RADS 1 and 2.Graphical

Abstract 3346: CD44 is an independent predictor of lymph node metastasis in differentiated thyroid cancer

Abstract Background: Differentiated Thyroid Carcinoma (DTC), a common endocrine malignancy, has witnessed a significant rise in global incidence over the past few decades. Despite its indolent nature and favorable prognosis, the clinical course of DTC varies extensively, with lymph node metastasis being an important risk factor in prognosis. CD44 has been shown to be associated with prognosis in various cancers. However, its role in DTC is not well explored. Methods: A total of 1703 DTC cases of Middle Eastern ethnicity were included in the study. CD44 expression was evaluated by immunohistochemistry using tissue microarray to analyze the clinico-pathological associations. Univariate and multivariate logistic regression analysis was used to determine the predictors of lymph node metastasis. Results: CD44 over-expression was noted in 69.7% (1187/1703) of DTC cases and was significantly associated with younger age (&amp;lt; 55 years; p &amp;lt; 0.0001), multifocality (p &amp;lt; 0.0001), lymphovascular invasion (p = 0.0007) and lymph node metastasis (p &amp;lt; 0.0001). Interestingly, on multivariate logistic regression analysis, CD44 was found to be an independent predictor of lymph node metastasis (Hazard ratio = 1.79; 95% confidence interval = 1.40 - 2.30; p &amp;lt; 0.0001). Conclusion: Our study showed that CD44 expression was an independent predictive biomarker of lymph node metastasis in Middle Eastern patients with DTC. Detection of CD44 expression could help identify patients at risk for developing lymph node metastasis and help in individualization of clinical management for DTC patients. Citation Format: Syed Zeeshan Qadri, Abdul K. Siraj, Sandeep K. Parvathareddy, Nabil Siraj, Padmanaban Annaiyappanaidu, Khawla S. Al-Kuraya. CD44 is an independent predictor of lymph node metastasis in differentiated thyroid cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3346.

Application of interpretable machine learning algorithm to predict lymph node metastasis in cutaneous malignant melanoma

Introduction: Cutaneous malignant melanoma (CMM) is the most lethal form of skin cancer worldwide. The precise prediction of lymph node metastasis is critical for personalized treatment and improved patient outcomes. However, no prior study has employed interpretable machine learning techniques to predict lymph node metastasis in CMM. This study aimed to utilize interpretable machine learning to integrate multidimensional data from the Surveillance, Epidemiology, and End Results (SEER) database—encompassing clinical characteristics, pathological information, and biomarkers of CMM—to construct various predictive models for lymph node metastasis. Methods: We constructed six machine learning models to predict lymph node metastasis using clinical, pathological, and biomarker data from 2448 patients with CMM in the SEER database.These models comprise a support vector machine, random forest (RF), XGBoost, LightGBM, adaptive boosting, and gradient boosting decision tree. The primary influential factors were identified using Gaussian Naive Bayes and gradient boosting algorithms. Shapley additive explanations (SHAP) analysis facilitates visual interpretation in individual patients. Model performance was evaluated based on accuracy, sensitivity, specificity, Brier score, and area under the receiver operating characteristic curve (AUC). Results: The RF algorithm exhibited the highest predictive performance with an AUC of 0.897, accuracy of 0.821, sensitivity of 0.876, specificity of 0.765, and Brier score of 0.086. The primary influential variables were T stage, chemotherapy, ulceration, pretreatment lactate dehydrogenase (LDH) levels, and radiation therapy. SHAP analysis confirmed a significant association and highlighted the critical function of (LDH) as a predictive biomarker. Conclusion: This study successfully established an accurate predictive model for lymph node metastasis in patients with CMM using machine-learning techniques, offering a significant reference to aid clinician treatment decisions.

Artificial neural network model enhancing the accuracy of clinical evaluation for high-risk population of lymph node metastasis in non-intestinal type early gastric cancer: a multicenter real-world study in China.

Artificial neural network model enhancing the accuracy of clinical evaluation for high-risk population of lymph node metastasis in non-intestinal type early gastric cancer: a multicenter real-world study in China.

A Hybrid Model-Based Clinicopathological Features and Radiomics Based on Conventional MRI for Predicting Lymph Node Metastasis and DFS in Cervical Cancer.

This study aimed to improve the accuracy of the diagnosis of lymph node metastasis (LNM) and prediction of patient prognosis in cervical cancer patients using a hybrid model based on MRI and clinical aspects. We retrospectively analyzed routine MR data from 485 patients with pathologically confirmed cervical cancer from January 2014 to June 2021. The data were divided into a training cohort (N = 261), internal cohort (N = 113), and external validation cohort (n = 111). A total of 2194 features were extracted from each ROI from T2WI and CE-T1WI. The clinical model (M1) was built with clinicopathological features including squamous cell carcinoma antigen, MRI-reported LNM, maximal tumor diameter (MTD). The radiomics model (M2) was built with four radiomics features. The hybrid model (M3) was constructed with squamous cell carcinoma antigen, MRI-reported LNM, MTD which consists of M1 and four radiomics features which consist of M2. GBDT algorithms were used to create the scores of M1 (clinical-score, C-score), M2 (radiomic score, R-score), and M3 (hybrid-score, H-score). M3 showed good performance in the training cohort (AUCs, M3 vs. M1 vs. M2, 0.917 vs. 0.830 vs. 0.788), internal validation cohorts (AUCs, M3 vs. M1 vs. M2, 0.872 vs. 0.750 vs. 0.739), and external validation cohort (AUCs, M3 vs. M1 vs. M2, 0.907 vs. 0.811 vs. 0.785). In addition, higher scores were significantly associated with worse disease-free survival (DFS) in the training cohort and the internal validation cohort (C-score, P = 0.001; R-score, P = 0.002; H-score, P = 0.006). Radiomics models can accurately predict LNM status in patients with cervical cancer. The hybrid model, which incorporates clinical and radiomics features, is a novel way to enhance diagnostic performance and predict the prognosis of cervical cancer.

Artificial Intelligence in Thoracic Surgery: A Review Bridging Innovation and Clinical Practice for the Next Generation of Surgical Care.

Background: Artificial intelligence (AI) is rapidly transforming thoracic surgery by enhancing diagnostic accuracy, surgical precision, intraoperative guidance, and postoperative management. AI-driven technologies, including machine learning (ML), deep learning, computer vision, and robotic-assisted surgery, have the potential to optimize clinical workflows and improve patient outcomes. However, challenges such as data integration, ethical concerns, and regulatory barriers must be addressed to ensure AI's safe and effective implementation. This review aims to analyze the current applications, benefits, limitations, and future directions of AI in thoracic surgery. Methods: This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive literature search was performed using PubMed, Scopus, Web of Science, and Cochrane Library for studies published up to January 2025. Relevant articles were selected based on predefined inclusion and exclusion criteria, focusing on AI applications in thoracic surgery, including diagnostics, robotic-assisted surgery, intraoperative guidance, and postoperative care. A risk of bias assessment was conducted using the Cochrane Risk of Bias Tool and ROBINS-I for non-randomized studies. Results: Out of 279 identified studies, 36 met the inclusion criteria for qualitative synthesis, highlighting AI's growing role in diagnostic accuracy, surgical precision, intraoperative guidance, and postoperative care in thoracic surgery. AI-driven imaging analysis and radiomics have improved pulmonary nodule detection, lung cancer classification, and lymph node metastasis prediction, while robotic-assisted thoracic surgery (RATS) has enhanced surgical accuracy, reduced operative times, and improved recovery rates. Intraoperatively, AI-powered image-guided navigation, augmented reality (AR), and real-time decision-support systems have optimized surgical planning and safety. Postoperatively, AI-driven predictive models and wearable monitoring devices have enabled early complication detection and improved patient follow-up. However, challenges remain, including algorithmic biases, a lack of multicenter validation, high implementation costs, and ethical concerns regarding data security and clinical accountability. Despite these limitations, AI has shown significant potential to enhance surgical outcomes, requiring further research and standardized validation for widespread adoption. Conclusions: AI is poised to revolutionize thoracic surgery by enhancing decision-making, improving patient outcomes, and optimizing surgical workflows. However, widespread adoption requires addressing key limitations through multicenter validation studies, standardized AI frameworks, and ethical AI governance. Future research should focus on digital twin technology, federated learning, and explainable AI (XAI) to improve AI interpretability, reliability, and accessibility. With continued advancements and responsible integration, AI will play a pivotal role in shaping the next generation of precision thoracic surgery.

Identification of predictors for lymph node metastasis in T2 colorectal cancer: retrospective cohort study from a high-volume hospital

BackgroundColorectal cancer (CRC) is the most prevalent malignant tumor of the digestive system globally, ranking third in incidence and second in mortality. In previous studies, the rate of lymph node metastasis (LNM) in T2 CRC ranged from 18.0 to 28.0%. We aim to identify T2 CRC patients without LNM and thereby mitigate the complications and potential impact on the quality of life associated with surgery.MethodsIn this retrospective study, 787 cases with T2 CRC were selected. The preoperative and postoperative clinicopathological features were retrospectively studied. Univariate analysis and multivariate analysis were performed using binary logistic regression to determine the predictive factor for LNM. Odds ratio (OR) and 95% confidence interval (CI) were conducted.Results184 (23.4%) patients were diagnosed with LNM, including 144 (78.3%) patients with N1stage and 40 (21.7%) patients with N2 stage. According to univariate analysis and multivariate analysis, poorly differentiated tumors (p = 0.003, OR = 4.405, 95%CI: 1.632–11.893), perineural invasion (p = 0.001, OR = 4.789, 95%CI: 1.958–11.716), and lymphovascular invasion (p = 0.001, OR = 2.779, 95%CI: 1.497–5.159) were independent risk factors of LNM, while male (p = 0.017, OR = 0.652, 95%CI: 0.459–0.926) and elevated preoperative PLR (p = 0.048, OR = 0.996, 95%CI: 0.993-1.000) seemed to be independent protective factors. Larger tumor size did not show significant association with LNM.ConclusionsApproximately three-quarters of T2 CRC patients are likely to avoid unnecessary surgery. Female, poorly differentiated tumors, perineural invasion, and lymphovascular invasion are expected to be used as predictors of LNM in T2 CRC.

Diagnostic value of systemic immune-inflammation index and prognostic nutritional index combined with CEA in gastric cancer with lymph node metastasis.

Carcinoembryonic antigen (CEA), systemic immune-inflammation index(SII), and prognostic nutritional index (PNI) are diagnostic markers for cancer, but their combined significance in gastric cancer (GC) with lymph node metastasis remains unclear. The aim of this study was to evaluate the association between these serum biomarkers and lymph node metastasis in patients with GC. Records of patients with GC were reviewed retrospectively. Univariate and multivariate logistic regression were performed to examine the association between tumor markers, serum biomarkers and lymph node metastasis in GC. Based on the results of multivariate regression, a nomogram was developed and verified. Of the 395 patients aged 68.5 ± 9.1 years, 192 (48.6%) were diagnosed with lymphatic node metastasis. After adjusting for confounding factors, CEA (Odd ratio (OR):2.21; 95%CI: 1.17-3.81) and SII (OR:1.02; 95%CI: 1.01-1.04) was identified as significant risk factors, while PNI (OR:0.90; 95%CI: 0.85~0.96) was a protective factor for lymph node metastasis. The established nomogram by incorporating CEA, SII, PNI, differentiation, and tumor diameter can effectively predict lymph node metastasis in GC. CEA, SII, PNI, differentiation, and tumor diameter were significantly associated with lymph node metastasis in patients with GC, and the combination of CEA, SII, PNI, differentiation, and tumor diameter has a better diagnostic value than either index alone.

Prediction of lymph node metastasis in stage I-III colon cancer patients younger than 40 years.

Developing a clinical model to predict the individual risk of lymph node metastasis (LNM) in young colon cancer (CC) patients may address an unmet clinical need. A total of 2,360 CC patients under 40 years old were extracted from the SEER database and randomly divided into development and validation cohorts. Risk factors for LNM were identified by using a logistic regression model. A weighted scoring system was built according to beta coefficients (β) calculated by a logistic regression model. Model discrimination was evaluated by C-statistics, model calibration was evaluated by H-L test and calibration plot. Risk factors were identified as T stage, tumor site, grade and histology. The area under the receiver operating characteristic curve (AUC-ROC) was 0.66 in both cohorts, indicating acceptable discriminatory power. The H-L test showed good calibration in the development cohort (χ2=2.869, P=0.837) and validation cohort (χ2=10.103, P=0.120) which also had been proved by calibration plot. Patients with total risk score of 0-1, 2-3 and 4-6 were considered as low, medium and high risk group. This clinical risk prediction model is accurate enough to identify young CC patients with high risk of LNM and can further provide individualized clinical reference.

Artificial intelligence in magnetic resonance imaging for predicting lymph node metastasis in rectal cancer patients: a meta-analysis.

This meta-analysis aims to evaluate the diagnostic performance of magnetic resonance imaging (MRI)-based artificial intelligence (AI) in the preoperative detection of lymph node metastasis (LNM) in patients with rectal cancer and to compare it with the diagnostic performance of radiologists. A thorough literature search was conducted across PubMed, Embase, and Web of Science to identify relevant studies published up to September 2024. The selected studies focused on the diagnostic performance of MRI-based AI in detecting rectal cancer LNM. A bivariate random-effects model was employed to calculate pooled sensitivity and specificity, each reported with 95% confidence intervals (CIs). Study heterogeneity was assessed using the I2 statistic. Furthermore, the modified quality assessment of diagnostic accuracy studies-2 (QUADAS-2) tool was applied to assess the methodological quality of the selected studies. Seventeen studies were included in this meta-analysis. The pooled sensitivity, specificity, and area under the curve (AUC) for MRI-based AI in detecting preoperative LNM in rectal cancer were 0.71 (95% CI: 0.66-0.74), 0.71 (95% CI: 0.67-0.75), and 0.77 (95% CI: 0.73-0.80), respectively. For radiologists, these values were 0.64 (95% CI: 0.49-0.77), 0.72 (95% CI: 0.62-0.80), and 0.74 (95% CI: 0.68-0.80). Both analyses showed no significant publication bias (p > 0.05). MRI-based AI demonstrates diagnostic performance similar to that of radiologists. The high heterogeneity among studies limits the strength of these findings, and further research with external validation datasets is necessary to confirm the results and assess their practical clinical value. Question How effective is MRI-based AI in detecting LNM in rectal cancer patients compared to traditional radiology methods? Findings The diagnostic performance of MRI-based AI is comparable to radiologists, with pooled sensitivity and specificity both at 0.71, indicating moderate accuracy. Clinical relevance Integrating MRI-based AI can enhance diagnostic efficiency in identifying LNM, especially in settings with limited access to skilled radiologists, but requires further validation.

Deep learning-based prediction of enhanced CT scans for lymph node metastasis in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) poses a significant global health challenge with a particularly grim prognosis. Accurate prediction of lymph node metastasis (LNM) in ESCC is crucial for optimizing treatment strategies and improving patient outcomes. This study leverages the power of deep learning, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to analyze arterial phase enhanced CT images and predict LNM in ESCC patients. A retrospective study included 441 ESCC patients who underwent radical esophagectomy and regional lymphadenectomy. CT imaging was performed using contrast-enhanced CT scanners. Tumor region segmentation was conducted to determine the region of interest (ROI), where local tumor 3D volumes were extracted as input for the model. The novel deep learning model, LymphoReso-Net, combined CNN and LSTM networks to process and learn from medical imaging data. The model outputs a binary prediction for LNM. GRAD-CAM was integrated to enhance model interpretability. Performance was evaluated using fivefold cross-validation with metrics including accuracy, sensitivity, specificity, and AUC-ROC. The gold standard for LNM confirmation was pathologically confirmed LNM shortly after the CT. LymphoReso-Net demonstrated promising performance with an average accuracy of 0.789, an AUC of 0.836, a sensitivity of 0.784, and a specificity of 0.797. GRAD-CAM provided visual explanations of the model's decision-making, aiding in identifying critical regions associated with LNM prediction. This study introduces a novel deep learning framework, LymphoReso-Net, for predicting LNM in ESCC patients. The model's accuracy and interpretability offer valuable insights into lymphatic spread patterns, enabling more informed therapeutic decisions.

PESI-MS combined with AI to build a prediction model for lymph node metastasis of papillary thyroid cancer.

PESI-MS combined with AI to build a prediction model for lymph node metastasis of papillary thyroid cancer.