Oncologic Imaging Research Articles

Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review.

The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.

Diagnostic accuracy and reliability of CT-based Node-RADS for colon cancer.

The Node-RADS classification was recently published as a classification system to better characterize lymph nodes in oncological imaging. The present analysis investigated the diagnostic benefit of the Node-RADS classification of staging computed tomography (CT) images to categorize and stage lymph nodes in patients with colon cancer. All patients were surgically resected and the lymph nodes were histopathological analyzed. All investigated lymph nodes were scored in accordance to the Node-RADS classification by two experienced radiologists. Interreader variability was assessed with Cohen's kappa analysis, discrimination analysis was performed with Mann-Whitney-U test and diagnostic accuracy was assessed with receiver-operating characteristics (ROC) curve analysis. Overall, 108 patients (n = 49 females, 45.3%) with a mean age of 70.08 ± 14.34 years were included. In discrimination analysis, the total Node-RADS score showed statistically significant differences between N- and N + stage (for reader 1: mean 1.89 ± 1.09 score for N- versus 2.93 ± 1.62 score for N+, for reader 2: 1.33 ± 0.48 score for N- versus 3.65 ± 0.94 score for N+, p = 0.001, respectively). ROC curve analysis for lymph node discrimination showed an area under the curve of 0.68. A threshold value of 2 resulted in a sensitivity of 0.62 and a specificity of 0.71. Node-RADS score derived from staging CT shows only limited diagnostic accuracy to correctly predict nodal positivity in colon cancer. The interreader variability seems to be high and should question the clinical translation for this tumour entity.

Deep learning CT reconstruction improves liver metastases detection

ObjectivesDetection of liver metastases is crucial for guiding oncological management. Computed tomography through iterative reconstructions is widely used in this indication but has certain limitations. Deep learning image reconstructions (DLIR) use deep neural networks to achieve a significant noise reduction compared to iterative reconstructions. While reports have demonstrated improvements in image quality, their impact on liver metastases detection remains unclear. Our main objective was to determine whether DLIR affects the number of detected liver metastasis. Our secondary objective was to compare metastases conspicuity between the two reconstruction methods.MethodsCT images of 121 patients with liver metastases were reconstructed using a 50% adaptive statistical iterative reconstruction (50%-ASiR-V), and three levels of DLIR (DLIR-low, DLIR-medium, and DLIR-high). For each reconstruction, two double-blinded radiologists counted up to a maximum of ten metastases. Visibility and contour definitions were also assessed. Comparisons between methods for continuous parameters were performed using mixed models.ResultsA higher number of metastases was detected by one reader with DLIR-high: 7 (2–10) (median (Q₁–Q₃); total 733) versus 5 (2–10), respectively for DLIR-medium, DLIR-low, and ASiR-V (p < 0.001). Ten patents were detected with more metastases with DLIR-high simultaneously by both readers and a third reader for confirmation. Metastases visibility and contour definition were better with DLIR than ASiR-V.ConclusionDLIR-high enhanced the detection and visibility of liver metastases compared to ASiR-V, and also increased the number of liver metastases detected.Critical relevance statementDeep learning-based reconstruction at high strength allowed an increase in liver metastases detection compared to hybrid iterative reconstruction and can be used in clinical oncology imaging to help overcome the limitations of CT.Key PointsDetection of liver metastases is crucial but limited with standard CT reconstructions.More liver metastases were detected with deep-learning CT reconstruction compared to iterative reconstruction.Deep learning reconstructions are suitable for hepatic metastases staging and follow-up.Graphical

Leveraging radiomics and AI for precision diagnosis and prognostication of liver malignancies.

Liver tumors, whether primary or metastatic, have emerged as a growing concern with substantial global health implications. Timely identification and characterization of liver tumors are pivotal factors in order to provide optimum treatment. Imaging is a crucial part of the detection of liver tumors; however, conventional imaging has shortcomings in the proper characterization of these tumors which leads to the need for tissue biopsy. Artificial intelligence (AI) and radiomics have recently emerged as investigational opportunities with the potential to enhance the detection and characterization of liver lesions. These advancements offer opportunities for better diagnostic accuracy, prognostication, and thereby improving patient care. In particular, these techniques have the potential to predict the histopathology, genotype, and immunophenotype of tumors based on imaging data, hence providing guidance for personalized treatment of such tumors. In this review, we outline the progression and potential of AI in the field of liver oncology imaging, specifically emphasizing manual radiomic techniques and deep learning-based representations. We discuss how these tools can aid in clinical decision-making challenges. These challenges encompass a broad range of tasks, from prognosticating patient outcomes, differentiating benign treatment-related factors and actual disease progression, recognizing uncommon response patterns, and even predicting the genetic and molecular characteristics of the tumors. Lastly, we discuss the pitfalls, technical limitations and future direction of these AI-based techniques.

An Update on MR Spectroscopy in Cancer Management: Advances in Instrumentation, Acquisition, and Analysis.

MR spectroscopy (MRS) is a noninvasive imaging method enabling chemical and molecular profiling of tissues in a localized, multiplexed, and nonionizing manner. As metabolic reprogramming is a hallmark of cancer, MRS provides valuable metabolic and molecular information for cancer diagnosis, prognosis, treatment monitoring, and patient management. This review provides an update on the use of MRS for clinical cancer management. The first section includes an overview of the principles of MRS, current methods, and conventional metabolites of interest. The remainder of the review is focused on three key areas: advances in instrumentation, specifically ultrahigh-field-strength MRI scanners and hybrid systems; emerging methods for acquisition, including deuterium imaging, hyperpolarized carbon 13 MRI and MRS, chemical exchange saturation transfer, diffusion-weighted MRS, MR fingerprinting, and fast acquisition; and analysis aided by artificial intelligence. The review concludes with future recommendations to facilitate routine use of MRS in cancer management. Keywords: MR Spectroscopy, Spectroscopic Imaging, Molecular Imaging in Oncology, Metabolic Reprogramming, Clinical Cancer Management © RSNA, 2024.

Establishment of National Diagnostic Reference Levels for Administered Activity in Diagnostic Nuclear Medicine in Thailand.

The diagnostic reference level (DRL) is a patient-exposure optimization tool used to evaluate radiation doses in medical imaging and provide guidance for protection from them. In Thailand, nuclear medicine DRLs have not been established yet. Therefore, this study surveyed dose levels in routine nuclear medicine procedures to provide national DRLs (NDRLs). Methods: NDRLs in Thailand were established by investigating the administered activity of radiopharmaceuticals in nuclear medicine examination studies. The NDRLs were determined on the basis of the 75th percentile (third quartile) of administered activity distribution as recommended by the International Commission on Radiological Protection. As part of a nationwide survey, datasets for the period between June 1, 2018, and August 31, 2019, were collected from 21 Thailand hospitals with nuclear medicine equipment. All hospitals were asked to report the nuclear medicine imaging devices in use, the standard protocol parameters for selected examinations, the injected activities, and the ages and weights of patients. All data were calculated to determine Thailand NDRLs, which were compared with international NDRLs. Results: The data reported by the 21 hospitals consisted of 4,641 examinations with SPECT or SPECT/CT for general nuclear medicine and 409 examinations with PET. The most widely performed examinations for SPECT were bone, thyroid, oncology, and cardiovascular imaging. The NDRLs for SPECT or SPECT/CT agreed well with published NDRLs for Europe, the United States, Japan, Korea, Kuwait, and Australia. In contrast, the NDRLs for 18F-FDG PET in oncology studies were higher than for Japan, Korea, Kuwait, and Australia but lower than for the United States, the United Kingdom, and the European Union. Conclusion: This study presents NDRL results for adults in Thailand as a way to optimize radiation protection in nuclear medicine imaging. Moreover, the reported injected activity levels were comparable to those of other countries.

Contrast-enhanced Susceptibility Weighted Imaging (CE-SWI) for the Characterization of Musculoskeletal Oncologic Pathology: A Pictorial Essay on the Initial Five-year Experience at a Cancer Institution

Susceptibility-weighted imaging (SWI) is based on a 3D high-spatial-resolution, velocity-corrected gradient-echo MRI sequence that uses magnitude and filtered-phase information to create images. It SWI uses tissue magnetic susceptibility differences to generate signal contrast that may arise from paramagnetic (hemosiderin), diamagnetic (minerals and calcifications) and ferromagnetic (metal) molecules. Distinguishing between calcification and blood products is possible through the filtered phase images, helping to visualize osteoblastic and osteolytic bone metastases or demonstrating calcifications and osteoid production in liposarcoma and osteosarcoma. When acquired in combination with the injection of an exogenous contrast agent, contrast-enhanced SWI (CE-SWI) can simultaneously detect the T2* susceptibility effect, T2 signal difference, contrast-induced T1 shortening, and out-of-phase fat and water chemical shift effect. Bone and soft tissue lesion SWI features have been described, including giant cell tumors in bone and synovial sarcomas in soft tissues. We expand on the appearance of benign soft-tissue lesions such as hemangioma, neurofibroma, pigmented villonodular synovitis, abscess, and hematoma. Most myxoid sarcomas demonstrate absent or just low-grade intra-tumoral hemorrhage at the baseline. CE-SWI shows superior differentiation between mature fibrotic T2* dark components and active enhancing T1 shortening components in desmoid fibromatosis. SWI has gained popularity in oncologic MSK imaging because of its sensitivity for displaying hemorrhage in soft tissue lesions, thereby helping to differentiate benign versus malignant soft tissue tumors. The ability to show the viable, enhancing portions of a soft tissue sarcoma separately from hemorrhagic/necrotic components also suggests its utility as a biomarker of tumor treatment response. It is essential to understand and appreciate the differences between spontaneous hemorrhage patterns in high-grade sarcomas and those occurring in the therapy-induced necrosis process in responding tumors. Ring-like hemosiderin SWI pattern is observed in successfully treated sarcomas. CE-SWI also demonstrates early promising results in separating the T2* blooming of healthy iron-loaded bone marrow from the T1-shortened enhancement in bone marrow that is displaced by the tumor. SWI and CE-SWI in MSK oncology learning objectives: SWI and CE-SWI can be used to identify calcifications on MRI. Certain SWI and CE-SWI patterns can correlate with tumor histologic type. CE-SWI can discriminate mature from immature components of desmoid tumors. CE-SWI patterns can help to assess treatment response in soft tissue sarcomas. Understanding CE-SWI patterns in post-surgical changes can also be useful in discriminating between residual and recurrent tumors with overlapping imaging features.

Spectral micro-CT for simultaneous gold and iodine detection, and multi-material identification

Multiple energy bin spectral micro-CT (SμCT) is an advanced imaging technique that allows multi-material decomposition according to their specific absorption patterns at a sub-100 μm scale. Typically, iodine is the preferred CT contrast agent for cardiovascular imaging, while gold nanoparticles have gained attention in recent years owing to their high absorption properties, biocompatibility and ability to target tumors.In this work, we demonstrate the potential for multi-material decomposition through SμCT imaging of a test sample at the PEPI lab of INFN Trieste. The sample, consisting of gold, iodine, calcium, and water, was imaged using a Pixirad1/PixieIII chromatic detector with multiple energy thresholds and a wide spectrum (100 kV) produced by a micro-focus X-ray tube. The results demonstrate the simultaneous detection and separation of the four materials at a spatial scale of 35 μm, suggesting the potential of this technique in improving material detectability and quantification in a range of pre-clinical applications, including cardiovascular and oncologic imaging.

Potential of photon counting computed tomography derived spectral reconstructions to reduce beam-hardening artifacts in chest CT

PurposeAim of the recent study is to point out a method to optimize quality of CT scans in oncological patients with port systems. This study investigates the potential of photon counting computed tomography (PCCT) for reduction of beam hardening artifacts caused by port-implants in chest imaging by means of spectral reconstructions. MethodIn this retrospective single-center study, 8 ROIs for 19 spectral reconstructions (polyenergetic imaging, monoenergetic reconstructions from 40 to 190 keV as well as iodine maps and virtual non contrast (VNC)) of 49 patients with pectoral port systems undergoing PCCT of the chest for staging of oncologic disease were measured. Mean values and standard deviation (SD) Hounsfield unit measurements of port-chamber associated hypo- and hyperdense artifacts, bilateral muscles and vessels has been carried out. Also, a structured assessment of artifacts and imaging findings was performed by two radiologists. ResultsA significant association of keV with iodine contrast as well as artifact intensity was noted (all p < 0.001). In qualitative assessment, utilization of 120 keV monoenergetic reconstructions could reduce severe and pronounced artifacts completely, as compared to lower keV reconstructions (p < 0.001). Regarding imaging findings, no significant difference between monoenergetic reconstructions was noted (all p > 0.05). In cases with very high iodine concentrations in the subclavian vein, image distortions were noted at 40 keV images (p < 0.01). ConclusionsThe present study demonstrates that PCCT derived spectral reconstructions can be used in oncological imaging of the thorax to reduce port-derived beam-hardening artefacts. When evaluating image data sets within a staging, it can be particularly helpful to consider the 120 keV VMIs, in which the artefacts are comparatively low.

Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography.

In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of medical images has rapidly increased during the past decade, and the main focus has been on oncological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity. The aim of our study was to evaluate an automatic machine learning model for AP detection using radiomics analysis. Patients with abdominal pain and contrast-enhanced CT of the abdomen in an emergency setting were retrospectively included in this single-center study. The pancreas was automatically segmented using TotalSegmentator and radiomics features were extracted using PyRadiomics. We performed unsupervised hierarchical clustering and applied the random-forest based Boruta model to select the most important radiomics features. Important features and lipase levels were included in a logistic regression model with AP as the dependent variable. The model was established in a training cohort using fivefold cross-validation and applied to the test cohort (80/20 split). From a total of 1012 patients, 137 patients with AP and 138 patients without AP were included in the final study cohort. Feature selection confirmed 28 important features (mainly shape and first-order features) for the differentiation between AP and controls. The logistic regression model showed excellent diagnostic accuracy of radiomics features for the detection of AP, with an area under the curve (AUC) of 0.932. Using lipase levels only, an AUC of 0.946 was observed. Using both radiomics features and lipase levels, we showed an excellent AUC of 0.933 for the detection of AP. Automated segmentation of the pancreas and consecutive radiomics analysis almost achieved the high diagnostic accuracy of lipase levels, a well-established predictor of AP, and might be considered an additional diagnostic tool in unclear cases. This study provides scientific evidence that automated image analysis of the pancreas achieves comparable diagnostic accuracy to lipase levels and might therefore be used in the future in the rapidly growing era of AI-based image analysis.

Imaging of Perirenal and Intrarenal Lymphatic Vessels: Anatomy-based Approach.

The lymphatic system (or lymphatics) consists of lymphoid organs and lymphatic vessels. Despite the numerous previously published studies describing conditions related to perirenal and intrarenal lymphoid organs in the radiology literature, the radiologic findings of conditions related to intrarenal and perirenal lymphatic vessels have been scarcely reported. In the renal cortex, interlobular lymphatic capillaries do not have valves; therefore, lymph can travel along the primary route toward the hilum, as well as toward the capsular lymphatic plexus. These two lymphatic pathways can be opacified by contrast medium via pyelolymphatic backflow at CT urography, which reflects urinary contrast agent leakage into perirenal lymphatic vessels via forniceal rupture. Pyelolymphatic backflow toward the renal hilum should be distinguished from urinary leakage due to urinary injury. Delayed subcapsular contrast material retention via pyelolymphatic backflow, appearing as hyperattenuating subcapsular foci on CT images, mimics other subcapsular cystic diseases. In contrast to renal parapelvic cysts originating from the renal parenchyma, renal peripelvic cysts are known to be of lymphatic origin. Congenital renal lymphangiectasia is mainly seen in children and assessed and followed up at imaging. Several lymphatic conditions, including lymphatic leakage as an early complication and acquired renal lymphangiectasia as a late complication, are sometimes identified at imaging follow-up of kidney transplant. Lymphangiographic contrast material accumulation in the renal hilar lymphatic vessels is characteristic of chylo-urinary fistula. Chyluria appears as a fat-layering fluid-fluid level in the urinary bladder or upper urinary tract. Recognition of the anatomic pathway of tumor spread via lymphatic vessels at imaging is of clinical importance for accurate management at oncologic imaging. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study

The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.

Multimodal Imaging in Oncology: Challenges and Future Directions

Multimodal imaging, a vital tool in oncology, combines various imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) to offer valuable insights for tumor detection, diagnosis, staging, and treatment planning. Despite its advantages, challenges persist in image registration, fusion, optimization of algorithms, hardware advancements, and the integration of artificial intelligence (AI) and computer vision techniques. This paper delves into the current state of multimodal imaging in oncology, addresses the challenges faced, explores potential solutions, and underscores future developments to augment diagnostic and therapeutic capacities, including the application of AI, computer vision, and big data analytics to enhance the efficiency and effectiveness of multimodal imaging in cancer diagnosis and treatment. Additionally, it highlights how AI-powered algorithms can automate image analysis, assist in tumor segmentation, and provide predictive insights, ultimately improving the precision and speed of diagnosis and treatment planning in oncology.

Dual-Energy CT in Oncologic Imaging.

Dual-energy CT (DECT) is an innovative technology that is increasingly widespread in clinical practice. DECT allows for tissue characterization beyond that of conventional CT as imaging is performed using different energy spectra that can help differentiate tissues based on their specific attenuation properties at different X-ray energies. The most employed post-processing applications of DECT include virtual monoenergetic images (VMIs), iodine density maps, virtual non-contrast images (VNC), and virtual non-calcium (VNCa) for bone marrow edema (BME) detection. The diverse array of images obtained through DECT acquisitions offers numerous benefits, including enhanced lesion detection and characterization, precise determination of material composition, decreased iodine dose, and reduced artifacts. These versatile applications play an increasingly significant role in tumor assessment and oncologic imaging, encompassing the diagnosis of primary tumors, local and metastatic staging, post-therapy evaluation, and complication management. This article provides a comprehensive review of the principal applications and post-processing techniques of DECT, with a specific focus on its utility in managing oncologic patients.

Applications of PET/MRI in prostate cancer

The advantages of both PET and MRI are combined with PET/MRI to provide near-perfect picture co-registration and simultaneous image capture. Prostate cancer and other pelvic oncologic lesions are increasingly being staged and restaged using magnetic resonance imaging (MRI). Long regarded as the cornerstone of oncologic imaging, fluorine 18-fluorodeoxyglucose PET/CT is now being studied and used in clinical practice more often due to the emergence of several tailored radiotracers. Therefore, PET/MRI performed concurrently offers a number of advantages over PET/CT and MR image collections, including the ability to obtain complementary imaging data. An overview of PET/MRI is given by the author, along with a discussion of the key distinctions between PET/MRI and PET/CT, case studies, and treatment guidelines for patients with common prostate cancers.

Development of the quantitative PET prostate phantom (Q3P) for improved quality assurance of 18F-PSMA PET imaging in metastatic prostate cancer.

Phantoms are commonly used to evaluate and compare the performance of imaging systems given the known ground truth. Positron emission tomography (PET) scanners are routinely validated using the NEMA image quality phantom, in which lesions are modeled using 10 to 37mm fillable spheres. The NEMA phantom neglects, however, to model focal (3-10-mm), high-uptake lesions that are increasingly observed in prostate-specific membrane antigen (PSMA) PET images. PSMA-targeting radiopharmaceuticals allow for enhanced detection of metastatic prostate cancers. As such, there is significant need to develop an updated phantom which considers both the quantitative and lesion detectability of this new paradigm in oncological PET imaging. In this work, we present the Quantitative PET Prostate Phantom (Q3P); a portable and modular phantom that can be used to improve and harmonize imaging protocols for 18F-PSMA PET scans. A one-piece cylindrical phantom was designed effectively in two halves, which we call modules. Module 1 was designed to mimic lesions in the presence of background, and Module 2 mimicked very high contrast conditions (i.e., very low background) that can be observed in 18F-PSMA PET scans. Shell-less radioactive spheres (3-16-mm) were cast using epoxy resin mixed with sodium-22 (22Na), a long half-life positron emitter with positron range similar to 18F. To establish realistic lesion contrast, the 22Na spheres were mounted in a cylindrical chamber that can be filled with an 18F background (module 1). Thirteen exchangeable spherical cavity inserts (3-37-mm) were machined in two parts and solvent welded together, and filled with 18F (50 kBq/mL) to model lesions with very high contrast (module 2). Five 2.5-min PET scans were acquired on a 5-ring GE Discovery MI PET/CT scanner (General Electric, USA). Lesions were segmented using 41% of SUVmax fixed thresholding (41% FT) and recovery coefficients (RCs) were computed from 5 noise realizations. The manufactured phantom is portable (5.7kg) and scan preparation takes less than 40min. The total 22Na activity is 250 kBq, allowing it to be shipped as an exempt package under International Atomic Energy Agency (IAEA) regulations. Recovery coefficients, computed using PSF modeling and no post-reconstruction smoothing, were 130.3% (16mm), 147.1% (10mm), 87.2% (6mm), and 7.0% (3mm) for RCmax, which decreased to 91.1% (16mm), 90.6% (10mm), 53.2% (6mm), and 3.6% (3mm) for RCmean in the 22Na spheres. Comparatively, 18F sphere recovery was 110.7% (17mm), 123.6% (10mm), 106.5% (7mm), and 23.3% (3mm) for RCmax, which was reduced to 76.7% (17mm), 77.7% (10mm), 66.8% (7mm), and 13.5% (3mm), for RCmean. A standardized imaging phantom was developed for lesion quantification assessment in 18F-PSMA PET images. The phantom is configurable, providing users with the opportunity to modify background activity levels or sphere sizes according to clinical demands. Distributed to the community, the Q3P phantom has the potential to enable better assessment of lesion quantification and harmonization of 18F-PSMA PET imaging, which may lead to more robust predictive metrics and better outcome prediction in metastatic prostate cancer.

Diagnostic and Therapeutic Application of Fibroblast Activation Protein Inhibitors in Oncologic and Nononcologic Diseases.

Fibroblast activation protein inhibitor positron emission tomography (PET) has gained interest for its ability to demonstrate uptake in a diverse range of tumors. Its molecular target, fibroblast activation protein, is expressed in cancer-associated fibroblasts, a major cell type in tumor microenvironment that surrounds various types of cancers. Although existing literature on FAPI PET is largely from single-center studies and case reports, initial findings show promise for some cancer types demonstrating improved imaging when compared with the widely used 18F-fludeoxyglucose PET for oncologic imaging. As we expand our knowledge of the utility of FAPI PET, accurate understanding of noncancerous uptake seen on FAPI PET is crucial for accurate evaluation. In this review, we summarize potential diagnostic and therapeutic applications of radiolabeled FAP inhibitors in oncological and nononcological disease processes.

Evaluation of organ dose, effective dose and cancer risk of head and neck dual-energy computed tomography

PurposeDual-energy computed tomography (DECT) is an emerging imaging modality with the aim of evaluating oncologic or vascular imaging diseases. Despite the benefits, the growing trend of DECT scans in recent decades is a major concerns regarding the induction of diseases such as cancer due to the use of a second radiation source. So, this survey was conducted to evaluate radiation dose and estimated the cancer risk of head and neck DECT examination.Methods: This investigation was performed on 122 patients who underwent head and neck DECT examination. The organ doses and effective doses for each patient were estimated using the ImpactDose program. The cancer risk values were calculated by using models developed in the BEIR VII (Biological Effects of Ionizing Radiation VII)-Phase 2 report. ResultsThe highest organ dose in both females and males were observed in thyroid. The overall mean effective dose in the females was higher than that in the males (1.78 vs. 1.36 mSv). The mean REID values were 55.07 per million males and 120.66 per million females. REID values decreased with the increasing age of patients in both genders and were much higher for younger females. ConclusionThe findings of this study will help referring physicians to justify requesting a DECT examination by considering its benefits for the diagnosis on the one hand and awareness of cancer risk on the other.

Clinical application of 99mTc‐labeled peptides for tumor imaging: Current status and future directions

AbstractTargeting receptors overexpressed on cancer cells with radiolabeled peptides has become a crucial aspect of molecular imaging in oncology. Small peptides offer favorable characteristics for tumor targeting with minimal side effects and toxicity owing to their small size and simple radiolabeling protocols. Among them, somatostatin analogs have received regulatory approval for the diagnosis and treatment of neuroendocrine tumors. Cyclic RGD (Arg‐Gly‐Asp) peptides, bombesin analogs, and glucagon‐like peptide‐1 analogs are currently under development and/or undergoing clinical trials. The most used radionuclides for tumor imaging include 99mTc and 111In for single‐photon emission computed tomography, 68Ga and 18F for positron emission tomography. This review highlights the clinical potential and future prospects of 99mTc‐labeled peptides for tumor imaging.

Perceptions of radiologists on structured reporting for cancer imaging—a survey by the European Society of Oncologic Imaging (ESOI)

ObjectivesTo assess radiologists’ current use of, and opinions on, structured reporting (SR) in oncologic imaging, and to provide recommendations for a structured report template.Materials and methodsAn online survey with 28 questions was sent to European Society of Oncologic Imaging (ESOI) members. The questionnaire had four main parts: (1) participant information, e.g., country, workplace, experience, and current SR use; (2) SR design, e.g., numbers of sections and fields, and template use; (3) clinical impact of SR, e.g., on report quality and length, workload, and communication with clinicians; and (4) preferences for an oncology-focused structured CT report. Data analysis comprised descriptive statistics, chi-square tests, and Spearman correlation coefficients.ResultsA total of 200 radiologists from 51 countries completed the survey: 57.0% currently utilized SR (57%), with a lower proportion within than outside of Europe (51.0 vs. 72.7%; p = 0.006). Among SR users, the majority observed markedly increased report quality (62.3%) and easier comparison to previous exams (53.5%), a slightly lower error rate (50.9%), and fewer calls/emails by clinicians (78.9%) due to SR. The perceived impact of SR on communication with clinicians (i.e., frequency of calls/emails) differed with radiologists’ experience (p < 0.001), and experience also showed low but significant correlations with communication with clinicians (r = − 0.27, p = 0.003), report quality (r = 0.19, p = 0.043), and error rate (r = − 0.22, p = 0.016). Template use also affected the perceived impact of SR on report quality (p = 0.036).ConclusionRadiologists regard SR in oncologic imaging favorably, with perceived positive effects on report quality, error rate, comparison of serial exams, and communication with clinicians.Clinical relevance statementRadiologists believe that structured reporting in oncologic imaging improves report quality, decreases the error rate, and enables better communication with clinicians. Implementation of structured reporting in Europe is currently below the international level and needs society endorsement.Key Points• The majority of oncologic imaging specialists (57% overall; 51% in Europe) use structured reporting in clinical practice.• The vast majority of oncologic imaging specialists use templates (92.1%), which are typically cancer-specific (76.2%).• Structured reporting is perceived to markedly improve report quality, communication with clinicians, and comparison to prior scans.