Established methods for early cancer detection rely on simple tissue visualization methods, accompanied by biopsy and histopathological evaluation, which is primarily based on morphological tissue features. These approaches are inaccurate or inefficient. Our long-term objective is to transform pre- and early epithelial cancer diagnosis through the use of functional metabolic, morphological and biomechanical tissue biomarkers that are extracted non-invasively, automatically and in near real time from fiber-probe-based endogenous two-photon images. Endogenous two-photon imaging is uniquely capable to provide label-free, functional, high resolution tissue images. In this proposal, we aim to establish and validate such measurements and biomarkers for the detection of human cervical pre-cancers using freshly excised tissues. The cervix is an ideal organ for developing and testing our approach as it relaxes some of the size limitations presented for endoscopic applications, enabling us to focus on demonstrating the principles of this innovative platform. In addition, we expect that our proposed methods will enable significant improvements in the specificity of detection of cervical pre-cancers. To achieve our goals, we will acquire images from fifty freshly excised human cervical tissue specimens from patients undergoing colposcopy, loop electrosurgical excision procedure, or hysterectomy. We will acquire endogenous two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) images and extract signals attributed to NADH, FAD, and collagen. We will process these images using methods we developed to assess: a) the depth-dependent optical redox ratio, mitochondrial organization and nuclear to cytoplasmic ratio variations from FAD and NADH TPEF images of the epithelium, and b) the collagen fiber organization and crosslinking from SHG and TPEF images of the stroma. We will use discriminant analysis to develop algorithms that include the optimal combination of the extracted optical parameters to distinguish high- grade from non-high grade cervical lesions, relying on histopathology as the gold standard. Our algorithms will be entirely automated and fast and will yield a diagnosis based on functional tissue characteristics. Thus, we expect that results from this study will motivate the development of a probe-based 2P imaging system for clinical in vivo imaging translation to enable real-time, highly accurate detection of cervical pre-cancerous lesions. Ultimately, we anticipate that probe-based 2P imaging will transform early cancer diagnosis for a wide range of tissues, such as the oral cavity, the esophagus, the colon, and the bladder.
Relevance: This project aims to establish the transformative potential of label-free, two photon imaging for pre- cancer diagnosis, through non-invasive imaging and automated extraction of a combination of metrics that report on important functional and structural tissue aspects that are altered during cancer development. Thus, this approach will enable accurate, real time detection of early cancers that need to be treated and will decrease the monetary and psychological costs associated with biopsy-based diagnosis for numerous cancer patients.
