Development and validation of a nomogram to predict the risk of occult cervical lymph node metastases in cN0 squamous cell carcinoma of the tongue

Jiang, Qunguang; Tang, Anliu; Long, Sang; Qi, Qi; Song, Chenjun; Yong, Xin; Zhang, C.; Cao, Zhen; Zhang, J.

doi:10.1016/j.bjoms.2019.09.024

Cited by 23 publications

(36 citation statements)

References 14 publications

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“…In study of Feng et al [4] suggests that END should be a preferred treatment strategy for tongue carcinoma in stage T2. Similar to resultsfrom a study of Sagheb et al [31]and Jiang et al [30] reported that the probability of cervical metastases rate of tongue SCC located in the posterior third region was signi cantly higher than when tumor was located in the anterior two-third of the tongue. Probably the posterior area of the tongue is supplied with plenty of lymphatic drainage to the super cial and deep cervical lymph nodes and had a greater chance of developing positive neck node disease.…”

Section: Discussionsupporting

confidence: 88%

“…Study of Swawantet al [29]also proposed a nomogram for predicting the risk of neck node metastasis in pathologically node-negative in 103 of T1-T4 oral cavity carcinoma patients by using a combination of protein expression, ultrastructural alterations and clinicopathological parameters. Likewise, Jiang et al [30] developed the nomogram to predict the probability of occult cervical lymph node metastasis before surgery in patients with cN0 SCC of the tongue with high (C-index = 0.846) and good calibration.…”

Section: Discussionmentioning

confidence: 99%

“…Nomograms are currently the most reliable discriminating tools for predicting individualized outcomes in clinical oncology to develop a treatment plan and follow-up strategies in several cancers [23][24][25]. Recently, previous studies have reported nomograms predicting long-term survival rate and predict cervical metastasis in head and neck cancer patients [26][27][28][29][30]. To date, a nomogram for predicting the likelihood of cervical lymph node metastasis pre-operatively of individual patients with early-stage of squamous cell carcinoma of lateral tongue (SCCLT) has not been widely investigated and no study has veri ed functionality of a MRI-based nomogram.…”

Section: Introductionmentioning

confidence: 99%

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Development and Validation of Multiparametric Mri-based Nomogram for Predicting Occult Metastasis Risk in Early Tongue Squamous Cell Carcinoma

Saenthaveesuk

Yang

Zeng

et al. 2020

Preprint

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Background: Nomograms are currently used in predicting individualized outcomes in clinical oncology of several cancers. However, nomograms for evaluating occult nodal metastasis of patients with squamous cell carcinoma of lateral tongue (SCCLT) have not been widely investigated for their functionality. This retrospective cohort study was designed to address this question.Methods: This study was divided into primary and validation cohorts. The primary cohort comprised 120 patients diagnosed between 2012 and 2017, whereas the validation cohort included 41 patients diagnosed thereafter. The diagnostic value of multiparametric MRI, including radiologic tumor thickness threshold (rTTT) in three-dimensions, paralingual distance, and sublingual distance were investigated. A nomogram was developed based on stepwise logistic regression of potential predictors associated with nodal metastasis in the primary cohort and then tested for predictive accuracy in the validation cohort using area under the curve (AUC) and goodness-of-fit tests.Results: Multivariate analysis, tumor size (odd ratio [OR] 15.175, 95% confidence interval [CI] 1.436-160.329, P = 0.024), rTTT (OR 11.528, 95% CI 2.483-53.530, P = 0.002), paralingual distance (OR 11.976, 95% CI 1.981-72.413, P = 0.005), and tumor location (OR 6.311, 95% CI 1.514-26.304, P = 0.011) were included in the nomogram to predict the likelihood of having cervical metastasis. A nomogram cutoff value of 210 points (sensitivity 93.8%, specificity 87.5%) was significantly different to classify the patients metastasis risk group (P< 0.001). Nomogram showed predictive accuracy with AUC 0.881 (95% CI 0.779–0.983, P< 0.001) and good calibration after the validation.Conclusions: A preoperative nomogram incorporating multiparametric MRI demonstrated good prediction and performed adequately in our study. Three-dimensional assessment of occult metastasis risk value obtained from this nomogram can assist in preoperative decision making whether to manage with neck surgery for individual patients with early-stage SCCLT. The probability of nodal metastasis tended to be greater than 20% in patients with high metastasis risk or nomogram total score > 210 points.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development and Validation of Multiparametric Mri-based Nomogram for Predicting Occult Metastasis Risk in Early Tongue Squamous Cell Carcinoma

Saenthaveesuk

Yang

Zeng

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Likewise, Jiang et al [30] developed the nomogram to predict the probability of occult cervical lymph node metastasis before surgery in patients with cN0 SCC of the tongue with high (C-index = 0.846) and good calibration.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, previous studies have reported nomograms predicting long-term survival rate and predict cervical metastasis in head and neck cancer patients [26][27][28][29][30]. To date, a nomogram for predicting the likelihood of cervical lymph node metastasis pre-operatively of individual patients with early-stage of squamous cell carcinoma of lateral tongue (SCCLT) has not been widely investigated and no study has veri ed functionality of a MRI-based nomogram.…”

mentioning

confidence: 99%

Development and validation of multiparametric MRI-based nomogram for predicting occult metastasis risk in early tongue squamous cell carcinoma

Saenthaveesuk

Yang

Zeng

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract Background Nomograms are currently used in predicting individualized outcomes in clinical oncology of several cancers. However, nomograms for evaluating occult nodal metastasis of patients with squamous cell carcinoma of lateral tongue (SCCLT) have not been widely investigated for their functionality. This retrospective cohort study was designed to address this question. Methods This study was divided into primary and validation cohorts. The primary cohort comprised 120 patients diagnosed between 2012 and 2017, whereas the validation cohort included 41 patients diagnosed thereafter. The diagnostic value of multiparametric MRI, including radiologic tumor thickness threshold (rTTT) in three-dimensions, paralingual distance, and sublingual distance were investigated. A nomogram was developed based on stepwise logistic regression of potential predictors associated with nodal metastasis in the primary cohort and then tested for predictive accuracy in the validation cohort using area under the curve (AUC) and goodness-of-fit tests. Results Multivariate analysis, tumor size (odd ratio [OR] 15.175, 95% confidence interval [CI] 1.436-160.329, P = 0.024), rTTT (OR 11.528, 95% CI 2.483–53.530, P = 0.002), paralingual distance (OR 11.976, 95% CI 1.981–72.413, P = 0.005), and tumor location (OR 6.311, 95% CI 1.514–26.304, P = 0.011) were included in the nomogram to predict the likelihood of having cervical metastasis. A nomogram cutoff value of 210 points (sensitivity 93.8%, specificity 87.5%) was significantly different to classify the patients metastasis risk group (P < 0.001). Nomogram showed predictive accuracy with AUC 0.881 (95% CI 0.779–0.983, P < 0.001) and good calibration after the validation. Conclusions A preoperative nomogram incorporating multiparametric MRI demonstrated good prediction and performed adequately in our study. Three-dimensional assessment of occult metastasis risk value obtained from this nomogram can assist in preoperative decision making whether to manage with neck surgery for individual patients with early-stage SCCLT. The probability of nodal metastasis tended to be greater than 20% in patients with high metastasis risk or nomogram total score > 210 points.

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Development of a machine learning model for the prediction of nodal metastasis in early T classification oral squamous cell carcinoma: SEER‐based population study

et al. 2021

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BackgroundThis study aimed to develop and compare machine learning (ML) based predictive models for lymph node metastasis (LNM) in early T classification oral squamous cell carcinoma (OSCC).MethodsWe used data from the Surveillance Epidemiology and End Results Database to develop and validate the predictive models for LNM in patients with T1, T2 OSCC. Using simple clinical and histopathological data, we developed six ML algorithms to predict LNM. The predictive performance of models was compared.ResultsThe areas under the receiver operating characteristic curves (AUCs) of the six models ranged from 0.768 to 0.956. The best prediction performance was achieved with a XGBoost (AUC = 0.956). Permutation importance analysis showed that tumor size is the most important feature in predicting metastasis.ConclusionsWe developed a simplified and reproducible ML‐based predictive model for metastasis in early T classification OSCC that could be helpful for the decision of a treatment strategy.

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Development and validation of a nomogram to predict the risk of occult cervical lymph node metastases in cN0 squamous cell carcinoma of the tongue

Cited by 23 publications

References 14 publications

Development and Validation of Multiparametric Mri-based Nomogram for Predicting Occult Metastasis Risk in Early Tongue Squamous Cell Carcinoma

Development and Validation of Multiparametric Mri-based Nomogram for Predicting Occult Metastasis Risk in Early Tongue Squamous Cell Carcinoma

Development and validation of multiparametric MRI-based nomogram for predicting occult metastasis risk in early tongue squamous cell carcinoma

Development of a machine learning model for the prediction of nodal metastasis in early T classification oral squamous cell carcinoma: SEER‐based population study

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