Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Wang Z

PROVIDER: S-EPMC9906958 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

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

Wang Zhongzhi Z Qu Limeng L Chen Qitong Q Zhou Yong Y Duan Hongtao H Li Baifeng B Weng Yao Y Su Juan J Yi Wenjun W

BMC cancer 20230208 1

<h4>Background</h4>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.<h4>Methods</h4>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 univ ...[more]

PMID: 36750791

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Project description:ObjectivesPrecise determination of cervical lymph node metastasis (CLNM) involvement in patients with early-stage thyroid cancer is fairly significant for identifying appropriate cervical treatment options. However, it is almost impossible to directly judge lymph node metastasis based on the imaging information of early-stage thyroid cancer patients with clinically negative lymph nodes.MethodsPreoperative US images (BMUS and CDFI) of 1031 clinically node negative PTC patients definitively diagnosed on pathology from two independent hospitals were divided into training set, validation set, internal test set, and external test set. An ensemble deep learning model based on ResNet-50 was built integrating clinical variables, BMUS, and CDFI images using a bagging classifier to predict metastasis of CLN. The final ensemble model performance was compared with expert interpretation.ResultsThe ensemble deep convolutional neural network (DCNN) achieved high performance in predicting CLNM in the test sets examined, with area under the curve values of 0.86 (95% CI 0.78-0.94) for the internal test set and 0.77 (95% CI 0.68-0.87) for the external test set. Compared to all radiologists averaged, the ensemble DCNN model also exhibited improved performance in making predictions. For the external validation set, accuracy was 0.72 versus 0.59 (p = 0.074), sensitivity was 0.75 versus 0.58 (p = 0.039), and specificity was 0.69 versus 0.60 (p = 0.078).ConclusionsDeep learning can non-invasive predict CLNM for clinically node-negative PTC using conventional US imaging of thyroid cancer nodules and clinical variables in a multi-institutional dataset with superior accuracy, sensitivity, and specificity comparable to experts.Critical relevance statementDeep learning efficiently predicts CLNM for clinically node-negative PTC based on US images and clinical variables in an advantageous manner.Key points• A deep learning-based ensemble algorithm for predicting CLNM in PTC was developed. • Ultrasound AI analysis combined with clinical data has advantages in predicting CLNM. • Compared to all experts averaged, the DCNN model achieved higher test performance.

Project description:BackgroundPapillary thyroid carcinoma (PTC) is the most frequent malignant tumor in thyroid carcinoma. The aim of this study was to explore the risk factors associated with central lymph node metastasis in papillary thyroid microcarcinoma (PTMC) and establish a nomogram model that can assess the probability of central lymph node metastasis (CLNM).MethodsThe clinicopathological data of 377 patients with cN0 PTMC were collected and analyzed from The Second Affiliated Hospital of Fujian Medical University from July 1st, 2019 to December 30th, 2021. All patients were examined by underwent ultrasound (US), found without metastasis to central lymph nodes, and diagnosed with PTMC through pathologic examination. All patients received thyroid lobectomy or total thyroidectomy with therapeutic or prophylactic central lymph node dissection (CLND). R software (Version 4.1.0) was employed to conduct a series of statistical analyses and establish the nomogram.ResultsA total of 119 patients with PTMC had central lymph node metastases (31.56%). After that, age (P < 0.05), gender (P < 0.05), tumor size (P < 0.05), tumor multifocality (P < 0.05), and ultrasound imaging-suggested tumor boundaries (P < 0.05) were identified as the risk factors associated with CLNM. Subsequently, multivariate logistic regression analysis indicated that the area under the receiver operating characteristic (ROC) curve (AUC) of the training cohort was 0.703 and that of the validation cohort was 0.656, demonstrating that the prediction ability of this model is relatively good compared to existing models. The calibration curves indicated a good fit for the nomogram model. Finally, the decision curve analysis (DCA) showed that a probability threshold of 0.15-0.50 could benefit patients clinically. The probability threshold used in DCA captures the relative value the patient places on receiving treatment for the disease, if present, compared to the value of avoiding treatment if the disease is not present.ConclusionCLNM is associated with many risk factors, including age, gender, tumor size, tumor multifocality, and ultrasound imaging-suggested tumor boundaries. The nomogram established in our study has moderate predictive ability for CLNM and can be applied to the clinical management of patients with PTMC. Our findings will provide a better preoperative assessment and treatment strategies for patients with PTMC whether to undergo central lymph node dissection.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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