Imaging Cancer Metabolism with Positron Emission Tomography (PET)

AI diagnostics in bone oncology for predicting bone metastasis in lung cancer patients using DenseNet-264 deep learning model and radiomics

This study aims to predict bone metastasis in lung cancer patients using radiomics and deep learning. Early prediction of bone metastasis is crucial for timely intervention and personalized treatment plans. This can improve patient outcomes and quality of life. By integrating advanced imaging techniques with artificial intelligence, this study seeks to enhance predictive accuracy and clinical decision-making.

Methods

We included 189 lung cancer patients, comprising 89 with non-bone metastasis and 100 with confirmed bone metastasis. Radiomic features were extracted from CT images, and feature selection was performed using Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO). We developed and validated a radiomics model and a deep learning model using DenseNet-264. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Statistical comparisons were made using the DeLong test.

Results

The radiomics model achieved an AUC of 0.815 on the training set and 0.778 on the validation set. The DenseNet-264 model demonstrated superior performance with an AUC of 0.990 on the training set and 0.971 on the validation set. The DeLong test confirmed that the AUC of the DenseNet-264 model was significantly higher than that of the radiomics model (p < 0.05).

Conclusions

The DenseNet-264 model significantly outperforms the radiomics model in predicting bone metastasis in lung cancer patients. The early and accurate prediction provided by the deep learning model can facilitate timely interventions and personalized treatment planning, potentially improving patient outcomes. Future studies should focus on validating these findings in larger, multi-center cohorts and integrating clinical data to further enhance predictive accuracy.

Multiplexing Autoradiography

Autoradiography, the direct imaging of radioactive distribution in tissue sections, is a powerful technique that has several key advantages for the validation of PET radiotracers. Using autoradiography, we can localize radiotracer uptake to neighbours of cells, and when multiplexed with additional radiotracers, fluorescent probes, or in situ tissue analysis, autoradiography can help to characterize the mechanism of radiotracer uptake and assess functional heterogeneity in tissue. In this chapter, the author outlines the basic ex vivo autoradiography protocol and shows how it can be multiplexed using dual radionuclides 18F and 14C. They also highlight where autoradiography can be combined with other technologies to provide synergistic information for interrogating spatial biology.

Imaging the Tumor Antioxidant Response with [18F]FSPG PET

(4S)-4-(3-[18F]Fluoropropyl)-L-glutamic acid ([18F]FSPG) is a flourine-18 labeled glutamate analog that enables the noninvasive in vivo imaging of cellular redox status. [18F]FSPG is transported across the cell membrane by the cystine/glutamate antiporter, system xc-, whose expression is upregulated in multiple cancer types. The requirement of cystine for the biosynthesis of glutathione, a major antioxidant, connects [18F]FSPG tissue retention to the intracellular redox response via system xc- activity. We herein describe the use of [18F]FSPG positron emission tomography (PET) to image the tumor antioxidant response and highlight key methodological considerations.

Advances in application of novel magnetic resonance imaging technologies in liver disease diagnosis

Liver disease is a major health concern globally, with high morbidity and mor-tality rates. Precise diagnosis and assessment are vital for guiding treatment approaches, predicting outcomes, and improving patient prognosis. Magnetic resonance imaging (MRI) is a non-invasive diagnostic technique that has been widely used for detecting liver disease. Recent advancements in MRI technology, such as diffusion weighted imaging, intravoxel incoherent motion, magnetic resonance elastography, chemical exchange saturation transfer, magnetic resonance spectroscopy, hyperpolarized MR, contrast-enhanced MRI, and ra-diomics, have significantly improved the accuracy and effectiveness of liver disease diagnosis. This review aims to discuss the progress in new MRI technologies for liver diagnosis. By summarizing current research findings, we aim to provide a comprehensive reference for researchers and clinicians to optimize the use of MRI in liver disease diagnosis and improve patient prognosis.

Asbestos accelerates disease onset in a genetic model of malignant pleural mesothelioma

Hypothesis: Asbestos-driven inflammation contributes to malignant pleural mesothelioma beyond the acquisition of rate-limiting mutations. Methods: Genetically modified conditional allelic mice that were previously shown to develop mesothelioma in the absence of exposure to asbestos were induced with lentiviral vector expressing Cre recombinase with and without intrapleural injection of amosite asbestos and monitored until symptoms required euthanasia. Resulting tumours were examined histologically and by immunohistochemistry for expression of lineage markers and immune cell infiltration. Results: Injection of asbestos dramatically accelerated disease onset and end-stage tumour burden. Tumours developed in the presence of asbestos showed increased macrophage infiltration. Pharmacological suppression of macrophages in mice with established tumours failed to extend survival or to enhance response to chemotherapy. Conclusion: Asbestos-driven inflammation contributes to the severity of mesothelioma beyond the acquisition of rate-limiting mutations, however, targeted suppression of macrophages in established epithelioid mesothelioma showed no therapeutic benefit.

Imaging modalities for measuring body composition in patients with cancer: opportunities and challenges

Body composition assessment (ie, the measurement of muscle and adiposity) impacts several cancer-related outcomes including treatment-related toxicities, treatment responses, complications, and prognosis. Traditional modalities for body composition measurement include body mass index, body circumference, skinfold thickness, and bioelectrical impedance analysis; advanced imaging modalities include dual energy x-ray absorptiometry, computerized tomography, magnetic resonance imaging, and positron emission tomography. Each modality has its advantages and disadvantages, thus requiring an individualized approach in identifying the most appropriate measure for specific clinical or research situations. Advancements in imaging approaches have led to an abundance of available data, however, the lack of standardized thresholds for classification of abnormal muscle mass or adiposity has been a barrier to adopting these measurements widely in research and clinical care. In this review, we discuss the different modalities in detail and provide guidance on their unique opportunities and challenges.

Changing Role of PET/CT in Cancer Care With a Focus on Radiotherapy

Positron emission tomography (PET) integrated with computed tomography (CT) has brought revolutionary changes in improving cancer care (CC) for patients. These include improved detection of previously unrecognizable disease, ability to identify oligometastatic status enabling more aggressive treatment strategies when the disease burden is lower, its use in better defining treatment targets in radiotherapy (RT), ability to monitor treatment responses early and thus improve the ability for early interventions of non-responding tumors, and as a prognosticating tool as well as outcome predicting tool. PET/CT has enabled the emergence of new concepts such as radiobiotherapy (RBT), radioimmunotherapy, theranostics, and pharmaco-radiotherapy. This is a rapidly evolving field, and this primer is to help summarize the current status and to give an impetus to developing new ideas, clinical trials, and CC outcome improvements.

Insights into the New Cancer Therapy through Redox Homeostasis and Metabolic Shifts

Modest levels of reactive oxygen species (ROS) are necessary for intracellular signaling, cell division, and enzyme activation. These ROS are later eliminated by the body’s antioxidant defense system. High amounts of ROS cause carcinogenesis by altering the signaling pathways associated with metabolism, proliferation, metastasis, and cell survival. Cancer cells exhibit enhanced ATP production and high ROS levels, which allow them to maintain elevated proliferation through metabolic reprograming. In order to prevent further ROS generation, cancer cells rely on more glycolysis to produce ATP and on the pentose phosphate pathway to provide NADPH. Pro-oxidant therapy can induce more ROS generation beyond the physiologic thresholds in cancer cells. Alternatively, antioxidant therapy can protect normal cells by activating cell survival signaling cascades, such as the nuclear factor erythroid 2-related factor 2 (Nrf2)-Kelch-like ECH-associated protein 1 (Keap1) pathway, in response to radio- and chemotherapeutic drugs. Nrf2 is a key regulator that protects cells from oxidative stress. Under normal conditions, Nrf2 is tightly bound to Keap1 and is ubiquitinated and degraded by the proteasome. However, under oxidative stress, or when treated with Nrf2 activators, Nrf2 is liberated from the Nrf2-Keap1 complex, translocated into the nucleus, and bound to the antioxidant response element in association with other factors. This cascade results in the expression of detoxifying enzymes, including NADH-quinone oxidoreductase 1 (NQO1) and heme oxygenase 1. NQO1 and cytochrome b5 reductase can neutralize ROS in the plasma membrane and induce a high NAD⁺/NADH ratio, which then activates SIRT1 and mitochondrial bioenergetics. NQO1 can also stabilize the tumor suppressor p53. Given their roles in cancer pathogenesis, redox homeostasis and the metabolic shift from glycolysis to oxidative phosphorylation (through activation of Nrf2 and NQO1) seem to be good targets for cancer therapy. Therefore, Nrf2 modulation and NQO1 stimulation could be important therapeutic targets for cancer prevention and treatment.