Detection Of Lesions Research Articles

Serum Albumin Alters [18F]FDG Activity in the Liver and Blood Pool

Abstract Objective This study aims to investigate the correlation between the 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) activity of the liver and blood pool, and the serum albumin. Methods A retrospective analysis was conducted on adult patients who underwent [18F]FDG positron emission tomography/computed tomography at the Nuclear Medicine Unit of a hospital in Hong Kong between January 1, 2023, and March 31, 2023. The mean standardized uptake value normalized to lean body mass (SULmean) was measured in the liver and blood pool. Pearson's correlation analyses between the SULmean of reference regions and serum albumin were performed. Multiple linear regression was used to analyze the effects of serum albumin and other parameters as the independent predictors on SULmean of the reference regions. Results A total of 146 patients were included, with their SULmean of the liver and blood pool showing significantly positive correlations with serum albumin (r = 0.393, p < 0.001 and r = 0.207, p = 0.012, respectively). Multiple linear regression analyses confirmed serum albumin as an independent variable on SULmean of the liver and blood pool (p < 0.001 and p = 0.014, respectively). Conclusion Serum albumin alters [18F]FDG biodistribution in the liver and blood pool. The decrease in liver background activity in patients with low serum albumin may produce a higher false-positive rate of lesion detection, particularly when there is a drop of serum albumin in serial scans. Nuclear medicine physicians should be cautious of image interpretation.

Efficacy of Autofluorescence in Detection of Tobacco-associated Oral Mucosal Lesions - A Systematic Review

Abstract Background: This study was conducted with the aim to systematically review the efficacy of autofluorescence in the detection of tobacco-associated oral mucosal lesions. Objectives and Data Sources: Clinical studies with patients that focused on the autofluorescence method in the detection, visualisation, or management of tobacco-associated oral mucosal lesions were included in the literature search across bibliographic databases. Results: Autofluorescence visualisation showed positive results, with high sensitivity scores when compared with the gold standard of histopathology in the clinical evaluation of common tobacco-associated oral mucosal lesions. However, the low specificity values indicate its limited ability to distinguish between dysplasia and other benign conditions. Conclusions and Implications: This review provides promising evidence for the application of autofluorescence in the detection of tobacco-associated oral mucosal lesions where autofluorescence is used in conjunction with conventional examination with the gold standard of histopathological examination. Registration number: PROSPERO 2023 CRD42023442292.

Enhancing deep-seated hepatocellular carcinoma detection: assessing the added value of high mechanical index setting in sonazoid-based contrast-enhanced ultrasound during post-vascular phase.

This retrospective study aimed to investigate the role of an additional high mechanical index (MI) setting scan during the post-vascular phase (PVP) in detecting deep-seated hepatocellular carcinoma (HCC) lesions. A total of 805 confirmed HCCs, which underwent Sonazoid-based contrast-enhanced ultrasound (CEUS) between January 2014 and October 2021, were included. Low MI scan was initially employed for lesion detection during the PVP, followed by high MI scan. Propensity score matching (PSM) was utilized to address confounding variables. Of the 805 study lesions, 668 were detected as perfusion defects at the initial low MI setting, while 137 remained undetected. Among these 137 undetected lesions, 77 were identified at the subsequent high MI setting, whereas 60 remained undetected. Lesions that were larger (18.69±11.27mm vs. 16.55±7.42mm, p = 0.006), more superficial (6.06±2.41cm vs. 7.40±2.74cm, p < 0.001), and hypoechoic (482/668 vs. 62/137, p < 0.001) were detectable at the initial low MI setting. Male patients benefited more from the additional high MI scan (63/97 vs. 14/40, p < 0.001). Lesions identified with additional high MI were larger (18.30±8.76mm vs. 14.30±4.34mm, p < 0.001) and deeper than undetected ones (8.48±2.48cm vs. 6.02±2.43cm, p < 0.001). After PSM, depth was shown to be an independent predictor in multivariate analysis (odds ratio: 1.557, 95% confidence interval: 1.249-1.941). The depth cutoff was 7.75 cm, with a sensitivity of 0.681, specificity of 0.851, and area under the curve of 0.774. The additional high MI setting scan during the PVP of Sonazoid-based CEUS leads to enhanced detection of deep-seated HCCs.

Clinical diagnostic study of Ramp lesion of medial meniscus based on knee MRI at 90° flexed position

To evaluate the clinical diagnostic value of knee MRI at 90° flexed position for Ramp lesions of medial meniscus. A total of 228 patients with knee pain as the main complaint who were admitted between September 2021 and September 2023 was selected as the research subjects, of which 51 patients met the selection criteria and were enrolled in the study. There were 31 males and 20 females with an average age of 38.6 years (range, 15-67 years). Body mass index was 17.2-28.7 kg/m 2 (mean, 23.9 kg/m 2). There were 25 cases of left knee and 36 cases of right knee. The time from injury to admission was 0.1-14.3 weeks (mean, 2.1 weeks). Preoperative knee MRI at fully extended position (knee extension position) and 90° flexed position (knee flexion position) were performed to determine the presence of irregular signs at the posterior edge of the medial meniscus, and PHMM fluid high signal [i.e. complete fluid filling between the posterior horn of the medial meniscus (PHMM) and the capsule margin]. Findings obtained under arthroscopy served as the "gold standard" to analyze the sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of MRI at knee extension and flexion positions for the two specific signs of Ramp lesion. Twenty-one patients (41.2%) were diagnosed with Ramp lesions by using arthroscopy, including 1 case of Thaunat type Ⅰ, 2 cases of type Ⅱ, 6 cases of type Ⅲ, 7 cases of type Ⅳ, and 5 cases of type Ⅴ. The positive rates of irregular signs at the posterior edge of the medial meniscus on MRI at knee extension and flexion positions were significantly different from the diagnosis of Ramp injury under arthroscopy ( P<0.05). The sensitivity, specificity, accuracy, PPV, and NPV of MRI in the diagnosis of irregular signs were 76.1%, 60.0%, 66.7%, 57.1%, and 78.3% respectively at knee extension position, and 85.7%, 73.3%, 78.4%, 69.2%, and 88.0% respectively at knee flexion position. The positive rates of PHMM fluid high signal on MRI at knee extension and flexion positions were significantly different from the diagnosis of Ramp injury under arthroscopy ( P<0.05). The sensitivity, specificity, accuracy, PPV, and NPV of MRI in diagnosing PHMM fluid high signal were 38.1%, 100%, 74.5%, 100%, and 69.8% respectively at knee extension position, and 85.7%, 100%, 94.1%, 100%, and 90.9% respectively at knee flexion position. Knee MRI at 90° flexed position improves the diagnostic performance of the detection of medial meniscal Ramp lesions compared with MRI at fully extended position.

Use of stool DNA for colorectal cancer screening: a meta-analysis and systematic review.

Colorectal cancer is the third most common malignancy in the USA and accounts for more than 1 million deaths worldwide with screening shown to reduce CRC mortality. This meta-analysis analyzed the use of stool DNA for screening average risk, asymptomatic subjects for colorectal cancer and advanced precancerous lesions and compared sDNA to FOBT tests (gFOBT and FIT). Eight studies were included from four different countries with a total of 39 665 subjects. Pooled sensitivity and specificity for sDNA for detecting CRC was 83.3% (95% CI: 60.8-94.2) and 92.4% (95% CI: 90.1-94.1), respectively, compared with FOBT, which had a lower sensitivity at 70.2% (95% CI: 45.5-86.9) but higher specificity 95.7% (95% CI: 95.1-96.2). Further analysis showed improved sensitivity of sDNA to 92.6% when only the studies employing sDNA tests that incorporate hemoglobin immunochemical test were used. Both sDNA and FOBT tests had low sensitivity for detecting advanced precancerous lesions. sDNA tests are sensitive and specific for the detection of CRC but show low sensitivity compared with colonoscopy for the detection of advanced precancerous lesions.

Enhancing Lesion Detection in Inflammatory Myelopathies: A Deep Learning-Reconstructed Double Inversion Recovery MRI Approach.

The imaging of inflammatory myelopathies has advanced significantly over time, with MRI techniques playing a pivotal role in enhancing lesion detection. However, the impact of deep learning (DL)-based reconstruction on 3D double inversion recovery (DIR) imaging for inflammatory myelopathies remains unassessed. This study aims to compare acquisition time, image quality, diagnostic confidence, and lesion detection rates among sagittal T2WI, standard DIR, and DL -reconstructed DIR in patients with inflammatory myelopathies. In this observational study, patients diagnosed with inflammatory myelopathies were recruited between June 2023 and March 2024. Each patient underwent sagittal conventional turbo spin-echo sequences and standard 3D DIR (T2WI and standard 3D DIR used as references for comparison), followed by an undersampled accelerated DIRDL examination. Three neuroradiologists evaluated the images using a four-point Likert scale (from 1 to 4) for overall image quality, perceived signal-tonoise ratio, sharpness, artifacts, and diagnostic confidence. The acquisition times, and lesion detection rates were also compared between the acquisition protocols. A total of 149 participants were evaluated (mean age 40.6 ± 16.8 years; 71 females). The median acquisition time for DIRDL was significantly lower than for standard DIR (298 seconds [interquartile range (IQR), 288-301] vs 151 seconds [IQR, 148-155]; P < 0.001), showing a 49%time reduction. DIRDL images scored higher in overall quality, perceived signal-to-noise ratio, and artifact noise reduction (all P < 0.001). There were no significant differences in sharpness (P = 0.07), or diagnostic confidence (P = 0.06) between the standard DIR and DIRDL protocols. Additionally, DIRDL detected 37% more lesions compared to T2WI (300 vs. 219; P < 0.001). DIRDL significantly reduces acquisition time and improves image quality compared to standard DIR without compromising diagnostic confidence. Additionally, DIRDL enhances lesion detection in patients with inflammatory myelopathies, making it a valuable tool in clinical practice. These findings underscore the potential for incorporating DIRDL into future imaging guidelines. DL = deep learning; DIR = double inversion recovery; IQR = interquartile range; MS = multiple sclerosis; AQP4+NMOSD = AQP4-IgG positive neuromyelitis optica spectrum disorders; MOG = myelin oligodendrocyte glycoprotein; MOGAD = MOG antibody-associated diseases.

Detection of Elementary White Mucosal Lesions by an AI System: A Pilot Study

Aim: Accurately identifying primary lesions in oral medicine, particularly elementary white lesions, is a significant challenge, especially for trainee dentists. This study aimed to develop and evaluate a deep learning (DL) model for the detection and classification of elementary white mucosal lesions (EWMLs) using clinical images. Materials and Methods: A dataset was created by collecting photographs of various oral lesions, including oral leukoplakia, OLP plaque-like and reticular forms, OLL, oral candidiasis, and hyperkeratotic lesions from the Unit of Oral Medicine. The SentiSight.AI (Neurotechnology Co.®, Vilnius, Lithuania) AI platform was used for image labeling and model training. The dataset comprised 221 photos, divided into training (n = 179) and validation (n = 42) sets. Results: The model achieved an overall precision of 77.2%, sensitivity of 76.0%, F1 score of 74.4%, and mAP of 82.3%. Specific classes, such as condyloma and papilloma, demonstrated high performance, while others like leucoplakia showed room for improvement. Conclusions: The DL model showed promising results in detecting and classifying EWMLs, with significant potential for educational tools and clinical applications. Expanding the dataset and incorporating diverse image sources are essential for improving model accuracy and generalizability.

Near-infrared fluorescent molecular probes with cetuximab in the in vivo fluorescence imaging for epithelial ovarian cancer

BackgroundNear-infrared fluorescence (NIRF) imaging is an excellent choice for image-guided surgery due to its simple operation and non-invasiveness. Developing tumor-specific fluorescent molecular probes is key to fluorescence imaging-guided surgery. EGFR (epidermal growth factor receptor) is closely related to the proliferation and growth of tumor cells and is highly expressed in epithelial ovarian cancer (EOC). The study aims to construct a NIR fluorescent molecular probe using cetuximab (an EGFR monoclonal antibody) and investigate its feasibility for targeting EOC in vivo through fluorescence imaging.MethodsWe determined the expression of EGFR in EOC. NIR fluorescent molecular probe with cetuximab (cetuximab-Cy7) was chemically engineered and identified. The subcutaneous xenografted tumor model of EOC was induced using SKOV3-Luc cell line with positive expression of EGFR. Cetuximab-Cy7 was used for in vivo fluorescence imaging, and phosphate-buffered saline, free Cy7 dye and mouse isotype immunoglobulin G-Cy7 were used as controls. NIRF imaging system was performed to study the distribution and targeting of the probes. Tumors were imaged in situ and ex vivo, and fluorescent intensity was quantified. Resected specimens were analyzed to confirm diagnosis, and immunohistochemical (IHC) staining was used to identify EGFR expression.ResultsEGFR expression was increased in EOC tissues than fallopian tube tissues. The high expression of EGFR was significantly correlated with well-differentiation, residual lesions ≤ 1 cm, no recurrence and increased survival. NIRF imaging showed that the cetuximab-Cy7 enabled detection of tumor lesions in EOC-bearing mice with the optimal dose of 30 µg. The suitable imaging time window may be 24–96 h post-injection. Ex vivo fluorescence imaging indicated that fluorescent signal was mainly detected in the tumor and the lung. IHC results confirmed that xenografts were EGFR positive.ConclusionCetuximab-Cy7 can specifically target the tumors of EOC xenografted nude mice. This research lays the foundation for future studies on EOC surgery navigation.

Comparison of clinical performance between late and standard total-body [68Ga]Ga-PSMA-11 in biochemical recurrent prostate cancer.

Enhanced lesion detection in prostate cancer is observed with late [68Ga]Ga-PSMA-11 PET/CT imaging compared to standard [68Ga]Ga-PSMA-11 PET/CT imaging (50-100min p.i.). However, the poor image quality of late imaging using short axial field of view (SAFOV) PET/CT has hindered its sole clinical adoption. Conversely, the image quality of late imaging with a long axial field of view (LAFOV) [68Ga]Ga-PSMA-11 PET/CT fulfills clinical diagnostic requirements. Nonetheless, the diagnostic efficacy of late LAFOV [68Ga]Ga-PSMA-11 PET/CT with forced diuresis and its impact on treatment decisions, compared to standard LAFOV [68Ga]Ga-PSMA-11 PET/CT, remains unclear. This study aims to compare the rate of PET positivity between late and standard LAFOV [68Ga]Ga-PSMA-11 PET/CT and to evaluate the influence of late LAFOV [68Ga]Ga-PSMA-11 PET/CT with forced diuresis on treatment decisions relative to standard scans. From January 2021 to April 2024, 127 patients with biochemical recurrence of prostate cancer post-radical prostatectomy were enrolled to undergo both standard and late LAFOV [68Ga]Ga-PSMA-11 PET/CT scans at Shanghai Renji Hospital. We compared the rate of PET positivity between the two modalities at the patient level and across different anatomical regions. We assessed the added diagnostic value of late LAFOV [68Ga]Ga-PSMA-11 PET/CT and its impact on modifying patient treatment plans. The image quality of late LAFOV [68Ga]Ga-PSMA-11 PET/CT with forced diuresis in all patients met clinical diagnostic requirements. The rate of PET positivity of late LAFOV [68Ga]Ga-PSMA-11 PET/CT with forced diuresis were significantly higher than those of standard LAFOV [68Ga]Ga-PSMA-11 PET/CT (80.31% [102/127] vs. 65.35% [83/127]; P < 0.001). Late LAFOV [68Ga]Ga-PSMA-11 PET/CT demonstrated higher lesion SUVmax (16.69 ± 16.42 vs. 11.91 ± 10.72, P < 0.001) and TBR (6.26 ± 7.21 vs. 3.44 ± 3.57, P < 0.001) compared to standard LAFOV scans. Additionally, 14.17% (18/127) of patients experienced changes in their treatment regimen due to the superior detection capabilities of late LAFOV [68Ga]Ga-PSMA-11 PET/CT with forced diuresis compared to the standard scan. The rate of PET positivity of late LAFOV [68Ga]Ga-PSMA-11 PET/CT with forced diuresis compared to standard LAFOV [68Ga]Ga-PSMA-11 PET/CT highlight its potential as a valuable diagnostic tool for biochemically recurrent prostate cancer. This study paves the way for using late LAFOV [68Ga]Ga-PSMA-11 PET/CT with forced diuresis for prostate cancer imaging in daily clinical practice, facilitating more accurate and timely diagnoses.

Enhancing detection of various pancreatic lesions on endoscopic ultrasound through artificial intelligence: a basis for computer-aided detection systems.

Endoscopic ultrasound (EUS) is the most sensitive method for evaluation of pancreatic lesions but is limited by significant operator dependency. Artificial intelligence (AI), in the form of computer-aided detection (CADe) systems, has shown potential in increasing accuracy and bridging operator dependency in several endoscopic domains. However, the complexity of integrating AI into EUS is far more challenging. This aims to develop and test the basis for a CADe system for real-time detection and segmentation of all pancreatic lesions. In this single-center study EUS studies of pancreatic findings were included. Lesions were outlined by two expert (>5years performing EUS) endoscopists, and the two leading types of models were benchmarked. The models' performance was evaluated through per-pixel intersection over union (IoU). A total of 1497 EUS images from 165 patients were evaluated. The dataset included malignancies, neuroendocrine tumors, benign cysts, chronic and acute pancreatitis, normal fatty pancreas, and benign lesions. The best model demonstrated detection and segmentation on the test set with a mean IoU of 0.73, achieving a PPV, NPV, total accuracy, and ROC of 0.82, 0.96, 0.95, and 0.95, respectively. The algorithm is adaptable for real-time processing. We developed and tested deep learning models for real-time detection and segmentation of pancreatic lesions during EUS with promising results. This constitutes the basis for a CADe system for EUS, which could be valuable in future detection and evaluation of pancreatic lesions. Further studies for validation and generalization are underway.

A Quantitative Comparison Between Human and Artificial Intelligence in the Detection of Focal Cortical Dysplasia.

Artificial intelligence (AI) is thought to improve lesion detection. However, a lack of knowledge about human performance prevents a comparative evaluation of AI and an accurate assessment of its impact on clinical decision-making. The objective of this work is to quantitatively evaluate the ability of humans to detect focal cortical dysplasia (FCD), compare it to state-of-the-art AI, and determine how it may aid diagnostics. We prospectively recorded the performance of readers in detecting FCDs using single points and 3-dimensional bounding boxes. We acquired predictions of 3 AI models for the same dataset and compared these to readers. Finally, we analyzed pairwise combinations of readers and models. Twenty-eight readers, including 20 nonexpert and 5 expert physicians, reviewed 180 cases: 146 subjects with FCD (median age: 25, interquartile range: 18) and 34 healthy control subjects (median age: 43, interquartile range: 19). Nonexpert readers detected 47% (95% confidence interval [CI]: 46, 49) of FCDs, whereas experts detected 68% (95% CI: 65, 71). The 3 AI models detected 32%, 51%, and 72% of FCDs, respectively. The latter, however, also predicted more than 13 false-positive clusters per subject on average. Human performance was improved in the presence of a transmantle sign ( P < 0.001) and cortical thickening ( P < 0.001). In contrast, AI models were sensitive to abnormal gyration ( P < 0.01) or gray-white matter blurring ( P < 0.01). Compared with single experts, expert-expert pairs detected 13% (95% CI: 9, 18) more FCDs ( P < 0.001). All AI models increased expert detection rates by up to 19% (95% CI: 15, 24) ( P < 0.001). Nonexpert+AI pairs could still outperform single experts by up to 13% (95% CI: 10, 17). This study pioneers the comparative evaluation of humans and AI for FCD lesion detection. It shows that AI and human predictions differ, especially for certain MRI features of FCD, and, thus, how AI may complement the diagnostic workup.

Mammography In Breast Cancer Screening – Current Knowledge, Challenges, The Impact of Artificial Intelligence, And Effectiveness With A Focus On Poland

Mammography is a critical tool in breast cancer screening and secondary prevention, enabling early detection and significantly reducing breast cancer mortality. This literature review assesses the effectiveness of mammography in identifying early-stage tumors, discussing its advantages, limitations, and specific challenges. Additionally, advancements in imaging techniques, such as 3D tomosynthesis, are examined, especially for their enhanced sensitivity in women with dense breast tissue, which reduces structural overlap and improves diagnostic precision. The integration of artificial intelligence (AI) in mammographic image analysis opens new opportunities, supporting radiologists by automating the detection and classification of potential lesions, thereby increasing screening efficiency and reducing false-positive rates. However, effective implementation of AI requires seamless integration with clinical systems and ongoing research to refine algorithms for diverse clinical needs. Furthermore, legal frameworks are essential to delineate responsibility in AI-supported diagnostics. This review emphasizes the status of breast cancer screening in Poland, where participation rates remain low despite the availability of free screenings. This highlights the need for improved public awareness and accessibility, especially in underserved areas. Overall, the findings underscore the fundamental role of mammography in breast cancer detection, with AI and other technological advancements significantly enhancing diagnostic accuracy while identifying areas for further improvement.

Different Evaluation Strategies of Oncological CT Examinations with Regard to Professional Experience: A Clinical Study Using Eye-tracking.

Contrast-enhanced CT is the standard imaging technique in oncological objectives. Rates of missed pathologies depend on work experience of the respective radiologists. Thus the aim of this study is to analyze the eye movements of professionals while reading CT images in order to evaluate whether the eye-fixation patterns and search strategies of experienced radiologists could explain higher detection rates of pathologies and whether such patterns can be learned.Anonymized images of 10 patients were presented to three medical students and six radiologists with different levels of work experience. During image analysis, ocular fixation positions were recorded using an eye-tracking software tool. The CT scans were analyzed retrospectively, considering the individual course of disease with the issue of successful detection of all pathologies. Visual attention and dwell time of ocular fixation on clinically important abnormalities or areas with pathological findings, general search patterns, and time efficiency were assessed. For statistical analysis, interobserver variability and accuracy of lesion detection were evaluated taking into account individual experience.The results revealed that observer sensitivity depends on work experience due to a more systematic order of inspection and a well-known course of disease, e.g. in case of metastatic spread. The areas of missed pathologies mostly included secondary findings. Inexperienced readers changed the stratification considerably more often and required more time for reporting or detecting pathologies.Our results suggest that experienced radiological physicians reduce their amount of missed findings by looking more systematically at images and by applying a more targeted inspection of clinically important regions. · CT interpretation by radiology residents is faster and less error-prone compared to postgraduate residents. · systematic image analysis is trainable. · engrams tend to be acquired through experience. · Kluge S, Schülke C, Ites HC et al. Different Evaluation Strategies of Oncological CT Examinations with Regard to Professional Experience: A Clinical Study Using Eye-tracking. Fortschr Röntgenstr 2024; DOI 10.1055/a-2452-2180.

Heuristic strategy using hybrid deep learning with transfer learning for oral cancer detection

Oral cancer becomes the most disastrous ailment that affects the oral cavity parts of the mouth. Oral cancer diagnosis is the main challenge in the medical field. It becomes expensive and less capable of classifying oral cancer. In some cases, it may cause unnecessary morbidity and mortality. Recently, the detection of malignant and premalignant oral lesions has been a critical process owing to their low image resolution and lower acquisition time. Thus, a novel hybrid deep learning with meta-heuristic-based optimization is proposed. The pre-processing occurs by median filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE). The CLAHE method is utilized to reduce unwanted noise. Finally, the classification is done by a proposed hybrid-based deep learning model termed as Recurrent Deep Belief Network (RDBN), in which the Deep Belief Network (DBN) is incorporated with the Recurrent Neural Network (RNN). Here, the RDBN helps to increase the performance classification. Furthermore, the hyperparameters of the RDBN model, such as learning rate, epochs and hidden neurons, are tuned using the proposed Hybrid Beetle-Barnacle Swarm Optimization (HBBSO) algorithm, where the Barnacles Mating Optimizer (BMO) is superimposed with Beetle Swarm Optimization (BSO) algorithms. In the given proposed model, the selected features are extracted by the optimization algorithms. Here, the parameters are fine-tuned to get the better optimal solution. From the experimental outcome, the developed model has acquired 5.2% better than PSO-RDBN, 5.6% improved than GWO-RDBN, 3.2% enhanced than BSO-RDBN and 3.5% superior to BMO-RDBN regarding accuracy. Thus, the proposed model achieves higher results for detecting oral cancer with enhanced classification performance than the existing approaches.

Comparison of early and standard 18F-PSMA-11 PET/CT imaging in treatment-naïve patients with prostate cancer.

To evaluate the diagnostic performance of dual-time-point 18F-PSMA-11 PET/CT imaging at 30 and 60min post-injection (p.i.) in treatment-naïve patients with prostate cancer (PCa). Twenty treatment-naïve patients with histology-proven PCa who underwent 18F-PSMA-11 PET/CT scans at both 30 and 60-min p.i. were retrospectively analyzed. Lesion detection, semi-quantitative analysis of lesion and background, and unspecific bone uptake (UBU) between two time points were evaluated and compared. Besides, interrater reliability was also evaluated. Lesion detection was consistent at both 30 and 60-min p.i. imaging of 20 patients (mean age 72 ± 9), identifying 27 primary prostate lesions, 84 lymph node metastases, bone metastases in 8 patients, and other metastases in 2 patients. Primary prostate lesions showed no significant difference in SUVmax and target-to-blood pool (T/B) ratios between the two imaging times, while these parameters significantly increased over time in bone metastases. Lymph node metastases showed no significant difference in SUVmax but higher T/B ratios at 60min compared to 30min. A higher frequency of UBU was observed at 60min (37.3%) compared to 30min (32.3%), with significantly higher SUVmax and T/B ratios at 60min. 85.6% UBU was categorized as PSMA-RADS 2 at 60min, and the others were PSMA-RADS 3 or 4. The most frequent localization was vertebrae, followed by ribs. Interrater reliability was almost perfect for lesion detection at both time points. Early 30-min 18F-PSMA-11 PET/CT imaging provided comparable PCa lesion detection and semi-quantitative analysis with reduced UBU to the standard 60-min imaging.

Evaluation of a real-time optoelectronic method for the detection of cervical intraepithelial neoplasia and cervical cancer in patients with different transformation zone types

We aimed to evaluate the diagnostic value of TruScreen, a real-time diagnostic technology, for cervical lesions in patients with different transformation zone (TZ) types. A total of 1908 women aged 34.0 ± 7.3 years who have received cytology, human papillomavirus (HPV) testing, TruScreen, and colposcopy were recruited. The clinical performances of these tests were evaluated for their detection of high-grade squamous intraepithelial lesion (HSIL), adenocarcinoma in situ (AIS), or more severe lesions in patients with different TZ types. For the detection of HSIL, AIS, or more severe lesions, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of TruScreen were 65.08%, 64.76%,11.55%, and 96.33%, respectively, which were similar to cytology (all P values > 0.05). For the detection of HSIL + in patients with type 3 TZ, the sensitivity, specificity, PPV, and NPV of TruScreen were 72.29%, 67.59%, 13.86%, and 97.13%, respectively, which were significantly higher than the sensitivity (51.16%, P = 0.029), specificity (59.59%, P = 0.001), PPV (7.94%, P = 0.016), and NPV (94.71%, P = 0.049) in type 1 and type 2 TZs. TruScreen has detection accuracy comparable to cytology and performs even better in patients with type 3 TZ.

Diabetic Foot Infection: Understanding the Disease its Diagnosis and Prevention

The purpose of this manuscript is to provide a thorough overview of current concepts regarding the evaluation and management of diabetic foot infections. One of the most serious and frequent complications that patients with diabetes mellitus come across is diabetic foot infections. A comprehensive investigation of the literature on diabetic foot infections has been conducted, with a focus on the Diabetes mellitus, pathophysiology, identifiable risk factors, evaluation (including physical examination and laboratory values), treatment approaches, and determining the infection's severity. Preventing severe limb loss and maintaining function are the goals of treating diabetic foot infections. Most diabetic foot infections are caused by polymicrobial organisms. It is probable that a variety of gram-positive and gram-negative aerobes are present at the infection site. Controlling the infection and avoiding more morbidity requires early lesion detection, timely initiation of the proper antibiotic therapy, and surgical excision of necrotic tissue and bone.

Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory study

BackgroundTo investigate the ability of artificial intelligence (AI)-based and semi-quantitative dynamic contrast enhanced (DCE) multiparametric MRI (mpMRI), performed within [18F]-PSMA-1007 PET/MRI, in differentiating benign from malignant prostate tissues in patients with primary prostate cancer (PC).ResultsA total of seven patients underwent whole-body [18F]-PSMA-1007 PET/MRI examinations including a pelvic mpMRI protocol with T2w, diffusion weighted imaging (DWI) and DCE image series. Conventional analysis included visual reading of PET/MRI images and Prostate Imaging Reporting & Data System (PI-RADS) scoring of the prostate. On the prostate level, we performed manual segmentations for time-intensity curve parameter formation and semi-quantitative analysis based on DCE segmentation data of PC-suspicious lesions. Moreover, we applied a recently introduced deep learning (DL) pipeline previously trained on 1010 independent MRI examinations with systematic biopsy-enhanced histopathological targeted biopsy lesion ground truth in order to perform AI-based lesion detection, prostate segmentation and derivation of a deep learning PI-RADS score. DICE coefficients between manual and automatic DL-acquired segmentations were compared. On patient-based analysis, PET/MRI revealed PC-suspicious lesions in the prostate gland in 6/7 patients (Gleason Score-GS ≥ 7b) that were histologically confirmed. Four of these patients also showed lymph node metastases, while two of them had bone metastases. One patient with GS 6 showed no PC-suspicious lesions. Based on DCE segmentations, a distinction between PC-suspicious and normal appearing tissue was feasible with the parameters fitted maximum contrast ratio (FMCR) and wash-in-slope. DICE coefficients (manual vs. deep learning) were comparable with literature values at a mean of 0.44. Further, the DL pipeline could identify the intraprostatic PC-suspicious lesions in all six patients with clinically significant PC.ConclusionFirstly, semi-quantitative DCE analysis based on manual segmentations of time-intensity curves was able to distinguish benign from malignant tissues. Moreover, DL analysis of the MRI data could detect clinically significant PC in all cases, demonstrating the feasibility of AI-supported approaches in increasing diagnostic certainty of PSMA-radioligand PET/MRI.

Targeting c-MET for Endoscopic Detection of Dysplastic Lesions Within Barrett's Esophagus Using EMI-137 Fluorescence Imaging.

Esophageal cancer (EC) carries a poor prognosis with 5-year overall survival of less than 20%. Barrett's esophagus (BE) increases the risk of esophageal adenocarcinoma (EAC). The aim of this study was to investigate the ability of EMI-137, a mesenchymal-epithelial transition factor (c-MET)-targeting optical imaging tracer, to detect dysplasia in BE. c-MET expression in human esophageal tissue was investigated using Gene Expression Omnibus (GEO) datasets, tissue microarrays and BE biopsies. EMI-137 was tested in a dual xenograft mouse model bearing OE33 (c-MET high expression) and FLO-1 (c-MET low expression) tumors. Fluorescence molecular endoscopy (FME) was performed in a mouse model of Barrett's-like metaplasia and dysplasia (L2-IL1β). Tumors and organs-of-interest were evaluated through ex vivo fluorescence imaging. MET mRNA expression analyses and c-MET immunostaining confirmed upregulation of c-MET in BE and EAC compared to normal epithelium. There was strong accumulation of EMI-137 in OE33 xenografts 3 h post injection decreasing by more than 50% on co-injection of a 10-fold molar excess of unlabeled EMI-137. The target-to background ratio (TBR) at 3 h p.i. for OE33 and FLO-1 tumors was 10.08 and 1.42, respectively. FME of L2-IL1β mice showed uptake of EMI-137 in dysplastic lesions within BE with a TBR of 1.9 in vivo, and greater than 2 in ex vivo fluorescence imaging. EMI-137 accumulates in dysplastic lesions within BE and in c-MET positive EAC. EMI-137 imaging has potential as a screening and surveillance tool for patients with BE and as a means to detecting dysplasia and EAC.

Feasibility of Early 68Ga-FAP-2286 PET Imaging: A Case Study.

Fibroblast activation protein (FAP) has emerged as a promising molecular target for diagnostic and therapeutic strategies. Previous research, including our own study and other published reports, has highlighted the potential of FAP inhibitors labeled with 68Ga as effective diagnostic radiopharmaceuticals. In this study, we present a comparative analysis of early and late PET/CT scans, using 68Ga-FAP-2286, for the detection of tumor lesions in a patient with metastatic breast adenocarcinoma.