A Noninvasive Multimodal Sono-contrast NIR Spectroscopy System for Breast Cancer Diagnosis

Yan, Kong; Podder, Tarun; Huang, Ke; Yan, Yu; Liao, Lydia

doi:10.1109/bibe.2010.55

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…A scanhead system was developed to incorporate these modules. [12][13][14] LIFU is generated by two 1 MHz focused transducers (Channel Industries, Santa Barbara, CA), aligned orthogonally inside the scanhead. The acoustic field was calibrated and maintained below FDA diagnostic ultrasound limits (0.72 W cm À2 ).…”

Section: Iia System Overviewmentioning

confidence: 99%

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

Yan

Tinney

et al. 2012

Med. Phys.

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Purpose: To present a noninvasive multimodal sono‐contrast induced spectroscopy (SCIS) system for breast cancer detection. Methods: 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). Results: 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. Conclusions: The feasibility of the multimodal system in characterizing breast cancer vs benign mass is established.

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Section: Iia System Overviewmentioning

confidence: 99%

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

Yan

Tinney

et al. 2012

Med. Phys.

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“…A detailed system design as well as safety considerations was previously presented in [15,16]. An IRB approved clinical trial has been carried out to evaluate the diagnostic power of the SCIS system.…”

Section: Introductionmentioning

confidence: 99%

Multimodal sono-contrast NIR spectroscopy for breast cancer diagnosis: Cancer characterization modeling and subgroup study

Yan

Liao

2012

2012 IEEE 11th International Conference on Signal Processing

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A noninvasive multimodal spectroscopy system was developed for breast cancer diagnosis. It includes three modules: diffuse reflectance spectroscopy (DRS), ultrasonography and low intensity focused ultrasound (LIFU). An IRB approved clinical trial was conducted to evaluate its diagnostic power. Fourteen patients with histopathologically-proven cancer and 26 with benign masses were enrolled with informed consent. Using histopathology as the gold standard, a cancer characterization model was developed to differentiate breast cancer from benign masses. Considering the small dataset, bootstrap resampling method was used to resample the dataset with replacement to create 1000 patient data. Trade-off between sensitivity and specificity was obtained by varying the threshold value in increments of 0.01, from which the receiver-operating characteristic (ROC) curve was generated. The area under the curve (AUC) was calculated to be 0.87. With threshold RMAD 2.28, sensitivity of 84.6% and specificity of 88.0% can be achieved. A sub-group study was also performed to investigate the detection capability of the system for different racial groups. By characterizing the patient population into different racial groups, the diagnostic power of the system can be improved. Current clinical results demonstrated the effectiveness of this promising technique in characterizing cancer from the benign masses. Combining this system with breast ultrasound has the potential to increase the specificity of sonographic breast cancer detection, and to reduce unnecessary invasive procedures.

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A Noninvasive Multimodal Sono-contrast NIR Spectroscopy System for Breast Cancer Diagnosis

Cited by 2 publications

References 5 publications

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

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

Multimodal sono-contrast NIR spectroscopy for breast cancer diagnosis: Cancer characterization modeling and subgroup study

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