Early Cancer Detection Research Articles

Exosomal miRNA as biomarker in cancer diagnosis and prognosis: A review.

Exosomes, which are extracellular vesicles with a diameter ranging from 40 to 160 nm, are abundantly present in various body fluids. Exosomal microRNA (ex-miR), due to its exceptional sensitivity and specificity, has garnered significant attention. Notably, ex-miR is consistently detected in almost all bodily fluids, highlighting its potential as a reliable biomarker. This attribute of ex-miR has piqued considerable interest in its application as a diagnostic tool for the early detection, continuous monitoring, and prognosis evaluation of cancer. Given the critical role of exosomes and their cargo in cancer biology, this review explores the intricate processes of exosome biogenesis and uptake, their multifaceted roles in cancer development and progression, and the potential of ex-miRs as biomarkers for tumor diagnosis and prognosis.

A New Computer-Aided Diagnosis System for Breast Cancer Detection from Thermograms Using Metaheuristic Algorithms and Explainable AI

Advances in the early detection of breast cancer and treatment improvements have significantly increased survival rates. Traditional screening methods, including mammography, MRI, ultrasound, and biopsies, while effective, often come with high costs and risks. Recently, thermal imaging has gained attention due to its minimal risks compared to mammography, although it is not widely adopted as a primary detection tool since it depends on identifying skin temperature changes and lesions. The advent of machine learning (ML) and deep learning (DL) has enhanced the effectiveness of breast cancer detection and diagnosis using this technology. In this study, a novel interpretable computer aided diagnosis (CAD) system for breast cancer detection is proposed, leveraging Explainable Artificial Intelligence (XAI) throughout its various phases. To achieve these goals, we proposed a new multi-objective optimization approach named the Hybrid Particle Swarm Optimization algorithm (HPSO) and Hybrid Spider Monkey Optimization algorithm (HSMO). These algorithms simultaneously combined the continuous and binary representations of PSO and SMO to effectively manage trade-offs between accuracy, feature selection, and hyperparameter tuning. We evaluated several CAD models and investigated the impact of handcrafted methods such as Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), Gabor Filters, and Edge Detection. We further shed light on the effect of feature selection and optimization on feature attribution and model decision-making processes using the SHapley Additive exPlanations (SHAP) framework, with a particular emphasis on cancer classification using the DMR-IR dataset. The results of our experiments demonstrate in all trials that the performance of the model is improved. With HSMO, our models achieved an accuracy of 98.27% and F1-score of 98.15% while selecting only 25.78% of the HOG features. This approach not only boosts the performance of CAD models but also ensures comprehensive interpretability. This method emerges as a promising and transparent tool for early breast cancer diagnosis.

Integrating Multi-Cancer Early Detection (MCED) Tests with Standard Cancer Screening: System Dynamics Model Development and Feasibility Testing.

Cancer screening plays a critical role in early disease detection and improving outcomes. In Australia, established screening protocols for colorectal, breast and cervical cancer have significantly contributed to timely cancer detection. However, the recent introduction of multi-cancer early detection (MCED) tests arguably can disrupt current screening, yet the extent to which these tests provide additional benefits remains uncertain. We present the development and initial validation of a system dynamics (SD) model that estimates the additional cancer detections and costs associated with MCED tests. This article describes the development of a simulation model built to evaluate the additional patient diagnoses and the economic impact of incorporating MCED testing alongside Australia's well-established standard of care (SOC) screening programs for colorectal, breast, cervical and lung cancers. The model was designed to estimate the additional number of patients diagnosed at each cancer stage (stage I, II, III, IV, or unknown) and the associated costs. This simulation model allows for the analysis of multiple scenarios under a plausible set of assumptions regarding population-level participation rates. An SD model was developed to represent the existing SOC national cancer screening pathways and to integrate potential clinical pathways that could be introduced by MCED tests. The SD model was built to investigate three scenarios for the use of MCED testing: firstly, to explore the viability of MCED testing as a substitute among individuals who are not opting for SOC screening for any reason; secondly, to implement MCED testing exclusively for individuals ineligible for SOC screening, yet have high-risk characteristics; and thirdly, to employ MCED testing after SOC screening to serve as a triaging/confirmatory tool for individuals receiving inconclusive test results. The three primary scenarios were constructed by varying diagnostic accuracy and uptake rates of MCED tests. The clinical utility and outcomes of MCED testing for screening and early detection still lack comprehensive evidence. Nonetheless, this simulation model facilitates a thorough analysis of MCED tests within the Australian healthcare context, providing insights into potential additional detections and costs to the healthcare system, which may help prioritise future evidence development. The adaptable yet novel SD model presented herein is anticipated to be of considerable interest to industry, policymakers, consumers and clinicians involved in informing clinical and economic decisions regarding integrating MCED tests as cancer screening and early detection tools. The expected results of applying this SD model will determine whether using MCED testing in conjunction with SOC screening offers any potential benefits, possibly guiding policy decisions and clinical practices towards the adoption of MCED tests.

XU-NetFullSharp: The Novel Deep Learning Architecture for Chest X-ray Bone Shadow Suppression

Background and objectivesChest X-ray image (CXR) is vital for screening, preventing, and monitoring various lung diseases. In particular, the early detection of lung cancer can significantly improve patients’ chances of survival and quality of life. Unfortunately, approximately 82–95 % of missed pulmonary nodules are estimated to be obscured by rib shadows, making them difficult to recognize. This study addresses this problem by considering the rib shadows in CXRs as noise that can be reduced using deep learning. The result of the proposed model is a CXR with improved clarity for easier and more accurate analysis by radiologists or computer algorithms. MethodsAn automated deep learning-based model for bone shadow suppression from frontal CXRs, called xU-NetFullSharp, was proposed. This network is inspired by the most modern U-NetSharp architecture and was modified using different approaches to preserve as many details as possible and accurately suppress bone shadows. For comparison, recent state-of-the-art models were implemented and trained. JSRT, VinDr-CXR, and Gusarev DES datasets were utilized for the experiments, where the JSRT dataset was extensively augmented. ResultsThe performance of the proposed xU-NetFullSharp was analyzed using statistical measures and compared with that of other architectures. The proposed model significantly outperformed the others, reaching the best values of the most used metrics (0.9846 SSIM; 0.9870 MS-SSIM). It also achieves a correlation of 96.31 % and an intersection of 10.0285 between the predicted and ground truth histograms, together with the smallest value of the Bhattacharyya distance. The obtained results were validated by experts from the University Hospital Olomouc with positive feedback, thus achieving the best objective and subjective results. The proposed method has the potential to be implemented in hospital environments. ConclusionA comprehensive comparison of the proposed architecture with state-of-the-art methods proves its efficiency in suppressing noise in CXRs and its ability to distinguish the signals of important tissues from noise components. This methodology can potentially improve the performance of the existing CXR processing methods. The source code is released in a GitHub repository that can be accessed from the following link: https://github.com/xKev1n/xU-NetFullSharp.

The Impact of the COVID-19 Lockdown on Cancer Referrals in Primary Care in the UK: Two Years On

Background: Cancer is common, with most cancer patients presenting initially to a general practitioner. The COVID-19 pandemic led to changes in the delivery of primary care, which could have affected cancer referrals. This observational study looked at two-week cancer referrals (2WRs) made before, during and after the first UK COVID-19 lockdown in 2020, at a GP practice in the Wirral, England. Methods: A search was conducted to find the cancer referrals made between 23rd March 2020 - 1st July 2020, during the first lockdown. Using the same methodology, cancer referral data was collected for the corresponding time periods in 2019 and 2021. The number of 2WRs and positive diagnostic yields were then compared. Results: The number of cancer referrals decreased by 40.4% in 2020, compared to 2019. In 2021, the number of referrals then increased by 225%, compared to 2020. Overall, the number of cancer referrals increased between 2019-2021. The positive diagnostic yield for the 2020 2WRs increased by 251.4%, compared to that of 2019. The calculated yield for the 2021 data then decreased by 10.8% compared to 2020. Overall, the positive diagnostic yield increased between 2019-2021. Conclusion: The numbers and outcomes of cancer referrals at this Wirral GP practice have changed considerably following the first UK COVID-19 lockdown in 2020, and the influence of the pandemic was still affecting cancer referrals in 2021. A greater focus on early cancer detection in primary care could help overcome the ways in which the pandemic has affected primary care delivery.

Abbreviated Breast MRI as a Supplement to Mammography in Family Risk History of Breast Cancer within the Croatian National Breast Screening Program.

To evaluate the diagnostic performance of abbreviated breast MRI compared with mammography in women with a family history of breast cancer included in the Croatian National Breast Screening Program. 178 women with a family history of breast cancer aged 50 to 69 underwent abbreviated breast MRI and mammography. Radiological findings for each method were categorized according to the BI-RADS classification. The gold standard for assessing the diagnostic accuracy of breast MRI and mammography, in terms of suspicious BI-RADS 4 and BI-RADS 5 findings, was the histopathological diagnosis. Performance measures, including cancer detection rates, specificity, sensitivity, and positive and negative predictive values, were calculated for both imaging methods. Twelve new cases of breast cancer were detected, with seven (58.3%) identified only by abbreviated breast MRI, four (33.3%) detected by both mammography and breast MRI, and one (8.3%) diagnosed only by mammography. Diagnostic accuracy parameters for abbreviated breast MRI were 91.67% sensitivity, 94.58% specificity, 55.0% positive predictive value (PPV), and 99.37% negative predictive value (NPV), while for mammography, the corresponding values were 41.67%, 96.39%, 45.46%, and 95.81%, respectively. Abbreviated breast MRI is a useful supplement to screening mammography in women with a family history of breast cancer. Considering the results of the conducted research, it is recommended to assess whether women with a family history of breast cancer have an increased risk and subsequently provide annual abbreviated breast MRI in addition to mammography for early detection of breast cancer.

Detection of Catalase and 4-Hydroxynonenal as Detective Biomarkers in Iraqi Breast Cancer

Breast cancer is the most common malignant tumor among women and a leading cause of cancer-related death. Early diagnosis is critical for improving treatment outcomes. The objective of this study is to determine and examine catalase (CAT) and 4-hydroxynonenal (4-HNE) markers as non-invasive biomarkers for early detection in Iraqi breast cancer. A case-control study of 150 participants was further divided into 75 women with a diagnosis of breast cancer and 75 healthy controls. In the present study, blood samples were collected for the determination of serum CAT and 4-HNE levels using an enzyme-linked immunosorbent assay technique. The biochemical markers urea and creatinine were determined by automated Cobas c311. Hemoglobin concentration (HB)-and total white blood cell (WBC) count was done by an automated hematology analyzer. Statistical analyses were performed using the SPSS version 24, where receiver operating characteristics (ROC) analysis was used to assess diagnostic performance. CAT activity was significantly reduced in the patient group (6.07±1.19 pg/ml) compared to controls (12.11±2.74 pg/ml), while 4-HNE was significantly elevated in the patient group (11.43±2.41 pg/ml) compared to control (3.83±1.02 pg/ml). Among hematology analyzer, there was a significant difference in HB and WBC between patients and controls. Roc analyses demonstrated a sensitivity of 100% for CAT and 98% for 4-HNE. Reduced serum levels of CAT and elevated 4-HNE levels in Iraqi breast cancer may serve as new diagnostic biomarkers for breast cancer, highlighting their role in disease progression.

OA17.03 Missed Opportunities for Early Lung Cancer Detection in a Multi-Disciplinary Thoracic Oncology Clinic Cohort

OA17.03 Missed Opportunities for Early Lung Cancer Detection in a Multi-Disciplinary Thoracic Oncology Clinic Cohort

Machine learning-derived peripheral blood transcriptomic biomarkers for early lung cancer diagnosis: Unveiling tumor-immune interaction mechanisms.

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection and a comprehensive understanding of tumor-immune interactions are crucial for improving patient outcomes. This study aimed to develop a novel biomarker panel utilizing peripheral blood transcriptomics and machine learning algorithms for early lung cancer diagnosis, while simultaneously providing insights into tumor-immune crosstalk mechanisms. Leveraging a training cohort (GSE135304), we employed multiple machine learning algorithms to formulate a Lung Cancer Diagnostic Score (LCDS) based on peripheral blood transcriptomic features. The LCDS model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) in multiple validation cohorts (GSE42834, GSE157086, and an in-house dataset). Peripheral blood samples were obtained from 20 lung cancer patients and 10 healthy control subjects, representing an in-house cohort recruited at the Sixth People's Hospital of Chengdu. We employed advanced bioinformatics techniques to explore tumor-immune interactions through comprehensive immune infiltration and pathway enrichment analyses. Initial screening identified 844 differentially expressed genes, which were subsequently refined to 87 genes using the Boruta feature selection algorithm. The random forest (RF) algorithm demonstrated the highest accuracy in constructing the LCDS model, yielding a mean AUC of 0.938. Lower LCDS values were significantly associated with elevated immune scores and increased CD4+ and CD8+ T-cell infiltration, indicative of enhanced antitumor-immune responses. Higher LCDS scores correlated with activation of hypoxia, peroxisome proliferator-activated receptor (PPAR), and Toll-like receptor (TLR) signaling pathways, as well as reduced DNA damage repair pathway scores. Our study presents a novel, machine learning-derived peripheral blood transcriptomic biomarker panel with potential applications in early lung cancer diagnosis. The LCDS model not only demonstrates high accuracy in distinguishing lung cancer patients from healthy individuals but also offers valuable insights into tumor-immune interactions and underlying cancer biology. This approach may facilitate early lung cancer detection and contribute to a deeper understanding of the molecular and cellular mechanisms underlying tumor-immune crosstalk. Furthermore, our findings on the relationship between LCDS and immune infiltration patterns may have implications for future research on therapeutic strategies targeting the immune system in lung cancer.

The Potential Benefit of a Novel Urine Biosensor Platform for Lung Cancer Detection in the Decision-Making Process: From the Bench to the Bedside

Background: Lung cancer is the leading cause of cancer-related mortality worldwide. Lung cancer screening and early detection resulted in a decrease in cancer-specific mortality; however, it introduced additional dilemmas and adherence barriers for patients and providers. Methods: Innovations such as biomolecular diagnosis and biosensor-based technology improve the detection and stratification of high-risk patients and might assist in overcoming adherence barriers, hence providing new horizons for better selection of screened populations. Conclusions: In the present manuscript, we discuss some of the dilemmas clinicians are currently facing during the diagnosis and treatment processes. We further highlight the potential benefits of a novel biosensor platform for lung cancer detection during the decision making process surrounding lung cancer.

Enhancing Early Detection of Prostate Cancer Using PSA-Guided Screening in High-Risk Men Under 50

Abstract Background Prostate Cancer remains a significant global public health concern. It is the most common cancer and the second leading cause of cancer death among men in the United States. Current guidelines, including those by the USPSTF, ACS and ASCO offer varying recommendations on PSA screening. Present clinical practice, primarily targeting men aged 50-69. Our study aimed to assess the feasibility and validity of using PSA for targeted screening in men under 50 who are at high risk, enhancing early detection of prostate cancer. Methods This retrospective analysis utilized data from our hospital’s prostate cancer screening program collected from 2022 to 2023, including104 individuals under 50. Participants were categorized based on PSA levels 4-10 ng/mL (n=71) and over10 ng/mL (n=33). We analyzed the number of cases diagnosed with prostate cancer and the correlation with PSA levels, Gleason Scores (GS), family history, and ethnicity/race. Results In the104 participants, 88 of them were diagnosed with prostate cancer, highlighting an 84.6% detection rate. Among PSA levels 4-10 ng/mL group, 36 out of 43 White or Caucasian and 13 out of 13 Black or African American men were identified as GS 6-10. In the group PSA over 10 ng/mL, 18 White or Caucasian and 6 Black or African American individuals with more aggressive disease (GS 6-10). Importantly, a substantial fraction of these cases had a first-degree relative with prostate cancer, emphasizing the influence of family history on PSA screening decision. Conclusion Our findings suggest that PSA-guided screening in men under 50 at high risk can enhance the early detection of prostate cancer. The significant presence of intermediate-risk prostate cancer (GS 6 or 7) in populations at higher risk due to ethnicity/race and family history. Our study highlights the potential benefits of revisiting current screening guidelines to lower the age threshold for high-risk populations.

Interpretable Machine Learning-Aided Optical Deciphering of Serum Exosomes for Early Detection, Staging, and Subtyping of Lung Cancer.

Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, underscoring an urgent need for strategies that enable early detection and phenotypic classification. Here, we conducted a label-free surface-enhanced Raman spectroscopic (SERS) analysis of serum exosomes from 643 participants to elucidate the biochemical deregulation associated with LC progression and the unique phenotypes of different LC subtypes. Iodide-modified silver nanofilms were prepared to rapidly acquire SERS spectra with a high signal-to-noise ratio using 0.5 μL of patient exosomes. We performed interpretable and automated machine learning (ML) analysis of differential SERS features of serum exosomes to build LC diagnostic models, which achieved accuracies of 100% and 81% for stage I lung adenocarcinoma and its preneoplasia, respectively. In addition, the ML-derived exosomal SERS models effectively recognized different LC subtypes and disease stages to guide precision treatment. Our findings demonstrate that spectral fingerprinting of circulating exosomes holds promise for decoding the clinical status of LC, thus aiding in improving the clinical management of patients.

Public perspectives on the benefits and harms of lung cancer screening: A systematic review and mixed-method integrative synthesis.

Screening for lung cancer with low dose computed tomography aims to reduce lung cancer mortality, but there is a lack of knowledge about how target populations consider its potential benefits and harms. We conducted a systematic review of primary empirical studies published in any jurisdiction since 2002 using an integrative meta-synthesis technique. We searched six health and social science databases. Two reviewers independently screened titles, abstracts, and potentially eligible full-text studies. Quantitative assessments and open-ended perspectives on benefits and harms were extracted and convergently integrated at analysis using a narrative approach. Study quality was assessed. The review included 26 quantitative, 18 qualitative, and 5 mixed methods studies. Study quality was acceptable. Lung cancer screening was widely perceived to be personally beneficial for early detection and reassurance. Radiation exposure and screening accuracy were recognised as harms, but these were frequently considered to be justified by early detection of lung cancer. Stigma, anxiety, and fear related to screening procedures and results were pervasive among current smokers. People with low incomes reported not participating in screening because of potential out-of-pocket costs and geographic access. Populations targeted for lung cancer screening tended to consider screening as personally beneficial and rationalised physical, but not psychological, harms. Screening programmes should be clear about benefits, use non-stigmatising design, and consider equity as a guiding principle.

The Multifaceted Role of miR-23a in Cancer and Disease Progression

Abstract: MicroRNAs (miRNAs) are small, non-coding RNAs that play a crucial role in post-transcriptional gene regulation by modulating mRNA stability and translation. These evolutionarily conserved molecules undergo processing by ribonucleases Drosha and Dicer, ultimately joining the RNA-induced silencing complex to silence gene expression. Among them, miR-23a, located on chromosome 19, is significant for its roles in cell growth, differentiation, and various diseases, including cancer. Depending on the cancer type, miR-23a can function as both an oncogene and a tumour suppressor. While its overexpression often correlates with aggressive tumours, miR-23a holds promise as a biomarker for early cancer detection and a therapeutic target. Its diverse functions in cancer include promoting tumour growth and hindering immune responses, highlighting its potential for personalized medicine. Beyond cancer, miR-23a is involved in blood sugar regulation, insulin resistance, muscle atrophy, and other diseases. It modulates pathways in type 2 diabetes mellitus, muscle atrophy, leukemia, epilepsy, and osteoarthritis. This paper aims to comprehensively analyze the roles of miR-23a in cancer and other diseases, focusing on its regulatory mechanisms, target genes, and pathways. It also evaluates the potential of miR-23a as a biomarker and therapeutic target. Despite its significance, research gaps remain, particularly in understanding the interactions of miR-23a with other miRNAs and the detailed mechanisms underlying its role in various diseases. More research into miR-23a clustering and how it works with other miRNAs could help us learn more about it and find better ways to use it to diagnose and treat diseases.

Differential methylation of circulating free DNA assessed through cfMeDiP as a new tool for breast cancer diagnosis and detection of BRCA1/2 mutation

BackgroundRecent studies have highlighted the importance of the cell-free DNA (cfDNA) methylation profile in detecting breast cancer (BC) and its different subtypes. We investigated whether plasma cfDNA methylation, using cell-free Methylated DNA Immunoprecipitation and High-Throughput Sequencing (cfMeDIP-seq), may be informative in characterizing breast cancer in patients with BRCA1/2 germline mutations for early cancer detection and response to therapy.MethodsWe enrolled 23 BC patients with germline mutation of BRCA1 and BRCA2 genes, 19 healthy controls without BRCA1/2 mutation, and two healthy individuals who carried BRCA1/2 mutations. Blood samples were collected for all study subjects at the diagnosis, and plasma was isolated by centrifugation. Cell-free DNA was extracted from 1 mL of plasma, and cfMeDIP-seq was performed for each sample. Shallow whole genome sequencing was performed on the immuno-precipitated samples. Then, the differentially methylated 300-bp regions (DMRs) between 25 BRCA germline mutation carriers and 19 non-carriers were identified. DMRs were compared with tumor-specific regions from public datasets to perform an unbiased analysis. Finally, two statistical classifiers were trained based on the GLMnet and random forest model to evaluate if the identified DMRs could discriminate BRCA-positive from healthy samples.ResultsWe identified 7,095 hypermethylated and 212 hypomethylated regions in 25 BRCA germline mutation carriers compared to 19 controls. These regions discriminate tumors from healthy samples with high accuracy and sensitivity. We show that the circulating tumor DNA of BRCA1/2 mutant breast cancers is characterized by the hypomethylation of genes involved in DNA repair and cell cycle. We uncovered the TFs associated with these DRMs and identified that proteins of the Erythroblast Transformation Specific (ETS) family are particularly active in the hypermethylated regions. Finally, we assessed that these regions could discriminate between BRCA positives from healthy samples with an AUC of 0.95, a sensitivity of 88%, and a specificity of 94.74%.ConclusionsOur study emphasizes the importance of tumor cell-derived DNA methylation in BC, reporting a different methylation profile between patients carrying mutations in BRCA1, BRCA2, and wild-type controls. Our minimally invasive approach could allow early cancer diagnosis, assessment of minimal residual disease, and monitoring of response to therapy.

Advances in microRNAs as Emerging Biomarkers for Colorectal Cancer Early Detection and Diagnosis.

Colorectal cancer (CRC) remains the second most common cause of cancer-related mortality worldwide, necessitating advancements in early detection and innovative treatment strategies. MicroRNAs (miRNAs), small non-coding RNAs involved in gene regulation, have emerged as crucial players in the pathogenesis of CRC. This review synthesizes the latest findings on miRNA deregulated in precancerous lesions and in CRC. By examining the deregulation patterns of miRNAs across different stages of CRC development, this review highlights their potential as diagnostic tools. We specifically analyse the roles and diagnostic relevance of four miRNAs-miR-15b, miR-21, miR-31, and miR-146a-that consistently exhibit altered expression in CRC. The current knowledge of their role in key oncogenic pathways, drug resistance, and clinical relevance is discussed. Despite challenges posed by the heterogeneity of the research findings on miRNA deregulation and their role in CRC, integrating miRNA diagnostics into current screening methods holds promise for enhancing personalized medicine approaches. This review emphasizes the transformative potential of miRNAs in CRC diagnosis, paving the way for improved patient outcomes and novel therapeutic paradigms.

Biomarkers suitable for early detection of intrathoracic cancers in primary care: a systematic review.

Intrathoracic cancers, including lung cancer, mesothelioma, and thymoma, present diagnostic challenges in primary care. Biomarkers could resolve some challenges. We synthesized evidence on biomarkers performance for intrathoracic cancer detection in low-prevalence settings. A search in EMBASE and MEDLINE included studies that recruited participants with suspected intrathoracic cancer and reported on at least one diagnostic measure for a validated, non-invasive biomarker. Studies were excluded if participants were recruited based on a pre-established diagnosis. Fifty-two studies were included, reporting on 108 individual biomarkers and panels. CEA, CYFRA 21.1, and VEGF were evaluated for lung cancer and mesothelioma. For lung cancer, CEA and CYFRA 21.1 were most studied, with AUCs of 0.48-0.90 and 0.48-0.83, respectively. Pro-GRP and NSE had the highest NPVs (98.2%, 96.9%), while Early-CDT and MSC panels showed NPVs of 99.3% and 99.0% in smokers. For mesothelioma, Fibrillin-3 and mesothelin plus osteopontin had AUCs of 0.93 and 0.91, respectively. Thymoma panels (Binding AcHR + StrAb) and (Binding AcHR + Modulating AcHR + StrAb) had 100% NPVs in myasthenia gravis patients. The review highlights the performance of some biomarkers. However, few were evaluated in low-prevalence settings. Further evaluation is necessary before implementing these biomarkers for intrathoracic cancers in primary care.

Serum exosomal small nucleolar RNA (snoRNA) signatures as a predictive biomarker for benign and malignant pulmonary nodules

Low-dose CT (LDCT) is increasingly recognized as the preferred method for detecting pulmonary nodules. However, distinguishing whether a nodule is benign or malignant often necessitates repeated scans or invasive tissue sampling procedures. Therefore, there is a pressing need for non-invasive techniques to minimize unnecessary interventions. This study aim to investigate the expression profile of exosomal snoRNA in the serum of patients with benign and malignant pulmonary nodules. We identified a total of 278 snoRNAs in serum exosomes, revealing significant differences in snoRNA levels between patients with malignant and benign nodules. Specifically, the upregulated snoRNAs U78 and U37 were validated through qRT-PCR and were found significantly elevated in the serum of patients with malignant pulmonary nodules, positioning them as promising biomarkers for the early detection of lung cancer. This study underscores the potential of serum exosomal U78 and U37 as critical tools for assessing the risk of pulmonary nodules identified through CT screening.

Chemical shift-encoded multishot EPI for navigator-free prostate DWI.

DWI is an important contrast for prostate MRI to enable early and accurate detection of cancer. This study introduces a Dixon 3-shot-EPI protocol with structured low-rank reconstruction for navigator-free DWI. The aim is to overcome the limitations of single-shot EPI (ssh-EPI), such as geometric distortions and fat signal interference, while addressing the motion-induced phase variations of multishot EPI and simultaneously allowing water/fat separation. DWI data were acquired from 7 healthy volunteers using both Dixon 3-shot EPI and standard fat-suppressed ssh-EPI with similar scan times for comparison. Two readers evaluated image quality using a 5-point Likert scale regarding different aspects. The ADC values were quantitatively compared between protocols. To show feasibility in a clinical setting, the protocol was applied to two patients. From the reader scores, Dixon 3-shot EPI significantly reduced geometric distortion compared with ssh-EPI (p < 0.01), with no significant differences in edge definition, SNR, or overall image quality. There was no significant difference in ADC values between the two protocols. However, the Dixon multishot-EPI protocol offered advantages such as self-referenced B0 map-driven distortion correction, greater flexibility in imaging parameters, and superior fat suppression. In the patient data, the lesion could be clearly identified in both protocols and on the associated ADC maps. The proposed Dixon 3-shot-EPI protocol shows promise as an alternative to ssh-EPI for prostate DWI, providing reduced geometric distortions and improved fat suppression. It addresses common DWI issues based on EPI and enhances scanning flexibility, indicating potential for optimized imaging.

Early Cervical Cancer Diagnosis with SWIN-Transformer and Convolutional Neural Networks.

Introduction: Early diagnosis of cervical cancer at the precancerous stage is critical for effective treatment and improved patient outcomes. Objective: This study aims to explore the use of SWIN Transformer and Convolutional Neural Network (CNN) hybrid models combined with transfer learning to classify precancerous colposcopy images. Methods: Out of 913 images from 200 cases obtained from the Colposcopy Image Bank of the International Agency for Research on Cancer, 898 met quality standards and were classified as normal, precancerous, or cancerous based on colposcopy and histopathological findings. The cases corresponding to the 360 precancerous images, along with an equal number of normal cases, were divided into a 70/30 train-test split. The SWIN Transformer and CNN hybrid model combines the advantages of local feature extraction by CNNs with the global context modeling by SWIN Transformers, resulting in superior classification performance and a more automated process. The hybrid model approach involves enhancing image quality through preprocessing, extracting local features with CNNs, capturing the global context with the SWIN Transformer, integrating these features for classification, and refining the training process by tuning hyperparameters. Results: The trained model achieved the following classification performances on fivefold cross-validation data: a 94% Area Under the Curve (AUC), an 88% F1 score, and 87% accuracy. On two completely independent test sets, which were never seen by the model during training, the model achieved an 80% AUC, a 75% F1 score, and 75% accuracy on the first test set (precancerous vs. normal) and an 82% AUC, a 78% F1 score, and 75% accuracy on the second test set (cancer vs. normal). Conclusions: These high-performance metrics demonstrate the models' effectiveness in distinguishing precancerous from normal colposcopy images, even with modest datasets, limited data augmentation, and the smaller effect size of precancerous images compared to malignant lesions. The findings suggest that these techniques can significantly aid in the early detection of cervical cancer at the precancerous stage.