Frequency Domain Analysis of Multiwavelength Photoacoustic Signals for Differentiating Among Malignant, Benign, and Normal Thyroids in an Ex Vivo Study With Human Thyroids

Abstract

Objective This study investigated the capability of spectral parameters, extracted using frequency domain analysis of photoacoustic (PA) signals, to differentiate between malignant, benign and normal thyroid tissue. Methods We acquired multiwavelength PA images of the freshly excised thyroid specimens, collected from 50 patients who underwent thyroidectomy after being diagnosed with suspected thyroid lesion. A thyroid cytopathologist marked histology slides of each tissue specimen. These marked histology slides were used as ground truth to identify the region of interests (ROI) corresponding to malignant, benign and normal thyroid tissue. Three spectral parameters, namely slope, midband fit and intercept were extracted from PA signals corresponding to different ROIs. Results Spectral parameters were extracted from a total of total of 65 ROIs. According to the ground truth, 12 out of 65 ROIs belonged to malignant thyroid, 28 out of 65 ROIs belonged to benign thyroid and 25 out of 65 ROIs belonged to normal thyroid. Besides slope, the other two spectral parameters and grayscale PA image pixel values were found to be significantly different (p < 0.05) between malignant and normal thyroid. Between benign and normal thyroid, all three spectral parameters and PA pixel values were significantly different (p < 0.05). Conclusion Preliminary results of our ex vivo human thyroid study show that the spectral parameters extracted from radio frequency PA signals as well as the pixel value of 2D PA images can be used for differentiating between malignant, benign and normal thyroid tissue.

Automatic cancer tissue detection using multispectral photoacoustic imaging

“…The entire process to return back the specimen to the surgical pathology laboratory took one hour. It was verified and approved by the pathologist to ensure that the histopathology procedure of the specimen was not compromised [90]. The laser intensity on the excised prostate specimen was maintained around 5 mJ/cm 2 , which is well below the safe human exposure limit according to Amer- The thermal expansion should be time-invariant to generate acoustic waves [115].…”

“…Where, H is the heating function and it is related to the optical absorption coefficient µ a by H = µ a Φ [90].…”

“…This may be a contributing factor for the difference in the frequency content of the PA signal in normal and malignant tissue. Sinha et al [90] reported that frequency content is useful for cancer tissue diagnosis using PA imaging when the size of the tissue specimen is important. Jnawali et al [47] implemented a k-wave Matlab toolbox [100] to simulate the effect of the size of prostate tissue specimen on the time content (frequency content) of the recorded PA signal.…”

“…There is theoretical evidence of the signature of the PA absorber size that can be seen in the corresponding pulse width of the PA signal. Therefore, in addition to the wavelength feature vector, the A-line data can be considered as a possible feature vector [47,90]. As the size of cancer tissue usually is bigger (more nodule vessels) than normal tissue and hence could be the stronger PA absorber.…”

“…Multiple high contrast PA images of the optically active chromophores such as oxyhemoglobin (high in oxygen content), deoxyhemoglobin (low in oxygen content), lipid, and water are generated by choosing multiple wavelengths to quantify the concentration of the chromophores [23]. These chromophores' concentration makes the PA imaging system to image optical biomarker for cancer tissue detection [28,30,75,90]. In multispectral photoacoustic (MPA) imaging, the large difference in light absorption coefficient between blood and other tissue constituents enables detection of tissue angiogenesis associated with rapid tumor growth in early stages [61].…”