Dataset Information

Quantitative molecular phenotyping with topically applied SERS nanoparticles for intraoperative guidance of breast cancer lumpectomy.

ABSTRACT: There is a need to image excised tissues during tumor-resection procedures in order to identify residual tumors at the margins and to guide their complete removal. The imaging of dysregulated cell-surface receptors is a potential means of identifying the presence of diseases with high sensitivity and specificity. However, due to heterogeneities in the expression of protein biomarkers in tumors, molecular-imaging technologies should ideally be capable of visualizing a multiplexed panel of cancer biomarkers. Here, we demonstrate that the topical application and quantification of a multiplexed cocktail of receptor-targeted surface-enhanced Raman scattering (SERS) nanoparticles (NPs) enables rapid quantitative molecular phenotyping (QMP) of the surface of freshly excised tissues to determine the presence of disease. In order to mitigate the ambiguity due to nonspecific sources of contrast such as off-target binding or uneven delivery, a ratiometric method is employed to quantify the specific vs. nonspecific binding of the multiplexed NPs. Validation experiments with human tumor cell lines, fresh human tumor xenografts in mice, and fresh human breast specimens demonstrate that QMP imaging of excised tissues agrees with flow cytometry and immunohistochemistry, and that this technique may be achieved in less than 15?minutes for potential intraoperative use in guiding breast-conserving surgeries.

SUBMITTER: Wang Y

PROVIDER: S-EPMC4754709 | biostudies-other | 2016 Feb

REPOSITORIES: biostudies-other

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Quantitative molecular phenotyping with topically applied SERS nanoparticles for intraoperative guidance of breast cancer lumpectomy.

Wang Yu Y Kang Soyoung S Khan Altaz A Ruttner Gabriel G Leigh Steven Y SY Murray Melissa M Abeytunge Sanjee S Peterson Gary G Rajadhyaksha Milind M Dintzis Suzanne S Javid Sara S Liu Jonathan T C JT

Scientific reports 20160216

There is a need to image excised tissues during tumor-resection procedures in order to identify residual tumors at the margins and to guide their complete removal. The imaging of dysregulated cell-surface receptors is a potential means of identifying the presence of diseases with high sensitivity and specificity. However, due to heterogeneities in the expression of protein biomarkers in tumors, molecular-imaging technologies should ideally be capable of visualizing a multiplexed panel of cancer ...[more]

PMID: 26878888

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Project description:Background Radiotracer extravasations, caused largely by faulty tracer injections, can occur in up to 23% of 18F-fluorodeoxyglucose (FDG) PET/CT scans and negatively impact radiological review and tracer quantification. Conventional radiological assessment of extravasation severity on PET has limited performance (e.g., extravasations frequently resolve before scanning) and practical drawbacks. In this study, we develop a new topical detector-based FDG extravasation severity classifier, calibrated from semi-quantitative PET measurements, and assess its performance on human subjects. Methods A retrospective study examined patients whose FDG injections had been monitored as part of their standard workup for PET/CT imaging. Topical uncollimated gamma ray detectors were applied proximal to the injection site and on the same location on the opposing arm, and readings were acquired continuously during radiotracer uptake. Patients were imaged with their arms in the PET field of view and total extravasation activity quantified from static PET images through a volume of interest approach. The image-derived activities were considered ground truth and used to calibrate and assess quantification of topical detector readings extrapolated to the start of PET imaging. The classifier utilizes the calibrated detector readings to produce four extravasation severity classes: none, minor, moderate, and severe. In a blinded study, a radiologist qualitatively labeled PET images for extravasation severity using the same classifications. The radiologist’s interpretations and topical detector classifications were compared to the ground truth PET results. Results Linear regression of log-transformed image-derived versus topical detector tracer extravasation activity estimates showed a strong correlation (R2 = 0.75). A total of 24 subject scans were cross-validated with the quantitatively based classifier through a leave-one-out methodology. For binary classification (none vs. extravasated), the topical detector classifier had the highest overall diagnostic performance for identifying extravasations. Specificity, sensitivity, accuracy, and positive predictive value were 100.0%, 80.0%, 95.8%, and 100.0%, respectively, for the topical detector classifier and 31.6%, 100.0%, 45.8%, and 27.8%, respectively, for the radiological analysis. The topical detector classifier, with an optimal detection threshold, produced a significantly higher Matthews correlation coefficient (MCC) than the radiological analysis (0.87 vs. 0.30). Conclusions The topical detector binary classifier, calibrated using quantitative static PET measurements, significantly improves extravasation detection compared to qualitative image analysis. Supplementary Information The online version contains supplementary material available at 10.1186/s40658-022-00488-6.

Dataset Information

Quantitative molecular phenotyping with topically applied SERS nanoparticles for intraoperative guidance of breast cancer lumpectomy.

Publications

Quantitative molecular phenotyping with topically applied SERS nanoparticles for intraoperative guidance of breast cancer lumpectomy.

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