Detection Of Cancer Research Articles

Ideal time of enzyme digestion in FISH detection of breast cancer and lymphoma

Ideal time of enzyme digestion in FISH detection of breast cancer and lymphoma

Boosting cervical cancer detection with a multi-stage architecture and complementary information fusion

Boosting cervical cancer detection with a multi-stage architecture and complementary information fusion

Adaptive genetic algorithm based deep feature selector for cancer detection in lung histopathological images

Cancer is a global health concern because of a significant mortality rate and a wide range of affected organs. Early detection and accurate classification of cancer types are crucial for effective treatment. Imaging tests on different image modalities such as Histopathology images, provide valuable insights into the cellular and architectural features of tissues, allowing pathologists to make diagnosis, determine disease stages, and guide treatment decisions. They are an essential tool in the study and understanding of diseases, aiding in research, education, and patient care. Convolutional neural network based pretrained deep learning models can be used successfully to detect lung cancer. In this study, we have used a channel attention-enabled deep learning model as a feature extractor followed by an adaptive Genetic Algorithm (GA) based feature selector. Here, we calculate the fitness score of each chromosome (i.e., a candidate solution) using a filter method, instead of a classifier. Further, the GA optimized feature vector is fed to the K-nearest neighbors classifier for final classification. The proposed method shows a promising result with an overall accuracy of 99.75% on the LC25000 dataset, which is a publicly available dataset of lung histopathological images. The source code for this work can be found https://github.com/priyam-03/GA-Feature-Selector-Lung-Cancer.

Detection of Human Cervical Cancer by Probe‐Based Quantitative Optical Coherence Tomography

Detection of Human Cervical Cancer by Probe‐Based Quantitative Optical Coherence Tomography

Are we ready? assessing effectiveness and implementation of cancer control strategies in primary care: a comprehensive review of systematic reviews.

Cancer is a major global cause of death, and primary care is crucial for cancer prevention and early detection. However, there is conflicting information on the effectiveness, implementation, and sustainability of cancer control interventions in primary care. This study aimed to summarize the evidence for cancer control in primary care, focussing on identifying relevant factors for implementation and sustainability. We conducted a narrative, mixed-methods review of systematic reviews, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Four databases were screened, and two independent reviewers selected studies reporting on cancer prevention, screening, or early detection in primary or community settings. We analysed findings using the extended Reach-Effectiveness-Adopt-Implementation-Maintenance (RE-AIM) Framework. From the 37 reviews that met the inclusion criteria, 6 focussed on primary prevention, 23 on screening, and 12 on early detection. Most reviews (78%) addressed intervention effectiveness, such as HPV vaccination, tobacco cessation, and cervical, breast, and colorectal screening. One-third of the reviews mentioned adoption and implementation factors, including barriers and facilitators to the implementation of cancer screening programs. Only one review addressed maintenance and sustainability factors, exploring continuous resources and funding strategies. While numerous interventions are effective for cancer prevention and detection in primary care, literature on implementation and sustainability strategies is lacking. Focusing on continuous resources and funding for cancer strategies in primary care may aid sustainability. Future research should prioritize reporting on implementation and sustainability factors to enhance cancer prevention and control in primary care settings.

3D-AJP: Fabrication of Advanced Microarchitected Multimaterial Ceramic Structures via Binder-Free and Auxiliary-Free Aerosol Jet 3D Nanoprinting.

Manufacturing of ceramics is challenging due to their low toughness and high hardness. Additive Manufacturing (AM) has been explored to create complex ceramic structures, but current techniques face a tradeoff between precisely controlled feature sizes and high shrinkage at the microscales. Here, we introduce 3D-AJP, a novel freeform ceramic fabrication method that enables highly complex microscale 3D ceramic architectures-such as micropillars, spirals, and lattices-with minimal shrinkage and no auxiliary support. Using a near-binder-free nanoparticle ink in an Aerosol Jet (AJ) 3D printer, our approach precisely controls feature sizes down to 20 µm with aspect ratios up to 30:1. The resulting structures exhibit exceptionally low linear shrinkage of 2-6% upon sintering, spanning five orders of magnitude in length scale. Bi-material 3D architectures (zinc oxide/zirconia, zinc oxide/titania, titania/zirconia) and hybrid ceramics further demonstrate the technique's versatility. We showcase two key applications. First, 3D ceramic photocatalysts improve water purification performance, achieving a 400% increase in photocatalytic efficiency compared to bulk ceramics. Second, we develop a highly sensitive Her2 biomarker sensor for breast cancer detection, achieving a 22-second response time and a record-low detection limit of 0.0193 fm. Our technique will lead to high-performance sensing, filtration, microelectronics packaging, catalysis, and tissue regeneration technologies.

Adaptive pelican optimization with optimized mask RCNN for automatic lung cancer detection

Adaptive pelican optimization with optimized mask RCNN for automatic lung cancer detection

Role of Deep Learning in Enhancing Medical Diagnosis Accuracy in Argentina

Purpose: To aim of the study was to analyze the role of deep learning in enhancing medical diagnosis accuracy in Argentina. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Deep learning has significantly enhanced medical diagnosis accuracy in Argentina, particularly in cardiology, radiology, and infectious disease detection. AI-driven echocardiograms have achieved high accuracy rates, reducing diagnostic errors and improving early disease detection. AI-powered models for COVID-19 diagnosis have streamlined response times and resource allocation. In cancer detection, AI has improved early tumor identification, but challenges remain due to biases in medical imaging datasets. Machine learning applications in diabetes risk assessment have proven effective in predicting high-risk patients. Despite these advancements, issues such as AI biases, limited dataset diversity, and challenges in integrating AI into healthcare systems persist. Unique Contribution to Theory, Practice and Policy: Artificial neural network (ANN) theory, computational learning theory (CLT) & pattern recognition theory may be used to anchor future studies on the role of deep learning in enhancing medical diagnosis accuracy in Argentina. Hospitals and diagnostic centers should develop AI-assisted workflows that seamlessly integrate deep learning models into radiology, pathology, and general diagnostics. Governments should encourage privacy-preserving AI techniques like differential privacy and federated learning to minimize data-sharing risks.

Liquid biopsies for early detection and monitoring of cancer: advances, challenges, and future directions

Abstract The article offers an extensive survey of the progressions, problems, and future trends of liquid biopsies in the early discovery and surveillance of cancer. Liquid biopsies can detect signals associated with cancer by looking at biological fluids like cerebrospinal fluid, blood, or urine, making them a less invasive alternative to traditional tissue biopsies. Methods: The review explores the molecular biology and techniques behind liquid biopsy, including circulating tumor DNA (ctDNA), circulatory tumor cells (CTCs), and exosomes. It evaluates clinical applications of liquid biopsy across different cancer types, showing their potential for early diagnosis, monitoring disease progression, and therapy response prediction. Results: The article identifies several critical issues with liquid biopsies, including achieving a balance between high sensitivity and specificity, standardizing protocols, addressing technological heterogeneity, and ensuring cost-effectiveness and accessibility. Also, ethical issues about informed consent, data privacy, incidental findings management, and equal testing access have been examined in this context. Conclusion: Finally, this article sheds light on future developments in liquid biopsies, such as enhanced specificity, sensitivity, and integration with artificial intelligence methods.

A fine-tuned adaptive weight deep dense meta stacked transfer learning model for effective cervical cancer prediction

Abstract In the digital world of remarkable technological advancements, the detection of cervical cancer at early stages is of important clinical significance as it can vastly improve the survival rate during treatment. Cervical cytopathology, often known as a Pap test is the frequently adopted screening method for cervical cancer. However, the test seems to be effective but investigation of images containing Pap smear with the help of a microscope is a difficult as well as laborious exercise. The procedure for the same demands an expert in the area and is often time-consuming. The serious pitfalls in subjective clinical evaluation evoke the need of developing an automated system for more reliable cervical cancer diagnosis. Therefore, the goal of this study primarily focuses on designing a Deep learning model to process the Pap smear images and correctly classify the cervical cells. For this purpose, firstly, a publically available dataset namely SIPaKMeD is utilized. Then, different data pre-processing methods are applied to intensify the data quality for effective analysis. Next, a novel stacking model is proposed that leverages a Support Vector Classifier (SVC) as a Meta model over a combination of different Transfer Learning Models including VGG16, ResNet101, InceptionV3, Xception, DenseNet169, and Inception ResNet. Furthermore, the dense layers are added to tune the underlying base transfer learning models to learn fine-tuned adaptive weights. The results obtained from experimental evaluation demonstrate the efficacy of the proposed stacking model by yielding the highest accuracy rate of 95.66% in comparison to other employed methods and existing state-of-the-art techniques.

PPIA enhances cell growth and metastasis through CD147 in oral cancer.

Oral cancer is a malignant tumor, and the associated death rate has significantly increased over the past few decades. Secreted fractions are involved in various physiological processes, and their analysis has become a promising approach for discovering diagnostic and prognostic biomarkers for cancer detection and monitoring metastasis. Therefore, the discovery of potential prognostic, diagnostic, and therapeutic biomarkers for oral cancer metastasis is beneficial for developing effective strategies in oral cancer therapy. In this study, we used secretomic analysis to identify the secreted proteins involved in oral cancer. One of the identified proteins, peptidylprolyl isomerase A (PPIA), was selected for further investigation. We used RNA interference to investigate the effect of PPIA secretion on invasion and migration of OC3-I5 cells. Our results showed that reducing the expression and secretion of PPIA significantly decreased invasion and migration of OC3-I5 cells. Next, we used recombinant PPIA to investigate its direct effect on OC3 cell metastasis. The results revealed that proliferation, migration, and invasion of OC3 cells were significantly increased by treatment with the recombinant PPIA. Immunohistochemical analyses revealed higher PPIA expression in tumor tissues compared to normal tissues. Concisely, PPIA activated the ERK1/2 and p38 MAPK signaling pathways and enhanced cell proliferation and metastasis through CD147. In summary, PPIA may prove to be a novel target for oral cancer therapy as well as a prognostic marker.

Circulating microRNA panels for multi-cancer detection and gastric cancer screening: leveraging a network biology approach

BackgroundScreening tests, particularly liquid biopsy with circulating miRNAs, hold significant potential for non-invasive cancer detection before symptoms manifest.MethodsThis study aimed to identify biomarkers with high sensitivity and specificity for multiple and specific cancer screening. 972 Serum miRNA profiles were compared across thirteen cancer types and healthy individuals using weighted miRNA co-expression network analysis. To prioritize miRNAs, module membership measure and miRNA trait significance were employed. Subsequently, for specific cancer screening, gastric cancer was focused on, using a similar strategy and a further step of preservation analysis. Machine learning techniques were then applied to evaluate two distinct miRNA panels: one for multi-cancer screening and another for gastric cancer classification.ResultsThe first panel (hsa-miR-8073, hsa-miR-614, hsa-miR-548ah-5p, hsa-miR-1258) achieved 96.1% accuracy, 96% specificity, and 98.6% sensitivity in multi-cancer screening. The second panel (hsa-miR-1228-5p, hsa-miR-1343-3p, hsa-miR-6765-5p, hsa-miR-6787-5p) showed promise in detecting gastric cancer with 87% accuracy, 90% specificity, and 89% sensitivity.ConclusionsBoth panels exhibit potential for patient classification in diagnostic and prognostic applications, highlighting the significance of liquid biopsy in advancing cancer screening methodologies.

Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging

BackgroundOver the next 5 years, new breast cancer screening guidelines recommending magnetic resonance imaging (MRI) for certain patients will significantly increase the volume of imaging data to be analyzed. While this increase poses challenges for radiologists, artificial intelligence (AI) offers potential solutions to manage this workload. However, the development of AI models is often hindered by manual annotation requirements and strict data-sharing regulations between institutions.MethodsIn this study, we present an integrated pipeline combining weakly supervised learning—reducing the need for detailed annotations—with local AI model training via swarm learning (SL), which circumvents centralized data sharing. We utilized three datasets comprising 1372 female bilateral breast MRI exams from institutions in three countries: the United States (US), Switzerland, and the United Kingdom (UK) to train models. These models were then validated on two external datasets consisting of 649 bilateral breast MRI exams from Germany and Greece.ResultsUpon systematically benchmarking various weakly supervised two-dimensional (2D) and three-dimensional (3D) deep learning (DL) methods, we find that the 3D-ResNet-101 demonstrates superior performance. By implementing a real-world SL setup across three international centers, we observe that these collaboratively trained models outperform those trained locally. Even with a smaller dataset, we demonstrate the practical feasibility of deploying SL internationally with on-site data processing, addressing challenges such as data privacy and annotation variability.ConclusionsCombining weakly supervised learning with SL enhances inter-institutional collaboration, improving the utility of distributed datasets for medical AI training without requiring detailed annotations or centralized data sharing.

Skin microbiome differences in pancreatic adenocarcinoma, other cancers, and healthy controls: a pilot study

IntroductionMany studies have reported the importance of the human microbiome in relationship to the overall health of its host. While recent studies have explored the microbiome’s role in various types of cancer compared to healthy patients, this pilot study is the first to investigate differences in the skin microbiome composition among pancreatic adenocarcinoma patients, individuals with other cancers, and cancer-free controls.MethodsThe study characterizes the skin microbiome’s potential associations with cancer status by analyzing skin swabs from the forehead and cheek of 58 participants using Next Generation Sequencing (NGS), differential abundance analysis, and machine learning techniques.ResultsThe study results indicated that the cancer group displayed a significantly higher mean alpha diversity compared to the control group. Additionally, a machine learning classification model achieved a mean F1 Score of 0.943 in predicting cancer status, indicating measurable differentiation in the skin microbiome between the study groups. This differentiation is supported by differential abundance methods, including ANCOM-BC and MaAsLin2.DiscussionThis pilot study suggests that skin microbiome profiling could serve as a non-invasive biomarker for cancer detection and monitoring, which warrants a larger, longitudinal study to validate these results.

Quantum computational infusion in extreme learning machines for early multi-cancer detection

A timely and accurate cancer diagnosis is essential for improving treatment outcomes. This study presents a hybrid model integrating Extreme Learning Machine (ELM) with FuNet transfer learning, applied on a multi-cancer dataset and optimized using the Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO). This model leverages a diverse feature fusion strategy, enhancing the extraction of critical imaging features, while Q-GBGWO optimizes ELM parameters to achieve superior classification performance. Results demonstrate that Q-GBGWO-ELM improves diagnostic accuracy by an average of 6.5% compared to traditional methods, with notable accuracy rates across various cancers: 98.80% for breast cancer, 92.30% for brain tumors, 97.00% for skin cancer, and 96.98% for lung cancer. The model integrates advanced feature extraction and optimization techniques, indicating significant potential for early cancer detection. The proposed Q-GBGWO-ELM model contributes to a more innovative diagnostic approach in clinical practice by offering enhanced precision, efficiency, and adaptability across multiple cancer types. This advancement supports a shift toward more personalized and rapid diagnostic procedures, aiming to improve patient outcomes and reshape current cancer care practices with AI-driven accuracy and efficiency.

Analysis of a mouse germ cell tumor model establishes pluripotency-associated miRNAs as conserved serum biomarkers for germ cell cancer detection

Malignant testicular germ cells tumors (TGCTs) are the most common solid cancers in young men. Current TGCT diagnostics include conventional serum protein markers, but these lack the sensitivity and specificity to serve as accurate markers across all TGCT subtypes. MicroRNAs (miRNAs) are small non-coding regulatory RNAs and informative biomarkers for several diseases. In humans, miRNAs of the miR-371-373 cluster are detectable in the serum of patients with malignant TGCTs and outperform existing serum protein markers for both initial diagnosis and subsequent disease monitoring. We previously developed a genetically engineered mouse model featuring malignant mixed TGCTs consisting of pluripotent embryonal carcinoma (EC) and differentiated teratoma that, like the corresponding human malignancies, originate in utero and are highly chemosensitive. Here, we report that miRNAs in the mouse miR-290-295 cluster, homologs of the human miR-371-373 cluster, were detectable in serum from mice with malignant TGCTs but not from tumor-free control mice or mice with benign teratomas. miR-291-293 were expressed and secreted specifically by pluripotent EC cells, and expression was lost following differentiation induced by the drug thioridazine. Notably, miR-291-293 levels were significantly higher in the serum of pregnant dams carrying tumor-bearing fetuses compared to that of control dams. These findings reveal that expression of the miR-290-295 and miR-371-373 clusters in mice and humans, respectively, is a conserved feature of malignant TGCTs, further validating the mouse model as representative of the human disease. These data also highlight the potential of serum miR-371-373 assays to improve patient outcomes through early TGCT detection, possibly even prenatally.

Utility of abdominal ultrasound in screening occult malignancy in the etiological study of idiopathic venous thromboembolism

To determine the sensibility of abdominal ultrasound (US) in the screening of occult cancer in patients with idiopathic venous thromboembolism (iVTE). To evaluate its sensitivity and specificity in different subgroups of patients with iVTE, in order to detect potential beneficiaries in whom the abdominal US screening may have greater diagnostic benefit. Descriptive analysis of the use of abdominal US in these patients, and evaluation of its impact on their management. We retrospectively searched all patients with confirmed iVTE (by Doppler-US, by CT angiography or V/Q scintigraphy) between 2011-2014, which had been performed an abdominal US for screening of occult malignancy as part as their aetiology study of VTE. As reference we set a follow-up period of 2years. 280 patients were found, and 209 were included after inclusion and exclusion criteria were applied. Abdominal US was positive for malignancy in 7 patients (3.3%). The sensitivity and specificity for cancer detection were 43.75% and 99.48%, respectively. Cancer was found in 16 patients in the 2-year follow-up (7.6%). No significant differences were found in the different subgroups of patients. Nevertheless, 28 patients (13.4%) were diagnosed of other cause of VTE during aetiology study, mainly hypercoagulability disorders. Abdominal US for screening of occult malignancy in patients with iVTE is a very specific test but has low sensitivity, which compromises its utility as a routine screening test in these patients. Its use could be optimised by performing it only in selected subgroups of patients that need to be established in future research.

Advancing Near-Infrared Probes for Enhanced Breast Cancer Assessment

Breast cancer remains a leading cause of cancer-related deaths among women, emphasizing the critical need for early detection and monitoring techniques. Conventional imaging modalities such as mammography, MRI, and ultrasound have face sensitivity, specificity, cost, and patient comfort limitations. This study introduces a handheld Near-Infrared Diffuse Optical Tomography (NIR DOT) probe for breast cancer imaging. The NIRscan probe utilizes multi-wavelength light-emitting diodes (LEDs) and a linear charge-coupled device (CCD) sensor to acquire real-time optical data, reconstructing cross-sectional images of breast tissue based on scattering and absorption coefficients. With wavelengths optimized for the differential optical properties of tissue components, the probe enables functional imaging, distinguishing between healthy and malignant tissues. Clinical evaluations have demonstrated its potential for precise tumor localization and monitoring therapeutic responses, achieving a sensitivity of 94.7% and specificity of 84.2%. By incorporating machine learning algorithms and a modified diffusion equation (MDE), the system enhances the accuracy and speed of image reconstruction, supporting rapid, non-invasive diagnostics. This development represents a significant step forward in portable, cost-effective solutions for breast cancer detection, with potential applications in low-resource settings and diverse clinical environments.

Current Status of Prostate Biopsy in Africa: Are We Ready for a Targeted Biopsy?

Prostate cancer remains a significant public health issue globally, with considerable disparities in diagnostic and management practices, especially in Africa. Traditional diagnostic methods such as transrectal ultrasound-guided biopsies have limitations in accuracy and are associated with potential complications. Emerging targeted biopsy techniques promise improved cancer detection rates and reduced morbidity but face adoption challenges across the African continent due to variable access to advanced imaging technologies and professional expertise. This study aims to evaluate the current practices of prostate biopsy techniques in Africa, focusing particularly on the readiness for and integration of targeted biopsy methods. It assesses the accessibility, quality, volume of procedures, and availability of advanced diagnostic tools across different regions. The survey included 58 centers across five African regions, representing a 70% response rate from an initial 83 invitations sent. Finger-guided biopsy was the most common method, used by 41 centers, while only one center reported using MRI fusion biopsy. Thirty-five centers routinely administered a rectal enema, and nearly all (n = 50) centers employed antibiotic prophylaxis, predominantly fluoroquinolones. For anesthesia, 12 centers used injectable lidocaine, and 38 centers used intrarectal lidocaine gel. The number of biopsy cores taken varied, with most centers taking 12, while others used fewer. The findings indicate a critical need for concerted efforts to bridge the gap in prostate cancer diagnostics and treatment in Africa. Enhancing the quality of prostate cancer care on the continent requires investments in training, infrastructure, and standardization of practices. Collaborative efforts towards adopting advanced diagnostic tools and methods are essential for aligning African practices with global standards, ultimately improving outcomes for prostate cancer patients.

Performance of Two Deep Learning-based AI Models for Breast Cancer Detection and Localization on Screening Mammograms from BreastScreen Norway.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate cancer detection and marker placement accuracy of two artificial intelligence (AI) models developed for interpretation of screening mammograms. Materials and Methods This retrospective study included data from 129 434 screening examinations (all female, mean age 59.2, SD = 5.8) performed between January 2008 and December 2018 in BreastScreen Norway. Model A was commercially available and B was an in-house model. Area under the receiver operating characteristic curve (AUC) with 95% confidence interval (CIs) were calculated. The study defined 3.2% and 11.1% of the examinations with the highest AI scores as positive, threshold 1 and 2, respectively. A radiologic review assessed location of AI markings and classified interval cancers as true or false negative. Results The AUC was 0.93 (95% CI: 0.92-0.94) for model A and B when including screen-detected and interval cancers. Model A identified 82.5% (611/741) of the screen-detected cancers at threshold 1 and 92.4% (685/741) at threshold 2. For model B, the numbers were 81.8% (606/741) and 93.7% (694/741), respectively. The AI markings were correctly localized for all screen-detected cancers identified by both models and 82% (56/68) of the interval cancers for model A and 79% (54/68) for B. At the review, 21.6% (45/208) of the interval cancers were identified at the preceding screening by either or both models, correctly localized and classified as false negative (n = 17) or with minimal signs of malignancy (n = 28). Conclusion Both AI models showed promising performance for cancer detection on screening mammograms. The AI markings corresponded well to the true cancer locations. ©RSNA, 2025.