Quantum Cascade Laser Spectral Histopathology: Breast Cancer Diagnostics Using High Throughput Chemical Imaging

Fourier transform infrared (FT-IR) microscopy, coupled with machine learning approaches, has been demonstrated to be a powerful technique for identifying abnormalities in human tissue. The ability to objectively identify the prediseased state, and diagnose cancer with high levels of accuracy, has the potential to revolutionise current histopathological practice. Despite recent technological advances in FT-IR microscopy, sample throughput and speed of acquisition are key barriers to clinical translation. Wide-field quantum cascade laser (QCL) infrared imaging systems with large focal plane array detectors utilising discrete frequency imaging, have demonstrated that large tissue microarrays (TMA) can be imaged in a matter of minutes. However this ground breaking technology is still in its infancy and its applicability for routine disease diagnosis is, as yet, unproven. In light of this we report on a large study utilising a breast cancer TMA comprised of 207 different patients. We show that by using QCL imaging with continuous spectra acquired between 912 and 1800 cm^-1, we can accurately differentiate between 4 different histological classes. We demonstrate that we can discriminate between malignant and non-malignant stroma spectra with high sensitivity (93.56%) and specificity (85.64%) for an independent test set. Finally, we classify each core in the TMA and achieve high diagnostic accuracy on a patient basis with 100% sensitivity and 86.67% specificity. The absence of false negatives reported here opens up the possibility of utilising high throughput chemical imaging for cancer screening, thereby reducing pathologist workload and improving patient care.傅里叶变换红外（Fourier Transform Infrared, FT-IR）显微镜技术结合机器学习方法，已被证实是识别人体组织异常的强有力手段。能够客观识别疾病前驱状态并以高准确度诊断癌症，有望彻底变革当前的组织病理学实践流程。尽管FT-IR显微镜技术近年来取得了技术进展，但样本通量与采集速度仍是制约其临床转化的关键障碍。采用离散频率成像模式、搭配大焦平面阵列探测器的宽场量子级联激光器（Quantum Cascade Laser, QCL）红外成像系统，已实现仅需数分钟即可完成大型组织微阵列（Tissue Microarray, TMA）的成像。然而这项突破性技术仍处于起步阶段，其在常规疾病诊断中的适用性尚未得到证实。鉴于此，本研究开展了一项大型队列研究，所用乳腺癌TMA包含207名不同患者的样本。研究结果表明，通过采用QCL成像并采集912至1800 cm⁻¹区间内的连续光谱，我们可以精准区分4种不同的组织学类别。本研究还证实，针对独立测试集，我们能够以93.56%的灵敏度与85.64%的特异度，有效区分恶性与非恶性间质光谱。最终，我们对TMA中的每一个芯样进行分类，并在患者层面实现了高诊断准确度：灵敏度达100%，特异度为86.67%。本研究未出现假阴性结果，这为利用高通量化学成像技术开展癌症筛查提供了可能，从而减轻病理学家的工作负担并改善患者诊疗质量。