title: On-the-spot lung cancer differential diagnosis by label-free, molecular vibrational imaging and knowledge-based classification.
abstract: We report the development and application of a knowledge-based coherent anti-Stokes Raman scattering (CARS) microscopy system for label-free imaging, pattern recognition, and classification of cells and tissue structures for differentiating lung cancer from non-neoplastic lung tissues and identifying lung cancer subtypes. A total of 1014 CARS images were acquired from 92 fresh frozen lung tissue samples. The established pathological workup and diagnostic cellular were used as prior knowledge for establishment of a knowledge-based CARS system using a machine learning approach. This system functions to separate normal, non-neoplastic, and subtypes of lung cancer tissues based on extracted quantitative features describing fibrils and cell morphology. The knowledge-based CARS system showed the ability to distinguish lung cancer from normal and non-neoplastic lung tissue with 91% sensitivity and 92% specificity. Small cell carcinomas were distinguished from nonsmall cell carcinomas with 100% sensitivity and specificity. As an adjunct to submitting tissue samples to routine pathology, our novel system recognizes the patterns of fibril and cell morphology, enabling medical practitioners to perform differential diagnosis of lung lesions in mere minutes. The demonstration of the strategy is also a necessary step toward in vivo point-of-care diagnosis of precancerous and cancerous lung lesions with a fiber-based CARS microendoscope.
authors.affiliation: Weill Cornell Medical College, The Methodist Hospital Research Institute, Department of Systems Medicine and Bioengineering, Houston, Texas 77030, USA.
authors.forename: Liang
authors.initials: L
authors.lastname: Gao
authors.forename: Fuhai
authors.initials: F
authors.lastname: Li
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Thrall
authors.forename: Yaliang
authors.initials: Y
authors.lastname: Yang
authors.forename: Jiong
authors.initials: J
authors.lastname: Xing
authors.forename: Ahmad A
authors.initials: AA
authors.lastname: Hammoudi
authors.forename: Hong
authors.initials: H
authors.lastname: Zhao
authors.forename: Yehia
authors.initials: Y
authors.lastname: Massoud
authors.forename: Philip T
authors.initials: PT
authors.lastname: Cagle
authors.forename: Yubo
authors.initials: Y
authors.lastname: Fan
authors.forename: Kelvin K
authors.initials: KK
authors.lastname: Wong
authors.forename: Zhiyong
authors.initials: Z
authors.lastname: Wang
authors.forename: Stephen T C
authors.initials: ST
authors.lastname: Wong
mesh_terms: D000230:Adenocarcinoma / Q000737:chemistry / Q000145:classification / Q000175:diagnosis; D000077192:Adenocarcinoma of Lung; D002289:Carcinoma, Non-Small-Cell Lung / Q000737:chemistry / Q000145:classification / Q000175:diagnosis; D002294:Carcinoma, Squamous Cell / Q000737:chemistry / Q000145:classification / Q000175:diagnosis; D016208:Databases, Factual; D003937:Diagnosis, Differential; D006651:Histocytochemistry; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods; D016018:Least-Squares Analysis; D008175:Lung Neoplasms / Q000737:chemistry / Q000145:classification* / Q000175:diagnosis*; D011014:Pneumonia; D012680:Sensitivity and Specificity; D055752:Small Cell Lung Carcinoma / Q000737:chemistry / Q000145:classification / Q000175:diagnosis; D013059:Spectrum Analysis, Raman / Q000379:methods*; D060388:Support Vector Machine