An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study
ObjectiveCentral lymph node metastasis (CLNM) is a predictor of poor prognosis for papillary thyroid carcinoma (PTC) patients. The options for surgeon operation or follow-up depend on the state of CLNM while accurate prediction is a challenge for radiologists. The present study aimed to develop and...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-02-01
|
| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2023.964074/full |
| _version_ | 1797901387966709760 |
|---|---|
| author | Luchen Chang Yanqiu Zhang Jialin Zhu Linfei Hu Xiaoqing Wang Haozhi Zhang Qing Gu Xiaoyu Chen Sheng Zhang Ming Gao Ming Gao Xi Wei |
| author_facet | Luchen Chang Yanqiu Zhang Jialin Zhu Linfei Hu Xiaoqing Wang Haozhi Zhang Qing Gu Xiaoyu Chen Sheng Zhang Ming Gao Ming Gao Xi Wei |
| author_sort | Luchen Chang |
| collection | DOAJ |
| description | ObjectiveCentral lymph node metastasis (CLNM) is a predictor of poor prognosis for papillary thyroid carcinoma (PTC) patients. The options for surgeon operation or follow-up depend on the state of CLNM while accurate prediction is a challenge for radiologists. The present study aimed to develop and validate an effective preoperative nomogram combining deep learning, clinical characteristics and ultrasound features for predicting CLNM.Materials and methodsIn this study, 3359 PTC patients who had undergone total thyroidectomy or thyroid lobectomy from two medical centers were enrolled. The patients were divided into three datasets for training, internal validation and external validation. We constructed an integrated nomogram combining deep learning, clinical characteristics and ultrasound features using multivariable logistic regression to predict CLNM in PTC patients.ResultsMultivariate analysis indicated that the AI model-predicted value, multiple, position, microcalcification, abutment/perimeter ratio and US-reported LN status were independent risk factors predicting CLNM. The area under the curve (AUC) for the nomogram to predict CLNM was 0.812 (95% CI, 0.794-0.830) in the training cohort, 0.809 (95% CI, 0.780-0.837) in the internal validation cohort and 0.829(95%CI, 0.785-0.872) in the external validation cohort. Based on the analysis of the decision curve, our integrated nomogram was superior to other models in terms of clinical predictive ability.ConclusionOur proposed thyroid cancer lymph node metastasis nomogram shows favorable predictive value to assist surgeons in making appropriate surgical decisions in PTC treatment. |
| first_indexed | 2024-04-10T09:01:15Z |
| format | Article |
| id | doaj.art-8e824090d5df44cf85f883ecc42267f9 |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-10T09:01:15Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-8e824090d5df44cf85f883ecc42267f92023-02-21T14:27:19ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-02-011410.3389/fendo.2023.964074964074An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter studyLuchen Chang0Yanqiu Zhang1Jialin Zhu2Linfei Hu3Xiaoqing Wang4Haozhi Zhang5Qing Gu6Xiaoyu Chen7Sheng Zhang8Ming Gao9Ming Gao10Xi Wei11Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Thyroid and Neck Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Thyroid and Neck Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Ultrasonography, Cangzhou Hospital of Integrated Traditional Chinese and Western Medicine of Hebei Province, Cangzhou, Hebei, ChinaDepartment of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Thyroid and Neck Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center of Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaDepartment of Breast and Thyroid Surgery, Tianjin Union Medical Center, Tianjin, ChinaDepartment of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin, ChinaObjectiveCentral lymph node metastasis (CLNM) is a predictor of poor prognosis for papillary thyroid carcinoma (PTC) patients. The options for surgeon operation or follow-up depend on the state of CLNM while accurate prediction is a challenge for radiologists. The present study aimed to develop and validate an effective preoperative nomogram combining deep learning, clinical characteristics and ultrasound features for predicting CLNM.Materials and methodsIn this study, 3359 PTC patients who had undergone total thyroidectomy or thyroid lobectomy from two medical centers were enrolled. The patients were divided into three datasets for training, internal validation and external validation. We constructed an integrated nomogram combining deep learning, clinical characteristics and ultrasound features using multivariable logistic regression to predict CLNM in PTC patients.ResultsMultivariate analysis indicated that the AI model-predicted value, multiple, position, microcalcification, abutment/perimeter ratio and US-reported LN status were independent risk factors predicting CLNM. The area under the curve (AUC) for the nomogram to predict CLNM was 0.812 (95% CI, 0.794-0.830) in the training cohort, 0.809 (95% CI, 0.780-0.837) in the internal validation cohort and 0.829(95%CI, 0.785-0.872) in the external validation cohort. Based on the analysis of the decision curve, our integrated nomogram was superior to other models in terms of clinical predictive ability.ConclusionOur proposed thyroid cancer lymph node metastasis nomogram shows favorable predictive value to assist surgeons in making appropriate surgical decisions in PTC treatment.https://www.frontiersin.org/articles/10.3389/fendo.2023.964074/fulldeep learningpapillary thyroid carcinomacentral lymph node metastasisnomogramultrasound |
| spellingShingle | Luchen Chang Yanqiu Zhang Jialin Zhu Linfei Hu Xiaoqing Wang Haozhi Zhang Qing Gu Xiaoyu Chen Sheng Zhang Ming Gao Ming Gao Xi Wei An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study Frontiers in Endocrinology deep learning papillary thyroid carcinoma central lymph node metastasis nomogram ultrasound |
| title | An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study |
| title_full | An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study |
| title_fullStr | An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study |
| title_full_unstemmed | An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study |
| title_short | An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study |
| title_sort | integrated nomogram combining deep learning clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer a multicenter study |
| topic | deep learning papillary thyroid carcinoma central lymph node metastasis nomogram ultrasound |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.964074/full |
| work_keys_str_mv | AT luchenchang anintegratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT yanqiuzhang anintegratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT jialinzhu anintegratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT linfeihu anintegratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT xiaoqingwang anintegratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT haozhizhang anintegratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT qinggu anintegratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT xiaoyuchen anintegratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT shengzhang anintegratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT minggao anintegratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT minggao anintegratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT xiwei anintegratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT luchenchang integratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT yanqiuzhang integratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT jialinzhu integratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT linfeihu integratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT xiaoqingwang integratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT haozhizhang integratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT qinggu integratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT xiaoyuchen integratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT shengzhang integratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT minggao integratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT minggao integratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy AT xiwei integratednomogramcombiningdeeplearningclinicalcharacteristicsandultrasoundfeaturesforpredictingcentrallymphnodemetastasisinpapillarythyroidcanceramulticenterstudy |