Because contemporary intraoperative tumor detection modalities, such as intraoperative MRI, are not ubiquitously available and can disrupt surgical workflow, there is an imperative for an accessible diagnostic device that can meet the surgeon’s needs in identifying tissue types. The objective of this paper is to determine the efficacy of a novel non-contact tumor detection device for metastatic melanoma boundary identification in a tissue-mimicking phantom, evaluate the identification of metastatic melanoma boundaries in ex vivo mouse brain tissue, and find the error associated with identifying this boundary. To validate the spatial and fluorescence resolution of the device, tissue-mimicking phantoms were created with modifiable optical properties. Phantom tissue provided ground truth measurements for fluorophore concentration differences with respect to spatial dimensions. Modeling metastatic disease, ex vivo melanoma brain metastases were evaluated to detect differences in fluorescence between healthy and neoplastic tissue. This analysis includes determining required-to-observe fluorescence differences in tissue. H&E staining confirmed tumor presence in mouse tissue samples. The device detected a difference in normalized average fluorescence intensity in all three phantoms. There were differences in fluorescence with the presence and absence of melanin. The estimated tumor boundary of all tissue phantoms was within 0.30 mm of the ground truth tumor boundary for all boundaries. Likewise, when applied to the melanoma-bearing brains from ex vivo mice, a difference in normalized fluorescence intensity was successfully detected. The potential prediction window for the tumor boundary location is less than 1.5 mm for all ex vivo mouse brain tumors boundaries. We present a non-contact, laser-induced fluorescence device that can identify tumor boundaries based on changes in laser-induced fluorescence emission intensity. The device can identify phantom ground truth tumor boundaries within 0.30 mm using instantaneous rate of change of normalized fluorescence emission intensity and can detect endogenous fluorescence differences in melanoma brain metastases in ex vivo mouse tissue.
The discovery of new treatments for cancer is imperative. Recently, we showed in a proof-of-concept study that SYnergistic IMmuno PHOtothermal NanotherapY (SYMPHONY) is a powerful treatment for metastatic bladder cancer and brain tumor in mouse models. Our work has recently demonstrated that combining immunotherapy checkpoint inhibitors and gold nanostar (AuNS) photothermal therapy (PTT) is more effective in killing primary tumors and activating the immune system to eradicate tumors at distant sites, than each individual treatment alone. When the tumor is being ablated via PTT in mice models, using low intensity heat from a near infrared laser, the dying tumor releases proteins that trigger the immune system to destroy remaining tumor cells. Immune checkpoint inhibitors prevent the tumor cells from hiding from the immune system’s mechanisms; thus, the immune system becomes capable of attacking distant secondary tumors, after the primary tumor has been eradicated using AuNS mediated PTT. The data shows that after the cured mice were rechallenged with bladder cancer cells, no tumor formation was observed after 60 days; hence these results indicate that the SYMPHONY treatment can function as a cancer vaccine and lead to long-lasting immunity.
The ability to differentiate healthy and tumorous tissue is vital during the surgical removal of tumors. This ability is especially critical during neurosurgical tumor resection due to the risk associated with removing healthy brain tissue. In this paper, we present an epifluorescence spectroscopy guided device that is not only capable of autonomously classifying a region of tissue as tumorous or healthy in real-time–but is also able to differentiate between different tumor types. For this study, glioblastoma and melanoma were chosen as the two different tumor types. Six mice were utilized in each of the three classes (healthy, glioblastoma, melanoma) for a total of eighteen mice. A “one-vs-the-all” approach was used to create a multi-class classifier. The multi-class classifier was capable of classifying with 100% accuracy. Future work includes increasing the number of mice in each of the three tumor classes to create a more robust classifier and expanding the number of tumor types beyond glioblastoma and melanoma.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.