Nanogenomics and Artificial Intelligence: A Dynamic Duo for the Fight Against Breast Cancer.
Nanogenomics and Artificial Intelligence: A Dynamic Duo for the Fight Against Breast Cancer.
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DOI：
10.3389/fmolb.2021.651588
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发表时间：
2021
影响因子：
5
通讯作者：
Al-Shareeda AT
中科院分区：
文献类型：
--
作者：
Al-Sowayan BS;Al-Shareeda AT
应用软件用于帮助诊断乳腺癌。然而，人工智能 (AI) 的最新进展正在解决与不同类型肿瘤的检测、分类和监测相关的挑战。人工智能可以应用深度学习算法对乳房X线照相或组织学检查进行自动分析。由数字化乳房X光照片或全幻灯片图像生成的大量数据可以通过先进的机器学习进行互操作。这使得能够快速评估图像上的每个组织斑块，从而与人类表现相比更快地提高灵敏度和更具可重复性的诊断。另一方面，癌细胞外泌体是癌细胞释放到血液循环中的细胞外囊泡，正在探索作为癌症生物标志物。最近对癌症外泌体含量的研究表明，封装的 miRNA 和其他生物分子可以指示肿瘤亚型、可能的转移和预后。因此，理论上，通过纳米基因组，可以构建每种乳腺肿瘤亚型、雌激素受体状态和潜在转移位点的概况。然后，配备人工智能程序的实验室仪器可以通过将疑似患者的血清 miRNA 和生物分子组成与可用的模板图谱进行匹配来诊断疑似患者。在本文中，我们讨论了与目前适用于人工智能的黄金标准放射学或组织学方法相比，建立基于纳米基因组学人工智能的乳腺癌诊断方法的优势。此外，我们还讨论了基于外泌体封装内容而不是游离循环 miRNA 和其他生物分子构建乳腺癌诊断和预后生物分子谱的优势。
Application software is utilized to aid in the diagnosis of breast cancer. Yet, recent advances in artificial intelligence (AI) are addressing challenges related to the detection, classification, and monitoring of different types of tumors. AI can apply deep learning algorithms to perform automated analysis on mammographic or histologic examinations. Large volume of data generated by digitalized mammogram or whole-slide images can be interoperated through advanced machine learning. This enables fast evaluation of every tissue patch on an image, resulting in a quicker more sensitivity, and more reproducible diagnoses compared to human performance. On the other hand, cancer cell-exosomes which are extracellular vesicles released by cancer cells into the blood circulation, are being explored as cancer biomarker. Recent studies on cancer-exosome-content revealed that the encapsulated miRNA and other biomolecules are indicative of tumor sub-type, possible metastasis and prognosis. Thus, theoretically, through nanogenomicas, a profile of each breast tumor sub-type, estrogen receptor status, and potential metastasis site can be constructed. Then, a laboratory instrument, fitted with an AI program, can be used to diagnose suspected patients by matching their sera miRNA and biomolecules composition with the available template profiles. In this paper, we discuss the advantages of establishing a nanogenomics-AI-based breast cancer diagnostic approach, compared to the gold standard radiology or histology based approaches that are currently being adapted to AI. Also, we discuss the advantages of building the diagnostic and prognostic biomolecular profiles for breast cancers based on the exosome encapsulated content, rather than the free circulating miRNA and other biomolecules.
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DOI：
10.1007/s12539-017-0253-4
发表时间：
2019-06-01
影响因子：
4.8
作者：
Gaur, Pallavi;Chaturvedi, Anoop
通讯作者：
Chaturvedi, Anoop
影响因子：
13.8
作者：
Shamai, Gil;Binenbaum, Yoav;Kimmel, Ron
通讯作者：
Kimmel, Ron
影响因子：
4.6
作者：
Shen, Li;Margolies, Laurie R.;Sieh, Weiva
通讯作者：
Sieh, Weiva
影响因子：
10.3
作者：
Rodriguez-Ruiz, Alejandro;Lang, Kristina;Sechopoulos, Ioannis
通讯作者：
Sechopoulos, Ioannis
影响因子：
7.5
作者：
Raciti, Patricia;Sue, Jillian;Ceballos, Rodrigo;Godrich, Ran;Kunz, Jeremy D.;Kapur, Supriya;Reuter, Victor;Grady, Leo;Kanan, Christopher;Klimstra, David S.;Fuchs, Thomas J.
通讯作者：
Fuchs, Thomas J.