Current Medical Imaging - Volume 20, Issue 1, 2024

Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) is considered a potential marker of hepatic fibrosis (HF).

To explore the influencing factors of repeatability and reliability in IVIM-DWI parameters of ROI-based liver segments in participants with HF and healthy volunteers (HV) and to assess the diagnostic efficiency of these parameters in HF.

Participants with early HF (EHF, n=59) or advanced HF (AHF, n=38) and HV (n=48) were recruited. Two examiners measured IVIM data using mono-, bi-exponential and stretched exponential models. The results and influencing factors of repeatability and reliability of IVIM-DWI, and the diagnostic efficiency were analyzed.

The repeatability of D* (CV: 26.62–41.47%) and DDC (CV: 18.01–34.40%) was poor, the repeatability of ADC (CV: 4.95–9.76%), D (CV: 7.09–15.52%), f (CV: 9.35–17.15%), and α (CV: 7.48–13.81%) was better; ordered logistic regression showed statistically significant results of IVIM-derived parameters; the reliability showed no obvious trend, and ordered logistic regression showed statistically significant results of IVIM-derived parameters, groups, and partial hepatic segments (all p<0.001). IVIM-derived parameters with relatively good repeatability (CV<20%) and reliability (ICC>0.4) were used to establish regression models for differential diagnosis. The AUC of regression models was 0.744–0.783 (EHF vs. AHF), but no statistically significant parameters were found in the HV vs EHF comparison.

IVIM-derived parameters were the most important factors affecting the repeatability and reliability, while staging of HF and hepatic segments may be the influencing factors of reliability. IVIM-derived parameters showed medium diagnostic efficiency in distinguishing between EHF and AHF.

Registered on Clinical Trial Management Public Platform (registration code: ChiCTR2100052114, date: 17^th Oct. 2021).

Brain tumor is a grave illness causing worldwide fatalities. The current detection methods for brain tumors are manual, invasive, and rely on histopathological analysis. Determining the type of brain tumor after its detection relies on biopsy measures and involves human subjectivity. The use of automated CAD techniques for brain tumor detection and classification can overcome these drawbacks.

The paper aims to create two deep learning-based CAD frameworks for automatic detection and severity grading of brain tumors – the first model for brain tumor detection in brain MR images and model 2 for the classification of tumors into three types: Glioma, Meningioma, and Pituitary based on severity grading.

The novelty of the research work includes the architectural design of deep learning frameworks for detection and classification of brain tumor using brain MR images. The hyperparameter tuning of the proposed models is done to achieve the optimal parameters that result in maximizing the models' performance and minimizing losses.

The proposed CNN models outperform the existing state of the art models in terms of accuracy and complexity of the models. The proposed model developed for detection of brain tumors achieved an accuracy of 98.56% and CNN Model developed for severity grading of brain tumor achieved an accuracy of 92.36% on BraTs dataset.

The proposed models have an edge over the existing CNN models in terms of less complexity of the structure and appreciable accuracy with low training and test errors. The proposed CNN Models can be employed for clinical diagnostic purposes to aid the medical fraternity in validating their initial screening for brain tumor detection and its multi-classification.

Embryonal carcinoma is a rare tissue type in germ cell tumors. According to our literature review, metastatic embryonal carcinoma misdiagnosed as lymphoma because of its high similarity to lymphoma is extremely rare and has not been reported yet.

A 46-year-old middle adulthood male presented with unexplained fever, night sweats, abdominal distension for 3 months, and weight loss of around 7kg during almost 6 months, which is extremely similar to lymphoma from the clinical features and imaging examinations. After a clear diagnosis, the case not only obtained the opportunity of surgery but was also exempted from radiotherapy. The treatment effect was good. We report a case of rare metastatic embryonal carcinoma, which can provide insight into the diagnosis and treatment of embryonal carcinoma.

Metastatic embryonal carcinoma of abdominal lymph nodes can be highly similar to lymphoma; the diagnosis can only be based on clinical manifestations and imaging examination but also combined with patient history, tumor markers and biochemical examination. However, the final diagnosis depends on pathological biopsy.

To investigate the feasibility of image characteristics and radiomics combined with machine learning based on Gd-EOB-DTPA-enhanced MRI for functional liver reserve assessment in cirrhotic patients.

123 patients with cirrhosis were retrospectively analyzed; all our patients underwent pre-contrast MRI, triphasic (arterial phase, venous phase, equilibrium phase) Gd-EOB-DTPA dynamic enhancement and hepatobiliary phase (20 minutes delayed). The relative enhancement (RE) of the patient's liver, the liver-spleen signal ratio in the hepatobiliary phase (SI liver/ spleen), the liver-vertical muscle signal ratio in the hepatobiliary phase (SI liver/ muscle), the bile duct signal intensity contrast ratio (SIR), and the radiomics features were evaluated. The support vector machine (SVM) was used as the core of machine learning to construct the liver function classification model using image and radiomics characteristics, respectively.

The area under the curve was the largest in SIR to identify Child-Pugh group A versus Child-Pugh group B+C in the image characteristics, AUC = 0.740, and Perc. 10% to identify Child-Pugh group A versus Child-Pugh group B+C in the radiomics characteristics, AUC = 0.9337. The efficacy of the SVM model constructed using radiomics characteristics was better, with an area under the curve of 0.918, a sensitivity of 95.45%, a specificity of 80.00%, and an accuracy of 89.19%.

The image and radiomics characteristics based on Gd-EOB-DTPA-enhanced MRI can reflect liver function, and the model constructed based on radiomics characteristics combined with machine learning methods can better assess functional liver reserve.

This study evaluates the effectiveness of artificial intelligence (AI) in mammography in a diverse population from a middle-income nation and compares it to traditional methods.

A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) results.

Of 543 mammograms, 69.2% had lesions detected. Biopsies were performed on 25%(n=136), with 66(48.5%) benign and 70(51.5%) malignant. Substantial agreement in density assessment between the radiologist and AI software (κ =0.606, p < 0.001) and the BI-RADS category with and without AI (κ =0.74, p < 0.001). The performance of the AI software was comparable to the traditional methods. The sensitivity, specificity, PPV, and NPV or radiologists alone, radiologist + AI, and AI alone were 81.9%,90.4%,56.0%, and 97.1%; 81.0%, 93.1%,55.5%, and 97.0%; and 90.0%,76.5%,36.2%, and 98.1%, respectively. AI software enhances the accuracy of lesion diagnosis and reduces unnecessary biopsies, particularly for BI-RADS 4 lesions. The AI software results for synthetic were almost similar to the original 2D mammography, with AUC of 0.925 and 0.871, respectively.

AI software may assist in the accurate diagnosis of breast lesions, enhancing the efficiency of breast lesion diagnosis in a mixed population of opportunistic screening and diagnostic patients.

• The use of artificial intelligence (AI) in mammography for population-based breast cancer screening has been validated in high-income nations, with reported improved diagnostic performance. Our study evaluated the usage of an AI tool in an opportunistic screening setting in a multi-ethnic and middle-income nation.

• The application of AI in mammography enhances diagnostic accuracy, potentially leading to reduced unnecessary biopsies.

• AI integration into the workflow did not disrupt the performance of trained breast radiologists, as there is a substantial inter-reader agreement for BI-RADS category assessment and breast density.

Imaging techniques such as X-rays and 3D Computed Tomography (CT) are used to diagnose and evaluate a patient's shoulder before and after surgery. Identifying the kind, location, and severity of a shoulder fracture helps surgeons choose the right treatment and surgery.

The study examines the effectiveness of small incision reduction and superior closure pinning in treating Ideberg type III glenoid fractures identified by X-ray and CT scans.

From October 2017 to June 2022, 40 patients with Ideberg type III glenoid fractures underwent mini-incision reduction and superior closed pinning fixation using the Anterior (AA) and Posterior (PA) approaches. Pre- and post-surgery shoulder scores and imaging data were analyzed. Outpatient review and shoulder anteroposterior radiographs were collected at 1, 3, 6, and 12 months after surgery. We assessed shoulder joint function using the American Shoulder and Elbow Society (ASES) shoulder score, VAS score, Constant-Murley Shoulder Outcome (Constant) score, and DASH score.

A total of 40 patients were monitored for 14-16 months, averaging 15.2 ± 0.3 months. All fractures were healed between 14-25 weeks from X-rays, averaging 17.6 ± 5.4 weeks. Both the AA and PA groups had similar shoulder score changes. However, the AA group did better. In all cases, ASES shoulder scores were outstanding at 80%. Radiographs demonstratedno traumatic arthritis or internal fixation failure consequences like screw loosening or breakage.

It was concluded that Ideberg type III glenoid fracture reduction with an anterior small incision and superior closed pinning hollow lag screw internal fixation could be successful.

A recently developed deep-learning-based automatic evaluation model provides reliable and efficient Cobb angle measurements for scoliosis diagnosis. However, few studies have explored its clinical application, and external validation is lacking. Therefore, this study aimed to explore the value of automated assessment models in clinical practice by comparing deep-learning models with manual measurement methods.

The 481 spine radiographs from an open-source dataset were divided into training and validation sets, and 119 spine radiographs from a private dataset were used as the test set. The mean Cobb angle values assessed by three physicians in the hospital's PACS system served as the reference standard. The results of Seg4Reg, VFLDN, and manual measurement were statistically analyzed. The intra-class correlation coefficients (ICC) and the Pearson correlation coefficient (PCC) were used to compare their reliability and correlation. The Bland-Altman method was used to compare their agreement. The Kappa statistic was used to compare the consistency of Cobb angles at different severity levels.

The mean Cobb angle values measured were 35.89° ± 9.33° with Seg4Reg, 31.54° ± 9.78° with VFLDN, and 32.23° ± 9.28° with manual measurement. The ICCs for the reliability of Seg4Reg and VFLDN were 0.809 and 0.974, respectively. The PCC and MAD between Seg4Reg and manual measurements were 0.731 (p<0.001) and 6.51°, while those between VFLDN and manual measurements were 0.952 (p<0.001) and 2.36°. The Kappa statistic indicated VFLDN (k= 0.686, p< 0.001) was superior to Seg4Reg and manual measurements for Cobb angle severity classification.

The deep-learning-based automatic scoliosis Cobb angle assessment model is feasible in clinical practice. Specifically, the keypoint-based VFLDN is more valuable in actual clinical work with higher accuracy, transparency, and interpretability.

⁶⁸Ga-labeled prostate-specific membrane antigen positron emission tomography-computed tomography (⁶⁸Ga-PSMA PET/CT) has led to altered treatment plans for prostate cancer (PCa) patients.

This study aimed to investigate the impact of ⁶⁸Ga-PSMA PET/CT on overall survival (OS) and management in PCa.

Consecutive 100 patients who had ⁶⁸Ga-PSMA PET/CT and conventional imaging (CI) were included in this retrospective study. Disease stages and treatment plans according to both CI and ⁶⁸Ga-PSMA PET/CT were compared. The effect of ⁶⁸Ga-PSMA PET/CT on OS was assessed.

After ⁶⁸Ga-PSMA PET/CT, the stage changed in 64 patients (64%). By the reason of ⁶⁸Ga-PSMA PET/CT findings, treatment plans based on CI were changed in 73 patients (73%). According to the ROC analysis, patients with a PSA value below 8 had higher rates of change in staging (p<0.0001) and treatment (p=0.034). Both a PSA below 8 (OR 8.79 95% CI (2.72-28.43), p<0.001), and having a hormone-sensitive disease at the time of imaging (OR 5.6 95% CI (1.35-23.08), p=0.017) were significant independent factors predicting change in staging with ⁶⁸Ga-PSMA PET/CT. The results of a phi correlation coefficient analysis showed a significant relationship between therapy and changes in staging (ϕ=0.638, p<0.0001). Two-year OS was statistically different in hormone-sensitive patients with and without treatment change (95% vs 81%, p=0.006).

⁶⁸Ga-PSMA PET/CT has the effect of changing the treatment in 73% of PCa patients. There is a positive correlation between the changes in staging and treatment. Survival of hormone sensitive patients has improved due to treatment changes based on PET/CT findings.

Medical imaging mechanization has reformed medical management, empowering doctors to recognize cancer prematurely and promote patient outcomes. Imaging tests are of significant influence in the detection and supervision of cancer patients. Cancer recognition generally necessitates imaging studies that, in most instances, utilize a trivial amount of radiation. Methodologies such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) are predominant in clinical managerial, incorporating remedy and research.

Over recent years, diagnostic imaging has progressed from a state of commencement to an advanced level. Numerous modern imaging procedures have evolved. Although contemporary medical imaging comprises image exhibition together with image refining, computer-aided diagnosis (CAD), image inscribing and conserving, and image transference, the majority of which are embraced in picture documentation and communication processes.

This review targets to encapsulate toxicology information on skin cancer unpredictability essential to interpretation measures, report important factor that helps in defining skin cancer condition, and possible medical care alternatives or medical attention endorsed referring to diverse aspects involving the size and site of malignancy, the complications, patient’s priority and well being. We concisely review various therapy alternatives, methods of radiation autoimmunity, prime observational study designs of medical and distinct radiation resources and cancer risks, and current analysis methodologies and research precision.

The detail of this paper covers a brief review of research and evolution in medical imaging discipline and mechanism. This review considers the physiology of melanocytes and the pathogenesis of skin cancer using medical imaging. Also, a description of risk factors, prevention methods, screening, various diagnosis methods and different stages of skin cancer, sub-types and different types of treatment methods is provided in this paper for research and development.

To identify age-matched healthy volunteers, non-cirrhotic chronic liver disease (CLD) and cirrhotic patients based on portal hemodynamic parameters using 4D flow MRI.

A total of 10 age-matched healthy volunteers and 69 CLD patients were enrolled and underwent 4D flow MRI prospectively. 4D flow MR images were processed by an MD in biomedical engineering working on the GTFlow platform. Portal hemodynamic parameters include net flow (mL/cycle), flow volume per second through the lumen (mL/sec), average flow velocity (cm/sec), and maximum flow velocity (cm/sec). The difference in portal hemodynamic parameters of 4D flow MRI was compared among healthy volunteers, non-cirrhotic CLD patients and patients with cirrhosis by one-way ANOVA or Kruskal-Wallis nonparametric test and post hoc tests.

10 CLD patients without cirrhosis and 56 patients with cirrhosis were eventually included, along with 10 healthy volunteers who were divided into three groups. 3 patients with cirrhosis whose image quality did not meet the requirements were excluded. There were no significant differences in portal hemodynamic parameters among the three groups except portal average velocity (P > 0.05). There was no statistical difference in all portal hemodynamic parameters of 4D flow MRI between healthy volunteers and patients with cirrhosis (P > 0.05). There were significant differences in portal average velocity between non-cirrhotic CLD patients, healthy volunteers and patients with cirrhosis, respectively (11.44±3.93 vs 8.10±2.66, P=0.013; 11.44±3.93 vs 8.60±2.22, P=0.007).

Portal average velocity obtained by 4D flow MRI can be an auxiliary means to identify cirrhosis in patients with CLD.

Cardiac chamber dimensions and left ventricle (LV) wall thickness change with the cardiac cycle, in which researchers have set different time points for systole and diastole.

This study aimed to provide characteristics of normal heart and choose the correct cardiac cycle to measure maximum cardiac parameters for cardiovascular disease.

The parameters of left atrium (LA), LV, right atrium (RA), and right ventricle (RV), as well as the wall thickness of LV, were measured in different cardiac phases using cardiac computed tomography (CT). Then, their differences in different phases and the correlation between these parameters and traditional risk factors were analyzed. In addition, receiver operator characteristic curve (ROC) analyses was performed to estimate LA enlargement.

The dimensions of LA and RA as well as the wall thickness of LV reached the maximum at the phase of 35% – 45%, while the dimensions of LV and RV reached the maximum at 95% – 5%. However, the changes of LA-B (antero-posterior diameter), LV-D1 (basal dimension), RA-B (minor dimension), and RV-D2 (mid cavity dimension) were relatively more stable than other diameters during the cardiac cycle. The maximum LA-B diameter, LV-D1 diameter, RA-B diameter, and RV-D2 diameter as well as the maximum interventricular septum thickness were acquired. Heart rate (HR) and smoking were potential indicators of LV-D2 (mid cavity dimension), while HR and LV myocardial mass were potential indicators of LV-D3 (apical-basal dimension). In phase 45%, the cut-off value of LA-B was 37.12 mm, with high sensitivity for predicting LA enlargement.

Choosing the adaptive cardiac phase for evaluating cardiac chamber dimensions and wall thickness obtained by cardiac CT could provide a more accurate clinical measurement of the heart.

Congenital enterocolic fistula, an abnormal connection between the small intestine and the colon, is a rare condition with the potential for significant complications affecting the patient’s quality of life.

A 2 year and 7 months old girl presented with abdominal pain and diarrhea lasting more than 10 days. The formation of the intestinal fistula was first detected by ultrasound, and the blood flow in the intestinal wall was preliminally analyzed. Surgical exploration revealed a colonic fistula formed by the attachment of the jejunum to the descending colon. Postoperatively, symptoms improved; no secondary infection occurred and the fistula healed well.

Congenital colon fistula is rarely reported, and ultrasound is becoming more and more important in its diagnosis. Here, we report a case of congenital colonic fistula diagnosed by ultrasound. Ultrasound can dynamically and in real-time observe the intestinal condition, which is conducive to the early diagnosis and staging of congenital intestinal diseases and the determination of diagnosis and treatment schemes.

Coronary Heart Disease (CHD) is one of the most common types of cardiovascular disease, and Heart Failure (HF) is an important factor in its progression. We aimed to evaluate the diagnostic value and predictors of multiparametric Cardiac Magnetic Resonance (CMR) in CHD patients with HF.

The study retrospectively included 145 CHD patients who were classified into CHD (HF+) (n = 91) and CHD (HF–) (n = 54) groups according to whether HF occurred. CMR assessed LV function, myocardial strain and T1 mapping. Multivariate linear regression analyses were performed to identify predictors of LV dysfunction, myocardial fibrosis, and LV remodeling.

CHD (HF+) group had impaired strain, with increased native T1, ECV, and LVM index. The impaired strain was associated with LVM index (p < 0.05), where native T1 and ECV were affected by log-transformed amino-terminal pro-B-type natriuretic peptide (NT-proBNP) levels. ROC analysis showed the combination of global circumferential strain (GCS), native T1, and LVM had a higher diagnostic value for the occurrence of HF in CHD patients.

Meanwhile, log-transformed NT-proBNP was an independent determinant of impaired strain, increased LVM index, native T1 and ECV.

HF has harmful effects on LV systolic function in patients with CHD. In CHD (HF+) group, LV dysfunction is strongly correlated with the degree of LV remodeling and myocardial fibrosis. The combination of the three is more valuable in diagnosing HF than conventional indicators.

The Glypican 3 (GPC3)-positive expression in Hepatocellular Carcinoma (HCC) is associated with a worse prognosis. Moreover, GPC3 has emerged as an immunotherapeutic target in advanced unresectable HCC systemic therapy. It is significant to diagnose GPC3-positive HCCs before therapy. Regarding imaging diagnosis of HCC, dynamic contrast-enhanced CT is more common than MRI in many regions.

The aim of this study was to construct and validate a radiomics model based on contrast-enhanced CT to predict the GPC3 expression in HCC.

This retrospective study included 141 (training cohort: n = 100; validation cohort: n = 41) pathologically confirmed HCC patients. Radiomics features were extracted from the Artery Phase (AP) images of contrast-enhanced CT. Logistic regression with the Least Absolute Shrinkage and Selection Operator (LASSO) regularization was used to select features to construct radiomics score (Rad-score). A final combined model, including the Rad-score of the selected features and clinical risk factors, was established. Receiver Operating Characteristic (ROC) curve analysis, Delong test, and Decision Curve Analysis (DCA) were used to assess the predictive performance of the clinical and radiomics models.

5 features were selected to construct the AP radiomics model of contrast-enhanced CT. The radiomics model of AP from contrast-enhanced CT was superior to the clinical model of AFP in training cohorts (P < 0.001), but not superior to the clinical model in validation cohorts (P = 0.151). The combined model (AUC = 0.867 vs. 0.895), including AP Rad-score and serum Alpha-Fetoprotein (AFP) levels, improved the predictive performance more than the AFP model (AUC = 0.651 vs. 0.718) in the training and validation cohorts. The combined model, with a higher decision curve indicating more net benefit, exhibited a better predictive performance than the AP radiomics model. DCA revealed that at a range threshold probability approximately above 60%, the combined model added more net benefit compared to the AP radiomics model of contrast-enhanced CT.

A combined model including AP Rad-score and serum AFP levels based on contrast-enhanced CT could preoperatively predict GPC3-positive expression in HCC.

Pelvic floor dysfunction is characterized by incomplete fecal defecation, negatively affecting the quality of life. Magnetic resonance defecography (MRD) is a useful examination that is ionizing radiation-free and easily reproducible, and provides anatomical and functional details that are obtainable through multiplanar and dynamic examinations. The study aims to detect pathology using MRD in patients with suspected pelvic floor dysfunction and determine its cause.

MRD was performed on 79 individuals. Dynamic images were obtained at rest, straining, and during defecation. Pelvic hiatus mediolateral diameters were compared between groups.

The defecation phase provided more accurate results than the straining phase for determining the existence and severity of pathology significantly.

The defecation phase is the most accurate phase for identifying the existence and severity of pathology, as the pelvic hiatus mediolateral diameter is thought to be an important factor in triggering pelvic dysfunction.

Intracranial hypertension (IH) is a neurological disease characterized by increased intracranial pressure. Idiopathic intracranial hypertension (IIH) is characterized by increased intracranial pressure without an underlying neuroradiological cause (1-3). The IH associated with a reason such as a mass, hydrocephalus, or drug use, is referred to as secondary intracranial hypertension (SIH). We aimed to detect and determine whether the increased intracranial pressure causes a change in the diffusion values of the brain in the diffusion MRI images.

The study includes 24 consecutive patients diagnosed with IIH and 18 consecutive patients diagnosed with secondary intracranial hypertension (SIH). The control group included 24 patients. Measurement of apparent diffusion coefficient (ADC) was performed using DWI sections obtained from subcortical white matter and the cortex of the frontal lobe in the basal ganglia plane, caudate nucleus head, thalamus, the posterior leg of the internal capsule, corpus callosum splenium; in the centrum semiovale plane, from the central white matter region. with 1.5T MRI using b=500s/mm² and b=1000s/mm² values both in patients and control groups. Mean ADC values were compared between IIH, SIH patients and control groups.

The ADC values from the head of the caudate nucleus and the cortex were significantly higher in the IIH group compared to the control group. When the ADC values of the SIH and control groups were compared, it was found that some of the ADC measurements (subcortical white matter, cortex and semioval center) were significantly different. The comparison of the IIH and the SIH groups revealed that the ADC measurements of central white matter in the centrum semiovale, the subcortical white matter and the posterior leg of the internal capsule were significantly different.

We have found increased diffusion of IIH and SIH patients, which supports the development of brain edema. Even though the mechanism of the brain edema in IIH is not entirely clear, it is thought that the mechanism is different from the brain edema caused by a mass or a sinus thrombosis.

Hepatic perivascular epithelioid cell tumors (PEComa) often mimic hepatocellular carcinoma (HCC) in patients without cirrhosis. This study aimed to develop a nomogram using imaging characteristics on Gd-EOB-DTPA-enhanced MRI and to distinguish PEComa from HCC in a non-cirrhotic liver.

Forty patients with non-cirrhotic Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) were included in our study. A multivariate logistic regression model was used to select significant variables to distinguish PEComa from HCC. A nomogram was developed based on the regression model. The performance of the nomogram was assessed with respect to the ROC curve and calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical usefulness of the nomogram.

Two significant predictors were identified: the appearance of an early draining vein and the T1D value of tumors. The ROC curve showed that the area under the curve (AUC) of the model to predict the risk of PEComa was 0.91 (95% CI: 0.80~1) and showed that the model had high specificity (92.3%) and sensitivity (88.9%). The nomogram incorporating these two predictors showed favorable calibration, which was validated using 1000 resampling procedures, and the corrected C-index of this model was 0.90. Furthermore, DCA analysis showed that the model had clinical practicability.

In conclusion, the nomogram model showed favorable predictive accuracy for distinguishing PEComa from HCC in non-cirrhotic patients and may aid in clinical decision-making.

Atypical carcinoid (AC) is one of the rarest lung neuroendocrine tumors (NETs) that rarely metastasize to the breast, and only a few cases have been reported in the literature. Positron emission tomography/computed tomography (PET/CT) with somatostatin analogs (SSAs) labeled with Gallium-68 (⁶⁸Ga) now represents the gold standard for diagnosis and management of NETs. A case of an unusual metastasis to the breast from an AC detected by ⁶⁸Ga-DOTATATE PET/CT was reported.

A 73-year-old woman was presented with a right breast lesion found on mammography screening, which revealed a metastatic neuroendocrine tumor by histopathological analysis with a tru-cut biopsy. Subsequently, ⁶⁸Ga-DOTATATE PET/CT imaging performed for the initial evaluation showed increased radiotracer uptake in the lesion in the right breast as well as the nodular lesion in the middle lobe of the right lung, which was histologically confirmed to be AC.

Metastasis of uncommon AC of the lung to the breast is extremely rare. However, it is essential to properly differentiate metastatic tumors from primary disease due to differences in clinical management and prognosis, and ⁶⁸Ga-DOTATATE PET/CT is a unique diagnostic tool with the advantage of whole-body imaging.

The human skeletal system relies heavily on the integrity of bones, which provide structural support and safeguard vital organs. Accurate detection is paramount for effective diagnosis. Conventional methods for identifying fractures manually are not only time-consuming but also susceptible to errors.

The proposed methodology hinges on a patch antenna operating at 2.4 GHz and a bone phantom housing a simulated fracture, where the antenna is scanned. The collected signals are then processed with Delay-and-Sum (DAS), and Delay-Multiply-and-Sum (DMAS) reconstruction algorithms. The resulting images offer visual insights into the location of fractures.

Through experimentation, the efficacy of the images varies considerably in terms of their capacity for noise and artifact suppression. While DAS exhibits reasonable effectiveness, it suppresses noise and artifacts comprehensively. In contrast, DMAS offers clearer and more precise images of bone fractures.

In summary, the research introduces a cost-effective and non-invasive strategy for detecting bone fractures. By involving a patch antenna at 2.4 GHz, along with image reconstruction algorithms like DMAS and DAS, one can effectively visualize the location of bone fractures. The experimental results highlight the superiority of DMAS over DAS in terms of contrast resolution, making it a highly promising avenue for fracture detection.