人工智能可以检测出超过 80% 的肝癌

StephenW

人工智能可以检测出超过 80% 的肝癌
2022 年 12 月 8 日
2 分钟
肝病科

一项包括 724 人在内的新研究表明，人工智能可以通过血液检测技术检测出 80% 以上的肝癌。 该技术此前已成功用于检测肺癌。

用于早期截取 (DELFI) 片段的 DNA 评估允许检测游离 DNA (cfDNA) 中的片段化变化，这意味着来自癌细胞的 DNA 脱落到血液中。 它使用血液测试通过分析循环中存在的 cfDNA 的数量和大小来测量 DNA 如何包装在细胞核内。 健康细胞将基因组的不同区域小心翼翼地放置在不同的隔间中，而癌细胞则杂乱无章，基因组中的项目被随意地扔进去。 当它们死亡时，癌细胞会将 DNA 片段释放到血液中。 通过分析 cfDNA 片段的异常模式，DELFI 可以识别癌症的存在。 由于它只需要低覆盖率测序，因此该技术在筛选环境中具有成本效益。

美国马里兰州巴尔的摩市约翰霍普金斯 Kimmel 癌症中心癌症遗传学和表观遗传学项目的研究联合负责人 Victor Velculescu 及其同事使用了 724 份血浆样本，其中包括来自 75 人的样本，以训练机器学习模型。 他们使用了额外的 223 份血浆样本，包括来自 90 名肝细胞癌患者、35 名乙型肝炎病毒相关肝硬化患者、66 名乙型肝炎病毒患者和 32 名没有潜在危险因素的人的样本进行验证。 分析碎片模式以制定 DELFI 分数。

结果显示，未患癌症但患有肝硬化或肝炎的人得分较低，但肝细胞癌患者的得分平均高出 5 到 10 倍。 这些高分出现在所有癌症阶段。 DELFI 能够检测出肝癌，即使是在早期阶段，总体敏感性为 88%，特异性为 98%，适用于处于平均风险的人群。 在高危人群中，敏感性为 85%，特异性为 80%。

StephenW

Artificial Intelligence Can Detect More Than 80% of Liver Cancers
8th December 2022
2 Mins
Hepatology

ARTIFICIAL intelligence can detect more than 80% of liver cancers through blood testing technology, according to a new study including 724 people. The technology had previously been used to detect lung cancer successfully.

DNA evaluation of fragments for early interception (DELFI) allows for the detection of fragmentation changes among cell-free DNA (cfDNA), meaning DNA from cancer cells that are shed into the bloodstream. It uses a blood test to measure how DNA is packaged inside the nucleus of a cell by analysing the amount and the size of cfDNA that is present in the circulation. While healthy cells place different regions of the genome in various compartments carefully, cancer cells are disorganised, and items from the genome are thrown in haphazardly. When they die, cancer cells release DNA fragments into the bloodstream. By analysing cfDNA fragments for abnormal patterns, DELFI can identify the presence of cancer. As it only requires low-coverage sequencing, the technique is cost-effective in a screening setting.

Study co-lead Victor Velculescu, Cancer Genetics and Epigenetics Program at the John Hopkins Kimmel Cancer Center, Baltimore, Maryland, USA, and colleagues, used 724 plasma samples, which included samples from 75 people in order to train the machine learning model. They used an additional 223 plasma samples, including samples from 90 people with hepatocellular carcinoma, 35 with hepatitis B virus-related liver cirrhosis, 66 with hepatitis B virus, and 32 people without underlying risk factors for validation. Patterns of fragmentation were analysed to develop a DELFI score.

Results showed that people who did not have cancer but had cirrhosis or hepatitis had low scores, but patients with hepatocellular cancer had scores that were on average five to 10 times higher. These high scores were seen across all cancer stages. DELFI was able to detect liver cancer, even at early stages, with an overall sensitivity of 88% and specificity of 98% in people at average risk. In those at high risk, sensitivity was of 85% and specificity of 80%.