Current Medical Imaging - Current Issue

Despite the increasing prevalence of hyperuricemia and gout, there remains a relative paucity of research focused on the use of straightforward clinical and laboratory markers to predict urate crystal formation. The identification of such predictive markers is crucial, as they would greatly enhance the ability of clinicians to make timely and accurate diagnoses, leading to more effective and targeted therapeutic interventions.

The aim of this study was to evaluate the diagnostic value of various easily obtainable clinical and laboratory indicators and to establish a decision tree (DT) model to analyze their predictive significance for monosodium urate (MSU) deposition in the first metatarsophalangeal (MTP) joint.

A retrospective study was conducted on 317 patients who presented to the outpatient clinic with a gout flare between January 2023 and June 2024 (181 cases with MSU deposition in the first MTP joint and 136 cases without such deposition). Clinical and laboratory indicators included gender, age, disease course, serum uric acid (SUA), glomerular filtration rate (GFR), serum creatinine (SCR), C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR). Statistical analysis methods, including T-test, logistic regression and decision tree, were used to analyze the predictors of MSU deposition in the first MTP joint. The performance of the DT model was evaluated using receiver operating characteristic (ROC) curves and a 5-fold cross-validation method was used to ensure the robustness of the study results.

Disease course, GFR, SUA, age, and SCR emerged as significant predictors of MSU deposition in the first MTP joint in both LR and DT analyses. The DT model exhibited superior diagnostic performance compared to the LR model, with a sensitivity of 83.4% (151/181), specificity of 56.6% (77/136), and overall accuracy of 71.9% (228/317). The importance of predictive variables in the DT model showed disease course, GFR, SUA, age, and SCR as 53.36%, 21.51%, 15.1%, 5.5% and 4.53%, respectively. The area under the ROC curve predicted by the DT model was 0.752 (95% CI: 0.700~0.800).

The DT model demonstrates strong predictive capability. Disease duration, GFR, SUA, age, and SCR are pivotal factors for predicting MSU deposition at the first MTP joint, with disease course being the most critical factor.

Reversible cerebral vasoconstriction syndrome (RCVS) is a condition characterized by thunderclap headaches, which are sudden and severe headaches that peak within a few seconds. These headaches present diagnostic difficulties due to their diversity and low specificity, often leading to misdiagnoses and patient dissatisfaction.

We present the case of a 52-year-old woman with a 10-day history of recurrent thunderclap headaches. Initial imaging revealed no abnormalities, but she experienced further episodes of thunderclap headaches during hospitalization. Subsequent neurovascular imaging revealed multiple intracranial stenoses with a “string of beads” appearance, confirming the diagnosis of reversible cerebral vasoconstriction syndrome. She was treated with nimodipine, and most symptoms had resolved upon discharge, with no recurrence of headache reported during a 3-month follow-up.

Prior reviews on reversible cerebral vasoconstriction syndrome predominantly emphasized isolated symptoms or advanced neuroimaging findings, offering limited applicability in primary care services. More attention should be given to identifying clinical manifestations warranting heightened reversible cerebral vasoconstriction syndrome suspicion.

Early recognition of reversible cerebral vasoconstriction syndrome counts in primary care services. We proposed a revised diagnostic routine that begins with clinical suspicion prompted by typical manifestations, like recurrent thunderclap headaches, female sex, and specific triggers, and recommends advanced neurovascular imaging when accessible. Extreme headache severity or deviation from prior migraine patterns should raise suspicion for reversible cerebral vasoconstriction syndrome, while diagnostic consideration should still remain in patients with transient neurological deficits, seizures, or cerebrovascular events.

Cor Triatriatum Sinister (CTS) is a rare congenital anomaly, accounting for 0.1%- 0.4% of congenital heart diseases. While often diagnosed and treated in infancy, some cases remain asymptomatic until adulthood due to large fenestrations. This report presents a unique case of CTS in an adult coexisting with chronic adhesive pericarditis, which may have contributed to chronic atrial dilatation, a condition not previously documented.

A 60-year-old asymptomatic Taiwanese male underwent a routine medical examination. Coronary computed tomography angiography revealed a fenestrated septum dividing the left atrium, consistent with CTS. Virtual endoscopy confirmed two wide fenestrations. Notably, chronic adhesive pericarditis, evidenced by curvilinear calcifications, was diagnosed. This condition likely exacerbated the hemodynamic impact of CTS, contributing to left atrial dilation and atrial fibrillation. Atrial fibrillation was identified, and the patient was treated with an anticoagulant for stroke prevention.

This is the first reported case of CTS coexisting with chronic adhesive pericarditis. Advanced imaging modalities, including cardiac computed tomography, angiography, and virtual endoscopy, are crucial for diagnosis and anatomical evaluation. Chronic adhesive pericarditis may amplify the effects of CTS, leading to complications, including atrial fibrillation. Anticoagulation is essential for stroke prevention in such cases.

Type 2 diabetes mellitus (T2DM) complicated with interstitial lung abnormalities (ILAs) is often overlooked and can progress to severe diabetes-induced pulmonary fibrosis (DiPF). Therefore, early diagnosis of T2DM complicated with ILAs is crucial. Chest computed tomography (CT) is an important method for diagnosing T2DM complicated with ILAs. Quantitative computed tomography (QCT) is more objective and accurate than visual assessment on CT. However, there are currently limited studies on T2DM complicated with ILAs based on quantitative CT.

This study aimed to explore the utility of quantitative computed tomography for early detection of lung injury in individuals with T2DM by examining CT-derived metrics in T2DM complicated with ILAs.

We collected data from 135 T2DM complicated with ILAs on chest CT scans retrospectively, alongside 135 non-diabetic controls with normal CT findings. Employing digital lung software, chest CT images were processed to extract quantitative parameters: total lung volume (TLV), emphysema index (LAA-950%, the percentage of lung area with attenuation < –950 Hu to total lung volume), pulmonary fibrosis index (LAA-700~-200%, the percentage of lung area with attenuation from –700Hu to –200 Hu to the total lung volume), and pulmonary peripheral vascular index (ratio TAV/TNV, the number of blood vessels TNV, the cross-sectional area of blood vessels TAV). Statistical comparisons between groups utilized Mann-Whitney U or t-tests. Correlations between Hemoglobin A1c (HbA1c) levels and CT parameters were assessed via Pearson or Spearman correlations. Parameters showing statistical significance were further examined through receiver operating characteristic (ROC) analysis.

The T2DM-ILAs cohort displayed a significantly higher LAA-700~-200% compared to controls (Z = -7.639, P< 0.001), indicative of increased fibrotic changes. Conversely, TLV (Z =-3.120, P=0.002), TAV/TNV (Z = -9.564, P< 0.001), and LAA-950% (Z = -4.926, P < 0.001) were reduced in T2DM-ILAs patients. The correlation between HbA1c and various CT quantitative indicators was not significant, HbA1c and TLV (r=-0.043, P=0.618), HbA1c and TAV (r=0.143, P=0.099), HbA1c and TNV (r=0.064, P=0.461), HbA1c and LAA-700~-200% (r=0.102, P=0.239), HbA1c and LAA-950% (r=-0.170, P=0.049), HbA1c and TAV/TNV (r=0.175, P=0.043). The peripheral vascular marker, TAV/TNV, excelled in distinguishing T2DM-related lung changes (AUC=0.84, P<0.001), outperforming LAA-700~-200% (AUC=0.77,P<0.001). A composite index incorporating multiple quantitative parameters achieved the highest diagnostic accuracy (AUC = 0.91, P< 0.001).

Quantitative CT parameters distinguish T2DM complicated with ILAs from non-diabetic individuals, suggesting a distinct pattern of lung injury. Our findings imply a particular susceptibility of small pulmonary blood vessels to injury in T2DM.

Non-gestational Ovarian Choriocarcinoma (NGOC) is an extremely rare and highly malignant ovarian germ cell tumor with nonspecific clinical manifestations, making early diagnosis challenging. At present, detailed reports on the clinical and imaging characteristics of NGOC are scarce. This case report discusses a rare instance of NGOC in a prepubertal adolescent, complemented by a literature review to enhance clinicians’ understanding of its presentation, diagnosis, and treatment.

A 10-year-old female with no history of menstruation or sexual activity presented with persistent lower abdominal pain and vaginal bleeding. Preoperative imaging revealed a large pelvic mass with heterogeneous echogenicity and vascularity. Serum Human Chorionic Gonadotropin (hCG) levels were markedly elevated (>297,000 IU/L).

Ultrasonography and CT demonstrated a large, heterogeneous, hypervascular adnexal mass with features of necrosis and cystic changes, suggesting malignancy.

The mass, originating from the right adnexa, was removed via laparotomy. Histopathology confirmed NGOC, supported by immunohistochemistry, showing strong positivity for markers like CD146, CK18, HCG, and HPL, along with a high Ki-67 index (>90%).

In young females with no sexual life, significantly elevated HCG levels and imaging findings of a large heterogeneous adnexal mass should raise suspicion for NGOC. Early recognition and multimodal diagnostic approaches, including imaging, biochemical, and pathological assessments, are essential for timely intervention, reducing metastatic risk and improving prognosis. This report contributes to the understanding of NGOC and emphasizes the importance of accurate diagnosis for better patient outcomes.

Pain with a persistent and recurrent onset is one of the most important symptoms of temporomandibular disorders (TMD). Recent evidence indicated the dysfunction of the central nervous system was more linked to TMD pain. This study aimed to explore the abnormal structural and perfusion alterations in patients with painful TMD (p-TMD) to understand the comprehension of neuro-pathophysiological mechanisms.

Forty-one p-TMD patients and 33 normal controls (NC) were recruited, and high-resolution structural brain and 3D PCASL data were obtained from a 3.0T MR scanner. The voxel-based analysis of the whole cerebral gray matter (GMV) was performed, and the GMV and cerebral blood flow (CBF) value of the altered positive areas were extracted to investigate the significant correlation with clinical variables.

The brain regions with significantly increased GMV in p-TMD group were listed as follows: right putamen, right superior frontal gyrus, left superior frontal gyrus medial segment, right supplementary motor cortex, left postcentral gyrus, right middle temporal gyrus, right postcentral gyrus medial segment, right temporal pole, right inferior temporal gyrus and right opercular part of the inferior frontal gyrus (Punc<0.001, cluster>39). However, there were no brain regions with significantly decreased GMV in the p-TMD group. Cerebral perfusion analysis identified that only the right postcentral gyrus medial segment presented significantly higher CBF value in the p-TMD group than in the NC group over all the brain regions with increased GMV. Within the p-TMD group, pain intensity, anxiety, depression, and jaw functional limitation scores were differentially associated with GMV and CBF value.

The voxel-based morphometric and perfusion findings collectively implicate maladaptive plasticity in both the sensory-discriminative and affective-motivational dimensions of pain processing in p-TMD pathophysiology.

Modic changes (MCs) are a common manifestation of lumbar degenerative disease, classified into three types. However, the relationship between Bone Mineral Density (BMD) and each type of MC at the vertebral lesion sites remains unclear.

This study included 144 patients who had both lumbar MR and CT images. The classification and grading of MCs were evaluated using MR images. On the CT images, BMD values, T-scores, and Z-scores were obtained from the normal T12 vertebrae, the corresponding lumbar Modic lesion sites, and the adjacent healthy regions at the same vertebra on the axial plane.

A total of 370 vertebrae (226 MCs and 144 normal T12 vertebrae) were assessed. No significant difference was found in the BMD of normal T12 vertebrae between males and females in the study. MCs were more commonly found in the lumbar 4 and 5 vertebrae. Of the MCs, 80 (36%) were classified as type I, 130 (57%) as type II, and 16 (7%) as type III. The BMD value, T-score, and Z-score of each Modic type lesion site were higher than those of adjacent healthy regions and normal T12 vertebrae. A strong correlation was found between the different Modic types, though no significant differences were observed between grades within the same Modic type.

The presence of any MCs was significantly associated with an increase in BMD in the corresponding lesion sites, with more severe MCs showing a stronger association with higher BMD. This is the first study to explore the relationship between all types of MCs and their BMD values.

Intrauterine growth restriction (IUGR) is associated with long-term metabolic disturbances, including obesity. Changes in hepatic lipid metabolism and adipose tissue function, mediated by lipin-1 and lipin-2, may contribute to these outcomes.

This study aimed to investigate the correlation between lipin-1 in visceral adipose tissues (VATs) and lipin-2 in the liver. It also examined hepatic T1 values using T1 mapping in IUGR rats.

The objective of this study was to explore the metabolic mechanisms linking IUGR and adult obesity by analyzing molecular and imaging markers.

Pregnant rats were fed either a low-protein diet (10%) to induce IUGR or a normal-protein diet (21%) as a control. Male offspring underwent conventional magnetic resonance imaging and native T1 mapping using a 3.0 T whole-body MR scanner at days 21, 56, and 84 post-birth. Liver tissues and VATs were collected for analysis. Lipin-1 and lipin-2 expression levels were measured using Western blot and real-time quantitative PCR.

The IUGR group exhibited significantly higher mRNA and protein expression levels of lipin-1 and lipin-2 compared to the control group at days 21, 56, and 84 after birth. Additionally, the IUGR group demonstrated significantly higher hepatic T1 values than the control group at the corresponding time points. Positive correlations were observed between the protein and mRNA expression levels of lipin-1 and hepatic T1 values. Similarly, the protein and mRNA expression levels of lipin-2 were positively correlated with hepatic T1 values. All results were statistically significant (P<0.05).

The upregulation of lipin-1 and lipin-2 expressions was found to be linked to elevated hepatic T1 values, potentially contributing to adult obesity in IUGR rats.

Semantic segmentation algorithms are essential for identifying and segmenting human organs and lesions in medical images. However, as U-Net variants enhance segmentation accuracy, they often increase in parameter count, demanding more sophisticated and costly hardware for training.

This study aims to introduce a lightweight U-Net that optimizes the trade-off between network parameters and segmentation accuracy, while fully leveraging the encoder's feature extraction capabilities.

We propose a lightweight full-encoder U-shaped network, termed LFE-UNet, which employs full-encoder skip connections, encompassing all encoder layers. This model is designed with a reduced number of basic channels—specifically, 8 instead of the typical 64 or 32—to achieve a more efficient architecture.

The LFE-UNet, when integrated with ResNet34, achieved a Dice score of 0.97385 on the ISBI LiTS 2017 liver dataset. For the BraTS 2018 brain tumor dataset, it obtained 0.87510, 0.93759, 0.87301, and 0.81469 on average, WT, TC, and ET, respectively. The paper also discusses the impact of varying basic channel numbers n and encoder layer counts N on the network's parameter efficiency, as well as the model's robustness to different levels of Gaussian noise in images and salt and pepper noise in labels. Additionally, the influence of different loss functions is explored.

The LFE-UNet proves that high segmentation accuracy can be attained with a markedly lower parameters, fully utilizing the full-scale encoder's feature extraction. It also highlights the significance of loss function selection and the effects of noise on segmentation accuracy.

The fundamental problem with the transmission and storage of medical images is their inherent redundancy and large size necessitating higher bandwidth and a significant amount of storage space.

The main objective is to enhance the compression efficiency through accurate segmentation followed by non-uniform compression through a cascade of encoders.

Due to a sharp growth in digital imaging data, it is highly desirable to reduce the size of medical images by a significant amount, without losing clinically important diagnostic information. The majority of the compression techniques reported in the literature use either manual or traditional segmentation techniques to extract the informative parts of the images. The methods based upon non-uniform compression require accurate extraction of the informative part of the image to achieve higher compression rate.

This research proposes unsupervised machine learning modified fuzzy c-means (FCM) clustering-based segmentation for accurate extraction of informative parts of MR images. The spatial constraints of the images are extracted using an automated region-growing algorithm and incorporated into the objective function of FCM clustering (RG-FCM) to enhance the performance of the segmentation process even in the presence of noise. Further, informative and background parts are subjected to two separate series of encoders, with higher bit rates for the informative part of the image.

Empirical analysis was done on the Magnetic Resonance Imaging (MRI)dataset, and experimental results indicate that the proposed technique outperforms similar existing techniques in terms of segmentation and compression metrics.

This integration of different segmentation techniques exhibits improvement in Jaccard and dice indexes, and cascade of different encoders endorse the superior performance of the proposed compression technique. The proposed technique can help in achieving higher compression of medical images without compromising clinically significant information.

Gastric schwannoma is a rare gastrointestinal mesenchymal tumor with Schwann cell differentiation. In the past, most of the published cases were single gastric schwannoma. Multiple gastric schwannoma is exceedingly rare. We herein report a case of multiple gastric schwannomas.

A 55-year-old male presented with postprandial vomiting of unclear etiology, accompanied by epigastric pain and bloating. Computed tomography revealed marked thickening of the gastric wall at the fundus-body junction along the greater curvature and gastric angle, with intraluminal nodular projections. Multiphase contrast-enhanced computed tomography demonstrated moderate progressive enhancement. The patient was misdiagnosed as having a gastric stromal tumor before the operation and subsequently underwent laparoscopic partial gastrectomy. However, pathological and immunohistochemical analysis confirmed multiple gastric schwannomas. The patient recovered uneventfully and was discharged without complications.

Gastric schwannoma is rare in clinical practice, especially gastric multiple schwannomas, which are easily confused with gastric stromal tumors, as illustrated in this case, where a preoperative misdiagnosis occurred. Clinicians should enhance their recognition of characteristic imaging features (including Computed tomography, Magnetic resonance imaging, and Positron emission tomography) and employ multimodal diagnostic approaches to optimize preoperative diagnosis.

Small cell neuroendocrine carcinoma (SCNEC) of the ureter is extremely rare, and tends to show a mixed histologic profile. Literature on its imaging features is limited.

We herein report the case of a 68-year-old woman who presented with two days of left flank pain. Ultrasound and CT scan revealed a lesion in the left distal ureter. The lesion exhibited intensive tracer activity on ¹⁸F-FDG PET/CT scan, corresponding to a malignant tumor, most likely a high-grade urothelial carcinoma, and no metastases were observed. Then, the patient underwent a radical left nephroureterectomy. Pathology revealed a carcinoma composed of SCNEC (approximately 83%) and urothelial carcinoma (approximately 17%). During one year of follow-up, the patient underwent six cycles of adjuvant chemotherapy (etoposide 100mg d1-3 + cisplatin 30mg d1-3, q3w), and no recurrence or metastases were found on the CT scan.

This case report has presented a case of ureteral SCNEC and explored the value of ¹⁸F-FDG PET/CT in the diagnosis and staging of the disease.

A transverse mesocolic internal hernia is a phenomenon in which a small intestinal loop protrudes through the natural orifice in the transverse colon mesentery. This type of internal hernia in adults, although rare, is one of the causes of closed-loop intestinal obstruction, which requires prompt diagnosis and treatment.

We report two cases of transverse mesocolic internal hernia that were examined and subsequently treated at Hospital 108, Hanoi, Vietnam. Both patients (53 and 66 years old) had atypical clinical symptoms, mainly dull epigastric pain. Upon admission, they were initially examined clinically, followed by blood testing and chest and abdominal X-ray radiography. Diagnostic imaging was mainly based on subsequent Multidetector-Row Computed Tomography (MDCT). Laparoscopic/surgical release of the hernia and closure of the natural orifice in the transverse colon mesentery were performed. The clinical symptoms and laboratory and radiographic findings did not suggest a causal diagnosis. However, MDCT provided several images suggestive of an internal hernia, including a closed intestinal loop passing through the transverse colon mesentery and located posteriorly in the left abdominal cavity near the Treitz angle, displacement of the mesenteric vascular bundle, and colon displacement. These displacements were the causes of intestinal inflammation/obstruction. Additionally, laparoscopic/surgical results confirmed the MDCT diagnosis.

Thin-slice thickness, high spatial resolution, multiplanar reconstruction MDCT was effective for diagnosing transverse mesocolic internal hernia. In our two cases, MDCT helped determine the cause and assess the state of intestinal ischemia.

The study investigates the creation and assessment of Machine Learning (ML) models using different classifiers such as Support Vector Machine (SVM), logistic regression, decision tree, k-nearest neighbour (kNN), and Artificial Neural Network (ANN) for the automated identification of tuberculosis (TB) from chest X-ray (CXR) images.

As a persistent worldwide health concern, TB requires early detection for effective treatment and control of the infection. The differential diagnosis of TB is a challenge, even for experienced radiologists. With the use of automated processing of CXR images which are reasonable and frequently used for TB diagnosis, employing Artificial Intelligence (AI) techniques provides novel possibilities.

The objective of the study was to identify respiratory disorders, radiologists devote a lot of time reviewing each of the CXR images. As such, they can identify the type of disease using automated methods based on AI algorithms. This work advances the diagnosis of TB via machine learning, which may result in early treatment options and enhanced outcomes for patients.

The disease was classified using distinct parameters like edge, shape, and Gray Level Difference Statistics (GLDS) on splitting of the dataset at 70:30 and 80:20.

It was observed that authors attained 93.5% accuracy using SVM with linear kernel for a 70:30 data split considering hybrid parameters. The comparison was made considering different feature extraction techniques, different dataset splitting, existing work, and another dataset.

The designed model using SVM, decision tree, kNN, ANN, and logistic regression was compared using other state-of-the-art techniques, other datasets, different feature extraction techniques, and different splitting of data. AI has great promise for enhancing tuberculosis detection, which will ultimately lead to an earlier diagnosis and improved disease management.

This study aimed to analyze the composition of renal staghorn calculi and their characteristics using spectral CT.

This study enrolled 111 cases of renal staghorn calculi from 94 patients (48 males and 46 females, aged 28–76 years; median age: 56 years). Using spectral CT, average Zeff and CT values were analyzed. The water/iodine-based images were generated by the material separation module. All stones were detected by FTIR spectroscopy.

111 cases of renal staghorn calculi included 53 cases of single composition (47.8%) and 58 cases of mixed composition (52.2%). In staghorn calculi of a single composition, urate (23 cases) and calcium oxalate monohydrate (16 cases) were more prevalent than struvite (5 cases) and brushite (5 cases). Mixed compositions included metabolic-metabolic (36 cases, 62.1%), metabolic-infectious (14 cases, 24.1%), and infectious-infectious (8 cases, 13.8%) cases, respectively. The average Zeff values showed some characteristics of carbapatite and urate. However, average Zeff and CT values had many overlappings among other compositions. All stones appeared homogeneous in water-based images. In iodine-based images, calcium oxalate monohydrate displayed homogeneous high density, but struvite and brushite showed heterogeneous high density. Single compositions of carbapatite, calcium oxalate monohydrate, and cystine exhibited homogeneous high density, similar to mixed compositions of carbapatite and calcium oxalate monohydrate. Furthermore, urate demonstrated homogeneous low density. Moreover, the mixture of struvite and brushite/urate showed heterogeneous high density.

In staghorn calculi of a single composition, the metabolic type was common, while metabolic-metabolic and metabolic-infectious types frequently occurred in staghorn calculi with mixed compositions. Except for average Zeff values, water-iodine material separation performed an important auxiliary function in differentiating stones’ compositions using spectral CT.

Optic nerve hemangioblastoma (ONH) is a rare benign tumor. It can be sporadic or associated with Von-Hippel Lindau (VHL) syndrome. Magnetic resonance imaging (MRI) is the most commonly used diagnostic technique for the tumor. However, an accurate diagnosis can be challenging due to the rarity of ONH and its similarity to glioma and meningioma.

A 49-year-old female experienced progressive vision loss for ten years in the right eye, accompanied by proptosis over two years. The ophthalmological examination found her visual acuity of the right eye to have no light perception. Optical coherence tomography showed decreased thickness of the right retinal ganglion cell layer. MRI revealed an oval solid mass within the right retrobulbar space, with isointensity on T1-weighted (T1WI) imaging and heterogeneous hyperintensity on T2-weighted imaging (T2WI). Heterogeneous enhancement was found on gadolinium-enhanced T1WI and dynamic contrast-enhanced MRI. At internal and marginal areas of the mass, multiple flow voids were observed on various sequences, especially on T2WI. Furthermore, the superior, inferior, medial, and lateral rectus muscles of the right eye distinctly atrophied, showing a lower signal intensity on T2WI and less apparent enhancement than the left normal ones. Postoperative pathological diagnosis was hemangioblastoma of the right optic nerve.

Hemangioblastoma should be considered as a differential diagnosis for the space-occupying mass of the optic nerve if there is the presence of flow voids, vivid enhancement, and absence of a dural attachment, regardless of VHL syndrome. Of note, this is the first reported case to consider altered extraocular muscles as a potential point to prompt the diagnosis on MRI.

The study aims to investigate the prognostic value of deep learning based pericoronary adipose tissue attenuation computed tomography (PCAT) and plaque volume beyond coronary computed tomography angiography (CTA) -derived fractional flow reserve (CT-FFR) in patients with percutaneous coronary intervention (PCI).

A total of 183 patients with PCI who underwent coronary CTA were included in this retrospective study. Imaging assessment included PCAT, plaque volume, and CT-FFR, which were performed using an artificial intelligence (AI) assisted workstation. Kaplan-Meier survival curves analysis and multivariate Cox regression were used to estimate major adverse cardiovascular events (MACE), including non-fatal myocardial infraction (MI), stroke, and mortality.

In total, 22 (12%) MACE occurred during a median follow-up period of 38.0 months (34.6-54.6 months). Kaplan-Meier analysis revealed that right coronary artery (RCA) PCAT (p = 0.007) and plaque volume (p = 0.008) were significantly associated with the increase in MACE. Multivariable Cox regression indicated that RCA PCAT (hazard ratios (HR): 7.05, 95%CI: 1.44-34.63, p = 0.016) and plaque volume (HR: 3.84, 95%CI: 1.44-10.27, p = 0.007) were independent predictors of MACE after adjustment by clinical risk factors. However, CT-FFR was not independently associated with MACE in multivariable Cox regression (p = 0.150).

Deep learning based RCA PCAT and plaque volume derived from coronary CTA were found to be more strongly associated with MACE than CT-FFR in patients with PCI.

Lung cancer is one of the main threats to global health, among lung diseases. Low-Dose Computed Tomography (LDCT) provides significant benefits for its screening but also brings new diagnostic challenges that require close attention.

By searching the Web of Science core collection, we selected articles and reviews published in English between 2005 and June 2024 on topics such as “Low-dose”, “CT image”, and “Lung”. These literatures were analyzed by bibliometric method, and CiteSpace software was used to explore the cooperation between countries, the cooperative relationship between authors, highly cited literature, and the distribution of keywords to reveal the research hotspots and trends in this field.

The number of LDCT research articles show a trend of continuous growth between 2019 and 2022. The United States is at the forefront of research in this field, with a centrality of 0.31; China has also rapidly conducted research with a centrality of 0.26. The authors' co-occurrence map shows that research teams in this field are highly cooperative, and their research questions are closely related. The analysis of highly cited literature and keywords confirmed the significant advantages of LDCT in lung cancer screening, which can help reduce the mortality of lung cancer patients and improve the prognosis. “Lung cancer” and “CT” have always been high-frequency keywords, while “image quality” and “low dose CT” have become new hot keywords, indicating that LDCT using deep learning techniques has become a hot topic in early lung cancer research.

The study revealed that advancements in CT technology have driven in-depth research from application challenges to image processing, with the research trajectory evolving from technical improvements to health risk assessments and subsequently to AI-assisted diagnosis. Currently, the research focus has shifted toward integrating deep learning with LDCT technology to address complex diagnostic challenges. The study also presents global research trends and geographical distributions of LDCT technology, along with the influence of key research institutions and authors. The comprehensive analysis aims to promote the development and application of LDCT technology in pulmonary disease diagnosis and enhance diagnostic accuracy and patient management efficiency.

The future will focus on LDCT reconstruction algorithms to balance image noise and radiation dose. AI-assisted multimodal imaging supports remote diagnosis and personalized health management by providing dynamic analysis, risk assessment, and follow-up recommendations to support early diagnosis.

Epithelioid Hemangioendothelioma (EHE) is a rare vascular neoplasm that typically occurs in the bone, soft tissue, liver, and lung but rarely in the mediastinum. Multimodal imaging of EHE is poorly understood, often leading to misdiagnosis as other mediastinal tumors.

Two female cases with incidental mediastinal masses were retrospectively analysed, focusing on multimodal presentations. For both cases, CT studies showed well-defined, low-density oval masses in the right anterior superior mediastinum with the Superior Vena Cava (SVC) invasion. Intralesional punctate calcifications were observed in Case 2. MRI revealed hypointense masses on T1WI and slightly hyperintense on T2WI, with partial diffusion restriction on DWI. Case 1 had mild enhancement, while Case 2 had significant enhancement. PET-CT showed significant FDG uptake with maximum standardized uptake values (SUVmax) of 9.2 and 5.1, respectively. Both patients underwent surgical resection, with pathology confirming mediastinal EHEs.

Mediastinal EHE presents as a well-defined soft-tissue mass with punctate calcifications and heterogeneous enhancement, typically located in the anterior mediastinum with invasion into medium or large veins. Moreover, it should be considered in the differential diagnosis of mediastinal tumors.

This study aimed to investigate the imaging features of primary fallopian tube carcinoma (PFTC).

Imaging findings of 12 PFTC patients were retrospectively studied. Multi-slice computed tomography (CT, MSCT) was performed to investigate tumor location, size, density, appearance (cystic/solid), enhancement pattern, and metastasis.

Twelve women aged 34–67 (mean=54.3) years were presented with pelvic pain (n=6), vaginal discharge (n=5), and incidental pelvic masses (n=3). The tumor diameters of PFTC varied from 3.3 to 6.8 cm (mean=4.7 cm). Ten cases were unilateral, and two were bilateral. The lesions were adnexal tubular-shaped cystic masses with mucosal papillary nodes in six cases, irregular cystic and solid masses in four cases, and sausage-shaped solid masses in two cases. The plain CT values ranged from 15 to 35 HU (mean, 28 HU). On enhanced CT, the enhancement of the solid composition was lower than that of the myometrium in all phases. CT values in arterial and venous phases were 55-62 and 60-63 HU, respectively, with average values of 58.6 and 61 HU. The metastasis sites included the ovary (n=2), omentum (n=3), retroperitoneal lymph nodes (n=5), pelvic lymph nodes (n=5), and inguinal lymph nodes (n=2). Seven cases exhibited pelvic fluid, and seven exhibited round ligament thickening on the lesioned side.

In patients presenting with vaginal discharge or genital bleeding and sausage-shaped or tubal-shaped cystic, solid, or solid-cystic complexes in the adnexal portion associated with hydrosalpinx and peritumoral ascites, PFTC should be considered in the diagnosis, especially in tumors associated with round ligament thickening.