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Simple Summary: Radiomics analysis of positron emission tomography (PET) images can provide objective measurements of tumor heterogeneity and spatial patterns. However, the relatively low resolution, high noise, and limited longitudinal data availability make it difficult to systematically investigate the relationship between the microscopic tumor phenotypes and corresponding PET radiomics signatures. To address this challenge, we use a multiscale, stochastic mathematical model of tumor growth to generate cross-sections of tumors in vascularized normal tissue on a microscopic level. By varying the biological parameters of the model, distinct tumor phenotypes are obtained, and their corresponding PET images are generated. The simulated data are then used to find the optimal combination of PET radiomics features that can reliably distinguish visually similar tumor phenotypes. In addition, we study the longitudinal changes in the discriminative power of radiomics features with tumor growth from a single cell to approximately one million cells. Cancers can manifest large variations in tumor phenotypes due to genetic and microenvironmental factors, which has motivated the development of quantitative radiomics-based image analysis with the aim to robustly classify tumor phenotypes in vivo. Positron emission tomography (PET) imaging can be particularly helpful in elucidating the metabolic profiles of tumors. However, the relatively low resolution, high noise, and limited PET data availability make it difficult to study the relationship between the microenvironment properties and metabolic tumor phenotype as seen on the images. Most of previously proposed digital PET phantoms of tumors are static, have an over-simplified morphology, and lack the link to cellular biology that ultimately governs the tumor evolution. In this work, we propose a novel method to investigate the relationship between microscopic tumor parameters and PET image characteristics based on the computational simulation of tumor growth. We use a hybrid, multiscale, stochastic mathematical model of cellular metabolism and proliferation to generate simulated cross-sections of tumors in vascularized normal tissue on a microscopic level. The generated longitudinal tumor growth sequences are converted to PET images with realistic resolution and noise. By changing the biological parameters of the model, such as the blood vessel density and conditions for necrosis, distinct tumor phenotypes can be obtained. The simulated cellular maps were compared to real histology slides of SiHa and WiDr xenografts imaged with Hoechst 33342 and pimonidazole. As an example application of the proposed method, we simulated six tumor phenotypes that contain various amounts of hypoxic and necrotic regions induced by a lack of oxygen and glucose, including phenotypes that are distinct on the microscopic level but visually similar in PET images. We computed 22 standardized Haralick texture features for each phenotype, and identified the features that could best discriminate the phenotypes with varying image noise levels. We demonstrated that "cluster shade" and "difference entropy" are the most effective and noise-resilient features for microscopic phenotype discrimination. Longitudinal analysis of the simulated tumor growth showed that radiomics analysis can be beneficial even in small lesions with a diameter of 3.5–4 resolution units, corresponding to 8.7–10.0 mm in modern PET scanners. Certain radiomics features were shown to change non-monotonically with tumor growth, which has implications for feature selection for tracking disease progression and therapy response. [ABSTRACT FROM AUTHOR]

A common feature of solid tumours that are resistant to therapy is the presence of regions with low oxygen content (i.e., hypoxia). Oxygen electrode studies suggest that localized prostate adenocarcinoma is commonly hypoxic, although conflicting data have been reported between immunohistochemical detection of hypoxia-induced proteins in biopsy specimens and positron emission tomography (PET) imaging of 18F-labeled hypoxia reporters. Although the 2-nitroimidazole 18F-EF5 is well-established to label hypoxic tumour cells in pre-clinical tumour models and clinical trials of multiple primary tumour sites, it has yet to be tested in prostate cancer. The purpose of this study was to evaluate the feasibility of using 18F-EF5 to detect hypoxia in clinical prostate tumours. Patients with localized adenocarcinoma of the prostate were recruited for pre-treatment 18F-EF5 PET scans. Immunohistochemistry was conducted on diagnostic biopsies to assess the expression of glucose transporter 1 (GLUT1), osteopontin (OPN), and carbonic anhydrase IX (CAIX). Immunoreactivity scores of staining intensity and frequency were used to indicate the presence of tumour hypoxia. We found low tumour-to-muscle ratios of 18F-EF5 uptake that were not consistent with tumour hypoxia, causing early termination of the study. However, we observed GLUT1 and OPN expression in all prostate tumour biopsies, indicating the presence of hypoxia in all tumours. Our data do not support the use of 18F-EF5 PET to detect hypoxia in prostate adenocarcinoma, and suggest the use of immunohistochemistry to quantify expression of the hypoxia-inducible proteins GLUT1 and OPN as indications of prostate tumour hypoxia. [ABSTRACT FROM AUTHOR]

Abstract The microenvironment within solid tumours can influence the metastatic dissemination of tumour cells, and recent evidence suggests that poorly oxygenated (hypoxic) cells in primary tumours can also affect the survival and proliferation of metastatic tumour cells in distant organs. Hypoxic tumour cells have been historically targeted during radiation therapy in attempts to improve loco-regional control rates of primary tumours since hypoxic cells are known to be resistant to ionizing radiation-induced DNA damage. There are, therefore, a number of therapeutic strategies to directly target hypoxic cells in primary (and metastatic) tumours, and several compounds are becoming available to functionally inhibit hypoxia-induced proteins that are known to promote metastasis. This mini-review summarizes several established and emerging experimental strategies to target hypoxic cells in primary tumours with potential clinical application to the treatment of patients with tumour metastases or patients at high risk of developing metastatic disease. Targeting hypoxic tumour cells to reduce metastatic disease represents an important advance in the way scientists and clinicians view the influence of tumour hypoxia on therapeutic outcome.