Abstract
Current approaches to predict central cervical lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) have failed to identify patients who would benefit from preventive treatment. Machine learning has offered the opportunity to improve accuracy by comparing the different algorithms. We assessed which machine learning algorithm can best improve CLNM prediction. This retrospective study used routine ultrasound data of 1,364 PTC patients. Six machine learning algorithms were compared to predict the possibility of CLNM. Predictive accuracy was assessed by sensitivity, specificity, positive predictive value, negative predictive value, and the area under the curve (AUC). The patients were randomly split into the training (70%), validation (15%), and test (15%) data sets. Random forest (RF) led to the best diagnostic model in the test cohort (AUC 0.731 ± 0.036, 95% confidence interval: 0.664–0.791). The diagnostic performance of the RF algorithm was most dependent on the following five top-rank features: extrathyroidal extension (27.597), age (17.275), T stage (15.058), shape (13.474), and multifocality (12.929). In conclusion, this study demonstrated promise for integrating machine learning methods into clinical decision-making processes, though these would need to be tested prospectively.
Highlights
According to the American Cancer Society 2020 American Cancer Data Statistics, thyroid cancer incidence has accounted as the fifth leading cause of cancer in women [1], similar to China [2]
We divided the patients into three parts randomly by IBM SPSS Modeler software: approximately 70% were conducted as the training cohort, about 15% were conducted as the validation cohort, and the remaining around 15% were used as the test cohort
Dummy variables were grouped according to the 8th American Joint Committee on Cancer (AJCC) staging systems [26] and American College of Radiology (ACR) TI-RADS [25], details as follows: location, background, diameter (T1a = “≤1 cm”, T1b = “1–2 cm”, T2 = “2–4 cm”, ≥T3 = “>4 cm”), margin, shape, composition, echogenicity, calcification, ETE, and multifocality
Summary
According to the American Cancer Society 2020 American Cancer Data Statistics, thyroid cancer incidence has accounted as the fifth leading cause of cancer in women [1], similar to China [2]. According to the American Thyroid Association (ATA) management guidelines for adult patients with thyroid cancer [6], whether there is CLNM or not directly affects the formulation of preoperative surgical procedures. Prophylactic central lymph node dissection (CLND) for cN0 papillary thyroid carcinoma (PTC) patients will undoubtedly cause excessive medical treatment. It is of great significance to distinguish CLNM with non-invasive methods before surgery for the treatment and prognosis in PTC patients. Ultrasound remains the most critical imaging modality in the evaluation of thyroid cancer according to the ATA Statement on Preoperative Imaging for Thyroid Cancer Surgery [7] due to its convenience, non-invasive, and non-radiation.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
