{"title":"Commentary on \"Large-Scale Pancreatic Cancer Detection via Non-Contrast CT and Deep Learning\".","authors":"Ibrahem Alshybani","doi":"10.1177/11795972241293521","DOIUrl":null,"url":null,"abstract":"<p><p>Cao et al. introduce PANDA, an AI model designed for the early detection of pancreatic ductal adenocarcinoma (PDAC) using non-contrast CT scans. While the model shows great promise, it faces several challenges. Notably, its training predominantly on East Asian datasets raises concerns about generalizability across diverse populations. Additionally, PANDA's ability to detect rare lesions, such as pancreatic neuroendocrine tumors (PNETs), could be improved by integrating other imaging modalities. High specificity is a strength, but it also poses risks of false positives, which may lead to unnecessary procedures and increased healthcare costs. Implementing a tiered diagnostic approach and expanding training data to include a wider demographic are essential steps for enhancing PANDA's clinical utility and ensuring its successful global implementation, ultimately shifting the focus from late diagnosis to proactive early detection.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528658/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Engineering and Computational Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/11795972241293521","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Cao et al. introduce PANDA, an AI model designed for the early detection of pancreatic ductal adenocarcinoma (PDAC) using non-contrast CT scans. While the model shows great promise, it faces several challenges. Notably, its training predominantly on East Asian datasets raises concerns about generalizability across diverse populations. Additionally, PANDA's ability to detect rare lesions, such as pancreatic neuroendocrine tumors (PNETs), could be improved by integrating other imaging modalities. High specificity is a strength, but it also poses risks of false positives, which may lead to unnecessary procedures and increased healthcare costs. Implementing a tiered diagnostic approach and expanding training data to include a wider demographic are essential steps for enhancing PANDA's clinical utility and ensuring its successful global implementation, ultimately shifting the focus from late diagnosis to proactive early detection.
Cao 等人介绍了 PANDA,这是一种利用非对比 CT 扫描早期检测胰腺导管腺癌(PDAC)的人工智能模型。虽然该模型前景广阔,但也面临着一些挑战。值得注意的是,它主要在东亚数据集上进行训练,这引起了人们对其在不同人群中通用性的担忧。此外,PANDA 检测胰腺神经内分泌肿瘤(PNET)等罕见病变的能力还可以通过整合其他成像模式来提高。高特异性是其优势,但也存在假阳性的风险,可能导致不必要的手术和医疗成本的增加。实施分级诊断方法和扩大培训数据以纳入更广泛的人群是提高 PANDA 临床实用性和确保其在全球成功实施的必要步骤,最终将重点从晚期诊断转移到主动早期检测。