Application of machine learning algorithms to predict lymph node metastasis in gastric neuroendocrine neoplasms

Machine Learning Based Assessment of Inguinal Lymph Node Metastasis in Patients with Squamous Cell Carcinoma of the Vulva

Background/Objectives: Despite great efforts from both clinical and pathological sides to address the extent of metastatic inguinal lymph node involvement in patients with vulvar cancer, current research attempts are still mostly aimed at identifying new imaging parameters or superior tissue diagnostic workflows rather than alternative ways of statistical data analysis. In the present study, we therefore establish a supervised machine learning algorithm to predict groin metastasis in patients with squamous cell carcinoma of the vulva (VSCC) based on classical histomorphological features. Methods: In total, 157 patients with VSCC were included in this retrospective study. After initial exploration of valuable clinicopathological predictor variables by means of Spearman correlation, a decision tree was trained and internally validated (5-fold cross-validation) using a training data set (n = 126) and afterwards externally validated employing a holdout validation data set (n = 31) using standard metrices such sensitivity, positive predictive value, and AUROC curve. Results: Our established classifier can predict inguinal lymph node status with an internal accuracy of 79.4% (AUROC value = 0.64). Reaching similar performances and an overall accuracy of 83.9% on an unknown data input (external validation set), our classifier demonstrates robustness. Conclusions: The presented results suggest that machine learning can predict groin lymph node status in VSCC based on histological findings of the primary tumor. Such research attempts may be useful in the future for an additional assessment of inguinal lymph nodes, aiming to maximize oncological safety when targeting the most accurate diagnosis of lymph node involvement.

Risk prediction of second primary malignancies in patients with primary gastric neuroendocrine neoplasms: a population-based study

PURPOSE

Patients with gastric neuroendocrine neoplasms (GNENs) face elevated risks of second primary malignancies (SPMs), yet tools for personalized risk prediction are lacking. This study aimed to explore the risk factors associated with the development of SPMs in patients with GNENs and to establish a new competing-risk nomogram to predict the occurrence of SPMs.

METHODS

We obtained clinical data for GNENs patients from the Surveillance, Epidemiology, and End Results (SEER) database covering the period from 2000 to 2015. Using Fine and Gray's proportional sub-distribution hazards model, potential risk factors for SPM development in GNENs' patients were identified. A competing-risk nomogram was then constructed to quantify the probability of SPMs' occurrence and was evaluated using the area under the receiver-operating characteristic (ROC) curves (AUC) and calibration curves.

RESULTS

Among the 5160 GNENs patients, 18.7% developed SPMs during a maximum follow-up period of approximately 131 months (median 82 months). Independent risk factors for SPMs included age, marital status, tumor size, histopathological grade, disease extent, and T staging. The nomogram based on these factors demonstrated relatively strong predictive accuracy.

CONCLUSION

This study identifies key risk factors for SPMs in GNENs patients and introduces a nomogram that effectively predicts SPM risk. This tool may help clinicians better assess patient risk and guide treatment decisions.

Interpretable machine learning models for predicting short-term prognosis in AChR-Ab+ generalized myasthenia gravis using clinical features and systemic inflammation index

Background

Myasthenia Gravis (MG) is an autoimmune disease that causes muscle weakness in 80% of patients, most of whom test positive for anti-acetylcholine receptor (AChR) antibodies (AChR-Abs). Predicting and improving treatment outcomes are necessary due to varying responses, ranging from complete relief to minimal improvement.

Objective

Our study aims to develop and validate an interpretable machine learning (ML) model that integrates systemic inflammation indices with traditional clinical indicators. The goal is to predict the short-term prognosis (after 6 months of treatment) of AChR-Ab+ generalized myasthenia gravis (GMG) patients to guide personalized treatment strategies.

Methods

We performed a retrospective analysis on 202 AChR-Ab+ GMG patients, dividing them into training and external validation cohorts. The primary outcome of this study was the Myasthenia Gravis Foundation of America (MGFA) post-intervention status assessed after 6 months of treatment initiation. Prognoses were classified as "unchanged or worse" for a poor outcome and "improved or better" for a good outcome. Accordingly, patients were categorized into "good outcome" or "poor outcome" groups. In the training cohort, we developed and internally validated various ML models using systemic inflammation indices, clinical indicators, or a combination of both. We then carried out external validation with the designated cohort. Additionally, we assessed the feature importance of our most effective model using the Shapley Additive Explanations (SHAP) method.

Results

In our study of 202 patients, 28.7% (58 individuals) experienced poor outcomes after 6 months of standard therapy. We identified 11 significant predictors, encompassing both systemic inflammation indexes and clinical metrics. The extreme gradient boosting (XGBoost) model demonstrated the best performance, achieving an area under the receiver operating characteristic (ROC) curve (AUC) of 0.944. This was higher than that achieved by logistic regression (Logit) (AUC: 0.882), random forest (RF) (AUC: 0.917), support vector machines (SVM) (AUC: 0.872). Further refinement through SHAP analysis highlighted five critical determinants-two clinical indicators and three inflammation indexes-as crucial for assessing short-term prognosis in AChR-Ab+ GMG patients.

Conclusion

Our analysis confirms that the XGBoost model, integrating clinical indicators with systemic inflammation indexes, effectively predicts short-term prognosis in AChR-Ab+ GMG patients. This approach enhances clinical decision-making and improves patient outcomes.