Prediction of central lymph node metastasis in patients with papillary thyroid microcarcinoma by gradient-boosting decision tree model based on ultrasound radiomics and clinical features
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ABSTRACT:
SUBMITTER: Zhao Y
PROVIDER: S-EPMC10788563 | biostudies-literature | 2023 Dec
REPOSITORIES: biostudies-literature
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Project description:BackgroundWhether prophylactic central lymph node dissection is necessary for patients with clinically node-negative (cN0) papillary thyroid microcarcinoma (PTMC) remains controversial. Herein, we aimed to establish an ultrasound (US) radiomics (Rad) score for assessing the probability of central lymph node metastasis (CLNM) in such patients.Methods480 patients (327 in the training cohort, 153 in the validation cohort) who underwent thyroid surgery for cN0 PTMC at two institutions between January 2018 and December 2020 were included. Radiomics features were extracted from the US images. Least absolute shrinkage and selection operator logistic regression were utilized to generate a Rad score. A nomogram consisting of the Rad score and clinical factors was then constructed for the training cohort. Both cohorts assessed model performance using discrimination, calibration, and clinical usefulness.ResultsBased on the six most valuable radiomics features, the Rad score was calculated for each patient. A multivariate analysis revealed that a higher Rad score (P < 0.001), younger age (P = 0.006), and presence of capsule invasion (P = 0.030) were independently associated with CLNM. A nomogram integrating these three factors demonstrated good calibration and promising clinical utility in the training and validation cohorts. The nomogram yielded areas under the curve of 0.795 (95% confidence interval [CI], 0.745-0.846) and 0.774 (95% CI, 0.696-0.852) in the training and validation cohorts, respectively.ConclusionsThe radiomics nomogram may be a clinically useful tool for the individual prediction of CLNM in patients with cN0 PTMC.
| S-EPMC10875890 | biostudies-literature
Project description:BackgroundThe aim of this study is to design a deep learning (DL) model to preoperatively predict the occurrence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC).MethodsThis research collected preoperative ultrasound (US) images and clinical factors of 611 PTMC patients. The clinical factors were analyzed using multivariate regression. Then, a DL model based on US images and clinical factors was developed to preoperatively predict CLNM. The model's efficacy was evaluated using the receiver operating characteristic (ROC) curve, along with accuracy, sensitivity, specificity, and the F1 score.ResultsThe multivariate analysis indicated an independent correlation factors including age ≥55 (OR = 0.309, p < 0.001), tumor diameter (OR = 2.551, p = 0.010), macrocalcifications (OR = 1.832, p = 0.002), and capsular invasion (OR = 1.977, p = 0.005). The suggested DL model utilized US images achieved an average area under the curve (AUC) of 0.65, slightly outperforming the model that employed traditional clinical factors (AUC = 0.64). Nevertheless, the model that incorporated both of them did not enhance prediction accuracy (AUC = 0.63).ConclusionsThe suggested approach offers a reference for the treatment and supervision of PTMC. Among three models used in this study, the deep model relied generally more on image modalities than the data modality of clinic records when making the predictions.
| S-EPMC10984175 | biostudies-literature
Project description: Not available
| S-EPMC10500345 | biostudies-literature
Project description:ObjectiveTo assess the utility of predictive models using ultrasound radiomic features to predict cervical lymph node metastasis (CLNM) in solitary papillary thyroid carcinoma (PTC) patients.MethodsA total of 570 PTC patients were included (456 patients in the training set and 114 in the testing set). Pyradiomics was employed to extract radiomic features from preoperative ultrasound images. After dimensionality reduction and meticulous selection, we developed radiomics models using various machine learning algorithms. Univariate and multivariate logistic regressions were conducted to identify independent risk factors for CLNM. We established clinical models using these risk factors. Finally, we integrated radiomic and clinical models to create a combined nomogram. We plotted ROC curves to assess diagnostic performance and used calibration curves to evaluate alignment between predicted and observed probabilities.ResultsA total of 1561 radiomics features were extracted from preoperative ultrasound images. After dimensionality reduction and feature selection, 16 radiomics features were identified. Among radiomics models, the logistic regression (LR) model exhibited higher predictive efficiency. Univariate and multivariate logistic regression results revealed that patient age, tumor size, gender, suspicious cervical lymph node metastasis, and capsule contact were independent predictors of CLNM (all P < 0.05). By constructing a clinical model, the LR model demonstrated favorable diagnostic performance. The combined model showed superior diagnostic efficacy, with an AUC of 0.758 (95% CI: 0.712-0.803) in the training set and 0.759 (95% CI: 0.669-0.849) in the testing set. In the training dataset, the AUC value of the nomogram was higher than that of the clinical and radiomics models (P = 0.027 and 0.002, respectively). In the testing dataset, the AUC value of the nomogram model was also greater than that of the radiomics models (P = 0.012). However, there was no significant statistical difference between the nomogram and the clinical model (P = 0.928). The calibration curve indicated a good fit of the combined model.ConclusionUltrasound radiomics technology offers a quantitative and objective method for predicting CLNM in PTC patients. Nonetheless, the clinical indicators persists as irreplaceable.
| S-EPMC10850287 | biostudies-literature
Project description:The surgical management of papillary thyroid microcarcinoma (PTMC), especially regarding the necessity of central/lateral lymph node dissection, remains controversial. This study investigated the clinicopathologic factors predictive of lymph node metastasis (LNM) in patients diagnosed with PTMC. Multivariate logistic regression analysis was used for PTMC patients identified from the Surveillance, Epidemiology, and End Results database who were treated by surgery between 2002 and 2012, to determine the association of clinicopathologic factors with LNM. According to the results, a total of 31,017 patients met the inclusion criteria of the study. Final histology confirmed 2,135 (6.9%) cases of N1a disease and 1,684 cases (5.4%) of N1b disease. Our multivariate logistic regression analysis identified variables associated with both central LNM and lateral lymph node metastasis (LLNM), including a younger age (<45 years), male sex, non-Hispanic white and other race, classical papillary histology, larger tumor size, multifocality, and extrathyroidal extension; distant metastasis was also significantly associated with LLNM. The significant predictors identified from multivariable logistic regression were integrated into a statistical model that showed that extrathyroidal extension had maximum weight in the predictive role for LNM. LLNM was validated to be a significant risk factor for cancer-specific death in Cox regression analyses, whereas central LNM failed to predict a worse cancer-specific survival according to our data. Therefore, we suggested that central lymph node dissection could be performed in certain patients with risk factors. Given the prevalence of LLNM in PTMC, a thorough inspection of the lateral compartment is recommended in PTMC patients with risk factors for precise staging; from the viewpoint of a radical treatment for tumors, prophylactic lateral lymph node dissection that aims to remove the occult lateral lymph nodes may be an option for PTMC with risk factors. Multicenter studies with long-term follow-up are recommended to better understand the risk factors and surgical management for cervical nodes in PTMC.
| S-EPMC5004998 | biostudies-literature
Project description: Not available
| S-EPMC11550994 | biostudies-literature
Project description:Background:Unilateral multifocal papillary thyroid microcarcinoma (UM-PTMC) is not rare, and the suitable extent of operation is controversial. Total thyroidectomy is associated with a high risk of hypoparathyroidism and recurrent laryngeal nerve injury. Hemithyroidectomy should be considered in patients without lymph node metastases. This study aimed to identify factors predicting central lymph node metastases (CLNM) in patients with UM-PTMC. Methods:A total of 1,453 patients with thyroid cancer underwent surgery at our hospital from January 2018 to June 2018, and 115 patients with UM-PTMC were selected and assessed retrospectively. The relationship between CLNM and clinicopathologic features of UM-PTMC were analyzed by both univariate and multivariate analyses. Results:Of the 115 patients with UM-PTMC, CLNM were confirmed by pathology in 57 patients. Univariate and multivariate analyses identified the sum diameter of all tumors (?1.0 cm) [odds ratio (OR) =2.295; 95% confidence interval (CI): 1.036-5.086; P=0.041] and positive CLNM via ultrasonography (OR =5.882; 95% CI: 1.562-22.153; P=0.009) as independent predictors of CLNM. Conclusions:CLNM are common in patients with UM-PTMC. The sum diameter of all tumors (?1.0 cm) and positive CLNM via ultrasonography are independent risk factors for CLNM. Our data should be considered in the decision process relating to performing total thyroidectomy or hemithyroidectomy in patients with UM-PTMC.
| S-EPMC7347816 | biostudies-literature
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.
| S-EPMC10877775 | biostudies-literature
Project description:BackgroundPapillary thyroid microcarcinoma (PTMC) is characterized by its favorable prognosis and potential for active surveillance (AS) as a management option. However, the presence of cervical lymph node (LN) metastasis, especially lateral LN metastasis, significantly impacts management and prognosis. Previous studies have focused on post-surgery risk factors for cervical LN metastasis. This study aims to identify predictors of lateral LN metastasis by analyzing pre-operative ultrasonographic findings alongside clinicopathological factors.MethodsA retrospective review of medical records was conducted for patients with PTMC who underwent surgery at Chonnam National University Hwasun Hospital between 2004 and 2013. This is a case-control study that compares patients with lateral LN metastasis (N1b) to age- and sex-matched patients without LN metastasis (N0). Subgroup analysis was performed to evaluate risk factors of skip metastasis.ResultsThe study included 90 patients with PTMC with lateral LN metastasis (N1b) and 268 age- and sex-matched patients without LN metastasis (N0). The mean age was 49.3 years, and female patients were dominant in both groups. Structural recurrences of 4.4% (4/90) were observed only in the N1b group. The N1b group exhibited a higher frequency of upper lobe tumor location compared to the N0 group (38.9% vs. 16.0%, p < 0.001). There was no significant difference in the locations with the presence of invasion to adjacent organs. A higher proportion of non-parallel shape was observed in the N1b group than the N0 group (80.0% vs. 66.0%, p = 0.013). There were no differences in echogenicity, sonographic feature, margin, and AP diameter of the thyroid gland between the two groups. In multivariate analysis, independent risk factors for lateral LN metastasis included extrathyroidal extension, multiplicity, upper lobe tumor location, and non-parallel shape. Skip metastasis in patients with PTMC was associated with upper lobe tumor location.ConclusionDetailed ultrasound examinations, evaluating tumor location, number, orientation, and the presence of ETE, are crucial in accurately predicting lateral LN metastasis especially when primary tumor was in the upper lobe to avoid missing skip metastasis. These evaluations can help guide the decision between AS and immediate surgery in patients with PTMC.
| S-EPMC11250241 | biostudies-literature
Project description:BackgroundWhether prophylactic central lymph node dissection is necessary for cN0 papillary thyroid microcarcinoma (PTMC) patients remains highly debatable. Surgeons desperately need a way to help with surgical decision-making. While traditional predictive models can better explain changes in variables, machine learning (ML) models may have better predictive performance. This study aims to develop models for predicting the risk of central lymph node metastasis (CLNM) by utilizing ML algorithms.MethodsThe clinical records of 1,121 patients with cN0 PTMC who underwent initial thyroid resection at our hospital between January 2014 and December 2018 were retrospectively retrieved. Univariate and multivariate analyses were performed to examine risk factors associated with CLNM. Six ML algorithms for predicting CLNM were established and internally validated. Indices including the area under the receiver operating characteristic (AUROC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were calculated to test the performance of the model.ResultsThe results showed 33.5% (376 out of 1,121) of patients had CLNM. In multivariate logistic regression (LR) analyses, gender, age, tumor size, multifocal lesions, and extrathyroidal extension (ETE) were all independent predictors of CLNM. The AUROC predictive values of the six ML algorithms were between 0.664 and 0.794, with the random forest (RF) model performing the best with an AUROC of 0.794. Therefore, we used the RF model and uploaded the results to a web-based risk calculator to predict an individual's probability of CLNM (https://xijing-thyroid.shinyapps.io/ptmc_clnm).ConclusionsDeveloping predictive models of CLNM in cN0 PTMC patients using the ML algorithm is a feasible method. Our online risk calculator based on the RF model may be a useful tool for surgical decisions.
| S-EPMC9469161 | biostudies-literature