Atta-Ur-Rahman School of Applied Biosciences, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Department of Electrical Engineering and Computer Science, The Henry Samueli School of Engineering, University of California, Irvine, Irvine, CA, United States of America.
PLoS One. 2023 Nov 15;18(11):e0287465. doi: 10.1371/journal.pone.0287465. eCollection 2023.
According to WHO 2019, Hepatocellular carcinoma (HCC) is the fourth highest cause of cancer death worldwide. More precise diagnostic models are needed to enhance early HCC and cirrhosis quick diagnosis, treatment, and survival. Breath biomarkers known as volatile organic compounds (VOCs) in exhaled air can be used to make rapid, precise, and painless diagnoses. Gas chromatography and mass spectrometry (GCMS) are utilized to diagnose HCC and cirrhosis VOCs. In this investigation, metabolically generated VOCs in breath samples (n = 35) of HCC, (n = 35) cirrhotic, and (n = 30) controls were detected via GCMS and SPME. Moreover, this study also aims to identify diagnostic VOCs for distinction among HCC and cirrhosis liver conditions, which are most closely related, and cause misleading during diagnosis. However, using gas chromatography-mass spectrometry (GC-MS) to quantify volatile organic compounds (VOCs) is time-consuming and error-prone since it requires an expert. To verify GC-MS data analysis, we present an in-house R-based array of machine learning models that applies deep learning pattern recognition to automatically discover VOCs from raw data, without human intervention. All-machine learning diagnostic model offers 80% sensitivity, 90% specificity, and 95% accuracy, with an AUC of 0.9586. Our results demonstrated the validity and utility of GCMS-SMPE in combination with innovative ML models for early detection of HCC and cirrhosis-specific VOCs considered as potential diagnostic breath biomarkers and showed differentiation among HCC and cirrhosis. With these useful insights, we can build handheld e-nose sensors to detect HCC and cirrhosis through breath analysis and this unique approach can help in diagnosis by reducing integration time and costs without compromising accuracy or consistency.
根据世界卫生组织 2019 年的数据，肝细胞癌（HCC）是全球癌症死亡的第四大主要原因。需要更精确的诊断模型来增强对早期 HCC 和肝硬化的诊断、治疗和生存。呼气中的挥发性有机化合物（VOCs）等呼吸生物标志物可用于快速、准确和无痛的诊断。气相色谱和质谱（GCMS）用于诊断 HCC 和肝硬化的 VOCs。在这项研究中，通过 GCMS 和 SPME 检测了 HCC（n=35）、肝硬化（n=35）和对照组（n=30）呼吸样本中代谢产生的 VOCs。此外，本研究还旨在确定用于区分最密切相关且在诊断中易产生误导的 HCC 和肝硬化肝脏状况的诊断 VOCs。然而，使用气相色谱-质谱（GC-MS）定量挥发性有机化合物（VOCs）既耗时又容易出错，因为它需要专家。为了验证 GC-MS 数据分析，我们提出了一个内部基于 R 的机器学习模型阵列，该模型应用深度学习模式识别从原始数据中自动发现 VOCs，无需人工干预。全机器学习诊断模型的灵敏度为 80%，特异性为 90%，准确性为 95%，AUC 为 0.9586。我们的结果证明了 GCMS-SMPE 与创新的 ML 模型相结合用于早期检测 HCC 和肝硬化特异性 VOCs 的有效性和实用性，这些 VOCs被认为是潜在的诊断性呼吸生物标志物，并显示出 HCC 和肝硬化之间的差异。有了这些有用的见解，我们可以通过呼吸分析来构建手持式电子鼻传感器来检测 HCC 和肝硬化，这种独特的方法可以通过减少集成时间和成本而不影响准确性或一致性来帮助诊断。
