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.

Dual-energy CT in musculoskeletal imaging: technical considerations and clinical applications.

Dual-energy CT stands out as a robust and innovative imaging modality, which has shown impressive advancements and increasing applications in musculoskeletal imaging. It allows to obtain detailed images with novel insights that were once the exclusive prerogative of magnetic resonance imaging. Attenuation data obtained by using different energy spectra enable to provide unique information about tissue characterization in addition to the well-established strengths of CT in the evaluation of bony structures. To understand clearly the potential of this imaging modality, radiologists must be aware of the technical complexity of this imaging tool, the different ways to acquire images and the several algorithms that can be applied in daily clinical practice and for research. Concerning musculoskeletal imaging, dual-energy CT has gained more and more space for evaluating crystal arthropathy, bone marrow edema, and soft tissue structures, including tendons and ligaments. This article aims to analyze and discuss the role of dual-energy CT in musculoskeletal imaging, exploring technical aspects, applications and clinical implications and possible perspectives of this technique.

The Clinical Role of CXCR4-Targeted PET on Lymphoproliferative Disorders: A Systematic Review.

Background/Objectives: We conducted a comprehensive investigation to explore the pathological expression of the CXCR4 receptor in lymphoproliferative disorders (LPDs) using [68Ga]Ga-Pentixafor PET/CT or PET/MRI technology. The PICO question was as follows: What is the diagnostic role (outcome) of [68Ga]Ga-Pentixafor PET (intervention) in patients with LPDs (problem/population)? Methods: The study was written based on the reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, and it was registered on the prospective register of systematic reviews (PROSPERO) website (CRD42024506866). A comprehensive computer literature search of Scopus, MEDLINE, Scholar, and Embase databases was conducted, including articles indexed up to February 2024. To the methodological evaluation of the studies used the quality assessment of diagnosis accuracy studies-2 (QUADAS-2) tool. Results: Of the 8380 records discovered, 23 were suitable for systematic review. Fifteen studies (on 571 LPD patients) focused on diagnosis and staging, and eight trials (194 LPD patients) assessed treatment response. Conclusions: The main conclusions that can be inferred from the published studies are as follows: (a) [68Ga]Ga-Pentixafor PET may have excellent diagnostic performance in the study of several LPDs; (b) [68Ga]Ga-Pentixafor PET may be superior to [18F]FDG or complementary in some LPDs variants and settings; (c) multiple myeloma seems to have a high uptake of [68Ga]Ga-Pentixafor. Overall, this technique is probably suitable for imaging, staging, and follow-up on patients with LPD. Due to limited data, further studies are warranted to confirm the promising role of [68Ga]Ga-Pantixafor in this context.

Posteromedial Corner Injuries of the Knee: Imaging Findings.

The posteromedial corner (PMC) of the knee is an anatomical region formed by ligamentous structures (medial collateral ligament, posterior oblique ligament, oblique popliteal ligament), the semimembranosus tendon and its expansions, the posteromedial joint capsule, and the posterior horn of the medial meniscus. Injuries to the structures of the PMC frequently occur in acute knee trauma in association with other ligamentous or meniscal tears. The correct assessment of PMC injuries is crucial because the deficiency of these supporting structures can lead to anteromedial rotation instability or the failure of cruciate ligaments grafts. This article reviews the anatomy and biomechanics of the PMC to aid radiologists in identifying injuries potentially involving PMC components.

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.

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.

EANM consensus document on the use of [18F]FDG PET/CT in fever and inflammation of unknown origin.

PURPOSE: Patients with fever and inflammation of unknown origin (FUO/IUO) are clinically challenging due to variable clinical presentations with nonspecific symptoms and many differential diagnoses. Positron emission tomography/computed tomography (PET/CT) with 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) is increasingly used in FUO and IUO, but the optimal diagnostic strategy remains controversial. This consensus document aims to assist clinicians and nuclear medicine specialists in the appropriate use of [18F]FDG-PET/CT in FUO and IUO based on current evidence. METHODS: A working group created by the EANM infection and inflammation committee performed a systematic literature search based on PICOs with "patients with FUO/IUO" as population, "[18F]FDG-PET/CT" as intervention, and several outcomes including pre-scan characteristics, scan protocol, diagnostic yield, impact on management, prognosis, and cost-effectiveness. RESULTS: We included 68 articles published from 2001 to 2023: 9 systematic reviews, 49 original papers on general adult populations, and 10 original papers on specific populations. All papers were analysed and included in the evidence-based recommendations. CONCLUSION: FUO and IUO remains a clinical challenge and [18F]FDG PET/CT has a definite role in the diagnostic pathway with an overall diagnostic yield or helpfulness in 50-60% of patients. A positive scan is often contributory by directly guiding treatment or subsequent diagnostic procedure. However, a negative scan may be equally important by excluding focal disease and predicting a favorable prognosis. Similar results are obtained in specific populations such as ICU-patients, children and HIV-patients.

CT Arthrography of the Elbow: What Radiologists Should Know.

Computed tomography (CT) arthrography is a quickly available imaging modality to investigate elbow disorders. Its excellent spatial resolution enables the detection of subtle pathologic changes of intra-articular structures, which makes this technique extremely valuable in a joint with very tiny chondral layers and complex anatomy of articular capsule and ligaments. Radiation exposure has been widely decreased with the novel CT scanners, thereby increasing the indications of this examination. The main applications of CT arthrography of the elbow are the evaluation of capsule, ligaments, and osteochondral lesions in both the settings of acute trauma, degenerative changes, and chronic injury due to repeated microtrauma and overuse. In this review, we discuss the normal anatomic findings, technical tips for injection and image acquisition, and pathologic findings that can be encountered in CT arthrography of the elbow, shedding light on its role in the diagnosis and management of different orthopedic conditions. We aspire to offer a roadmap for the integration of elbow CT arthrography into routine clinical practice, fostering improved patient outcomes and a deeper understanding of elbow pathologies.

How young radiologists use contrast media and manage adverse reactions: an international survey.

OBJECTIVES: To collect real-world data about the knowledge and self-perception of young radiologists concerning the use of contrast media (CM) and the management of adverse drug reactions (ADR). METHODS: A survey (29 questions) was distributed to residents and board-certified radiologists younger than 40 years to investigate the current international situation in young radiology community regarding CM and ADRs. Descriptive statistics analysis was performed. RESULTS: Out of 454 respondents from 48 countries (mean age: 31.7 ± 4 years, range 25-39), 271 (59.7%) were radiology residents and 183 (40.3%) were board-certified radiologists. The majority (349, 76.5%) felt they were adequately informed regarding the use of CM. However, only 141 (31.1%) received specific training on the use of CM and 82 (18.1%) about management ADR during their residency. Although 266 (58.6%) knew safety protocols for handling ADR, 69.6% (316) lacked confidence in their ability to manage CM-induced ADRs and 95.8% (435) expressed a desire to enhance their understanding of CM use and handling of CM-induced ADRs. Nearly 300 respondents (297; 65.4%) were aware of the benefits of contrast-enhanced ultrasound, but 249 (54.8%) of participants did not perform it. The preferred CM injection strategy in CT parenchymal examination and CT angiography examination was based on patient's lean body weight in 318 (70.0%) and 160 (35.2%), a predeterminate fixed amount in 79 (17.4%) and 116 (25.6%), iodine delivery rate in 26 (5.7%) and 122 (26.9%), and scan time in 31 (6.8%) and 56 (12.3%), respectively. CONCLUSION: Training in CM use and management ADR should be implemented in the training of radiology residents. CRITICAL RELEVANCE STATEMENT: We highlight the need for improvement in the education of young radiologists regarding contrast media; more attention from residency programs and scientific societies should be focused on training about contrast media use and the management of adverse drug reactions. KEY POINTS: • This survey investigated training of young radiologists about use of contrast media and management adverse reactions. • Most young radiologists claimed they did not receive dedicated training. • An extreme heterogeneity of responses was observed about contrast media indications/contraindications and injection strategy.

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.

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.

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.

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.

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review.

BACKGROUND: The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL). MATERIALS AND METHODS: An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural networks (DCNN), radiology, detection, diagnosis and dental caries (DC). The quality assessment was performed using the guidelines of QUADAS-2. RESULTS: Twenty articles that met the selection criteria were evaluated. Five studies were performed on periapical radiographs, nine on bitewings, and six on orthopantomography. The number of imaging examinations included ranged from 15 to 2900. Four studies investigated ANN models, fifteen CNN models, and two DCNN models. Twelve were retrospective studies, six cross-sectional and two prospective. The following diagnostic performance was achieved in detecting CL: sensitivity from 0.44 to 0.86, specificity from 0.85 to 0.98, precision from 0.50 to 0.94, PPV (Positive Predictive Value) 0.86, NPV (Negative Predictive Value) 0.95, accuracy from 0.73 to 0.98, area under the curve (AUC) from 0.84 to 0.98, intersection over union of 0.3-0.4 and 0.78, Dice coefficient 0.66 and 0.88, F1-score from 0.64 to 0.92. According to the QUADAS-2 evaluation, most studies exhibited a low risk of bias. CONCLUSION: AI-based models have demonstrated good diagnostic performance, potentially being an important aid in CL detection. Some limitations of these studies are related to the size and heterogeneity of the datasets. Future studies need to rely on comparable, large, and clinically meaningful datasets. PROTOCOL: PROSPERO identifier: CRD42023470708.

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.

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.