Cervical Cancer Tissue Analysis Using Photothermal Midinfrared Spectroscopic Imaging

• Published in: Chemical & biomedical imaging Vol. 2; no. 9; pp. 651 - 658
• Main Authors: Reihanisaransari, Reza, Gajjela, Chalapathi Charan, Wu, Xinyu, Ishrak, Ragib, Zhong, Yanping, Mayerich, David, Berisha, Sebastian, Reddy, Rohith
• Format: Journal Article
• Language: English
• Published: United States Nanjing University and American Chemical Society 23.09.2024; American Chemical Society
• Subjects: label-free imaging; photothermal; chemical imaging; O-PTIR; mid-infrared spectroscopic imaging; cervical cancer
• ISSN: 2832-3637; 2832-3637
• DOI: 10.1021/cbmi.4c00031

Hyperspectral photothermal mid-infrared spectroscopic imaging (HP-MIRSI) is an emerging technology with promising applications in cervical cancer diagnosis and quantitative, label-free histopathology. This study pioneers the application of HP-MIRSI to the evaluation of clinical cervical cancer tissues, achieving excellent tissue type segmentation accuracy of over 95%. This achievement stems from an integrated approach of optimized data acquisition, computational data reconstruction, and the application of machine learning algorithms. The results are statistically robust, drawing from tissue samples of 98 cervical cancer patients and incorporating over 40 million data points. Traditional cervical cancer diagnosis methods entail biopsy, staining, and visual evaluation by a pathologist. This process is qualitative, subject to variations in staining and subjective interpretations, and requires extensive tissue processing, making it costly and time-consuming. In contrast, our proposed alternative can produce images comparable to those from histological analyses without the need for staining or complex sample preparation. This label-free, quantitative method utilizes biochemical data from HP-MIRSI and employs machine-learning algorithms for the rapid and precise segmentation of cervical tissue subtypes. This approach can potentially transform histopathological analysis by offering a more accurate and label-free alternative to conventional diagnostic processes.