Artificial Intelligence–Assisted ACR TI-RADS and Bethesda-like Risk Prediction for Indeterminate Thyroid Nodules: A Retrospective Proof-of-Concept Evaluation

Background/Objectives: Cytologically indeterminate thyroid nodules (Bethesda III–IV) remain a diagnostic challenge. We evaluated the TRAICK (version 1.9.4; TRAICK, Istanbul, Türkiye) artificial intelligence (AI) system, which generates an American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) category and an imaging-derived Bethesda-like risk category from gray-scale ultrasound images. Methods: This retrospective proof-of-concept study included 52 surgically resected nodules with Bethesda III (n = 29) or IV (n = 23) cytology, with histopathology of the index nodule as the reference standard. The two TRAICK outputs were analyzed separately, and the ACR TI-RADS output was compared with two radiologists using the McNemar and DeLong tests. Results: Twenty-three nodules (44.2%) were malignant on index-nodule histopathology. The TRAICK ACR TI-RADS output had an accuracy of 71.2% (95% CI 57.7–81.7) and an area under the ROC curve (AUC) of 0.762 (0.640–0.884). Its AUC did not differ significantly from that of Radiologist 1 (0.762 vs. 0.777, p = 0.783); specificity was higher (65.5% vs. 37.9%, p = 0.021), whereas sensitivity was lower without a statistically significant difference (78.3% vs. 91.3%, p = 0.250). Compared with Radiologist 2, the TRAICK ACR TI-RADS output had higher accuracy (71.2% vs. 51.9%, p = 0.013), specificity (65.5% vs. 24.1%, p < 0.001), and AUC (0.762 vs. 0.629, p = 0.042). The Bethesda-like output had an AUC of 0.822 (0.707–0.938), with a sensitivity of 60.9% and specificity of 86.2% at the V–VI threshold. In sensitivity analyses, the AUC of the ACR TI-RADS output did not differ significantly from that of Radiologist 1, whereas its AUC advantage over Radiologist 2 was not statistically significant when low-risk neoplasms were classified as malignant. Conclusions: In this small, selected surgical cohort, the TRAICK ACR TI-RADS output showed discrimination that did not differ significantly from that of the more accurate of two experienced radiologists, with higher specificity, and its Bethesda-like output showed promising discrimination. These findings support further study of TRAICK as a complementary aid for preoperative decision-making; independent prospective validation is needed.

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Journal
Diagnostics
Published
2026-11-02
DOI
https://doi.org/10.3390/diagnostics16213616
Primary Topic
Thyroid Cancer Diagnosis and Treatment
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article

Artificial Intelligence–Assisted ACR TI-RADS and Bethesda-like Risk Prediction for Indeterminate Thyroid Nodules: A Retrospective Proof-of-Concept Evaluation

Düzgün Yıldırım, Eren Yılmaz, Aytuğ Altundağ, Filiz Çelebi et al.
Diagnostics
Thyroid Cancer Diagnosis and Treatment
article

Artificial Intelligence–Assisted ACR TI-RADS and Bethesda-like Risk Prediction for Indeterminate Thyroid Nodules: A Retrospective Proof-of-Concept Evaluation

Düzgün Yıldırım, Eren Yılmaz, Aytuğ Altundağ, Filiz Çelebi, Deniz Esin Tekcan Şanlı, Burcu Bektas Gunes, Ahmet Necati Şanlı, Bülent Çolakoğlu, Leonardo O. Iheme, Meltem Kurşun, Enes YERDEŞ, Mustafa Hurmuzlı, Ayten Paşazade
article en

Abstract

Background/Objectives: Cytologically indeterminate thyroid nodules (Bethesda III–IV) remain a diagnostic challenge. We evaluated the TRAICK (version 1.9.4; TRAICK, Istanbul, Türkiye) artificial intelligence (AI) system, which generates an American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) category and an imaging-derived Bethesda-like risk category from gray-scale ultrasound images. Methods: This retrospective proof-of-concept study included 52 surgically resected nodules with Bethesda III (n = 29) or IV (n = 23) cytology, with histopathology of the index nodule as the reference standard. The two TRAICK outputs were analyzed separately, and the ACR TI-RADS output was compared with two radiologists using the McNemar and DeLong tests. Results: Twenty-three nodules (44.2%) were malignant on index-nodule histopathology. The TRAICK ACR TI-RADS output had an accuracy of 71.2% (95% CI 57.7–81.7) and an area under the ROC curve (AUC) of 0.762 (0.640–0.884). Its AUC did not differ significantly from that of Radiologist 1 (0.762 vs. 0.777, p = 0.783); specificity was higher (65.5% vs. 37.9%, p = 0.021), whereas sensitivity was lower without a statistically significant difference (78.3% vs. 91.3%, p = 0.250). Compared with Radiologist 2, the TRAICK ACR TI-RADS output had higher accuracy (71.2% vs. 51.9%, p = 0.013), specificity (65.5% vs. 24.1%, p < 0.001), and AUC (0.762 vs. 0.629, p = 0.042). The Bethesda-like output had an AUC of 0.822 (0.707–0.938), with a sensitivity of 60.9% and specificity of 86.2% at the V–VI threshold. In sensitivity analyses, the AUC of the ACR TI-RADS output did not differ significantly from that of Radiologist 1, whereas its AUC advantage over Radiologist 2 was not statistically significant when low-risk neoplasms were classified as malignant. Conclusions: In this small, selected surgical cohort, the TRAICK ACR TI-RADS output showed discrimination that did not differ significantly from that of the more accurate of two experienced radiologists, with higher specificity, and its Bethesda-like output showed promising discrimination. These findings support further study of TRAICK as a complementary aid for preoperative decision-making; independent prospective validation is needed.

DiagnosticsVol. 16(21)
Yeditepe University (TR), Taksim German Hospital (TR), Acıbadem University (TR), Gaziantep Children's Hospital (TR), Biruni University (TR), Sivas State Hospital (TR), Gedik University (TR), Amerikan Hastanesi (TR), Gaziantep University (TR)
Openalex Percentile: Top 12%
Thyroid Cancer Diagnosis and Treatment
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