The physics of boron neutron capture therapy: an emerging and innovative treatment for glioblastoma and melanoma

There is no one treatment for cancer, and the search for ways to combat cancer have led to many different treatments, including surgery, chemotherapy, and radiation therapy. However, these treatments are not always effective, and in such cases new treatments must be developed. Boron neutron capture therapy (BNCT) is a treatment that has been proposed to combat glioblastomas of the brain and malignant melanomas, two tumors that are resistant to traditional cancer therapies. BNCT is based on the 10B(n,α)7Li reaction, which can potentially deliver a very high and fatal radiation dose to cancerous cells by concentrating boron in them. It is a promising, though complicated treatment. Neutron beams must be generated with an adequate neutron flux, and moderated to therapeutically useful energy levels. The dose is difficult to calculate in BNCT because of all the types of radiation involved: photons, neutrons, and heavy charged particles. Dose is also highly dependent on boron distributions, which are not uniform and are difficult to measure. This makes accurate treatment plans difficult to develop. However, progress has been made on all these fronts and clinical trials have been conducted and shown that BNCT is a potentially safe and effective treatment for glioblastoma and melanoma. It provides an excellent example of the importance of innovation in the search for a cure to cancer

Spatial relationship of 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography and magnetic resonance diffusion imaging metrics in cervical cancer

Abstract Background This study investigated the spatial relationship of 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography ([18F]FDG-PET) standardized uptake values (SUVs) and apparent diffusion coefficients (ADCs) derived from magnetic resonance (MR) diffusion imaging on a voxel level using simultaneously acquired PET/MR data. We performed an institutional retrospective analysis of patients with newly diagnosed cervical cancer who received a pre-treatment simultaneously acquired [18F]FDG-PET/MR. Voxel SUV and ADC values, and global tumor metrics including maximum SUV (SUVmax), mean ADC (ADCmean), and mean tumor-to-muscle ADC ratio (ADCT/M) were compared. The impacts of histology, grade, and tumor volume on the voxel SUV to ADC relationship were also evaluated. The potential prognostic value of the voxel SUV/ADC relationship was evaluated in an exploratory analysis using Kaplan-Meier/log-rank and univariate Cox analysis. Results Seventeen patients with PET/MR scans were identified. There was a significant inverse correlation between SUVmax and ADCmean, and SUVmax and ADCT/M. In the voxelwise analysis, squamous cell carcinomas (SCCAs) and poorly differentiated tumors showed a consistent significant inverse correlation between voxel SUV and ADC values; adenocarcinomas (AdenoCAs) and well/moderately differentiated tumors did not. The strength of the voxel SUV/ADC correlation varied with metabolic tumor volume (MTV). On log-rank analysis, the correlation between voxel SUV/ADC values was prognostic of disease-free survival (DFS). Conclusions In this hypothesis-generating study, a consistent inverse correlation between voxel SUV and ADC values was seen in SCCAs and poorly differentiated tumors. On univariate statistical analysis, correlation between voxel SUV and ADC values was prognostic for DFS

A platform-independent AI tumor lineage and site (ATLAS) classifier

Abstract Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.g. prostate) and lineage (e.g. adenocarcinoma) and have minimal validation in metastatic disease, where classification is more difficult. Utilizing gradient-boosted machine learning, we developed ATLAS, a pair of separate AI Tumor Lineage and Site-of-origin models from RNA expression data on 8249 tumor samples. We assessed performance independently in 10,376 total tumor samples, including 1490 metastatic samples, achieving an accuracy of 91.4% for cancer site-of-origin and 97.1% for cancer lineage. High confidence predictions (encompassing the majority of cases) were accurate 98–99% of the time in both localized and remarkably even in metastatic samples. We also identified emergent properties of our lineage scores for tumor types on which the model was never trained (zero-shot learning). Adenocarcinoma/sarcoma lineage scores differentiated epithelioid from biphasic/sarcomatoid mesothelioma. Also, predicted lineage de-differentiation identified neuroendocrine/small cell tumors and was associated with poor outcomes across tumor types. Our platform-independent single-sample approach can be easily translated to existing RNA-seq platforms. ATLAS can complement and guide traditional histopathologic assessment in challenging situations and tumors of unknown primary