Current Medical Imaging - Volume 20, Issue 1, 2024

Non-invasive bio-diagnostics are essential for providing patients with safer treatment. In this subject, significant growth is attained for non-invasive anaemia detection in terms of Hb concentration by means of spectroscopic and image analysis. The lower satisfaction rate is found due to inconsistent results in various patient settings.

This observational study aims to present an adaptable point-of-care Near-Infrared (NIR) spectrophotometric approach with a constructive Machine Learning (ML) algorithm for monitoring Haemoglobin (Hb) concentration by considering dominating influencing factors into account.

To accomplish this objective, 121 subjects (19.2-55.4 years) were enrolled in the study, having a wide range of Hb concentrations (8.2-17.4 g/dL) obtained from two standard Laboratory analyzers. To inspect the performance, the unique dimensionality reduction approaches are applied with numerous regression models using 5-fold cross-validation.

The optimum accuracy is found using support vector regression (SVR) and mutual information having 3 independent features i.e. Pearson correlation (r)= 0.79, standard deviation (SD)= 1.07 g/dL, bias=-0.13 g/dL and limits of agreement (LoA)=-2.22 to 1.97 g/dL. Additionally, comparability between two standard laboratory analyzers is found as; r=0.97, SD=0.50 g/dL, bias=0.21 g/dL, and LoA= -0.77 to 1.19 g/dL.

The precision of ±1 g/dL in 5-fold cross-validation ensures the same performance irrespective of different age groups, gender, BMI, smoking level, drinking level, and skin type. The outcomes with the offered NIR sensing system and an exclusive ML algorithm can accelerate its’ requirement at remote locative rural areas and critical care units where continuous Hb monitoring is compulsory.

In order to perform a full surgical resection on non-palpable breast lesions, a current method necessitates correct intraoperative localization. Additionally, because it is an important prognostic factor for these patients, the examination of the lymph node status is crucial.

The aim of this study was to evaluate the efficiency of the sentinel node and occult lesion localization (SNOLL) technique in localizing non-palpable breast lesions together with sentinel lymph node (SLN) using a single radiotracer, that is, nanocolloid particles of human serum albumin (NC) labeled with technetium-99m (^99mTc).

39 patients were included, each having a single non-palpable breast lesion and clinically no evidence of axillary disease. Patients received ^99mTc-NC intratumorally on the same day as surgery under the guidance of ultrasound. Planar and single-photon emission computed tomography/computed tomography lymphoscintigraphy were performed to localize the breast lesion and the SLN. The occult breast lesion and SLN were both localized using a hand-held gamma-probe, which was also utilized to determine the optimal access pathway for surgery. In order to ensure a radical treatment in a single surgical session and reduce the amount of normal tissue that would need to be removed, the surgical field was checked with the gamma probe after the specimen was removed to confirm the lack of residual sources of considerable radioactivity.

Breast lesions were successfully localized and removed in all patients. Pathological findings revealed breast carcinoma in 11/39 patients (28%) and benign lesions in 28 (72%). Axillary SLNs were detected in 31/39 (79.5%) patients. The metastatic involvement of SLN was only seen in two cases.

While the identification rate of the SNOLL technique performed with an intratumoral injection of ^99mTc-NC as the sole radiotracer in non-palpable breast lesions was great, it was not fully satisfactory in SLNs.

Pulmonary enteric adenocarcinoma (PEAC) is an extremely rare variant of lung adenocarcinoma characterized by pathological features similar to those of colorectal adenocarcinoma. It is mostly observed on computed tomography (CT) and positron emission tomography (PET)/CT as solitary or multiple nodules/masses in the lung. It tends to grow rapidly and is difficult to distinguish from lung metastatic colorectal cancer. Herein, we have presented a case of PEAC with special imaging findings.

A chest CT scan of a 72-year-old man with suspected chronic pneumonia revealed a well-defined consolidation in the upper lobe of the left lung. The lesion was slightly enlarged at the 9-month follow-up, and low FDG accumulation was subsequently observed using ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT scans. The patient was later diagnosed with PEAC through percutaneous lung biopsy.

Our case has demonstrated specific imaging findings of PEAC.

Immune thrombocytopenic purpura (ITP) is a rare auto-antibody mediated disease of isolated thrombocytopenia (<100,000/µL) with normal haemoglobin levels and leukocyte counts. Only a small number of ITP cases have been reported with accompanying ophthalmological findings. Herein, we report an ITP case with demonstrative retinal haemorrhages.

A fifty-five-year-old woman with a known history of type 2 diabetes mellitus was referred to our clinic with blurred vision. After detailed anamnesis and clinical assessment, she was diagnosed as primary ITP in haematology department, and systemic steroid (1.5mg/kg) therapy was initiated. During her follow-up, a concomitant peripheral facial paralysis (PFP) emerged. In the course of follow-up, her platelet counts increased gradually, the retinal haemorrhages regressed partially, and the PFP recovered completely.

ITP is a rare haematologic disease that sometimes manifests with additional systemic involvements, and this disease should be remembered in the differential diagnosis of unusual retinal haemorrhages, which might be the only presenting feature.

Persistent trigeminal artery (PTA) is the most common vascular anastomosis between the carotid artery and vertebrobasilar systems. We report a very rare case of dissecting aneurysm in the right internal carotid artery (ICA) with ipsilateral PTA and discuss its clinical importance.

A 38-year-old male presented to the emergency department with paroxysmal dysphasia for 6h. Brain magnetic resonance (MR) imaging showed acute cerebral infarction of the right corona radiata and right parietal lobe. Three-dimensional time-of-flight MR angiography (3D TOF MRA) revealed severe stenosis of the petrous segment (C1 portion) of the right internal carotid artery and a PTA originating from the right ICA cavernous segment (C4 portion), with a length of approximately 1.8cm and a diameter of approximately 0.2cm. The ICA segments are all named according to the Bouthilier classification. The basilar artery (BA) under union was well developed. The bilateral posterior communicating arteries were also present. One day later, the high-resolution vessel-wall MR demonstrated a dissecting aneurysm in the C1 portion of the right ICA. The length of the dissecting aneurysm is approximately 4.4cm, the diameter of the true lumen at the most severe stenosis is approximately 0.2cm, and the diameter of the false lumen is approximately 0.8cm. Subsequent digital subtraction angiography (DSA) confirmed a dissecting aneurysm in the C1 portion of the right ICA. The patient was treated conservatively and did not undergo interventional surgery. Four months later, head and neck MRA showed that the right ICA blood flow was smooth and that the dissecting aneurysm had disappeared.

The Ethics Committee of Liaocheng People’s Hospital approved the research protocol in compliance with the Helsinki Declaration. Written informed consent was obtained from the individual for the publication of any potentially identifiable images or data included in this article.

Flow alteration with PTA may have influenced the formation of ICA dissection in this patient. Awareness of this is crucial in clinical practice because it can influence treatment options and intervention procedures.

The objective of this study was to evaluate the robustness of proton density fat fraction (PDFF) data determined by magnetic resonance imaging (MRI) and spectroscopy (MRS) via spatially resolved error estimation.

Using standard T2* relaxation time measurement protocols, in-vivo and ex-vivo MRI data with water and fat nominally in phase or out of phase relative to each other were acquired on a 7 T small animal scanner. Based on a total of 24 different echo times, PDFF maps were calculated in a magnitude-based approach. After identification of the decisive error-prone variables, pixel-wise error estimation was performed by simple propagation of uncertainty. The method was then used to evaluate PDFF data acquired for an explanted mouse liver and an in vivo mouse liver measurement.

The determined error maps helped excluding measurement errors as cause of unexpected local PDFF variations in the explanted liver. For in vivo measurements, severe error maps gave rise to doubts in the acquired PDFF maps and triggered an in-depth analysis of possible causes, yielding abdominal movement or bladder filling as in vivo occurring reasons for the increased errors.

The combination of pixel-wise acquisition of PDFF data and the corresponding error maps allows for a more specific, spatially resolved evaluation of the PDFF value reliability.

The prediction power of MRI radiomics for microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains uncertain.

To investigate the prediction performance of MRI radiomics for MVI in HCC.

Original studies focusing on preoperative prediction performance of MRI radiomics for MVI in HCC, were systematically searched from databases of PubMed, Embase, Web of Science and Cochrane Library. Radiomics quality score (RQS) and risk of bias of involved studies were evaluated. Meta-analysis was carried out to demonstrate the value of MRI radiomics for MVI prediction in HCC. Influencing factors of the prediction performance of MRI radiomics were identified by subgroup analyses.

13 studies classified as type 2a or above according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement were eligible for this systematic review and meta-analysis. The studies achieved an average RQS of 14 (ranging from 11 to 17), accounting for 38.9% of the total points. MRI radiomics achieved a pooled sensitivity of 0.82 (95%CI: 0.78 – 0.86), specificity of 0.79 (95%CI: 0.76 – 0.83) and area under the summary receiver operator characteristic curve (AUC) of 0.88 (95%CI: 0.84 – 0.91) to predict MVI in HCC. Radiomics models combined with clinical features achieved superior performances compared to models without the combination (AUC: 0.90 vs 0.85, P < 0.05).

MRI radiomics has the potential for preoperative prediction of MVI in HCC. Further studies with high methodological quality should be designed to improve the reliability and reproducibility of the radiomics models for clinical application.

The systematic review and meta-analysis was registered prospectively in the International Prospective Register of Systematic Reviews (No. CRD42022333822).

This study aimed to describe the characteristics of computed tomography (CT) and magnetic resonance imaging (MRI) of clear cell papillary renal cell carcinoma (CCPRCC).

This retrospective study comprised 27 patients diagnosed with 29 tumors of CCPRCC. The study was approved by the Medical Ethics Committee and the requirement for the informed consent was waived. The inclusion criteria stipulated pathology-confirmed CCPRCCs with at least one preoperative imaging examination, including CT or MRI. Two experienced radiologists independently analyzed the imaging characteristics, including size, location, growth mode, morphology, texture, density, and enhancement pattern. Paired t-test was used to compare differences in CT Hounsfield unit values and apparent diffusion coefficient (ADC) imaging between the tumor and the renal cortex.

The mean age of the 27 patients was 57.0 ± 14.2 years. Nineteen patients underwent CT, while 12 underwent MRI (There are 4 patients underwent not only CT but also MRI). Among the cases, 26 (96%) were single, and 1 (4%) was multiple, consisting of three lesions. Out of the 29 tumors, 15 (52%) were located in the left kidney and 14 (48%) in the right kidney. The mean tumor diameter was 3.3 ± 1.7 cm. Furthermore, 19 (66%), 3 (10%), and 7 (24%) tumors were solid, cystic, mixed solid, and cystic type, respectively. The growth mode was endogenous and exogenous in 8 (28%) and 21 (72%) tumors, respectively. The tumor shape was irregular and round in 5 (17%) and 24 (83%) tumors, respectively. The CT value of the tumor was approximately 33.2 ± 9.8 HU, which was not significantly different from that of the renal cortex(31.1 ± 6.3HU)(p = 0.343). Furthermore, 7 (24%), 12 (41%), and 3 (10%) had calcification, cystic degeneration, and hemorrhage, respectively. In 12 tumors, hypointense and hyperintense were predominant on T1 and T2-weighted images, respectively. The tumor capsule was found at the edge of 12 tumors. The average ADC value of the tumor (1.54 ± 0.74 × 10⁻³ mm²/s) and that of the renal cortex(1.68 ± 0.63×10^–3mm² /s) was not statistically significantly different (p = 0.260). The enhancement scanning revealed “wash-in and wash-out” enhancement in 19 (68%) tumors, continuous or progressive enhancement in 6 (21%) tumors, and enhanced cystic wall and central separation in 3 (11%) tumors.

CCPRCC occurs more likely in middle-aged and elderly individuals, and the tumor is prone to cystic degeneration, with rare bleeding and calcification, and no obvious limitation on MRI diffusion-weighted imaging, which enhancement form performs as mainly “wash-in and wash-out,” but the final diagnosis depends on histopathology.

Follicular Dendritic Cell Sarcomas (FDCS)was first found in 1986; the specificity of the disease is its rarity, with an incidence of only 0.4%, numerous doctors for its lack of understanding, the accuracy of imaging diagnosis is not great, which is easy to delay the treatment. This article summarizes several characteristic imaging manifestations of FDCS to provide imaging physicians with an understanding of the imaging properties of this rare disease. When faced with complex cases, the radiologist can consider this disease and include it in the differential diagnosis. FDCS occurs mainly in lymph nodes, mainly in the head and neck. The main symptoms are fatigue, local pain, or painless mass. The treatment method is not uniform, but scholars agree that we should strive for the opportunity of surgery as much as possible.

This paper reported a case of FDCS with pelvic recurrence 3 years after surgery. The patient was suspected to have lymphoma by postoperative pathology in the local hospital, and it is recommended that the patient be reexamined regularly. A soft tissue mass was recently found again in the left pelvic cavity. After an enhanced CT examination, the radiologist was skeptical of the previous diagnosis of lymphoma. Subsequently, a needle biopsy was performed at Peking University Shougang Hospital. The pathological results rejected the prior diagnosis of lymphoma after consultation with additional hospitals, and the patient was diagnosed with FDCS.

The imaging manifestations of FDCS lack absolute specificity, but it also has imaging characteristics, such as large areas of necrosis in the huge mass, rough mass calcification in the mass, enhanced scan showed “fast in and slow out” mode, and there were blood vessels in the tumor. FDCS mainly occurs in lymph nodes and is easily misdiagnosed as GIST, inflammatory myoblastoma, lymphoma, etc. Radiologists should continue to collect cases of this disease and include suspected cases in the differential diagnosis in clinical work.

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a genetic disorder that causes uncontrolled kidney cyst growth, leading to kidney volume enlargement and renal function loss over time. Total kidney volume (TKV) and cyst burdens have been used as prognostic imaging biomarkers for ADPKD.

This study aimed to evaluate nnUNet for automatic kidney and cyst segmentation in T2-weighted (T2W) MRI images of ADPKD patients.

756 kidney images were retrieved from 95 patients in the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) cohort (95 patients × 2 kidneys × 4 follow-up scans). The nnUNet model was trained, validated, and tested on 604, 76, and 76 images, respectively. In contrast, all images of each patient were exclusively assigned to either the training, validation, or test sets to minimize evaluation bias. The kidney and cyst regions defined using a semi-automatic method were employed as ground truth. The model performance was assessed using the Dice Similarity Coefficient (DSC), the intersection over union (IoU) score, and the Hausdorff distance (HD).

The test DSC values were 0.96±0.01 (mean±SD) and 0.90±0.05 for kidney and cysts, respectively. Similarly, the IoU scores were 0.91± 0.09 and 0.81±0.06, and the HD values were 12.49±8.71 mm and 12.04±10.41 mm, respectively, for kidney and cyst segmentation.

The nnUNet model is a reliable tool to automatically determine kidney and cyst volumes in T2W MRI images for ADPKD prognosis and therapy monitoring.

Dural sinus malformation (DSM) is a rather rare congenital condition that can be encountered in the fetus and infants. The cause and etiology of DSM remain unclear. Obstetric ultrasound plays a key role in screening fetal brain malformations, and MRI is frequently used as a complementary method to confirm the diagnosis and provide more details.

Here, we present a fetus with DSM by multiple imaging methods to help better understand the imaging characteristics of this malformation.

A 22-year-old primipara was referred to our hospital at 25 weeks of gestation following the detection of a fetal intracranial mass without any symptoms. A prenatal ultrasound performed in our hospital at 25 ^{+ 2} gestational weeks showed a large anechoic mass with liquid dark space, while no blood flow was detected. After the initial evaluation, this primipara received a prenatal MRI in our hospital. This examination at 25 ^{+ 5} gestational weeks delineated a fan-shaped mass in the torcular herophili, which was iso-to hyperintense on T1WI and hypointense on T2WI. At the lower part of this lesion, a quasi-circular hyperintense on T1WI and a signal slightly hyperintense on T2WI could be seen. Meanwhile, the adjacent brain parenchyma was compressed by the mass.

We reviewed the current literature to obtain a better understanding of the mechanisms, imaging characteristics, and survival status of DSM. Although the primipara of the present study regretfully opted for elective termination of pregnancy, the reevaluation of DSM survival deserves more attention because of the better survival data from recent studies.

Mixed epithelial and stromal tumor of the kidney (MESTK) is a rare benign lesion that appears as a solid cystic renal lesion or complex renal cystic lesion on medical imaging. There are no definite imaging criteria for METSK diagnosis.

We present a case of a solid cystic renal mass that was evaluated by contrast-enhanced ultrasound (CEUS) during an imaging workup. The patient underwent nephrectomy and histopathological confirmation of MESTK. The lesions showed hypoenhancement during the process. Quantitative perfusion analysis showed the septation of the solid cystic lesion to have lower peak enhancement with a longer rise time compared to the normal renal cortex.

CEUS can visualize the microcirculation of the organ and reconstruction of the vessels. By providing a more detailed visualization of the microvessel, CEUS is a useful tool for further characterizing renal lesions that show indeterminate enhancement on CT. This study determined the time to peak to be shorter for the cancerous lesion than the normal renal cortex, while peak intensity did not differ between the cancerous lesion and the normal renal cortex.

Quantitative perfusion analysis of CEUS may be useful for differentiating benign and malignant solid cystic renal masses. Further investigation is needed to determine whether peak intensity is a useful parameter in differentiating benign and malignant solid cystic lesions of the kidney.

Cytoarchitectonic mapping has revealed distinct subregions within Broadmann area 4 (BA 4) – BA 4a and BA 4p – with varying functional roles across tasks. We investigate their functional connectivity using resting-state functional magnetic resonance imaging (rsfMRI) to explore bilateral differences and the impact of handedness on connectivity within major brain networks.

This retrospective study involved 54 left- and right-handed subjects. We employed regions-to-regions-network rsfMRI analysis to examine the Cytoarchitectonic mapping of BA 4a and BA 4p functional connectivity with eight major brain networks.

Our findings reveal differential connectivity patterns in both right-handed and left-handed subjects:

Both right-handed subjects' BA 4a and BA 4p subregions exhibit connections to sensorimotor, dorsal attention, frontoparietal, and anterior cerebellar networks. Notably, BA 4a shows unique connectivity to the posterior cerebellum, lateral visual networks, and select salience regions. Similar connectivity patterns are observed in left-handed subjects, with BA 4a linked to sensorimotor, dorsal attention, frontoparietal, and anterior cerebellar networks. However, BA 4a in left-handed subjects shows distinct connectivity only to the posterior cerebellum. In both groups, the right portion of BA 4 demonstrates heightened connectivity compared to the left portion within each subregion.

Our study uncovers complex patterns of functional connectivity within BA 4a and BA 4p, influenced by handedness. These findings emphasize the importance of considering hemisphere-specific and handedness-related factors in functional connectivity analyses, with potential implications for understanding brain organization in health and neurodegenerative diseases.

Patients with cancer can develop bone metastasis when a solid tumor invades the bone, which is the third most commonly affected site by metastatic cancer, after the lung and liver. The early detection of bone metastases is crucial for making appropriate treatment decisions and increasing survival rates. Deep learning, a mainstream branch of machine learning, has rapidly become an effective approach to analyzing medical images.

To automatically diagnose bone metastasis with bone scintigraphy, in this work, we proposed to cast the bone metastasis diagnosis problem into automated image classification by developing a deep learning-based automated classification model.

A self-defined convolutional neural network consisting of a feature extraction sub-network and feature classification sub-network was proposed to automatically detect lung cancer bone metastasis, with a feature extraction sub-network extracting hierarchal features from SPECT bone scintigrams and feature classification sub-network classifying high-level features into two categories (i.e., images with metastasis and without metastasis).

Using clinical data of SPECT bone scintigrams, the proposed model was evaluated to examine its detection accuracy. The best performance was achieved if the two images (i.e., anterior and posterior scans) acquired from each patient were fused using pixel-wise addition operation on the bladder-excluded images, obtaining the best scores of 0.8038, 0.8051, 0.8039, 0.8039, 0.8036, and 0.8489 for accuracy, precision, recall, specificity, F-1 score, and AUC value, respectively.

The proposed two-class classification network can predict whether an image contains lung cancer bone metastasis with the best performance as compared to existing classical deep learning models. The high accumulation of ^99mTc MDP in the urinary bladder has a negative impact on automated diagnosis of bone metastasis. It is recommended to remove the urinary bladder before automated analysis.

Deep learning reconstruction for free-breathing pulmonary MRI.

To propose a motion-resolved 3D pulmonary MRI reconstruction scheme using the sinusoidal representation network (SIREN).

The proposed scheme learns the registration maps using SIREN to register an averaging image to get the final reconstructions. The learning of the network relies only on the undersampled data from the specific subject. The usage of the network for outputting the registration maps enables a memory-efficient algorithm, as outputting registration maps instead of images only requires small networks. The training of the network based on only undersampled data enables an unsupervised learning scheme, which makes the proposed scheme useful in cases in which fully sampled data is not available.

We compare the proposed SIREN-based motion-resolved reconstruction with two state-of-the-art methods for ten datasets. Both visual and quantitative comparison indicates the better performance of the proposed method.

In conclusion, the use of SIREN for 3D pulmonary MRI reconstruction allows for the efficient and accurate reconstruction of data that has been undersampled.

The purpose of this work was to identify which Glioblastoma (GBM) problems can be handled by Magnetic Resonance Imaging (MRI) and Machine Learning (ML) techniques. Results, limitations, and trends through a review of the scientific literature in the last 5 years were performed. Google Scholar, PubMed, Elsevier databases, and forward and backward citations were used for searching articles applying ML techniques in GBM. The 50 most relevant papers fulfilling the selection criteria were deeply analyzed. The PRISMA statement was followed to structure our report.

A partial taxonomy of the GBM problems tackled with ML methods was formulated with 15 subcategories grouped into four categories: extraction of characteristics from tumoral regions, differentiation, characterization, and problems based on genetics.

The dominant techniques in solving these problems are: Radiomics for feature extraction, Least Absolute Shrinkage and Selection Operator for feature selection, Support Vector Machines and Random Forest for classification, and Convolutional Neural Networks for characterization. A noticeable trend is that the application of Deep Learning on GBM problems is growing exponentially. The main limitations of ML methods are their interpretability and generalization.

The diagnosis, treatment, and characterization of GBM have advanced with the aid of ML methods and MRI data, and this improvement is expected to continue. ML methods are effective in solving GBM-related problems with different precisions, Overall Survival being the hardest problem to solve with accuracies ranging from 57%-71%, and GBM differentiation the one with the highest accuracy ranging from 80%-97%.

Renal cell carcinoma, especially in small renal masses (≤ 4 cm) (SRM), has increased. Pathological analysis revealed a high proportion of benign masses, highlighting the urgent need for precise SRM differentiation.

This research aimed to independently validate the performance of machine learning-based ultrasound (US) radiomics analysis in differentiating benign from malignant SRM, and to compare its performance with that of radiologists.

A total of 499 patients from two hospitals were retrospectively included in this study and divided into two cohorts. US images were used to extract radiomics features. To obtain the most robust features, inter-observer correlation coefficient, Spearman correlation coefficient, and least absolute shrinkage and selection operator methods were applied for feature selection. Three models were developed in the training data using the stochastic gradient boosting algorithm, including a clinical model, a radiomics model, and a combined model that integrated clinical factors and radiomics features. The performance of these models was evaluated in the independent external validation data, including discrimination, calibration, and clinical usefulness, and compared with pooled radiologists' assessments.

The AUCs of the clinical, radiomics, and combined models were 0.844, 0.942, and 0.954, respectively. The radiomics and combined models significantly outperformed the clinical model (all p < 0.05), while no significant difference was observed between them (p = 0.32). The radiomics and combined models showed good discrimination and calibration. Decision curve analysis exhibited that the combined model had clinical usefulness. Compared with the pooled radiologists’ assessment (AUC, 0.799), the combined model showed superior classification results (p < 0.01) and higher specificity (p < 0.01) with similar sensitivity (p = 0.62).

The combined model incorporating clinical factors and radiomics features accurately distinguished benign from malignant SRM.

Multisystem information, including musculoskeletal information, can be captured from chest CT scans of patients with COVID-19 without further examination.

This study aims to assess the relationship between chest CT-extracted baseline bone mineral density (BMD) and body composition parameters and the length of hospital stay in these patients.

A retrospective analysis was performed in a cohort of 88 patients with COVID-19. Correlation analysis and a generalized linear model (GLM) were used to assess the associations between the length of hospital stay and covariates, including age, sex, body mass index (BMI), BMD and body composition variables.

The mean length of hospital stay was 27.4±8.7 days. The length of hospital stay was significantly positively associated with age (r=0.202, p=0.046) and the paraspinal muscle fat ratio (r=0.246, p=0.021). The GLM involving age, sex, BMD, paraspinal muscle fat ratio, subcutaneous adipose tissue (SAT) area, visceral adipose tissue (VAT) area, and liver fat fraction (LFF) showed that the length of hospital stay was positively correlated with VAT area (β coefficients, 95% CI: 9.304, 1.141-17.478, p=0.025).

The musculoskeletal features extracted from chest CT correlated with the prognosis of COVID-19 patients. Factors including old age, a higher paraspinal muscle fat ratio and a larger VAT area in patients with COVID-19 were associated with longer hospital stays.

Coronary artery diseases are the leading cause of death worldwide. Stenting or angioplasty of coronary arteries as interventional management requires knowledge about the morphology of the coronary tree, including luminal diameters.

This work aimed to study the diameters of the left main coronary artery and its branches measured by QCA in relation to the diameters derived by Finet’s law.

This was a cross-sectional, retrospective, hospital-based study. The number of angiograms used was 357. The diameters of the left main coronary artery (LM1), left anterior interventricular artery (LAD1), and left circumflex artery (LCx1) were measured by QCA. The diameter of LM1 was measured by 5 mm before its termination, and the diameters of LAD1 and LCx1 were obtained by 5 mm from their origins. Finet’s law was used to calculate the diameters of LM2, LAD2 and LCx2 using the QCA measurements.

The mean age of participants was 53.3±8.8 years. Female patients represented 58.9%. The mean diameter of the left main coronary using QCA was 3.75±0.85 mm, and the diameter calculated using Finet’s law was 3.89±0.80 mm. The diameters of LAD1 and LCx1 were larger than those calculated with Finet’s law. The Z-test showed a significant difference between the diameter of the LM1 calculated by Finet’s law; both diameters were positively associated. The diameters of LAD1 and LAD2 showed a non-significant correlation (r = 0.0653, P = 0.259526) and a negative correlation between LCx1 and LCX2 (r = -0.2659, P = 0.00001). The Z-test showed a significant difference in the diameter of LAD and LCx measured by QCA and Finet’s law.

An association was found between the diameter of LM measured by QCA and calculated with Finet’s law; the diameter calculated by Finet’s law was larger. The diameters of LAD and LCx measured by QCA were larger than those calculated by Finet’s law. A positive correlation existed between the diameters measured by QCA and Finet’s law, and they had significant differences. Finet’s law can assist in the selection of stent size. Despite the literature about Finet’s law, generalising its use requires more studies on different ethnicities.