Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning.
Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning.
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DOI：
10.1038/s41551-022-00860-y
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发表时间：
2022-03
影响因子：
28.1
通讯作者：
中科院分区：
文献类型：
--
作者：
血清生物标志物往往不够敏感或特异性，以促进癌症筛查或诊断测试。在卵巢癌中，少数已建立的血清生物标志物具有高度特异性，但灵敏度不足以检测早期疾病并影响该癌症患者的死亡率。在这里，我们展示了通过机器学习从具有量子缺陷功能化的碳纳米管阵列的近红外荧光发射光谱中获得的“疾病指纹”，以87%的灵敏度和98%的特异性检测有症状个体的血清样本中的高级别浆液性卵巢癌（相比而言，目前最好的临床筛选试验的灵敏度为84%，特异性为98%）。它使用癌症抗原125的测量和经阴道超声检查)。我们使用269个血清样本来训练和验证几个机器学习分类器，以区分卵巢癌患者与其他疾病患者和健康个体。通过已知的蛋白质生物标志物无法获得最佳分类器的预测值，这表明纳米管传感器阵列对未知的血清生物标志物有反应。
Serum biomarkers are often insufficiently sensitive or specific to facilitate cancer screening or diagnostic testing. In ovarian cancer, the few established serum biomarkers are highly specific, yet insufficiently sensitive to detect early-stage disease and to impact the mortality rates of patients with this cancer. Here we show that a ‘disease fingerprint’ acquired via machine learning from the spectra of near-infrared fluorescence emissions of an array of carbon nanotubes functionalized with quantum defects detects high-grade serous ovarian carcinoma in serum samples from symptomatic individuals with 87% sensitivity at 98% specificity (compared with 84% sensitivity at 98% specificity for the current best clinical screening test, which uses measurements of cancer antigen 125 and transvaginal ultrasonography). We used 269 serum samples to train and validate several machine-learning classifiers for the discrimination of patients with ovarian cancer from those with other diseases and from healthy individuals. The predictive values of the best classifier could not be attained via known protein biomarkers, suggesting that the array of nanotube sensors responds to unidentified serum biomarkers.
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DOI：
10.1016/j.annonc.2020.02.011
发表时间：
2020-06
期刊：
Annals of oncology : official journal of the European Society for Medical Oncology
影响因子：
--
作者：
Liu MC;Oxnard GR;Klein EA;Swanton C;Seiden MV;CCGA Consortium
通讯作者：
CCGA Consortium
影响因子：
78.8
作者：
Bera, Kaustav;Schalper, Kurt A.;Madabhushi, Anant
通讯作者：
Madabhushi, Anant
影响因子：
15
作者：
Ming, Z;Diner, BA
通讯作者：
Diner, BA
影响因子：
45.3
作者：
Dupont, J;Tanwar, MK;Spriggs, DR
通讯作者：
Spriggs, DR
影响因子：
14.8
作者：
Docter, Dominic;Distler, Ute;Stauber, Roland H.
通讯作者：
Stauber, Roland H.