The value of diagnostic nomogram based on CT radiomics for the preoperative differentiation between benign and malignant thyroid follicular neoplasms
10.3760/cma.j.cn112149-20210423-00403
- VernacularTitle:基于CT影像组学列线图鉴别甲状腺良性与恶性滤泡性肿瘤的价值
- Author:
Pengzhou TANG
1
;
Caiyue REN
;
Yueming WANG
;
Zhengrong ZHOU
Author Information
1. 复旦大学附属肿瘤医院放射科，上海 200032
- Keywords:
Thyroid neoplasms;
Adenocarcinoma, follicular;
Tomography, X-ray computed;
Radiomics;
Nomogram
- From:
Chinese Journal of Radiology
2022;56(2):136-141
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of nomogram constructed by CT-based radiomics for differentiating benign and malignant thyroid follicular neoplasms.Methods:Totally 200 post-surgery patients with pathologically confirmed thyroid follicular neoplasms in Fudan University Shanghai Cancer Center from January 2016 to December 2018 were retrospectively analyzed. Among the patients, 46 were follicular thyroid carcinoma (FTC) and 154 patients were follicular thyroid adenoma (FTA). The patients were randomly divided into a training set ( n=140) and validation set ( n=60) using a random number table. CT signs and radiomics features of each patient were analyzed within the LIFEx package. A predictive model was developed by the least absolute shrinkage and selection operator regression to build a nomogram based on selected parameters. The predictive effectiveness of differentiating benign and malignant thyroid follicular neoplasms was evaluated by the area under receiver operating characteristic curve (AUC). Calibration plots were formulated to evaluate the reliability and accuracy of the nomogram based on internal (training set) and external (validation set) validity. The clinical value of the nomogram was estimated through the decision curve analysis. Results:The prediction nomogram was built with 4 selected parameters, including grey level zone length matrix (GLZLM)-gray-level zone length matrix_zone length non-uniformity, GLZLM-gray-level zone length matrix_low gray-level zone emphasis, CONVENTIONAL_HUQ3, CONVENTIONAL_HUmean. In training and validation sets, the AUCs for differentiating FTC and FTA were 0.863 (95%CI 0.746-0.932), 0.792 (95%CI 0.658-0.917), accuracy were 87.9% and 75.0%, sensitivity were 67.9% and 66.7%, specificity were 91.1% and 90.5%, respectively. The calibration curves indicated good consistency between actual observation and prediction for differentiating the malignancy. Decision curve analysis demonstrated the nomogram was clinically useful.Conclusions:The CT radiomics mode shows the certain value and great potential to identify benign or malignant thyroid follicular neoplasms and the nomogram can accurately and intuitively predict the malignancy potential in patients with thyroid follicular neoplasms.
