A Prototype Artificial Intelligence Algorithm Versus Liver Imaging Reporting and Data System (LI-RADS) Criteria in Diagnosing Hepatocellular Carcinoma on Computed Tomography: a Randomized Trial
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 300
- 试验地点
- 4
- 主要终点
- Diagnostic accuracy for HCC
研究概览
简要总结
This study aims to prospective validate this AI algorithm in comparison with the current standard of radiological reporting in a randomized manner in the at-risk population undergoing triphasic contrast CT. This research project is totally independent and separated from the actual clinical reporting of the CT scan by the duty radiologist. The primary study outcome is to compare the diagnostic performance of the prototype AI algorithm versus LI-RADS criteria in determining HCC on CT in the at-risk population.
详细描述
Liver cancer is the sixth most commonly diagnosed cancer and the fourth leading cause of cancer death worldwide. The main disease burden is found in East Asia, in which the age-standardized incidence is 26.8 and 8.7 per 100,000 in men and women respectively. In 2017, among the top 10 most common cancers in Hong Kong, liver cancer had the highest case fatality rate of 84.6%. The five-year survival rates of hepatocellular carcinoma (HCC) differ greatly with disease staging, ranging from 91.5% in <2 cm with surgical resection to 11% in >5 cm with adjacent organ involvement. The early and accurate diagnosis of HCC is paramount in improving cancer survival.
Unlike other common cancers, HCC is diagnosed by highly characteristic dynamic patterns on contrast-enhanced cross sectional imaging, without the need of pathological confirmation. The Liver Imaging Reporting and Data System (LI-RADS) was established to standardize the lexicon, interpretation and communication of radiological findings related to HCC. However, up to 49% of nodules identified in computed tomography (CT) in the at-risk population are categorized by LI-RADS as indeterminate, further delaying the establishment of diagnosis.
There are currently studies pioneering the application of artificial intelligence (AI) in the field of medical imaging. An interdisciplinary research team of clinicians, radiologists and statistical scientists, based on the clinical and radiological database of over 4,000 liver images, have developed an AI algorithm to accurately diagnose liver cancer on CT. Based on retrospective data, an interim analysis found the AI algorithm able to achieve a diagnostic accuracy of >97% and a negative predictive value of >99%.
If the prototype AI algorithm proves to have a better one-off diagnostic performance when compared to LI-RADS, it can facilitate the earlier diagnosis of HCC, allowing earlier definitive treatment and improving cancer survival.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- Single (Investigator)
盲法说明
Both radiologists will be blinded to the clinical characteristics and subsequent management of participants, with any discordance in assessment resolved by consensus before reaching a final decision.
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •1. Age >=18 years.
- •2. Defined as the at-risk population requiring regular liver ultrasonography surveillance.
- •These include:
- •Cirrhotic patients of any disease etiology,
- •Chronic hepatitis B patients of age ≥40 years for men, age ≥50 years for women or with a family history of HCC.
- •3. At least one new-onset focal liver nodule detected on liver ultrasonography.
排除标准
- •1. Liver nodules of <1 cm. Currently such nodules are not reported using LI-RADS criteria but are recommended for a repeat scan in 3-6 months. In patients with multiple liver nodules, the largest nodule will be assessed.
- •2. Patients with contraindications for contrast CT imaging, including a history of contrast anaphylaxis and impaired renal function (glomerular filtration rate <30 ml/min).
- •3. Patients with prior transarterial chemoembolization or other interventional procedures with intrahepatic injection of lipiodol. Lipiodol is extremely hyperdense on computed tomography and will preclude objective interpretation. Such patients were also excluded in the development of our prototype AI algorithm.
研究组 & 干预措施
Prototype AI algorithm
In-house prototype deep learning artificial intelligence algorithm
干预措施: Prototype artificial intelligence algorithm (Diagnostic Test)
LI_RADS interpretation
LI-RADS criteria will be assessed independently by two specified abdominal radiologists with at least 10 years of experience in cross-sectional abdominal imaging
干预措施: LI-RADS (Diagnostic Test)
结局指标
主要结局
Diagnostic accuracy for HCC
时间窗: 12 months
Number of participants diagnosed with HCC using a composite clinical reference standard. A lesion will be considered positive for HCC based on histology (biopsy, surgical resection or explant) or achieving LR-5 criteria in subsequent imaging. A lesion will be considered negative for HCC if it demonstrated stability at imaging for at least 12 months, unequivocal spontaneous reduction, or disappearance in the absence of tumor treatment.
次要结局
- Other diagnostic performance parameters for HCC(12 months)
- Interpretation time(12 months)
- Occurrence of technical failures(12 months)