Automatic detection of thyroid nodules with a real-time artificial intelligence system in a real clinical scenario and the associated influencing factors.

BACKGROUND: At present, most articles mainly focused on the diagnosis of thyroid nodules by using artificial intelligence (AI), and there was little research on the detection performance of AI in thyroid nodules. OBJECTIVE: To explore the value of a real-time AI based on computer-aided diagnosis system in the detection of thyroid nodules and to analyze the factors influencing the detection accuracy. METHODS: From June 1, 2022 to December 31, 2023, 224 consecutive patients with 587 thyroid nodules were prospective collected. Based on the detection results determined by two experienced radiologists (both with more than 15 years experience in thyroid diagnosis), the detection ability of thyroid nodules of radiologists with different experience levels (junior radiologist with 1 year experience and senior radiologist with 5 years experience in thyroid diagnosis) and real-time AI were compared. According to the logistic regression analysis, the factors influencing the real-time AI detection of thyroid nodules were analyzed. RESULTS: The detection rate of thyroid nodules by real-time AI was significantly higher than that of junior radiologist (P = 0.013), but lower than that of senior radiologist (P = 0.001). Multivariate logistic regression analysis showed that nodules size, superior pole, outside (near carotid artery), close to vessel, echogenicity (isoechoic, hyperechoic, mixed-echoic), morphology (not very regular, irregular), margin (unclear), ACR TI-RADS category 4 and 5 were significant independent influencing factors (all P < 0.05). With the combination of real-time AI and radiologists, junior and senior radiologist increased the detection rate to 97.4% (P < 0.001) and 99.1% (P = 0.015) respectively. CONCLUSONS: The real-time AI has good performance in thyroid nodule detection and can be a good auxiliary tool in the clinical work of radiologists.

Liver failure after hepatectomy: A risk assessment using the pre-hepatectomy shear wave elastography technique.

OBJECTIVE: To determine the efficacy of liver stiffness (LS) measurements utilizing the Shear Wave Elastography (SWE) technique for predicting post-hepatectomy liver failure (PHLF) among patients with hepatocellular carcinoma (HCC). METHODS: Data from eighty consecutive patients who were undergoing hepatectomy for HCC were prospectively identified and evaluated with preoperative SWE. The SWE was measured with advanced ultrasound equipment (Philips EPIQ7; Philips Healthcare, Seattle, WA, USA). PHLF classification was defined according to the International Study Group of Liver Surgery Recommendations (ISGLS). RESULTS: SWE was successfully performed in 77 patients. According to the ISGLS criteria, PHLF occurred in 35.1% of patients (27 patients), including 2/25 patients with Grade A/B, respectively. Elevated SWE values (P=0.002) and histological cirrhosis (P=0.003) were independent predictors of PHLF according to the multivariate analysis. Patients with SWE values higher than or equal to 6.9kPa were identified at higher risk of PHLF (area under the curve: 0.843, sensitivity: 77.8% and specificity: 78.0%). Postoperative dynamic course of the median the Model For End-stage Liver Disease (MELD) score showed irregular changes among patients with an SWE >6.9kPa. Patients with an SWE <6.9kPa, postoperative dynamic course of the median MELD score gradually decreased. CONCLUSION: LS measured with SWE is a valid and reliable method for the prediction of PHLF grade A/B among patients with HCC. SWE could become a routine examination for the preoperative evaluation of PHLF.