Detection Of Lesions Research Articles

The Effect of Hsa-miR-504 Targeting MUC16 in Ovarian Cancer Progression

Ovarian cancer (OC) is the most fatal gynecological tumor. Early diagnosis of OC is difficult and recurrence rate is high after treatment. Studies on the early detection of OC lesions using nanotechnology and nanomaterials are limited by the large number of OC subtypes and cannot achieve effective early detection. Understanding the molecular mechanism of OC and identifying new therapeutic targets is important. MUC16 is an important diagnostic indicator of OC, and hsa-miR-504 may be a potential biomarker of OC. However, the effects of miR-504 on cell cycle, apoptosis, and proliferation of OC and its relationship with MUC16 must be further clarified. The relationship between miR-504 and OC was determined by Gene Expression Omnibus (GEO) and meta-analysis, and the molecular pathways of miR-504 and MUC16 intervening in OC were screened by GSEA analysis. The expression of miR-504 and MUC16 in Skov3IP cells and their correlation with clinical features were detected by qRT-PCR and western blotting (WB). The correlation between miR-504 and MUC16 was detected with the luciferase reporter assay. The effects of miR-504 and MUC16 on the cell cycle and apoptosis of Skov3IP cells were detected by flow cytometry. Meta-analysis of the GSE dataset showed that miR-504 expression is downregulated in OC (95% CI [−0.39; 0.40]). GSEA enrichment analysis combined with literature review showed that MUC16 is involved in the TP53 signaling pathway to regulate cell proliferation and apoptosis. qRT-PCR and WB confirmed that the expression of MUC16 was upregulated and miR-504 was downregulated in Skov3IP cells. A luciferase reporter assay confirmed that miR-504 targeted MUC16. In OC, downregulation of miR-504 can increase the expression of MUC16, inhibit OC cell apoptosis, and promote OC cell proliferation. The miR-504 target MUC16 may participate in OC through the TP53 signaling pathway. miR-504 can be used as a potential tumor biomarker of OC.

Early detection of pancreatic cancer

The diagnosis of pancreatic cancer associates an appalling significance. Detection of preinvasive stage of pancreatic cancer will ameliorate the survival of this deadly disease. Premalignant lesions such as Intraductal Papillary Mucinous Neoplasms or Mucinous Cystic Neoplasms of the pancreas are detectable on imaging exams and this permits their management prior their invasive development. Pancreatic intraepithelial neoplasms (PanIN) are the most frequent precursors of pancreatic adenocarcinoma (PDAC), and its particular type PanIN high-grade represents the malignant non-invasive form of PDAC. Unfortunately, PanINs are not detectable on radiologic exams. Nevertheless, they can associate indirect imaging signs which would rise the diagnostic suspicion. When this suspicion is established, the patient will be enrolled in a follow-up strategy that includes performing of blood test and serial imaging test such as computed tomography or magnetic resonance imaging, which will cost in the best-case scenario a burden of healthcare systems, and potential mortality in the worst-case scenario when the patient underwent resection surgery, worthless when there is no moderate or high grade dysplasia in the final histopathology. This issue will be avoid having at its disposal a diagnostic technique capable of detecting high-grade PanIN lesions, such is the cytology of pancreatic juice obtained by nasopancreatic intubation. Herein, we review the possibility of detection of early malignant lesions before they become invasive PADC.

Endocrinology application of molecular imaging: current role of PET/CT.

In recent years, nuclear medicine imaging methods have proven to be of paramount importance in a wide variety of diseases, particularly in oncology, where they are crucial for assessing the extent of disease when conventional methods fall short. Moreover, nuclear imaging modalities are able to better characterize lesions using target agents related to specific pathways (e.g. glucose metabolism, cellular proliferation, amino acid transport, lipid metabolism, specific receptor ligands). The clinical presentation of endocrine diseases encompasses a broad spectrum of sign and symptoms. Moreover, endocrine tumors show varying degrees of aggressiveness from well differentiated and indolent to highly aggressive cancers, respectively. With the application of new medicinal radio-compounds and increasingly advanced tomographic imaging technology, the utility of Positron Emission Tomography/Computed Tomography (PET/CT) in the field of endocrine diseases is expanding. This review aims to analyze and summarize the primary indications of PET/CT, providing a practical approach for clinicians. A comprehensive literature search on PubMed was conducted to provide an updated overview of the available evidence regarding the use of PET/CT in endocrinology. Within this review, we will discuss the applications of PET/CT, compare different radiopharmaceuticals and highlight the uptake mechanism, excluding neuroendocrine carcinomas from discussion. PET/CT is a valuable tool in diagnosing and managing endocrine disorders due to its capacity to furnish both functional and anatomical information, facilitate early lesion detection, guide treatment decisions, and monitor treatment response. Its non-invasive nature and precision make it an integral component of modern endocrine healthcare. This review aims to provide physicians with a clear perspective on the role of PET/CT imaging, discussing its emerging opportunities and appropriateness of use in endocrinological diseases.

Clinical impact of using [18F]AlF-NOTA-octreotide PET/CT instead of [68Ga]Ga-DOTA-SSA PET/CT: Secondary endpoint analysis of a multicenter, prospective trial.

[18F]AlF-NOTA-octreotide ([18F]AlF-OC) is a promising alternative for [68Ga]Ga-DOTA-somatostatin analogs (SSAs) in positron emission tomography (PET) imaging of the somatostatin receptor (SSTR). Our aim is to assess changes in TNM staging and differences in patient management between [18F]AlF-OC PET/CT and [68Ga]Ga-DOTA-SSA PET/CT in the work-up of neuroendocrine tumor (NET) patients. Patients who underwent both [18F]AlF-OC and [68Ga]Ga-DOTA-TATE or [68Ga]Ga-DOTA-NOC PET/CT in our multicenter study (Pauwels et al., J Nucl Med.2023;63:632-638) with a NET were included for analysis. TNM staging was determined and compared for both tracers. For each patient, the blinded [68Ga]Ga-DOTA-SSA or [18F]AlF-OC PET/CT images were presented in random order at a multidisciplinary team board. The images were presented together with clinical information and compared with previous SSTR and [18F]FDG PET/CT imaging. After a consensus decision for patient management was recorded, the board was presented with the PET/CT images from the other SSTR tracer and a decision was made for the second tracer. Differences in management were classified as major if it entailed an intermodality change and minor if it led to an intramodality change. Compared with [68Ga]Ga-DOTA-SSA, the use of [18F]AlF-OC led to a change in 16/75 patients: TNM staging changes in 10/75 patients (13.3%; downstaging in 3/10, upstaging in 7/10) and differences in clinical management were seen in 10/75 patients (13.3%), leading to a major difference in 7/10 cases and a minor change in 3/10 cases. All 10 cases with a difference in patient management between both PET tracers were caused by additional lesion detection by [18F]AlF-OC. The use of [18F]AlF-OC did not impact TNM staging or clinical management in the large majority of the patients (86.7%), further validating the potential for routine clinical use of [18F]AlF-OC PET/CT as an alternative for [68Ga]Ga-DOTA-SSA PET/CT. The trial is registered under ClinicalTrials.gov identifier NCT04552847 and EudraCT 2020-000549-15.

Utilizing deep learning for automated detection of oral lesions: A multicenter study

ObjectivesWe aim to develop a YOLOX-based convolutional neural network model for the precise detection of multiple oral lesions, including OLP, OLK, and OSCC, in patient photos. Materials and methodsWe collected 1419 photos for model development and evaluation, conducting both a comparative analysis to gauge the model’s capabilities and a multicenter evaluation to assess its diagnostic aid, where 24 participants from 14 centers across the nation were invited. We further integrated this model into a mobile application for rapid and accurate diagnostics. ResultsIn the comparative analysis, our model overperformed the senior group (comprising three most experienced experts with more than 10 years of experience) in macro-average recall (85 % vs 77.5 %), precision (87.02 % vs 80.29 %), and specificity (95 % vs 92.5 %). In the multicenter model-assisted diagnosis evaluation, the dental, general, and community hospital groups showed significant improvement when aided by the model, reaching a level comparable to the senior group, with all macro-average metrics closely aligning or even surpassing with those of the latter (recall of 78.67 %, 74.72 %, 83.54 % vs 77.5 %, precision of 80.56 %, 76.42 %, 85.15 % vs 80.29 %, specificity of 92.89 %, 91.57 %, 94.51 % vs 92.5 %). ConclusionOur model exhibited a high proficiency in detection of oral lesions, surpassing the performance of highly experienced specialists. The model can also help specialists and general dentists from dental and community hospitals in diagnosing oral lesions, reaching the level of highly experienced specialists. Moreover, our model’s integration into a mobile application facilitated swift and precise diagnostic procedures.

Deep Learning-Based Approach for Identifying and Measuring Focal Liver Lesions on Contrast-Enhanced MRI.

The number of focal liver lesions (FLLs) detected by imaging has increased worldwide, highlighting the need to develop a robust, objective system for automatically detecting FLLs. To assess the performance of the deep learning-based artificial intelligence (AI) software in identifying and measuring lesions on contrast-enhanced magnetic resonance imaging (MRI) images in patients with FLLs. Retrospective. 395 patients with 1149 FLLs. The 1.5 T and 3 T scanners, including T1-, T2-, diffusion-weighted imaging, in/out-phase imaging, and dynamic contrast-enhanced imaging. The diagnostic performance of AI, radiologist, and their combination was compared. Using 20 mm as the cut-off value, the lesions were divided into two groups, and then divided into four subgroups: <10, 10-20, 20-40, and ≥40 mm, to evaluate the sensitivity of radiologists and AI in the detection of lesions of different sizes. We compared the pathologic sizes of 122 surgically resected lesions with measurements obtained using AI and those made by radiologists. McNemar test, Bland-Altman analyses, Friedman test, Pearson's chi-squared test, Fisher's exact test, Dice coefficient, and intraclass correlation coefficients. A P-value <0.05 was considered statistically significant. The average Dice coefficient of AI in segmentation of liver lesions was 0.62. The combination of AI and radiologist outperformed the radiologist alone, with a significantly higher detection rate (0.894 vs. 0.825) and sensitivity (0.883 vs. 0.806). The AI showed significantly sensitivity than radiologists in detecting all lesions <20 mm (0.848 vs. 0.788). Both AI and radiologists achieved excellent detection performance for lesions ≥20 mm (0.867 vs. 0.881, P = 0.671). A remarkable agreement existed in the average tumor sizes among the three measurements (P = 0.174). AI software based on deep learning exhibited practical value in automatically identifying and measuring liver lesions. Stage 2.

Oral cancer detection and progression prediction using noninvasive cytology-based DNA ploidy approach.

Despite the oral cavity being readily accessible, oral cancer (OC) remains a significant burden. The objective of this study is to develop a DNA ploidy-based cytology test for early detection of high-risk oral lesions. This retrospective study was conducted using 569 oral brushing samples collected from 95 normal and 474 clinically abnormal mucosa with biopsy diagnosis of reactive, low-grade or high-grade precancer or cancers. Brushing cells were processed to characterize DNA ploidy. A two-step DNA ploidy-based algorithm, the DNA ploidy oral cytology (DOC) test, was developed using a training set, and verified in test and validation sets to differentiate high-grade lesions (HGLs) from normal. The prognostic value of the test was evaluated by an independent outcome cohort, including progressed and non-progressing normal, reactive and low-grade lesions. Classification performance was assessed by accuracy, sensitivity, and specificity, while the prognostic value was evaluated by using the Cox proportional hazards analysis on 3-year progression-free survival (PFS). The developed DOC test exhibited high accuracy for detecting HGLs in the test and validation sets, with a sensitivity of 0.97 and 0.96, respectively. Its application to the Outcome cohort demonstrated significant prognostic value for 3-year PFS (log rank, p < 0.001). Multivariate analysis showed that high-grade pathology was the only variable explaining positive DOC test, not age, smoking, or lesional site. Clinical implementation of the DOC test could provide an effective screening method for detecting HGLs for biopsy and lesions at risk of progression.

PREVECOL, a predictive model for early detection of colorectal cancer and precancerous lesions in liquid biopsy.

3629 Background: Colorectal cancer (CRC) is the second cause of cancer-related deaths worldwide. Most of these deaths could have been prevented by increasing the effectiveness of CRC screening programs, which exhibit a low participation rate, partly due to the limited available screening options, alongside discomfort associated with the screening procedure (faecal test). Liquid biopsy-based technology offers new alternatives to improve CRC early detection. CRC and advanced adenoma (AA) patients show molecular alterations at different biological levels that can be detected in peripheral blood and used for diagnostic purposes. AMADIX has developed PreveCol, an innovative blood test based on molecular biomarkers and clinical features for early detection of CRC and AA, before the onset of the first symptoms. We present the validation of PreveCol predictive model through a European prospective clinical study involving average-risk population actively engaged in a real CRC screening program. PreveCol has been granted with the Breakthrough Device Designation by the FDA in December 2023. Methods: Samples from two prospective clinical studies were used for training and validation. A case-control study (n= 538 individuals: 173 CRC, 181 AA, 184 Controls; 13 Spanish sites) and a clinical study based on a real CRC screening program (n= 3163 individuals: 78 CRC, 156 AA, 146 HRPL, and 2783 Controls; 9 European sites). PreveCol molecular signature (11 proteins and 10 miRNAs), was determined in plasma samples using RT-qPCR and ELISA/CLIA. Machine learning techniques were used to develop an ensemble algorithm. K-fold cross validation techniques were used in the training to set the hyperparameters for each model. Hyperparameter optimalization was performed to find the best parameters of the grid. Once the algorithm was locked and the clinical cut-off was established in the training set, the signature was validated in the European prospective study sub-cohort. Results: For CRC detection, PreveCol demonstrates an Area Under the ROC curve (AUC) of 92%, with a sensitivity (SS) of 81.8% and specificity (SP) of 86%. Moreover, the model exhibits high sensitivity (above 90%) in identifying CRC in early stages (stages 1 and 2), as well as lesions located in the proximal colon. In addition, the AUC of PreveCol for Advanced Adenomas detection is 86.88%, (SS of 70.8% and SP of 86%), showing that PreveCol presents higher sensitivity compared to other identified tests for detecting precancerous lesions. Conclusions: These findings highlight PreveCol's potential to significantly impact on patient prognosis and survival by facilitating the early detection of CRC and premalignant lesions as well as lesions in critical areas of the colon, thereby contributing to improved screening efficacy and patient outcomes.

A novel, noninvasive, multimodal screening test for the early detection of precancerous lesions and colorectal cancers using an artificial intelligence–based algorithm.

3627 Background: Colorectal cancer (CRC) ranks as the second leading cause of cancer-related mortality worldwide, and its incidence is increasing in younger populations. Detection of early-stage CRC and its precursor lesions, such as advanced adenomas (AA), is crucial for successful treatment and reduces CRC-related mortality. Although non-invasive methods for early detection are available and increase screening compliance, their performance with respect to detection of advanced adenomas and early-stage cancers is limited. Here, we describe a novel and non-invasive stool-based approach combining diagnostic biomarkers with an algorithm generated by machine learning/artificial intelligence (ML/AI) resulting in significantly improved diagnostic performance for the detection of not only CRC, but especially precancerous lesions like AA. Methods: Data were generated from a combined cohort of stool samples collected at 10 clinical sites in Europe (COLOFUTURE study) and 21 clinical sites in the US (eAArly DETECT study). The evaluable study cohort consists of 690 subjects, including 78 CRC, 146 AA, 147 with non-advanced adenoma (AD) and 319 normal colonoscopy negative control (NC) subjects (49% female, 51% male, average age 61.8 years). Results were compared with colonoscopy and pathology findings to determine sensitivity and specificity for detection of early-stage CRC and AA vs. AD and NC. Nucleic acids were extracted from stabilized stool samples using a silica bead-based extraction method. Expression of mRNA biomarkers was analyzed utilizing Real-Time PCR. Human hemoglobin was quantified using FIT. The Emerge Quantitative Evolution ML/AI platform was leveraged to develop classifiers capable of distinguishing CRC and AA from AD and NC. Results: Applying the combined approach of non-invasive diagnostic testing with an AI/ML generated algorithm, the sensitivity for detection of CRC overall was 92.3%. In addition, this method enabled AA detection with a sensitivity of 82.2%. Specificity turned out to be 90.1 % (AD+NC). Conclusions: This innovative, non-invasive, multimodal screening strategy based on self-collected stool samples which combines mRNA expression patterns and FIT analysis with an AI/ML-generated algorithm represents a substantial improvement in the effectiveness of non-invasive CRC screening. Such improvements in usability and performance leading to reliable detection of early-stage CRC and precursor lesions, such as advanced adenomas, are required for wide-spread availability and adaption of non-invasive screening tests to accepted clinically relevant usage and to prevent the development of CRC effectively.

Cervical cancer screening in 48000 females over two decades: Feasibility and success of community-based screening in Kerala, India.

10550 Background: Cervical Cancer is still significant cause of mortality and morbidity among females in India. Various methods of cancer screening are currently practiced for cervical cancer prevention including human papilloma virus (HPV) screening, pap smear, visual inspection with Lugol’s iodine (VILI), visual inspection with acetic acid (VIA) etc. The success of any cancer screening method is based on the feasibility of large-scale implementation at community level and the scientific validity of the screening test. VIA/VILI has been proven to prevent mortality in cervical cancer. This study is based on analysis of prospectively maintained database of community based cervical cancer screening using VIA/VILI in northern Kerala from 2002 to 2020. Methods: Retrospective analysis of prospective database of cervical cancer screening from 2002 to 2020 done by Malabar Cancer Care Society in Kerala, a non-governmental organization with support from governmental bodies like ISRO. Data was collected into excel sheets and were rechecked. The data on demography, type of screening, detection of precancerous and cancerous lesions was analyzed. Descriptive statistics were used to analyze the data using SPSS Version 20.0. Results: A total of 48008 individuals were screened over 18 years. 55% were premenopausal. 33% were fully asymptomatic and remaining 66% had white discharge per vaginum (25%), bleeding per vaginum (14%) or combination of symptoms (25%). 18% had abnormal per speculum findings (bleeds on touch, discharge etc.) 86% had unhealthy cervix.26% (12650) had VIA/VILI and 74% (35658) had pap smear. The pap smear findings were normal (41%), atrophic (17%), inflammatory (40%) and 2% had pre-cancerous/ cancerous changes. Of these lesions, 46% were HSIL, 36% were LSIL, 5% ASUS and 13% were squamous cell carcinoma (52 cases). Screen positive rate was 325 (0.005%) out of 48008. Treatment of precancerous lesions was by Cryotherapy (54%), LEEP (10%) and Surgery (36%). Treatment of invasive carcinoma was by surgery (91%) and radiation (85%). Referral to regional cancer center was done in 26% patients. The screen positive rate shows decreasing trend over the last two decades. Conclusions: Cervical Cancer screening in large scale in low resource setting is feasible in India by both pap smear and VIA/VILI with community based interventions led by NGO with support from governmental bodies. This model can be considered ideal for India and other low middle income countries with logistic difficulties for large scale HPV based screening.

Deep learning-based anatomical position recognition for gastroscopic examination.

The gastroscopic examination is a preferred method for the detection of upper gastrointestinal lesions. However, gastroscopic examination has high requirements for doctors, especially for the strict position and quantity of the archived images. These requirements are challenging for the education and training of junior doctors. The purpose of this study is to use deep learning to develop automatic position recognition technology for gastroscopic examination. A total of 17182 gastroscopic images in eight anatomical position categories are collected. Convolutional neural network model MogaNet is used to identify all the anatomical positions of the stomach for gastroscopic examination The performance of four models is evaluated by sensitivity, precision, and F1 score. The average sensitivity of the method proposed is 0.963, which is 0.074, 0.066 and 0.065 higher than ResNet, GoogleNet and SqueezeNet, respectively. The average precision of the method proposed is 0.964, which is 0.072, 0.067 and 0.068 higher than ResNet, GoogleNet, and SqueezeNet, respectively. And the average F1-Score of the method proposed is 0.964, which is 0.074, 0.067 and 0.067 higher than ResNet, GoogleNet, and SqueezeNet, respectively. The results of the t-test show that the method proposed is significantly different from other methods (p< 0.05). The method proposed exhibits the best performance for anatomical positions recognition. And the method proposed can help junior doctors meet the requirements of completeness of gastroscopic examination and the number and position of archived images quickly.

Non-invasive early detection on esophageal squamous cell carcinoma and precancerous lesions by microbial biomarkers combining epidemiological factors in China.

Microbiota may be associated with esophageal squamous cell carcinoma (ESCC) development. However, it is not known the predictive value of microbial biomarkers combining epidemiological factors for the early detection of ESCC and precancerous lesions. A total of 449 specimens (esophageal swabs and saliva) were collected from 349 participants with different esophageal statuses in China to explore and validate ESCC-associated microbial biomarkers from genes level to species level by 16S rRNA sequencing, metagenomic sequencing and real-time quantitative polymerase chain reaction. A bacterial biomarker panel including Actinomyces graevenitzii (A.g_1, A.g_2, A.g_3, A.g_4), Fusobacteria nucleatum (F.n_1, F.n_2, F.n_3), Haemophilus haemolyticus (H.h_1), Porphyromonas gingivalis (P.g_1, P.g_2, P.g_3) and Streptococcus australis (S.a_1) was explored by metagenomic sequencing to early detect the participants in Need group (low-grade intraepithelial neoplasia, high-grade intraepithelial neoplasia and ESCC) vs participants without these lesions as the Noneed group. Significant quantitative differences existed for each microbial target in which the detection efficiency rate was higher in saliva than esophageal swab. In saliva, the area under the curve (AUC) based on the microbial biomarkers (A.g_4 ∩ P.g_3 ∩ H.h_1 ∩ S.a_1 ∩ F.n_2) was 0.722 (95% CI 0.621-0.823) in the exploration cohort. Combining epidemiological factors (age, smoking, drinking, intake of high-temperature food and toothache), the AUC improved to 0.869 (95% CI 0.802-0.937) in the exploration cohort, which was validated with AUC of 0.757 (95% CI 0.663-0.852) in the validation cohort. It is feasible to combine microbial biomarkers in saliva and epidemiological factors to early detect ESCC and precancerous lesions in China.

High-sensitivity troponin T for detection of culprit lesions in patients with out-of-hospital cardiac arrest.

Patients with an out-of-hospital cardiac arrest (OHCA) often undergo coronary angiography, although a culprit lesion is found in only 30%-40% of patients. The aim of this study was to investigate high-sensitivity troponin T (hsTnT) levels in post cardiac arrest patients with and without coronary culprit lesions; factors affecting hsTnT levels after return of spontaneous circulation (ROSC); and the diagnostic ability of hsTnT in identifying patients with culprit lesions. We hypothesized that peak hsTnT levels were higher during the initial 48 h after cardiac arrest in patients with a coronary culprit lesion. This was a retrospective observational study, which included patients admitted to the Intensive Care Unit after an OHCA and who received a coronary angiography. Peak values and dynamic changes in hsTnT were analyzed in relation to the presence of a culprit lesion at coronary angiography. A total of 238 patients were studied, of whom 140 had a culprit lesion. HsTnT levels during the initial 48 h were higher in patients with culprit lesions, longer time to ROSC and an unwitnessed cardiac arrest. At 6 to 12 h after ROSC, a hsTnT cut-off level of 1690 ng/L had a sensitivity of 64% and specificity of 84% to identify a culprit lesion. In patients without ST-elevations, hsTnT measured between 6 and 12 h after ROSC had a specificity above 90%, with a sensitivity of 46%. HsTnT levels after cardiac arrest are higher in patients with coronary culprit lesions. Presence of a culprit lesion, witnessed status and the duration of CPR are important factors affecting hsTnT levels. Repeated measurement of hsTnT within the first 12 h after admission improved diagnostic accuracy but the value of hsTnT as a predictor of culprit lesions early after OHCA is limited.

Pioneering Dermatological Disease Detection with CNNs

Abstract: Skin cancer poses a significant public health concern globally, with its incidence steadily rising. Early and accurate detection plays a pivotal role in improving patient outcomes and survival rates. With leveraging advancements in deep learning, this project explores the efficacy of Convolutional Neural Networks (CNNs) in automating the classification and detection of skin cancer from dermatoscopic images. The methodology employed in this project involves the development and optimization of a CNN architecture tailored for skin cancer analysis. The proposed model harnesses the hierarchical feature learning capabilities of CNN to automatically extract intricate patterns and discriminative features from dermatoscopic images, enabling accurate differentiation among various skin lesion types, including melanoma, basal cell carcinoma, and benign nevi. In addition to classification, the developed CNN architecture integrates a lesion detection mechanism. This auxiliary functionality enables the identification and delineation of potentially cancerous regions within skin lesions. The proposed model’s detection capabilities are evaluated through bounding box prediction and segmentation masks, exhibit notable performance in localizing suspicious areas, aiding dermatologists in targeted examinations. With the help of this project, we can prevent the cause of damage to skin at an early stage.

Malignant melanotic schwannoma of the cervical spinal cord: a case report

Spinal cord malignant melanotic schwannoma (MMNST) is a rare central nervous system tumor that originates from the spinal cord or spinal myelin sheath cells and can produce melanin. This type of tumor is usually highly aggressive and malignant, with a poor prognosis. The clinical manifestations of spinal cord MMNST are mainly pain, paresthesia, muscle weakness, muscle atrophy, etc., and symptoms of spinal cord compression, such as intestinal and bladder dysfunction, paraplegia, etc. Early detection of tumor lesions can facilitate tumor removal, improve patients’ quality of life, and prolong patients’ survival. In this case report, a 27-year-old young woman was diagnosed with MMNST of the cervical spinal cord due to weakness of her limbs in our hospital, and underwent surgical resection. The patient’s limbs returned to normal after surgery. It is worth mentioning that the patient visited our hospital 7 months ago for “right upper limb pain for 3 days” and was diagnosed with a cervical spine space-occupying lesion at the same position this time, but the pathology report was “hemosiderosis”. This case report aims to raise awareness of the problem of spinal cord MMNST and contribute to greater knowledge of this rare tumor.

Dual-energy CT applications on liver imaging: what radiologists and radiographers should know? A systematic review.

This review aims to provide a comprehensive summary of DECT techniques, acquisition workflows, and post-processing methods. By doing so, we aim to elucidate the advantages and disadvantages of DECT compared to conventional single-energy CT imaging. A systematic search was conducted on MEDLINE/EMBASE for DECT studies in liver imaging published between 1980 and 2024. Information regarding study design and endpoints, patient characteristics, DECT technical parameters, radiation dose, iodinated contrast agent (ICA) administration and postprocessing methods were extracted. Technical parameters, including DECT phase, field of view, pitch, collimation, rotation time, arterial phase timing (from injection), and venous timing (from injection) from the included studies were reported, along with formal narrative synthesis of main DECT applications for liver imaging. Out of the initially identified 234 articles, 153 met the inclusion criteria. Extensive variability in acquisition parameters was observed, except for tube voltage (80/140 kVp combination reported in 50% of articles) and ICA administration (1.5mL/kg at 3-4mL/s, reported in 91% of articles). Radiation dose information was provided in only 40% of articles (range: 6-80mGy), and virtual non-contrast imaging (VNC) emerged as a common strategy to reduce the radiation dose. The primary application of DECT post-processed images was in detecting focal liver lesions (47% of articles), with predominance of study focusing on hepatocellular carcinoma (HCC) (27%). Furthermore, a significant proportion of the articles (16%) focused on enhancing DECT protocols, while 15% explored metastasis detection. Our review recommends using 80/140 kVp tube voltage with 1.5mL/kg ICA at 3-4mL/s flow rate. Post-processing should include low keV-VMI for enhanced lesion detection, IMs for tumor iodine content evaluation, and VNC for dose reduction. However, heterogeneous literature hinders protocol standardization.

Diagnosis of Multiple Sclerosis Lesion using Deep Learning Models

Exploring the identification of Multiple Sclerosis (MS) lesions involves leveraging diverse machine learning classifiers. Using Magnetic Resonance Imaging (MRI) scans, the study aims to detect and characterize these lesions, evaluating their attributes, progression stages, and the pivotal role of Artificial Intelligence (AI) in diagnosis. The focus is on analyzing automated detection algorithms, particularly Deep Learning techniques. Through comprehensive assessment, various classifiers including MLP Classifier, Random Forest (RF), Support Vector Machine (SVM), and DL are evaluated using metrics like precision, recall, F1-score, and accuracy. The DL classifier stands out, achieving remarkable precision (99%), recall (99%), and overall accuracy (99.5%). Comparative analysis confirms its superiority, reinforcing its efficacy over alternative methods. The research underscores the DL model's potential in generalizing to new samples due to its robustness and precision. This study significantly advances automated MS lesion detection, highlighting the promise of AI-based methodologies in medical image analysis.

Synthesis and preclinical evaluation of 68Ga-labeled PSMA tracers with improved pharmacological properties

Prostate cancer (PCa) is one of the most common tumors in men, with the overexpression of prostate-specific membrane. In this study, we developed four new 68Ga-labeled PSMA-targeting tracers by introducing quinoline, phenylalanine and decanoic acid groups to enhance their lipophilicity, strategically limiting their metabolic pathway through the urinary system. Four radiotracers were synthesized with radiochemical purity >95 %, and exhibited high stability in vivo and in vitro. The inhibition constants (Ki) of SDTWS01-04 to PSMA were in the nanomolar range (<10 nM). Micro PET/CT imaging and biodistribution analysis revealed that 68Ga-SDTWS01 enabled clear tumor visualization in PET images at 1.5 h post-injection, with excellent pharmacokinetic properties. Notably, the kidney uptake of 68Ga-SDTWS01 significantly reduced, with higher tumor-to-kidney ratio (0.36 ± 0.02), tumor-to-muscle ratio (24.31 ± 2.10), compared with 68Ga-PSMA-11 (T/K: 0.15 ± 0.01; T/M: 14.97 ± 1.40), suggesting that 68Ga-SDTWS01 is a promising radiotracer for the diagnosis of PCa. Moreover, SDTWS01 with a chelator DOTA could also label 177Lu and 225Ac, which could be used for the treatment of PCa.

Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: A systematic review.

The purpose of this systematic review (SR) is to gather evidence on the use of machine learning (ML) models in the diagnosis of intraosseous lesions in gnathic bones and to analyze the reliability, impact, and usefulness of such models. This SR was performed in accordance with the PRISMA 2022 guidelines and was registered in the PROSPERO database (CRD42022379298). The acronym PICOS was used to structure the inquiry-focused review question "Is Artificial Intelligence reliable for the diagnosis of intraosseous lesions in gnathic bones?" The literature search was conducted in various electronic databases, including PubMed, Embase, Scopus, Cochrane Library, Web of Science, Lilacs, IEEE Xplore, and Gray Literature (Google Scholar and ProQuest). Risk of bias assessment was performed using PROBAST, and the results were synthesized by considering the task and sampling strategy of the dataset. Twenty-six studies were included (21 146 radiographic images). Ameloblastomas, odontogenic keratocysts, dentigerous cysts, and periapical cysts were the most frequently investigated lesions. According to TRIPOD, most studies were classified as type 2 (randomly divided). The F1 score was presented in only 13 studies, which provided the metrics for 20 trials, with a mean of 0.71 (±0.25). There is no conclusive evidence to support the usefulness of ML-based models in the detection, segmentation, and classification of intraosseous lesions in gnathic bones for routine clinical application. The lack of detail about data sampling, the lack of a comprehensive set of metrics for training and validation, and the absence of external testing limit experiments and hinder proper evaluation of model performance.

Why Are Explainable AI Methods for Prostate Lesion Detection Rated Poorly by Radiologists?

Deep learning offers significant advancements in the accuracy of prostate identification and classification, underscoring its potential for clinical integration. However, the opacity of deep learning models presents interpretability challenges, critical for their acceptance and utility in medical diagnosis and detection. While explanation methods have been proposed to demystify these models, enhancing their clinical viability, the efficacy and acceptance of these methods in medical tasks are not well documented. This pilot study investigates the effectiveness of deep learning explanation methods in clinical settings and identifies the attributes that radiologists consider crucial for explainability, aiming to direct future enhancements. This study reveals that while explanation methods can improve clinical task performance by up to 20%, their perceived usefulness varies, with some methods being rated poorly. Radiologists prefer explanation methods that are robust against noise, precise, and consistent. These preferences underscore the need for refining explanation methods to align with clinical expectations, emphasizing clarity, accuracy, and reliability. The findings highlight the importance of developing explanation methods that not only improve performance but also are tailored to meet the stringent requirements of clinical practice, thereby facilitating deeper trust and a broader acceptance of deep learning in medical diagnostics.