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Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect and grade liver tumors non-invasively. A total of 95 patients with liver tumors (M = 65, F = 30, age range = 34–82 years) were enrolled in the study after consents were obtained. 38 patients had benign tumors (LR1 = 19 and LR2 = 19), 19 patients had intermediate tumors (LR3), and 38 patients had hepatocellular carcinoma (HCC) malignant tumors (LR4 = 19 and LR5 = 19). A multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) was collected to extract the imaging markers. A comprehensive CAD system was developed, which includes the following main steps: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of textural markers using a novel rotation invariant gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) models, and iii) calculation of the functional markers by estimating the wash-in/wash-out slopes, which enable quantification of the enhancement characteristics across different CE-MR phases. These markers were subsequently processed using a two-stages random forest-based classifier to classify the liver tumor as benign, intermediate, or malignant and determine the corresponding grade (LR1, LR2, LR3, LR4, or LR5). The overall CAD system using all the identified imaging markers achieved a sensitivity of 91.8%±0.9%, specificity of 91.2%±1.9%, and F 1 score of 0.91±0.01, using the leave-one-subject-out (LOSO) cross-validation approach. Importantly, the CAD system achieved overall accuracies of 88 % ± 5 % , 85%±2%, 78%±3%, 83%±4%, and 79%±3% in grading liver tumors into LR1, LR2, LR3, LR4, and LR5, respectively. In addition to LOSO, the developed CAD system was tested using randomly stratified 10-fold and 5-fold cross-validation approaches. Alternative classification algorithms, including support vector machine, naive Bayes classifier, k-nearest neighbors, and linear discriminant analysis all produced inferior results compared to the proposed two stage random forest classification model. These experiments demonstrate the feasibility of the proposed CAD system as a novel tool to objectively assess liver tumors based on the new comprehensive imaging markers. The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.

The accurate classification of brain tumors is an important step for early intervention. Artificial intelligence (AI)-based diagnostic systems have been utilized in recent years to help automate the process and provide more objective and faster diagnosis. This work introduces an enhanced AI-based architecture for improved brain tumor classification. We introduce a hybrid architecture that integrates vision transformer (ViT) and deep neural networks to create an ensemble classifier, resulting in a more robust brain tumor classification framework. The analysis pipeline begins with preprocessing and data normalization, followed by extracting three types of MRI-derived information-rich features. The latter included higher-order texture and structural feature sets to harness the spatial interactions between image intensities, which were derived using Haralick features and local binary patterns. Additionally, local deeper features of the brain images are extracted using an optimized convolutional neural networks (CNN) architecture. Finally, ViT-derived features are also integrated due to their ability to handle dependencies across larger distances while being less sensitive to data augmentation. The extracted features are then weighted, fused, and fed to a machine learning classifier for the final classification of brain MRIs. The proposed weighted ensemble architecture has been evaluated on publicly available and locally collected brain MRIs of four classes using various metrics. The results showed that leveraging the benefits of individual components of the proposed architecture leads to improved performance using ablation studies.

Objectives To assess the diagnostic accuracy of diffusion tensor imaging (DTI) in the characterization of hepatic focal lesions (HFLs) and compare it to diffusion-weighted imaging (DWI). Methods Prospective analysis was done for 49 patients (23 male and 26 female) with 74 HFLs who underwent dynamic MRI, DWI, and DTI. Apparent diffusion coefficient (ADC) values from DWI, fractional anisotropy (FA) values, and mean diffusivity (MD) values from DTI were measured by two independent radiologists. HFLs were classified into benign and malignant HFLs; the latter were subdivided into HCC and non-HCC lesions. Binary logistic regression was performed to analyze the associations between the DTI parameters and the distinction of malignant lesions. Results The ADC, MD, and FA at cutoff values of ≤ 1.17 × 10 −3 mm 2 /s, ≤ 1.71 × 10 −3 mm 2 /s, and > 0.29, respectively, are excellent discriminators for differentiating malignant and benign HFLs. The mean ADC and MD values of hemangiomas were significantly higher than HCC and non-HCC malignant lesions. In contrast, the mean FA values of hemangiomas were significantly lower than those of non-HCC malignant lesions and HCCs. The ADC and MD were very good discriminators at cutoff values of > 1.03 × 10 −3 mm 2 /s and > 1.12 × 10 −3 mm 2 /s, respectively. The FA at a cutoff value > 0.38 is an excellent discriminator for HCC versus non-HCC malignant lesions. Only FA value > 0.38 was a statistically significant independent predictor of HCC versus non-HCC lesions among the three parameters. There was an excellent inter-observer agreement with ICC > 0.9. Conclusion MD and FA of DTI are non-invasive, very good, and excellent discriminators superior to ADC measured by DWI for the differentiation of HFLs. Key Points • The ADC, MD, and FA at cutoff values of ≤ 1.17 × 10 −3 mm 2 /s, ≤ 1.71 × 10 −3 mm 2 /s, and > 0.29, respectively, are excellent discriminators for differentiating malignant and benign HFLs. • The mean ADC and MD values of hemangiomas were significantly higher than those of HCC and non-HCC malignant lesions. In contrast, the mean FA values of hemangiomas were significantly lower than those of non-HCC malignant lesions and HCCs, respectively. • Multivariate regression analysis revealed that only FA value > 0.38 was a statistically significant independent predictor of HCC vs. non-HCC lesions. A lesion with FA > 0.38 has 34 times higher odds of being HCC rather than non-HCC lesions

Background Although endometrial cancer (EC) is staged surgically, magnetic resonance imaging (MRI) plays a critical role in assessing and selecting the most appropriate treatment planning. We aimed to assess the diagnostic performance of quantitative analysis of diffusion-weighted imaging (DWI) in preoperative assessment of EC. Methods Prospective analysis was done for sixty-eight patients with pathology-proven endometrial cancer who underwent MRI and DWI. Apparent diffusion coefficient (ADC) values were measured by two independent radiologists and compared with the postoperative pathological results. Results There was excellent inter-observer reliability in measuring ADCmean values. There were statistically significant lower ADCmean values in patients with deep myometrial invasion (MI), cervical stromal invasion (CSI), type II EC, and lympho-vascular space involvement (LVSI) (AUC = 0.717, 0.816, 0.999, and 0.735 respectively) with optimal cut-off values of ≤ 0.84, ≤ 0.84, ≤ 0.78 and ≤ 0.82 mm 2 /s respectively. Also, there was a statistically significant negative correlation between ADC values and the updated 2023 FIGO stage and tumor grade (strong association), and the 2009 FIGO stage (medium association). Conclusions The preoperative ADCmean values of EC were significantly correlated with main prognostic factors including depth of MI, CSI, EC type, grade, nodal involvement, and LVSI.

Traditional dilated ophthalmoscopy can reveal diseases, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), diabetic macular edema (DME), retinal tear, epiretinal membrane, macular hole, retinal detachment, retinitis pigmentosa, retinal vein occlusion (RVO), and retinal artery occlusion (RAO). Among these diseases, AMD and DR are the major causes of progressive vision loss, while the latter is recognized as a world-wide epidemic. Advances in retinal imaging have improved the diagnosis and management of DR and AMD. In this review article, we focus on the variable imaging modalities for accurate diagnosis, early detection, and staging of both AMD and DR. In addition, the role of artificial intelligence (AI) in providing automated detection, diagnosis, and staging of these diseases will be surveyed. Furthermore, current works are summarized and discussed. Finally, projected future trends are outlined. The work done on this survey indicates the effective role of AI in the early detection, diagnosis, and staging of DR and/or AMD. In the future, more AI solutions will be presented that hold promise for clinical applications.

ObjectivesTo evaluate the role of arterial spin labelling (ASL) and blood oxygen level dependent (BOLD) magnetic resonance imaging (MRI) in assessment of perfusion and oxygenation in kidney transplant donor-recipient pairs.Subjects and methodsThis prospective study included 40 kidney transplant recipients (30 males and 10 females) and 40 corresponding donors (8 males and 32 females) and was done in the period from June, 2022 to January, 2024. Donors underwent ASL and BOLD MRI by using 3 Tesla MRI scanner the day prior to kidney donation (baseline), both donor and recipient underwent the same sequences early (7–10 days) after,3 months and 6 months post-transplantation. Demographic data were recorded. ASL value, BOLD values (MR2*, CR2* and MCR) were correlated with laboratory investigations (serum creatinine and estimated glomerular filtration rate) and compared using ROC curve analyses.ResultsIn donors there were no significant changes as regard the ASL mean value immediately after donation (402.08 ± 42.29 ml/min/100gm), 3 months later (402.06 ± 40.96 ml/min/100gm) or 6 months later (400.17 ± 44.13 ml/min/100gm) also the mean value of the CR2* showed no significant changes either immediately after donation ( 17.65 ± 0.23 /s),3 months after (17.64 ± 0.29 /s) or 6 months later ( 17.60 ± 0.32/s) in the remaining kidney. In recipients there was reduction in the cortical perfusion immediately after donation ( 252.41 ± 109.89 ml/min/100gm) and 3 months later ( 253.35 ± 103.41 ml/min/100gm) compared with the mean ASL value of the pre transplanted donor’s kidney ( 394.12 ± 54.0 ml/min/100gm) ( P = 0.001), after 6 months it showed mild increase in its mean value ( 263.52 ± 91.50 ml/min/100gm) but still less than the baseline mean value (p = 0.001). There was reduction in the immediate post transplantation cortical R2* mean value (16.89 ± 1.49 /s) and 3 months post transplantation (17.02 ± 1.59 /s) p value (0.004) as well as 6 months post transplantation (24.15 ± 1.69/s). There was a significant difference between the mean ASL value of the recipients with impaired function (114 ± 15.06 ml/min/100gm) and of those with good function group (298.55 ± 85.79 ml/min/100gm) p = 0.001, also as regard the CR2* between the impaired function group (17.91 ± 1.88/s) and of normal function group (16.56 ± 1.21 /s) p value (0.012). The diagnostic accuracy, sensitivity and specificity of ASL for assessment of renal function were 90%, 96.7% and 70% respectively, As the regard the BOLD parameters; MR2* diagnostic accuracy was 85%, sensitivity 93.3% and specificity 60%, CR2* diagnostic accuracy was 80%, sensitivity86.7% and specificity 60%, MCR diagnostic accuracy was 57.5%, sensitivity 50% and specificity 80%.ConclusionThe ASL and BOLD are efficient noninvasive methods in functional follow up of the donor-recipients pairs kidneys and efficient in early detection of renal impairment.

Lymphoma is the most common primary hematological malignancy. FDG PET/CT has recently become the standard imaging modality for clinical management owing to its ability to provide precise, non-invasive anatomical and functional data. The purpose of this study was to highlight the role of 18F FDG-PET/CT in the management of lymphoma by monitoring treatment response, providing a guide for response-adapted therapy, and predicting the final therapeutic outcome. This was a prospective monocentric cohort observational study in which thirty-three patients with histopathologically proved lymphoma of different types performed FDG-PET/CT scanning several times throughout the 24-month duration of the study. Early-stage interim SUVmax of the most active lesion (both nodal and/or extra-nodal) was measured and statistically analyzed together with data of the international prognostic index parameters and score. Among the included 33 patients of lymphoma, international prognostic index parameters and score together with the early-stage interim SUVmax of the predominant nodal and extra-nodal sites showed statistical significance in predicting the initial as well as the final treatment response after 24 months. Using ROC analysis, we could obtain cutoff values of SUVmax of the predominant nodal lesion of 2.75 (AUC 72%, 95% CI 0.42-1.0) and SUVmax of the predominant extra-nodal lesion of 3 (AUC 70.8% and 95% CI 0.23-1.0); therefore, SUVmax of higher than these values was related to stable or progressive disease, and lower levels than these values were related to complete or partial metabolic response based on Deauville 5-point scale and Lugano response criteria. Early-stage interim PET-CT SUVmax of the predominant nodal and extra-nodal lesion could be a reliable parameter in predicting initial and final therapeutic outcome in lymphoma patients.

To estimate the diagnostic utility of chest CT qualitative assessment and chest CT total severity score (TSS) to predict mortality in oncology patients with COVID-19 infection. This retrospective study included 151 oncology patients with COVID-19 infection. 67, 84 were male and female, respectively. Their mean age (years) ± SD was 49.7 ± 14.9. Two radiologists individually reviewed the chest CT and scored the pulmonary abnormalities using TSS. Inter-observer agreement was determined using the Bland-Altman plot. Correlation between TSS and COVID-19 severity, complication, mortality, cancer status and effect in anticancer therapy plan was done. There was a statistically significant excellent agreement between the independent observers in quantitative pulmonary assessment using TSS with interclass correlation (ICC) > 0.9 (P < 0.001). ROC curve analysis revealed that TSS was statistically significantly higher in non-survivors using an optimum cut-off value of 5 to predict in-hospital mortality. Univariate analysis showed that age, pulmonary predominant pattern, pleural effusion, tree-in-bud, ECOG PS, tumour stage 4 and post-COVID cancer status were a statistically significant predictor of mortality. Multivariate analysis reported that consolidation versus ground-glass opacity (GGO), crazy paving pattern versus GGO and progressive versus remittent cancer diseases were statistically significant independent predictors of mortality among those patients. TSS demonstrated excellent inter-observer agreement to assess COVID-19 in oncology patients with low cut-off value to predict in-hospital mortality, thus raising the attention to rapid proper care in this setting. There was a statistically significant positive correlation between TSS and delayed chemotherapeutic schedule.

Hepatocellular carcinoma (HCC) is the most frequent primary liver tumor globally and a leading cause of mortality in cirrhotic patients. Our study aimed to estimate the diagnostic performance of triphasic CT and inter-observer reliability in the preoperative detection of microvascular invasion (MVI) in HCC. Two independent radiologists accomplished a retrospective analysis for 99 patients with HCC to assess the CT features for MVI in each lesion. Postoperative histopathology was considered the gold standard. Multivariate regression analysis revealed that incomplete or absent tumor capsules, presence of TTPV, and absence of hypodense halo were statistically significant independent predictors of MVI. There was excellent agreement among observers in evaluating peritumoral enhancement, identifying intratumoral arteries, hypodense halo, TTPV, and macrovascular invasion. Also, our results revealed moderate agreement in assessing the tumor margin and tumor capsule. Triphasic CT features of MVI are reliable imaging predictors that may be helpful for standard preoperative interpretation of HCC.

Colorectal carcinoma still represents a global health burden despite the advances in its management. The most common sites of distant metastasis from colorectal carcinoma are hepatic and pulmonary metastases while metastases are rarely reported to affect the bone marrow. Though being rare, bone marrow metastasis should be suspected in colorectal carcinoma cases with abnormalities in peripheral blood count.