Real-time Resection Research Articles

SERS surgical navigation with postsurgical immunotherapy of local microtumors and distant metastases for improved anticancer outcomes.

The standard of clinical care of most malignant solid cancers is surgery, followed by postsurgical adjuvant therapy, but microtumor lesions left behind after surgery and invisible distant metastases are the major reasons for treatment failure. Here, we report an integrated strategy combining surface-enhanced Raman spectroscopy (SERS) surgical navigation with postsurgical immunotherapy elicited by near-infrared II photothermal treatment and programmed death-1 antibody. The SERS surgical navigation is principally based on the multifunctional optical probes (namely, MATRA probes) integrating with T1-weighted magnetic resonance (MR) imaging, photothermal effect and Raman spectroscopic detection. We demonstrate in a 4T1 breast tumor mouse model that the pre-surgical MR/SERS dual-modal imaging is capable of providing comprehensive tumor information, and intraoperative SERS detection allows accurately delineating the tumor margins and guiding the surgical resection in real time with the least residual microscopic foci. We verify that the postsurgical immunotherapy effectively eradicates those local microtumor lesions and invisible distant metastases, greatly inhibiting the postsurgical cancer recurrence and distant metastasis.

From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery.

Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice. Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports. The best-performing model using both quantitative evaluation and our visual ranking framework achieved a mean Dice score of 0.959. Quantitative metrics are positively associated with expert visual rankings. However, the predictive value of generated contours was limited with a sensitivity of 0.750 and a specificity of 0.433 when tested against pathology reports. We present a clinical evaluation of deep learning models trained for intraoperative tumor segmentation in breast-conserving surgery. We demonstrate that automatic contouring is limited in predicting pathology margins despite achieving high performance on quantitative metrics.

Fluorescence-Guided Surgery Allows for More Complete Resection of Pancreatic Cancer, Resulting in Longer Disease-Free Survival Compared with Standard Surgery in Orthotopic Mouse Models

Negative surgical margins are vital to achieve cure and prolong survival in patients with pancreatic cancer. We inquired if fluorescence-guided surgery (FGS) could improve surgical outcomes and reduce recurrence rates in orthotopic mouse models of human pancreatic cancer. A randomized active-control preclinical trial comparing bright light surgery (BLS) to FGS was used. Orthotopic mouse models of human pancreatic cancer were established using the BxPC-3 pancreatic cancer cell line expressing red fluorescent protein (RFP). Two weeks after orthotopic implantation, tumors were resected with BLS or FGS. Pre- and postoperative images were obtained with the OV-100 Small Animal Imaging System to assess completeness of surgical resection in real time. Postoperatively, noninvasive whole body imaging was done to assess recurrence and follow tumor progression. Six weeks postoperatively, mice were sacrificed to evaluate primary pancreatic and metastatic tumor burden at autopsy. A more complete resection of pancreatic cancer was achieved using FGS compared with BLS: 98.9% vs 77.1%, p = 0.005. The majority of mice undergoing BLS (63.2%) had evidence of gross disease with no complete resections; 20% of mice undergoing FGS had complete resection and an additional 75% had only minimal residual disease (p = 0.0001). The mean postoperative tumor burden was significantly less with FGS compared with BLS: 0.08 ± 0.06 mm(2) vs 2.64 ± 0.63 mm(2), p = 0.001. The primary tumor burden at termination was significantly less with FGS compared with BLS: 19.3 ± 5.3 mm(2) vs 6.2 ± 3.6 mm(2), p = 0.048. FGS resulted in significantly longer disease-free survival than BLS (p = 0.02, hazard ratio = 0.39, 95% CI 0.17, 0.88). Surgical outcomes were improved in pancreatic cancer using fluorescence-guidance. This novel approach has significant potential to improve surgical treatment of cancer.

Hydrogel Nanoparticles with Covalently Linked Coomassie Blue for Brain Tumor Delineation Visible to the Surgeon

Delineation of tumor margins is a critical and challenging objective during brain cancer surgery. A tumor-targeting deep-blue nanoparticle-based visible contrast agent is described, which, for the first time, offers in vivo tumor-specific visible color staining. This technology thus enables color-guided tumor resection in real time, with no need for extra equipment or special lighting conditions. The visual contrast agent consists of polyacrylamide nanoparticles covalently linked to Coomassie Blue molecules (for nonleachable blue color contrast), which are surface-conjugated with polyethylene glycol and F3 peptides for efficient in vivo circulation and tumor targeting, respectively.