Detection Of Lesions Research Articles

Predictable changes in the accuracy of human papillomavirus tests after vaccination: review with implications for performance monitoring in cervical screening.

Vaccination against human papillomavirus (HPV) is changing the performance of cytology as a cervical screening test, but its effect on HPV testing is unclear. We review the effect of HPV16/18 vaccination on the epidemiology and the detection of HPV infections and high-grade cervical lesions (CIN2+) to evaluate the likely direction of changes in HPV test accuracy. The reduction in HPV16/18 infections and cross-protection against certain non-16/18 high-risk genotypes, most notably 31, 33, and/or 45, will likely increase the test's specificity but decrease its positive predictive value (PPV) for CIN2+. Post-vaccination viral unmasking of non-16/18 genotypes due to fewer HPV16 co-infections might reduce the specificity and the PPV for CIN2+. Post-vaccination clinical unmasking exposing a higher frequency of CIN2+ related to non-16/18 high-risk genotypes is likely to increase the specificity and the PPV of HPV tests. The effect of HPV16/18 vaccination on HPV test sensitivity is difficult to predict based on these changes alone. Programmes relying on HPV detection for primary screening should monitor the frequency of false-positive and false-negative tests in vaccinated (younger) vs. unvaccinated (older) cohorts, to assess the outcomes and performance of their service.

Comparison of 3T and 7T magnetic resonance imaging for direct visualization of finger flexor pulley rupture: an ex-vivo study.

To compare image quality and diagnostic performance of 3T and 7T magnetic resonance imaging (MRI) for direct depiction of finger flexor pulleys A2, A3 and A4 before and after artificial pulley rupture in an ex-vivo model using anatomic preparation as reference. 30 fingers from 10 human cadavers were examined at 3T and 7T before and after being subjected to iatrogenic pulley rupture. MRI protocols were comparable in duration, both lasting less than 22 min. Two experienced radiologists evaluated the MRIs. Image quality was graded according to a 4-point Likert scale. Anatomic preparation was used as gold standard. In comparison, 7T versus 3T had a sensitivity and specificity for the detection of A2, A3 and A4 pulley lesions with 100% vs. 95%, respectively 98% vs. 100%. In the assessment of A3 pulley lesions sensitivity of 7T was superior to 3T MRI (100% vs. 83%), whereas specificity was lower (95% vs. 100%). Image quality assessed before and after iatrogenic rupture was comparable with 2.74 for 7T and 2.61 for 3T. Visualization of the A3 finger flexor pulley before rupture creation was significantly better for 7T (p < 0.001). Interobserver variability showed substantial agreement at 3T (κ = 0.80) and almost perfect agreement at 7T (κ = 0.90). MRI at 3T allows a comparable diagnostic performance to 7T for direct visualization and characterization of finger flexor pulleys before and after rupture, with superiority of 7T MRI in the visualization of the normal A3 pulley.

Adaptive Feature Medical Segmentation Network: an adaptable deep learning paradigm for high-performance 3D brain lesion segmentation in medical imaging.

In neurological diagnostics, accurate detection and segmentation of brain lesions is crucial. Identifying these lesions is challenging due to its complex morphology, especially when using traditional methods. Conventional methods are either computationally demanding with a marginal impact/enhancement or sacrifice fine details for computational efficiency. Therefore, balancing performance and precision in compute-intensive medical imaging remains a hot research topic. We introduce a novel encoder-decoder network architecture named the Adaptive Feature Medical Segmentation Network (AFMS-Net) with two encoder variants: the Single Adaptive Encoder Block (SAEB) and the Dual Adaptive Encoder Block (DAEB). A squeeze-and-excite mechanism is employed in SAEB to identify significant data while disregarding peripheral details. This approach is best suited for scenarios requiring quick and efficient segmentation, with an emphasis on identifying key lesion areas. In contrast, the DAEB utilizes an advanced channel spatial attention strategy for fine-grained delineation and multiple-class classifications. Additionally, both architectures incorporate a Segmentation Path (SegPath) module between the encoder and decoder, refining segmentation, enhancing feature extraction, and improving model performance and stability. AFMS-Net demonstrates exceptional performance across several notable datasets, including BRATs 2021, ATLAS 2021, and ISLES 2022. Its design aims to construct a lightweight architecture capable of handling complex segmentation challenges with high precision. The proposed AFMS-Net addresses the critical balance issue between performance and computational efficiency in the segmentation of brain lesions. By introducing two tailored encoder variants, the network adapts to varying requirements of speed and feature. This approach not only advances the state-of-the-art in lesion segmentation but also provides a scalable framework for future research in medical image processing.

Assessment of drainage vein of focal nodular hyperplasia using contrast-enhanced ultrasound.

Identification of drainage vessels is useful for differential diagnosis of hepatic tumors. Direct drainage to the hepatic vein has been reported to occur in focal nodular hyperplasia (FNH), but studies evaluating the drainage veins of FNH are limited. We aimed to investigate the detection rate of the FNH drainage vein and the factors related to visualization of the drainage vein on contrast-enhanced ultrasound (CEUS). Fifty consecutive patients with 50 FNH lesions were retrospectively evaluated in this study. We calculated and compared the detection rate of the FNH drainage vein on CEUS, contrast-enhanced magnetic resonance imaging (CEMRI), and contrast-enhanced computed tomography (CECT), and identified the factors correlated with visualization of the FNH drainage vein on CEUS by using multivariate logistic regression analyses. Visualization of the drainage vein was confirmed in 31 of 50 lesions (62%) using CEUS, three of 44 lesions (6.8%) using CEMRI, and one of 18 lesions (5.6%) using CECT. The detection rate of the FNH drainage vein on CEUS was significantly higher than that on CEMRI and CECT (p < 0.001). Multivariate analysis identified lesion size (≥ 25mm) and detection of the spoke-wheel pattern on Doppler US as independent factors for drainage vein detection in FNH. Our study showed that rapid FNH drainage to the hepatic vein was observed at a relatively high rate on CEUS, suggesting that CEUS focusing on detection of drainage veins is important for diagnosing FNH.

Linked color imaging improves colorectal lesion detection especially for low performance endoscopists: An international trial in Asia.

Linked color imaging (LCI) is an image-enhanced endoscopy technique that accentuates the color difference between red and white, potentially improving the adenoma detection rate (ADR). However, it remains unclear whether LCI performance in detecting colorectal lesions differs based on endoscopists' experience levels. We aimed to evaluate the differences in LCI efficacy based on the experience levels of endoscopists by conducting an exploratory analysis. In this post hoc analysis of an international randomized controlled trial comparing the detection of adenoma and other lesions using colonoscopy with LCI and high-definition white light imaging (WLI), we included patients from 11 institutions across four countries/regions: Japan, Thailand, Taiwan, and Singapore. We retrospectively reviewed differences in the lesion detection of LCI according to endoscopists' colonoscopy history or ADR. We included 1692 and 1138 patients who underwent colonoscopies performed by 54 experts (experience of ≥5000 colonoscopies) and by 43 non-experts (experience of <5000 colonoscopies), respectively. Both expert and non-expert groups showed a significant improvement in ADR with LCI compared to WLI (expert, 61.7% vs 46.4%; P<0.001; non-expert, 56.6% vs 46.4%; P<0.001). LCI had no effect on sessile serrated lesion detection rate in non-experts (3.1% vs 2.5%; P=0.518). LCI significantly improved detection rates in endoscopists with relatively low detection performance, defined as an ADR<50%. This exploratory study analyzed data from a previous trial and revealed that LCI is useful for both experts and non-experts and is even more beneficial for endoscopists with relatively low detection performance using WLI.

New-onset diabetes is a predictive risk factor for pancreatic lesions in high-risk individuals: An observational cohort study

ABSTRACT Background and Objectives Pancreatic cancer (PC) is the third cause of cancer-related deaths. Early detection and interception of premalignant pancreatic lesions represent a promising strategy to improve outcomes. We evaluated risk factors of focal pancreatic lesions (FPLs) in asymptomatic individuals at hereditary high risk for PC. Methods This is an observational single-institution cohort study conducted over a period of 5 years. Surveillance was performed through imaging studies (EUS or magnetic resonance imaging/magnetic resonance cholangiopancreatography) and serum biomarkers. We collected demographic characteristics and used univariate and multivariate logistic regression models to evaluate associations between potential risk factors and odd ratios (ORs) for FPL development. Results A total of 205 patients completed baseline screening. Patients were followed up to 53 months. We detected FPL in 37 patients (18%) at baseline; 2 patients had lesions progression during follow-up period, 1 of them to PC. Furthermore, 13 patients developed new FPLs during the follow-up period. Univariate and multivariate analyses revealed that new-onset diabetes (NOD) is strongly associated with the presence of FPL (OR, 10.94 [95% confidence interval, 3.01–51.79; P &lt; 0.001]; OR, 9.98 [95% confidence interval, 2.15–46.33; P = 0.003]). Follow-up data analysis revealed that NOD is also predictive of lesions progression or development of new lesions during screening (26.7% vs. 2.6%; P = 0.005). Conclusions In a PC high-risk cohort, NOD is significantly associated with presence of FPL at baseline and predictive of lesions progression or new lesions during surveillance.

Safe and Informed Use of Gadolinium-Based Contrast Agent in Body Magnetic Resonance Imaging: Where We Were and Where We Are.

Gadolinium-based contrast agents (GBCAs) have helped to improve the role of magnetic resonance imaging (MRI) for the diagnosis and treatment of diseases. There are currently nine different commercially available gadolinium-based contrast agents (GBCAs) that can be used for body MRI cases, and which are classifiable according to their structures (cyclic or linear) or biodistribution (extracellular-space agents, target/specific-agents, and blood-pool agents). The aim of this review is to illustrate the commercially available MRI contrast agents, their effect on imaging, and adverse reaction on the body, with the goal to lead to their proper selection in different clinical contexts. When we have to choose between the different GBCAs, we have to consider several factors: (1) safety and clinical impact; (2) biodistribution and diagnostic application; (3) higher relaxivity and better lesion detection; (4) higher stability and lower tissue deposit; (5) gadolinium dose/concentration and lower volume injection; (6) pulse sequences and protocol optimization; (7) higher contrast-to-noise ratio at 3.0 T than at 1.5 T. Knowing the patient's clinical information, the relevant GBCAs properties and their effect on body MRI sequences are the key features to perform efficient and high-quality MRI examination.

Detection of structural lesions of the sacroiliac joints in patients with spondyloarthritis: A comparison of T1-weighted 3D spoiled gradient echo MRI and MRI-based synthetic CT versus T1-weighted turbo spin echo MRI.

To investigate the detection of erosion, sclerosis and ankylosis using 1mm 3D T1-weighted spoiled gradient echo (T1w-GRE) MRI and 1mm MRI-based synthetic CT (sCT), compared with conventional 4mm T1w-TSE. Prospective, cross-sectional study. Semi-coronal 4mm T1w-TSE and axial T1w-GRE with 1.6mm slice thickness and 0.8mm spacing between overlapping slices were performed. The T1w-GRE images were processed into sCT images using a commercial deep learning algorithm, BoneMRI. Both were reconstructed into 1mm semi-coronal images. T1w-TSE, T1w-GRE and sCT images were assessed independently by 3 expert and 4 non-expert readers for erosion, sclerosis and ankylosis. Cohen's kappa for inter-reader agreement, exact McNemar test for lesion frequencies and Wilcoxon signed-rank test for confidence in lesion detection were used. Nineteen patients with axial spondyloarthritis were evaluated. T1w-GRE increased inter-reader agreement for detecting erosion (kappa 0.42 vs 0.21 in non-experts), increased detection of erosion (57 vs 43 of 152 joint quadrants) and sclerosis (26 vs 17 of 152 joint quadrants) among experts, and increased reader confidence for scoring erosion and sclerosis. sCT increased inter-reader agreement for detecting sclerosis (kappa 0.69 vs 0.37 in experts) and ankylosis (0.71 vs 0.52 in non-experts), increased detection of sclerosis (34 vs 17 of 152 joint quadrants) and ankylosis (20 vs 13 of 76 joint halves) among experts, and increased reader confidence for scoring erosion, sclerosis and ankylosis. T1w-GRE and sCT increase sensitivity and reader confidence for the detection of erosion, sclerosis and ankylosis, compared with T1w-TSE. These methods improve the detection of sacroiliac joint structural lesions and might be a useful addition to SIJ MRI protocols both in routine clinical care and as structural outcome measures in clinical trials.

A protocol for annotation of total body photography for machine learning to analyze skin phenotype and lesion classification.

Artificial Intelligence (AI) has proven effective in classifying skin cancers using dermoscopy images. In experimental settings, algorithms have outperformed expert dermatologists in classifying melanoma and keratinocyte cancers. However, clinical application is limited when algorithms are presented with 'untrained' or out-of-distribution lesion categories, often misclassifying benign lesions as malignant, or misclassifying malignant lesions as benign. Another limitation often raised is the lack of clinical context (e.g., medical history) used as input for the AI decision process. The increasing use of Total Body Photography (TBP) in clinical examinations presents new opportunities for AI to perform holistic analysis of the whole patient, rather than a single lesion. Currently there is a lack of existing literature or standards for image annotation of TBP, or on preserving patient privacy during the machine learning process. This protocol describes the methods for the acquisition of patient data, including TBP, medical history, and genetic risk factors, to create a comprehensive dataset for machine learning. 500 patients of various risk profiles will be recruited from two clinical sites (Australia and Spain), to undergo temporal total body imaging, complete surveys on sun behaviors and medical history, and provide a DNA sample. This patient-level metadata is applied to image datasets using DICOM labels. Anonymization and masking methods are applied to preserve patient privacy. A two-step annotation process is followed to label skin images for lesion detection and classification using deep learning models. Skin phenotype characteristics are extracted from images, including innate and facultative skin color, nevi distribution, and UV damage. Several algorithms will be developed relating to skin lesion detection, segmentation and classification, 3D mapping, change detection, and risk profiling. Simultaneously, explainable AI (XAI) methods will be incorporated to foster clinician and patient trust. Additionally, a publicly released dataset of anonymized annotated TBP images will be released for an international challenge to advance the development of new algorithms using this type of data. The anticipated results from this protocol are validated AI-based tools to provide holistic risk assessment for individual lesions, and risk stratification of patients to assist clinicians in monitoring for skin cancer.

Sulfuric Acid Ingestion: May the Severity of the Metabolic Acidosis be Considered as a Predictive Sign of Late Damage to the Gastrointestinal Tract?

Caustic substances ingestion results in a complex syndrome. The patient characteristics and severity of injury are important prognostic predictors. The monitoring of clinical changes and the multidisciplinary approach are necessary to prevent death in the early stages of the poisoning. The case report describes the suicide of a woman by ingestion of a large amount of 15% sulfuric acid for suicidal purposes (15-20 ml). The initial conditions were stable, and no changes were found on a CT scan. However, the main sign was a severe metabolic acidosis. After 7 hours, haematemesis and oedema of the larynx appeared, and oro-tracheal intubation and ICU admission were necessary. Consequent progressive haemodynamic deterioration with persistent severe metabolic acidosis, increasing lactates and septic shock appeared. A new CT scan with contrast was performed 22 hours later detecting diffuse perforations and liquid in pleurae and abdomen. A pleural sample showed necrotic liquid. The death was 24 hours after ingestion and no surgical treatment was performed because of the diffuse lesions to the thoracoabdominal districts. Early detection of gastroenteric lesions and the monitoring of clinical changes are mandatory to avoid the death of the patient. Gastroenteric perforations require an immediate radiological evaluation and surgical treatment. The clinical case report states the severity of prognosis was related to high doses of sulfuric acid ingestion. The immediate metabolic acidosis is related to quick subsequent severe systemic pathological lesions of the gastrointestinal tract. The severity of absorption metabolic acidosis, consequently, may be an early and prognostic sign of the late chest and abdominal lesions. Severity of metabolic acidosis after sulfuric acid ingestion may anticipate late damage to the gastrointestinal tract.

3D printed phantom with 12 000 submillimeter lesions to improve efficiency in CT detectability assessment.

The detectability performance of a CT scanner is difficult to precisely quantify when nonlinearities are present in reconstruction. An efficient detectability assessment method that is sensitive to small effects of dose and scanner settings is desirable. We previously proposed a method using a search challenge instrument: a phantom is embedded with hundreds of lesions at random locations, and a model observer is used to detect lesions. Preliminary tests in simulation and a prototype showed promising results. In this work, we fabricated a full-size search challenge phantom with design updates, including changes to lesion size, contrast, and number, and studied our implementation by comparing the lesion detectability from a nonprewhitening (NPW) model observer between different reconstructions at different exposure levels, and by estimating the instrument sensitivity to detect changes in dose. Designed to fit into QRM anthropomorphic phantoms, our search challenge phantom is a cylindrical insert 10cm wide and 4cm thick, embedded with 12 000 lesions (nominal width of 0.6mm, height of 0.8mm, and contrast of -350 HU), and was fabricated using PixelPrint, a 3D printing technique. The insert was scanned alone at a high dose to assess printing accuracy. To evaluate lesion detectability, the insert was placed in a QRM thorax phantom and scanned from 50 to 625 mAs with increments of 25 mAs, once per exposure level, and the average of all exposure levels was used as high-dose reference. Scans were reconstructed with three different settings: filtered-backprojection (FBP) with Br40 and Br59, and Sinogram Affirmed Iterative Reconstruction (SAFIRE) with strength level 5 and Br59 kernel. An NPW model observer was used to search for lesions, and detection performance of different settings were compared using area under the exponential transform of free response ROC curve (AUC). Using propagation of uncertainty, the sensitivity to changes in dose was estimated by the percent change in exposure due to one standard deviation of AUC, measured from 5 repeat scans at 100, 200, 300, and 400 mAs. The printed insert lesions had an average position error of 0.20mm compared to printing reference. As the exposure level increases from 50 mAs to 625 mAs, the lesion detectability AUCs increase from 0.38 to 0.92, 0.42 to 0.98, and 0.41 to 0.97 for FBP Br40, FBP Br59, and SAFIRE Br59, respectively, with a lower rate of increase at higher exposure level. FBP Br59 performed best with AUC 0.01 higher than SAFIRE Br59 on average and 0.07 higher than FBP Br40 (all P<0.001). The standard deviation of AUC was less than 0.006, and the sensitivity to detect changes in mAs was within 2% for FBP Br59. Our 3D-printed search challenge phantom with 12 000 submillimeter lesions, together with an NPW model observer, provide an efficient CT detectability assessment method that is sensitive to subtle effects in reconstruction and is sensitive to small changes in dose.

Design of an Iterative Method for Enhanced Retinal Image Analysis Using Stacked Deep Learning Operations

The necessity for advanced diagnostic techniques in ophthalmology has become increasingly evident, particularly for conditions such as glaucoma and diabetic retinopathy, which necessitate precise retinal image analysis. Current methodologies, while effective to a degree, fall short in terms of accuracy, efficiency, and adaptability, especially in semantic segmentation and disease classification tasks. These limitations underscore the need for more sophisticated and reliable approaches to retinal image analysis. In response, this work introduces a comprehensive framework leveraging Fully Convolutional Neural Networks (FCNNs) for optic disc segmentation and Cup-to-Disc Ratio (CDR) estimation. The selection of FCNNs is predicated on their demonstrated proficiency in semantic segmentation, ability to discern spatial dependencies, and capacity to learn intricate structures within fundus images without relying on manually engineered features. This approach aims to surpass existing segmentation accuracy benchmarks, targeting over 95% Intersection over Union (IoU) while reducing the mean absolute error in CDR estimation to below 10%. To address the challenges of data variability and quality, the framework incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) for synthetic data generation, enhancing local contrast while preserving image integrity levels. This method is expected to improve contrast in augmented images by 30%, simultaneously enhancing image quality metrics. Furthermore, the introduction of an overlapping sliding window technique with adaptive patch sizes ensures meticulous coverage and analysis of fundus images, significantly elevating lesion detection sensitivity and reducing false positives. Lastly, the framework employs an innovative multi-disease classification strategy utilizing ensemble learning and stacking of CNN architectures. This synergistic approach amalgamates multiple base models to diminish overfitting risks and enhance generalization capabilities, aiming for a classification accuracy surpassing 95% in binary assessments and 85% for specific retinal diseases. The proposed model not only addresses the current gaps in retinal image analysis but also sets a new standard for precision, reliability, and efficiency in diagnosing and managing ocular diseases, marking a significant step forward in the application of artificial intelligence in ophthalmology.

Effective abdominal ultrasonographic detection of pancreatic cystic lesions using artificial intelligence‐assisted noise reduction

AbstractIntroductionPatients with intraductal papillary mucinous neoplasms (IPMNs) exhibit a high incidence of pancreatic cancer. An IPMN presents as a pancreatic cystic lesion (PCL). Artificial intelligence (AI) can be used to enhance ultrasonographic images. We retrospectively evaluated the utility of AI‐assisted ultrasonography in terms of PCL detection.MethodsIn total, 969 patients who underwent artificial narrow intelligence‐assisted transabdominal ultrasound examinations (AI group) were compared with 943 patients who underwent non‐AI‐assisted examinations (control group). The morphological changes and the frequencies of newly detected PCLs were compared between the groups.ResultsThe PCL detection rate was significantly higher in the AI group than in the control group (1.25 ± 0.36 and 0.21 ± 0.15%, respectively; p = .008). AI‐assisted ultrasound evaluation was independently associated with enhanced PCL detection (odds ratio: 5.83, 95% confidence interval: 1.29–26.29, p = .02). PCLs smaller than 8 mm and all PCLs in the pancreatic tail were detected in the AI group.PCL morphological changes were more common in the AI group than in the control group (63.6 ± 14.5 and 16.7 ± 15.2%, respectively; p = .04).ConclusionAI‐assisted transabdominal ultrasonography enhances the PCL detection rate. The use of AI is likely to increase the IPMN detection rate.

Gamma camera imaging characteristics of 166Ho and 99mTc used in Selective Internal Radiation Therapy

BackgroundThe administration of a 166Ho scout dose is available as an alternative to 99mTc particles for pre-treatment imaging in Selective Internal Radiation Therapy (SIRT). It has been reported that the 166Ho scout dose may be more accurate for the prediction of microsphere distribution and the associated therapy planning. The aim of the current study is to compare the scintigraphic imaging characteristics of both isotopes, considering the objectives of the pre-treatment imaging using clinically geared phantoms.MethodsPlanar and SPECT/CT images were obtained using a NEMA image quality phantom in different phantom setups and another body-shaped phantom with several inserts. The influence of collimator type, count statistics, dead time effects, isotope properties and patient obesity on spatial resolution, contrast recovery and the detectability of small activity accumulations was investigated. Furthermore, the effects of the imaging characteristics on personalized dosimetry are discussed.ResultsThe images with 99mTc showed up to 3 mm better spatial resolution, up to two times higher contrast recovery and significantly lower image noise than those with 166Ho. The contrast-to-noise ratio was up to five times higher for 99mTc than for 166Ho. Only when using 99mTc all activity-filled spheres could be distinguished from the activity-filled background. The measurements mimicking an obese patient resulted in a degraded image quality for both isotopes.ConclusionsOur measurements demonstrate better scintigraphic imaging properties for 99mTc compared to 166Ho in terms of spatial resolution, contrast recovery, image noise, and lesion detectability. While the 166Ho scout dose promises better prediction of the microsphere distribution, it is important to consider the inferior imaging characteristics of 166Ho, which may affect individualized treatment planning in SIRT.

AI Applications to Breast MRI: Today and Tomorrow.

In breast imaging, there is an unrelenting increase in the demand for breast imaging services, partly explained by continuous expanding imaging indications in breast diagnosis and treatment. As the human workforce providing these services is not growing at the same rate, the implementation of artificial intelligence (AI) in breast imaging has gained significant momentum to maximize workflow efficiency and increase productivity while concurrently improving diagnostic accuracy and patient outcomes. Thus far, the implementation of AI in breast imaging is at the most advanced stage with mammography and digital breast tomosynthesis techniques, followed by ultrasound, whereas the implementation of AI in breast magnetic resonance imaging (MRI) is not moving along as rapidly due to the complexity of MRI examinations and fewer available dataset. Nevertheless, there is persisting interest in AI-enhanced breast MRI applications, even as the use of and indications of breast MRI continue to expand. This review presents an overview of the basic concepts of AI imaging analysis and subsequently reviews the use cases for AI-enhanced MRI interpretation, that is, breast MRI triaging and lesion detection, lesion classification, prediction of treatment response, risk assessment, and image quality. Finally, it provides an outlook on the barriers and facilitators for the adoption of AI in breast MRI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

Abstract LB219: DCLK1 is a novel therapeutic target for gastric and colorectal cancers as demonstrated by patient-derived organoids assays

Abstract Gastrointestinal cancers, including gastric cancer (GC) and colorectal cancer (CRC), continue to be devastating diseases despite advances in screening, early detection, and removal of pre-neoplastic and early cancerous lesions. Even with advancements in targeted chemotherapy and immunotherapies, advanced and metastatic diseases continue to be responsible for the high mortality associated with GC and CRC. Newer, safer, and more effective therapies to prevent, inhibit, or reverse advanced disease progression are urgently needed. Doublecortin-like kinase 1 (DCLK1) is a microtubule associated protein kinase that has been reported to drive tumor initiation, progression, metastasis, and drug resistance in many solid tumor cancers including GC and CRC, where it is upregulated in patient tumors and associated with poor overall survival. An anti-DCLK1 monoclonal antibody has demonstrated potential therapeutic activity against CRC. In this study, we established patient-derived organoids (PDOs) to evaluate the effects of inhibiting DCLK1 kinase activity (DCLK1-IN-1) or targeting DCLK1 with a highly specific CAR-T cells on the treated PDOs. Two GC PDOs were treated with various concentrations of DCLK1 kinase inhibitor DCLK1-IN-1 (0-32 μM) for 72 hrs, viability of the PDOs were determined, histochemistry and immunohistochemistry staining were used to image the live cells and DCLK1 expression. For CRC PDOs, five CRC PDOs were cultured in the presence or absence of PBMC, treated with Mock CAR-T cells or DCLK1 CAR-T cells (three doses) for 4 days. Tumor size, tumor total area, and viability of these PDOs were determined. Here, we demonstrate that treating GC PDOs with DCLK1 kinase inhibitor resulted in a significant dose-dependent inhibition in PDO numbers in both PDOs (two-way ANOVA P&amp;lt;0.0001), a decrease of cell viability, revealing IC50 values of 7.566 and 9.958 μM respectively, and a near total loss of GC PDO viability at 10 μM using CellTiterGlo assay. In CRC PDOs, two of five CRC PDOs showed a significant decrease in total tumor size, tumor areas, and an increase in cell death measured by the DRAQ7 method. Compared to Mock CAR-T cells, treatment with DCLK1 CAR-T cells resulted in a dose dependent decrease of tumor size and total tumor area, with more than a 50% reduction at the 50000 cells dose (p&amp;lt;0.05). Treatment with DCLK1 CAR-T cells also resulted in a dose dependent increase in cell death, with approximately 20% cell death without PBMC, and 50% cell death with PBMC at the 50000 cells dose (p&amp;lt;0.05). These results suggest that targeting DCLK1 represents a potential novel therapeutic strategy for GI cancers, especially in combination with FDA approved drugs: 5-fluorouracil (5-FU) or Gefitinib. Citation Format: Dongfeng Qu, Jinsen Shi, Zhiyun Cao, Eddie BAnnerman-Menson, Nathaniel Weygant, Courtney Houchen. DCLK1 is a novel therapeutic target for gastric and colorectal cancers as demonstrated by patient-derived organoids assays [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB219.

A Review on the Design of Carbon-Based Nanomaterials as MRI Contrast Agents.

The administration of magnetic resonance imaging (MRI) contrast agents (CAs) has been conducted since 1988 by clinicians to enhance the clarity and interpretability of MR images. CAs based on gadolinium chelates are the clinical standard used worldwide for the diagnosis of various pathologies, such as the detection of brain lesions, the visualization of blood vessels, and the assessment of soft tissue disorders. However, due to ongoing concerns associated with the safety of gadolinium-based contrast agents, considerable efforts have been directed towards developing contrast agents with better relaxivities, reduced toxicity, and eventually combined therapeutic modalities. In this context, grafting (or encapsulating) paramagnetic metals or chelates onto (within) carbon-based nanoparticles is a straightforward approach enabling the production of contrast agents with high relaxivities while providing extensive tuneability regarding the functionalization of the nanoparticles. Here, we provide an overview of the parameters defining the efficacy of lanthanide-based contrast agents and the subsequent developments in the field of nanoparticular-based contrast agents incorporating paramagnetic species.

Equine skeletal scintigraphy: Comparing lesion detection ability of methylene diphosphonate and hydroxymethylene diphosphonate in the caudal cervical and proximal metacarpal/metatarsal regions.

Data regarding the lesion detection ability of different radiotracers are lacking in equine bone scintigraphy. In this prospective study, hydroxymethylene diphosphonate (HMDP) and methylene diphosphonate (MDP) were compared in horses with increased radiopharmaceutical uptake either in the caudal cervical region (CS group) or in the proximal metacarpal/metatarsal region (PMR group). Region of interest analysis was used to determine normal bone-to-soft tissue ratios, lesion-to-normal bone ratios and lesion-to-soft tissue ratios. Qualitative scoring and total count rates were recorded for each image. A total of 213 scintigrams were included. Within the PMR group, there were significantly higher lesion-to-normal bone ratios for MDP compared with HMDP (p=0.02). In the CS group, normal bone-to-soft tissue ratios were significantly higher for HMDP (p=0.01). The interobserver agreement with regard to the qualitative assessment of the scintigrams was poor. Paired studies, comparing the different radiotracers in the same patient, were not feasible. This study revealed minor differences between the two radiotracers, although thesehave no practical implications. Both radiopharmaceuticals are well suited for detecting lesions at the investigated sites using equine bone scintigraphy.

The role of endoscopic ultrasound in the detection of pancreatic lesions in high-risk individuals.

Individuals at high risk of developing pancreatic ductal adenocarcinoma are eligible for surveillance within research programs. These programs employ periodic imaging in the form of magnetic resonance imaging/magnetic resonance cholangiopancreatography or endoscopic ultrasound for the detection of early cancer or high-grade precursor lesions. This narrative review discusses the role of endoscopic ultrasound within these surveillance programs. It details its overall strengths and limitations, yield, burden on patients, and how it compares to magnetic resonance imaging. Finally, recommendations are given when and how to incorporate endoscopic ultrasound in the surveillance of high-risk individuals.

Parametric net influx rate imaging of 68Ga-DOTATATE in patients with neuroendocrine tumors: assessment of lesion detectability.

There has been developed a clinical dynamic total-body 68Ga-DOTATATE PET/CT imaging protocol that allows quantitative imaging of net influx rate (Ki). Using qualitative and quantitative analyses of clinical studies, this retrospective study aims to assess whether parametric Ki images improve lesion detectability. Using a 194-cm axial field-of-view PET/CT scanner, 52 patients with neuroendocrine tumors underwent a 60-min dynamic total-body 68Ga-DOTATATE scan. Parametric Ki images and static standardized uptake value (SUV) images were generated. In addition to visual inspection of both sets of images, a quantitative analysis of 249 individual lesions was conducted using the target-to-background (TBR) metric. There were 52 patients who underwent dynamic total-body 68Ga-DOTATATE PET/CT scans. A total of 249 lesions were evaluated, of which 66 lesions were biopsy-proven and 183 lesions were unproven. Ki images produced two fewer false positives than the SUV images. Overall, our results from 66 proven NET lesions suggested similar sensitivity (98.5%) but improved accuracy (from 95.6 to 97.1%) and potentially enhanced specificity with Ki over SUV imaging. Besides, there was no difference in the number of pathological lesions identified visually in both images. However, Ki TBR was significantly higher than SUV TBR quantitatively (P < 0.001). Patlak Ki imaging provides nuclear physicians with a PET image with higher tumor contrast which may enhance confidence in diagnosis with possibly reduced false positive results, albeit an equivalent detectability, compared to static SUV image.