Quing Zhu

Integrated spectral and depth compensation approach for optimizing oxygen saturation and total hemoglobin estimation in photoacoustic tomography for ovarian lesion diagnosis

Photoacoustic tomography (PAT) holds promise for non-invasive functional imaging in ovarian cancer diagnostics. However, accurate estimation of oxygen saturation ( We aim to improve the accuracy and clinical utility of We introduce a spectral compensation strategy derived from Monte Carlo simulations and integrate it with depth-wise fluence correction to construct the proposed ISDC method. The approach has been validated using phantoms with known optical properties and applied to clinical PAT data from 82 ovarian lesions (67 benign and 15 malignant). Diagnostic performance was evaluated using logistic regression and receiver operating characteristic analysis. In phantom experiments, ISDC improved The ISDC approach significantly enhances the quantitative accuracy and diagnostic performance of PAT by compensating for both spectral and depth fluence variations within biological tissue. These improvements support the integration of ISDC into US-PAT systems for ovarian lesion characterization and future clinical applications.

Characterization of adnexal lesions using photoacoustic imaging to improve sonographic O‐RADS risk assessment

Abstract Objective To assess the impact of photoacoustic imaging (PAI) on the assessment of ovarian/adnexal lesion(s) of different risk categories using the sonographic ovarian‐adnexal imaging‐reporting‐data system (O‐RADS) in women undergoing planned oophorectomy. Method This prospective study enrolled women with ovarian/adnexal lesion(s) suggestive of malignancy referred for oophorectomy. Participants underwent clinical ultrasound (US) examination followed by coregistered US and PAI prior to oophorectomy. Each ovarian/adnexal lesion was graded by two radiologists using the US O‐RADS scale. PAI was used to compute relative total hemoglobin concentration (rHbT) and blood oxygenation saturation (%sO 2 ) colormaps in the region of interest. Lesions were categorized by histopathology into malignant ovarian/adnexal lesion, malignant Fallopian tube only and several benign categories, in order to assess the impact of incorporating PAI in the assessment of risk of malignancy with O‐RADS. Malignant and benign histologic groups were compared with respect to rHbT and %sO 2 and logistic regression models were developed based on tumor marker CA125 alone, US‐based O‐RADS alone, PAI‐based rHbT with %sO 2 , and the combination of CA125, O‐RADS, rHbT and %sO 2. Areas under the receiver‐operating‐characteristics curve (AUC) were used to compare the diagnostic performance of the models. Results There were 93 lesions identified on imaging among 68 women (mean age, 52 (range, 21–79) years). Surgical pathology revealed 14 patients with malignant ovarian/adnexal lesion, two with malignant Fallopian tube only and 52 with benign findings. rHbT was significantly higher in malignant compared with benign lesions. %sO 2 was lower in malignant lesions, but the difference was not statistically significant for all benign categories. Feature analysis revealed that rHbT, CA125, O‐RADS and %sO 2 were the most important predictors of malignancy. Logistic regression models revealed an AUC of 0.789 (95% CI, 0.626–0.953) for CA125 alone, AUC of 0.857 (95% CI, 0.733–0.981) for O‐RADS only, AUC of 0.883 (95% CI, 0.760–1) for CA125 and O‐RADS and an AUC of 0.900 (95% CI, 0.815–0.985) for rHbT and %sO 2 in the prediction of malignancy. A model utilizing all four predictors (CA125, O‐RADS, rHbT and %sO 2 ) achieved superior performance, with an AUC of 0.970 (95% CI, 0.932–1), sensitivity of 100% and specificity of 82%. Conclusions Incorporating the additional information provided by PAI‐derived rHbT and %sO 2 improves significantly the performance of US‐based O‐RADS in the diagnosis of adnexal lesions. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Depth‐resolved attenuation mapping of the human ovary and fallopian tube using optical coherence tomography

AbstractDue to the lack of reliable early‐diagnostic tools, most ovarian cancers are diagnosed at late stages. Although optical coherence tomography (OCT) has shown promise for identifying diseased ovaries and fallopian tubes at an earlier stage, previous studies either did not provide quantitative scattering mapping or simply used Beer's law to fit the scattering coefficients of each A‐line. In this paper, we calculated the pixel‐wise attenuation coefficients of ovaries and fallopian tubes in OCT images. Data from 73 freshly excised human ovaries and fallopian tubes from 36 patients have shown that statistical features are statistically different between cancerous ovaries, infundibula, and fimbriae and normal ones.

Role of blood oxygenation saturation in ovarian cancer diagnosis using multi‐spectral photoacoustic tomography

AbstractIn photoacoustic tomography (PAT), a tunable laser typically illuminates the tissue at multiple wavelengths, and the received photoacoustic waves are used to form functional images of relative total haemoglobin (rHbT) and blood oxygenation saturation (%sO2). Due to measurement errors, the estimation of these parameters can be challenging, especially in clinical studies. In this study, we use a multi‐pixel method to smooth the measurements before calculating rHbT and %sO2. We first perform phantom studies using blood tubes of calibrated %sO2 to evaluate the accuracy of our %sO2 estimation. We conclude by presenting diagnostic results from PAT of 33 patients with 51 ovarian masses imaged by our co‐registered PAT and ultrasound system. The ovarian masses were divided into malignant and benign/normal groups. Functional maps of rHbT and %sO2 and their histograms as well as spectral features were calculated using the PAT data from all ovaries in these two groups. Support vector machine models were trained on different combinations of the significant features. The area under ROC (AUC) of 0.93 (0.95%CI: 0.90‐0.96) on the testing data set was achieved by combining mean %sO2, a spectral feature, and the score of the study radiologist.