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Revisiting the standards of cancer detection and therapy alongside their comparison to modern methods

World Journal of Methodology 2024年第2期14卷 17-37页

作者： Piotr Gromek Zuzanna Senkowska Elzbieta Pluciennik Zbigniew Pasieka Lin-Yong Zhao Adrianna Gielecinska Mateusz Kciuk Karol Klosinski Zaneta Kaluzinska-Kolat Damian Kolat Department of Functional Genomics Medical University of LodzLodz 90-752LodzkiePoland Department of Biomedicine and Experimental Surgery Medical University of LodzLodz 90-136LodzkiePoland Department of General Surgery&Laboratory of Gastric cancer State Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer CenterWest China HospitalSichuan UniversityChengdu 610041Sichuan ProvinceChina Gastric cancer Center West China HospitalSichuan UniversityChengdu 610041Sichuan ProvinceChina Department of Molecular Biotechnology and Genetics University of LodzLodz 90-237LodzkiePoland Doctoral School of Exact and Natural Sciences University of LodzLodz 90-237LodzkiePoland

In accordance with the World Health Organization data,cancer remains at the forefront of fatal diseases.An upward trend in cancer incidence and mortality has been observed globally,emphasizing that efforts in developing detection and treatment methods should continue.The diagnostic path typically begins with learning the medical history of a patient;this is followed by basic blood tests and imaging tests to indicate where cancer may be located to schedule a needle biopsy.Prompt initiation of diagnosis is crucial since delayed cancer detection entails higher costs of treatment and hospitalization.Thus,there is a need for novel cancer detection methods such as liquid biopsy,elastography,synthetic biosensors,fluorescence imaging,and reflectance confocal microscopy.Conventional therapeutic methods,although still common in clinical practice,pose many limitations and are unsatisfactory.Nowadays,there is a dynamic advancement of clinical research and the development of more precise and effective methods such as oncolytic virotherapy,exosome-based therapy,nanotechnology,dendritic cells,chimeric antigen receptors,immune checkpoint inhibitors,natural product-based therapy,tumor-treating fields,and photodynamic therapy.The present paper compares available data on conventional and modern methods of cancer detection and therapy to facilitate an understanding of this rapidly advancing field and its future directions.As evidenced,modern methods are not without drawbacks;there is still a need to develop new detection strategies and therapeutic approaches to improve sensitivity,specificity,safety,and efficacy.Nevertheless,an appropriate route has been taken,as confirmed by the approval of some modern methods by the Food and Drug Administration.

关键词： cancer detection Liquid biopsy Synthetic biosensors Fluorescence imaging Reflectance confocal microscopy Elastography cancer therapy Tumor-treating fields Oncolytic virotherapy Nanotechnology

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An ultra-sensitive metasurface biosensor for instant cancer detection based on terahertz spectra

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Nano Research 2023年第5期16卷 7304-7311页

作者： Peiliang Wang Jing Lou Yun Yu Lang Sun Lan Sun Guangyou Fang Chao Chang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Innovation Laboratory of Terahertz Biophysics National Innovation Institute of Defense TechnologyBeijing 100071China The Key Laboratory of Electromagnetic Radiation and Sensing Technology Chinese Academy of SciencesBeijing 100190China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China Air and Missile Defense College Air Force Engineering UniversityXi’an 710051China Institute of Acupuncture and Moxibustion China Academy of Chinese Medical SciencesBeijing 100007China School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental HealthPeking UniversityBeijing 100871China School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China

Metasurface biosensors have become the core label-free and rapid-detection technology in bioanalysis.Lung cancer and brain cancer are the first leading causes of cancer death among adults and adolescents,respectively,where poor early diagnosis results from expensive detection costs and time consumption.To tackle the above problems,here,we introduce a terahertzdomain metasurface biosensor for cancer diagnosis,relying on a perfectly symmetrical periodic surface structure,which significantly exhibits polarization-insensitivity at 2.05 THz and the high-sensitivity of 504 GHz/RIU(RIU=refractive index unit).According to the frequency shifts and transmittance variations,four cell types are successfully distinguished from each other.The minimum number of cells is required for thousands of cells to display the differences of spectra,which is 1/30 of clinical methods.Furthermore,the results were consistent with pathological results(the gold standard in clinic)by Gaussian curve fitting.The proposed biosensor has really achieved the characterization of cells in normal and cancerous state.This detection strategy dramatically reduced the cost of detection by reuse and time consumption was reduced to 1/20 of the pathology testing.In addition,it is flexible to set samples and easy to realize automatic operation due to the great polarization-insensitivity of the proposed biosensor,which can further reduce labor costs in the future.It is envisioned that the proposed biosensor will present immense potential in the fields of cancer detection,distinguishing different cancers,identifying primary lesion cancer or metastatic cancer.

关键词： terahertz metasurface biosensor cancer detection

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Deep Learning-Based cancer detection-Recent Developments,Trend and Challenges

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Computer Modeling in Engineering & Sciences 2022年第3期130卷 1271-1307页

作者： Gulshan Kumar Hamed Alqahtani Shaheed Bhagat Singh State University FerozepurIndia King Khalid University AbhaSaudi Arabia

cancer is one of the most critical diseases that has caused several deaths in today’s world.In most cases,doctors and practitioners are only able to diagnose cancer in its later stages.In the later stages,planning cancer treatment and increasing the patient’s survival rate becomes a very challenging task.Therefore,it becomes the need of the hour to detect cancer in the early stages for appropriate treatment and surgery planning.Analysis and interpretation of medical images such as MRI and CT scans help doctors and practitioners diagnose many diseases,including cancer disease.However,manual interpretation of medical images is costly,time-consuming and biased.Nowadays,deep learning,a subset of artificial intelligence,is gaining increasing attention from practitioners in automatically analysing and interpreting medical images without their intervention.Deep learning methods have reported extraordinary results in different fields due to their ability to automatically extract intrinsic features from images without any dependence on manually extracted features.This study provides a comprehensive review of deep learning methods in cancer detection and diagnosis,mainly focusing on breast cancer,brain cancer,skin cancer,and prostate cancer.This study describes various deep learningmodels and steps for applying deep learningmodels in detecting cancer.Recent developments in cancer detection based on deep learning methods have been critically analysed and summarised to identify critical challenges in applying them for detecting cancer accurately in the early stages.Based on the identified challenges,we provide a few promising future research directions for fellow researchers in the field.The outcome of this study provides many clues for developing practical and accurate cancer detection systems for its early diagnosis and treatment planning.

关键词： Autoencoders(AEs) cancer detection convolutional neural networks(CNNs) deep learning generative adversarial models(GANs) machine learning

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DNA methylation signatures in circulating cell-free DNA as biomarkers for the early detection of cancer

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Science China(Life Sciences) 2017年第4期60卷 356-362页

作者： Junyun Wang Xiao Han Yingli Sun Key Laboratory of Genomic and Precision Medicine China Gastrointestinal Cancer Research Center Beijing Institute of Genomics Chinese Academy of Sciences Beijing 100101 China University of Chinese Academy of Sciences Beijing 100049 China

Detecting cell-free DNA(cfDNA) or circulating tumor DNA(ctDNA) in plasma or serum could serve as a "liquid biopsy", which would be useful for numerous diagnostic applications. cfDNA methylation detection is one of the most promising approaches for cancer risk assessment. Here, we reviewed the literature related to the use of serum or plasma circulating cell-free DNA for cancer diagnosis in the early stage and their power as future biomarkers.

关键词： circulating cell-free DNA DNA methylation diagnostic markers cancer detection

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Breast cancer detection Based on Multi-Slotted Patch Antenna at ISM Band

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Circuits and Systems 2023年第5期14卷 1-9页

作者： Mussa Elsaadi Rema Hamad Department of Electronic and Electrical Engineering Tobruk University Tobruk Libya Department of Electronic and Electrical Engineering University of Omar Al-Mukhtar Beida Libya

The present work designed and investigated a 3D basic model for breast cancer detection at the ISM band. The model consists of two multi-slotted rectangular patch antennas and a three-layer breast phantom containing two tumors. A multi-slotted antenna was designed at 2.45 GHz using CST STUDIO SUITE 2018, where the simulated results showed a return loss better than -35 dB and attended more than 77 MHz bandwidth. The diagnosis approach is based on exploiting the electrical properties (frequency dependent) of breast tissues, i.e., mass density, relative permittivity, and conductivity. Once the proposed slotted antenna radiates electromagnetic signals toward the breast model (with and without tumors), the radiation properties in terms of the scattering parameters (S11 and S21), the electrical field, the power flow, the current density, and the power loss density were altered. As a result, the values of these radiation parameters increased when tumors were implanted inside the breast model, informing the presence of cancerous tissues. Moreover, the specific absorption rate (SAR) was estimated as a function of input powers, where the proposed antenna showed a set of low SAR values compared to the IEEE standard of 1.6 W/kg, validating its potential use for diagnosing purposes. The simulated results indicated the prospective use of two slotted antennas (in the first instance) to detect multiple tumors which could be a challenging task using a single-element antenna, where the ultimate goal is to realize a compact antenna array to detect multi-tumors.

关键词： Antenna Array Breast cancer cancer detection Microstrip Tumor detection

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The outpost against cancer:universal cancer only markers

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cancer Biology & Medicine 2023年第11期20卷 806-815页

作者： Chengchen Qian Xiaolong Zou Wei Li Yinshan Li Wenqiang Yu Shanghai Epiprobe Biotechnology Co. LtdShanghai 200233China Department of General Surgery the First Affiliated Hospital of Harbin Medical UniversityHarbin 150001China Shandong Epiprobe Medical Laboratory Co. LtdHeze 274108China People's Hospital of Ningxia Hui Autonomous Region Ningxia Medical UniversityYinchuan 750002China Shanghai Public Health Clinical Center&Department of General Surgery Huashan Hospital&Cancer Metastasis Institute&Laboratory of RNA EpigeneticsInstitutes of Biomedical SciencesShanghai Medical CollegeFudan UniversityShanghai 200032China

cancer is the leading cause of death worldwide.Early detection of cancer can lower the mortality of all types of cancer;however,effective early-detection biomarkers are lacking for most types of cancers.DNA methylation has always been a major target of interest because DNA methylation usually occurs before other detectable genetic changes.While investigating the common features of cancer using a novel guide positioning sequencing for DNA methylation,a series of universal cancer only markers(UCOMs)have emerged as strong candidates for effective and accurate early detection of cancer.While the clinical value of current cancer biomarkers is diminished by low sensitivity and/or low specificity,the unique characteristics of UCOMs ensure clinically meaningful results.Validation of the clinical potential of UCOMs in lung,cervical,endometrial,and urothelial cancers further supports the application of UCOMs in multiple cancer types and various clinical scenarios.In fact,the applications of UCOMs are currently under active investigation with further evaluation in the early detection of cancer,auxiliary diagnosis,treatment efficacy,and recurrence monitoring.The molecular mechanisms by which UCOMs detect cancers are the next important topics to be investigated.The application of UCOMs in real-world scenarios also requires implementation and refinement.

关键词： cancer detection cancer screening DNA methylation cancer epigenetics cancer biomarkers

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Identifying cancer Disease Using Softmax-Feed Forward Recurrent Neural Classification

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Intelligent Automation & Soft Computing 2023年第4期36卷 1137-1149页

作者： P.Saranya P.Asha Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia

In today’s growing modern world environment,as human food activities are changing,it is affecting human health,thus leading to diseases like cancer.cancer is a complex disease with many subtypes that affect human health without premature treatment and cause death.So the analysis of early diagnosis and prognosis of cancer studies can improve clinical management by analyzing various features of observa-tion,which has become necessary to classify the type in cancer research.The research needs importance to organize the risk of the cancer patients based on data analysis to predict the result of premature treatment.This paper introduces a Maximal Region-Based Candidate Feature Selection(MRCFS)for early risk diagnosing using Soft-Max Feed Forward Neural Classiﬁcation(SMF2NC)to solve the above pro-blem.The predictive model is based on a different relational feature learning model,which is possessed to candidate selection to reduce the dimensionality.The redundant features are processed marginal weight rates for observing similar features’variants and the absolute value.Softmax neural hidden layers are trained using the Sigmoid Activation Function(SAF)to create the logical condition for feed-forward layers.Further,the maximal features are introduced to invite a deep neural network con-structed on the Feed Forward Recurrent Neural Network(FFRNN).The classiﬁer produces higher classiﬁcation accuracy than the previous methods and observes the cancer detection,which is recommended for early diagnosis.

关键词： cancer detection extensive data analysis candidate feature selection deep neural classiﬁcation clustering disease inﬂuence rate

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Oral microbiome and pancreatic cancer

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World Journal of Gastroenterology 2020年第48期26卷 7679-7692页

作者： Ai-Lin Wei Mao Li Guo-Qing Li Xuan Wang Wei-Ming Hu Zhen-Lu Li Jue Yuan Hong-Ying Liu Li-Li Zhou Ka Li Ang Li Mei Rosemary Fu Department of Pancreatic Surgery West China HospitalSichuan UniversityChengdu 610000Sichuan ProvinceChina Key Laboratory of Transplant Engineering and Immunology Regenerative Medicine Research CenterWest China HospitalSichuan UniversityChengdu 610000Sichuan ProvinceChina West China School of Public Health/West China fourth Hospital Sichuan UniversityChengdu 610000Sichuan ProvinceChina West China Hospital/West China School of Nursing Sichuan UniversityChengdu 610000Sichuan ProvinceChina Boston College William F.Connell School of Nursing Boston College William F.Connell SchoolMA02467United States

BACKGROUND Microbiota profiles differ between patients with pancreatic cancer and healthy people,and understanding these differences may help in early detection of pancreatic cancer.Saliva sampling is an easy and cost-effective way to determine microbiota profiles compared to fecal and tissue sample collection.AIM To investigate the saliva microbiome distribution in patients with pancreatic adenocarcinoma(PDAC)and the role of oral microbiota profiles in detection and risk prediction of pancreatic cancer.METHODS We conducted a prospective study of patients with pancreatic cancer(n=41)and healthy individuals(n=69).Bacterial taxa were identified by 16S ribosomal ribonucleic acid gene sequencing,and a linear discriminant analysis effect size algorithm was used to identify differences in taxa.Operational taxonomic unit values of all selected taxa were converted into a normalized Z-score,and logistic regressions were used to calculate risk prediction of pancreatic cancer.RESULTS Compared with the healthy control group,carriage of Streptococcus and Leptotrichina(z-score)was associated with a higher risk of PDAC[odds ratio(OR)=5.344,95%confidence interval(CI):1.282-22.282,P=0.021 and OR=6.886,95%CI:1.423-33.337,P=0.016,respectively].Veillonella and Neisseria(z-score)were considered a protective microbe that decreased the risk of PDAC(OR=0.187,95%CI:0.055-0.631,P=0.007 and OR=0.309,95%CI:0.100-0.952,P=0.041,respectively).Among the patients with PDAC,patients reporting bloating have a higher abundance of Porphyromonas(P=0.039),Fusobacterium(P=0.024),and Alloprevotella(P=0.041);while patients reporting jaundice had a higher amount of Prevotella(P=0.008);patients reporting dark brown urine had a higher amount of Veillonella(P=0.035).Patients reporting diarrhea had a lower amount of Neisseria and Campylobacter(P=0.024 and P=0.034),and patients reporting vomiting had decreased Alloprevotella(P=0.036).CONCLUSION Saliva microbiome was able to distinguish patients with pancreatic cancer and healthy individuals.Leptotrichia may be

关键词： Oral microbiota Dysbiosis Pancreatic cancer cancer detection 16s rRNA High-throughput sequencing

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

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Computer Systems Science & Engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human body.It produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and platelets.Leukemia can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia cells.Furthermore,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for leukemia.However,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and dedication.Different authors have proposed automated detection systems for leukemia diagnosis to overcome these limitations.They have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast cells.However,these systems are more efficient,reliable,and fast than previous manual diagnosing methods.However,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class similarity.In this paper,we have proposed a robust automated system to diagnose leukemia and its sub-types.We have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better performance.We have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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The significance and clinical utility of the detection of primary malignant circulating prostate cells:a review of the evidence

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Journal of cancer Metastasis and Treatment 2016年第1期2卷 453-462页

作者： Nigel P.Murray Hospital Carabineros of Chile Nunoa7770199 SantiagoChile Faculty of Medicine University Finis TerraeProvidencia7501015 SantiagoChile

Primary malignant circulating prostate cells(CPCs)are those detected in blood before definitive treatment for prostate cancer.CPCs can be detected in men with benign prostate disease;however,some methods to distinguish between benign and malignant prostate cells have to be validated.This study presents a review of the subject,including theoretical considerations for the selection of markers to detect them,the different methods used,and the utility of their detection in identifying men with prostate cancer and as a prognostic factor.

关键词： Prostate cancer circulating prostate cells cancer detection prognostic factor

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