## 1 Introduction  
Lung cancer is one of the most frequently diagnosed cancers and the leading cause of cancer deaths around the world [1; 2]. The five-year survival rate of lung cancer patients is approximately 18% [2]. Eighty-five percent of lung cancer patients were non-small cell lung cancer (NSCLC), whose five-year survival rate is about 21% [3]. The common types of NSCLC are adenocarcinoma and squamous cell carcinomas with five-year survival rates of 42.8% and 43.4% [4]. Small cell lung cancer (SCLC) represents 12%-13% of lung cancers, with five-year survival rate of 6% [5]. As a rare form of cancer, five-year survival of mesothelioma was 13.4% in women and 4.5% in men [6]. Early detection and visualization of lung cancer is crucial to improve the accurate diagnosis and treatment performance. Currently, the haematoxylin and eosin (HE)-stained histopathology is applied as the gold standard to assess and diagnose disease by pathologists, but is time-consuming, usually taking up to one week [7]. During tumor resection, intraoperative frozen section examination can aid the surgeon to distinguish tumor and normal tissues, which still requires the staining process and takes about 20-30 min [8; 9].  
In recent years, autofluorescence of cancer was investigated based on the knowledge that some endogenous fluorophores in the tissues change when normal tissue turns malignant [10; 11; 12]. Autofluorescence imaging and spectroscopy is a non-invasive, label-free, real-time technique for cancer detection. Autofluorescence emitting from tissues has many intrinsic fluorophores, such as reduced nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD), and porphyrins [13; 14]. NADH and FAD are metabolic coenzymes in oxidative phosphorylation, and NADH has a free or protein-bound state [15; 16]. Porphyrins are organic compounds and are often used as a tumor-specific marker [17]. Tumor cells exhibit a different metabolic state or microenvironment from normal cells, which associates with changes of these intrinsic fluorophores fluorescence intensity. Thus the difference between normal and cancerous tissues can be investigated based on their autofluorescence spectra. However, this approach is limited by that autofluorescence spectrum features exhibit less specific differences and the origin of the contrasts mechanisms are not clearly identified [18; 19].  
There have been some reports using spectroscopy with a combination of endoscopic equipment [20; 21; 22; 23; 24; 25]. [PERSON] et al. used white-light reflectance spectroscopy during bronchoscopy examination to differentiate normal bronchus mucosa from malignant lesions [15; 20]. [PERSON] et al. differentiated bronchial cancerous lesions with fluorescence spectroscopy, and reached a sensitivity and specificity of 71% and 74% [21]. Most of bronchoscopes measured the decrease of autofluorescence intensity within the green spectral region for early cancerous lesions as compared to healthy tissues under blue-violet light (around 405 nm) excitation [22; 24]. According to [PERSON] et al. [23; 24], the additional detection of backscattered red light significantly improved the autofluorescence image quality. This phenomenon could be due to the increased vasculature around cancerous lesions, and partially due to changes in the fluorescent properties of the tissues. Previous studies have also been performed on blood samples [26], urine samples [27], animal models [28], cell lines [29], and human cervical tissue sections [30; 31] based on the fluorescence spectroscopy. Direct studies on the optical properties of lung tissues may be particularly useful for intraoperative margin assessment during tumor resection [32].  
In the present study, we assessed the possibilities for introducing fluorescence imaging and spectroscopy of lung tissue samples as a potential tool to differentiate tumor from normal tissues. The optical measurement technique is more efficient than the HE stained histopathology, and may ultimately be used to aid the diagnosis of lung cancer.