SM-S1: An early lung cancer detection test

癌症是全球疾病的主要原因。每年约有180万人死于肺癌（LC）。早期诊断对提高生存率至关重要。然而，I-III期LC是无症状或无描述性的（例如咳嗽），通常归因于“变老”或最近的COVID。因此，约75%的患者是在晚期才被诊断出来的，而此时生存率要低得多，治疗费用也最昂贵，导致LC成为国家卫生系统所有癌症中经济负担最高的癌症。目前的金标准诊断方法对于检测无症状的早期LC患者的筛查方案并不理想。成像（x射线、低剂量CT）昂贵且不易获得，需要复杂、昂贵的设备、训练有素的操作人员和专业顾问。此外，大量的设备积压导致等待使用这些设备的时间过长，而训练有素的放射科医生的持续短缺又加剧了这种情况。相比之下，对于其他癌症，特别是乳腺癌、宫颈癌和肠癌，已经有了可获得的、具有成本效益的筛查测试。Sierra Medical正在开发一种将人工智能（AI）应用于患者脸颊细胞生物化学的创新型早期LC检测试剂盒SM-S1。我们的新型分析算法结合了红外光谱、生物/医学信息、机器学习和人工智能，以提取健康细胞和病变细胞之间的细微光谱差异。我们的系统是非破坏性的（可以在相同的样品上进行测试），只需要几百个细胞，任何医疗保健专业人员都可以轻松使用，无需经过广泛的培训。SM-S1测试很简单：使用口腔拭子从患者口腔中收集口腔细胞样本。棉签被送到实验室。使用市售的现成红外（IR）光谱仪从拭子收集数据。LC患者的脸颊细胞显示生化变化，改变细胞的红外指纹图谱。数据被自动上传到Sierra Medical的安全云平台上，在那里光谱被自动清洗，根据环境进行校正，并通过我们的算法进行分析。通过我们的基础研究，我们已经训练了我们的处理算法，将生化变化与环境条件分开，使我们能够检测肺部癌细胞的存在（早期结果显示灵敏度为93%）。1天内可获得早期癌症检测报告。

Cancer is the leading cause of illness worldwide. Each year, about 1.8 M deaths are caused by lung cancer (LC). Early diagnosis is crucial to improve survival rates. However Stage I-III LC is asymptomatic or non-descript (e.g. a cough) and often ascribed to "getting older" or, more recently, COVID. Consequently about 75% of patients are diagnosed at the late stage, when survival rates are much lower and treatment is most expensive, resulting in LC having the highest economical burden of all cancers on national health systems.Current gold standard diagnostic methods are not ideal for screening programmes to detect symptomless early stage LC patients. Imaging (x-ray, low-dose CT) is expensive and not readily accessible, requiring sophisticated, costly equipment, highly trained operators and specialist consultants. Furthermore, there are significant backlogs causing long waiting times to use such equipment, exacerbated by an ongoing shortage of trained radiologists.In contrast there are already accessible, cost effective screening tests for other cancers, particularly breast, cervical and bowel.Sierra Medical is developing SM-S1, an innovative early-stage LC detection test which applies artificial intelligence (AI) to the biochemistry of a patient's cheek cells. Our novel analytical algorithm combines infrared spectroscopy, biological/medical information, machine learning and AI to extract subtle spectral differences between healthy and diseased cells. Our system is non-destructive (tests can be performed on the same sample), requires only a few hundred cells and is easy-to-use by any healthcare professionals without extensive training.The SM-S1 test is simple: a sample of buccal cells is collected from the patient's mouth using a cheek swab. The swab is sent to a laboratory. Data is collected from the swab using a commercially available off-the-shelf infrared (IR) spectrometer. Cheek cells in LC patients display biochemical changes which change the cell's IR fingerprint. The data is automatically uploaded onto Sierra Medical's secure cloud platform where the spectra are automatically cleaned, corrected for their environment and analysed by our algorithm. Through our underpinning research we have trained our processing algorithms to separate biochemical changes from environmental conditions allowing us to detect the presence of cancerous cells within the lung (early results show a sensitivity of 93%). The early cancer detection test report is available in less than 1 day.