Clinical study of a noninvasive multimodal sono-contrast induced spectroscopy system for breast cancer diagnosis

To present a noninvasive multimodal sono-contrast induced spectroscopy (SCIS) system for breast cancer detection.

An IRB approved clinical study was carried out to evaluate its diagnostic power. A total of 66 subjects were enrolled with informed consent. The study data were grouped into healthy breast tissue (26), histologically proven cancer (14), and benign mass (26). The diffuse reflectance optical intensity and low intensity focused ultrasound (LIFU) signals, as well as ultrasound images, were collected during each study. The ratio of optical intensities at wavelengths 685 and 830 nm was analyzed using wavelet technique to compare the LIFU effects in cancer and noncancerous tissues. The ultrasound images were also processed to obtain tissue texture parameters, such as correlation, energy, contrast, homogeneity, etc. Backward stepwise regression method was performed to identify the statistically significant factors correlating to tissue types (cancer vs benign mass).

Comparison of the optical signals showed that LIFU induced transitory fluctuation in noncancerous tissue, but not in malignant tissue, as quantified by the ratio of mean absolute deviation (RMAD) of the high frequency component. Statistical analysis revealed that the RMAD ratios were significantly different in tumor vs noncancerous masses (p ≪ 0.01). For tissue texture parameters, energy and correlation were found to statistically correlate with the tissue types. A cancer characterization model was developed using the weighted factors to differentiate the tumor from the benign mass. Trade-off between sensitivity and specificity was obtained by varying the threshold value that estimated the upper-bound of the cancer output factor, from which the receiver-operating characteristic (ROC) curve was generated. The characterization model was optimized using ten modeling datasets and verified using another ten validation datasets randomly generated from the database. The optimization results show that an AUC of 0.93 can be achieved. With threshold 0.3, sensitivity of 96.0%, specificity of 84.1%, and negative predictive value (NPV) of 97.3% can be achieved.

The feasibility of the multimodal system in characterizing breast cancer vs benign mass is established.