Validating the ‘CUT score’ risk stratification tool for indeterminate thyroid nodules using the Bethesda system for reporting thyroid cytopathology

Low-cost Thyroid Nodule Diagnosis Using a Supervised Autoencoder Neural network

“…Thus, it would be interesting to develop less expensive methods to improve the risk stratification of malignancy among ITNs. In this sense, it is well known that certain routine characteristics may refine the risk of malignancy among one cytological category [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [51]. Some studies have led to the development of classification methods based on clinical, biological, radiological, and cytological data [23].…”

“…Some studies have led to the development of classification methods based on clinical, biological, radiological, and cytological data [23]. Those methods yielded scores and nomograms with performance ranging from 76.2 % to 91 % and 72.1 %to 91 %in terms of accuracy and AUC, respectively [14], [15], [16], [18], [19], [20], [21], [22], [23]. However, they were based on reliable but outdated mathematical models.…”

“…We also evaluated the metrics of four classical machine learning methods on the same dataset: a partial least squares-discriminant Of the initial features, we picked 12 features of higher importance: the TI-RADS score, the cytological category according to TBSRTC, the cytological subclassification according to Valderrabano et al [5], the nodule's size in echography, the presence of colloid in the cytologic sample, the age, clinical hyperthyroidism, the Hashimoto's thyroiditis, the gender, clinical hypothyroidism, the existence of a thyroidopathy of any kind, and the cellularity of the cytologic sample, in order of decreasing importance. Features like the TI-RADS score, the Bethesda category are well known to be associated with the risk of malignancy of one TN, and remain the spearheads of the current diagnostic strategy [14], [15], [16], [18], [19], [20], [21], [22], [23]. The cytologic subclassification according to Valderrabano et al is an emerging classification, which has already proven its ability to refine the risk of malignancy among ITNs [5].…”

“…The cytologic subclassification according to Valderrabano et al is an emerging classification, which has already proven its ability to refine the risk of malignancy among ITNs [5]. Features like age and male gender were also already reported as associated with the nature of a TN [16], [20], [21], [22]. The literature suggests that enlarged TN (especially those larger than 2 cm) have a higher risk of malignancy, in particular for those cytologically classified as Hurtle cell neoplasms, but the evidence remains controversial [52], [53], [55].…”

“…In fact, classical nodule sub-classifications, such as the TI-RADS classification, only use a few features of the nodule [12], [13]. Moreover, more advanced efforts using modern methods either were realised using only echography or cytology data, or tried to integrate all sources of nodule information, to the avail of an ungeneralizable model because of a lacking modus operandi [11], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23]; the only successful methods exhibit prohibitive costs, as mentioned previously. In this paper, we take an integrative approach and use three sources of nodule information: clinical, echography and cytology data.…”