Abstract：
Essential thrombocythemia (ET) is a type of myeloproliferative neoplasm that increases the risk of thrombosis. To diagnose this disease, the analysis of mutations in the Janus Kinase 2 (JAK2), thrombopoietin receptor (MPL), or calreticulin (CALR) gene is recommended. Disease poses diagnostic challenges due to overlapping mutations with other neoplasms and the presence of triple-negative cases. This study explores the potential of Raman spectroscopy combined with machine learning for ET diagnosis. We assessed two laser wavelengths (785, 1064 nm) to differentiate between ET patients and healthy controls. The PCR results indicate that approximately 50% of patients in our group have a mutation in the JAK2 gene, while only 5% of patients harbor a mutation in the ASXL1 gene. Additionally, only one patient had a mutation in the IDH1 and one had a mutation in IDH2 gene. Consequently, patients having no mutations were also observed in our group, making diagnosis challenging. Raman spectra at 1064 nm showed lower amide, polysaccharide, and lipid vibrations in ET patients, while 785 nm spectra indicated significant decreases in amide II and C-H lipid vibrations. Principal Component Analysis (PCA) confirmed that both wavelengths could distinguish ET from healthy subjects. Support Vector Machine (SVM) analysis revealed that the 800-1800 cm-1 range provided the highest diagnostic accuracy, with 89% for 785 nm and 72% for 1064 nm. These findings suggest that FT-Raman spectroscopy, paired with multivariate and machine learning analyses, offers a promising method for diagnosing ET with high accuracy by detecting specific molecular changes in serum. Principal Component Analysis (PCA) confirmed that both wavelengths could distinguish ET from healthy subjects. Support Vector Machine (SVM) analysis revealed that the 800-1800 cm-1 range provided the highest diagnostic accuracy, with 89% for 785 nm and 72% for 1064 nm. These findings suggest that FT-Raman spectroscopy, paired with multivariate and machine learning analyses, offers a promising method for diagnosing ET with high accuracy by detecting specific molecular changes in serum.
摘要：
原发性血小板增多症（ET）是一种骨髓增殖性肿瘤，可增加血栓形成的风险。为了诊断这种疾病，Janus激酶2（JAK2）的突变分析，血小板生成素受体（MPL），或建议使用钙网蛋白（CALR）基因。由于与其他肿瘤的重叠突变和三阴性病例的存在，疾病带来了诊断挑战。本研究探索拉曼光谱与机器学习相结合用于ET诊断的潜力。我们评估了两种激光波长（785，1064nm）以区分ET患者和健康对照。PCR结果表明，我们组中大约50％的患者在JAK2基因中存在突变，而只有5％的患者在ASXL1基因中存在突变。此外,只有1例患者发生IDH1基因突变,1例患者发生IDH2基因突变.因此,我们组中也观察到没有突变的患者，诊断具有挑战性。在1064nm的拉曼光谱显示较低的酰胺，多糖，和内皮素患者的脂质振动，而785nm光谱表明酰胺II和C-H脂质振动显着降低。主成分分析（PCA）证实，这两种波长都可以将ET与健康受试者区分开。支持向量机（SVM）分析显示，800-1800cm-1范围提供了最高的诊断精度，785nm为89％，1064nm为72％。这些发现表明，FT-拉曼光谱，与多变量和机器学习分析配对，通过检测血清中特定的分子变化，为高精度诊断ET提供了一种有前途的方法。主成分分析（PCA）证实，这两种波长都可以将ET与健康受试者区分开。支持向量机（SVM）分析显示，800-1800cm-1范围提供了最高的诊断精度，785nm为89％，1064nm为72％。这些发现表明，FT-拉曼光谱，与多变量和机器学习分析配对，通过检测血清中特定的分子变化，为高精度诊断ET提供了一种有前途的方法。
