Photoacoustic signal characterization of cancer treatment response: Correlation with changes in tumor oxygenation

Hysi, Eno; Wirtzfeld, Lauren A.; May, Jonathan P.; Undzys, Elijus; Li, Shyh-Dar; Kolios, Michael C.

doi:10.1016/j.pacs.2017.03.003

Cited by 50 publications

(74 citation statements)

References 76 publications

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“…Toi et al [39] demonstrated that the tumor associated blood vessels in a patient with IBC became visible in PA images after chemotherapy. Hysi et al [126] performed a PA imaging study at 750/850 nm on EMT-6 breast cancer tumor injected into mice to monitor the response of a thermosensitive liposome treatment. They observed the response of the treatment though PA images obtained at different time points 30 min before and after treatment up to seven days after treatment.…”

Section: Breast Cancer Treatment Monitoringmentioning

confidence: 99%

Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives

2020

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In this review article, a detailed chronological account of the research related to photoacoustic imaging for the management of breast cancer is presented. Performing a detailed analysis of the breast cancer detection related photoacoustic imaging studies undertaken by different research groups, this review attempts to present the clinical evidence in support of using photoacoustic imaging for breast cancer detection. Based on the experimental evidence obtained from the clinical studies conducted so far, the performance of photoacoustic imaging is compared with that of conventional breast imaging modalities. While we find that there is enough experimental evidence to support the use of photoacoustic imaging for breast cancer detection, additional clinical studies are required to be performed to evaluate the diagnostic potential of photoacoustic imaging for identifying different types of breast cancer. To establish the utility of photoacoustic imaging for breast cancer screening, clinical studies with high-risk asymptomatic patients need to be done.

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Section: Breast Cancer Treatment Monitoringmentioning

confidence: 99%

Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives

2020

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“…Imaging of collagen phantoms, as well as imaging of mouse, pig, and human kidneys, was performed using the VevoLAZR-X system (Fujifilm VisualSonics Inc.) (56)(57)(58). This commercially available system uses an Nd:YAG laser 512 nm source through an optical parametric oscillator operating at 10 Hz pulse repetition frequency.…”

Section: Pa Imagingmentioning

confidence: 99%

Photoacoustic imaging of kidney fibrosis for assessing pretransplant organ quality

Hysi¹,

He²,

Fadhel³

et al. 2020

JCI Insight

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“…Photoacoustic spectral features have been shown to be valuable tools in clinical applications, such as characterization of bone microstructure [20], quantification of normal and fatty livers [21], cancer diagnosis [22,23], and monitoring cancer treatment response [24]. In earlier studies we evaluated the feasibility of these features for distinguishing malignant from benign ex vivo ovaries [16,17].…”

Section: Pat Spectral Feature Extractionmentioning

confidence: 99%

Classification of human ovarian cancer using functional, spectral, and imaging features obtained from in vivo photoacoustic imaging

Amidi¹,

Mostafa²,

Nandy³

et al. 2019

Biomed. Opt. Express

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We report in this pilot study the diagnostic results of in vivo imaging of patients with ovarian lesions, using a co-registered photoacoustic and ultrasound (PAT/US) system. A total of 39 ovaries from 24 patients were imaged in vivo. PAT functional features, i.e., blood oxygen saturation (sO2) and relative total hemoglobin (rHbT), PAT image features, and PAT spectral features within a region of interest (ROI) in each ovarian tissue were extracted. To select the significant features, a t-test on each feature was performed, and the independent predictors were determined by evaluating correlation between each pair of predictors. To classify the ovarian lesions, we employed a generalized linear model (GLM) and a support vector machine (SVM). We used these classifiers first to distinguish benign/normal lesions from ovaries with invasive epithelial tumors and then to separate normal/benign lesions from all types of ovarian tumors. We developed classifiers once by inclusion of PAT functional features to assess the best diagnostic performance of the classifiers when multiple wavelengths data are available. Second time, we excluded the PAT functional features from the features set to evaluate the best diagnostic performance if only a single wavelength is available. Our results show that using functional features improves the classification performance, especially for distinguishing normal/benign ovarian lesions from all types of tumors. In this case, an area under ROC curve (AUC) of 0.92, 0.93 of testing data was achieved using a GLM and SVM classifier when functional features were included in the feature set while excluding these features resulted in an AUC of 0.89, 0.92, respectively.

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Photoacoustic signal characterization of cancer treatment response: Correlation with changes in tumor oxygenation

Cited by 50 publications

References 76 publications

Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives

Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives

Photoacoustic imaging of kidney fibrosis for assessing pretransplant organ quality

Classification of human ovarian cancer using functional, spectral, and imaging features obtained from in vivo photoacoustic imaging

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