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Tomography (Ann Arbor, Mich.) Jun 2024CAR-T-cell therapy, also referred to as chimeric antigen receptor T-cell therapy, is a novel method in the field of immunotherapy for the treatment of non-Hodgkin's... (Review)
Review
CAR-T-cell therapy, also referred to as chimeric antigen receptor T-cell therapy, is a novel method in the field of immunotherapy for the treatment of non-Hodgkin's lymphoma (NHL). In patients receiving CAR-T-cell therapy, fluorodeoxyglucose Positron Emission Tomography/Computer Tomography ([F]FDG PET/CT) plays a critical role in tracking treatment response and evaluating the immunotherapy's overall efficacy. The aim of this study is to provide a systematic review of the literature on the studies aiming to assess and predict toxicity by means of [F]FDG PET/CT in patients with NHL receiving CAR-T-cell therapy. PubMed/MEDLINE and Cochrane Central Register of Controlled Trials (CENTRAL) databases were interrogated by two investigators to seek studies involving the use of [F]FDG PET/CT in patients with lymphoma undergoing CAR-T-cell therapy. The comprehensive computer literature search allowed 11 studies to be included. The risk of bias for the studies included in the systematic review was scored as low by using version 2 of the "Quality Assessment of Diagnostic Accuracy Studies" tool (QUADAS-2). The current literature emphasizes the role of [F]FDG PET/CT in assessing and predicting toxicity in patients with NHL receiving CAR-T-cell therapy, highlighting the evolving nature of research in CAR-T-cell therapy. Additional studies are warranted to increase the collected evidence in the literature.
Topics: Humans; Fluorodeoxyglucose F18; Positron Emission Tomography Computed Tomography; Lymphoma, Non-Hodgkin; Immunotherapy, Adoptive; Radiopharmaceuticals; Receptors, Chimeric Antigen; Treatment Outcome
PubMed: 38921943
DOI: 10.3390/tomography10060066 -
Cureus May 2024Neuroendocrine tumors (NETs) represent a heterogeneous group of neoplasms with diverse clinical presentations and prognoses. Accurate and timely diagnosis of these... (Review)
Review
Neuroendocrine tumors (NETs) represent a heterogeneous group of neoplasms with diverse clinical presentations and prognoses. Accurate and timely diagnosis of these tumors is crucial for appropriate management and improved patient outcomes. In recent years, exciting advancements in artificial intelligence (AI) technologies have been revolutionizing medical diagnostics, particularly in the realm of detecting and characterizing pulmonary NETs, offering promising avenues for improved patient care. This article aims to provide a comprehensive overview of the role of AI in diagnosing lung NETs. We discuss the current challenges associated with conventional diagnostic approaches, including histopathological examination and imaging modalities. Despite advancements in these techniques, accurate diagnosis remains challenging due to the overlapping features with other pulmonary lesions and the subjective interpretation of imaging findings. AI-based approaches, including machine learning and deep learning algorithms, have demonstrated remarkable potential in addressing these challenges. By leveraging large datasets of radiological images, histopathological samples, and clinical data, AI models can extract complex patterns and features that may not be readily discernible to human observers. Moreover, AI algorithms can continuously learn and improve from new data, leading to enhanced diagnostic accuracy and efficiency over time. Specific AI applications in the diagnosis of lung NETs include computer-aided detection and classification of pulmonary nodules on CT scans, quantitative analysis of PET imaging for tumor characterization, and integration of multi-modal data for comprehensive diagnostic assessments. These AI-driven tools hold promise for facilitating early detection, risk stratification, and personalized treatment planning in patients with lung NETs.
PubMed: 38910787
DOI: 10.7759/cureus.61012 -
PloS One 2024To evaluate the diagnostic accuracy of the aortic dissection detection risk score (ADD-RS) used alone or in combination with D-dimer for detecting acute aortic syndrome... (Meta-Analysis)
Meta-Analysis
OBJECTIVES
To evaluate the diagnostic accuracy of the aortic dissection detection risk score (ADD-RS) used alone or in combination with D-dimer for detecting acute aortic syndrome (AAS) in patients presenting with symptoms suggestive of AAS.
METHODS
We searched MEDLINE, EMBASE, and the Cochrane Library from inception to February 2024. Additionally, the reference lists of included studies and other systematic reviews were thoroughly searched. All diagnostic accuracy studies that assessed the use of ADD-RS alone or with D-Dimer for diagnosing AAS compared with a reference standard test (e.g. computer tomographic angiography (CTA), ECG-gated CTA, echocardiography, magnetic resonance angiography, operation, or autopsy) were included. Two reviewers independently selected and extracted data. Risk of bias was appraised using QUADAS-2 tool. Data were synthesised using hierarchical meta-analysis models.
RESULTS
We selected 13 studies from the 2017 citations identified, including six studies evaluating combinations of ADD-RS alongside D-dimer>500ng/L. Summary sensitivities and specificities (95% credible interval) were: ADD-RS>0 94.6% (90%, 97.5%) and 34.7% (20.7%, 51.2%), ADD-RS>1 43.4% (31.2%, 57.1%) and 89.3% (80.4%, 94.8%); ADD RS>0 or D-Dimer>500ng/L 99.8% (98.7%, 100%) and 21.8% (12.1%, 32.6%); ADD RS>1 or D-Dimer>500ng/L 98.3% (94.9%, 99.5%) and 51.4% (38.7%, 64.1%); ADD RS>1 or ADD RS = 1 with D-dimer>500ng/L 93.1% (87.1%, 96.3%) and 67.1% (54.4%, 77.7%).
CONCLUSIONS
Combinations of ADD-RS and D-dimer can be used to select patients with suspected AAS for imaging with a range of trade-offs between sensitivity (93.1% to 99.8%) and specificity (21.8% to 67.1%).
Topics: Humans; Fibrin Fibrinogen Degradation Products; Aortic Dissection; Syndrome; Sensitivity and Specificity; Acute Disease; Computed Tomography Angiography; Acute Aortic Syndrome
PubMed: 38905181
DOI: 10.1371/journal.pone.0304401 -
Journal of Clinical Medicine May 2024Psoas muscle abscess (PMA) is an uncommon yet severe condition characterized by diagnostic and therapeutic challenges due to its varied etiology and nonspecific... (Review)
Review
Psoas muscle abscess (PMA) is an uncommon yet severe condition characterized by diagnostic and therapeutic challenges due to its varied etiology and nonspecific symptoms. This study aimed to evaluate the effectiveness and accuracy of various imaging techniques used in the image-guided percutaneous drainage (PD) of PMA. A systematic review was conducted following the PRISMA guidelines. We searched PubMed, Google Scholar, and Science Direct for studies published in English from 1998 onwards that reported on the use of PD in treating PMA, detailing outcomes and complications. Imaging modalities guiding PD were also examined. We identified 1570 articles, selecting 39 for full review. Of these, 23 met the inclusion criteria; 19 were excluded due to unspecified PMA, absence of imaging guidance for PD, or inconclusive results. Eleven studies utilized computed tomography (CT) for PD, with six also using magnetic resonance imaging (MRI). Ten studies implemented ultrasound (US)-guided PD; variations in diagnostic imaging included combinations of US, CT, and MRI. A mixed approach using both CT and US was reported in two articles. Most studies using CT-guided PD showed complete success, while outcomes varied among those using US-guided PD. No studies employed MRI-guided PD. This review supports a multimodal approach for psoas abscess management, using MRI for diagnosis and CT for drainage guidance. We advocate for Cone Beam CT (CBCT)-MRI fusion techniques with navigation systems to enhance treatment precision and outcomes, particularly in complex cases with challenging abscess characteristics.
PubMed: 38892910
DOI: 10.3390/jcm13113199 -
Journal of Nanobiotechnology Jun 2024Manganese (Mn) is widely recognized owing to its low cost, non-toxic nature, and versatile oxidation states, leading to the emergence of various Mn-based nanomaterials...
Manganese (Mn) is widely recognized owing to its low cost, non-toxic nature, and versatile oxidation states, leading to the emergence of various Mn-based nanomaterials with applications across diverse fields, particularly in tumor diagnosis and therapy. Systematic reviews specifically addressing the tumor diagnosis and therapy aspects of Mn-derived biomaterials are lacking. This review comprehensively explores the physicochemical characteristics and synthesis methods of Mn-derived biomaterials, emphasizing their role in tumor diagnostics, including magnetic resonance imaging, photoacoustic and photothermal imaging, ultrasound imaging, multimodal imaging, and biodetection. Moreover, the advantages of Mn-based materials in tumor treatment applications are discussed, including drug delivery, tumor microenvironment regulation, synergistic photothermal, photodynamic, and chemodynamic therapies, tumor immunotherapy, and imaging-guided therapy. The review concludes by providing insights into the current landscape and future directions for Mn-driven advancements in the field, serving as a comprehensive resource for researchers and clinicians.
Topics: Animals; Humans; Biocompatible Materials; Drug Delivery Systems; Magnetic Resonance Imaging; Manganese; Nanostructures; Neoplasms; Tumor Microenvironment
PubMed: 38879519
DOI: 10.1186/s12951-024-02629-8 -
Translational Psychiatry Jun 2024Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one... (Review)
Review
Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one brain and one behavioural variable (univariate) or multiple variables against one brain/behaviour feature ('single' multivariate). Recently, large multimodal datasets have propelled a new wave of studies that leverage on 'doubly' multivariate approaches capable of parsing the multifaceted nature of both brain and behaviour simultaneously. Within this movement, canonical correlation analysis (CCA) and partial least squares (PLS) emerge as the most popular techniques. Both seek to capture shared information between brain and behaviour in the form of latent variables. We provide an overview of these methods, review the literature in psychiatric disorders, and discuss the main challenges from a predictive modelling perspective. We identified 39 studies across four diagnostic groups: attention deficit and hyperactive disorder (ADHD, k = 4, N = 569), autism spectrum disorders (ASD, k = 6, N = 1731), major depressive disorder (MDD, k = 5, N = 938), psychosis spectrum disorders (PSD, k = 13, N = 1150) and one transdiagnostic group (TD, k = 11, N = 5731). Most studies (67%) used CCA and focused on the association between either brain morphology, resting-state functional connectivity or fractional anisotropy against symptoms and/or cognition. There were three main findings. First, most diagnoses shared a link between clinical/cognitive symptoms and two brain measures, namely frontal morphology/brain activity and white matter association fibres (tracts between cortical areas in the same hemisphere). Second, typically less investigated behavioural variables in multivariate models such as physical health (e.g., BMI, drug use) and clinical history (e.g., childhood trauma) were identified as important features. Finally, most studies were at risk of bias due to low sample size/feature ratio and/or in-sample testing only. We highlight the importance of carefully mitigating these sources of bias with an exemplar application of CCA.
Topics: Humans; Brain; Mental Disorders; Autism Spectrum Disorder; Depressive Disorder, Major; Canonical Correlation Analysis; Attention Deficit Disorder with Hyperactivity; Least-Squares Analysis
PubMed: 38824172
DOI: 10.1038/s41398-024-02954-4 -
BMC Women's Health May 2024To demonstrate and analyze the F-FDG positron emission tomography/computed tomography (PET/CT) findings in this rare nevoid basal cell carcinoma syndrome (NBCCS).
BACKGROUND
To demonstrate and analyze the F-FDG positron emission tomography/computed tomography (PET/CT) findings in this rare nevoid basal cell carcinoma syndrome (NBCCS).
CASE PRESENTATION
A 71-year-old woman with the left invasive breast cancer was treated with hormone therapy for six months and underwent the F-FDG PET/CT examination for efficacy evaluation. F-FDG PET/CT revealed the improvement after treatment and other unexpected findings, including multiple nodules on the skin with F-FDG uptake, bone expansion of cystic lesions in the bilateral ribs, ectopic calcifications and dilated right ureter. She had no known family history. Then, the patient underwent surgical excision of the all skin nodules and the postoperative pathology were multiple basal cell carcinomas. Finally, the comprehensive diagnosis of NBCCS was made. The patient was still in follow-up. Additionally, we have summarized the reported cases (n = 3) with F-FDG PET/CT from the literature.
CONCLUSIONS
It is important to recognize this syndrome on F-FDG PET/CT because of different diagnoses and therapeutic consequences.
Topics: Humans; Female; Positron Emission Tomography Computed Tomography; Fluorodeoxyglucose F18; Aged; Basal Cell Nevus Syndrome; Breast Neoplasms; Skin Neoplasms; Radiopharmaceuticals
PubMed: 38802808
DOI: 10.1186/s12905-024-03145-5 -
Radiotherapy and Oncology : Journal of... Jul 2024We performed this systematic review and meta-analysis to investigate the performance of ML in detecting genetic mutation status in NSCLC patients. (Meta-Analysis)
Meta-Analysis Review
BACKGROUND AND PURPOSE
We performed this systematic review and meta-analysis to investigate the performance of ML in detecting genetic mutation status in NSCLC patients.
MATERIALS AND METHODS
We conducted a systematic search of PubMed, Cochrane, Embase, and Web of Science up until July 2023. We discussed the genetic mutation status of EGFR, ALK, KRAS, and BRAF, as well as the mutation status at different sites of EGFR.
RESULTS
We included a total of 128 original studies, of which 114 constructed ML models based on radiomic features mainly extracted from CT, MRI, and PET-CT data. From a genetic mutation perspective, 121 studies focused on EGFR mutation status analysis. In the validation set, for the detection of EGFR mutation status, the aggregated c-index was 0.760 (95%CI: 0.706-0.814) for clinical feature-based models, 0.772 (95%CI: 0.753-0.791) for CT-based radiomics models, 0.816 (95%CI: 0.776-0.856) for MRI-based radiomics models, and 0.750 (95%CI: 0.712-0.789) for PET-CT-based radiomics models. When combined with clinical features, the aggregated c-index was 0.807 (95%CI: 0.781-0.832) for CT-based radiomics models, 0.806 (95%CI: 0.773-0.839) for MRI-based radiomics models, and 0.822 (95%CI: 0.789-0.854) for PET-CT-based radiomics models. In the validation set, the aggregated c-indexes for radiomics-based models to detect mutation status of ALK and KRAS, as well as the mutation status at different sites of EGFR were all greater than 0.7.
CONCLUSION
The use of radiomics-based methods for early discrimination of EGFR mutation status in NSCLC demonstrates relatively high accuracy. However, the influence of clinical variables cannot be overlooked in this process. In addition, future studies should also pay attention to the accuracy of radiomics in identifying mutation status of other genes in EGFR.
Topics: Humans; Lung Neoplasms; Machine Learning; Mutation; Carcinoma, Non-Small-Cell Lung; Positron Emission Tomography Computed Tomography; ErbB Receptors; Proto-Oncogene Proteins p21(ras)
PubMed: 38734145
DOI: 10.1016/j.radonc.2024.110325 -
Clinical Neurophysiology : Official... Jul 2024Increasing evidence suggests that the seizure-onset pattern (SOP) in stereo-electroencephalography (SEEG) is important for localizing the "true" seizure onset....
OBJECTIVE
Increasing evidence suggests that the seizure-onset pattern (SOP) in stereo-electroencephalography (SEEG) is important for localizing the "true" seizure onset. Specifically, SOPs with low-voltage fast activity (LVFA) are associated with seizure-free outcome (Engel I). However, several classifications and various terms corresponding to the same pattern have been reported, challenging its use in clinical practice.
METHOD
Following the Preferred Reporting Items of Systematic reviews and Meta-Analyses (PRISMA) guideline, we performed a systematic review of studies describing SOPs along with accompanying figures depicting the reported SOP in SEEG.
RESULTS
Of 1799 studies, 22 met the selection criteria. Among the various SOPs, we observed that the terminology for low frequency periodic spikes exhibited the most variability, whereas LVFA is the most frequently used term of this pattern. Some SOP terms were inconsistent with standard EEG terminology. Finally, there was a significant but weak association between presence of LVFA and seizure-free outcome.
CONCLUSION
Divergent terms were used to describe the same SOPs and some of these terms showed inconsistencies with the standard EEG terminology. Additionally, our results confirmed the link between patterns with LVFA and seizure-free outcomes. However, this association was not strong.
SIGNIFICANCE
These results underline the need for standardization of SEEG terminology.
Topics: Humans; Electroencephalography; Seizures; Stereotaxic Techniques
PubMed: 38733701
DOI: 10.1016/j.clinph.2024.04.016 -
International Journal of Medical... Aug 2024Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's... (Review)
Review
BACKGROUND
Radiomics is a rapidly growing field used to leverage medical radiological images by extracting quantitative features. These are supposed to characterize a patient's phenotype, and when combined with artificial intelligence techniques, to improve the accuracy of diagnostic models and clinical outcome prediction.
OBJECTIVES
This review aims at examining the application areas of artificial intelligence-based radiomics (AI-based radiomics) for the management of head and neck cancer (HNC). It further explores the workflow of AI-based radiomics for personalized and precision oncology in HNC. Finally, it examines the current challenges of AI-based radiomics in daily clinical oncology and offers possible solutions to these challenges.
METHODS
Comprehensive electronic databases (PubMed, Medline via Ovid, Scopus, Web of Science, CINAHL, and Cochrane Library) were searched following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The quality of included studies and their risk of biases were evaluated using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)and Prediction Model Risk of Bias Assessment Tool (PROBAST).
RESULTS
Out of the 659 search hits retrieved, 45 fulfilled the inclusion criteria. Our review revealed that the application of AI-based radiomics model as an ancillary tool for improved decision-making in HNC management includes radiomics-based cancer diagnosis and radiomics-based cancer prognosis. The radiomics-based cancer diagnosis includes tumor staging, tumor grading, and classification of malignant and benign tumors. Similarly, radiomics-based cancer prognosis includes prediction for treatment response, recurrence, metastasis, and survival. In addition, the challenges in the implementation of these models for clinical evaluations include data imbalance, feature engineering (extraction and selection), model generalizability, multi-modal fusion, and model interpretability.
CONCLUSION
Considering the highly subjective and interobserver variability that is peculiar to the interpretation of medical images by expert clinicians, AI-based radiomics seeks to offer potentially useful quantitative information, which is not visible to the human eye or unintentionally often remain ignored during clinical imaging practice. By enabling the extraction of this type of information, AI-based radiomics has the potential to revolutionize HNC oncology, providing a platform for more personalized, higher quality, and cost-effective care for HNC patients.
Topics: Humans; Head and Neck Neoplasms; Artificial Intelligence; Precision Medicine; Prognosis; Radiomics
PubMed: 38728812
DOI: 10.1016/j.ijmedinf.2024.105464