Development and validation of a nomogram for preoperative prediction of cervical lymph node involvement in thyroid microcarcinoma

Si, Lu; Zhao, Rongjie; Ni, Yeqin; Ding, Jinwang; Qiu, Fuqiang; Peng, You; Pan, Gang; Wu, Fan; Shi, Jingjing; Zhou, Tianhan; Luo, Dingcun

doi:10.18632/aging.102915

Cited by 14 publications

(14 citation statements)

References 41 publications

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“…The finding that younger age is highly predictive of CLNM in our research is similar to previous studies ( 23 , 24 ). In addition, multifocal PTCs have been shown to be prone to CLNM and our results are consistent with previous reports, suggesting that multifocality is a positive predictor of CLNM ( 25 , 26 ). It has been previously demonstrated by Thompson et al ( 27 ) and Yang et al ( 28 ) that larger tumors are significantly associated with an increased risk of nodal spread while we have found that rates of lymph node involvement surge in tumor sizes > 20 mm, compared with those in tumor sizes of 10–20 mm and < 10 mm (81.2 vs. 62.3 and 45.3%).…”

Section: Discussionsupporting

confidence: 93%

Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Zhu

Zheng

et al. 2021

Front. Med.

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Purpose: While there are no clear indications of whether central lymph node dissection is necessary in patients with T1-T2, non-invasive, clinically uninvolved central neck lymph nodes papillary thyroid carcinoma (PTC), this study seeks to develop and validate models for predicting the risk of central lymph node metastasis (CLNM) in these patients based on machine learning algorithms.Methods: This is a retrospective study comprising 1,271 patients with T1-T2 stage, non-invasive, and clinically node negative (cN0) PTC who underwent surgery at the Department of Endocrine and Breast Surgery of The First Affiliated Hospital of Chongqing Medical University from February 1, 2016, to December 31, 2018. We applied six machine learning (ML) algorithms, including Logistic Regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), and Neural Network (NNET), coupled with preoperative clinical characteristics and intraoperative information to develop prediction models for CLNM. Among all the samples, 70% were randomly selected to train the models while the remaining 30% were used for validation. Indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models' performance.Results: The results showed that ~51.3% (652 out of 1,271) of the patients had pN1 disease. In multivariate logistic regression analyses, gender, tumor size and location, multifocality, age, and Delphian lymph node status were all independent predictors of CLNM. In predicting CLNM, six ML algorithms posted AUROC of 0.70–0.75, with the extreme gradient boosting (XGBoost) model standing out, registering 0.75. Thus, we employed the best-performing ML algorithm model and uploaded the results to a self-made online risk calculator to estimate an individual's probability of CLNM (https://jin63.shinyapps.io/ML_CLNM/).Conclusions: With the incorporation of preoperative and intraoperative risk factors, ML algorithms can achieve acceptable prediction of CLNM with Xgboost model performing the best. Our online risk calculator based on ML algorithm may help determine the optimal extent of initial surgical treatment for patients with T1-T2 stage, non-invasive, and clinically node negative PTC.

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Section: Discussionsupporting

confidence: 93%

Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Zhu

Zheng

et al. 2021

Front. Med.

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“…The cut-off value of examined LNs was 8 and we divided patients into two groups by the number of examined LNs: <=8 examined LNs and >8 examined LNs. As we all know, Lasso regression analysis could well avoid confounding factors and made our predicting model more acurate [20]. According to the results (Figure 3), six characters including tumor size, T stage, age, examined LNs, N stage and M stage were identi ed as the most suitable variables for constructing model.…”

Section: Discussionmentioning

confidence: 97%

Validation of a nomogram to predict survival in patients with ESCC based on examined lymph nodes

Liao

Cao

Chen

et al. 2020

Preprint

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Background: Current the number of examined LNs are controversial in predicting the survival of ESCC. We aimed to develop an alternative LN-classification-based nomogram to individualize ESCC prognosis.Methods: Using the data of patients diagnosed with ESCC from SEER database between 2004 and 2015, we determined the cut-off values for the number of LNs examined via the K-adaptive partitioning (KAPS) algorithm. A nomogram predicting the survival of ESCC was performed, internally and externally validated, and evaluated by calibration plot, C-index, and decision curve analysis, and compared to the 7th TNM stage.Results: Totally, we included 3629 patients with detailed information. The optimal cut-off for examined LN number was 8. The C-index for the nomogram was higher than the 7th TNM staging (internal: 0.708; 95%CI, 0.678-0.753 vs 0.601; 95%CI, 0.573-0.656, P<0.001; external: 0.687; 95%CI, 0.601-0.734 vs 0.605; 95%CI, 0.563-0.659, P<0.001). Additionally, the nomogram showed good agreement between internal and external validation. DCA analysis showed no matter in the internal cohort or external cohort, the nomogram showed a greater benefit across the period of follow-up compared to 7th TNM stage.Conclusion: We found examining LNs that was more than 8 benefited for prognosis of patients. Based on these, a nomogram with greater benefit for predicting survival of EC patients than TNM staging was constructed.

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“…Nonetheless, some countries such as China and Japan underline the significance of the routine pCND for cN0 PTMC, [ 25 ] which can not only reduce local recurrence rate, but also identify the stage of disease to guide subsequent treatment such as thyroid-stimulating hormone-suppression and radioactive iodine treatment. [ 26 ] A meta-analysis enrolled in 17 studies observed the locoregional recurrence of PTC patients after surgery, showed that pCND reduced the risk of lymph node recurrence by 34%. [ 27 ] Barczyński et al also showed the 10-year local recurrence rates of patients with and without pCND were 5.6% and 13.4%, respectively.…”

Section: Discussionmentioning

confidence: 99%

Predicting non-small-volume central lymph node metastases (>5 or ≥2 mm) preoperatively in cN0 papillary thyroid microcarcinoma without extrathyroidal extension

Shou

Shi

et al. 2020

Medicine

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The ability to identify patients with aggressive papillary thyroid microcarcinoma (PTMC) from the low-risk patients is critical to planning proper management of PTMC. Lymph node metastases showed association with recurrence and low survival rate, especially in patients with >5 or ≥2 mm metastatic lymph nodes. Therefore, this study aimed to investigate the preoperatively predictive factors of non-small-volume (metastatic lymph nodes >5 or ≥2 mm in size) central lymph node metastases (NSVCLNM) in PTMC patients. A total of 420 patients with clinically node-negative (cN0) PTMC without extrathyroidal extension underwent thyroidectomy plus central neck dissection (CND) between January 2013 and December 2015, were retrospectively analyzed. Of the 420 patients, 33 (7.9%) had NSVCLNM. The 5-year recurrence-free survival was significantly less in cN0 PTMC patients with NSVCLNM, when compared with patients without NSVCLNM (80.8% vs 100%, P < .001). Multivariate logistic regression revealed age ≤36 years ( P < .001), male sex ( P = .002), ultrasonic tumor sizes of >0.65 cm ( P < .001), and ultrasonic multifocality ( P = .039) were independent predictive factors of NSVCLNM. A prediction equation (Y = 1.714 × age + 1.361 × sex + 1.639 × tumor size + 0.842 × multifocality −5.196) was developed, with a sensitivity (69.7%) and a specificity (84.0%), respectively, at an optimal cutoff point of −2.418. In conclusion, if the predictive value was >−2.418 according to the equation, immediate surgery including CND rather than active surveillance might be considered for cN0 PTMC patients.

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Development and validation of a nomogram for preoperative prediction of cervical lymph node involvement in thyroid microcarcinoma

Cited by 14 publications

References 41 publications

Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Application of Machine Learning Algorithms to Predict Central Lymph Node Metastasis in T1-T2, Non-invasive, and Clinically Node Negative Papillary Thyroid Carcinoma

Validation of a nomogram to predict survival in patients with ESCC based on examined lymph nodes

Predicting non-small-volume central lymph node metastases (>5 or ≥2 mm) preoperatively in cN0 papillary thyroid microcarcinoma without extrathyroidal extension

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