Diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest

Abstract

Background: Various combinations of ultrasonographic (US) characteristics are increasingly utilized to classify thyroid nodules. But they lack theories, and heavily depend on radiologists' experience, and cannot correctly classify thyroid nodules. Hence, our main purpose of this manuscript is to select the US characteristics significantly associated with malignancy and to develop an efficient scoring system for facilitating ultrasonic clinicians to correctly identify thyroid malignancy.

Methods: A logistic regression (LR) model is utilized to identify the potential thyroid malignancy, and the least absolute shrinkage and selection operator (LASSO) method is adopted to simultaneously select US characteristics significantly associated with malignancy and estimate parameters in LR model. Based on the selected US characteristics, we calculate the probability for each of thyroid nodules via random forest (RF) and extreme learning machine (ELM), and develop a scoring system to classify thyroid nodules. For comparison, we also consider eight state-of-the-art methods such as support vector machine (SVM), neural network (NET), etc. The area under the receiver operating characteristic curve (AUC) is employed to measure the accuracy of various classifiers.

Results: The US characteristics: nodule size, AP/T≥1, solid component, micro-calcifications, hackly border, hypoechogenicity, presence of halo, unclear border, irregular margin, and central vascularity are selected as the significant predictors associated with thyroid malignancy via the LASSO LR (LLR). Using the developed scoring system, thyroid nodules are classified into the following four categories: benign, low suspicion, intermediate suspicion, and high suspicion, whose rates of malignancy correctly identified for RF (ELM) method on the testing dataset are 0.0% (4.3%), 14.3% (50.0%), 58.1% (59.1%) and 96.1% (97.7%), respectively.

Conclusion: LLR together with RF performs better than other methods in identifying malignancy, especially for abnormal nodules, in terms of risk scores. The developed scoring system can well predict the risk of malignancy and guide medical doctors to make management decisions for reducing the number of unnecessary biopsies for benign nodules.

Keywords: Random forest; Risk score; Thyroid nodule; Ultrasonographic characteristic.

Fig. 1

US scans show characteristics of thyroid modules: a beign nodules; b malignant nodule with hackly border; c malignant nodule with micro-calcifications; d malignant nodule with (AP/T ≥1) and irregular margin

Fig. 2

Flow chart of our proposed method

Fig. 3

Cutoff of echogenicity ratio vs. rate of malignancy. The vertical line corresponds to the cutoff =1.3

Fig. 4

Cutoff and performance of hypoechogenicity in the diagnosis of malignant nodules. Scatter diagram of ER distribution for benign and malignant thyroid nodules. The vertical line in a and horizontal line in b correspond to the cutoff = 1.3, respectively

Fig. 5

Importance of US characteristics by RF

Fig. 6

The risk score of malignancy for each thyroid nodule, as calculated by statistics methods respectively. The green crossings represent malignant nodules and blue dots benign nodules

Fig. 7

The box bar graphs show the risk score of malignancy for benign and malignant nodules