Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 3/4)
유사 논문P · Population 대상 환자/모집단
151 patients with DTC without distant metastasis and who received RAI treatment was determined (ER/nonER).
I · Intervention 중재 / 시술
RAI treatment was determined (ER/nonER)
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
The model with the highest AUC value was extreme gradient boosting (AUC = 0.871), the highest accuracy shown by gradient boosting (81%). [CONCLUSIONS] ML models may be used to predict ER in patients with DTC without distant metastasis.
[OBJECTIVE] If excellent response (ER) occurs after radioactive iodine (RAI) treatment in patients with differentiated thyroid carcinoma (DTC), the recurrence rate is low.
- p-value p = 0.007
APA
Bülbül O, Nak D (2024). Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning.. Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale, 44(4), 261-268. https://doi.org/10.14639/0392-100X-N3029
MLA
Bülbül O, et al.. "Predicting excellent response to radioiodine in differentiated thyroid cancer using machine learning.." Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale, vol. 44, no. 4, 2024, pp. 261-268.
PMID
39347551
Abstract
[OBJECTIVE] If excellent response (ER) occurs after radioactive iodine (RAI) treatment in patients with differentiated thyroid carcinoma (DTC), the recurrence rate is low. Our study aims to predict ER at 6-24 months after RAI by using machine learning (ML) methods in which clinicopathological parameters are included in patients with DTC without distant metastasis.
[METHODS] Treatment response of 151 patients with DTC without distant metastasis and who received RAI treatment was determined (ER/nonER). Thyroidectomy ± neck dissection pathology data, laboratory, and imaging findings before and after RAI treatment were introduced to ML models.
[RESULTS] After RAI treatment, 118 patients had ER and 33 had nonER. Before RAI treatment, TgAb was positive in 29% of patients with ER and 55% of patients with nonER (p = 0.007). Eight of the ML models predicted ER with high area under the ROC curve (AUC) values (> 0.700). The model with the highest AUC value was extreme gradient boosting (AUC = 0.871), the highest accuracy shown by gradient boosting (81%).
[CONCLUSIONS] ML models may be used to predict ER in patients with DTC without distant metastasis.
[METHODS] Treatment response of 151 patients with DTC without distant metastasis and who received RAI treatment was determined (ER/nonER). Thyroidectomy ± neck dissection pathology data, laboratory, and imaging findings before and after RAI treatment were introduced to ML models.
[RESULTS] After RAI treatment, 118 patients had ER and 33 had nonER. Before RAI treatment, TgAb was positive in 29% of patients with ER and 55% of patients with nonER (p = 0.007). Eight of the ML models predicted ER with high area under the ROC curve (AUC) values (> 0.700). The model with the highest AUC value was extreme gradient boosting (AUC = 0.871), the highest accuracy shown by gradient boosting (81%).
[CONCLUSIONS] ML models may be used to predict ER in patients with DTC without distant metastasis.
MeSH Terms
Humans; Iodine Radioisotopes; Thyroid Neoplasms; Machine Learning; Male; Female; Middle Aged; Adult; Treatment Outcome; Retrospective Studies; Aged