Quantitative collagen analysis using second harmonic generation images for the detection of basal cell carcinoma with ex vivo multiphoton microscopy.
Mercedes Sendín-MartínJasmine PosnerUcalene HarrisMatthew MorontaJulián Conejo-Mir SánchezSushmita MukherjeeMilind RajadhyakshaKivanc KoseManu JainPublished in: Experimental dermatology (2022)
Basal cell carcinoma (BCC) is the most common skin cancer and its incidence is rising. Millions of benign biopsies are performed annually for BCC diagnosis, increasing morbidity and healthcare costs. Noninvasive in vivo technologies such as multiphoton microscopy (MPM) can aid in diagnosing BCC, reducing the need for biopsies. Furthermore, the second harmonic generation (SHG) signal generated from MPM can classify and prognosticate cancers based on extracellular matrix changes, especially collagen type I. We explored the potential of MPM to differentiate collagen changes associated with different BCC subtypes compared to normal skin structures and benign lesions. Quantitative analysis such as frequency band energy analysis in Fourier domain, CurveAlign, and CT-FIRE fiber analysis was performed on SHG images from 52 BCC and 12 benign lesions samples. Our results showed that collagen distribution is more aligned surrounding BCCs nests compared to the skin's normal structures (p<0.001) and benign lesions (p<0.001). Also, collagen was orientated more parallelly surrounding indolent BCC subtypes (superficial and nodular) versus those with more aggressive behavior (infiltrative BCC) (p=0.021). In conclusion, SHG signal from type I collagen can aid not only in the diagnosis of BCC but could be useful for prognosticating these tumors. Our initial results are limited to a small number of samples, requiring large-scale studies to validate them. These findings represent the groundwork for future in vivo MPM for diagnosis and prognosis of BCC.
Keyphrases
- wound healing
- basal cell carcinoma
- high resolution
- healthcare
- extracellular matrix
- tissue engineering
- optical coherence tomography
- deep learning
- computed tomography
- skin cancer
- convolutional neural network
- high throughput
- high speed
- risk assessment
- magnetic resonance
- human health
- soft tissue
- young adults
- image quality
- current status
- single cell
- pet ct
- affordable care act