Krishnan Arunkumar
Department of Supportive Oncology, Atrium Health Levine Cancer, Charlotte, NC 28204, United States.
Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, United States.
World J Gastrointest Oncol. 2025 Jul 15;17(7):103282. doi: 10.4251/wjgo.v17.i7.103282.
A recent study by Luo examined the relationship between the pathological types of pancreatic cancer (PC) and their imaging characteristics. While this study presented an important step toward improving diagnostic accuracy for PC, we have several concerns regarding its generalizability, cohort selection, imaging variability, statistical methods, and potential confounding factors. We recommended that future research adopt multi-center, prospective designs to improve representation and minimize bias. Additionally, incorporating advanced imaging techniques such as radiomics and artificial intelligence and conducting more comprehensive statistical analyses would be valuable. By implementing these strategies, future studies can yield more reliable and externally validated findings that improve the clinical applicability of imaging-based differentiation of PC. Addressing these methodological issues could significantly advance the field of gastrointestinal oncology and improve patient management and outcomes.
罗最近的一项研究探讨了胰腺癌（PC）的病理类型与其影像学特征之间的关系。虽然这项研究朝着提高PC诊断准确性迈出了重要一步，但我们对其普遍性、队列选择、影像变异性、统计方法以及潜在混杂因素存在一些担忧。我们建议未来的研究采用多中心、前瞻性设计，以提高代表性并尽量减少偏差。此外，纳入如放射组学和人工智能等先进成像技术并进行更全面的统计分析将很有价值。通过实施这些策略，未来的研究可以产生更可靠且经过外部验证的结果，从而提高基于影像的PC鉴别诊断在临床中的适用性。解决这些方法学问题可以显著推动胃肠肿瘤学领域的发展，并改善患者管理和治疗结果。
