Exploring the Utility of Radiomic Feature Extraction to Improve the Diagnostic Accuracy of Cardiac Sarcoidosis Using FDG PET

Infection and Inflammation in Nuclear Medicine Imaging: The Role of Artificial Intelligence

Infectious and inflammatory diseases represent a global challenge. Delayed diagnosis and treatment lead to death, disabilities and impairment of the quality of life. The detection of low-grade inflammation and occult infections remains challenging. Nuclear medicine techniques are well established in the assessment of the severity and extent of the disease. However, high-level expertise is required to process and interpret the images. Additionally, the workflows are frequently time consuming. Artificial intelligence (AI)-based techniques can be efficiently applied in this setting. We reviewed the literature to assess the state of the application of AI in nuclear medicine imaging in infectious and inflammatory diseases. We included 22 studies, which applied AI-based methods for any of the steps of their workflow. In this review we report and critically discuss the state-of-the-art knowledge on the application of AI models in Infection and Inflammation nuclear medicine imaging.

An assessment of PET and CMR radiomic features for the detection of cardiac sarcoidosis

Background

Visual interpretation of PET and CMR may fail to identify cardiac sarcoidosis (CS) with high specificity. This study aimed to evaluate the role of [18F]FDG PET and late gadolinium enhancement (LGE)-CMR radiomic features in differentiating CS from another cause of myocardial inflammation, in this case patients with cardiac-related clinical symptoms following COVID-19.

Methods

[18F]FDG PET and LGE-CMR were treated separately in this work. There were 35 post-COVID-19 (PC) and 40 CS datasets. Regions of interest were delineated manually around the entire left ventricle for the PET and LGE-CMR datasets. Radiomic features were then extracted. The ability of individual features to correctly identify image data as CS or PC was tested to predict the clinical classification of CS vs. PC using Mann-Whitney U-tests and logistic regression. Features were retained if the P-value was <0.00053, the AUC was >0.5, and the accuracy was >0.7. After applying the correlation test, uncorrelated features were used as a signature (joint features) to train machine learning classifiers. For LGE-CMR analysis, to further improve the results, different classifiers were used for individual features besides logistic regression, and the results of individual features of each classifier were screened to create a signature that included all features that followed the previously mentioned criteria and used it them as input for machine learning classifiers.

Results

The Mann-Whitney U-tests and logistic regression were trained on individual features to build a collection of features. For [18F]FDG PET analysis, the maximum target-to-background ratio (TBRmax ) showed a high area under the curve (AUC) and accuracy with small P-values (<0.00053), but the signature performed better (AUC 0.98 and accuracy 0.91). For LGE-CMR analysis, the Gray Level Dependence Matrix (gldm)-Dependence Non-Uniformity showed good results with small error bars (accuracy 0.75 and AUC 0.87). However, by applying a Support Vector Machine classifier to individual LGE-CMR features and creating a signature, a Random Forest classifier displayed better AUC and accuracy (0.91 and 0.84, respectively).

Conclusion

Using radiomic features may prove useful in identifying individuals with CS. Some features showed promising results in differentiating between PC and CS. By automating the analysis, the patient management process can be accelerated and improved.

The clinical value of quantitative cardiovascular molecular imaging: a step towards precision medicine

Cardiovascular diseases (CVD) are the leading cause of death worldwide and have an increasing impact on society. Precision medicine, in which optimal care is identified for an individual or a group of individuals rather than for the average population, might provide significant health benefits for this patient group and decrease CVD morbidity and mortality. Molecular imaging provides the opportunity to assess biological processes in individuals in addition to anatomical context provided by other imaging modalities and could prove to be essential in the implementation of precision medicine in CVD. New developments in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) systems, combined with rapid innovations in promising and specific radiopharmaceuticals, provide an impressive improvement of diagnostic accuracy and therapy evaluation. This may result in improved health outcomes in CVD patients, thereby reducing societal impact. Furthermore, recent technical advances have led to new possibilities for accurate image quantification, dynamic imaging, and quantification of radiotracer kinetics. This potentially allows for better evaluation of disease activity over time and treatment response monitoring. However, the clinical implementation of these new methods has been slow. This review describes the recent advances in molecular imaging and the clinical value of quantitative PET and SPECT in various fields in cardiovascular molecular imaging, such as atherosclerosis, myocardial perfusion and ischemia, infiltrative cardiomyopathies, systemic vascular diseases, and infectious cardiovascular diseases. Moreover, the challenges that need to be overcome to achieve clinical translation are addressed, and future directions are provided.

Exploring the Utility of Cardiovascular Magnetic Resonance Radiomic Feature Extraction for Evaluation of Cardiac Sarcoidosis

BACKGROUND

The aim of this study is to explore the utility of cardiac magnetic resonance (CMR) imaging of radiomic features to distinguish active and inactive cardiac sarcoidosis (CS).

METHODS

Subjects were classified into active cardiac sarcoidosis (CSactive) and inactive cardiac sarcoidosis (CSinactive) based on PET-CMR imaging. CSactive was classified as featuring patchy [18F]fluorodeoxyglucose ([18F]FDG) uptake on PET and presence of late gadolinium enhancement (LGE) on CMR, while CSinactive was classified as featuring no [18F]FDG uptake in the presence of LGE on CMR. Among those screened, thirty CSactive and thirty-one CSinactive patients met these criteria. A total of 94 radiomic features were subsequently extracted using PyRadiomics. The values of individual features were compared between CSactive and CSinactive using the Mann-Whitney U test. Subsequently, machine learning (ML) approaches were tested. ML was applied to two sub-sets of radiomic features (signatures A and B) that were selected by logistic regression and PCA, respectively.

RESULTS

Univariate analysis of individual features showed no significant differences. Of all features, gray level co-occurrence matrix (GLCM) joint entropy had a good area under the curve (AUC) and accuracy with the smallest confidence interval, suggesting it may be a good target for further investigation. Some ML classifiers achieved reasonable discrimination between CSactive and CSinactive patients. With signature A, support vector machine and k-neighbors showed good performance with AUC (0.77 and 0.73) and accuracy (0.67 and 0.72), respectively. With signature B, decision tree demonstrated AUC and accuracy around 0.7; Conclusion: CMR radiomic analysis in CS provides promising results to distinguish patients with active and inactive disease.

Hybrid PET/MRI in Infection and Inflammation: An Update About the Latest Available Literature Evidence

PET/MRI has been reported to be promising in the diagnosis and evaluation of infection and inflammation including brain disorders, bone and soft tissue infections and inflammations, cardiovascular, abdominal, and systemic diseases. However, evidence came out manly from anecdotal cases or small cohorts. The present review aimed to update the latest available evidence about the role of PET/MRI in infection and inflammation. The search (January, 1 2018-July, 8 2022) on PubMed produced 504 results. Sixty-five articles were selected and included in the qualitative synthesis. The number of publications on PET/MRI in the 3 years 2018-2020 was comparable, while it increased in 2021 and 2022 (from 11 to 17 and 15, respectively). [¹⁸F]FDG and ⁶⁸Ga-DOTA-FAPI-04 were the most frequently used (42/65) and innovative radiopharmaceuticals, respectively. [¹⁸F]fluoride (9/65), translocator protein (TSPO)-targeted PET agents (6/65), CXCR4 receptor targeting tracer and β-amyloid plaques binding radiopharmaceuticals (2/65 and 2/65, respectively) were also used. Most PET/MRI studies in the period 2018-2022 focused on inflammation (55/65), and cardiovascular diseases represented the most frequent field of interest (30/65), also when considering each year singularly. An increasing trend in bone and joint publications was observed in the considered period (12/65). Other topics included neurology (11/65), inflammatory bowel disease (8/65), and other (4/65). PET/MRI technology demonstrated to be useful in infection and inflammation, being superior to each single modality and/or facilitating diagnosis in a number of conditions (eg, cardiac sarcoidosis, myocarditis, endocarditis), and/or allowing to provide insightful information about disease biology and apply innovative radiopharmaceuticals (eg, neurology, atherosclerosis). Publications focused on PET/MRI in large vessel vasculitis and aortic diseases include both diagnostic and discovery objectives. The current review corroborates the potential of PET/MRI - combining in a single examination the high soft tissue contrast, high resolution, and functional information of MRI, with molecular data provided by PET technology - to positively impact on the management of infectious diseases and inflammatory conditions.

Automatic detection of abnormal hand gestures in patients with radial, ulnar, or median nerve injury using hand pose estimation

Background

Radial, ulnar, or median nerve injuries are common peripheral nerve injuries. They usually present specific abnormal signs on the hands as evidence for hand surgeons to diagnose. However, without specialized knowledge, it is difficult for primary healthcare providers to recognize the clinical meaning and the potential nerve injuries through the abnormalities, often leading to misdiagnosis. Developing technologies for automatically detecting abnormal hand gestures would assist general medical service practitioners with an early diagnosis and treatment.

Methods

Based on expert experience, we selected three hand gestures with predetermined features and rules as three independent binary classification tasks for abnormal gesture detection. Images from patients with unilateral radial, ulnar, or median nerve injuries and healthy volunteers were obtained using a smartphone. The landmark coordinates were extracted using Google MediaPipe Hands to calculate the features. The receiver operating characteristic curve was employed for feature selection. We compared the performance of rule-based models with logistic regression, support vector machine and of random forest machine learning models by evaluating the accuracy, sensitivity, and specificity.

Results

The study included 1,344 images, twenty-two patients, and thirty-four volunteers. In rule-based models, eight features were finally selected. The accuracy, sensitivity, and specificity were (1) 98.2, 91.7, and 99.0% for radial nerve injury detection; (2) 97.3, 83.3, and 99.0% for ulnar nerve injury detection; and (3) 96.4, 87.5, and 97.1% for median nerve injury detection, respectively. All machine learning models had accuracy above 95% and sensitivity ranging from 37.5 to 100%.

Conclusion

Our study provides a helpful tool for detecting abnormal gestures in radial, ulnar, or median nerve injuries with satisfying accuracy, sensitivity, and specificity. It confirms that hand pose estimation could automatically analyze and detect the abnormalities from images of these patients. It has the potential to be a simple and convenient screening method for primary healthcare and telemedicine application.