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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]

Background: Prostate‐specific membrane antigen (PSMA) PET imaging represents a valuable source of information reflecting disease stage, response rate, and treatment optimization options, particularly with PSMA radioligand therapy. Quantification of radiopharmaceutical uptake in healthy organs from PSMA images has the potential to minimize toxicity by extrapolation of the radiation dose delivery towards personalization of therapy. However, segmentation and quantification of uptake in organs requires labor‐intensive organ delineations that are often not feasible in the clinic nor scalable for large clinical trials. Purpose: In this work we develop and test the PSMA Healthy organ segmentation network (PSMA‐Hornet), a fully‐automated deep neural net for simultaneous segmentation of 14 healthy organs representing the normal biodistribution of [18F]DCFPyL on PET/CT images. We also propose a modified U‐net architecture, a self‐supervised pre‐training method for PET/CT images, a multi‐target Dice loss, and multi‐target batch balancing to effectively train PSMA‐Hornet and similar networks. Methods: The study used manually‐segmented [18F]DCFPyL PET/CT images from 100 subjects, and 526 similar images without segmentations. The unsegmented images were used for self‐supervised model pretraining. For supervised training, Monte‐Carlo cross‐validation was used to evaluate the network performance, with 85 subjects in each trial reserved for model training, 5 for validation, and 10 for testing. Image segmentation and quantification metrics were evaluated on the test folds with respect to manual segmentations by a nuclear medicine physician, and compared to inter‐rater agreement. The model's segmentation performance was also evaluated on a separate set of 19 images with high tumor load. Results: With our best model, the lowest mean Dice coefficient on the test set was 0.826 for the sublingual gland, and the highest was 0.964 for liver. The highest mean error in tracer uptake quantification was 13.9% in the sublingual gland. Self‐supervised pretraining improved training convergence, train‐to‐test generalization, and segmentation quality. In addition, we found that a multi‐target network produced significantly higher segmentation accuracy than single‐organ networks. Conclusions: The developed network can be used to automatically obtain high‐quality organ segmentations for PSMA image analysis tasks. It can be used to reproducibly extract imaging data, and holds promise for clinical applications such as personalized radiation dosimetry and improved radioligand therapy. [ABSTRACT FROM AUTHOR]