Abstract
Purpose
Papillary thyroid microcarcinomas (PTMC), small tumors under 10 mm, represent a major part of the increase in papillary thyroid cancer cases. The treatment plans for PTMC patients with lymph node metastasi should be different from those without lymph node metastasis. Therefore, accurately identifying patients with cervical lymph node metastasis is of great clinical significance.
Methods
We analyzed data from 256 patients diagnosed with PTMC, using age, gender, tumor size, lesion count, and ACR score as predictors. Outcomes were based on cervical lymph node pathology. Four machine learning models—Random Forest, Multivariate Logistic Regression, Support Vector Machine, and Xgboost—were tested for their predictive accuracy and clinical utility. We then created an online website for direct prediction and designed online platforms that allow other researchers to upload their data for model building and prediction. The website and platform design is based on "shiny" package.
Results
The Random Forest model proved optimal, achieving an AUC of 0.92. It showed high sensitivity (0.83) and specificity (0.90) at the best threshold of 0.46. The link to the website we built based on this model is as follows: http://yucemoxing.online:8082. Additionally, the link to the online platforms that allows userss to upload their own data for model building and prediction is as follows: http://yucemoxing.online:8081,http://yucemoxing.site:8089,http://yucemoxing.online:8084,http://yucemoxing.online:8085,http://yucemoxing.online:8083,http://yucemoxing.online:8088, http://yucemoxing.online:8087, http://yucemoxing.online:8086
Conclusions
Machine learning tools can reliably predict cervical lymph node metastasis in PTMC patients. The developed websites offer valuable tools for clinical application, enhancing decision-making in treatment strategies.