Vibrational spectroscopy-are we close to finding a solution for early pancreatic cancer diagnosis?

Pancreatic cancer(PC)is an aggressive and lethal neoplasm,ranking seventh in the world for cancer deaths,with an overall 5-year survival rate of below 10%.The knowledge about PC pathogenesis is rapidly expanding.New aspects of tumor biology,including its molecular and morphological heterogeneity,have been reported to explain the complicated“cross-talk”that occurs between the cancer cells and the tumor stroma or the nature of pancreatic ductal adenocarcinoma-associated neural remodeling.Nevertheless,currently,there are no specific and sensitive diagnosis options for PC.Vibrational spectroscopy(VS)shows a promising role in the development of early diagnosis technology.In this review,we summarize recent reports about improvements in spectroscopic methodologies,briefly explain and highlight the drawbacks of each of them,and discuss available solutions.The important aspects of spectroscopic data evaluation with multivariate analysis and a convolutional neural network methodology are depicted.We conclude by presenting a study design for systemic verification of the VS-based methods in the diagnosis of PC.

SNSVM:SqueezeNet-Guided SVM for Breast Cancer Diagnosis

Breast cancer is a major public health concern that affects women worldwide.It is a leading cause of cancer-related deaths among women,and early detection is crucial for successful treatment.Unfortunately,breast cancer can often go undetected until it has reached advanced stages,making it more difficult to treat.Therefore,there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early stage.The proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography images.The extracted features are then utilized to train a support vector machine(SVM)for mammography image classification.The SqueezeNet-guided SVMmodel,known as SNSVM,achieved promising results,with an accuracy of 94.10% and a sensitivity of 94.30%.A 10-fold cross-validation was performed to ensure the robustness of the results,and the mean and standard deviation of various performance indicators were calculated across multiple runs.This model also outperforms state-of-the-art models in all performance indicators,indicating its superior performance.This demonstrates the effectiveness of the proposed approach for breast cancer diagnosis using mammography images.The superior performance of the proposed model across all indicators makes it a promising tool for early breast cancer diagnosis.This may have significant implications for reducing breast cancer mortality rates.

Recent Progress in Nanoscale Covalent Organic Frameworks for Cancer Diagnosis and Therapy

Covalent organic frameworks(COFs)as a type of porous and crystalline covalent organic polymer are built up from covalently linked and periodically arranged organic molecules.Their precise assembly,welldefined coordination network,and tunable porosity endow COFs with diverse characteristics such as low density,high crystallinity,porous structure,and large specific-surface area,as well as versatile functions and active sites that can be tuned at molecular and atomic level.These unique properties make them excellent candidate materials for biomedical applications,such as drug delivery,diagnostic imaging,and disease therapy.To realize these functions,the components,dimensions,and guest molecule loading into COFs have a great influence on their performance in various applications.In this review,we first introduce the influence of dimensions,building blocks,and synthetic conditions on the chemical stability,pore structure,and chemical interaction with guest molecules of COFs.Next,the applications of COFs in cancer diagnosis and therapy are summarized.Finally,some challenges for COFs in cancer therapy are noted and the problems to be solved in the future are proposed.

Fluorescence lifetime imaging microscopy and its applications in skin cancer diagnosis

Fluorescence lifetime(FLT)of fluorophores is sensitive to the changes in their surrounding microenvironment,and hence it can quantitatively reveal the physiological characterization of the tissue under investigation.Fluorescence lifetime imaging microscopy(FLIM)provides not only morphological but also functional information of the tisse by producing spatially resolved image of fuorophore lifetime,which can be used as a signature of disorder and/or malignancy in diseased tissues.In this paper,we begin by introducing the basic principle and common detection methods of FLIM.Then the recent advances in the FLIM-based diagnosis of three different skin cancers,including basal cell carcinoma(BCC),squamous cell carcinoma(SCC)and malignant melanoma(MM)are reviewed.Furthermore,the potential advantages of FLIM in skin cancer diagnosis and the challenges that may be faced in the future are prospected.

OPTICAL COHERENCE TOMOGRAPHY FOR BLADDER CANCER DIAGNOSIS:FROM ANIMAL STUDY TO CLINICAL DIAGNOSIS

This paper summarizes the recent technological development in our lab on cystoscopic optical coherence tomography(COCT)by integrating time-domain OCT(TDOCT)and spectral-domain OCT(SDOCT)with advanced MEMS-mirror technology for endoscopic laser scanning imaging.The COCT catheter can be integrated into the instrument channel of a commercial 22Fr rigid cystoscopic sheath for in vivo imaging of human bladder under the cystosocopic visual guidance;the axial/transverse resolutions of the COCT catheter are roughly 9μm and 12μm,respectively,and 2D COCT imaging can be performed with over 110dB dynamic range at 4–8 fps.To examine the utility and potential limitations of OCT for bladder cancer diagnosis,systemic ex vivo rat bladder carcinogenesis studies were performed to follow various morphological changes induced by tumor growth and in vivo porcine study was performed to examine the feasibility of COCT for in vivo imaging.Justified by promising results of the animal studies,preliminary clinical study was conducted on patients scheduled for operating-room cystoscopy for bladder cancers.Double-blind clinical results reveal that COCT can delineate detailed bladder architectures(e.g.,urothelium,lamina propria,muscularis)at high resolution and detect bladder cancers based on enhanced urothelial heterogeneity as a result of excessive growing nature of bladder cancers.The diagnostic sensitivity and specificity can be enhanced to 92%and 85%,respectively.Results also suggest that due to reduced imaging depth of COCT in cancerous lesions,staging of bladder cancers may be limited to Ta or T1 for non-outgrowing cancerous lesions.

Nanomedicine has opened new avenues for cancer diagnosis and therapy

A crucial feature of nanoparticles,such as liposomes,magnetic nanoparticles,quantum dots,metallic nanoparticles,silica nanoparticles,polymersomes and dendrimers etc.,is their higher accumulation in the tumor than in normal tissues1-3.Various nanoparticles have been intensively used as vehicles to deliver

DNA Processor Modules Enabled Pattern Recognition of Extracellular Vesicles for Facile Cancer Diagnosis

Comprehensive Summary,Extracellular vesicles(EVs)carry rich protein and nucleic acid information of host cells,thus,they are considered to be reliable biomarkers for cancer diagnosis.However,current EVs detection relies on technical expertise that requires special equipment to readout signals that prevent its point-of-care testing.In this study,we propose a Pattern Recognition of Molecular in Interest on Single EVs(PROMISE)strategy for clinical EVs detection.This strategy combines an aptamer-based DNA processor on single EVs,and a color-rendering enzyme to provide a visual output for naked-eyes enabled profiling.We demonstrate 100%accuracy in breast cancer discrimination.Furthermore,by utilizing thin-layer chromatography(TLC),we achieve a simultaneous screening of two types of cancers(breast and prostate cancer)in one sample.This PROMISE strategy could serve as a versatile platform for point-of-care EVs diagnosis.

Inorganic imaging nanoprobes for breast cancer diagnosis

Breast cancer is one of the most prevalent cancers worldwide,and early diagnosis and screening are vital to its successful treatment.Although medical imaging methods can assist in the early detection of breast cancer,imaging methods that are currently used for clinical diagnosis have drawbacks,such as low sensitivity and accuracy.Contrast agents are often used in diagnostic imaging to address these drawbacks.Nanocontrast agents have attracted considerable attention in recent years due to their unique physicochemical characteristics.Among these agents,inorganic nanoprobes have been substantially developed through improvements in synthesis techniques and pairings with other organic molecules.This paper mainly summarizes the specific applications of inorganic nanoprobes in the magnetic resonance imaging,fluorescence imaging,radionuclide imaging,and bimodal/multimodal imaging of breast cancer.

A Novel Method for Diagnosis of Breast Cancer Tumors Based on Random Forest

GLOBOCAN 2020 cancer data shows that female breast cancer has become the most common cancer over lung cancer for the first time. As a disease threatening the life safety of women all over the world, how to improve the accuracy of breast cancer diagnosis and help patients get treatment as early as possible is of great importance. This paper introduces a new random forest-based breast cancer diagnosis method (NRFM), using the average radius, average texture, average circumference and other 30 indicators in the nucleus of breast mass as characteristics, to diagnose the benign and malignant breast cancer. NRFM proposed to randomly miss a certain percentage of breast cancer data, using random forest regression to fill in the experiment proved that using the method proposed in this paper, when the proportion of missing data reached 50%, the accuracy of breast cancer diagnosis will be as high as 96.85%. Experiments show that NRFM is easy to understand, convenient to operate, and has practical application value, which can assist doctors to improve the accuracy of breast cancer diagnosis.

Metal oxide semiconductor gas sensing materials for early lung cancer diagnosis

The urgency of early lung cancer(LC)diagnosis and treatment has been more and more significant.Exhaled breath analysis using gas sensors is a promising way to find out if someone has LC due to its low-cost,non-invasive,and real-time monitoring compared with traditional invasive diagnostic techniques.Among sensor-based gas detection techniques,metal oxide semiconductor’s gas sensors are one of the most important types.This review presents the-state-of-art in metal oxide gas sensors for the diagnosis of early LC.First,the exhaled breath biomarkers are described with emphasis on the concentration of abnormal volatile organic compounds(VOCs)caused by the metabolic process of LC cells.Then,the research status of metal oxide gas sensors in LC diagnosis is summarized.The sensing performance and enhancement strategy of biomarkers provided by metal oxide semiconductor materials are reviewed.Another effective way to improve VOC detection performance is to build a gas sensor array.At the same time,various gas sensors combined with self-powered techniques are mentioned to display a broad development prospect in breath diagnosis.Finally,metal oxide gas sensor-based LC diagnosis is prospected.

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Over the past decade,artificial intelligence(AI)has contributed substantially to the resolution of various medical problems,including cancer.Deep learning(DL),a subfield of AI,is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data.On the basis of a large quantity of medical data and novel computational technologies,AI,especially DL,has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment.These applications range from early cancer detection,diagnosis,classification and grading,molecular characterization of tumors,prediction of patient outcomes and treatment responses,personalized treatment,automatic radiotherapy workflows,novel anti-cancer drug discovery,and clinical trials.In this review,we introduced the general principle of AI,summarized major areas of its application for cancer diagnosis and treatment,and discussed its future directions and remaining challenges.As the adoption of AI in clinical use is increasing,we anticipate the arrival of AI-powered cancer care.

Diagnostic accuracy of the multi-target stool DNA test in detecting colorectal cancer:A hospital-based study

BACKGROUND The multi-target stool DNA test(MT-sDNA)has potential utility in the detection of colorectal cancer(CRC),but validation of its clinical accuracy has been limited in China.AIM To evaluate the diagnostic performance of MT-sDNA and investigate the combined diagnostic value of alpha-fetoprotein(AFP),carcinoembryonic antigen(CEA),and carbohydrate antigen 199(CA199)with MT-sDNA in CRC and adenomas.METHODS We evaluated the performance of the MT-sDNA kit based on a hospital clinical trial.In this case-control study,135 participants from the Affiliated Hospital of Medical School of Ningbo University,including 51 CRC patients,23 patients with adenomas,and 61 healthy controls were enrolled.We used a risk scoring system to determine the positivity of tests with histological diagnosis or colonoscopy as the reference standard.RESULTS The main indices of sensitivity,specificity and accuracy were evaluated.The sensitivity and specificity for CRC detection were 90.2%and 83.3%,respectively,with an accuracy of 89.8%.For adenoma,the sensitivity and specificity were 56.5%and 68.9%,respectively,with an accuracy of 73.1%.The sensitivity and specificity of MT-sDNA combined with CEA in the diagnosis of adenoma were 78.3%and 60.7%,respectively.CONCLUSION The MT-sDNA test showed better performance in the detection of CRC,which was superior to AFP,CEA,and CA199 separately,but not for predicting adenomas.The combination of MT-sDNA with CEA further improved the sensitivity for adenoma diagnosis.

Glyco-functionalised quantum dots and their progress in cancer diagnosis and treatment

Despite all major breakthroughs in recent years of research,we are still unsuccessful to effctively diagnose and treat cancer that has express and metasta-sizes.Thus,the development of a novel approach for cancer detection and treatment is crucial.Recent progress in Glyconanotechnology has allowed the use of glycans and lectins as bio-functional molecules for many biological and biomedical applications.With the known advantages of quantum dots(QDs)and versatility of carbohydrates and lectins,Glyco-functionalised QD is a new prospect in constructing biomedical imaging platform for cancer behaviour study as well as treatment.In this review,we aim to describe the current utilisation of Glyco-functiona-lised QDs as well as their future prospective to interpret and confront cancer.

DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this area.To address this problem,researchers start looking for information out of the medical datasets.Previous efforts mainly leverage information from natural images via transfer learning.More recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra annotations.In this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)images.In DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the networks.We have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public dataset.The results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their performance.For example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

Levels of chemical element contents in thyroid as potential biomarkers for cancer diagnosis (a preliminary study)

Aim:Thyroid cancer is an internationally important health problem.The aim of this exploratory study was to evaluate whether significantchanges in the thyroid tissue levels of Al,B,Ba,Br,Ca,Cl,Cu,Fe,I,K,Li,Mg,Mn,Na,P,S,Si,Sr,V,and Zn exist in the malignantly transformed thyroid.Methods:Thyroid tissue levels of twenty chemical elements were prospectively evaluated in 41 patients with thyroid malignant tumors and 105 healthy inhabitants.Measurements were performed using a combination of non-destructive and destructive methods:instrumental neutron activation analysis and inductively coupled plasma atomic emission spectrometry,respectively.Tissue samples were divided into two portions.One was used for morphological study while the other was intended for trace element analysis.Results:It was found that contents of Al,B,Br,Ca,Cl,Cu,K,Mg,Mn,Na,P,S,and Si were significantly higher(approximately 3.2,4.6,9.3,1.8,2.3,3.6,1.6,1.6,1.6,1.2,2.5,1.1,and 2.8 times,respectively)while content of I lower(nearly 26 times)in cancerous tissues than in normal tissues.Conclusion:There are considerable changes in chemical element contents in the malignantly transformed tissue of thyroid.

Self-assembling bile pigments for cancer diagnosis and therapy

Nanomaterials that integrate multiple functions provide promising opportunities for noninvasive and targeted cancer diagnosis and therapy.However,the unclear metabolic pathway to nanomaterials brought difficulties to clinical application.Selfassembling bile pigments are endogenous functional materials with excellent biocompatibility and low toxicity.Functional materials based on endogenous bile pigments provide a decent solution to this dilemma.In this review,the features and functions of self-assembling bile pigments are discussed in detail for cancer diagnosis and treatment applications.Emphases are put on the intrinsic physicochemical characteristics of bile pigments and their applications,including drug delivery,photoacoustic imaging,photothermal therapy,and anti-inflammation therapy.This review will promote the exploration of these areas and tremendously realize the innovative applications of self-assembling biliverdin/bilirubin nanomaterials toward cancer diagnosis and therapy.

Improved Model for Genetic Algorithm-Based Accurate Lung Cancer Segmentation and Classification

Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients.For lung cancer diagnosis,the computed tomography(CT)scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis.In present scenario of medical data processing,the cancer detection process is very time consuming and exactitude.For that,this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm.In the model,the input CT images are pre-processed with the filters called adaptive median filter and average filter.The filtered images are enhanced with histogram equalization and the ROI(Regions of Interest)cancer tissues are segmented using Guaranteed Convergence Particle Swarm Optimization technique.For classification of images,Probabilistic Neural Networks(PNN)based classification is used.The experimentation is carried out by simulating the model in MATLAB,with the input CT lung images LIDC-IDRI(Lung Image Database Consortium-Image Database Resource Initiative)benchmark Dataset.The results ensure that the proposed model outperforms existing methods with accurate classification results with minimal processing time.

Urine Diagnosis for Cancer

The key to saving the life of a person suffering from a malignant tumor lies in early diagnosis and surgery. Chinese scientists have developed a new method of diagnosing cancer by analyzing a person's urine. This feat was acclaimed by a panel of experts at a meeting under the auspices of the Chinese Academy of Sciences (CAS) in July 30 in Dalian, in northeast China's Liaoning Province.

Depression and quality of life before and after diagnosis of lung cancer

Objective The aim of the present study was to assess the frequency of depression and quality of life(QoL) in lung cancer patients before and after diagnosis,and to investigate the potential related factors. Methods The subjects consisted of 115 consecutive adult patients newly diagnosed for lung cancer in Shanghai Pulmonary Hospital between April 2008 and October 2008. Depression

Value of autofluorescence bronchoscope in diagnosis of central type lung cancer

Objective To evaluate the value of autofluorescence bronchoscope (AFB) in airway examination in central type lung cancer. Methods From Sep 2009 to Mar 2010,29 patients (23 men,6 women,median age 62. 2 years,range from 34 to 81 years) underwent AFB procedure. There were 3 lesions located at trachea,1 at