Malignant Pulmonary Nodules Research Articles

Diagnostic Study of Nodular Pulmonary Cryptococcosis Based on Radiomic Features Captured from CT Images.

Radiomics can quantify pulmonary nodule characteristics non-invasively by applying advanced imaging feature algorithms. Radiomic textural features derived from Computed Tomography (CT) imaging are broadly used to predict benign and malignant pulmonary nodules. However, few studies have reported on the radiomics-based identification of nodular Pulmonary Cryptococcosis (PC). This study aimed to evaluate the diagnostic and differential diagnostic value of radiomic features extracted from CT images for nodular PC. This retrospective analysis included 44 patients with PC (29 males, 15 females), 58 with Tuberculosis (TB) (39 males, 19 females), and 60 with Lung Cancer (LC) (20 males, 40 females) confirmed pathologically. Models 1 (PC vs. non-PC), 2 (PC vs. TB), and 3 (PC vs. LC) were established using radiomic features. Models 4 (PC vs. TB) and 5 (PC vs. LC) were established based on radiomic and CT features. Five radiomic features were predictive of PC vs. non-PC model, but accuracy and Area Under the Curve (AUC) were 0.49 and 0.472, respectively. In model 2 (PC vs. TB) involving six radiomic features, the accuracy and AUC were 0.80 and 0.815, respectively. Model 3 (PC vs. LC) with six radiomic features performed well, with AUC=0.806 and an accuracy of 0.76. Between the PC and TB groups, model 4 combining radiomics, distribution, and PI, showed AUC=0.870. In differentiating PC from LC, the combination of radiomics, distribution, PI, and RBNAV achieved AUC=0.926 and an accuracy of 0.90. The prediction models based on radiomic features from CT images performed well in discriminating PC from TB and LC. The individualized prediction models combining radiomic and CT features achieved the best diagnostic performance.

Imaging features and clinical evaluation of pulmonary nodules in children.

With the widespread use of computed tomography (CT), the detection rate of pulmonary nodules in children has gradually increased. Due to the lack of epidemiological evidence and clinical guideline on pulmonary nodule treatment in children, we aimed to provide a reference for the clinical diagnosis and management of pediatirc pulmonary nodules. This retrospective study collected consecutive cases from April 2012 to July 2021 in the Shanghai Children's Medical Center. The sample included children with pulmonary nodules on chest CT scans and met the inclusion criteria. All patients were categorized into tumor and non-tumor groups by pre-CT clinical diagnosis. Nodule characteristics between groups were analyzed. To establish a clinical assessment model for the benign versus malignant pulmonary nodules, patients who have been followed-up for three months were detected and a decision tree model for nodule malignancy prediction was constructed and validated. The sample comprised 1341 patients with an average age of 7.2 ± 4.6 years. More than half of them (51.7%) were diagnosed with malignancies before CT scan. 48.3% were diagnosed with non-tumor diseases or healthy. Compared to non-tumor group, children with tumor were more likely to have multiple nodules in both lungs, with larger size and often be accompanied by osteolytic or mass lesions. Based on the decision tree model, patients' history of malignancies, nodules diameter size≥5mm, and specific nodule distribution (multiple in both lungs, multiple in the right lung or solitary in the upper or middle right lobe) were important potential predictors for malignity. In the validation set, sensitivity, specificity and AUC were 0.855, 0.833 and 0.828 (95%CI: 0.712-0.909), respectively. This study conducted a clinical assessment model to differentiate benignity and malignancy of pediatric pulmonary nodules. We suggested that a nodule's diameter, distribution and patient's history of malignancies are predictable factors in benign or malignant determination.

An artificial intelligence-assisted diagnostic system for the prediction of benignity and malignancy of pulmonary nodules and its practical value for patients with different clinical characteristics.

This study aimed to explore the value of an artificial intelligence (AI)-assisted diagnostic system in the prediction of pulmonary nodules. The AI system was able to make predictions of benign or malignant nodules. 260 cases of solitary pulmonary nodules (SPNs) were divided into 173 malignant cases and 87 benign cases based on the surgical pathological diagnosis. A stratified data analysis was applied to compare the diagnostic effectiveness of the AI system to distinguish between the subgroups with different clinical characteristics. The accuracy of AI system in judging benignity and malignancy of the nodules was 75.77% (p < 0.05). We created an ROC curve by calculating the true positive rate (TPR) and the false positive rate (FPR) at different threshold values, and the AUC was 0.755. Results of the stratified analysis were as follows. (1) By nodule position: the AUC was 0.677, 0.758, 0.744, 0.982, and 0.725, respectively, for the nodules in the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe. (2) By nodule size: the AUC was 0.778, 0.771, and 0.686, respectively, for the nodules measuring 5-10, 10-20, and 20-30 mm in diameter. (3) The predictive accuracy was higher for the subsolid pulmonary nodules than for the solid ones (80.54 vs. 66.67%). The AI system can be applied to assist in the prediction of benign and malignant pulmonary nodules. It can provide a valuable reference, especially for the diagnosis of subsolid nodules and small nodules measuring 5-10 mm in diameter.

Analysis of the correlation between clinical and imaging features of malignant lung nodules and pathological types.

To explore the correlation between clinical and imaging features of malignant lung nodules and pathology types. Patients with lung nodules admitted to the Affiliated Hospital of Jiangsu University from January 1, 2020 to December 31, 2020 were collected as study subjects, and all of them underwent surgical treatment and were clearly diagnosed by pathology. The correlation between clinical and imaging features and pathological types of lung cancer patients was analyzed. Among them, The pathological types of malignant pulmonary nodules are correlated with age, gender, smoking history, ground glass sign, nodule size, solid to solid ratio, lobulation sign, pleural indentation sign, hair prick sign, CEA, SCCA. The imaging features of ground glass sign and nodule size are most significantly correlated with the pathological type. It was found that, the clinical and imaging characteristics of patients with malignant lung nodules have a certain correlation with the pathological type, and gender, age, smoking history, nodule size, nodule nature, burr sign, pleural depression sign, and tumor markers are of great value for pathological typing.

Principal Component Analysis Applied to Radiomics Data: Added Value for Separating Benign from Malignant Solitary Pulmonary Nodules.

Here, we report on the added value of principal component analysis applied to a dataset of texture features derived from 39 solitary pulmonary lung nodule (SPN) lesions for the purpose of differentiating benign from malignant lesions, as compared to the use of SUVmax alone. Texture features were derived using the LIFEx software. The eight best-performing first-, second-, and higher-order features for separating benign from malignant nodules, in addition to SUVmax (MaximumGreyLevelSUVbwIBSI184IY), were included for PCA. Two principal components (PCs) were retained, of which the contributions to the total variance were, respectively, 87.6% and 10.8%. When included in a logistic binomial regression analysis, including age and gender as covariates, both PCs proved to be significant predictors for the underlying benign or malignant character of the lesions under study (p = 0.009 for the first PC and 0.020 for the second PC). As opposed to SUVmax alone, which allowed for the accurate classification of 69% of the lesions, the regression model including both PCs allowed for the accurate classification of 77% of the lesions. PCs derived from PCA applied on selected texture features may allow for more accurate characterization of SPN when compared to SUVmax alone.

Diagnosis of Malignant Pulmonary Nodules Using a Combination of Tumor-associated Autoantibodies and Computed Tomography.

Diagnosis of malignant pulmonary nodules can greatly reduce the occurrence of lung cancer death, and computed tomography (CT) is commonly used in diagnosis. In addition, tumor-associated autoantibodies (TAAbs) show high specificity and stability. We aim to establish a computable risk model of pulmonary nodules by combining CT with TAAb detection. The concentrations of 7 TAAbs (p53, PGP9.5, SOX2, GAGE7, GBU4-5, CAGE, MAGEA1, and CAGE) were assayed using the enzyme-linked immunosorbent assay in 136 patients with pulmonary nodules (84 with newly diagnosed lung adenocarcinoma, 21 with squamous cell carcinoma, and 31 with benign nodules) and 42 control subjects without pulmonary nodules. We then drew receiver operating characteristic curves and conducted logistic regression to analyze the diagnostic efficiency of our method in the detection of lung cancer. The positivity rate of the 7 TAAbs was 49.5%, and the specificity was 83.6%. Our regression results indicated 65% overall accuracy, 44.76% sensitivity, and 76.71% specificity. Notably, when combined with CT imaging and the demographic characteristics, diagnostic accuracy increased to 73.4%, sensitivity to 61.5%, and specificity to 87.1%. The positive predictive value and negative predictive value were 93% and 41%, respectively. Our study provides a method that combines 7 serum TAAbs with imaging and demographic characteristics to diagnose malignant pulmonary nodules more accurately than existing methods.

MiR-1246b, a novel miRNA molecule of extracellular vesicles in bronchoalveolar lavage fluid, promotes nodule growth through FGF14 in patients with lung cancer

With the widespread development of chest computed tomography (CT), the detection rate of pulmonary nodules has increased; therefore, the classification of benign vs. malignant nodules has become a common problem in the clinic. MicroRNA, a potential tool, is expected to become a good choice for diagnosing and studying the occurrence and development of diseases through the vector of bronchoalveolar lavage fluid extracellular vesicles (BALF-EVs). In this study, radial endobronchial ultrasound (R-EBUS) was used to locate pulmonary nodules in patients. BALF was obtained, EVs were isolated, and small RNA sequencing was performed to screen differentially expressed miRNAs between benign and malignant pulmonary nodules. The binding targets and underlying mechanisms of the differentially expressed miRNAs were verified by in vitro and in vivo experiments. R-EBUS localization and sampling was used to obtain BALF, and EVs were successfully isolated and characterized. Differentially expressed miRNAs in BALF-EVs of patients with benign vs. malignant pulmonary nodules were screened by high-throughput small RNA sequencing. A new miRNA, miR-1246b, was identified. We found that FGF14 was the binding target of miR-1246b by luciferase assay. Subsequent mechanistic studies showed that miR-1246b inhibited the expression of FGF14 in lung cancer cells, further leading to ERK phosphorylation and epithelial-to-mesenchymal transition (EMT), which ultimately contributed to lung cancer cell proliferation, migration and invasion. In summary, our study demonstrates that the detection of miRNAs in BALF-EVs, a means of liquid biopsy, could assist in distinguishing malignant nodules from benign nodules. miR-1246b, which was extracted from BALF-EVs, targets FGF14 to promote lung cancer cell proliferation, migration and invasion.

Feasibility, safety and tolerability of CT-guided percutaneous thermal ablation in COPD with malignant pulmonary nodules

For early-stage non-small cell lung cancer, surgical resection was used as the first treatment. However, approximately 20% of patients were not suitable for surgery due to severe comorbidities. We verified the feasibility, safety, and tolerability of percutaneous thermal ablation for patients of chronic obstructive pulmonary disease (COPD) with peripheral high-risk pulmonary nodules in the real world. The patients with peripheral high-risk pulmonary nodules ineligible or unwilling to undergo surgery who were ineligible or unwilling tosurgery underwent CT-guided thermal ablation in our hospital from January 1st, 2019 to May 31th, 2022 were retrospectively collected, and divided into COPD and non COPD group. Incidence, severity, risk factors of complications between in different severity of COPD and non-COPD group were compared. A total of 216 high-risk were enrolled, including 73 in COPD group and 143 in the non-COPD group. The average age, male gender, MMRC score, size of nodules, incidence of confirmed pathological diagnosis, and pneumothorax after thermal ablation were higher in the COPD group than in the non-COPD group. COPD was the only independent risk factor for pneumothorax after ablation. The incidence of pneumothorax increased with the severity of COPD, but no statistical significance. Compared to the baseline, the MMRC score was significantly increased in the COPD group, but there was no significant difference in the discharge time and hospitalization expenses between the COPD patients with or without pneumothorax. CT-guided percutaneous thermal ablation is a safe and feasible therapy for different severities of COPD with high-risk pulmonary nodules, and it is well-tolerated without increasing medical burden.

Development of an autoantibody panel for early detection of lung cancer in the Chinese population

IntroductionTumor-associated autoantibodies have been revealed as promising biomarkers for the early detection of lung cancer. This study was designed to develop an autoantibody panel for early detection of lung cancer in the Chinese population.MethodsRecruited prospectively in three clinical centers, the subjects (n = 991) who had a definite diagnosis during follow-up were included in the development of the autoantibody panel. The levels of 14 autoantibody candidates in plasma were detected.ResultsA panel of nine autoantibody markers (named as CN9), namely, P53, SOX2, SSX1, HuD, NY-ESO-1, CAGE, MAGE-A4, P62, and CK20, was preferably selected from 14 candidates. The overall specificity, sensitivity, and AUC were 90.5%, 40.8%, and 0.64, respectively. The CN9 panel demonstrated a reasonable detection rate in lung cancer patients at all stages, histological types, sizes of lesions, and risk levels. Its estimated overall accuracy is 85.5% and 90%, with PPV at 0.32 and 0.04, and NPV at 0.93 and 0.99 in the scenario of pulmonary nodules' characterizing and lung cancer screening, respectively. Two risk models were developed within the subgroups of malignant and benign pulmonary nodules in this study. By adding the CN9 result to the Mayo model indicators, it achieved a sensitivity of 41.3% and an AUC of 0.74 at a specificity of 91.3%. By adding the CN9 result to the Brock model indicators, it achieved a sensitivity of 47.7% and an AUC of 0.78 at a specificity of 91.3%. Both were improved compared with either the standalone Mayo or Brock model.DiscussionThis multi-center prospective study indicates a panel of nine autoantibody markers that can help in the detection of lung cancer and the classification of pulmonary nodules in the Chinese population.

Research of correlation between personality traits and hormones with the nature of pulmonary nodules

BackgroundRising rates of lung cancer screening have contributed to an increase in pulmonary nodule diagnosis rates. Studies have shown that psychosocial factors and hormones have an impact on the development of the oncological diseases. Therefore, we conducted this study to explore the potential relationship between pulmonary nodules pathology and patient personality traits and hormone levels. MethodsThis study enrolled 245 individuals who had first been diagnosed with pulmonary nodules in Tangdu Hospital and admitted for surgery. The personality profile of these patients was analyzed on admission using the C-Type Behavioral Scale and hormone levels were measured in preoperative serum samples. Associations between nodule pathology, personality scores, and hormone levels， were then assessed through Statistical methods analysis. ResultsBehavioral scale analyses revealed significant differences four items, including depression, anger outward, optimism, and social support (P< 0.05). Specifically, patients with higher depression scores were more likely to harbor malignant pulmonary nodules, as were patients with lower levels of anger outward, social support, and optimism. Univariate analyses indicated that nodule pathology was associated with significant differences in nodule imaging density, CT value, testosterone levels, and T4 levels(P< 0.05), and logistic regression analyses revealed pulmonary nodule imaging density and T4 levels to be significant differences of nodule pathology. ConclusionThe results showed a significant association between nodules pathology and the personality characteristics of the patients (depression, anger outward, optimism, social support), the patients' T4 levels and the imaging density of the nodules.

Factors associated with overdiagnosis of benign pulmonary nodules as malignancy: a retrospective cohort study

ObjectiveTo establish a preoperative model for the differential diagnosis of benign and malignant pulmonary nodules (PNs), and to evaluate the related factors of overdiagnosis of benign PNs at the time of imaging assessments.Materials and methodsIn this retrospective study, 357 patients (median age, 52 years; interquartile range, 46–59 years) with 407 PNs were included, who underwent surgical histopathologic evaluation between January 2020 and December 2020. Patients were divided into a training set (n = 285) and a validation set (n = 122) to develop a preoperative model to identify benign PNs. CT scan features were reviewed by two chest radiologists, and imaging findings were categorized. The overdiagnosis rate of benign PNs was calculated, and bivariate and multivariable logistic regression analyses were used to evaluate factors associated with benign PNs that were over-diagnosed as malignant PNs.ResultsThe preoperative model identified features such as the absence of part-solid and non-solid nodules, absence of spiculation, absence of vascular convergence, larger lesion size, and CYFRA21-1 positivity as features for identifying benign PNs on imaging, with a high area under the receiver operating characteristic curve of 0.88 in the validation set. The overdiagnosis rate of benign PNs was found to be 50%. Independent risk factors for overdiagnosis included diagnosis as non-solid nodules, pleural retraction, vascular convergence, and larger lesion size at imaging.ConclusionWe developed a preoperative model for identifying benign and malignant PNs and evaluating factors that led to the overdiagnosis of benign PNs. This preoperative model and result may help clinicians and imaging physicians reduce unnecessary surgery.

Radiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [18F]FDG and [18F]FLT PET/CT

PurposeTo investigate the clinical value of radiomic analysis on [18F]FDG and [18F]FLT PET on the differentiation of [18F]FDG-avid benign and malignant pulmonary nodules (PNs).MethodsData of 113 patients with inconclusive PNs based on preoperative [18F]FDG PET/CT who underwent additional [18F]FLT PET/CT scans within a week were retrospectively analyzed in the present study. Three methods of analysis including visual analysis, radiomic analysis based on [18F]FDG PET/CT images alone, and radiomic analysis based on dual-tracer PET/CT images were evaluated for differential diagnostic value of benign and malignant PNs.ResultsA total of 678 radiomic features were extracted from volumes of interest (VOIs) of 123 PNs. Fourteen valuable features were thereafter selected. Based on a visual analysis of [18F]FDG PET/CT images, the diagnostic accuracy, sensitivity, and specificity were 61.6%, 90%, and 28.8%, respectively. For the test set, the area under the curve (AUC), sensitivity, and specificity of the radiomic models based on [18F]FDG PET/CT plus [18F]FLT signature were equal or better than radiomics based on [18F]FDG PET/CT only (0.838 vs 0.810, 0.778 vs 0.778, 0.750 vs 0.688, respectively).ConclusionRadiomic analysis based on dual-tracer PET/CT images is clinically promising and feasible for the differentiation between benign and malignant PNs.Clinical relevance statementRadiomic analysis will add differential diagnostic value of benign and malignant pulmonary nodules: a hybrid imaging study based on [18F]FDG and [18F]FLT PET/CT.Key points• Radiomics brings new insights into the differentiation of benign and malignant pulmonary nodules beyond the naked eyes.• Dual-tracer imaging shows the biological behaviors of cancerous cells from different aspects.• Radiomics helps us get to the histological view in a non-invasive approach.Graphical

Tumor blood vessel in 3D reconstruction CT imaging as an risk indicator for growth of pulmonary nodule with ground-glass opacity

ObjectiveDespite the vital role of blood perfusion in tumor progression, in patients with persistent pulmonary nodule with ground-glass opacity (GGO) is still unclear. This study aims to investigate the relationship between tumor blood vessel and the growth of persistent malignant pulmonary nodules with ground-glass opacity (GGO).MethodsWe collected 116 cases with persistent malignant pulmonary nodules, including 62 patients as stable versus 54 patients in the growth group, from 2017 to 2021. Three statistical methods of logistic regression model, Kaplan–Meier analysis regression analysis were used to explore the potential risk factors for growth of malignant pulmonary nodules with GGO.ResultsMultivariate variables logistic regression analysis and Kaplan–Meier analysis identified that tumor blood vessel diameter (p = 0.013) was an significant risk factor in the growth of nodules and Cut-off value of tumor blood vessel diameter was 0.9 mm with its specificity 82.3% and sensitivity 66.7%.While in subgroup analysis, for the GGO CTR < 0.5[C(the maximum diameter of consolidation in tumor)/T(the maximum diameter of the whole tumor including GGO) ratio], tumor blood vessel diameter (p = 0.027) was important during the growing processes of nodules.ConclusionsThe tumor blood vessel diameter of GGO lesion was closely associated with the growth of malignant pulmonary nodules. The results of this study would provide evidence for effective follow-up strategies for pulmonary nodule screening.

Machine Learning Model of ResNet50-Ensemble Voting for Malignant-Benign Small Pulmonary Nodule Classification on Computed Tomography Images.

The early detection of benign and malignant lung tumors enabled patients to diagnose lesions and implement appropriate health measures earlier, dramatically improving lung cancer patients' quality of living. Machine learning methods performed admirably when recognizing small benign and malignant lung nodules. However, exploration and investigation are required to fully leverage the potential of machine learning in distinguishing between benign and malignant small lung nodules. The aim of this study was to develop and evaluate the ResNet50-Ensemble Voting model for detecting the benign and malignant nature of small pulmonary nodules (<20 mm) based on CT images. In this study, 834 CT imaging data from 396 patients with small pulmonary nodules were gathered and randomly assigned to the training and validation sets in an 8:2 ratio. ResNet50 and VGG16 algorithms were utilized to extract CT image features, followed by XGBoost, SVM, and Ensemble Voting techniques for classification, for a total of ten different classes of machine learning combinatorial classifiers. Indicators such as accuracy, sensitivity, and specificity were used to assess the models. The collected features are also shown to investigate the contrasts between them. The algorithm we presented, ResNet50-Ensemble Voting, performed best in the test set, with an accuracy of 0.943 (0.938, 0.948) and sensitivity and specificity of 0.964 and 0.911, respectively. VGG16-Ensemble Voting had an accuracy of 0.887 (0.880, 0.894), with a sensitivity and specificity of 0.952 and 0.784, respectively. Machine learning models that were implemented and integrated ResNet50-Ensemble Voting performed exceptionally well in identifying benign and malignant small pulmonary nodules (<20 mm) from various sites, which might help doctors in accurately diagnosing the nature of early-stage lung nodules in clinical practice.

Discussion to: Computational risk model for predicting 2-year malignancy of pulmonary nodules using demographic and radiographic characteristics

Discussion to: Computational risk model for predicting 2-year malignancy of pulmonary nodules using demographic and radiographic characteristics

Diagnostic value of peripheral blood eosinophils for benign and malignant pulmonary nodule.

This retrospective study aims to assess the diagnostic utility of peripheral blood eosinophil counts in distinguishing between benign and malignant pulmonary nodules (PNs) prior to surgical intervention. We involved patients presenting with PNs measuring ≤30 mm as the primary CT imaging finding prior to surgical procedures at the General Hospital of Northern Theater Command in Shenyang, China, during the period spanning 2021 to 2022. Multivariable logistic regression analysis and receiver operator characteristic curve analysis, along with area under the curve (AUC) calculations, were used to determine the diagnostic value of eosinophil. A total of 361 patients with PN were included, consisting of 135 with benign PN and 226 with malignant PN. Multivariable logistic regression analysis showed that eosinophil percentage (OR = 1.909, 95% CI: 1.323-2.844, P < .001), absolute eosinophil value (OR = 0.001, 95% CI: 0.000-0.452, P = .033), tumor diameter (OR = 0.918, 95% CI: 0.877-0.959, P < .001), nodule type (OR = 0.227, 95% CI: 0.125-0.400, P < .001), sex (OR = 2.577, 95% CI: 1.554-4.329, P < .001), and age (OR = 0.967, 95% CI: 0.945-0.989, P = .004) were independently associated with malignant PN. The diagnostic value of regression model (AUC [95% CI]: 0.775 [0.725-0.825]; sensitivity: 74.3%; specificity: 71.1%) was superior to eosinophil percentage (AUC [95% CI]: 0.616 [0.556-0.677]; specificity: 66.8%; specificity: 51.1%) (Delong test: P < .001). Peripheral blood eosinophil percentage might be useful for early malignant PN diagnosis, and combining that with other characteristics might improve the diagnostic performance.

MA19.10 Saliva Metabolic Profiling for Benign and Malignant Pulmonary Nodules Distinguish and Early Lung Cancer Detection

MA19.10 Saliva Metabolic Profiling for Benign and Malignant Pulmonary Nodules Distinguish and Early Lung Cancer Detection

Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans

BackgroundOutside a screening program, early-stage lung cancer is generally diagnosed after the detection of incidental nodules in clinically ordered chest CT scans. Despite the advances in artificial intelligence (AI) systems for lung cancer detection, clinical validation of these systems is lacking in a non-screening setting.MethodWe developed a deep learning-based AI system and assessed its performance for the detection of actionable benign nodules (requiring follow-up), small lung cancers, and pulmonary metastases in CT scans acquired in two Dutch hospitals (internal and external validation). A panel of five thoracic radiologists labeled all nodules, and two additional radiologists verified the nodule malignancy status and searched for any missed cancers using data from the national Netherlands Cancer Registry. The detection performance was evaluated by measuring the sensitivity at predefined false positive rates on a free receiver operating characteristic curve and was compared with the panel of radiologists.ResultsOn the external test set (100 scans from 100 patients), the sensitivity of the AI system for detecting benign nodules, primary lung cancers, and metastases is respectively 94.3% (82/87, 95% CI: 88.1–98.8%), 96.9% (31/32, 95% CI: 91.7–100%), and 92.0% (104/113, 95% CI: 88.5–95.5%) at a clinically acceptable operating point of 1 false positive per scan (FP/s). These sensitivities are comparable to or higher than the radiologists, albeit with a slightly higher FP/s (average difference of 0.6).ConclusionsThe AI system reliably detects benign and malignant pulmonary nodules in clinically indicated CT scans and can potentially assist radiologists in this setting.

Value of CT examination combined with serum tumor markers assisted with artificial intelligence diagnostic system in the diagnosis of benign and malignant pulmonary nodules

Value of CT examination combined with serum tumor markers assisted with artificial intelligence diagnostic system in the diagnosis of benign and malignant pulmonary nodules

Hybrid Algorithm for the Detection of Lung Cancer Using CNN and Image Segmentation

When it comes to cancer-related fatalities, lung cancer is by far the most common cause. Early detection is the key to a successful diagnosis and treatment plan for lung cancer, just as it is for other types of cancer. Automatic CAD systems for lung cancer screening using computed tomography scans primarily involve two steps: the first step is to detect all potentially malignant pulmonary nodules, and the second step is to determine whether or not the nodules are malignant. There have been a lot of books published recently about the first phase, but not many about the second stage. Screening for lung cancer requires a careful investigation on each suspicious nodule and the integration of information from all nodules. This is because the presence of pulmonary nodules does not always indicate cancer, and the morphology of nodules, including their shape, size, and contextual information, has a complex relationship with cancer. In order to overcome this problem, we suggest a deep CNN architecture that is different from the architectures that are commonly utilised in computer vision. After the suspicious nodules have been formed with the modified version of U-Net, they are used as an input data for our model. First, the suspicious nodules are generated with U-Net. To automatically diagnose lung cancer, the suggested model is a multi-path CNN that concurrently makes use of local characteristics as well as more general contextual characteristics from a wider geographical area. In order to accomplish this, the model consisted of three separate pathways, each of which used a different receptive field size, which contributed to the modelling of distant dependencies (short and long-range dependencies of the neighbouring pixels). After that, we concatenate characteristics from the three different pathways in order to further improve our model's performance. In conclusion, one of the contributions that we have made is the development of a retraining phase system. This system enables us to address issues that are caused by an uneven distribution of picture labels. The experimental findings from the KDSB 2017 challenge demonstrate that our model is more adaptable to the described inconsistency among the nodules' sizes and shapes. Furthermore, our model obtained better results in comparison to other researches.