Second near-infrared photothermal-amplified immunotherapy using photoactivatable composite nanostimulators

Background The construction of a nanoimmune controlled-release system that spatiotemporally recognizes tumor lesions and stimulates the immune system response step by step is one of the most potent cancer treatment strategies for improving the sensitivity of immunotherapy response. Results Here, a composite nanostimulator (CNS) was constructed for the release of second near-infrared (NIR-II) photothermal-mediated immune agents, thereby achieving spatiotemporally controllable photothermal-synergized immunotherapy. CNS nanoparticles comprise thermosensitive liposomes as an outer shell and are internally loaded with a NIR-II photothermal agent, copper sulfide (CuS), toll-like receptor-9 (TLR-9) agonist, cytosine-phospho-guanine oligodeoxynucleotides, and programmed death-ligand 1 (PD-L1) inhibitors (JQ1). Following NIR-II photoirradiation, CuS enabled the rapid elevation of localized temperature, achieving tumor ablation and induction of immunogenic cell death (ICD) as well as disruption of the lipid shell, enabling the precise release of two immune-therapeutical drugs in the tumor region. Combining ICD, TLR-9 stimulation, and inhibited expression of PD-L1 allows the subsequent enhancement of dendritic cell maturation and increases infiltration of cytotoxic T lymphocytes, facilitating regional antitumor immune responses. Conclusion CNS nanoparticle-mediated photothermal-synergized immunotherapy efficiently suppressed the growth of primary and distant tumors in two mouse models and prevented pulmonary metastasis. This study thus provides a novel sight into photo-controllably safe and efficient immunotherapy. Graphical Abstract

NI-14 estimation of property of MRI non-contrast enhanced lesion of Glioblastoma using T1/T2 ratio

Background: Tumor mass of glioblastoma is considered to exist beyond gadolinium-enhancing lesion into T2/FLAIR-high intensity lesions (T2/FL-HIL) on MRI. However, it is challenging to differentiate non-enhancing tumor region (NET) from pure brain edema for T2/FL-HIL. The T1/T2 ratio (rT1/T2) is an MRI metric considered to semi-quantify the tissue relaxation time on MRI. This research tested the hypothesis that rT1/T2 is useful for identifying NET within T2/FL-HIL by comparing it with 11C-methionine positron emission tomography (MET-PET). Method: Forty-six glioblastoma (GBM) patients at Osaka International Cancer Institute and Osaka University Hospital where T1-, T2- and contrast-enhanced T1-weighted MRI and MET-PET were available were included in this study. rT1/T2 maps were obtained after signal corrections were performed, as reported previously. Region-of-interests (ROIs) were defined within T2/FL-HILs beyond the gadolinium-enhanced lesion. MET-PET and rT1/T2 maps were co-registered to the same coordinate system, and the relationship between methionine uptake and rT1/T2 values was examined in a voxel-wise manner.ResultApproximately three million voxels were included for analysis. Lesions with methionine uptake higher than 5.0 on T/N showed 0.7 &lt rT1/T2 &lt 0.98. For those with methionine uptake higher than 3.0, rT1/T2 was between 0.70 and 1.04.DiscussionThis report suggested that rT1/T2 represents histological characteristics of the glioblastoma within T2/FL-HIL. It also indicated that rT1/T2 could be a useful biomarker for detecting NET within T2/FL-HIL for glioblastoma.

Theranostic near-infrared-IIb emitting nanoprobes for promoting immunogenic radiotherapy and abscopal effects against cancer metastasis

Radiotherapy is an important therapeutic strategy for cancer treatment through direct damage to cancer cells and augmentation of antitumor immune responses. However, the efficacy of radiotherapy is limited by hypoxia-mediated radioresistance and immunosuppression in tumor microenvironment. Here, we construct a stabilized theranostic nanoprobe based on quantum dots emitting in the near-infrared IIb (NIR-IIb, 1,500–1,700 nm) window modified by catalase, arginine–glycine–aspartate peptides and poly(ethylene glycol). We demonstrate that the nanoprobes effectively aggregate in the tumor site to locate the tumor region, thereby realizing precision radiotherapy with few side-effects. In addition, nanoprobes relieve intratumoral hypoxia and reduce the tumor infiltration of immunosuppressive cells. Moreover, the nanoprobes promote the immunogenic cell death of cancer cells to trigger the activation of dendritic cells and enhance T cell-mediated antitumor immunity to inhibit tumor metastasis. Collectively, the nanoprobe-mediated immunogenic radiotherapy can boost the abscopal effect to inhibit tumor metastasis and prolong survival.

Classification of Spine Image from MRI Image Using Convolutional Neural Network

The spine tumor is a fast-growing abnormal cell in the spinal canal or vertebrae of the spine, it affected many people. Thousands of researchers have focused on this disease for better understanding of tumor classification to provide more effective treatment to the patients. The main objective of this paper is to form a methodology for classification of spine image. We proposed an efficient and effective method that helpful for classifying the spine image and identified tumor region without any human assistance. Basically, Contrast Limited Adaptive Histogram Equalization used to improve the contrast of spine images and to eliminate the effect of unwanted noise. The proposed methodology will classify spine images as Normal or Abnormal using Convolutional Neural Network (CNN) model algorithm. The CNN model can classify spine image as Normal or Abnormal with 99.4% Accuracy, 94.5% Sensitivity, 95.6% Precision, and 99.9% specificity. Compared with the previous existing methods, our proposed solution achieved the highest performance in terms of classification based on the spine dataset. From the experimental results performed on the different images, it is clear that the analysis for the spine tumor detection is fast and accurate when compared with the manual detection performed by radiologists or clinical experts, So, anyone can easily identify the tumor affected area also determine abnormal images.

Computational modeling of PET tracer distribution in solid tumors integrating microvasculature

Background We present computational modeling of positron emission tomography radiotracer uptake with consideration of blood flow and interstitial fluid flow, performing spatiotemporally-coupled modeling of uptake and integrating the microvasculature. In our mathematical modeling, the uptake of fluorodeoxyglucose F-18 (FDG) was simulated based on the Convection–Diffusion–Reaction equation given its high accuracy and reliability in modeling of transport phenomena. In the proposed model, blood flow and interstitial flow are solved simultaneously to calculate interstitial pressure and velocity distribution inside cancer and normal tissues. As a result, the spatiotemporal distribution of the FDG tracer is calculated based on velocity and pressure distributions in both kinds of tissues. Results Interstitial pressure has maximum value in the tumor region compared to surrounding tissue. In addition, interstitial fluid velocity is extremely low in the entire computational domain indicating that convection can be neglected without effecting results noticeably. Furthermore, our results illustrate that the total concentration of FDG in the tumor region is an order of magnitude larger than in surrounding normal tissue, due to lack of functional lymphatic drainage system and also highly-permeable microvessels in tumors. The magnitude of the free tracer and metabolized (phosphorylated) radiotracer concentrations followed very different trends over the entire time period, regardless of tissue type (tumor vs. normal). Conclusion Our spatiotemporally-coupled modeling provides helpful tools towards improved understanding and quantification of in vivo preclinical and clinical studies.

ITVT-06. Application of artificial intelligence and radiomics for the analysis of intraoperative ultrasound images of brain tumors

BACKGROUND Intraoperative ultrasound (ioUS) images of brain tumors contain information that has not yet been exploited. The present work aims to analyze images in both B-mode and strain-elastography using techniques based on artificial intelligence and radiomics. We pretend to assess the capacity for differentiating glioblastomas (GBM) from solitary brain metastases (SBM) and also to assess the ability to predict the overall survival (OS) in GBM. METHODS We performed a retrospective analysis of patients who underwent craniotomy between March 2018 to June 2020 with GBM and SBM diagnoses. Cases with an ioUS study were included. In the first group of patients, an analysis based on deep learning was performed. An existing neural network (Inception V3) was used to classify tumors into GBM and SBM. The models were evaluated using the area under the curve (AUC), classification accuracy, and precision. In the second group, radiomic features from the tumor region were extracted. Radiomic features associated with OS were selected employing univariate correlations. Then, a survival analysis was conducted using Cox regression. RESULTS For the classification task, a total of 36 patients were included. 26 GBM and 10 SBM. Models were built using a total of 812 ultrasound images. For B-mode, AUC and accuracy values ranged from 0.790 to 0.943 and from 72 to 89 % respectively. For elastography, AUC and accuracy values ranged from 0.847 to 0.985 and from 79 to 95 % respectively. Sixteen patients were available for the survival analysis. A total of 52 radiomic features were extracted. Two texture features from B-mode (Conventional mean and GLZLM_SZLGE) and one texture feature from strain-elastography (GLZLM_LZHGE) were significantly associated with OS. CONCLUSIONS Automated processing of ioUS images through deep learning can generate high-precision classification algorithms. Radiomic tumor region features in B-mode and elastography appear to be significantly associated with OS in GBM.

EXTH-75. APPLICATION OF TUMOR TREATING FIELDS (TTFIELDS) TO THE HEAD AND TORSO OF MICE WITH THE DEDICATED INOVIVO SYSTEM

INTRODUCTION Tumor Treating Fields (TTFields) therapy is an approved anti-cancer treatment for glioblastoma and mesothelioma. TTFields are delivered to patients continuously by two sets of arrays placed on opposite sides of the body at the tumor region to generate two perpendicular electric fields. Previously, in vivo studies of TTFields were limited due to the lack of a dedicated system that could maintain continuous and adequate contact of the arrays with the animal’s skin as well as the stress imposed on the animals by individual housing and the motility limitations they experience during treatment. METHODS Different electrode layouts were explored to optimize the intensity of the electric fields delivered to the target locations (therapeutic threshold >1 V/cm). The ability of various adhesive materials and wire coiling prevention strategies to increase TTFields device usage was examined. Stress reduction with different housing methods was evaluated via clinical examination of the animals. RESULTS Optimal array layouts were identified based on simulation data for TTFields delivery to the torso or the head of the mouse. Compacting conductors into a single printed circuit cable connected to a novel electric swivel machine resulted in fewer wire entanglements, and the improved adhesives resulted in fewer array replacements, overall elevating device usage. Improved cage design permitted pairs of mice to maintain social interactions while individually housed. Less weight loss was seen for animals housed in the dyadic relative to the standard solitary cages, indicating reduced stress. CONCLUSIONS The inovivo system provides means for continuous delivery of therapeutic levels of TTFields to the head and torso of mice while minimizing animal stress and increasing device usage. The new head arrays enable application of TTFields to the head of mice for the first time, allowing expansion of glioblastoma treatment research.