Our purpose is to investigate the feasibility of imaging tumor metabolism in breast cancer patients using ¹³ C magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized ¹³ C label exchange between injected [1- ¹³ C]pyruvate and the endogenous tumor lactate pool. Treatment-naïve breast cancer patients were recruited: four triple-negative grade 3 cancers; two invasive ductal carcinomas that were estrogen and progesterone receptor-positive (ER/PR+) and HER2/neu-negative (HER2−), one grade 2 and one grade 3; and one grade 2 ER/PR+ HER2− invasive lobular carcinoma (ILC). Dynamic ¹³ C MRSI was performed following injection of hyperpolarized [1- ¹³ C]pyruvate. Expression of lactate dehydrogenase A (LDHA), which catalyzes ¹³ C label exchange between pyruvate and lactate, hypoxia-inducible factor-1 (HIF1α), and the monocarboxylate transporters MCT1 and MCT4 were quantified using immunohistochemistry and RNA sequencing. We have demonstrated the feasibility and safety of hyperpolarized ¹³ C MRI in early breast cancer. Both intertumoral and intratumoral heterogeneity of the hyperpolarized pyruvate and lactate signals were observed. The lactate-to-pyruvate signal ratio (LAC/PYR) ranged from 0.021 to 0.473 across the tumor subtypes (mean ± SD: 0.145 ± 0.164), and a lactate signal was observed in all of the grade 3 tumors. The LAC/PYR was significantly correlated with tumor volume ( R = 0.903, P = 0.005) and MCT 1 ( R = 0.85, P = 0.032) and HIF1α expression ( R = 0.83, P = 0.043). Imaging of hyperpolarized [1- ¹³ C]pyruvate metabolism in breast cancer is feasible and demonstrated significant intertumoral and intratumoral metabolic heterogeneity, where lactate labeling correlated with MCT1 expression and hypoxia.

Hyperpolarized 13C Magnetic Resonance Imaging (13C-MRI) provides a highly sensitive tool to probe tissue metabolism in vivo and has recently been translated into clinical studies. We report the cerebral metabolism of intravenously injected hyperpolarized [1-13C]pyruvate in the brain of healthy human volunteers for the first time. Dynamic acquisition of 13C images demonstrated 13C-labeling of both lactate and bicarbonate, catalyzed by cytosolic lactate dehydrogenase and mitochondrial pyruvate dehydrogenase respectively. This demonstrates that both enzymes can be probed in vivo in the presence of an intact blood-brain barrier: the measured apparent exchange rate constant (kPL) for exchange of the hyperpolarized 13C label between [1-13C]pyruvate and the endogenous lactate pool was 0.012 ± 0.006 s-1 and the apparent rate constant (kPB) for the irreversible flux of [1-13C]pyruvate to [13C]bicarbonate was 0.002 ± 0.002 s-1. Imaging also revealed that [1-13C]pyruvate, [1-13C]lactate and [13C]bicarbonate were significantly higher in gray matter compared to white matter. Imaging normal brain metabolism with hyperpolarized [1-13C]pyruvate and subsequent quantification, have important implications for interpreting pathological cerebral metabolism in future studies.

Convolutional neural networks (CNNs) constitute a widely used deep learning approach that has frequently been applied to the problem of brain tumor diagnosis. Such techniques still face some critical challenges in moving towards clinic application. The main objective of this work is to present a comprehensive review of studies using CNN architectures to classify brain tumors using MR images with the aim of identifying useful strategies for and possible impediments in the development of this technology. Relevant articles were identified using a predefined, systematic procedure. For each article, data were extracted regarding training data, target problems, the network architecture, validation methods, and the reported quantitative performance criteria. The clinical relevance of the studies was then evaluated to identify limitations by considering the merits of convolutional neural networks and the remaining challenges that need to be solved to promote the clinical application and development of CNN algorithms. Finally, possible directions for future research are discussed for researchers in the biomedical and machine learning communities. A total of 83 studies were identified and reviewed. They differed in terms of the precise classification problem targeted and the strategies used to construct and train the chosen CNN. Consequently, the reported performance varied widely, with accuracies of 91.63–100% in differentiating meningiomas, gliomas, and pituitary tumors (26 articles) and of 60.0–99.46% in distinguishing low-grade from high-grade gliomas (13 articles). The review provides a survey of the state of the art in CNN-based deep learning methods for brain tumor classification. Many networks demonstrated good performance, and it is not evident that any specific methodological choice greatly outperforms the alternatives, especially given the inconsistencies in the reporting of validation methods, performance metrics, and training data encountered. Few studies have focused on clinical usability.

Management of brain tumors is based on clinical and radiological information with presumed grade dictating treatment. Hence, a non-invasive assessment of tumor grade is of paramount importance to choose the best treatment plan. Convolutional Neural Networks (CNNs) represent one of the effective Deep Learning (DL)-based techniques that have been used for brain tumor diagnosis. However, they are unable to handle input modifications effectively. Capsule neural networks (CapsNets) are a novel type of machine learning (ML) architecture that was recently developed to address the drawbacks of CNNs. CapsNets are resistant to rotations and affine translations, which is beneficial when processing medical imaging datasets. Moreover, Vision Transformers (ViT)-based solutions have been very recently proposed to address the issue of long-range dependency in CNNs. This survey provides a comprehensive overview of brain tumor classification and segmentation techniques, with a focus on ML-based, CNN-based, CapsNet-based, and ViT-based techniques. The survey highlights the fundamental contributions of recent studies and the performance of state-of-the-art techniques. Moreover, we present an in-depth discussion of crucial issues and open challenges. We also identify some key limitations and promising future research directions. We envisage that this survey shall serve as a good springboard for further study.

The objectives of this study were to evaluate the efficacy in pain management of magnetic resonance (MR)-guided focused ultrasound for the primary treatment of painful bone metastases and to assess its potential for local control of bone metastases. This was a prospective, single-arm research study with approval from the institutional review board. Eighteen consecutive patients (female, 8; male, 10; mean [SD] age, 62.7 [11.5] years) with painful bone metastases were enrolled. The patients were examined clinically for pain severity and pain interference in accordance with the Brief Pain Inventory-Quality of Life criteria before and at each follow-up visit. Computed tomography and MR imaging were performed before and at 1 and 3 months after the magnetic resonance-guided focused ultrasound treatment. The nonperfused volume (NPV) was calculated to correlate the extension of the ablated pathological tissue in the responder and nonresponder patients. No treatment-related adverse events were recorded during the study. The evaluation of pain palliation revealed a statistically significant difference between baseline and follow-up values for pain severity and pain interference (P = 0.001, both evaluations). In the evaluation of local tumor control, we observed increased bone density with restoration of cortical borders in 5 of the 18 patients (27.7%). In accordance with the MD Anderson criteria, complete and partial responses were obtained in 2 of the 18 patients (11.1%) and 4 of the 18 patients (22.2%), respectively. Nonperfused volume values ranged between 20% and 93%. Mean NPV values remained substantially stable after the treatment (P = 0.08). There was no difference in the NPV values between the responder and nonresponder patients (46.7% [24.2%] [25%-90%] versus 45% [24.9%] [20%-93%]; P = 0.7). Magnetic resonance-guided focused ultrasound can be safely and effectively used as the primary treatment of pain palliation in patients with bone metastases and has a potential role in local tumor control.