The Diagnostic Performance of 2-[18F]FDG PET/CT in Identifying Richter Transformation in Chronic Lymphocytic Leukemia: An Updated Systematic Review and Bivariate Meta-Analysis.

Richter transformation is a rare phenomenon characterized by the transformation of cell chronic lymphocytic leukemia (CLL) into a more aggressive lymphoma variant. The early identification of CLLs with a high risk of RT is fundamental. In this field, 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography/computed tomography (2-[18F]FDG PET/CT) has been shown to be a non-invasive and promising tool, but apparently, unclear data seem to be present in the literature. This systematic review and bivariate meta-analysis aimed to investigate the diagnostic performance of 2-[18F]FDG PET/CT and its parameters in predicting RT. Between 2006 and 2024, 15 studies were published on this topic, including 1593 CLL patients. Among semiquantitative variables, SUVmax was the most investigated, and the best threshold derived for detecting RT was five. With this cut-off value, a pooled sensitivity of 86.8% (95% CI: 78.5-93.3), a pooled specificity of 48.1% (95% CI: 27-69.9), a pooled negative predictive value of 90.5% (95% CI: 88.4-92.4), a pooled negative likelihood ratio of 0.35 (95% CI: 0.17-0.70), a pooled positive likelihood ratio of 1.8 (95% CI: 1.3-2.4), and a pooled diagnostic odds ratio of 6.7 (3.5-12.5) were obtained. With a higher cut-off (SUVmax = 10), the specificity increased while the sensitivity reduced. The other metabolic features, like metabolic tumor volume, total lesion glycolysis, and radiomic features, were only marginally investigated with controversial evidence.

Artificial intelligence in interventional radiology: state of the art.

Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.Relevance statement Exploring AI's transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.Key points• AI adoption in IR is more complex compared to diagnostic radiology.• Current literature about AI in IR is in its early stages.• AI has the potential to revolutionise every aspect of IR.

High incidence of RAMP lesions and a nonnegligible incidence of anterolateral ligament and posterior oblique ligament rupture in acute ACL injury.

PURPOSE: The purpose of this study was to assess the frequency of medial collateral ligament (MCL), posterior oblique ligament (POL) and anterolateral ligament (ALL) tears and different types of RAMP lesions of patients with verified acute anterior cruciate ligament (ACL) tears by magnetic resonance imaging (MRI). METHODS: MRI was performed on patients with a clinical diagnosis of acute ACL injury. Patients were eligible for inclusion if they had an initially clinically noted ACL tear confirmed on MRI within 30 days of trauma. RESULTS: A total of 146 patients were included in the study, 42 (28.8%) females and 104 (71.2%) males. The mean age at MRI was 27.2 ± 9.4 years, and the mean time from injury to MRI was 15.7 ± 7.8 days. Thirty-four (23.3%) patients had a complete MCL lesion, 32 (21.9%) had a complete POL lesion and 28 (19.2%) had a complete ALL lesion. One hundred and fourteen patients (78.1%) presented with RAMP lesions, while 20 (13.7%) patients reported other meniscal lesions. The mean medial and lateral tibial slopes were 4.0° ± 2.7° and 4.0° ± 3.1°, respectively. Only 10 (6.8%) patients reported no lesions associated with ACL rupture. The most common injuries were isolated RAMP type 3 (18-12.3%) and isolated RAMP type 1 (17-11.6%). Thirteen (8.9%) patients had a combination of MCL, POL and ALL rupture. CONCLUSIONS: Isolated lesions of the ACL are extremely rare. In most cases, a single RAMP lesion should be investigated. In the presence of MCL injury, POL injury should always be suspected as well, while nearly 20% of patients present a rupture of the ALL. About one in 10 patients had three lesions (MCL, ALL and POL), and most of them had a combined RAMP lesion. LEVEL OF EVIDENCE: Level IV.

Diagnostic yield of FAP-guided positron emission tomography in thyroid cancer: a systematic review.

Background: Several recent studies have proposed the possible application of positron emission tomography/computed tomography (PET/CT) administering radiolabelled fibroblast-activation protein (FAP) inhibitors for various forms of thyroid cancer (TC), including differentiated TC (DTC), and medullary TC (MTC). Methods: The authors conducted an extensive literature search of original studies examining the effectiveness of FAP-guided PET/CT in patients with TC. The papers included were original publications exploring the use of FAP-targeted molecular imaging in restaging metastatic DTC and MTC patients. Results: A total of 6 studies concerning the diagnostic yield of FAP-targeted PET/CT in TC (274 patients, of which 247 DTC and 27 MTC) were included in this systematic review. The included articles reported high values of FAP-targeted PET/CT detection rates in TC, ranging from 81 to 100% in different anatomical sites and overall superior to the comparative imaging method. Conclusion: Although there are promising results, the existing literature on the diagnostic accuracy of FAP-guided PET in this context is still quite limited. To thoroughly evaluate its potential significance in TC patients, it is needed to conduct prospective randomized multicentric trials.

The role of baseline 2-[18 F]-FDG-PET/CT metrics and radiomics features in predicting primary gastric lymphoma diagnosis.

Diffuse Large B-Cell Lymphomas (DLCBL) and mucosa-associated lymphoid tissue (MALT) are the two most common primary gastric lymphomas (PGLs), but have strongly different features. DLBCL is more aggressive, is frequently diagnosed at an advanced stage and has a poorer prognosis. The aim of this retrospective study was to explore the role of fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (2-[18 F]-FDG-PET/CT) and radiomics features (RFs) in predicting the final diagnosis of patients with PGLs. Ninety-one patients with newly diagnosed PGLs who underwent pre-treatment 2-[18 F]-FDG-PET/CT were included. PET images were qualitatively and semi-quantitatively analyzed by deriving maximum standardized uptake value body weight (SUVbw), maximum standardized uptake value lean body mass (SUVlbm), maximum standardized uptake value body surface area (SUVbsa), lesion to liver SUVmax ratio (L-L SUV R), lesion to blood-pool SUVmax ratio (L-BP SUV R), metabolic tumor volume (gMTV) and total lesion glycolysis of gastric lesion (gTLG), total MTV (tMTV), TLG, and first-order RFs (histogram-related and shape related). Receiver-operating characteristic (ROC) curve analyses were performed to determine the differential diagnostic values of PET parameters. The final diagnosis was DLBCL in 54 (59%) cases and MALT in 37 cases (41%). PGLs showed FDG avidity in 83 cases (90%), 54/54 of DLBCL and 29/37 of MALT. All PET/CT metabolic features, such as stage of disease and tumor size, were significantly higher in DLBCL than MALT; while the presence of H. Pylori infection was more common in MALT. At univariate analysis, all PET/CT metrics were significantly higher in DLBCL than MALT lymphomas, while among RFs only Shape volume_vx and Shape sphericity showed a significant difference between the two groups. In conclusion we demonstrated that 2-[18 F]-FDG-PET/CT parameters can potentially discriminate between DLBCL and MALT lymphomas with high accuracy. Among first-order RFs, only Shape volume_vx and Shape sphericity helped in the differential diagnosis.

X-rays radiomics-based machine learning classification of atypical cartilaginous tumour and high-grade chondrosarcoma of long bones.

BACKGROUND: Atypical cartilaginous tumour (ACT) and high-grade chondrosarcoma (CS) of long bones are respectively managed with active surveillance or curettage and wide resection. Our aim was to determine diagnostic performance of X-rays radiomics-based machine learning for classification of ACT and high-grade CS of long bones. METHODS: This retrospective, IRB-approved study included 150 patients with surgically treated and histology-proven lesions at two tertiary bone sarcoma centres. At centre 1, the dataset was split into training (n = 71 ACT, n = 24 high-grade CS) and internal test (n = 19 ACT, n = 6 high-grade CS) cohorts, respectively, based on the date of surgery. At centre 2, the dataset constituted the external test cohort (n = 12 ACT, n = 18 high-grade CS). Manual segmentation was performed on frontal view X-rays, using MRI or CT for preliminary identification of lesion margins. After image pre-processing, radiomic features were extracted. Dimensionality reduction included stability, coefficient of variation, and mutual information analyses. In the training cohort, after class balancing, a machine learning classifier (Support Vector Machine) was automatically tuned using nested 10-fold cross-validation. Then, it was tested on both the test cohorts and compared to two musculoskeletal radiologists' performance using McNemar's test. FINDINGS: Five radiomic features (3 morphology, 2 texture) passed dimensionality reduction. After tuning on the training cohort (AUC = 0.75), the classifier had 80%, 83%, 79% and 80%, 89%, 67% accuracy, sensitivity, and specificity in the internal (temporally independent) and external (geographically independent) test cohorts, respectively, with no difference compared to the radiologists (p ≥ 0.617). INTERPRETATION: X-rays radiomics-based machine learning accurately differentiates between ACT and high-grade CS of long bones. FUNDING: AIRC Investigator Grant.

CT and MRI radiomics of bone and soft-tissue sarcomas: an updated systematic review of reproducibility and validation strategies.

OBJECTIVE: To systematically review radiomic feature reproducibility and model validation strategies in recent studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas, thus updating a previous version of this review which included studies published up to 2020. METHODS: A literature search was conducted on EMBASE and PubMed databases for papers published between January 2021 and March 2023. Data regarding radiomic feature reproducibility and model validation strategies were extracted and analyzed. RESULTS: Out of 201 identified papers, 55 were included. They dealt with radiomics of bone (n = 23) or soft-tissue (n = 32) tumors. Thirty-two (out of 54 employing manual or semiautomatic segmentation, 59%) studies included a feature reproducibility analysis. Reproducibility was assessed based on intra/interobserver segmentation variability in 30 (55%) and geometrical transformations of the region of interest in 2 (4%) studies. At least one machine learning validation technique was used for model development in 34 (62%) papers, and K-fold cross-validation was employed most frequently. A clinical validation of the model was reported in 38 (69%) papers. It was performed using a separate dataset from the primary institution (internal test) in 22 (40%), an independent dataset from another institution (external test) in 14 (25%) and both in 2 (4%) studies. CONCLUSIONS: Compared to papers published up to 2020, a clear improvement was noted with almost double publications reporting methodological aspects related to reproducibility and validation. Larger multicenter investigations including external clinical validation and the publication of databases in open-access repositories could further improve methodology and bring radiomics from a research area to the clinical stage. CRITICAL RELEVANCE STATEMENT: An improvement in feature reproducibility and model validation strategies has been shown in this updated systematic review on radiomics of bone and soft-tissue sarcomas, highlighting efforts to enhance methodology and bring radiomics from a research area to the clinical stage. KEY POINTS: • 2021-2023 radiomic studies on CT and MRI of musculoskeletal sarcomas were reviewed. • Feature reproducibility was assessed in more than half (59%) of the studies. • Model clinical validation was performed in 69% of the studies. • Internal (44%) and/or external (29%) test datasets were employed for clinical validation.

2-[18F]-FDG PET/CT Semiquantitative and Radiomics Predictive Parameters of Richter's Transformation in CLL Patients.

Background and Objectives: Chronic lymphocytic leukemia (CLL) is the most common type of leukemia in developed countries, which can evolve into aggressive lymphoma variants, a process called Richter transformation (RT). The aim of this retrospective study was to analyze the role of 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography (2-[18F]-FDG PET/CT) and its semiquantitative and radiomics features in detecting RT and evaluate the impact on overall survival (OS). Materials and Methods: One hundred and thirty-seven patients with histologically proven CLL were retrospectively recruited. PET/CT images were qualitatively and semiquantitatively examined by estimating the main metabolic parameters (the maximum standardized uptake value body weight (SUVbw), lean body mass (SUVlbm), body surface area (SUVbsa), lesion-to-blood-pool SUV ratio (L-BP SUV R), lesion-to-liver SUV ratio (L-L SUV R), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) and radiomics first- and second- order variables of the lesion with highest uptake. The role of these parameters in predicting RT and OS was analyzed. Results: One hundred and thirty (95%) PET/CT scans were positive, showing an increased tracer uptake at the site of disease, whereas the remaining 7 (5%) scans were negative. SUVbw, SUVlbm, SUVbsa, L-L SUV ratio, and L-BP SUV ratio were significantly higher in the RT group (p < 0.001 in all cases). Radiomics first- and second-order features were not significantly associated with RT. After a median follow-up of 44 months, 56 patients died; OS was significantly shorter in patients with RT than patients without RT (28 vs. 34 months; p = 0.002). Binet-stage, RT, and L-BP SUV R were shown to be independent prognostic features. Conclusions: Semiquantitative PET/CT parameters such as SUVbw, SUVlbm, SUVbsa, L-L SUV ratio and L-BP SUV ratio may be useful in discriminating patients with a high risk of developing RT, whereas Binet-stage, RT, and L-BP SUV R are also significant in predicting OS.

2-[18]F FDG PET/CT dissemination features in adult burkitt lymphoma Are predictive of outcome.

This retrospective study investigated the prognostic role of disease dissemination features (Dmax and Dmaxbsa) measured by 2-[18F]FDG PET/CT in newly diagnosed Burkitt Lymphoma (BL) patients, comparing their performance with other metabolic parameters. We included 78 patients diagnosed with BL between 2010 and 2022 with an available baseline PET, interim PET/CT (iPET) and end of treatment PET/CT (eotPET) and with a minimum of two 2-[18F]FDG avid lesions present at the baseline scan. Dmax was calculated from the three-dimensional coordinates of the baseline metabolic tumor volume (MTV) by using LIFEx software; Dmaxbsa was calculated as Dmax normalized for body surface area according to the Du Bois method. We evaluated their effect on metabolic treatment response evaluated by PET, on progression free survival (PFS) and on overall survival (OS). Dmaxbsa was significantly associated with tumor stage, bulky and extranodal disease, MTV and TLG. At a median follow-up of 49 months, the median PFS and OS were 45 and 48 months. Dmax and Dmaxbsa were significantly higher in not complete metabolic response than complete metabolic response group at iPET and eotPET.As far as PFS, parameters including iPET/CT, eotPET/CT outcomes, MTV and TLG showed to be independent prognostic factors while Dmax and Dmaxbsa were not significantly associated with the outcome. Dissemination features, together with eotPET/CT results, MTV and TLG, demonstrated to be significantly correlated with OS. In conclusion, in this study we demonstrated that dissemination features derived by 2[18F]-FDG PET/CT were significantly correlated with response to treatment and long-term outcome, independently from other PET features.

AI applications in musculoskeletal imaging: a narrative review.

This narrative review focuses on clinical applications of artificial intelligence (AI) in musculoskeletal imaging. A range of musculoskeletal disorders are discussed using a clinical-based approach, including trauma, bone age estimation, osteoarthritis, bone and soft-tissue tumors, and orthopedic implant-related pathology. Several AI algorithms have been applied to fracture detection and classification, which are potentially helpful tools for radiologists and clinicians. In bone age assessment, AI methods have been applied to assist radiologists by automatizing workflow, thus reducing workload and inter-observer variability. AI may potentially aid radiologists in identifying and grading abnormal findings of osteoarthritis as well as predicting the onset or progression of this disease. Either alone or combined with radiomics, AI algorithms may potentially improve diagnosis and outcome prediction of bone and soft-tissue tumors. Finally, information regarding appropriate positioning of orthopedic implants and related complications may be obtained using AI algorithms. In conclusion, rather than replacing radiologists, the use of AI should instead help them to optimize workflow, augment diagnostic performance, and keep up with ever-increasing workload.Relevance statement This narrative review provides an overview of AI applications in musculoskeletal imaging. As the number of AI technologies continues to increase, it will be crucial for radiologists to play a role in their selection and application as well as to fully understand their potential value in clinical practice. Key points • AI may potentially assist musculoskeletal radiologists in several interpretative tasks.• AI applications to trauma, age estimation, osteoarthritis, tumors, and orthopedic implants are discussed.• AI should help radiologists to optimize workflow and augment diagnostic performance.

Effects of Interobserver Segmentation Variability and Intensity Discretization on MRI-Based Radiomic Feature Reproducibility of Lipoma and Atypical Lipomatous Tumor.

Segmentation and image intensity discretization impact on radiomics workflow. The aim of this study is to investigate the influence of interobserver segmentation variability and intensity discretization methods on the reproducibility of MRI-based radiomic features in lipoma and atypical lipomatous tumor (ALT). Thirty patients with lipoma or ALT were retrospectively included. Three readers independently performed manual contour-focused segmentation on T1-weighted and T2-weighted sequences, including the whole tumor volume. Additionally, a marginal erosion was applied to segmentations to evaluate its influence on feature reproducibility. After image pre-processing, with included intensity discretization employing both fixed bin number and width approaches, 1106 radiomic features were extracted from each sequence. Intraclass correlation coefficient (ICC) 95% confidence interval lower bound ≥ 0.75 defined feature stability. In contour-focused vs. margin shrinkage segmentation, the rates of stable features extracted from T1-weighted and T2-weighted images ranged from 92.68 to 95.21% vs. 90.69 to 95.66% after fixed bin number discretization and from 95.75 to 97.65% vs. 95.39 to 96.47% after fixed bin width discretization, respectively, with no difference between the two segmentation approaches (p ≥ 0.175). Higher stable feature rates and higher feature ICC values were found when implementing discretization with fixed bin width compared to fixed bin number, regardless of the segmentation approach (p < 0.001). In conclusion, MRI radiomic features of lipoma and ALT are reproducible regardless of the segmentation approach and intensity discretization method, although a certain degree of interobserver variability highlights the need for a preliminary reliability analysis in future studies.

The evolution of PET imaging in renal, bladder, upper urinary tract urothelial, testicular and penile carcinoma - Today's impact, tomorrow's potential.

INTRODUCTION: The advancement of hybrid PET/CT or PET/MRI imaging for non-prostate genitourinary cancers has not experienced the rapid progress of prostate cancer. Nevertheless, these neoplasms are aggressive and reliable imaging plays a pivotal role in enhancing patients' quality of life and prognosis. AREAS COVERED: the main evidence regarding [18F]FDG and non-[18F]FDG PET/CT or PET/MRI in non-prostate uro-oncological malignancies are summarized and discussed. Moreover, potential future directions concerning PET imaging in these neoplasms are debated, with the aim to stimulate future research projects covering these fields. EXPERT OPINION: In Renal Cell Carcinoma (RCC), [18F]FDG PET/CT demonstrates varying efficacy in staging, restaging, and prognostic stratification, but PSMA PET/CT is emerging as a potential game-changer, particularly in advanced, high-grade aggressive clear cell RCC. [18F]FDG PET/CT may see an increased use in N and M-staging of bladder cancer, as well as for detecting recurrence and response to neoadjuvant chemotherapy. Preliminary data regarding [68Ga]-FAPI also looks promising in this context. [18F]FDG PET/MRI could be useful for the T-staging of bladder cancer, while upper tract urothelial carcinoma still lacks of molecular imaging literature reports. In testicular and penile cancer [18F]FDG PET/CT has demonstrated its usefulness in several clinical settings, although experiences with non-[18F]FDG radiotracers are lacking.

Does it work in childhood and adolescence? The predictive role of postoperative/preablative stimulated thyroglobulin levels in paediatric thyroid cancer. A systematic review of the literature.

BACKGROUND: Thyroglobulin is a well-established disease marker during follow-up in paediatric differentiated thyroid cancer. However, no conclusive data on the role of endogenously stimulated thyroglobulin after thyroidectomy (ptTg) in predicting disease-specific outcomes are available. This review aims to establish the prognostic value of ptTg in children with DTC. METHODS: Online medical databases were searched for studies evaluating the association between ptTg and disease-specific outcomes in DTC-affected children. Documents not in English, preclinical studies, other review articles, case reports, and small case series were excluded. The risk of bias was assessed with the QUADAS-2 tool. RESULTS: Twelve studies, analysing 1043 children in total, were included in the review. They all had a retrospective design and were published between 2016 and 2022. Of all patients, 1008 (97%) and 849 (81%) had undergone thyroidectomy and RAI, respectively. Eight studies (756 children) evaluated the correlation between ptTg and disease persistence/relapse: six reported a significant association between these parameters; a specific ptTg cut-off (10-14 ng/ml) was identified at the multivariate analysis in three studies. The remaining four studies assessed the link between ptTg levels and disease extension, with three reporting a correlation between ptTg and lung/nodal metastases. DISCUSSION: ptTg is a readily available and inexpensive parameter, bearing a strong prognostic power in identifying disease persistence, relapse, and the presence of metastases in children affected by DTC.

Application of radiomics and machine learning to thyroid diseases in nuclear medicine: a systematic review.

BACKGROUND: In the last years growing evidences on the role of radiomics and machine learning (ML) applied to different nuclear medicine imaging modalities for the assessment of thyroid diseases are starting to emerge. The aim of this systematic review was therefore to analyze the diagnostic performances of these technologies in this setting. METHODS: A wide literature search of the PubMed/MEDLINE, Scopus and Web of Science databases was made in order to find relevant published articles about the role of radiomics or ML on nuclear medicine imaging for the evaluation of different thyroid diseases. RESULTS: Seventeen studies were included in the systematic review. Radiomics and ML were applied for assessment of thyroid incidentalomas at 18 F-FDG PET, evaluation of cytologically indeterminate thyroid nodules, assessment of thyroid cancer and classification of thyroid diseases using nuclear medicine techniques. CONCLUSION: Despite some intrinsic limitations of radiomics and ML may have affect the results of this review, these technologies seem to have a promising role in the assessment of thyroid diseases. Validation of preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting.

Diagnostic Accuracy of PET with 18F-Fluciclovine ([18F]FACBC) in Detecting High-Grade Gliomas: A Systematic Review and Meta-Analysis.

BACKGROUND: 18F-Fluciclovine ([18F]FACBC) has been recently proposed as a synthetic radiolabeled amino acid for positron emission tomography (PET) imaging in patients with brain neoplasms. Our aim is to evaluate the diagnostic performance of [18F]FACBC PET in high-grade glioma (HGG) patients, taking into account the literature data. METHODS: A comprehensive literature search was performed. We included original articles evaluating [18F]FACBC PET in the detection of HGG before therapy and for the suspicion of tumor recurrence. Pooled sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-), and diagnostic odds ratios (DOR), including 95% confidence intervals (95% CI), were measured. Statistical heterogeneity and publication bias were also assessed. RESULTS: ten studies were included in the review and eight in the meta-analysis (113 patients). Regarding the identification of HGG, the sensitivity of [18F]FACBC PET ranged between 85.7% and 100%, with a pooled estimate of 92.9% (95% CI: 84.4-96.9%), while the specificity ranged from 50% to 100%, with a pooled estimate of 70.7% (95% CI: 47.5-86.5%). The pooled LR+, LR-, and DOR of [18F]FACBC PET were 2.5, 0.14, and 37, respectively. No significant statistical heterogeneity or publication bias were found. CONCLUSIONS: evidence-based data demonstrate the good diagnostic accuracy of [18F]FACBC PET for HGG detection. Due to the still limited data, further studies are warranted to confirm the promising role of [18F]FACBC PET in this context.

Positron Emission Tomography-Derived Radiomics and Artificial Intelligence in Multiple Myeloma: State-of-the-Art.

Multiple myeloma (MM) is a heterogeneous neoplasm accounting for the second most prevalent hematologic disorder. The identification of noninvasive, valuable biomarkers is of utmost importance for the best patient treatment selection, especially in heterogeneous diseases like MM. Despite molecular imaging with positron emission tomography (PET) has achieved a primary role in the characterization of MM, it is not free from shortcomings. In recent years, radiomics and artificial intelligence (AI), which includes machine learning (ML) and deep learning (DL) algorithms, have played an important role in mining additional information from medical images beyond human eyes' resolving power. Our review provides a summary of the current status of radiomics and AI in different clinical contexts of MM. A systematic search of PubMed, Web of Science, and Scopus was conducted, including all the articles published in English that explored radiomics and AI analyses of PET/CT images in MM. The initial results have highlighted the potential role of such new features in order to improve the clinical stratification of MM patients, as well as to increase their clinical benefits. However, more studies are warranted before these approaches can be implemented in clinical routines.

Diagnostic Accuracy of [68Ga]Ga Labeled Fibroblast-Activation Protein Inhibitors in Detecting Head and Neck Cancer Lesions Using Positron Emission Tomography: A Systematic Review and a Meta-Analysis.

Several studies have examined the use of positron emission tomography (PET) using [68Ga]Ga-radiolabeled fibroblast-activation protein inhibitors (FAPi) across multiple subtypes of head and neck cancer (HNC). The purpose of the present study was to evaluate the diagnostic accuracy of a newly developed molecular imaging approach in the context of HNC through a comprehensive review and meta-analysis. A thorough literature review was conducted to identify scholarly articles about the diagnostic effectiveness of FAP-targeted PET imaging. The present study incorporates original publications assessing the efficacy of this innovative molecular imaging test in both newly diagnosed and previously treated HNC patients. This systematic review examined eleven investigations, of which nine were deemed suitable for inclusion in the subsequent meta-analysis. The quantitative synthesis yielded a pooled detection rate of 99% for primary HNC lesions. Additionally, on a per patient-based analysis, the pooled sensitivity and specificity for regional lymph node metastases were found to be 90% and 84%, respectively. The analysis revealed a statistical heterogeneity among the studies for the detection rate of primary HNC lesions. The quantitative findings presented in this study indicate a favorable diagnostic performance of FAP-targeted PET imaging in detecting primary HNC tumors. In contrast, discordant results concerning the diagnostic accuracy of lymph node metastases were found. However, further multicentric trials are required to validate the efficacy of FAP-targeted PET in this specific group of patients.

Prevalence of Incidental Findings Suspicious for Transthyretin Cardiac Amyloidosis among Patients Undergoing Bone Scintigraphy: A Systematic Review and a Meta-Analysis.

BACKGROUND: The myocardial uptake of bone-seeking tracers suspicious for transthyretin cardiac amyloidosis (ATTR-CA) can be incidentally detected in patients undergoing bone scintigraphy for noncardiac reasons. We conducted a systematic review and meta-analysis to assess the prevalence of these scintigraphic findings. METHODS: A comprehensive literature search was performed using two bibliographic databases (PubMed/MEDLINE and Cochrane Library), searching for articles related to the review question. Eligible articles were selected, and relevant data were extracted by two authors. The pooled prevalence of incidental findings suspicious for ATTR-CA among patients undergoing bone scintigraphy was calculated on a per-patient-based analysis using a random-effects model. The pooled measure was provided with 95% confidence interval (95% CI) values. RESULTS: Among 219 records, 11 articles were selected for the systematic review and 10 for the meta-analysis. The pooled prevalence of incidental findings suspicious for ATTR-CA was 1.1% (95% CI: 0.7-1.4%) with heterogeneity due to the characteristics of the included studies, patients, and index tests. These findings are more prevalent in older men. CONCLUSIONS: The prevalence of incidental findings of ATTR-CA among patients undergoing bone scintigraphy is low but not negligible. Nuclear medicine physicians should suggest, in the scintigraphic report, further clinical investigations when these findings are detected. Prospective studies are warranted.