Artificial intelligence and imaging for risk prediction of pancreatic cancer: a narrative review.
Artificial intelligence and imaging for risk prediction of pancreatic cancer: a narrative review.
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DOI：
10.21037/cco-21-117
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发表时间：
2022-03
影响因子：
2.8
通讯作者：
Li, Debiao
中科院分区：
文献类型：
--
作者：
Qureshi, Touseef Ahmad;Javed, Sehrish;Sarmadi, Tabasom;Pandol, Stephen Jacob;Li, Debiao
强调胰腺影像学和人工智能（AI）在胰腺导管腺癌（PDAC）风险预测中的重要性。在早期阶段检测PDAC是具有挑战性的，因为该疾病要么保持无症状，要么呈现非特异性症状。PDAC的风险预测是一种有效的策略，因为随后的靶向筛查可以帮助在早期阶段甚至在症状出现之前诊断癌症。然而，缺乏具体的临床和流行病学预测PDAC的预测，使预测一个极具挑战性的任务。检测胰腺中的前驱变化可能有助于PDAC的风险预测，因为癌前胰腺通过生物学适应而演变-在腹部成像上表现为形态和纹理变化。然而，这种微观层面的“线索”通常被忽视或不被重视，部分原因是没有工具来检测和解释这种复杂的测量，使PDAC的风险预测成为一个悬而未决的问题。本综述强调了目前PDAC风险预测模型的局限性和腹部成像预测PDAC的重要性。一个暗示性的叙述是关于最近的人工智能工具如何帮助提取生物标志物的精确测量，检测早期体征和癌前异常，量化组织特征，并揭示复杂的功能，可能表明未来胰腺癌的发病率使用腹部成像。在其他癌症的同行例子的帮助下，建立了一个关于AI在利用胰腺图像特征来增强PDAC风险预测方面的应用的案例。此外，还讨论了人工智能应用面临的挑战，包括模型训练数据不足、数据隐私侵犯风险、数据标签不一致和计算资源有限，以及潜在的解决方案。人工智能领域的最新进展是利用自动化工具识别PDAC的成像指标并增强癌症风险预测的潜在机会。有了这种意识和动机，更好地管理PDAC的期望。
To emphasize the importance of pancreatic imaging and the application of Artificial Intelligence (AI) for enhanced risk prediction of pancreatic ductal adenocarcinoma (PDAC). Detecting PDAC at the early stage is challenging as the disease either remains asymptomatic or presents nonspecific symptoms. Risk prediction of PDAC is an efficient strategy as subsequent targeted screening can assist in diagnosing cancer at the early stage even before the symptoms appear. However, the lack of specific clinical and epidemiological predictors of PDAC makes prediction a highly challenging task. Detecting precursor changes in the pancreas can potentially assist in the risk prediction of PDAC as the precancerous pancreas evolves through biological adaptations–presented as morphological and textural changes on abdominal imaging. However, such microlevel “clues” usually remain unnoticed or unappreciated, partly due to the unavailability of tools to detect and interpret such complex measurements, making the risk prediction of PDAC an unresolved problem. This review study highlights the limitations of the current risk prediction models of PDAC and the importance of abdominal imaging for predicting PDAC. A suggestive narrative is made as to how recent AI tools can assist in extracting precise measurements of biomarkers, detecting early signs and precancerous abnormalities, quantifying tissue characteristics, and revealing complex features potentially indicative of future incidence of pancreatic cancer using abdominal imaging. With the help of peer examples of other cancers, a case is built about the application of AI in utilizing image features of the pancreas to enhance risk prediction of PDAC. Furthermore, the challenges of AI applications including insufficient data for model training, risk of data privacy violation, inconsistent data labeling, and limited computational resources, and their potential solutions are also discussed. The recent advancement in the domain of AI is a potential opportunity to utilize automated tools for the identification of imaging-based indicators of PDAC and perform enhanced risk prediction of cancer. With this awareness and motivation, better management of PDAC has expected.
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影响因子：
5.2
作者：
Casari I;Falasca M
通讯作者：
Falasca M
DOI：
10.1016/j.pan.2012.02.003
发表时间：
2012-03
期刊：
Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]
影响因子：
--
作者：
Aggarwal G;Rabe KG;Petersen GM;Chari ST
通讯作者：
Chari ST
影响因子：
3.6
作者：
Illes, Dora;Terzin, Viktoria;Czako, Laszlo
通讯作者：
Czako, Laszlo
影响因子：
5.9
作者：
Ahn, Sung Soo;Kim, Myeong-Jin;Lim, Joon Seok
通讯作者：
Lim, Joon Seok
影响因子：
7.2
作者：
Damiano, J;Bordier, L;Bauduceau, B
通讯作者：
Bauduceau, B