RETRACTION: Artificial Intelligence Algorithm-Based Feature Extraction of Computed Tomography Images and Analysis of Benign and Malignant Pulmonary Nodules

Objective: To explore the value of detection of epidermal growth factor receptor (EGFR) gene amplification in peripheral blood rare cells in the assessment of benign and malignant pulmonary nodules. Methods: A total of 262 patients with pulmonary nodules were selected as the retrospectively study subjects from the Second Affiliated Hospital of Army Military Medical University and Peking Union Medical College Hospital from July 2022 to August 2023. There were 98 males and 164 females, with the age range from 16 to 79 (52.1±12.1) years. The EGFR gene amplification testing was performed on the rare cells enriched from patients' peripheral blood, and the clinical manifestations, CT imaging features, histopathological and/or pathological cytological confirmed results of patients were collected. The receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of the method of detection of EGFR gene amplification in peripheral blood rare cells, and its diagnostic efficacy was evaluated. Results: Among the 262 patients, 143 were malignant pulmonary nodules and 119 were benign pulmonary nodules. The differences between malignant pulmonary nodules and benign pulmonary nodules in nodule diameter and nodule density were statistically significant (both P<0.001), while the differences in age, gender and nodule number were not statistically significant (all P>0.05). The number [M (Q1, Q3)] of EGFR gene amplification positive rare cells in patients with malignant pulmonary nodule was 8 (6, 11), which was higher than that in patients with benign pulmonary nodule [2 (1, 4), P<0.001]. The ROC curve results showed that when the optimal cut-off value was 5 (that was, the number of EGFR gene amplification positive rare cells was>5), the area under the curve (AUC) of the detection of EGFR gene amplification in peripheral blood rare cells for discrimination of benign and malignant pulmonary lesions was 0.816 (95%CI: 0.761-0.870), with a sensitivity of 83.2%, a specificity of 80.7%, and an accuracy of 82.1%. Based on the analysis of the diameter of the nodules, the AUC for distinguishing between benign and malignant pulmonary nodules with diameter 5-9 mm and 10-30 mm was 0.797 (95%CI: 0.707-0.887) and 0.809 (95%CI: 0.669-0.949), respectively, with sensitivity, specificity and accuracy reached 75% or above. Based on the analysis of nodule density, the AUC for distinguishing between benign and malignant solid nodule and subsolid nodule was 0.845 (95%CI: 0.751-0.939) and 0.790 (95%CI: 0.701-0.880), respectively, with sensitivity, specificity and accuracy reached 75% or above. Based on the analysis of nodule number, the AUC for distinguishing between benign and malignant solitary pulmonary nodule and multiple pulmonary nodule was 0.830 (95%CI: 0.696-0.965) and 0.817 (95%CI: 0.758-0.877), respectively, with sensitivity, specificity and accuracy reached 80% or above. Conclusion: The detection of EGFR gene amplification in peripheral blood rare cells contributes to the evaluation of benign and malignant pulmonary nodules, and can be used in the auxiliary diagnosis of benign and malignant pulmonary nodules.

Background: LDCT screening can significantly lower the mortality rate of lung cancer among high-risk individuals. Nevertheless, the limitations of CT might lead to frequent follow-up examinations and false positive outcomes, thereby causing unnecessary interventions and overtreatment. Therefore, the development of reliable and convenient biomarkers to accurately differentiate between benign and malignant nodules and to assess the likelihood of cancerous transformation is essential. We attempted to provide meaningful biomarkers based on plasma proteomic studies. Methods: The participants in this study were chosen from individuals aged 40 to 74 years in the Chinese Colorectal, Breast, Lung, Liver, And Stomach Cancer Screening Trial (C-BLAST). We selected 10 patients with malignant lung nodules and 10 with benign lung nodules, matched for age and sex. Malignant lung nodules were defined as those with a LUNG-RADS diagnostic category of 4A, 4B, or 4X, accompanied by a biopsy confirming malignancy; benign nodules were those with a diagnostic category not exceeding 3. Plasma samples from two groups were collected and subjected to proteomic analysis using the Somascan Assay 11k detection platform. Paired t-tests were employed to identify the differential proteins between malignant and benign pulmonary nodules. The functional pathways enriched by these proteins were determined based on Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, the STRING was utilized to construct a protein-protein interaction network (PPI) and determine the key proteins related to malignant nodules. Results: The average age of both groups was 61.4 years. A comparison of the proteomics between the malignant pulmonary nodule group and the benign pulmonary nodule group identified 188 differentially expressed proteins (P &amp;lt; 0.05), among which 102 were up-regulated proteins and 86 were down-regulated proteins. GO analysis of the differential proteins indicated functional enrichment in pathways such as chemical carcinogenesis, fluid shear stress and atherosclerosis, and biosynthesis of cofactors. According to KEGG analysis, they were mainly enriched in pathways like chemical carcinogenesis-reactive oxygen species, fluid shear stress and atherosclerosis, and metabolism of xenobiotics by cytochrome P450. Through PPI analysis, ten key proteins were determined, including CRP, FCGR3B, CCL2, CYP3A5, GSTA3, GSTM1, GSTM3, GSTM5, CD163, and GSTM4. These molecules possess anti-atherosclerotic and anti-inflammatory activities, as well as chemotactic activity for monocytes and basophils, and play roles in hydrolyzing nucleotides and host defense. Conclusions: Our research results provide ten potential plasma protein biomarkers for the discrimination of benign and malignant pulmonary nodules, which might broaden our understanding of their characteristics. Citation Format: Ximin Gao, Zhangyan Lyu, Guojin Si, Fengju Song. Proteomics unveil characteristic proteins of patients with benign and malignant pulmonary nodules [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1887.

There is some doubt that all nodules <8 mm are really mainly benign and that simple follow-up is adequate in all cases. The purpose of this study is to create a predictive model for the diagnosis of benign and malignant small pulmonary nodules.This was a retrospective case–control study of patients who had undergone pulmonary nodule resection at the Zhejiang University Jinhua Hospital. Patients with pulmonary nodules of ≤10 mm in size on chest high-resolution computed tomography were included. Patients’ demographic characteristics, clinical features, and high-resolution computed tomography findings were collected. Logistic regression and receiver-operating characteristic analysis were used to create a predictive model for malignancy.A total of 216 patients were included: 160 with malignant and 56 with benign nodules. Nodule density (odds ratio [OR] = 0.996, 95% confidence interval [CI]: 0.993–0.998, P = .001), vascular penetration sign (OR = 3.49, 95% CI: 1.39–8.76, P = .008), nodule type (OR = 4.27, 95% CI: 1.48–12.29, P = .007), and incisure surrounding nodules (OR = 0.18, 95% CI: 0.04–0.84, P = .03) were independently associated with malignant nodules. These factors were used to create a mathematical model that had an area under the receiver-operating characteristic curve of 0.744. Using a cut-off of 0.762 resulted in 63.1% sensitivity and 75.0% specificity.This study proposes a pulmonary nodule prediction model that can estimate benign/malignant lung nodules with good sensitivity and specificity. Mixed ground-glass nodules, vascular penetration sign, density of lung nodules, and the absence of incisure signs are independently associated with malignant lung nodules.

This study was aimed to explore the effect of CT image feature extraction of pulmonary nodules based on an artificial intelligence algorithm and the image performance of benign and malignant pulmonary nodules. In this study, the CT images of pulmonary nodules were collected as the research object, and the lung nodule feature extraction model based on expectation maximization (EM) was used to extract the image features. The Dice similarity coefficient, accuracy, benign and malignant nodule edges, internal signs, and adjacent structures were compared and analyzed to obtain the extraction effect of this feature extraction model and the image performance of benign and malignant pulmonary nodules. The results showed that the detection sensitivity of pulmonary nodules in this model was 0.955, and the pulmonary nodules and blood vessels were well preserved in the image. The probability of burr sign detection in the malignant group was 73.09% and that in the benign group was 8.41%. The difference was statistically significant (P < 0.05). The probability of malignant component leaf sign (69.96%) was higher than that of a benign component leaf sign (0), and the difference was statistically significant (P < 0.05). The probability of cavitation signs in the malignant group (59.19%) was higher than that in the benign group (3.74%), and the probability of blood vessel collection signs in the malignant group (74.89%) was higher than that in the benign group (11.21%), with statistical significance (P < 0.05). The probability of the pleural traction sign in the malignant group was 17.49% higher than that in the benign group (4.67%), and the difference was statistically significant (P < 0.05). In summary, the feature extraction effect of CT images based on the EM algorithm was ideal. Imaging findings, such as the burr sign, lobulation sign, vacuole sign, vascular bundle sign, and pleural traction sign, can be used as indicators to distinguish benign and malignant nodules.

The ability to differentiate between benign, suspicious, and malignant pulmonary nodules is imperative for definitive intervention in patients with early stage lung cancers. Here, we report that plasma protein functional effector sncRNAs (pfeRNAs) serve as non-invasive biomarkers for determining both the existence and the nature of pulmonary nodules in a three-stage study that included the healthy group, patients with benign pulmonary nodules, patients with suspicious nodules, and patients with malignant nodules. Following the standards required for a clinical laboratory improvement amendments (CLIA)-compliant laboratory-developed test (LDT), we identified a pfeRNA classifier containing 8 pfeRNAs in 108 biospecimens from 60 patients by sncRNA deep sequencing, deduced prediction rules using a separate training cohort of 198 plasma specimens, and then applied the prediction rules to another 230 plasma specimens in an independent validation cohort. The pfeRNA classifier could (1) differentiate patients with or without pulmonary nodules with an average sensitivity and specificity of 96.2% and 97.35% and (2) differentiate malignant versus benign pulmonary nodules with an average sensitivity and specificity of 77.1% and 74.25%. Our biomarkers are cost-effective, non-invasive, sensitive, and specific, and the qPCR-based method provides the possibility for automatic testing of robotic applications.

BackgroundAccurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5–10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules.MethodsBlood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules.ResultsOur results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5–10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones.ConclusionsOur study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making.Trial registration: This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic.clinicaltrials.gov/ct2/show/NCT05432128.

Objective To analyze retrospectively the clinical data of solitary pulmonary nodules and summarize the clinical characteristics to provide evidences for the diagnosis of benign and malignant solitary pulmonary nodules. Methods 148 cases of solitary pulmonary nodules were selected in the Northern Jiangsu People′s Hospital from January 2016 to June 2018.All the lesions of these patients were confirmed by surgery, bronchoscopy or lung puncture and had definite pathological diagnosis.The clinical data were collected to judge benign and malignant solitary pulmonary nodules, including gender, age, smoking history, cancer history, family history of cancer, maximum diameter of nodule, location, lobulation, spicule, pleural indentation, vascular convergence sign, tumor markers.Single factor binary regression was performed in the univariate analysis and logistic regression in multivariate analysis of these clincial data. Results 148 cases collected included 40 benign nodules and 108 malignant nodules.The univariate analysis showed that there were statistical differences in gender, age, cancer history, maximum diameter of nodule, location, lobulation, spicule, pleural indentation, vascular convergence sign between benign and malignant pulmonary nodules (all P<0.05). Multivariate logistic regression analysis showed that there were statistical differences in gender, age, maximum diameter of nodule and vascular convergence sign between benign and malignant pulmonary nodules (all P<0.05). Conclusions Patient gender, age, maximum diameter of nodule, vascular convergence sign are independent predictors of malignance in patients with solitary pulmonary nodules. Key words: Solitary pulmonary nodule; Adenocarcinoma; Risk factors; Logistic regression analysis

We aim to quantitatively investigate the difference between benign and malignant solid pulmonary nodules that appeared on dual-energy spectral computed tomography, and assess the diagnostic accuracy of several parameters derived from computed tomography in differentiating malignant from benign pulmonary nodules. Between September 2021 and December 2022, spectral images of 71 patients (male:female = 44:27, mean age = 71.0 years) confirmed by pathology were retrospectively analyzed in the venous phase. Patients were classified into the malignant group and the benign group. The iodine concentration values of the nodules, normalized iodine concentration of the nodules to the neighboring vessels, virtual monochromatic images of 40 and 80 keV, and slope of the spectral curve were calculated and compared between the benign and malignant groups. Receiver operating characteristic curves and the area under the curve were performed to assess the diagnostic performance of the above parameters. Both virtual monochromatic images and iodine concentration maps prove to be highly useful in differentiating benign and malignant pulmonary nodules. The malignant pulmonary nodules have higher iodine density and slope of the spectral curve than the benign lesions. The combined model of iodine density and curve slope with an optimal cutoff of 0.39 (area under the curve = 0.82) yielded a sensitivity of 95% and a specificity of 63%. Contrast-enhanced dual-energy spectral computed tomography allows promising capability of distinguishing malignant from benign lesions, potential for avoiding unnecessary invasive procedure or surgery.

Malignant pulmonary nodules (PNs) are often accompanied by vascular dilatation and structural abnormalities. Pulmonary transit time (PTT) measurement by contrast echocardiograghy has used to assess the cardiopulmonary function and pulmonary vascular status, such as hepatopulmonary syndrome and pulmonary arteriovenous fistula, but has not yet been attempted inthe diagnosis and differential diagnosis of PNs. The aim of this work was to evaluate the feasibility and performance of myocardial contrast echocardiography (MCE) for differentiating malignant PNs from benign ones. The study population consisted of 201 participant: 66 healthy participants, 65 patients with benign PNs and 70 patients with malignant PNs. Their clinical and conventional echocardiographic characteristics were collected. MCE with measurements of PTT were performed. There was no difference in age, sex, heart rate, blood pressure, smoking rate, background lung disease, pulmonary function, ECG, myocardial enzymes, cardiac size and function among the healthy participant, patients with benign and malignant PNs (P > 0.05). PTT did not differ significantly in patients with PNs of different sizes, nor did they differ in patients with PNs of different enhancement patterns (P > 0.05). However, the PTT were far shorter (about one half) in patients with malignant PNs than in patients with benign ones (1.88 ± 0.37 vs. 3.73 ± 0.35, P < 0.001). There was no significantly different between patients with benign PNs and healthy participant (3.73 ± 0.35 vs.3.89 ± 0.36, P > 0.05). The area under the receiver operating characteristics curve (AUC) of PTT was 0.99(0.978-1.009) in discriminating between benign and malignant PNs. The optimal cutoff value was 2.78s, with a sensitivity of 98.52%, a specificity of 97.34%, and a accuracy of 97.69%. MCE had a powerful performance in differentiating between benign and malignant PNs, and a pulmonary circulation time of < 2.78s indicated malignant PNs.

Under the background of urgent need for computer-aided technology to provide physicians with objective decision support, aiming at reducing the false positive rate of nodule CT detection in pulmonary nodules detection and improving the accuracy of lung nodule recognition, this paper puts forward a method based on ensemble learning to distinguish between malignant and benign pulmonary nodules. Firstly, trained on a public data set, a multi-layer feature fusion YOLOv3 network is used to detect lung nodules. Secondly, a CNN was trained to differentiate benign from malignant pulmonary nodules. Then, based on the idea of ensemble learning, the confidence probability of the above two models and the label of the training set are taken as data features to build a Logistic regression model. Finally, two test sets (public data set and private data set) were tested, and the confidence probability output by the two models was fused into the established logistic regression model to determine benign and malignant pulmonary nodules. The YOLOv3 network was trained to detect chest CT images of the test set. The number of pulmonary nodules detected in the public and private test sets was 356 and 314, respectively. The accuracy, sensitivity and specificity of the two test sets were 80.97%, 81.63%, 78.75% and 79.69%, 86.59%, 72.16%, respectively. With CNN training pulmonary nodules benign and malignant discriminant model analysis of two kinds of test set, the result of accuracy, sensitivity and specificity were 90.12%, 90.66%, 89.47% and 88.57%, 85.62%, 90.87%, respectively. Fused model based on YOLOv3 network and CNN is tested on two test sets, and the result of accuracy, sensitivity and specificity were 93.82%, 94.85%, 92.59% and 92.31%, 92.68%, 91.89%, respectively. The ensemble learning model is more effective than YOLOv3 network and CNN in removing false positives, and the accuracy of the ensemble. Learning model is higher than the other two networks in identifying pulmonary nodules.

Background: Advancements in technology have significantly improved the diagnosis of solitary pulmonary nodules in the lungs. Various computed tomography (CT) imaging techniques, including modern dual-energy computed tomography (DECT), have enhanced the ability to accurately classify pulmonary nodules as benign or malignant. In this study, three different dual-energy parameters — iodine load, contrast load, and visual assessment — were evaluated for their potential in characterizing pulmonary nodules. Objectives: The aim of this study was to assess the reliability and effectiveness of DECT in distinguishing benign from malignant pulmonary nodules using different parameters, including visual assessment, iodine concentration, and contrast load. Patients and Methods: This prospective study included patients who underwent contrast-enhanced thoracic DECT for solitary pulmonary nodules, had histopathological examination results, or had at least a two-year follow-up CT scan. Patients with nodules smaller than 6 mm or completely calcified nodules were excluded. Patients diagnosed with a suspicious solitary pulmonary nodule on chest radiography and subsequently underwent contrast-enhanced DECT, or those diagnosed with a lung nodule on routine non-contrast CT scans and later evaluated using DECT, were included in the study. Benign and malignant nodules were compared based on gender, age, contrast load, iodine load, and color map assessment. Nodule images were obtained 40 seconds after intravenous contrast administration using single-source DECT (120 kV split filter) with twin-beam technology. The visual enhancement and color map evaluation, including contrast and iodine load measurements, were separately calculated and recorded for each lung nodule. Results: A total of 59 patients [30 males (50.8%) and 29 females (49.2%)] with a solitary pulmonary nodule met the inclusion criteria. Among the 59 pulmonary nodules, 16 (27.1%) were malignant, and 43 (72.9%) were benign. Of the benign lesions, 23 (53.5%) were found in males and 20 (46.5%) in females. The mean age of patients with benign nodules was 53.5 ± 12 years (range: 25 - 73 years), while for those with malignant nodules, it was 69.2 ± 5.59 years (range: 57 - 75 years). There was no statistically significant difference in age between the two groups (P = 0.506). The median contrast load was 0.0 Hounsfield units (HU) [interquartile range (IQR: 64)] in benign nodules and 63 HU (IQR: 154) in malignant nodules. Malignant nodules had a significantly higher contrast load than benign nodules (P = 0.003). Using a cut-off value of 22 HU for contrast load in malignancy diagnosis, the sensitivity was 100%, specificity was 58.14%, positive predictive value (PPV) was 47.06%, and negative predictive value (NPV) was 100%. The area under the curve (AUC) was 0.746. The median iodine load was 0.0 mg/dL (IQR: 4.5) in benign nodules and 4.5 mg/dL (IQR: 11.8) in malignant nodules. Malignant nodules had a significantly higher iodine load than benign nodules (P &lt; 0.001). Using a cut-off value of 1 mg/mL for malignancy diagnosis, the sensitivity was 100%, specificity was 62.79%, PPV was 50%, and NPV was 100% (AUC: 0.768). Conclusion: Dual-energy computed tomography provides valuable contributions in differentiating benign and malignant pulmonary nodules. In this study, the diagnostic value of three different approaches — visual iodine coverage color map, iodine concentration, and contrast load — was demonstrated in distinguishing these lesions.

BackgroundtRNA-derived small RNAs (tsRNAs) have garnered significant attention in the field of cancer research, however, exosomal tsRNAs remain relatively understudied as potential biomarkers in the pulmonary nodules. This study aims to identify exosomal tsRNAs that are differentially expressed between benign and malignant pulmonary nodules, integrate these findings with other clinical parameters, and develop a novel predictive model to estimate the likelihood of malignancy in pulmonary nodules.MethodsExosomes were extracted from plasma of patients with benign pulmonary nodules and malignant pulmonary nodules (early-stage lung cancer), then characterized using transmission electron microscopy (TEM), qNano, and western blot. Differentially expressed tsRNAs were identified through small RNA microarray screening and validated by Quantitative Real-Time PCR (qRT-PCR). Receiver operating characteristic (ROC) analysis evaluated their diagnostic efficiency, while logistic regression integrated blood and imaging data to build a predictive model. Diagnostic performance was further assessed using random forest and nomogram analyses.ResultsA total of 43 differentially expressed tsRNAs were identified through small RNA array analysis. Among these, the expression levels of 3’tiRNA-43-GlyGCC-4 and tRF3-17-GlyTCC were significantly higher in patients with benign pulmonary nodules compared to those with early-stage lung cancer. Conversely, the expression of 5’Leader-ValAAC-1-2 was significantly lower in benign cases than in early-stage lung cancer patients. Using logistic regression, a predictive model was constructed by combining these tsRNA biomarkers with blood-based and imaging parameters. The model demonstrated excellent performance in distinguishing early-stage lung cancer from benign pulmonary nodules, achieving an area under the curve (AUC) of 0.9559, with a sensitivity of 91.06% and a specificity of 91.53%.ConclusionOur model highlights its potential as a robust tool for predicting the malignancy probability of pulmonary nodules.

With the rapid development in computed tomography (CT), the establishment of artificial intelligence (AI) technology and improved awareness of health in folks in the decades, it becomes easier to detect and predict pulmonary nodules with high accuracy. The accurate identification of benign and malignant pulmonary nodules has been challenging for radiologists and clinicians. Therefore, this study applied the unenhanced CT imagesbased radiomics to identify the benign or malignant pulmonary nodules. One hundred and four cases of pulmonary nodules confirmed by clinicopathology were analyzed retrospectively, including 79 cases of malignant nodules and 25 cases of benign nodules. They were randomly divided into a training group (n = 74 cases) and test group (n = 30 cases) according to the ratio of 7:3. Using ITK-SNAP software to manually mark the region of interest (ROI), and using AK software (Analysis kit, Version 3.0.0.R, GE Healthcare, America) to extract image radiomics features, a total of 1316 radiomics features were extracted. Then, the minimum-redundancy-maximum-relevance (mRMR) algorithms were used to preliminarily reduce the dimension, and retain the 30 most meaningful features, and then the least absolute shrinkage and selection operator (LASSO) algorithm was used to select the optimal subset of features, so as to establish the final model. The performance of the model was evaluated by using the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), accuracy, sensitivity and specificity. Calibration refers to the agreement between observed endpoints and predictions, and the clinical benefit of the model to patients was evaluated by decision curve analysis (DCA). The accuracy, sensitivity, and specificity of the training and testing groups were 81.0%, 77.7%, 82.1% and 76.6%, 85.7%, 73.9%, respectively, and the corresponding AUCs were of 0.83 in both groups. CT image-based radiomics could differentiate benign from malignant pulmonary nodules, which might provide a new method for clinicians to detect benign and malignant pulmonary nodules.

Objective: To assess the clinical value of a radiomics model basedon low-dose computed tomography (LDCT) in diagnosing benign and malignantpulmonary ground-glass nodules. Methods: A retrospective analysiswas performed on 274 patients who underwent LDCT scanning with theidentification of pulmonary ground-glass nodules from January 2018 to March2021. All patients had complete clinical and pathological data. The cases wererandomly divided into 191 cases in a training set and 83 cases in a validationset using the random sampling method and a 7:3 ratio. Based on the predictorsources, we established clinical, radiomics, and combined prediction models inthe training set. A receiver operating characteristic (ROC) curve was generatedfor the training and validation sets, the predictive abilities of the differentmodels for benign and malignant nodules were compared according to the areaunder the curve (AUC), and the model with the best predictive ability wasselected. A calibration curve was plotted to test the good-of-fitness of themodel in the validation set. Results: Of the 274 patients (84 malesand 190 females), 156 had malignant, and 118 had benign nodules. The univariateanalysis showed a statistically significant difference in nodule positionbetween benign nodules and lung adenocarcinoma in both data sets(P <.001 and .021). In the training set, when the nodulediameter was >8 mm, the probability of nodule malignancy increased(P < .001). The results showed that the combined modelhad a higher prediction ability than the other two models. The combined modelcould distinguish between benign and malignant pulmonary nodules in the trainingset (AUC: 0.711; 95%CI: 0.634-0.787; ACC: 0.696; sensitivity: 0.617;specificity: 0.816; PPV:0.835; NPV: 0.585). Moreover, this model could predictbenign and malignant nodules in the validation set (AUC: 0.695; 95%CI:0.574-0.816; ACC: 9.747; sensitivity: 0.694; specificity: 0.824; PPV: 0.850;NPV: 0.651). The calibration curve had a P value of 0.775,indicating that in the validation set, there was no difference between the valuepredicted by the combined model and the actual observed value and that theresult was a good fit. Conclusion: The prediction model combiningclinical information and radiomics parameters had a good ability to distinguishbenign and malignant pulmonary ground-glass nodules.

Purpose: Lung cancer is the most common malignancy with the highest morbidity and mortality worldwide. Low-dose computed tomography (LDCT) is one of the main tools for lung cancer screening and diagnosis. However, high false positive, high cost, over-diagnosis and radiation exposure are major drawbacks to screen pulmonary nodules by LDCT. Thus, a noninvasive diagnostic approach with high specificity and accuracy is badly needed to enhance LDCT. Liquid biopsy represent a valuable non-invasive approach when biopsy or resection is not the first choice. Till now, it is rare to see the studies on exosome-derived miRNAs as early diagnosis biomarkers to distinguish benign and malignant pulmonary nodules using small RNA sequencing. Here, we aimed to explore the diagnostic value of a panel of significantly differential expressed plasma exosomal miRNAs between benign and malignant pulmonary nodule samples in Chinese cohorts. Materials and Methods: This study was registered at Chinese Clinical Trial Registry (www.chictr.org.cn) with registration number ChiCTR1800019877. Forty-five patients including twenty-six lung adenocarcinoma and nineteen benign nodules with various pathological characteristics were enrolled as a training cohort. A test cohort consisted of sixty-two patients with twenty-four benign nodules patients similar to training cohort and thirty-eight lung adenocarcinoma. Exosomes were precipitated from the plasma, and small RNA sequencing was performed to identify the differential expressed miRNAs. A statistical model consisting of a panel of exosomal miRNAs was trained to discriminate benign nodules from cancerous ones. The model was validated in the independent test cohort, and re-confirmed in an external dataset from another Chinese cohort. Enriched Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were also performed. Results: Characteristic proteins and morphology of exosomes were characterised by western blotting, nanoparticle tracking analysis and scanning electron microscopy. Five differential expressed miRNAs (let-7b-3p, miR-101-3p, miR-125b-5p, miR-150-5p, and miR-3168) with median expression &amp;gt; 50 were selected by LASSO-penalized regression as a linear model to classify samples into benign or maligant groups with 10-fold cross-validation to determine the model parameters. When the specificity set 94.7% and 91.7% for the training and test cohorts, respectively, the model had 57.7% and 57.9% sensitivity in both cohorts. The model was also confirmed in an external dataset with 87.5% specificity and 53.1% sensitivity. The expression of each biomarker in benign, adenocarcinoma in situ/microinvasive adenocarcinoma and invasive adenocarcinoma nodules were gradually altered. Four of the five biomarkers were gradually increased, whereas one miRNA was gradually decreased. GO and KEGG analysis demonstrated that biological process and pathways of the genes targeted by five biomarkers were associated with tumor development. Conclusions: This study using small RNA sequencing identified five plasma exosome-derived differentially expressed miRNAs as a diagnosis model to distinguish benign and malignant pulmonary nodules, which provides insights into the feasibility of exosomal miRNAs as a novel early diagnosis approach for lung adenocarcinoma. Citation Format: Di Zheng, Yang Yang, Chunyan Wu, Huizhen Wang, Jiyang Zhang, Shiyi Liu, Xiaoya Xu, Hao Chen, Dadong Zhang, Fugen Li, Jian Ni, Gening Jiang, Jianfang Xu. A panel of plasma exosomal miRNAs as diagnosis biomarker to distinguish benign and malignant nodules in non-small cell lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 756.