Rationale: Positive margin status due to incomplete removal of tumor tissue during breast conserving surgery (BCS) is a prevalent diagnosis usually requiring a second surgical procedure. These follow-up procedures increase the risk of morbidity and delay the use of adjuvant therapy; thus, significant efforts are underway to develop new intraoperative strategies for margin assessment to eliminate re-excision procedures. One strategy under development uses topical application of dual probe staining and a fluorescence imaging strategy termed dual probe difference specimen imaging (DDSI). DDSI uses a receptor-targeted fluorescent probe and an untargeted, spectrally-distinct fluorescent companion imaging agent topically applied to fresh resected specimens, where the fluorescence from each probe is imaged and a normalized difference image is computed to identify tumor-target distribution in the specimen margins. While previous reports suggested this approach is a promising new tool for surgical guidance, advancing the approach into the clinic requires methodical protocol optimization and further validation.Methods: In the present study, we used breast cancer xenografts and receiver operator characteristic (ROC) curve analysis to evaluate a wide range of staining and imaging parameters, and completed a prospective validation study on multiple tumor phenotypes with different target expression. Imaging fluorophore-probe pair, concentration, and incubation times were systematically optimized using n=6 tissue specimen replicates per staining condition. Resulting tumor vs. normal adipose tissue diagnostic performance were reported and staining patterns were validated via receptor specific immunohistochemistry colocalization. Optimal staining conditions were tested in receptor positive and receptor negative cohorts to confirm specificity.Results: The optimal staining conditions were found to be a one minute stain in a 200 nM probe solution (area under the curve (AUC) = 0.97), where the choice of fluorescent label combination did not significantly affect the diagnostic performance. Using an optimal threshold value determined from ROC curve analysis on a training data set, a prospective study on xenografts resulted in an AUC=0.95 for receptor positive tumors and an AUC = 0.50 for receptor negative (control) tumors, confirming the diagnostic performance of this novel imaging technique.Conclusions: DDSI provides a robust, molecularly specific imaging methodology for identifying tumor tissue over benign mammary adipose tissue. Using a dual probe imaging strategy, nonspecific accumulation of targeted probe was corrected for and tumor vs. normal tissue diagnostic potential was improved, circumventing difficulties with ex vivo tissue specimen staining and allowing for rapid clinical translation of this promising technology for tumor margin detection during BCS procedures.

Intraoperative margin assessment is imperative to cancer cure but is a continued challenge to successful surgery. Breast conserving surgery is a relevant example, where a cosmetically improved outcome is gained over mastectomy, but re-excision is required in >25% of cases due to positive or closely involved margins. Clinical translation of margin assessment modalities that must directly contact the patient or required administered contrast agents are time consuming and costly to move from bench to bedside. Tumor resections provide a unique surgical opportunity to deploy margin assessment technologies including contrast agents on the resected tissues, substantially shortening the path to the clinic. However, staining of resected tissues is plagued by nonspecific uptake. A ratiometric imaging approach where matched targeted and untargeted probes are used for staining has demonstrated substantially improved biomarker quantification over staining with conventional targeted contrast agents alone. Our group has developed an antibody-based ratiometric imaging technology using fluorescently labeled, spectrally distinct targeted and untargeted antibody probes termed dual-stain difference specimen imaging (DDSI). Herein, the targeted biomarker expression level and pattern are evaluated for their effects on DDSI diagnostic potential. Epidermal growth factor receptor expression level was correlated to DDSI diagnostic potential, which was found to be robust to spatial pattern expression variation. These results highlight the utility of DDSI for accurate margin assessment of freshly resected tumor specimens.

Background Re-excision rates following breast conserving surgery (BCS) remain as high as ~ 35%, with positive margins detected during follow-up histopathology. Additional breast cancer resection surgery is not only taxing on the patient and health care system, but also delays adjuvant therapies, increasing morbidity and reducing the likelihood of a positive outcome. The ability to precisely resect and visualize tumor margins in real time within the surgical theater would greatly benefit patients, surgeons and the health care system. Current tumor margin assessment technologies utilized during BCS involve relatively lengthy and labor-intensive protocols, which impede the surgical work flow. Methods In previous work, we have developed and validated a fluorescence imaging method termed dual probe difference specimen imaging (DDSI) to accurately detect benign and malignant tissue with direct correlation to the targeted biomarker expression levels intraoperatively. The DDSI method is currently on par with touch prep cytology in execution time (~ 15-min). In this study, the main goal was to shorten the DDSI protocol by decreasing tissue blocking and washing times to optimize the DDSI protocol to < 10-min whilst maintaining robust benign and malignant tissue differentiation. Results We evaluated the utility of the shortened DDSI staining methodology using xenografts grown from cell lines with varied epidermal growth factor receptor (EGFR) expression levels, comparing accuracy through receiver operator characteristic (ROC) curve analyses across varied tissue blocking and washing times. An optimized 8-min DDSI methodology was developed for future clinical translation. Conclusions Successful completion of this work resulted in substantial shortening of the DDSI methodology for use in the operating room, that provided robust, highly receptor specific, sensitive diagnostic capabilities between benign and malignant tissues.

Identification of tumor margins in the operating room in real time is a critical challenge for surgical procedures that serve as cancer cure. Breast conserving surgery (BCS) is particularly affected by this problem, with current re-excision rates above 25%. Due to a lack of clinically available methodologies for detection of involved or close tumor margins, much effort is focused on developing intraoperative margin assessment modalities that can aid in addressing this unmet clinical need. BCS provides a unique opportunity to design contrast-based technologies that are able to assess tumor margins independent from the patient, providing a rapid pathway from bench to bedside at a much lower cost. Since resected tissue is removed from the patient's blood supply, non-specific contrast agent uptake becomes a challenge due to the lack of clearance. Therefore, a dual probe, ratiometric fluorescence imaging approach was taken in an effort to reduce nonspecific signal, and provide a modality that could demonstrate rapid, robust margin assessment on resected patient samples. Termed, dual-stain difference specimen imaging (DDSI), DDSI includes the use of spectrally unique, and fluorescently labeled target-specific, as well as non-specific biomarkers. In the present study, we have applied epidermal growth factor receptor (EGFR) targeted DDSI to tumor xenografts with variable EGFR expression levels using a previously optimized staining protocol, allowing for a quantitative assessment of the predictive power of the technique under biologically relevant conditions. Due to the presence of necrosis in the model tumors, ring analysis was employed to characterize diagnostic accuracy as measured by receiver operator characteristic (ROC) curve analysis. Our findings demonstrate the robust nature of the DDSI technique even in the presence of variable biomarker expression and spatial patterns. These results support the continued development of this technology as a robust diagnostic tool for tumor margin assessment in resected specimens during BCS.

Using optical coherence tomography angiography images from mouse ears, a convolutional neural network successfully recognized diabetes with an accuracy of 0.81 and an area under the curve of 0.90.

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