Wang Rong, Sabzian Roya, Gibson Tanya M, Wang Yong
Department of Oral and Craniofacial Sciences, School of Dentistry, University of Missouri Kansas City, Kansas City, MO, USA.
Department of Restorative Dentistry, Rutgers School of Dental Medicine, Newark, NJ, USA.
Analyst. 2025 Jun 23;150(13):2809-2821. doi: 10.1039/d5an00117j.
Oral squamous cell carcinoma (OSCC) is an aggressive cancer with a poor prognosis. Oral epithelial dysplasia (OED) is a precancerous lesion associated with an increased risk of malignant transformation (MT) into OSCC. However, current histopathological methods for diagnosing OED are subjective and ineffective in assessing MT risk. FTIR provides a comprehensive biochemical profile of tissues, known as "biomolecular fingerprinting". Previously we developed an FTIR-based OSCC-Benign classifier that accurately distinguishes OSCC from benign tissue. In this study, we evaluated whether this classifier could also predict MT risk in OED. Thirty OED patient biopsies with documented MT outcomes were analyzed, including 12 with and 18 without MT. FTIR images were acquired from six regions of interest (ROIs) per tissue section, yielding an average epithelial spectrum for each ROI, and a total of 180 spectra for model evaluation. The OSCC-Benign classifier achieved an accuracy of 81.7% with an F1 score of 0.77 at the ROI level, and an accuracy of 83.3% with an F1 score of 0.8 at the biopsy level in predicting MT in OED. Our findings suggest that OEDs with biomolecular fingerprints similar to OSCC carry a higher risk of MT, while those resembling benign tissue carry a lower risk, providing new insight into the malignant transformation process. In summary, the FTIR-based machine learning approach outperforms traditional histopathology in predicting MT risk in OED, potentially offering a quantitative and objective tool for clinical diagnosis.
口腔鳞状细胞癌（OSCC）是一种侵袭性癌症，预后较差。口腔上皮发育异常（OED）是一种癌前病变，与恶变为OSCC的风险增加相关。然而，目前用于诊断OED的组织病理学方法主观性强，在评估恶变风险方面效果不佳。傅里叶变换红外光谱（FTIR）提供了组织的全面生化特征，即“生物分子指纹图谱”。此前我们开发了一种基于FTIR的OSCC-良性分类器，可准确区分OSCC与良性组织。在本研究中，我们评估了该分类器是否也能预测OED中的恶变风险。分析了30例有恶变结果记录的OED患者活检样本，其中12例发生恶变，18例未发生恶变。从每个组织切片的六个感兴趣区域（ROI）获取FTIR图像，为每个ROI生成平均上皮光谱，共180个光谱用于模型评估。在预测OED中的恶变时，OSCC-良性分类器在ROI水平上的准确率为81.7%，F1分数为0.77；在活检水平上的准确率为83.3%，F1分数为0.8。我们的研究结果表明，生物分子指纹图谱与OSCC相似的OED恶变风险较高，而与良性组织相似的OED恶变风险较低，这为恶变过程提供了新的见解。总之，基于FTIR的机器学习方法在预测OED中的恶变风险方面优于传统组织病理学，可能为临床诊断提供一种定量和客观的工具。
