An Intelligent Decision Support System for Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related death around the globe.The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis.Most diagnostic techniques can identify and classify only one type of lung cancer.It is crucial to close this gap with a system that detects all lung cancer types.This paper proposes an intelligent decision support system for this purpose.This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives.Its algorithm uses a Convolutional Neural Network(CNN)tool to perform deep learning and a Random Forest Algorithm(RFA)to help classify the type of cancer present using several extracted features,including histograms and energy.Numerous simulation experiments were conducted on MATLAB,evidencing that this system achieves 98.7%accuracy and over 98%precision and recall.A comparative assessment assessing accuracy,recall,precision,specificity,and F-score between the proposed algorithm and works from the literature shows that the proposed system in this study outperforms existing methods in all considered metrics.This study found that using CNNs and RFAs is highly effective in detecting lung cancer,given the high accuracy,precision,and recall results.These results lead us to believe that bringing this kind of technology to doctors diagnosing lung cancer is critical.

EZH2 is a biomarker associated with lung cancer diagnosis and immune infiltrates without prognostic specificity: a study based on the cancer genome atlas data

Enhancer of zeste homolog 2(EZH2)is the catalytic subunit of polycomb repressive complex 2(PRC2).Dysregulation of EZH2 causes alteration of gene expression and functions,thereby promoting cancer development.Recent studies suggest that EZH2 has a potential prognostic role in patients with nonsmall cell lung cancer(NSCLC).However,the prognostic value of EZH2 expression levels in NSCLC is controversial.In this study,we evaluated the prognostic value in lung cancer(LC-LUAD/LUSC)based on data from The Cancer Genome Atlas(TCGA)database.Kruskal-Wallis test,Wilcoxon signed-rank test,and logistic regression were used to evaluate the relationship between EZH2 expression and clinicopathological features.Cox regression and the Kaplan-Meier method were adopted to evaluate prognosis-related factors.Gene set enrichment analysis(GSEA)was performed to identify the key pathways related to EZH2.The correlations between EZH2 and cancer immune infiltrates were investigated by single-sample Gene Set Enrichment Analysis(ssGSEA).EZH2 was found to be up regulated with amplification in tumor tissues in multiple LC cohorts.High EZH2 expression was associated with poorer overall survival(OS).GSEA suggested that EZH2 regulates innate immune system,ECM affiliated,matrisome,surfactant metabolism.Notably,ssGSEA indicated that EZH2 expression was positively correlated with infiltrating levels of Th2 cells and significantly negatively correlated with mast cell infiltration level.These results suggest that EZH2 is associated with LC immune infiltration and significantly over-expressed in lung cancer,and its diagnostic value is better than prognosis,which lays a foundation for further study of the immunomodulatory role of EZH2 in LC.

Multilevel Attention Unet Segmentation Algorithmfor Lung Cancer Based on CT Images

Lung cancer is a malady of the lungs that gravely jeopardizes human health.Therefore,early detection and treatment are paramount for the preservation of human life.Lung computed tomography(CT)image sequences can explicitly delineate the pathological condition of the lungs.To meet the imperative for accurate diagnosis by physicians,expeditious segmentation of the region harboring lung cancer is of utmost significance.We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner,erect an interpretable model,and attain segmentation of lung cancer.The specific advancements can be encapsulated as follows:1)Concentration on the lung parenchyma region:Based on 16-bit CT image capturing and the luminance characteristics of lung cancer,we proffer an intercept histogram algorithm.2)Focus on the specific locus of lung malignancy:Utilizing the spatial interrelation of lung cancer,we propose a memory-based Unet architecture and incorporate skip connections.3)Data Imbalance:In accordance with the prevalent situation of an overabundance of negative samples and a paucity of positive samples,we scrutinize the existing loss function and suggest a mixed loss function.Experimental results with pre-existing publicly available datasets and assembled datasets demonstrate that the segmentation efficacy,measured as Area Overlap Measure(AOM)is superior to 0.81,which markedly ameliorates in comparison with conventional algorithms,thereby facilitating physicians in diagnosis.

Primary biliary cholangitis presenting with granulomatous lung disease misdiagnosed as lung cancer:A case report

BACKGROUND There are few cases of pulmonary granulomatous changes secondary to primary biliary cirrhosis(PBC).No case of granulomatous lung disease secondary to PBC misdiagnosed as lung cancer had been reported.CASE SUMMARY A middle-aged woman presented with lung nodules and was misdiagnosed with lung cancer by positron emission tomography/computed tomography.She underwent left lobectomy,and the pathology of the nodules showed granulomatous inflammation,which was then treated with antibiotics.However,a new nodule appeared.Further investigation with lung biopsy and liver serology led to the diagnosis of PBC,and chest computed tomography indicated significant reduction in the pulmonary nodule by treatment with methylprednisolone and ursodeoxycholic acid.CONCLUSION Diagnosis of pulmonary nodules requires integrating various clinical data to avoid unnecessary pulmonary lobectomy.

Tongue image feature correlation analysis in benign lung nodules and lung cancer

Objective To analyze the differences in the correlation of tongue image indicators among patients with benign lung nodules and lung cancer.Methods From July 1;2020 to March 31;2022;clinical information of lung cancer patients and benign lung nodules patients was collected at the Oncology Department of Longhua Hos-pital Affiliated to Shanghai University of Traditional Chinese Medicine and the Physical Ex-amination Center of Shuguang Hospital Affiliated to Shanghai University of Traditional Chi-nese Medicine;respectively.We obtained tongue images from patients with benign lung nod-ules and lung cancer using the TFDA-1 digital tongue diagnosis instrument;and analyzed these images with the TDAS V2.0 software.The extracted indicators included color space pa-rameters in the Lab system for both the tongue body(TB)and tongue coating(TC)(TB/TC-L;TB/TC-a;and TB/TC-b);textural parameters[TB/TC-contrast(CON);TB/TC-angular second moment(ASM);TB/TC-entropy(ENT);and TB/TC-MEAN];as well as TC parameters(perAll and perPart).The bivariate correlation of TB and TC features was analyzed using Pearson’s or Spearman’s correlation analysis;and the overall correlation was analyzed using canonical correlation analysis(CCA).Results Samples from 307 patients with benign lung nodules and 276 lung cancer patients were included after excluding outliers and extreme values.Simple correlation analysis indi-cated that the correlation of TB-L with TC-L;TB-b with TC-b;and TB-b with perAll in lung cancer group was higher than that in benign nodules group.Moreover;the correlation of TB-a with TC-a;TB-a with perAll;and the texture parameters of the TB(TB-CON;TB-ASM;TB-ENT;and TB-MEAN)with the texture parameters of the TC(TC-CON;TC-ASM;TC-ENT;and TC-MEAN)in benign nodules group was higher than lung cancer group.CCA further demon-strated a strong correlation between the TB and TC parameters in lung cancer group;with the first and second pairs of typical variables in benign nodules and lung cancer groups indicat-ing correlation coefficients of 0.918 and 0.817(P<0.05);and 0.940 and 0.822(P<0.05);re-spectively.Conclusion Benign lung nodules and lung cancer patients exhibited differences in correla-tion in the L;a;and b values of the TB and TC;as well as the perAll value of the TC;and the texture parameters(TB/TC-CON;TB/TC-ASM;TB/TC-ENT;and TB/TC-MEAN)between the TB and TC.Additionally;there were differences in the overall correlation of the TB and TC be-tween the two groups.Objective tongue diagnosis indicators can effectively assist in the diag-nosis of benign lung nodules and lung cancer;thereby providing a scientific basis for the ear-ly detection;diagnosis;and treatment of lung cancer.

Chinese guidelines for diagnosis and treatment of primary lung cancer 2018 (English version)

Contents1. Overview2. Screening and diagnosis2.1 Risk factors for lung cancer2.1.1 Smoking and passive smoking2.1.2 Indoor pollution2.1.3 Indoor radon exposure2.1.4 Outdoor air pollution2.1.5 Occupational factors2.1.6 Family history of lung cancer and genetic susceptibility2.1.7 Other factors.

Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis

The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computeraided diagnosis(CAD) vs human judgment alone in characterizing solitary pulmonary nodules(SPNs) at computed tomography(CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator(BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic(ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions(P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs(15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses(mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.

National guidelines for diagnosis and treatment of lung cancer 2022 in China(English version)

Contents 1.Overview 2.Screening and diagnosis 2.1 Risk factors for lung cancer 2.1.1 Smoking and involuntary smoking 2.1.2 History of chronic obstructive pulmonary disease(COPD)2.1.3 Occupational exposures 2.1.4 Family history of lung cancer and genetic susceptibility 2.1.5 Other factors 2.2 Screening for high-risk population。

Patient, Provider and System Factors Impacting on the Diagnosis and Management of Lung Cancer Care in Australia

Background: Lung cancer is the leading cause of cancer death in Australia, with only modest improvements in survival. This study aims to identify factors impacting on diagnosis and management of lung cancer with particular reference to Australian primary care. Methods: A sequential mixed method modified approach employing interview and a two-phased survey technique. Following telephonic interviews with 31 health professionals (individuals representing general practitioners, specialized physicians, nurses and allied health practitioners), interview data was analysed using qualitative thematic analysis, and surveys using descriptive statistics. Emergent themes were organised under patient, provider and system factors. Interviews ceased upon saturation of data. Results: Multiple patient, provider and systems issues were seen to contribute to adverse health outcomes. There is a strong relationship between smoking and outcomes, and factors related to higher smoking rates such as a lower socioeconomic status. For smokers, guilt and/or denial was considered a reason for delay in the decision to seek medical care for cough or shortness of breath. Aboriginal people under-report morbidity related to smoking and chronic obstructive pulmonary disease;other patients fail to recognise the significance of their symptoms. Discussion: Despite the poor prognosis of lung cancer diagnosis, increased awareness of presentation and treatment options can address disparities in health outcomes.

Nanotechnology and Nanomedicine: A Promising Avenue for Lung Cancer Diagnosis and Therapy

Lung cancer is a leading cause of cancer-related death worldwide,with a very poor overall five-year survival rate.The intrinsic limitations associated with the conventional diagnosis and therapeutic strategies used for lung cancer have motivated the development of nanotechnology and nanomedicine approaches,in order to improve early diagnosis rate and develop more effective and safer therapeutic options for lung cancer.Cancer nanomedicines aim to individualize drug delivery,diagnosis,and therapy by tailoring them to each patient’s unique physiology and pathological features—on both the genomic and proteomic levels—and have attracted widespread attention in this field.Despite the successful application of nanomedicine techniques in lung cancer research,the clinical translation of nanomedicine approaches remains challenging due to the limited understanding of the interactions that occur between nanotechnology and biology,and the challenges posed by the toxicology,pharmacology,immunology,and largescale manufacturing of nanoparticles.In this review,we highlight the progress and opportunities associated with nanomedicine use for lung cancer treatment and discuss the prospects of this field,together with the challenges associated with clinical translation.

Circulating tumor cells with epithelial-mesenchymal transition markers as potential biomarkers for the diagnosis of lung cancer

BACKGROUND Circulating tumor cells(CTCs) can be clustered into three subtypes according to epithelial-mesenchymal transition(EMT) markers: CTCs with epithelial markers(E-CTCs), CTCs with mesenchymal markers(M-CTCs), and CTCs with both markers(E&M-CTCs). CTC detection has clinical implications in the diagnosis of lung cancer(LC).AIM To clarify the diagnostic value of CTCs categorized by EMT markers in LC.METHODS The study included 106 patients with lung adenocarcinoma, including 42 groundglass opacities(GGO) and 64 solid lesions, who underwent surgery between July 2015 and December 2019. Eleven patients with benign tumors and seventeen healthy controls were included. CTCs in peripheral blood and associated EMT markers were detected preoperatively using the CanPatrol TM technique. The diagnostic power of CTCs for discriminating LC cases from controls was analyzed by the receiver operating characteristic(ROC) curve. The CytoploRare technique was used in 20 cases and 18 controls for validation, and Kappa values were calculated to evaluate consistency between techniques.RESULTS Of the 106 LC cases, 94(89.6%) had at least one CTC. CTCs were detectable in 35(83.3%) of 42 GGO cases. Total CTCs and E&M-CTCs were significantly more frequent in LC cases than in benign or healthy controls. The proportion of MCTCs plus E&M-CTCs increased gradually from healthy controls, to benign controls, to LC cases. The area under the ROC curve of total CTCs and E&M-CTCs was > 0.8 and > 10.75, respectively. The combined sensitivity of total-CTCs and E&M-CTCs was 85.85% for LC patients(80.95% for GGO patients) and the specificity was 78.57%.The Kappa value was 0.415,indicating relative consistency between CanPatrol TM and CytoploRare.CONCLUSION CTC detection is valuable for distinguishing LC from controls,and particularly E&M-CTC detection warrants further study.

Progress in diagnosis and treatment of small cell lung cancer with integrated traditional Chinese and Western medicine

Lung cancer is one of the most common major diseases that seriously threaten human health,lung cancer includes small cell lung cancer(SCLC)and non-small cell lung cancer(NSCLC).Although patients with SCLC account for about 20%of the total number of patients with lung cancer,the mortality rate is much higher than that of patients with NSCLC.Integrated traditional Chinese and Western medicine has obvious advantages in the treatment of patients with SCLC.According to the relevant literature reports on the treatment of SCLC in recent years,this article will summarize the research progress of integrated traditional Chinese and western medicine in the treatmentof SCLC from the aspects of traditional Chinese medicine(TCM)combined with surgery,chemotherapy,radiotherapy,and molecular targeted therapy.

EXAMINATION OF SPUTUM SEDIMENT PARAFFIN SECTION OF DIAGNOSIS OF LUNG CANCER

To establish the rapid and accurate methods for detecting cancer cells from the sputum of patients with lung cancer. Methods: Sputum sediment section examination and sputum smear examination were performed. For 142 cases of lung cancer patients diagnosed by bronchia fibrous lens and confirmed by pathologic diagnosis. Results: (a) The positive diagnosis rate of lung cancer cell was 71.83% (102/142) when using sputum sediment examination while that was 32.39% (46/142) when using sputum smear examination, when using sputum sediment examination jointly with sputum smear examination, the positive diagnosis rate of lung cancer was 91.55% (130/142). According to the positive diagnosis rate of lung cancer, the sputum sediment examination was significantly higher than sputum smear examination (P<0.001), joint examination of both of them was significantly higher than the single sputum sediment examination (P<0.001); (b) As to the histopathological types of lung cancers, 66 cases could be identified among 102 cases whom were confirmed by sputum sediment examination, the identification rate was 64.71%; and 8 cases could be identified among 46 cases whom were confirmed by sputum smear examination, with an identification rate of 17.39%, it was significantly lower than that by sputum sediment examination (P<0.001); (c) Immunohistochemistry technique could be used in the sections of sputum sediment to identify some histopathological types which could not be identified by routine HE stain, with an identification rate of 94.44% (34/36). Conclusion: the sputum sediment examination has some advantages compared to the sputum smear examination such as the specimen may include more materials, the higher cancer cell identification positive rate and immunohistochemistry method can be used to identify the histopathological type.

Non-Small Cell Lung Cancer: Treatment, Diagnosis, and Life after Treatment

Lung cancer is becoming the most common cancer globally. In China, Lung cancer has become prevalent among preceding compared to present smokers. There are many treatments for lung cancer globally like Chemotherapy, Radiotherapy, Surgery, and Targeted therapy [1] [2]. Generally, lung cancer starts in the lungs. The spongy lungs in the chest inhale oxygen and exhale carbon dioxide. Those who smoke regularly have the highest risk of lung cancer than nonsmokers. This risk increases with an increase in length, time, and the number of cigarettes smoked. Immediate treatment will help in reducing the severity of cancer. The complications of lung cancer include shortness of breath, coughing up blood, pain, and fluid in the chest. Therefore, the primary step in preventing lung cancer is quitting smoking [3].

Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses

Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses. Materials and Methods: Ultrasound images of biopsy proven solid breast masses were analyzed in three independent observations for BI-RADS sonographic features. The BI-RADS features from each observation were used with Bayes classifier to determine probability of malignancy. The observer agreement in the sonographic features was measured by kappa coefficient and the difference in the diagnostic performances between observations was determined by the area under the ROC curve, Az, and interclass correlation coefficient. Results: While some features were repeatedly observed, κ = 0.95, other showed a significant variation, κ = 0.16. For all features, combined intra-observer agreement was substantial, κ = 0.77. The agreement, however, decreased steadily to 0.66 and 0.56 as time between the observations increased from 1 to 2 and 3 months, respectively. Despite the variation in features between observations the probabilities of malignancy estimates from Bayes classifier were robust and consistently yielded same level of diagnostic performance, Az was 0.772-0.817 for sonographic features alone and 0.828-0.849 for sonographic features and age combined. The difference in the performance, ΔAz, between the observations for the two groups was small (0.003-0.044) and was not statistically significant (p < 0.05). Interclass correlation coefficient for the observations was 0.822 (CI: 0.787-0.853) for BI-RADS sonographic features alone and for those combined with age was 0.833 (CI: 0.800-0.862). Conclusion: Despite the differences in the BI-RADS sonographic features between different observations, the diagnostic performance of computer-aided analysis for differentiating breast masses did not change. Through continual retraining, the computer-aided analysis provides consistent diagnostic performance independent of the variations in the observed sonographic features.

Current status and prospects of biomarkers in early diagnosis of lung cancer

Lung cancer is the most common malignant tumor with morbidity and mortality worldwide. The early diagnosis of lung cancer is crucial for the treatment and prognosis of patients. Therefore, this is one of the main challenges in the field of lung cancer. The occurrence of lung cancer is a multi-gene and multi-factor involved in complex processes. In recent years, research on lung cancer-related biomarkers has become a hot spot in the early diagnosis of lung cancer. Serum biomarkers such as circulating tumor cells, ctDNA, ctRNA, and lung cancer autoantibodies have been discovered one after another with the rapid development of detection technology. This article summarizes the research progress of related tumor markers for early diagnosis of lung cancer.

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

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

Optimal Deep Belief Network Based Lung Cancer Detection and Survival Rate Prediction

The combination of machine learning(ML)approaches in healthcare is a massive advantage designed at curing illness of millions of persons.Several efforts are used by researchers for detecting and providing primary phase insights as to cancer analysis.Lung cancer remained the essential source of disease connected mortality for both men as well as women and their frequency was increasing around the world.Lung disease is the unrestrained progress of irregular cells which begin off in one or both Lungs.The previous detection of cancer is not simpler procedure however if it can be detected,it can be curable,also finding the survival rate is a major challenging task.This study develops an Ant lion Optimization(ALO)with Deep Belief Network(DBN)for Lung Cancer Detection and Classification with survival rate prediction.The proposed model aims to identify and classify the presence of lung cancer.Initially,the proposed model undergoes min-max data normalization approach to preprocess the input data.Besides,the ALO algorithm gets executed to choose an optimal subset of features.In addition,the DBN model receives the chosen features and performs lung cancer classification.Finally,the optimizer is utilized for hyperparameter optimization of the DBN model.In order to report the enhanced performance of the proposed model,a wide-ranging experimental analysis is performed and the results reported the supremacy of the proposed model.