Cook Hiroki, Crisford Anna, Bourdakos Konstantinos, Dunlop Douglas, Oreffo Richard O C, Mahajan Sumeet
School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK.
Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK.
Biomed Opt Express. 2024 Jun 13;15(7):4264-4280. doi: 10.1364/BOE.520171. eCollection 2024 Jul 1.
Osteoarthritis (OA) is the most common degenerative joint disease, presented as wearing down of articular cartilage and resulting in pain and limited mobility for 1 in 10 adults in the UK [Osteoarthr. Cartil.28(6), 792 (2020)10.1016/j.joca.2020.03.004]. There is an unmet need for patient friendly paradigms for clinical assessment that do not use ionizing radiation (CT), exogenous contrast enhancing dyes (MRI), and biopsy. Hence, techniques that use non-destructive, near- and shortwave infrared light (NIR, SWIR) may be ideal for providing label-free, deep tissue interrogation. This study demonstrates multimodal "spectromics", low-level abstraction data fusion of non-destructive NIR Raman scattering spectroscopy and NIR-SWIR absorption spectroscopy, providing an enhanced, interpretable "fingerprint" for diagnosis of OA in human cartilage. This is proposed as method level innovation applicable to both arthro- or endoscopic (minimally invasive) or potential exoscopic (non-invasive) optical approaches. Samples were excised from femoral heads post hip arthroplasty from OA patients (n = 13) and age-matched control (osteoporosis) patients (n = 14). Under multivariate statistical analysis and supervised machine learning, tissue was classified to high precision: 100% segregation of tissue classes (using 10 principal components), and a classification accuracy of 95% (control) and 80% (OA), using the combined vibrational data. There was a marked performance improvement (5 to 6-fold for multivariate analysis) using the spectromics fingerprint compared to results obtained from solely Raman or NIR-SWIR data. Furthermore, clinically relevant tissue components were identified through discriminatory spectral features - spectromics biomarkers - allowing interpretable feedback from the enhanced fingerprint. In summary, spectromics provides comprehensive information for early OA detection and disease stratification, imperative for effective intervention in treating the degenerative onset disease for an aging demographic. This novel and elegant approach for data fusion is compatible with various NIR-SWIR optical devices that will allow deep non-destructive penetration.
骨关节炎（OA）是最常见的退行性关节疾病，表现为关节软骨磨损，导致英国十分之一的成年人疼痛且行动受限[《骨关节炎与软骨》28(6)，792 (2020)10.1016/j.joca.2020.03.004]。对于不使用电离辐射（CT）、外源性造影增强染料（MRI）和活检的临床评估而言，目前仍未满足患者友好型范例的需求。因此，使用非破坏性近红外和短波红外光（NIR，SWIR）的技术可能是提供无标记、深部组织检测的理想选择。本研究展示了多模态“光谱组学”，即非破坏性近红外拉曼散射光谱和近红外 - 短波红外吸收光谱的低层次抽象数据融合，为人类软骨中OA的诊断提供了增强的、可解释的“指纹”。这被提议作为适用于关节镜或内窥镜（微创）或潜在外窥镜（无创）光学方法的方法层面创新。样本取自OA患者（n = 13）和年龄匹配的对照（骨质疏松）患者（n = 14）髋关节置换术后的股骨头。在多变量统计分析和监督机器学习下，组织被高精度分类：使用10个主成分时组织类别完全分离，使用组合振动数据时对照（95%）和OA（80%）的分类准确率。与仅从拉曼或近红外 - 短波红外数据获得的结果相比，使用光谱组学指纹有显著的性能提升（多变量分析提高5至6倍）。此外，通过鉴别光谱特征——光谱组学生物标志物——识别出临床相关的组织成分，从而从增强的指纹中获得可解释的反馈。总之，光谱组学为早期OA检测和疾病分层提供了全面信息，这对于有效干预治疗老龄化人群的退行性发病疾病至关重要。这种新颖且精巧的数据融合方法与各种近红外 - 短波红外光学设备兼容，可实现深部非破坏性穿透。
