Liberda-Matyja Danuta, Stopa Kinga B, Krzysztofik Daria, Ferdek Pawel E, Jakubowska Monika A, Wrobel Tomasz P
Doctoral School of Exact and Natural Sciences, Jagiellonian University, ul. Łojasiewicza 11, 30-348 Krakow, Poland.
Solaris National Synchrotron Radiation Centre, Jagiellonian University, ul. Czerwone Maki 98, 30-392 Krakow, Poland.
ACS Pharmacol Transl Sci. 2025 Mar 20;8(4):1096-1105. doi: 10.1021/acsptsci.4c00689. eCollection 2025 Apr 11.
With the challenge of limited early stage detection and a resulting five-year survival rate of only 13%, pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal cancers. Replacing the high-cost and time-consuming grading of pancreatic samples by pathologists with automated diagnostic approaches can revolutionize PDAC detection and thus accelerate patient admission into the clinical setting for treatment. To address this unmet diagnostic need and facilitate the shift of tissue screening toward automated systems, we combined stain-free histology-specifically, Fourier-transform infrared (FT-IR) imaging-with machine learning. The obtained stain-free model was trained to distinguish between normal, benign, and malignant areas in analyzed specimens using hematoxylin and eosin stained pancreatic tissues isolated from KC (Kras; Pdx1-Cre) or KPC mice (Kras; Trp53; Pdx1-Cre). Due to the pancreas-specific mosaic expression of the mutant and genes, changes in pancreatic tissues of this mouse model of PDAC closely mirror the gradual transformation of normal pancreatic epithelia into (pre)malignant structures. Thus, this mouse model provides a reliable representation of human disease progression, which we tracked in our study with a Random Forest classifier to achieve accurate detection at the cellular level. This approach yielded a comprehensive model that distinguishes normal pancreatic tissues from pathological features such as pancreatic intraepithelial neoplasia (PanIN), cancerous regions, hemorrhages, and collagen fibers, as well as a streamlined model designed to rapidly identify normal tissues versus pathologically altered regions, including PanINs. These models offer highly accurate diagnostic tools for the early detection of pancreatic malignancies, thus significantly improving the chance for timely therapeutic intervention against PDAC.
由于早期检测手段有限，胰腺导管腺癌（PDAC）的五年生存率仅为13%，它仍然是最致命的癌症之一。用自动化诊断方法取代病理学家对胰腺样本进行的高成本且耗时的分级，可能会彻底改变PDAC的检测方式，从而加快患者进入临床治疗的进程。为满足这一未被满足的诊断需求，并推动组织筛查向自动化系统转变，我们将无染色组织学——具体而言是傅里叶变换红外（FT-IR）成像——与机器学习相结合。使用从KC（Kras；Pdx1-Cre）或KPC小鼠（Kras；Trp53；Pdx1-Cre）分离的苏木精和伊红染色的胰腺组织，对所获得的无染色模型进行训练，以区分分析样本中的正常、良性和恶性区域。由于突变基因在胰腺中的特异性镶嵌表达，这种PDAC小鼠模型的胰腺组织变化密切反映了正常胰腺上皮向（癌前）恶性结构的逐渐转变。因此，该小鼠模型提供了人类疾病进展的可靠表征，我们在研究中使用随机森林分类器对其进行跟踪，以在细胞水平实现准确检测。这种方法产生了一个综合模型，可将正常胰腺组织与胰腺上皮内瘤变（PanIN）、癌区、出血和胶原纤维等病理特征区分开来，以及一个简化模型，旨在快速识别正常组织与包括PanIN在内的病理改变区域。这些模型为早期检测胰腺恶性肿瘤提供了高度准确的诊断工具，从而显著提高了对PDAC进行及时治疗干预的机会。
