A Fluorescence-Guided Laser Ablation System for Removal of Residual Cancer in a Mouse Model of Soft Tissue Sarcoma.

The treatment of soft tissue sarcoma (STS) generally involves tumor excision with a wide margin. Although advances in fluorescence imaging make real-time detection of cancer possible, removal is limited by the precision of the human eye and hand. Here, we describe a novel pulsed Nd:YAG laser ablation system that, when used in conjunction with a previously described molecular imaging system, can identify and ablate cancer in vivo. Mice with primary STS were injected with the protease-activatable probe LUM015 to label tumors. Resected tissues from the mice were then imaged and treated with the laser using the paired fluorescence-imaging/ laser ablation device, generating ablation clefts with sub-millimeter precision and minimal underlying tissue damage. Laser ablation was guided by fluorescence to target tumor tissues, avoiding normal structures. The selective ablation of tumor implants in vivo improved recurrence-free survival after tumor resection in a cohort of 14 mice compared to 12 mice that received no ablative therapy. This prototype system has the potential to be modified so that it can be used during surgery to improve recurrence-free survival in patients with cancer.

Structured Illumination Microscopy and a Quantitative Image Analysis for the Detection of Positive Margins in a Pre-Clinical Genetically Engineered Mouse Model of Sarcoma.

Intraoperative assessment of surgical margins is critical to ensuring residual tumor does not remain in a patient. Previously, we developed a fluorescence structured illumination microscope (SIM) system with a single-shot field of view (FOV) of 2.1 × 1.6 mm (3.4 mm2) and sub-cellular resolution (4.4 µm). The goal of this study was to test the utility of this technology for the detection of residual disease in a genetically engineered mouse model of sarcoma. Primary soft tissue sarcomas were generated in the hindlimb and after the tumor was surgically removed, the relevant margin was stained with acridine orange (AO), a vital stain that brightly stains cell nuclei and fibrous tissues. The tissues were imaged with the SIM system with the primary goal of visualizing fluorescent features from tumor nuclei. Given the heterogeneity of the background tissue (presence of adipose tissue and muscle), an algorithm known as maximally stable extremal regions (MSER) was optimized and applied to the images to specifically segment nuclear features. A logistic regression model was used to classify a tissue site as positive or negative by calculating area fraction and shape of the segmented features that were present and the resulting receiver operator curve (ROC) was generated by varying the probability threshold. Based on the ROC curves, the model was able to classify tumor and normal tissue with 77% sensitivity and 81% specificity (Youden's index). For an unbiased measure of the model performance, it was applied to a separate validation dataset that resulted in 73% sensitivity and 80% specificity. When this approach was applied to representative whole margins, for a tumor probability threshold of 50%, only 1.2% of all regions from the negative margin exceeded this threshold, while over 14.8% of all regions from the positive margin exceeded this threshold.

Tailoring Adjuvant Radiation Therapy by Intraoperative Imaging to Detect Residual Cancer.

For many solid cancers, radiation therapy is offered as an adjuvant to surgical resection to lower rates of local recurrence and improve survival. However, a subset of patients treated with surgery alone will not have a local recurrence. Currently, there is no way to accurately determine which patients have microscopic residual disease in the tumor bed after surgery and therefore are most likely to benefit from adjuvant radiation therapy. To address this problem, a number of technologies have been developed to try to improve margin assessment of resected tissue and to detect residual cancer in the tumor bed. Moreover, some of these approaches have been translated from the preclinical arena into clinical trials. Here, we review different types of intraoperative molecular imaging systems for cancer. Optical imaging techniques like epi-illumination, fluorescence molecular tomography and optoacoustic imaging can be coupled with exogenous fluorescent imaging probes that accumulate in tumors passively via the enhanced permeability and retention effect or are targeted to tumor tissues based on affinity or enzyme activity. In these approaches, detection of fluorescence in the tumor bed may indicate residual disease. Protease activated probes have generated great interest because of their potential for leading to high tumor to normal contrast. Recently, the first Phase I clinical trial to assess the safety and activation of a protease activated probe was conducted. Spectroscopic methods like radiofrequency spectroscopy and Raman spectroscopy, which are based on energy absorption and scattering, respectively, have also been tested in humans and are able to distinguish between normal and tumors tissues intraoperatively. Most recently, multimodal contrast agents have been developed that target tumors and contain both fluorescent dyes and magnetic resonance imaging contrast agents, allowing for preoperative planning and intraoperative margin assessment with a single contrast agent. Further clinical testing of these various intraoperative imaging approaches may lead to more accurate methods for margin assessment and the intraoperative detection of microscopic residual disease, which could guide further resection and the use of adjuvant radiation therapy.

A Plasmonic Gold Nanostar Theranostic Probe for In Vivo Tumor Imaging and Photothermal Therapy.

Nanomedicine has attracted increasing attention in recent years, because it offers great promise to provide personalized diagnostics and therapy with improved treatment efficacy and specificity. In this study, we developed a gold nanostar (GNS) probe for multi-modality theranostics including surface-enhanced Raman scattering (SERS) detection, x-ray computed tomography (CT), two-photon luminescence (TPL) imaging, and photothermal therapy (PTT). We performed radiolabeling, as well as CT and optical imaging, to investigate the GNS probe's biodistribution and intratumoral uptake at both macroscopic and microscopic scales. We also characterized the performance of the GNS nanoprobe for in vitro photothermal heating and in vivo photothermal ablation of primary sarcomas in mice. The results showed that 30-nm GNS have higher tumor uptake, as well as deeper penetration into tumor interstitial space compared to 60-nm GNS. In addition, we found that a higher injection dose of GNS can increase the percentage of tumor uptake. We also demonstrated the GNS probe's superior photothermal conversion efficiency with a highly concentrated heating effect due to a tip-enhanced plasmonic effect. In vivo photothermal therapy with a near-infrared (NIR) laser under the maximum permissible exposure (MPE) led to ablation of aggressive tumors containing GNS, but had no effect in the absence of GNS. This multifunctional GNS probe has the potential to be used for in vivo biosensing, preoperative CT imaging, intraoperative detection with optical methods (SERS and TPL), as well as image-guided photothermal therapy.

A quantitative microscopic approach to predict local recurrence based on in vivo intraoperative imaging of sarcoma tumor margins.

The goal of resection of soft tissue sarcomas located in the extremity is to preserve limb function while completely excising the tumor with a margin of normal tissue. With surgery alone, one-third of patients with soft tissue sarcoma of the extremity will have local recurrence due to microscopic residual disease in the tumor bed. Currently, a limited number of intraoperative pathology-based techniques are used to assess margin status; however, few have been widely adopted due to sampling error and time constraints. To aid in intraoperative diagnosis, we developed a quantitative optical microscopy toolbox, which includes acriflavine staining, fluorescence microscopy, and analytic techniques called sparse component analysis and circle transform to yield quantitative diagnosis of tumor margins. A series of variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82 and 75%. The utility of this approach was tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78 and 82%. For comparison, if pathology was used to predict local recurrence in this data set, it would achieve a sensitivity of 29% and a specificity of 71%. These results indicate a robust approach for detecting microscopic residual disease, which is an effective predictor of local recurrence.