Journal Article

Deep learning-based multifeature integration robustly predicts central lymph node metastasis in papillary thyroid cancer

Duan, Hongtao, Chen, Qitong, Li, Baifeng, Weng, Yao, Su, Juan, Yi, Wenjun, Zhou, Yong, Wang, Zhongzhi, Qu, Limeng

2023

Overview

Background Few highly accurate tests can diagnose central lymph node metastasis (CLNM) of papillary thyroid cancer (PTC). Genetic sequencing of tumor tissue has allowed the targeting of certain genetic variants for personalized cancer therapy development. Methods This study included 488 patients diagnosed with PTC by ultrasound-guided fine-needle aspiration biopsy, collected clinicopathological data, analyzed the correlation between CLNM and clinicopathological features using univariate analysis and binary logistic regression, and constructed prediction models. Results Binary logistic regression analysis showed that age, maximum diameter of thyroid nodules, capsular invasion, and BRAF V600E gene mutation were independent risk factors for CLNM, and statistically significant indicators were included to construct a nomogram prediction model, which had an area under the curve (AUC) of 0.778. A convolutional neural network (CNN) prediction model built with an artificial intelligence (AI) deep learning algorithm achieved AUCs of 0.89 in the training set and 0.78 in the test set, which indicated a high prediction efficacy for CLNM. In addition, the prediction models were validated in the subclinical metastasis and clinical metastasis groups with high sensitivity and specificity, suggesting the broad applicability of the models. Furthermore, CNN prediction models were constructed for patients with nodule diameters less than 1 cm. The AUCs in the training set and test set were 0.87 and 0.76, respectively, indicating high prediction efficacy. Conclusions The deep learning-based multifeature integration prediction model provides a reference for the clinical diagnosis and treatment of PTC.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Age / Artificial Intelligence / Biomedical and Life Sciences / Biomedicine / Biopsy / BRAF V600E gene mutation / Calibration / Cancer / Cancer Research / Cancer therapies / Care and treatment / Central lymph node metastasis(CLNM) / Convolutional neural network(CNN) / Data mining / Deep Learning / Development and progression / Dissection / Gene mutations / Genetic aspects / Genetic diversity / Health aspects / Health Promotion and Disease Prevention / Humans / Lymph nodes / Lymph Nodes - pathology / Lymphatic metastasis / Lymphatic Metastasis - pathology / Lymphatic system / Machine learning / Medicine/Public Health / Metastases / Metastasis / Methods / Mutation / Neck / Neural networks / Nodules / Nomograms / Oncology / Oncology, Experimental / Papillary thyroid cancer / Papillary thyroid cancer(PTC) / Pathology / Patients / Point mutation / Prediction models / Prevention / Regression analysis / Retrospective Studies / Risk Factors / Statistical analysis / Surgeons / Surgical Oncology / Thyroid cancer / Thyroid Cancer, Papillary - genetics / Thyroid diseases / Thyroid Neoplasms - pathology / Thyroidectomy / Ultrasonic imaging