Computed tomography-derived radiomics models for distinguishing difficult-to-diagnose inflammatory and malignant pulmonary nodules

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.

Objective: To investigate the value of computed tomography (CT) texture analysis in differential diagnosis of inflammatory and malignant pulmonary nodules. Methods: The image data of 54 patients with lung cancer and 36 patients with pulmonary inflammatory nodules were retrospectively collected in our hospital. All the patients received chest CT scan. CT texture analysis of entropy, correlation degree and contrast ratio were performed by the MaZda software. The receiver operating characteristic curve (ROC) was established and the area under the curve (AUC) was calculated to evaluate the value of CT texture analysis in differential diagnosis of inflammatory and malignant pulmonary nodules. Results: In the lung cancer group, the value of entropy, correlation degree and contrast ratio were 1.58±0.07, 0.02±0.17 and 8.79±2.59, respectively. In the inflammatory nodules group, the value of entropy, correlation degree and contrast ratio were 1.51±0.04, 0.22±0.16 and 12.53±2.24, respectively. The differences were all statistically significant (P values were 0.008, 0.027, and 0.006, respectively) between two groups. There was not statistically significant difference (P>0.05) in the CT values between the lung cancer group and the inflammatory nodule group based on the non-contrast enhanced CT scan. Meanwhile, there was no statistically significant difference (P>0.05) in the value of entropy, correlation degree or contrast ratio between two groups based on arterial phase or venous phase of contrast enhanced CT. The ROC analysis showed that the AUC in differentiating the lung cancer and inflammatory nodules was 0.821, 0.778 and 0.875, respectively. The AUC of combination of three phases was 0.931, which was higher than the AUC of entropy, correlation degree and contrast ratio respectively (P<0.01). The sensitivity was 88.9%, and the specificity was 87.5%. Conclusion: CT texture analysis is a high-potential image analysis method, which can provide more information for the differential diagnosis of benign and malignant pulmonary nodules.

PURPOSE The aim of this study was to evaluate the diagnostic performance of iodine uptake parameters using dual-energy computed tomography (DECT) in discriminating inflammatory nodules from malignant tumors. METHODS This retrospective study included 116 solid pulmonary nodules from 112 patients who were admitted to our hospital between January and September 2018. All nodules were confirmed by surgery or puncture. The degree of enhancement of a single-section region of interest was evalu ated. After total tumor volume-of-interest segmentation, the mean iodine density of the whole tumor was measured. Meanwhile, iodine uptake parameters, including total iodine uptake vol ume, total iodine concentration, vital iodine uptake volume, and vital iodine concentration, were calculated, and a predictive model was established. The overall ability to discriminate between inflammatory and malignant nodules was analyzed using an independent samples t-test for normally distributed variables. The diagnostic accuracy and prognostic performance of DECT parameters were evaluated and compared using receiver operating characteristic curve analysis and logistic regression analysis. A multivariate logistic regression analysis was used to determine the prognostic factors and goodness-of-fit of the whole tumor mean iodine and iodine uptake parameters for discriminating malignant nodules. RESULTS There were 116 non-calcified nodules, including 64 inflammatory nodules and 52 malignant nodules. The degree of enhancement in malignant nodules was significantly lower than that in inflammatory nodules (P=.043). All iodine uptake parameters in malignant nodules were signifi cantly higher than those in inflammatory nodules (P < .001). The area under the receiver operat ing curve value, accuracy, sensitivity, and specificity of the established model based on iodine uptake parameters were 0.803, 76.72%, 82.69%, and 84.37%, respectively, which exhibited bet ter diagnostic performance than the degree of enhancement on weighted average images with respective values of 0.609, 59.48%, 61.54%, and 59.38%. CONCLUSION The iodine uptake parameters of DECT exhibited better diagnostic accuracy in discriminating inflammatory nodules from malignant nodules than the degree of enhancement on weighted average images.

The detection of pulmonary nodules has become increasingly common; however, accurate qualitative diagnosis remains a clinical challenge. This study sought to distinguish between malignant and inflammatory solid lung nodules using clinical data and computed tomography (CT) imaging features. A total of 948 patients with pulmonary nodules who underwent surgery or percutaneous biopsy from four centers were included in the study. The patients were divided into the following four groups based on nodule diameter: Group 1: nodules ≤10 mm; Group 2: nodules >10 and ≤20 mm; Group 3: nodules >20 and ≤30 mm; and Group 4: all nodules. The independent risk factors were identified and merged by univariate and multivariate analyses in the four groups to establish four models. The overall performance of the four models was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve. Differences between Models 1-3 and Model 4 were compared using the DeLong test. Of the nodules, 638 were classified as malignant and 310 as inflammatory. The patients with malignant and inflammatory nodules had median ages of 64.3±9.8 and 56.0±11.9 years, respectively (P<0.001). To build the four models, 17 features were identified, of which 2 were clinical features and 15 were imaging features. Notably, the frequency of lobulation, age, multiple lesions, and satellite lesions was relatively high in the four models. The AUC, accuracy, sensitivity, and specificity of Models 1-4 were 0.861 (0.803-0.921), 73.5%, 81.0%, and 78.9%; 0.902 (0.873-0.931), 82.8%, 74.7%, and 88.0%; 0.943 (0.914-0.972), 90.5%, 87.3%, and 89.7%; and 0.921 (0.903-0.940), 84.7%, 83.1%, and 86.8%; respectively. However, there were no statistically significant differences between Models 1-3 and Model 4. Our novel subgrouping models were able to effectively distinguish between inflammatory and malignant lung nodules using a reduced feature set. Our models could facilitate the accurate diagnosis of patients with potentially malignant lesions.

To investigate a novel diagnostic model for benign and malignant pulmonary nodule diagnosis based on radiomic and clinical features, including urine energy metabolism index. A total of 107 pulmonary nodules were prospectively recruited and pathologically confirmed as malignant in 86 cases and benign in 21 cases. A chest CT scan and urine energy metabolism test were performed in all cases. A nomogram model was established in combination with radiomic and clinical features, including urine energy metabolism levels. The nomogram model was compared with the radiomic model and the clinical feature model alone to test its diagnostic validity, and receiver operating characteristic (ROC) curves were plotted to assess diagnostic validity. The nomogram was established using a logistic regression algorithm to combine radiomic features and clinical characteristics including urine energy metabolism results. The predictive performance of the nomogram was evaluated using the area under the ROC and calibration curve, which showed the best performance, area under the curve (AUC) = 0.982, 95% CI = 0.940-1.000, compared to clinical and radiomic models in the testing cohort. The clinical benefit of the model was assessed using the decision curve analysis (DCA) and using the nomogram for benign and malignant pulmonary nodules, and preoperative prediction of benign and malignant pulmonary nodules using nomograms showed better clinical benefit. This study shows that a coupled model combining CT imaging features and clinical features (including urine energy metabolism) in combination with the nomogram model has higher diagnostic performance than the radiomic and clinical models alone, suggesting that the combination of both methods is more advantageous in identifying benign and malignant 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.

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.

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.

To explore the effect of the full iterative model reconstruction algorithm (IMR) on chest CT image processing and its adoption value in the clinical diagnosis of lung cancer patients, multislice spiral CT (MSCT) scans were performed on 96 patients with pulmonary nodules. Reconstruction was performed by hybrid iterative reconstruction (iDose4) and IMR2 algorithms. Then, the image contrast, spatial resolution, density resolution, image uniformity, and noise of the CT reconstructed image were recorded. The benign and malignant pulmonary nodules of patients were collected and classified into malignant pulmonary nodule group and benign pulmonary nodule group, and the differences in chest CT imaging characteristics between the two groups were compared. The subject’s receiver operating characteristic (ROC) curve was used to analyze the diagnostic sensitivity, specificity, and area under the curve (AUC) of CT for benign and malignant pulmonary nodules. It was found that the spatial resolution, density resolution, image uniformity, and contrast of the CT image reconstructed by the IMR2 algorithm were remarkably greater than those of the iDose4 algorithm, and the noise was considerably less than that of the iDose4 algorithm ( P &lt; 0.05 ). Among 96 patients with pulmonary nodules, 65 were malignant nodules, including 15 squamous cell carcinoma, 31 adenocarcinoma, and 19 small cell carcinomas. There were 31 cases of benign nodules, including 14 cases of hamartoma, 10 cases of tuberculous granuloma, 2 cases of sclerosing hemangioma, and 5 cases of diffuse lymphocyte proliferation. The pulmonary nodule malignant group and the pulmonary nodule benign group had statistical differences in pulmonary nodule size, nodule morphology, burr sign, lobular sign, vascular sign, bronchial sign, and pleural depression sign ( P &lt; 0.05 ). The sensitivity, specificity, and area under the curve (AUC) of IMR2 algorithm processing chest CT images for liver cancer diagnosis were 85.7%, 82.3%, and 0.815, respectively, which were significantly higher than the original CT images ( P &lt; 0.05 ). In short, chest MSCT based on the IMR2 algorithm can greatly improve the diagnosis efficiency of lung cancer and had practical significance for the timely detection of early lung cancer.

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.

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.

背景与目的近年来，孤立性肺结节（solitary pulmonary nodule, SPN）受到越来越多的关注，部分肺结节被认为是早期肺癌，但如何鉴别肺结节良恶性却是亟待解决的临床难题。本研究旨在探讨细胞因子与肿瘤标志物联合检测对SPN良恶性的鉴别诊断价值，从而提高SPN诊断的准确性。方法纳入81例诊断明确的SPN患者作为研究对象，收集病例的一般临床资料、结节影像学特征、病理学诊断资料、细胞因子系列和肿瘤标志物表达水平。利用单因素和多因素分析筛选可预测肺结节性质的影响指标，并用二元Logistic回归分析构造联合指标；绘制受试者工作特征曲线（receiver operating characteristic curve, ROC），计算曲线下面积及相应的灵敏度、特异度、阳性预测值、阴性预测值和准确率。结果一般临床资料分析示恶性结节出现在右肺上叶的比例最高（40.4%）。恶性结节组中的癌胚抗原（carcinoembryonic antigen, CEA）、细胞角蛋白19片段（cytokeratin 19 fragment 21-1, CYFRA21-1）、白介素6（interleukin-6, IL-6）和白介素8（interleukin-8, IL-8）血清水平高于良性结节组。Logistic回归分析提示，CEA、IL-6、IL-8为预测恶性结节的独立危险因子。ROC曲线分析表明，单项指标CEA、IL-6和IL-8的曲线下面积分别为0.642、0.684和0.749，CEA+IL-6+IL-8联合检测曲线下面积更大，检测效能更高。结论CEA、IL-6和IL-8为恶性结节的独立危险因素。细胞因子和肿瘤标志物联合检测在SPN良恶性鉴别诊断中具有一定的价值。其中CEA+IL-6+IL-8联合检测的诊断价值最高。

Background Deep learning may help to improve computer-aided detection of volume (CADv) measurement of pulmonary nodules at chest CT. Purpose To determine the efficacy of a deep learning method for improving CADv for measuring the solid and ground-glass opacity (GGO) volumes of a nodule, doubling time (DT), and the change in volume at chest CT. Materials and Methods From January 2014 to December 2016, patients with pulmonary nodules at CT were retrospectively reviewed. CADv without and with a convolutional neural network (CNN) automatically determined total nodule volume change per day and DT. Area under the curves (AUCs) on a per-nodule basis and diagnostic accuracy on a per-patient basis were compared among all indexes from CADv with and without CNN for differentiating benign from malignant nodules. Results The CNN training set was 294 nodules in 217 patients, the validation set was 41 nodules in 32 validation patients, and the test set was 290 nodules in 188 patients. A total of 170 patients had 290 nodules (mean size ± standard deviation, 11 mm ± 5; range, 4-29 mm) diagnosed as 132 malignant nodules and 158 benign nodules. There were 132 solid nodules (46%), 106 part-solid nodules (36%), and 52 ground-glass nodules (18%). The test set results showed that the diagnostic performance of the CNN with CADv for total nodule volume change per day was larger than DT of CADv with CNN (AUC, 0.94 [95% confidence interval {CI}: 0.90, 0.96] vs 0.67 [95% CI: 0.60, 0.74]; P < .001) and CADv without CNN (total nodule volume change per day: AUC, 0.69 [95% CI: 0.62, 0.75]; P < .001; DT: AUC, 0.58 [95% CI: 0.51, 0.65]; P < .001). The accuracy of total nodule volume change per day of CADv with CNN was significantly higher than that of CADv without CNN (P < .001) and DT of both methods (P < .001). Conclusion Convolutional neural network is useful for improving accuracy of computer-aided detection of volume measurement and nodule differentiation capability at CT for patients with pulmonary nodules. © RSNA, 2020 Online supplemental material is available for this article.

BackgroundIncidental indeterminate solitary solid pulmonary nodule is a progressively common finding on CT worldwide. Once detected, there are a number of imaging modalities that can be done to help in nodule characterization and differentiating benign from malignant nodules. Through these imaging modalities, there are PET CT, SPECT and dynamic CE-CT. Dynamic CE-CT is a functional test that help in assessment of the vascularity of the nodule which reverb the degree of angiogenesis of that nodule so can help in differentiating benign from malignant pulmonary nodules. The purpose of this study was to evaluate the role of Dynamic CE-CT in characterization of solitary pulmonary nodules. Detect what are the important parameters on dynamic CE-CT to differentiate benign from malignant nodules and detect their cutoff values.ResultsThe pre-enhancement value shows cutoff point of 26.50 HU with sensitivity 93.8% and specificity 75% with accuracy rate 90% in differentiating benign from malignant pulmonary nodules. Peak enhancement value (at 2 min) shows cutoff point of 40.00 HU with sensitivity 96.9% and specificity 87.5% with accuracy rate 95% in differentiating benign from malignant pulmonary nodules. Net enhancement value shows cutoff point of 19.00 HU with sensitivity 96.9% and specificity 87.5% with accuracy rate 95% in differentiating benign from malignant pulmonary nodules.ConclusionDynamic CE-CT is a useful tool in differentiating benign from malignant pulmonary nodules. Peak and net enhancement values are important parameters with high sensitivity and specificity in differentiating benign from malignant pulmonary nodules.

Deep learning-based CT image for pulmonary nodule classification with intrathoracic fat: A multicenter study