Application of multi-spectral CT imaging in Crohn's disease: a systematic review.

BACKGROUND: No quantitative computed tomography (CT) biomarker is actually sufficiently accurate to assess Crohn's disease (CD) lesion activity, with adequate precision to guide clinical decisions. PURPOSE: To assess the available literature on the use of iodine concentration (IC), from multi-spectral CT acquisition, as a quantitative parameter able to distinguish healthy from affected bowel and assess CD bowel activity and heterogeneity of activity along the involved segments. MATERIAL AND METHODS: A literature search was conducted to identify original research studies published up to February 2022. The inclusion criteria were original research papers (>10 human participants), English language publications, focus on dual-energy CT (DECT) of CD with iodine quantification (IQ) as an outcome measure. The exclusion criteria were animal-only studies, languages other than English, review articles, case reports, correspondence, and study populations <10 patients. RESULTS: Nine studies were included in this review; all of which showed a strong correlation between IC measurements and CD activity markers, such as CD activity index (CDAI), endoscopy findings and simple endoscopic score for Crohn's disease (SES-CD), and routine CT enterography (CTE) signs and histopathologic score. Statistically significant differences in IC were reported between affected bowel segments and healthy ones (higher P value was P < 0.001), normal segments and those with active inflammation (P < 0.0001) as well as between patients with active disease and those in remission (P < 0.001). CONCLUSION: The mean normalized IC at DECTE could be a reliable tool in assisting radiologists in the diagnosis, classification and grading of CD activity.

Advanced imaging and Crohn's disease: An overview of clinical application and the added value of artificial intelligence.

PURPOSE: The purpose of this narrative review is to describe the clinical applications of advanced computed tomography (CT) and magnetic resonance (MRI) techniques in patients affected by Crohn's disease (CD), giving insights about the added value of artificial intelligence (AI) in this field. METHODS: We performed a literature search comparing standardized and advanced imaging techniques for CD diagnosis. Cross-sectional imaging is essential for the identification of lesions, the assessment of active or relapsing disease and the evaluation of complications. RESULTS: The studies reviewed show that new advanced imaging techniques and new MRI sequences could be integrated into standard protocols, to achieve a reliable quantification of CD activity, improve the lesions' characterization and the evaluation of therapy response. These promising tools are: dual-energy CT (DECT) post-processing techniques, diffusion-weighted MRI (DWI-MRI), dynamic contrast-enhanced MRI (DCE-MRI), Magnetization Transfer MRI (MT-MRI) and CINE-MRI. Furthermore, AI solutions show a potential when applied to radiological techniques in these patients. Machine learning (ML) algorithms and radiomic features prove to be useful in improving the diagnostic accuracy of clinicians and in attempting a personalized medicine approach, stratifying patients by predicting their prognosis. CONCLUSIONS: Advanced imaging is crucial in the diagnosis, lesions' characterisation and in the estimation of the abdominal involvement in CD. New AI developments are promising tools that could support doctors in the management of CD affected patients.

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework.

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.

Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs.

BACKGROUND: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans. METHODOLOGY: Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation. RESULTS: Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models. CONCLUSIONS: We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.

Cardiac computed tomography radiomics: an emerging tool for the non-invasive assessment of coronary atherosclerosis.

In the last decades, significant advances have been made in the preventive approaches to cardiovascular disease. Even so, coronary artery disease remains one of the main causes of morbidity and mortality worldwide. Invasive imaging modalities, such as intravascular ultrasound or optical coherence tomography, have played a key role in the comprehension of the pathological processes underlying myocardial infarction and cerebrovascular disease. These imaging techniques have contributed greatly to the identification and phenotyping of the culprit lesion, the so-called vulnerable plaque. Coronary computed tomographic angiography (CCTA) has emerged in more recent years as the non-invasive modality of choice in the study of coronary atherosclerosis, showing in many studies a diagnostic yield comparable to invasive approaches. Moreover, being able to describe extra-luminal characteristics of the affected vessel, CCTA has greatly contributed towards shifting the attention of researchers from the mere quantification of luminal stenosis to the identification of adverse plaque features, which appear to have a stronger prognostic value. However, the identification of some of the hallmarks of vulnerable plaques is qualitative in nature and, therefore, subject to some degree of inter-reader variability. Moreover, CCTA is still unable to identify some fine markers of plaque vulnerability which can be detected by invasive techniques, such as neovascularization and plaque erosion, among others. Nonetheless, radiological images can be viewed as vast 3-D datasets which, via the use of recent technology, allow for the extraction of numerous quantitative features that may be used to accurately phenotype a given lesion. Radiomics is the process of extrapolating innumerable parameters from a given region of interest, with the goal of establishing correlations between quantitative variables and clinical data. These datasets can then be manipulated to create predictive models via the use of automated algorithms in a process called machine learning. As a result of these approaches, radiological images may offer information regarding the characterization of a plaque which can go much beyond the boundaries of what can be qualitatively asserted by the human eye, contributing to expanding the knowledge of the disease and ultimately assist clinical decisions. Thus far, radiomics has found its more consistent area of application in the field of oncology; to present date, the amount of clinical data regarding coronary artery disease is still relatively small, partly due to the technical difficulties associated with the implementation of such techniques to the study of a small and geometrically complex lesion such as the coronary plaque. The present review, after a summary of the imaging modalities most commonly used nowadays in the study of coronary plaques, will provide a perspective on the application of radiomic analysis to coronary artery disease.

Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm.

MOTIVATION: Brain or central nervous system cancer is the tenth leading cause of death in men and women. Even though brain tumour is not considered as the primary cause of mortality worldwide, 40% of other types of cancer (such as lung or breast cancers) are transformed into brain tumours due to metastasis. Although the biopsy is considered as the gold standard for cancer diagnosis, it poses several challenges such as low sensitivity/specificity, risk during the biopsy procedure, and relatively long waiting times for the biopsy results. Due to an increase in the sheer volume of patients with brain tumours, there is a need for a non-invasive, automatic computer-aided diagnosis tool that can automatically diagnose and estimate the grade of a tumour accurately within a few seconds. METHOD: Five clinically relevant multiclass datasets (two-, three-, four-, five-, and six-class) were designed. A transfer-learning-based Artificial Intelligence paradigm using a Convolutional Neural Network (CCN) was proposed and led to higher performance in brain tumour grading/classification using magnetic resonance imaging (MRI) data. We benchmarked the transfer-learning-based CNN model against six different machine learning (ML) classification methods, namely Decision Tree, Linear Discrimination, Naive Bayes, Support Vector Machine, K-nearest neighbour, and Ensemble. RESULTS: The CNN-based deep learning (DL) model outperforms the six types of ML models when considering five types of multiclass tumour datasets. These five types of data are two-, three-, four-, five, and six-class. The CNN-based AlexNet transfer learning system yielded mean accuracies derived from three kinds of cross-validation protocols (K2, K5, and K10) of 100, 95.97, 96.65, 87.14, and 93.74%, respectively. The mean areas under the curve of DL and ML were found to be 0.99 and 0.87, respectively, for p < 0.0001, and DL showed a 12.12% improvement over ML. Multiclass datasets were benchmarked against the TT protocol (where training and testing samples are the same). The optimal model was validated using a statistical method of a tumour separation index and verified on synthetic data consisting of eight classes. CONCLUSION: The transfer-learning-based AI system is useful in multiclass brain tumour grading and shows better performance than ML systems.

Early diagnosis of chemotherapy-induced cardiotoxicity by cardiac MRI.

Survival rate in cancer patients has improved over the course of the years. In cancer survivors, cardiovascular disease is the second leading cause of mortality and early detection and serial monitoring of cardiotoxicity are key factors towards the improvement of patients' outcomes. This review article will provide an overview of the existing literature regarding the tools that MRI can offer in the early diagnosis of myocardial damage.

Erdheim-Chester disease presenting with cough, abdominal pain, and headache.

Erdheim-Chester disease (ECD) is a rare non-Langerhans cell histiocytic disorder. The diagnosis was based on the relationship between radiologic findings, clinical manifestations, and pathologic features of the bone biopsy. We report a case of ECD with unusual presenting symptoms: a 56 year-old man presented with cough, abdominal pain, and recurrent episodes of headache associated without any seizures. Peculiar computer tomography (CT) findings were key for the diagnostic suspicion. Bone biopsy and other radiological investigations confirmed the diagnosis. CT findings can help raise the suspicion of ECD. CT is easy to perform and widely available in comparison with kinetic cardiac magnetic resonance imaging and nuclear imaging. Therefore, CT findings of ECD can reduce the therapeutic delay between diagnosis and therapy prescription.

Does the clinical information play a role in the magnetic resonance diagnostic confidence analysis of ovarian and deep endometriosis?

OBJECTIVE:: Some recent studies have explored how the experience in the observers change their performance in the endometriosis detection using MRI but the effects of the clinical information remains uncertain. The purpose of this study was to assess the effect of the clinical information in the diagnostic confidence in the MRI diagnosis of endometriosis. METHODS AND MATERIALS:: Institutional Review Board was obtained. This study is compliant to STARD method. 80 patients (mean age 32 years; range 19 - 46 years) who had undergone MRI study and surgery for suspected endometriosis were retrospectively evaluated. MRI exams were performed with a 1.5 T scanner and the following five locations were assessed: ovary, anterior compartment, vaginal fornix, utero-sacral ligaments, and Rectum\Sigmoid\Pouch of Douglas. Data sets were evaluated twice on a 5-point scale by four radiologists with different level of expertise; the first time blinded to the clinical information and the second time, after 3 months together with the clinical chart. Statistical analysis included receiver operating characteristics curve analysis, the Cohen weighted test and sensitivity, specificity, positive predictive value, negative predictive value, accuracy, LR+ and LR-. RESULTS:: A total of 140 localization of endometriosis (47 endometriomas and 93 endometriotic nodules) were found. The pairwise comparison demonstrated that in all cases the presence of clinical information improved the Az value. The concordance analysis indicated a mixed pattern from modest agreement (weighted κ value 0.556 for anterior compartment) to excellent agreement values (weighted κ value 0.867 for ovarian endometriomas). CONCLUSION:: The results of our study suggest that clinical information is useful in diagnosing endometriosis in general anterior compartment, but not in other locations. Less experienced radiologists (resident) may benefit from it at utero-sacral ligaments or Rectum\Sigmoid\Pouch of Douglas. ADVANCES IN KNOWLEDGE:: In this era of sometimes indiscriminate use of diagnostic methods, it is important to emphasis the context for interpretation of diagnostic results. Our paper confirms that clinical information is useful in diagnosing endometriosis.

Preoperative Angio-CT preliminary study of the TRAM flap after selective vascular delay.

BACKGROUND: The pedicled TRAM (pTRAM) flap is one of the best options for autologous breast reconstruction, but vascular complications reported in the standard versions are about 30%. To reduce complication rate, especially in high-risk patients, surgical delay has been suggested. Individual precise preoperative location and evaluation of perforating vessels and of variations of the diameter of the deep superior epigastric artery (DSEA) are highly desirable for improving surgical strategy. Previous reports using color duplex scanning, although generally confirming the validity of the delay maneuver, have showed several pitfalls. The aim of this report was to demonstrate the usefulness of multidetector computed tomography angiography (MDCTA) for preoperative planning in patients undergoing pTRAM flap breast reconstruction after selective vascular delay. METHODS: Three patients were considered for breast reconstruction with the pTRAM flap. An MDCTA was performed before and after selective delay to locate the muscle perforators and to show increase in DSEA diameter. Axial images, multiplanar reconstruction, and 3D volume images were analyzed. RESULTS: Accurate identification of the main perforators was achieved. Location, course, and anatomic variations of DSEA were reported. The average increase in diameter of the DSEA was 29.3%. CONCLUSION: Preoperative planning of pTRAM flap with MDCTA allows surgeons to visualize and locate the dominant perforators and to select the best DSEA. Consequently, the choice between the homolateral or contralateral rectus muscle is facilitated. The high sensitivity and specificity and the ease of interpreting data have made MDCTA a highly promising diagnostic tool for planning a pTRAM flap.