PET/CT imaging in cancer: current applications and future directions

Diagnostic and prognostic role of 18F-FDG PET/CT for sarcomatoid differentiation in renal cell carcinoma

BACKGROUND

Sarcomatoid differentiation is an invasive dedifferentiated feature of tumor and associated with poor prognosis in renal cell carcinoma (RCC) patients. This study aimed to evaluate the utility of 18F-FDG PET/CT in predicting sarcomatoid differentiation in RCC and its potential prognostic value.

RESULTS

This retrospective study assessed newly diagnosed sarcomatoid differentiation renal cell carcinoma (SDRCC) patients who were staged using 18F-FDG PET/CT. Patients were categorized into high-grade sarcomatoid differentiation RCC (HG-SDRCC), low-grade sarcomatoid differentiation RCC (LG-SDRCC), and non-sarcomatoid differentiation RCC (non-SDRCC). The maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were compared. Overall survival (OS) and disease-free survival (DFS) were analyzed. SUVmax, MTV, TLG, and SUVmean values were significantly higher in SDRCC compared to non-SDRCC (P < 0.05). Additionally, SUVmax, TLG, and SUVmean were significantly higher in HG-SDRCC compared to non-HG-SDRCC (P < 0.05). ROC curves revealed that SUVmax and SUVmean were effective for distinguishing HG-SDRCC from non-HG-SDRCC. The log-rank test identified SUVmax > 11, MTV > 95, TLG > 500, SUVmean > 5.2, invasion of peripheral tissue and/or organs, and metastasis as risk factors for SDRCC patients. Multivariate Cox proportional hazards model analyses indicated that TLG > 500 was a risk factor for poor DFS, while SUVmax > 11 and SUVmean > 5.2 were risk factors for poor OS.

CONCLUSIONS

18F-FDG PET/CT can effectively differentiate HG-SDRCC with more aggressive malignancy. The prognostic model developed in this study demonstrates that metabolic parameters, particularly TLG for DFS and SUVmax/SUVmean for OS, serve as more robust predictors of patient outcomes than the degree of sarcomatoid differentiation.

Multimodal Imaging of Osteosarcoma: From First Diagnosis to Radiomics

Osteosarcoma is a primary malignant bone tumor characterized by the production of an osteoid matrix. Although histology remains the definitive diagnostic standard, imaging plays a crucial role in diagnosis, therapeutic planning, and follow-up. Conventional radiography serves as the initial checkpoint for detecting this pathology, which often presents diagnostic challenges due to vague and nonspecific symptoms, especially in its early stages. Today, the integration of different imaging techniques enables an increasingly personalized diagnosis and management, with each contributing unique and complementary information. Conventional radiography typically initiates the imaging assessment, and the Bone Reporting and Data System (Bone-RADS) of the Society of Skeletal Radiology (SSR) is a valuable tool for stratifying the risk of suspicious bone lesions. CT is the preferred modality for evaluating the bone matrix, while bone scans and PET/CT are effective for detecting distant metastases. MRI reveals the extent of the lesion in adjacent soft tissues, the medullary canal, and joints, as well as its relationship to neurovascular structures and the presence of skip lesions. Advanced techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion-weighted imaging (DWI), and perfusion MRI help characterize the tumor environment and assess treatment response. Osteosarcoma comprises a range of subtypes with differing clinical and imaging characteristics, some of which are particularly distinctive, such as in the case of telangiectatic osteosarcoma. Knowledge of these variants can guide radiologists in the differential diagnosis, which includes both central and surface forms, ranging from highly aggressive to more indolent types. In this review, we present a wide range of representative cases from our hospital case series to illustrate both typical and atypical imaging presentations. Finally, we discuss recent advancements and challenges in applying artificial intelligence approaches to the imaging of osteosarcoma.

Current and Future Applications of PET Radiomics in Radiation Oncology

This review delves into the principles of PET imaging and radiomics, emphasizing their importance in detecting, staging, and monitoring various cancers. It highlights the clinical applications of PET radiomics in oncology, showcasing its impact on personalized cancer care. Additionally, the review addresses challenges such as standardizing PET radiomics, integrating multiomics data, and ethical concerns in clinical decision-making. Future directions are also discussed, including broader applications of PET radiomics in clinical trials, artificial intelligence integration for automated analysis, and incorporating multiomics data for a comprehensive understanding of tumor biology.

Recent Breakthroughs in PET-CT Multimodality Imaging: Innovations and Clinical Impact

This review presents a detailed examination of the most recent advancements in positron emission tomography-computed tomography (PET-CT) multimodal imaging over the past five years. The fusion of PET and CT technologies has revolutionized medical imaging, offering unprecedented insights into both anatomical structure and functional processes. The analysis delves into key technological innovations, including advancements in image reconstruction, data-driven gating, and time-of-flight capabilities, highlighting their impact on enhancing diagnostic accuracy and clinical outcomes. Illustrative case studies underscore the transformative role of PET-CT in lesion detection, disease characterization, and treatment response evaluation. Additionally, the review explores future prospects and challenges in PET-CT, advocating for the integration and evaluation of emerging technologies to improve patient care. This comprehensive synthesis aims to equip healthcare professionals, researchers, and industry stakeholders with the knowledge and tools necessary to navigate the evolving landscape of PET-CT multimodal imaging.

Joint segmentation of tumors in 3D PET-CT images with a network fusing multi-view and multi-modal information

Objective. Joint segmentation of tumors in positron emission tomography-computed tomography (PET-CT) images is crucial for precise treatment planning. However, current segmentation methods often use addition or concatenation to fuse PET and CT images, which potentially overlooks the nuanced interplay between these modalities. Additionally, these methods often neglect multi-view information that is helpful for more accurately locating and segmenting the target structure. This study aims to address these disadvantages and develop a deep learning-based algorithm for joint segmentation of tumors in PET-CT images.Approach. To address these limitations, we propose the Multi-view Information Enhancement and Multi-modal Feature Fusion Network (MIEMFF-Net) for joint tumor segmentation in three-dimensional PET-CT images. Our model incorporates a dynamic multi-modal fusion strategy to effectively exploit the metabolic and anatomical information from PET and CT images and a multi-view information enhancement strategy to effectively recover the lost information during upsamping. A Multi-scale Spatial Perception Block is proposed to effectively extract information from different views and reduce redundancy interference in the multi-view feature extraction process.Main results. The proposed MIEMFF-Net achieved a Dice score of 83.93%, a Precision of 81.49%, a Sensitivity of 87.89% and an IOU of 69.27% on the Soft Tissue Sarcomas dataset and a Dice score of 76.83%, a Precision of 86.21%, a Sensitivity of 80.73% and an IOU of 65.15% on the AutoPET dataset.Significance. Experimental results demonstrate that MIEMFF-Net outperforms existing state-of-the-art models which implies potential applications of the proposed method in clinical practice.

Advancements of non‐invasive imaging technologies for the diagnosis and staging of liver fibrosis: Present and future

Liver fibrosis is a reparative response triggered by liver injury. Non‐invasive assessment and staging of liver fibrosis in patients with chronic liver disease are of paramount importance, as treatment strategies and prognoses depend significantly on the degree of fibrosis. Although liver fibrosis has traditionally been staged through invasive liver biopsy, this method is prone to sampling errors, particularly when biopsy sizes are inadequate. Consequently, there is an urgent clinical need for an alternative to biopsy, one that ensures precise, sensitive, and non‐invasive diagnosis and staging of liver fibrosis. Non‐invasive imaging assessments have assumed a pivotal role in clinical practice, enjoying growing popularity and acceptance due to their potential for diagnosing, staging, and monitoring liver fibrosis. In this comprehensive review, we first delved into the current landscape of non‐invasive imaging technologies, assessing their accuracy and the transformative impact they have had on the diagnosis and management of liver fibrosis in both clinical practice and animal models. Additionally, we provided an in‐depth exploration of recent advancements in ultrasound imaging, computed tomography imaging, magnetic resonance imaging, nuclear medicine imaging, radiomics, and artificial intelligence within the field of liver fibrosis research. We summarized the key concepts, advantages, limitations, and diagnostic performance of each technique. Finally, we discussed the challenges associated with clinical implementation and offer our perspective on advancing the field, hoping to provide alternative directions for the future research.

Demonstrating the use of population level data to investigate trends in the rate, radiation dose and cost of Computed Tomography across clinical groups: Are there any areas of concern?

INTRODUCTION

Increases in computed tomography (CT) use may not always reflect clinical need or improve outcomes. This study aimed to demonstrate how population level data can be used to identify variations in care between patient groups, by analysing system-level changes in CT use around the diagnosis of new conditions.

METHODS

Retrospective repeated cross-sectional observational study using West Australian linked administrative records, including 504,723 adults diagnosed with different conditions in 2006, 2012 and 2015. For 90 days pre/post diagnosis, CT use (any and 2+ scans), effective dose (mSv), lifetime attributable risk (LAR) of cancer incidence and mortality from CT, and costs were assessed.

RESULTS

CT use increased from 209.4 per 1000 new diagnoses in 2006 to 258.0 in 2015; increases were observed for all conditions except neoplasms. Healthcare system costs increased for all conditions but neoplasms and mental disorders. Effective dose increased substantially for respiratory (+2.5 mSv, +23.1%, P < 0.001) and circulatory conditions (+2.1 mSv, +15.4%, P < 0.001). The LAR of cancer incidence and mortality from CT increased for endocrine (incidence +23.4%, mortality +18.0%) and respiratory disorders (+21.7%, +23.3%). Mortality LAR increased for circulatory (+12.1%) and nervous system (+11.0%) disorders. The LAR of cancer incidence and mortality reduced for musculoskeletal system disorders, despite an increase in repeated CT in this group.

CONCLUSIONS

Use and costs increased for most conditions except neoplasms and mental and behavioural disorders. More strategic CT use may have occurred in musculoskeletal conditions, while use and radiation burden increased for respiratory, circulatory and nervous system conditions. Using this high-level approach we flag areas requiring deeper investigation into appropriateness and value of care.

18F-FDG PET/MR for diagnosis of primary central nervous system lymphoma: protocol for a systematic review and meta-analysis

BACKGROUND

Primary central nervous system lymphoma (PCNSL) is a rare form of extranodal non-Hodgkin's lymphoma with poor prognosis. 18F-flourodeoxyglucose positron emission tomography (PET)/magnetic resonance (MR) combines the advantages of PET and MR. The aim of this study is to evaluate the validity of PET/MR for the diagnosis of PCNSL by means of a meta-analysis.

METHODS AND ANALYSIS

Wanfang Database, SinoMed, China National Knowledge Infrastructure, the Cochrane Library, PubMed and Embase will be searched for candidate studies about PET/MRI in PCNSL diagnosis from database inception to October 2024. The following keywords will be applied: "Primary central nervous system lymphoma", "Primary intracerebral lymphoma", "Positron Emission Tomography Magnetic Resonance" and "PET-MR". Studies meeting the inclusion criteria will be included. Studies without full true positive, false positive, false negative and true negative values; studies reported in languages other than English and Chinese; conference abstracts not available in full text and case reports will be excluded. Quality Assessment of Diagnostic Accuracy Studies will be used to evaluate the study quality. The STATA software (V.15.0) and Meta-Disc software (V.1.4) will be used to carry out meta-analysis. When heterogeneity is evident, subgroup analysis will be used to investigate the origin of heterogeneity. The robustness of the analysis will be checked with sensitivity analysis.

ETHICS AND DISSEMINATION

This research is based on public databases and does not require ethical approval. The results will seek publication in a peer-reviewed journal after the completion of this systematic review and meta-analysis.

PROSPERO REGISTRATION NUMBER

CRD42023472570.

Plasma Metabolite Profiling in the Search for Early-Stage Biomarkers for Lung Cancer: Some Important Breakthroughs

Lung cancer is the leading cause of cancer-related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster, and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already been shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterized by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation to their metabolic pathways and in light of the altered cancer metabolism.

Activity-Based Fluorescence Diagnostics for Cancer

Fluorescence imaging is one of the most promising approaches to achieve intraoperative assessment of the tumor/normal tissue margins during cancer surgery. This is critical to improve the patients' prognosis, and therefore various molecular fluorescence imaging probes have been developed for the identification of cancer lesions during surgery. Among them, "activatable" fluorescence probes that react with cancer-specific biomarker enzymes to generate fluorescence signals have great potential for high-contrast cancer imaging due to their low background fluorescence and high signal amplification by enzymatic turnover. Over the past two decades, activatable fluorescence probes employing various fluorescence control mechanisms have been developed worldwide for this purpose. Furthermore, new biomarker enzymatic activities for specific types of cancers have been identified, enabling visualization of various types of cancers with high sensitivity and specificity. This Review focuses on recent advances in the design, function and characteristics of activatable fluorescence probes that target cancer-specific enzymatic activities for cancer imaging and also discusses future prospects in the field of activity-based diagnostics for cancer.

MRI compatibility study of a prototype radiofrequency penetrable oval PET insert at 3 T

PURPOSE

To perform an MRI compatibility study of an RF field-penetrable oval-shaped PET insert that implements an MRI built-in body RF coil both as a transmitter and a receiver.

METHODS

Twelve electrically floating RF shielded PET detector modules were used to construct the prototype oval PET insert with a major axis of 440 mm, a minor axis of 350 mm, and an axial length of 225 mm. The electric floating of the PET detector modules was accomplished by isolating the cable shield from the detector shield using plastic tape. Studies were conducted on the transmit (B1) RF field, the image signal-to-noise ratio (SNR), and the RF pulse amplitude for a homogeneous cylindrical (diameter: 160 mm and length: 260 mm) phantom (NaCl + NiSO4 solution) in a 3 T clinical MRI system (Verio, Siemens, Erlangen, Germany).

RESULTS

The B1 maps for the oval insert were similar to the MRI-only field responses. Compared to the MRI-only values, SNR reductions of 51%, 45%, and 59% were seen, respectively, for the spin echo (SE), gradient echo (GE), and echo planar (EPI) images for the case of oval PET insert. Moreover, the required RF pulse amplitudes for the SE, GE, and EPI sequences were, respectively, 1.93, 1.85, and 1.36 times larger. However, a 30% reduction in the average RF reception sensitivity was observed for the oval insert.

CONCLUSIONS

The prototype floating PET insert was a safety concern for the clinical MRI system, and this compatibility study provided clearance for developing a large body size floating PET insert for the existing MRI system. Because of the RF shield of the insert, relatively large RF powers compared to the MRI-only case were required. Because of this and also due to low RF sensitivity of the body coil, the SNRs reduced largely.

Deep learning techniques in PET/CT imaging: A comprehensive review from sinogram to image space

Positron emission tomography/computed tomography (PET/CT) is increasingly used in oncology, neurology, cardiology, and emerging medical fields. The success stems from the cohesive information that hybrid PET/CT imaging offers, surpassing the capabilities of individual modalities when used in isolation for different malignancies. However, manual image interpretation requires extensive disease-specific knowledge, and it is a time-consuming aspect of physicians' daily routines. Deep learning algorithms, akin to a practitioner during training, extract knowledge from images to facilitate the diagnosis process by detecting symptoms and enhancing images. This acquired knowledge aids in supporting the diagnosis process through symptom detection and image enhancement. The available review papers on PET/CT imaging have a drawback as they either included additional modalities or examined various types of AI applications. However, there has been a lack of comprehensive investigation specifically focused on the highly specific use of AI, and deep learning, on PET/CT images. This review aims to fill that gap by investigating the characteristics of approaches used in papers that employed deep learning for PET/CT imaging. Within the review, we identified 99 studies published between 2017 and 2022 that applied deep learning to PET/CT images. We also identified the best pre-processing algorithms and the most effective deep learning models reported for PET/CT while highlighting the current limitations. Our review underscores the potential of deep learning (DL) in PET/CT imaging, with successful applications in lesion detection, tumor segmentation, and disease classification in both sinogram and image spaces. Common and specific pre-processing techniques are also discussed. DL algorithms excel at extracting meaningful features, and enhancing accuracy and efficiency in diagnosis. However, limitations arise from the scarcity of annotated datasets and challenges in explainability and uncertainty. Recent DL models, such as attention-based models, generative models, multi-modal models, graph convolutional networks, and transformers, are promising for improving PET/CT studies. Additionally, radiomics has garnered attention for tumor classification and predicting patient outcomes. Ongoing research is crucial to explore new applications and improve the accuracy of DL models in this rapidly evolving field.

Breast Imaging Physics in Mammography (Part II)

One of the most frequently detected neoplasms in women in Italy is breast cancer, for which high-sensitivity diagnostic techniques are essential for early diagnosis in order to minimize mortality rates. As addressed in Part I of this work, we have seen how conditions such as high glandular density or limitations related to mammographic sensitivity have driven the optimization of technology and the use of increasingly advanced and specific diagnostic methodologies. While the first part focused on analyzing the use of a mammography machine from a physical and dosimetric perspective, in this paper, we will examine other techniques commonly used in breast imaging: contrast-enhanced mammography, digital breast tomosynthesis, radio imaging, and include some notes on image processing. We will also explore the differences between these various techniques to provide a comprehensive overview of breast lesion detection techniques. We will examine the strengths and weaknesses of different diagnostic modalities and observe how, with the implementation of improvements over time, increasingly effective diagnoses can be achieved.

Nuclear medicine radiomics in digestive system tumors: Concept, applications, challenges, and future perspectives

Radiomics aims to develop novel biomarkers and provide relevant deeper subvisual information about pathology, immunophenotype, and tumor microenvironment. It uses automated or semiautomated quantitative analysis of high-dimensional images to improve characterization, diagnosis, and prognosis. Recent years have seen a rapid increase in radiomics applications in nuclear medicine, leading to some promising research results in digestive system oncology, which have been driven by big data analysis and the development of artificial intelligence. Although radiomics advances one step further toward the non-invasive precision medical analysis, it is still a step away from clinical application and faces many challenges. This review article summarizes the available literature on digestive system tumors regarding radiomics in nuclear medicine. First, we describe the workflow and steps involved in radiomics analysis. Subsequently, we discuss the progress in clinical application regarding the utilization of radiomics for distinguishing between various diseases and evaluating their prognosis, and demonstrate how radiomics advances this field. Finally, we offer our viewpoint on how the field can progress by addressing the challenges facing clinical implementation.

Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images

BACKGROUND

Dynamic positron emission tomography (PET) images are useful in clinical practice because they can be used to calculate the metabolic parameters (Ki) of tissues using graphical methods (such as Patlak plots). Ki is more stable than the standard uptake value and has a good reference value for clinical diagnosis. However, the long scanning time required for obtaining dynamic PET images, usually an hour, makes this method less useful in some ways. There is a tradeoff between the scan durations and the signal-to-noise ratios (SNRs) of Ki images. The purpose of our study is to obtain approximately the same image as that produced by scanning for one hour in just half an hour, improving the SNRs of images obtained by scanning for 30 min and reducing the necessary 1-h scanning time for acquiring dynamic PET images.

METHODS

In this paper, we use U-Net as a feature extractor to obtain feature vectors with a priori knowledge about the image structure of interest and then utilize a parameter generator to obtain five parameters for a two-tissue, three-compartment model and generate a time activity curve (TAC), which will become close to the original 1-h TAC through training. The above-generated dynamic PET image finally obtains the Ki parameter image.

RESULTS

A quantitative analysis showed that the network-generated Ki parameter maps improved the structural similarity index measure and peak SNR by averages of 2.27% and 7.04%, respectively, and decreased the root mean square error (RMSE) by 16.3% compared to those generated with a scan time of 30 min.

CONCLUSIONS

The proposed method is feasible, and satisfactory PET quantification accuracy can be achieved using the proposed deep learning method. Further clinical validation is needed before implementing this approach in routine clinical applications.

The role of clinical imaging in oncology drug development: progress and new challenges

In 2008, the role of clinical imaging in oncology drug development was reviewed. The review outlined where imaging was being applied and considered the diverse demands across the phases of drug development. A limited set of imaging techniques was being used, largely based on structural measures of disease evaluated using established response criteria such as response evaluation criteria in solid tumours. Beyond structure, functional tissue imaging such as dynamic contrast-enhanced MRI and metabolic measures using [18F]flourodeoxyglucose positron emission tomography were being increasingly incorporated. Specific challenges related to the implementation of imaging were outlined including standardisation of scanning across study centres and consistency of analysis and reporting. More than a decade on the needs of modern drug development are reviewed, how imaging has evolved to support new drug development demands, the potential to translate state-of-the-art methods into routine tools and what is needed to enable the effective use of this broadening clinical trial toolset. In this review, we challenge the clinical and scientific imaging community to help refine existing clinical trial methods and innovate to deliver the next generation of techniques. Strong industry-academic partnerships and pre-competitive opportunities to co-ordinate efforts will ensure imaging technologies maintain a crucial role delivering innovative medicines to treat cancer.

Stability of metabolic tumor volume may enable radiotherapy dose painting in anal cancer

PURPOSE

To evaluate the variability of the 18F-FDG-PET/CT-based metabolic tumor volume (MTV) in anal cancers during fractionated chemoradiotherapy (CRT), and assess the impact of this variability on dosimetric accuracy in MTV-targeted dose painting.

METHODS

Eleven patients with anal squamous cell carcinoma who received fractionated chemoradiotherapy with curative intent were included. 18F-FDG PET/CT images were acquired at pre- and mid-treatment. Target volumes and organs at risk (OARs) were contoured manually on both image series. The MTV was generated from the PET images by thresholding. Treatment plans were retrospectively optimized for both image series using volumetric modulated arc therapy (VMAT). Standard plans prescribed 48.6 Gy, 54 Gy and 57.5 Gy in 27 fractions to elective regions, lymph node metastases and primary tumor, respectively. Dose painting plans included an extra dose level of 65 Gy to the MTV. Pre-treatment plans were transferred and re-calculated at mid-treatment basis.

RESULTS

MTV decreased from pre- to mid-treatment in 10 of the 11 patients. On average, 71 % of MTVmid overlapped with MTVpre. The median and mean doses to the MTV were robust against anatomical changes, but the transferred dose painting plans had lower D98% values than the original and re-optimized plans. No major differences were found between standard and dose painting plans for OARs.

CONCLUSIONS

Despite volumetric changes in the MTV, adequate dose coverage was observed in most dose painting plans. The findings indicate little or no need for adaptive dose painting at mid-treatment. Dose painting appears to be a safe treatment alternative with similar dose sparing of OARs.

PET assessment of acute gastrointestinal graft versus host disease

Acute gastrointestinal graft versus host disease (GI-GVHD) is a common complication following allogeneic haematopoietic cell transplantation (HCT), and is characterised by severe morbidity, frequent treatment-refractoriness, and high mortality. Early, accurate identification of GI-GVHD could allow for therapeutic interventions to ameliorate its severity, improve response rates and survival; however, standard endoscopic biopsy is inadequately informative in terms of diagnostic sensitivity or outcome prediction. In an era where rapid technological and laboratory advances have dramatically expanded our understanding of GI-GVHD biology and potential therapeutic targets, there is substantial scope for novel investigations that can precisely guide GI-GVHD management. In particular, the combination of tissue-based biomarker assessment (plasma cytokines, faecal microbiome) and molecular imaging by positron emission tomography (PET) offers the potential for non-invasive, real-time in vivo assessment of donor:recipient immune activity within the GI tract for GI-GVHD prediction or diagnosis. In this article, we review the evidence regarding GI-GVHD diagnosis, and examine the potential roles and translational opportunities posed by these novel diagnostic tools, with a focus on the evolving role of PET.

Portable optical spectroscopic assay for non-destructive measurement of key metabolic parameters on in vitro cancer cells and organotypic fresh tumor slices

To enable non-destructive metabolic characterizations on in vitro cancer cells and organotypic tumor models for therapeutic studies in an easy-to-access way, we report a highly portable optical spectroscopic assay for simultaneous measurement of glucose uptake and mitochondrial function on various cancer models with high sensitivity. Well-established breast cancer cell lines (MCF-7 and MDA-MB-231) were used to validate the optical spectroscopic assay for metabolic characterizations, while fresh tumor samples harvested from both animals and human cancer patients were used to test the feasibility of our optical metabolic assay for non-destructive measurement of key metabolic parameters on organotypic tumor slices. Our optical metabolic assay captured that MCF-7 cells had higher mitochondrial metabolism, but lower glucose uptake compared to the MDA-MB-231 cells, which is consistent with our microscopy imaging and flow cytometry data, as well as the published Seahorse Assay data. Moreover, we demonstrated that our optical assay could non-destructively measure both glucose uptake and mitochondrial metabolism on the same cancer cell samples at one time, which remains challenging by existing metabolic tools. Our pilot tests on thin fresh tumor slices showed that our optical assay captured increased metabolic activities in tumors compared to normal tissues. Our non-destructive optical metabolic assay provides a cost-effective way for future longitudinal therapeutic studies using patient-derived organotypic fresh tumor slices through the lens of tumor energetics, which will significantly advance translational cancer research.

Ultrasmall, elementary and highly translational nanoparticle X-ray contrast media from amphiphilic iodinated statistical copolymers

To expand the single-dose duration over which noninvasive clinical and preclinical cancer imaging can be conducted with high sensitivity, and well-defined spatial and temporal resolutions, a facile strategy to prepare ultrasmall nanoparticulate X-ray contrast media (nano-XRCM) as dual-modality imaging agents for positron emission tomography (PET) and computed tomography (CT) has been established. Synthesized from controlled copolymerization of triiodobenzoyl ethyl acrylate and oligo(ethylene oxide) acrylate monomers, the amphiphilic statistical iodocopolymers (ICPs) could directly dissolve in water to afford thermodynamically stable solutions with high aqueous iodine concentrations (>140 mg iodine/mL water) and comparable viscosities to conventional small molecule XRCM. The formation of ultrasmall iodinated nanoparticles with hydrodynamic diameters of ca. 10 nm in water was confirmed by dynamic and static light scattering techniques. In a breast cancer mouse model, in vivo biodistribution studies revealed that the 64Cu-chelator-functionalized iodinated nano-XRCM exhibited extended blood residency and higher tumor accumulation compared to typical small molecule imaging agents. PET/CT imaging of tumor over 3 days showed good correlation between PET and CT signals, while CT imaging allowed continuous observation of tumor retention even after 10 days post-injection, enabling longitudinal monitoring of tumor retention for imaging or potentially therapeutic effect after a single administration of nano-XRCM.

Favorable Mortality-to-Incidence Ratio Trends of Lung Cancer in Countries with High Computed Tomography Density

Background and Objectives: The prognoses of lung cancer deteriorate dramatically as the cancer progresses through its stages. Therefore, early screening using techniques such as low-dose computed tomography (LDCT) is critical. However, the epidemiology of the association between the popularization of CT and the prognosis for lung cancer is not known. Materials and Methods: Data were obtained from GLOBOCAN and the health data and statistics of the World Health Organization. Mortality-to-incidence ratios (MIRs) and the changes in MIR over time (δMIR; calculated as the difference between MIRs in 2018 and 2012) were used to evaluate the correlation with CT density disparities via Spearman's rank correlation coefficient. Results: Countries with zero CT density presented a relatively low incidence crude rate and a relatively high MIR in 2018 and a negative δMIR. Conversely, countries with a CT density over 30 had a positive δMIR. The CT density was significantly associated with the HDI score and MIR in 2018, whereas it demonstrated no association with MIR in 2012. The CT density and δMIR also showed a significant linear correlation. Conclusions: CT density was significantly associated with lung cancer MIR in 2018 and with δMIR, indicating favorable clinical outcomes in countries in which CT has become popularized.

Skin Cancer Pathobiology at a Glance: A Focus on Imaging Techniques and Their Potential for Improved Diagnosis and Surveillance in Clinical Cohorts

Early diagnosis is essential for completely eradicating skin cancer and maximizing patients' clinical benefits. Emerging optical imaging modalities such as reflectance confocal microscopy (RCM), optical coherence tomography (OCT), magnetic resonance imaging (MRI), near-infrared (NIR) bioimaging, positron emission tomography (PET), and their combinations provide non-invasive imaging data that may help in the early detection of cutaneous tumors and surgical planning. Hence, they seem appropriate for observing dynamic processes such as blood flow, immune cell activation, and tumor energy metabolism, which may be relevant for disease evolution. This review discusses the latest technological and methodological advances in imaging techniques that may be applied for skin cancer detection and monitoring. In the first instance, we will describe the principle and prospective clinical applications of the most commonly used imaging techniques, highlighting the challenges and opportunities of their implementation in the clinical setting. We will also highlight how imaging techniques may complement the molecular and histological approaches in sharpening the non-invasive skin characterization, laying the ground for more personalized approaches in skin cancer patients.

Review on Metal-Based Theranostic Nanoparticles for Cancer Therapy and Imaging

Theranostic agents are promising due to their ability to diagnose, treat and monitor different types of cancer using a variety of imaging modalities. The advantage specifically of nanoparticles is that they can accumulate easily at the tumor site due to the large gaps in blood vessels near tumors. Such high concentration of theranostic agents at the target site can lead to enhancement in both imaging and therapy. This article provides an overview of nanoparticles that have been used for cancer theranostics, and the different imaging, treatment options and signaling pathways that are important when using nanoparticles for cancer theranostics. In particular, nanoparticles made of metal elements are emphasized due to their wide applications in cancer theranostics. One important aspect discussed is the ability to combine different types of metals in one nanoplatform for use as multimodal imaging and therapeutic agents for cancer.

Current investigative modalities for detecting and staging lung cancers: a comprehensive summary

This narrative review compares the advantages and drawbacks of imaging and other investigation modalities which currently assist with lung cancer diagnosis and staging, as well as those which are not routinely indicated for this. We examine plain film radiography, computed tomography (CT) (alone, as well as in conjunction with positron emission tomography (PET)), magnetic resonance imaging (MRI), ultrasound, and newer techniques such as image-guided bronchoscopy (IGB) and robotic bronchoscopy (RB). While a chest X-ray is the first-line imaging investigation in patients presenting with symptoms suggestive of lung cancer, it has a high positive predictive value (PPV) even after negative X-ray findings, which calls into question its value as part of a potential national screening programme. CT lowers the mortality for high-risk patients when compared to X-ray and certain scoring systems, such as the Brock model can guide the need for further imaging, like PET-CT, which has high sensitivity and specificity for diagnosing solitary pulmonary nodules as malignant, as well as for assessing small cell lung cancer spread. In practice, PET-CT is offered to everyone whose lung cancer is to be treated with a curative intent. In contrast, MRI is only recommended for isolated distant metastases. Similarly, ultrasound imaging is not used for diagnosis of lung cancer but can be useful when there is suspicion of intrathoracic lymph node involvement. Ultrasound imaging in the form of endobronchial ultrasonography (EBUS) is often used to aid tissue sampling, yet the diagnostic value of this technique varies widely between studies. RB is another novel technique that offers an alternative way to biopsy lesions, but further research on it is necessary. Lastly, thoracic surgical biopsies, particularly minimally invasive video-assisted techniques, have been used increasingly to aid in diagnosis and staging.

Assessment of Brown and Beige Adipose Tissue Activation in Mice Using PET/CT Imaging

In response to cold induction, brown adipose tissues (BAT) and emerged brown-like adipocytes (beige adipocytes) in subcutaneous white adipose tissues (WAT browning/beiging) are activated. Thermogenesis is increased during glucose and fatty acid uptake and metabolism in adult humans and mice. This activation of BAT or WAT beiging to generate heat helps to counteract diet-induced obesity. This protocol applies the glucose analog radiotracer ¹⁸F-fluorodeoxyglucose (FDG), coupled with positron emission tomography and computed tomography (PET/CT) scanning to evaluate cold-induced thermogenesis in the active BAT (interscapular region) and browned/beiged WAT (subcutaneous adipose region) in mice. The PET/CT scanning technique not only can quantify cold-induced glucose uptake in well-known BAT and beige-fat depots but also helps to visualize the anatomical location of novel uncharacterized mouse BAT and beige fat where cold-induced glucose uptake is high. Histological analysis is further employed to validate signals of delineated anatomical regions in PET/CT images as bona fide mouse BAT or beiged WAT fat depots.

Using PSMA imaging for prognostication in localized and advanced prostate cancer

The use of prostate-specific membrane antigen (PSMA)-directed applications in modern prostate cancer management has evolved rapidly over the past few years, helping to establish new treatment pathways and provide further insights into prostate cancer biology. However, the prognostic implications of PSMA-PET have not been studied systematically, owing to rapid clinical implementation without long follow-up periods to determine intermediate-term and long-term oncological outcomes. Currently available data suggest that traditional prognostic factors and survival outcomes are associated with high PSMA expression (both according to immunohistochemistry and PET uptake) in men with localized and biochemically recurrent disease. Treatment with curative intent (primary and/or salvage) often fails when PSMA-positive metastases are present; however, the sensitivity of PSMA-PET in detecting all metastases is poor. Low PSMA-PET uptake in recurrent disease is a favourable prognostic factor; however, it can be associated with poor prognosis in conjunction with high ¹⁸F-fluorodeoxyglucose uptake in metastatic castration-resistant prostate cancer. Clinical trials embedding PSMA-PET for guiding management with reliable oncological outcomes are needed to support ongoing clinical use.

The diagnostic value of imaging techniques for keratoacanthoma: A review

Keratoacanthoma (KA) is a fast-growing skin tumor with solitary KA being the most common type. KAs rarely metastasize and subside spontaneously. Although histopathology is the gold standard for the diagnosis of KA, its histopathological features are sometimes difficult to distinguish from those of other skin tumors. Imaging studies have certain advantages in the preoperative diagnosis of KA; they not only show the exact shape of the lesion but can also accurately determine the extent of the lesion. Combined with histopathological examination, these findings help establish a diagnosis. By summarizing the imaging features of KA, this article aimed to improve radiologists' understanding of the disease and help in the clinical and differential diagnosis of KA.

Direct C-H Radiocyanation of Arenes via Organic Photoredox Catalysis

Innovative labeling methods to incorporate the short-lived positron emitter carbon-11(¹¹C) into bioactive molecules are attractive for positron emission tomography (PET) tracer discovery. Herein, we report a direct C-H radiocyanation method that incorporates [¹¹C]cyanide (¹¹CN^-) to a series of functional electron-rich arenes via photoredox catalysis. This photoredox-mediated radiocyanation can proceed in an aerobic environment and is not moisture sensitive, which allows for ease of reaction setup and for scalable synthesis of ¹¹C-aryl nitriles from readily available precursors.

Reproducibility of lung cancer radiomics features extracted from data-driven respiratory gating and free-breathing flow imaging in [18F]-FDG PET/CT

BACKGROUND

Quality and reproducibility of radiomics studies are essential requirements for the standardisation of radiomics models. As recent data-driven respiratory gating (DDG) [18F]-FDG has shown superior diagnostic performance in lung cancer, we evaluated the impact of DDG on the reproducibility of radiomics features derived from [18F]-FDG PET/CT in comparison to free-breathing flow (FB) imaging.

METHODS

Twenty four lung nodules from 20 patients were delineated. Radiomics features were derived on FB flow PET/CT and on the corresponding DDG reconstruction using the QuantImage v2 platform. Lin's concordance factor (Cb) and the mean difference percentage (DIFF%) were calculated for each radiomics feature using the delineated nodules which were also classified by anatomical localisation and volume. Non-reproducible radiomics features were defined as having a bias correction factor Cb  < 0.8 and/or a mean difference percentage DIFF% > 10.

RESULTS

In total 141 features were computed on each concordance analysis, 10 of which were non-reproducible on all pulmonary lesions. Those were first-order features from Laplacian of Gaussian (LoG)-filtered images (sigma = 1 mm): Energy, Kurtosis, Minimum, Range, Root Mean Squared, Skewness and Variance; Texture features from Gray Level Cooccurence Matrix (GLCM): Cluster Prominence and Difference Variance; First-order Standardised Uptake Value (SUV) feature: Kurtosis. Pulmonary lesions located in the superior lobes had only stable radiomics features, the ones from the lower parts had 25 non-reproducible radiomics features. Pulmonary lesions with a greater size (defined as long axis length > median) showed a higher reproducibility (9 non-reproducible features) than smaller ones (20 non-reproducible features).

CONCLUSION

Calculated on all pulmonary lesions, 131 out of 141 radiomics features can be used interchangeably between DDG and FB PET/CT acquisitions. Radiomics features derived from pulmonary lesions located inferior to the superior lobes are subject to greater variability as well as pulmonary lesions of smaller size.

Novel applications of molecular imaging to guide breast cancer therapy

The goals of precision oncology are to provide targeted drug therapy based on each individual’s specific tumor biology, and to enable the prediction and early assessment of treatment response to allow treatment modification when necessary. Thus, precision oncology aims to maximize treatment success while minimizing the side effects of inadequate or suboptimal therapies. Molecular imaging, through noninvasive assessment of clinically relevant tumor biomarkers across the entire disease burden, has the potential to revolutionize clinical oncology, including breast oncology. In this article, we review breast cancer positron emission tomography (PET) imaging biomarkers for providing early response assessment and predicting treatment outcomes. For 2-¹⁸fluoro-2-deoxy-D-glucose (FDG), a marker of cellular glucose metabolism that is well established for staging multiple types of malignancies including breast cancer, we highlight novel applications for early response assessment. We then review current and future applications of novel PET biomarkers for imaging the steroid receptors, including the estrogen and progesterone receptors, the HER2 receptor, cellular proliferation, and amino acid metabolism.

Performance of digital PET/CT compared with conventional PET/CT in oncologic patients: a prospective comparison study

PURPOSE

Digital PET systems (dPET) improve lesion detectability as compared to PET systems with conventional photomultiplier tubes (cPET). We prospectively studied the performance of high-resolution digital PET scans in patients with cancer, as compared with high- and standard-resolution conventional PET scans, taking the acquisition order into account.

METHODS

We included 212 patients with cancer, who were referred for disease staging or restaging. All patients underwent FDG-PET/CT on a dPET scanner and on a cPET scanner in a randomized order. The scans were acquired immediately after each other. Three image reconstructions were generated: 1) standard-resolution (4 × 4 × 4 mm³ voxels) cPET, 2) high-resolution (2 × 2 × 2 mm³ voxels) cPET, and 3) high-resolution dPET. Two experienced PET readers visually assessed the three reconstructions side-by-side and ranked them according to scan preference, in an independent and blinded fashion.

RESULTS

On high-resolution dPET, the PET readers detected more lesions or they had a higher diagnostic confidence than on high- and standard-resolution cPET (p < 0.001). High-resolution dPET was preferred in 90% of the cases, as compared to 44% for high-resolution cPET and 1% for standard-resolution cPET (p < 0.001). However, for the subgroup of patients where dPET was made first (n = 103, 61 ± 10 min after FDG administration) and cPET was made second (93 ± 15 min after FDG administration), no significant difference in preference was found between the high-resolution cPET and dPET reconstructions (p = 0.41).

CONCLUSIONS

DPET scanners in combination with high-resolution reconstructions clinically outperform cPET scanners with both high- and standard-resolution reconstructions as the PET readers identified more FDG-avid lesions, their diagnostic confidence was increased, and they visually preferred dPET. However, when dPET was made first, high-resolution dPET and high-resolution cPET showed similar performance, indicating the positive effect of a prolonged FDG uptake time. Therefore, high-resolution cPET in combination with a prolonged FDG uptake time can be considered as an alternative.

Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology

Digital twins employ mathematical and computational models to virtually represent a physical object (e.g., planes and human organs), predict the behavior of the object, and enable decision-making to optimize the future behavior of the object. While digital twins have been widely used in engineering for decades, their applications to oncology are only just emerging. Due to advances in experimental techniques quantitatively characterizing cancer, as well as advances in the mathematical and computational sciences, the notion of building and applying digital twins to understand tumor dynamics and personalize the care of cancer patients has been increasingly appreciated. In this review, we present the opportunities and challenges of applying digital twins in clinical oncology, with a particular focus on integrating medical imaging with mechanism-based, tissue-scale mathematical modeling. Specifically, we first introduce the general digital twin framework and then illustrate existing applications of image-guided digital twins in healthcare. Next, we detail both the imaging and modeling techniques that provide practical opportunities to build patient-specific digital twins for oncology. We then describe the current challenges and limitations in developing image-guided, mechanism-based digital twins for oncology along with potential solutions. We conclude by outlining five fundamental questions that can serve as a roadmap when designing and building a practical digital twin for oncology and attempt to provide answers for a specific application to brain cancer. We hope that this contribution provides motivation for the imaging science, oncology, and computational communities to develop practical digital twin technologies to improve the care of patients battling cancer.

Minimally invasive nanomedicine: nanotechnology in photo-/ultrasound-/radiation-/magnetism-mediated therapy and imaging

Traditional treatments such as chemotherapy and surgery usually cause severe side effects and excruciating pain. The emergence of nanomedicines and minimally invasive therapies (MITs) has brought hope to patients with malignant diseases. Especially, minimally invasive nanomedicines (MINs), which combine the advantages of nanomedicines and MITs, can effectively target pathological cells/tissues/organs to improve the bioavailability of drugs, minimize side effects and achieve painless treatment with a small incision or no incision, thereby acquiring good therapeutic effects. In this review, we provide a comprehensive review of the research status and challenges of MINs, which generally refers to the medical applications of nanotechnology in photo-/ultrasound-/radiation-/magnetism-mediated therapy and imaging. Additionally, we also discuss their combined application in various fields including cancers, cardiovascular diseases, tissue engineering, neuro-functional diseases, and infectious diseases. The prospects, and potential bench-to-bedside translation of MINs are also presented in this review. We expect that this review can inspire the broad interest for a wide range of readers working in the fields of interdisciplinary subjects including (but not limited to) chemistry, nanomedicine, bioengineering, nanotechnology, materials science, pharmacology, and biomedicine.

Validation of a computational chain from PET Monte Carlo simulations to reconstructed images

The study aimed to create a pipeline from Monte Carlo simulated projections of a Gate PET system to reconstructed images. The PET system was modelled after the GE Discovery MI (DMI) PET/CT, and the simulated projections were reconstructed with the stand-alone reconstruction software CASToR. Attenuation correction, normalisation calibration, random estimation, and scatter estimation for the simulations were computed with in-house programs. The pipeline was compared in both projection and image space with data acquired on a clinical DMI and reconstructed with GE's off-line PET reconstruction software (PET Toolbox) and CASToR. The simulated and measured data were compared for the number of prompt coincidences, scatter fraction, contrast recovery coefficient (CRC), signal-to-noise ratio (SNR), background variability, residual lung error, and image profiles. A slight discrepancy was noted in the projection space, but good agreements were generally achieved in image space between simulated and measured data. The CRC was found to be 81 % for Gate – CASToR, 84 % for GE – CASToR, and 84 % for GE - PET Toolbox for the largest sphere of the NEMA image quality (IQ) phantom, and the SNR was found to be 98 for Gate – CASToR, 91 for GE – CASToR, and 93 for GE – PET Toolbox. Profiles drawn over the spheres for the NEMA IQ phantom and the Data Spectrum (DS) phantom show a good match between measurement and simulation. The results indicate feasibility to utilise the pipeline as a tool for off-line simulation-based studies. A complete pipeline introduces possibilities to study the impact of single parameters in the whole chain from simulation to reconstructed images.

Medical Radiation Exposure Reduction in PET via Super-Resolution Deep Learning Model

In positron emission tomography (PET) imaging, image quality correlates with the injected [18F]-fluorodeoxyglucose (FDG) dose and acquisition time. If image quality improves from short-acquisition PET images via the super-resolution (SR) deep learning technique, it is possible to reduce the injected FDG dose. Therefore, the aim of this study was to clarify whether the SR deep learning technique could improve the image quality of the 50%-acquisition-time image to the level of that of the 100%-acquisition-time image. One-hundred-and-eight adult patients were enrolled in this retrospective observational study. The supervised data were divided into nine subsets for nested cross-validation. The mean peak signal-to-noise ratio and structural similarity in the SR-PET image were 31.3 dB and 0.931, respectively. The mean opinion scores of the 50% PET image, SR-PET image, and 100% PET image were 3.41, 3.96, and 4.23 for the lung level, 3.31, 3.80, and 4.27 for the liver level, and 3.08, 3.67, and 3.94 for the bowel level, respectively. Thus, the SR-PET image was more similar to the 100% PET image and subjectively improved the image quality, as compared to the 50% PET image. The use of the SR deep-learning technique can reduce the injected FDG dose and thus lower radiation exposure.

Shining Damaged Hearts: Immunotherapy-Related Cardiotoxicity in the Spotlight of Nuclear Cardiology

The emerging use of immunotherapies in cancer treatment increases the risk of immunotherapy-related cardiotoxicity. In contrast to conventional chemotherapy, these novel therapies have expanded the forms and presentations of cardiovascular damage to a broad spectrum from asymptomatic changes to fulminant short- and long-term complications in terms of cardiomyopathy, arrythmia, and vascular disease. In cancer patients and, particularly, cancer patients undergoing (immune-)therapy, cardio-oncological monitoring is a complex interplay between pretherapeutic risk assessment, identification of impending cardiotoxicity, and post-therapeutic surveillance. For these purposes, the cardio-oncologist can revert to a broad spectrum of nuclear cardiological diagnostic workup. The most promising commonly used nuclear medicine imaging techniques in relation to immunotherapy will be discussed in this review article with a special focus on the continuous development of highly specific molecular markers and steadily improving methods of image generation. The review closes with an outlook on possible new developments of molecular imaging and advanced image evaluation techniques in this exciting and increasingly growing field of immunotherapy-related cardiotoxicity.

Study on the RF transparency of electrically floating and ground PET inserts in a 3T clinical MRI system

PURPOSE

The positron emission tomography (PET) insert for a magnetic resonance imaging (MRI) system that implements the radiofrequency (RF) built-in body coil of the MRI system as a transmitter is designed to be RF-transparent, as the coil resides outside the RF-shielded PET ring. This approach reduces the design complexities (e.g., large PET ring diameter) related to implementing a transmit coil inside the PET ring. However, achieving the required field transmission into the imaging region of interest (ROI) becomes challenging because of the RF shield of the PET insert. In this study, a modularly RF-shielded PET insert is used to investigate the RF transparency considering two electrical configurations of the RF shield, namely the electrical floating and ground configurations. The purpose is to find the differences, advantages and disadvantages of these two configurations.

METHODS

Eight copper-shielded PET detector modules (intermodular gap: 3 mm) were oriented cylindrically with an inner-diameter of 234 mm. Each PET module included four-layer LYSO scintillation crystal blocks and front-end readout electronics. RF-shielded twisted-pair cables were used to connect the front-end electronics with the power sources and PET data acquisition systems located outside the MRI room. In the ground configuration, both the detector and cable shields were connected to the RF ground of the MRI system. In the floating configuration, only the RF shields of the PET modules were isolated from the RF ground. Experiments were conducted using two cylindrical homogeneous phantoms in a 3T clinical MRI system, in which the built-in body RF coil (a cylindrical volume coil of diameter 700 mm and length 540 mm) was implemented as a transceiver.

RESULTS

For both PET configurations, the RF and MR imaging performances were lower than those for the MRI-only case, and the MRI-system provided SAR values that were almost double. The RF homogeneity and field strength, and the SNR of the MR images were mostly higher for the floating PET configuration than they were for the ground PET configuration. However, for a shorter axial FOV of 125 mm, both configurations offered almost the same performance with high RF homogeneities (e.g., 76 ± 10%). Moreover, for both PET configurations, 56 ± 6% larger RF pulse amplitudes were required for MR imaging purposes. The increased power is mostly absorbed in the conductive shields in the form of shielding RF eddy currents; as a result, the SAR values only in the phantoms were estimated to be close to the MRI-only values.

CONCLUSIONS

The floating PET configuration showed higher RF transparency under all experimental setups. For a relatively short axial FOV of 125 mm, the ground configuration also performed well which indicated that an RF-penetrable PET insert with the conventional design (e.g., the ground configuration) might also become possible. However, some design modifications (e.g., a wider intermodular gap and using the RF receiver coil inside the PET insert) should improve the RF performance to the level of the MRI-only case. This article is protected by copyright. All rights reserved.

Protein-Based Nanoparticles for the Imaging and Treatment of Solid Tumors: The Case of Ferritin Nanocages, a Narrative Review

Protein nanocages have been studied extensively, due to their unique architecture, exceptional biocompatibility and highly customization capabilities. In particular, ferritin nanocages (FNs) have been employed for the delivery of a vast array of molecules, ranging from chemotherapeutics to imaging agents, among others. One of the main favorable characteristics of FNs is their intrinsic targeting efficiency toward the Transferrin Receptor 1, which is overexpressed in many tumors. Furthermore, genetic manipulation can be employed to introduce novel variants that are able to improve the loading capacity, targeting capabilities and bio-availability of this versatile drug delivery system. In this review, we discuss the main characteristics of FN and the most recent applications of this promising nanotechnology in the field of oncology with a particular emphasis on the imaging and treatment of solid tumors.

Noise2Void: unsupervised denoising of PET images

Objective:Elevated noise levels in positron emission tomography (PET) images lower image quality and quantitative accuracy and are a confounding factor for clinical interpretation. The objective of this paper is to develop a PET image denoising technique based on unsupervised deep learning.Significance:Recent advances in deep learning have ushered in a wide array of novel denoising techniques, several of which have been successfully adapted for PET image reconstruction and post-processing. The bulk of the deep learning research so far has focused on supervised learning schemes, which, for the image denoising problem, require paired noisy and noiseless/low-noise images. This requirement tends to limit the utility of these methods for medical applications as paired training datasets are not always available. Furthermore, to achieve the best-case performance of these methods, it is essential that the datasets for training and subsequent real-world application have consistent image characteristics (e.g. noise, resolution, etc), which is rarely the case for clinical data. To circumvent these challenges, it is critical to develop unsupervised techniques that obviate the need for paired training data.Approach:In this paper, we have adapted Noise2Void, a technique that relies on corrupt images alone for model training, for PET image denoising and assessed its performance using PET neuroimaging data. Noise2Void is an unsupervised approach that uses a blind-spot network design. It requires only a single noisy image as its input, and, therefore, is well-suited for clinical settings. During the training phase, a single noisy PET image serves as both the input and the target. Here we present a modified version of Noise2Void based on a transfer learning paradigm that involves group-level pretraining followed by individual fine-tuning. Furthermore, we investigate the impact of incorporating an anatomical image as a second input to the network.Main Results:We validated our denoising technique using simulation data based on the BrainWeb digital phantom. We show that Noise2Void with pretraining and/or anatomical guidance leads to higher peak signal-to-noise ratios than traditional denoising schemes such as Gaussian filtering, anatomically guided non-local means filtering, and block-matching and 4D filtering. We used the Noise2Noise denoising technique as an additional benchmark. For clinical validation, we applied this method to human brain imaging datasets. The clinical findings were consistent with the simulation results confirming the translational value of Noise2Void as a denoising tool.

Multimodal Spatial Attention Module for Targeting Multimodal PET-CT Lung Tumor Segmentation

Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of PET and anatomical information from CT. Tumor segmentation is a critical element of PET-CT but at present, the performance of existing automated methods for this challenging task is low. Segmentation tends to be done manually by different imaging experts, which is labor-intensive and prone to errors and inconsistency. Previous automated segmentation methods largely focused on fusing information that is extracted separately from the PET and CT modalities, with the underlying assumption that each modality contains complementary information. However, these methods do not fully exploit the high PET tumor sensitivity that can guide the segmentation. We introduce a deep learning-based framework in multimodal PET-CT segmentation with a multimodal spatial attention module (MSAM). The MSAM automatically learns to emphasize regions (spatial areas) related to tumors and suppress normal regions with physiologic high-uptake from the PET input. The resulting spatial attention maps are subsequently employed to target a convolutional neural network (CNN) backbone for segmentation of areas with higher tumor likelihood from the CT image. Our experimental results on two clinical PET-CT datasets of non-small cell lung cancer (NSCLC) and soft tissue sarcoma (STS) validate the effectiveness of our framework in these different cancer types. We show that our MSAM, with a conventional U-Net backbone, surpasses the state-of-the-art lung tumor segmentation approach by a margin of 7.6% in Dice similarity coefficient (DSC).

Building the Bridge: Molecular Imaging Biomarkers for 21st Century Cancer Therapies

Precision medicine, where the molecular underpinnings of the disease are assessed for tailored therapies, has greatly impacted cancer care. In parallel, a new pillar of therapeutics has emerged with profound success, including immunotherapies such as checkpoint inhibitors and cell-based therapies. Nonetheless, it remains essential to develop paradigms to predict and monitor for therapeutic response. Molecular imaging has the potential to add substantially to all phases of cancer patient care: predicative, companion diagnostics can illuminate therapeutic target density within a tumor, and pharmacodynamic imaging biomarkers can complement traditional modalities to judge a favorable treatment response. This "Focus on Molecular Imaging" article discusses the current role of molecular imaging in oncology and highlights an additional step in clinical paradigm termed a "therapeutic biomarker," which serves to assess whether next generation drugs reach their target to elicit a favorable clinical response.

A comparative analysis: international variation in PET-CT service provision in oncology-an International Cancer Benchmarking Partnership study

Objective

To explore differences in position emission tomography-computed tomography (PET-CT) service provision internationally to further understand the impact variation may have upon cancer services. To identify areas of further exploration for researchers and policymakers to optimize PET-CT services and improve the quality of cancer services.

Design

Comparative analysis using data based on pre-defined PET-CT service metrics from PET-CT stakeholders across seven countries. This was further informed via document analysis of clinical indication guidance and expert consensus through round-table discussions of relevant PET-CT stakeholders. Descriptive comparative analyses were produced on use, capacity and indication guidance for PET-CT services between jurisdictions.

Setting

PET-CT services across 21 jurisdictions in seven countries (Australia, Denmark, Canada, Ireland, New Zealand, Norway and the UK).

Participants

None.

Intervention(s)

None.

Main Outcome Measure(s)

None.

Results

PET-CT service provision has grown over the period 2006–2017, but scale of increase in capacity and demand is variable. Clinical indication guidance varied across countries, particularly for small-cell lung cancer staging and the specific acknowledgement of gastric cancer within oesophagogastric cancers. There is limited and inconsistent data capture, coding, accessibility and availability of PET-CT activity across countries studied.

Conclusions

Variation in PET-CT scanner quantity, acquisition over time and guidance upon use exists internationally. There is a lack of routinely captured and accessible PET-CT data across the International Cancer Benchmarking Partnership countries due to inconsistent data definitions, data linkage issues, uncertain coverage of data and lack of specific coding. This is a barrier in improving the quality of PET-CT services globally. There needs to be greater, richer data capture of diagnostic and staging tools to facilitate learning of best practice and optimize cancer services.

Clinical value of 18FDG PET/MRI in muscle-invasive, locally advanced, and metastatic bladder cancer

OBJECTIVE

Metastatic bladder cancer is an aggressive disease that can often be difficult to diagnose and stage with conventional cross-sectional imaging. The primary objective of this study was to determine the clinical value of fluorine-18 2-fluoro-2-deoxy-D-glucose (¹⁸F-FDG) PET/MRI for surveillance and restaging of patients with muscle-invasive, locally advanced, and metastatic bladder cancer compared to conventional imaging methods.

MATERIALS AND METHODS

This retrospective study enrolled patients with muscle-invasive, locally advanced and metastatic bladder cancer in a single institute evaluated with ¹⁸F-FDG PET/MRI. All patients also underwent conventional imaging with CT. Additional imaging may also have included ¹⁸F-FDG PET/CT (¹⁸F-FDG PET), or sodium fluoride (NaF) PET/CT in some patients. Images were reviewed by a diagnostic radiologist/nuclear medicine physician. Number of lesions and sites of disease were captured and compared between ¹⁸F-FDG PET/MRI and conventional imaging. Lesions were confirmed by sequential imaging or lesion biopsy. All patients were followed for survival.

RESULTS

Fifteen patients (4 for surveillance; 11 for restaging) underwent 34 ¹⁸F-FDG PET/MRI scans. Each patient received a corresponding conventional CT around the time of the ¹⁸F-FDG PET/MRI (median 6 days). The 15 patients (11 male; 4 female) had a median age of 61.5 years (range 37-73) and histologies of urothelial carcinoma (n = 13) and small-cell carcinoma of the bladder (n = 2) diagnosed as stage 4 (n = 13), stage 3 (n = 1), or stage 2 (n = 1). ¹⁸F-FDG PET/MRI detected 82 metastatic malignant lesions involving lymph nodes (n = 22), liver (n = 10), lung (n = 34), soft tissue (n = 12), adrenal glands (n = 1), prostate (n = 1), and bone (n = 2) with a resultant advantage of 36% for lesion visibility in comparison with CT. Serial imaging or biopsy confirmed these lesions as malignant.

CONCLUSION

¹⁸F-FDG PET/MRI can detect metastatic lesions which cannot be identified on conventional CT, and this can allow for better treatment planning and improved disease monitoring during therapy.

Systematic imaging in medicine: a comprehensive review

Systematic imaging can be broadly defined as the systematic identification and characterization of biological processes at multiple scales and levels. In contrast to "classical" diagnostic imaging, systematic imaging emphasizes on detecting the overall abnormalities including molecular, functional, and structural alterations occurring during disease course in a systematic manner, rather than just one aspect in a partial manner. Concomitant efforts including improvement of imaging instruments, development of novel imaging agents, and advancement of artificial intelligence are warranted for achievement of systematic imaging. It is undeniable that scientists and radiologists will play a predominant role in directing this burgeoning field. This article introduces several recent developments in imaging modalities and nanoparticles-based imaging agents, and discusses how systematic imaging can be achieved. In the near future, systematic imaging which combines multiple imaging modalities with multimodal imaging agents will pave a new avenue for comprehensive characterization of diseases, successful achievement of image-guided therapy, precise evaluation of therapeutic effects, and rapid development of novel pharmaceuticals, with the final goal of improving human health-related outcomes.

Copper-mediated nucleophilic radiofluorination of [18 F]β-CFT for positron emission tomography imaging of dopamine transporter

[18 F]β-CFT is a positron emission tomography (PET) ligand for imaging of dopamine transporter. It was proved to be a sensitive PET marker to detect presynaptic dopaminergic hypofunction in Parkinson's disease. In recent years, copper-mediated 18 F-fluorination of aryl boronic esters has been successful in some molecules containing aromatic groups. In this study, we describe the novel synthetic strategy of [18 F]β-CFT by copper-mediated nucleophilic radiofluorination with pinacol-derived aryl boronic esters upon reaction with [18 F]KF/K222 and Cu (OTf)2 (py)4 . The radiolabeling protocol was optimized with [18 F]fluoride elution method and amount of copper catalyst used. [18 F]β-CFT is obtained from boronic ester precursors in 2.2% to 10.6% non-isolated radiochemical yield (RCY). Purified [18 F]β-CFT with >99% radiochemical purity (RCP) and high molar activity was obtained in validation runs. The radiolabeling procedure is straightforward and can easily be adapted for clinical use.

A Pulmonary Pleomorphic Carcinoma Patient with Exudative Retinal Detachment Secondary to Choroid Metastasis as Initial Presentation-A Case Report

Choroid metastasis is the initial presentation of pleomorphic carcinoma (PC) of the lung. PC is classified as poorly differentiated non-small cell lung carcinoma. It has a tendency to metastasize early and has a poor response to chemotherapy, which often results in poor prognosis. We report the case of a 63-year-old woman with a one-month history of deteriorating vision in the left eye. Fundus examination, fluorescein angiography, indocyanine green angiography, and B-scan sonography demonstrated choroidal metastasis of the left eye. Positron emission tomography/computed tomography (PET/CT) revealed a tumor with increased uptake in the left upper lung. Subsequent bronchoscopic biopsy confirmed a pleomorphic carcinoma of the lungs. Choroid metastasis as an initial presentation of PC in the lung is rare. Usually, it represents the late course of disseminated disease with hematogenous spread. Prompt diagnosis is imperative for patients to immediately initiate treatment.

Internet of Things and Robotics in Transforming Current-Day Healthcare Services

Technology has become an integral part of everyday lives. Recent years have witnessed advancement in technology with a wide range of applications in healthcare. However, the use of the Internet of Things (IoT) and robotics are yet to see substantial growth in terms of its acceptability in healthcare applications. The current study has discussed the role of the aforesaid technology in transforming healthcare services. The study also presented various functionalities of the ideal IoT-aided robotic systems and their importance in healthcare applications. Furthermore, the study focused on the application of the IoT and robotics in providing healthcare services such as rehabilitation, assistive surgery, elderly care, and prosthetics. Recent developments, current status, limitations, and challenges in the aforesaid area have been presented in detail. The study also discusses the role and applications of the aforementioned technology in managing the current pandemic of COVID-19. A comprehensive knowledge has been provided on the prospect of the functionality, application, challenges, and future scope of the IoT-aided robotic system in healthcare services. This will help the future researcher to make an inclusive idea on the use of the said technology in improving the healthcare services in the future.

Carbon Based Nanodots in Early Diagnosis of Cancer

Detection of cancer at an early stage is one of the principal factors associated with successful treatment outcome. However, current diagnostic methods are not capable of making sensitive and robust cancer diagnosis. Nanotechnology based products exhibit unique physical, optical and electrical properties that can be useful in diagnosis. These nanotech-enabled diagnostic representatives have proved to be generally more capable and consistent; as they selectively accumulated in the tumor site due to their miniscule size. This article rotates around the conventional imaging techniques, the use of carbon based nanodots viz Carbon Quantum Dots (CQDs), Graphene Quantum Dots (GQDs), Nanodiamonds, Fullerene, and Carbon Nanotubes that have been synthesized in recent years, along with the discovery of a wide range of biomarkers to identify cancer at early stage. Early detection of cancer using nanoconstructs is anticipated to be a distinct reality in the coming years.

EpCAM-Targeting Aptamer Radiotracer for Tumor-Specific PET Imaging

Noninvasive in vivo imaging to measure the expression of EpCAM, a biomarker overexpressed in the majority of carcinoma tumors and metastatic lesions, is highly desirable for accurate tumor staging and therapy evaluation. Here, we report the use of an aptamer radiotracer to enable tumor-specific EpCAM-targeting PET imaging. Oligonucleotide aptamers are small molecular ligands that specifically bind with high affinity to their target molecules. For specific tumor imaging, an aptamer radiotracer was formulated by chelating a ⁶⁴Cu isotope and DOTA-PEGylated aptamer sequence to target EpCAM. In vitro cell uptake assays demonstrated that the aptamer radiotracer specifically bound EpCAM-expressing breast cancer cells but did not react with off-target tumor cells. For in vivo tumor imaging, aptamer radiotracer was systemically administered into xenograft mice. MicroPET/CT scans revealed that the aptamer radiotracer rapidly highlighted xenograft tumors derived from MDA-MB-231 breast cancer cells (EpCAM positive) as early as 2 h postadministration with a gradually increasing tumor uptake signal that peaked at 24 h but not in lymphoma 937 tumors (EpCAM negative). In contrast, nonspecific background signals in the liver and kidneys were rapidly decreased postadministration. This proof-of-concept study demonstrates the utility of aptamer radiotracers for tumor-specific PET imaging.