A Modified Model for Preoperatively Predicting Malignancy of Solitary Pulmonary Nodules: An Asia Cohort Study

Risk of adenocarcinoma in patients with a suspicious ground-glass opacity: a retrospective review

BACKGROUND

Both primary lung adenocarcinoma and benign processes can have a ground-glass opacity (GGO) appearance on imaging. This study evaluated the incidence of and risk factors for malignancy in a diverse cohort of patients who underwent resection of a GGO suspicious for lung cancer.

METHODS

All patients who underwent resection of a pulmonary nodule with a GGO component and suspected to be primary lung cancer at a single institution from 2001-2017 were retrospectively reviewed. Risk factors for malignancy were evaluated using multivariable logistic regression analysis that included nodule size, age, sex, and race as potential predictors.

RESULTS

The incidence of pulmonary adenocarcinoma in the 243 patients who met inclusion criteria was 86% (n=208). The most common pathologic findings in 35 patients with a benign pathology was granulomatous inflammation (n=14, 40%). Risk factors for adenocarcinoma in multivariable logistic regression were age [odds ratio (OR) 1.06, P=0.003], GGO size (OR 2.76, P<0.001), female sex (OR 4.47, P=0.002), and Asian race (OR 8.35, P=0.002). In this cohort, adenocarcinoma was found in 100% (44/44) of Asian females, 86% (25/29) of Asian males, 84% (98/117) of non-Asian females, and 77% (41/53) of non-Asian males.

CONCLUSIONS

The likelihood of adenocarcinoma in lung nodules with a ground-glass component is influenced by sex and race. Asian females with a GGO have a much higher likelihood of having adenocarcinoma than men and non-Asians. This data can be used when deciding whether to pursue nodule resection or surveillance in a patient with a GGO.

Factors associated with concurrent malignancy risk among patients with incidental solitary pulmonary nodule: A systematic review taskforce for developing rapid recommendations

OBJECTIVE

To assess the association between prespecified factors and the malignancy risk of solitary pulmonary nodules (SPNs) to support the development of rapid recommendations for daily use in the Chinese setting.

METHODS

The expert panel for the rapid recommendations voted for 12 candidate factors based on published guidelines, selected publications, and clinical experiences. We then searched Medline, Embase, and Web of Science up to October 17, 2021, for studies investigating the association between these factors and the diagnosis of malignant SPNs in patients with CT-identified SPNs through multivariable regression analysis. The risk of bias was assessed using the Agency for Healthcare Research and Quality (AHRQ) Checklist. We pooled adjusted odds ratios (aOR) between candidate factors and the diagnosis of the malignant SPNs.

RESULTS

A total of 32 cross-sectional studies were included. Nine factors were statistically associated with malignant SPNs: age (aOR 1.06, 95% confidence interval [CI]: 1.05-1.07), smoking history (2.83, 1.84-4.36), history of extrathoracic malignancy (5.66, 2.80-11.46), history of malignancy (4.64, 3.37-6.39), family history of malignancy (3.11, 1.66-5.83), nodule diameter (1.23, 1.17-1.31), spiculation (3.41, 2.64-4.41), lobulation (3.85, 2.47-6.01), and mixed ground-glass opacity (mGGO) density of the nodule (5.56, 2.47-12.52). No statistical association was found between family history of lung cancer, emphysema, nodule border, and malignant SPNs.

CONCLUSION

Nine prespecified factors were associated with the concurrent malignancy risk among patients with SPNs. Risk stratification for SPNs is warranted in clinical practice.

Lung cancer risk prediction models based on pulmonary nodules: A systematic review

BACKGROUND

Screening with low-dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false-positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models.

METHODS

The keywords "lung cancer," "lung neoplasms," "lung tumor," "risk," "lung carcinoma" "risk," "predict," "assessment," and "nodule" were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed.

RESULTS

A total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single-center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets.

CONCLUSION

The existing models showed good discrimination for identifying high-risk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required to improve the quality of deep learning models, particularly for the Asian population.

The use of carcinoembryonic antigen levels to predict lung nodule malignancy: a meta-analysis

OBJECTIVES

To assess the diagnostic value of serum carcinoembryonic antigen (CEA) as a diagnostic biomarker that can be used to differentiate between benign and malignant lung nodules (LNs).

METHODS

PubMed, Cochrane Library, and Embase were reviewed from January 2000 to April 2020 for eligible studies. Stata v12.0 was used to conduct this meta-analysis.

RESULTS

Our initial literature search identified 511 potentially relevant studies, of which 11 were ultimately included in the present meta-analysis. Ten studies were retrospective and only 1 study was prospective. Overall these studies incorporated 2760 patients and 2760 total LNs (1733 malignant, 1027 benign). Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) values for these studies were 0.33 (95% CI: 0.20-0.49), 0.92 (95% CI: 0.85-0.96), 3.96 (95% CI: 2.84-5.54), 0.73 (95% CI: 0.62-0.87), and 5.42 (95% CI: 3.77-7.78), respectively. The area under curve (AUC) value was 0.77, consistent with moderate diagnostic accuracy. We detected significant heterogeneity when calculating pooled sensitivity (I² = 95.9%, P = 0.00), specificity (I² = 92.0%, P = 0.00), PLR (I² = 61.7%, P = 0.00), NLR (I² = 92.8%, P = 0.00), and DOR (I² = 93.8%, P = 0.00). No significant evidence of publication bias was detected via Deeks' funnel plot asymmetry test (P = 0.371). Meta-regression analysis revealed different reference standards to be closely associated with both sensitivity and specificity.

CONCLUSIONS

Serum CEA can achieve moderate diagnostic performance as a means of differentiating between malignant and benign LNs.

Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review

BACKGROUND

Solitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice.

METHODS

We performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles.

RESULTS

A total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice.

CONCLUSIONS

The studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42020161559.

Calcification sign for prediction of benignity in pulmonary nodules: A meta-analysis

BACKGROUND

The calcification sign assessed by computed tomography appears to be a potential marker for benignities among patients diagnosed with pulmonary nodules (PNs). The following meta-analysis has been purposefully designed to figure-out the diagnostic value of the calcification signature as a way of identifying benignities from PNs.

METHODS

Cochrane Library, Embase and PubMed were considered as a reference to obtain the required data from January 2000 until October 2020. Stata v12.0 was used as a standard tool for statistical assessment.

RESULTS

Eleven retrospective studies were assessed via this meta-analysis, which included 6136 PNs (1827 benign and 4309 malignant). The pooled diagnostic odd ratios, positive likelihood ratio (PLR), negative likelihood ratio (NLR), sensitivity and specificity were 6.79, 6.06, 0.89, 13% and 98%, respectively. The value obtained for the area under the curve was 0.65, showing moderate overall diagnostic accuracy. A significant heterogeneity was found while calculating the pooled sensitivity (I²  = 85.5%), specificity (I²  = 75.0%), PLR (I²  = 59.0%), NLR (I²  = 79.5%) and DOR (I²  = 100.0%) in the current analysis. Sub-group analyses presented better PLR and specificity values for the study with a sample size ≥ 400. Deeks' funnel plot asymmetry test detected no potential evidence of significant publication bias (p = 0.091).

CONCLUSIONS

Calcification signs have been identified as moderate regulators corresponding to overall diagnostic performance (via marking a distinct differentiation between malignant and benign) for PNs. However, the manifestation of the calcification sign had a good directive property for benign PNs.

Establishment and verification of a prediction model based on clinical characteristics and positron emission tomography/computed tomography (PET/CT) parameters for distinguishing malignant from benign ground-glass nodules

Background

To develop and verify a prediction model for distinguishing malignant from benign ground-glass nodules (GGNs) combined with clinical characteristics and ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) parameters.

Methods

We retrospectively analyzed 170 patients (56 males and 114 females) with GGNs who underwent PET/CT and high-resolution CT examination in our hospital from November 2011 to December 2019. The clinical and imaging data of all patients were collected, and the nodules were randomly divided into a derivation set and a validation set. For the derivation set, we used multivariate logistic regression to develop a prediction model for distinguishing benign from malignant GGNs. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the model, and the data in the validation set were used to verify the prediction model.

Results

Among the 170 patients, 197 GGNs were confirmed via postoperative pathological examination or clinical follow-up. There were 21 patients with 27 GGNs in the benign group and 149 patients with 170 GGNs in the adenocarcinoma group. A total of five parameters, including the patient's sex, nodule location, margin, pleural indentation, and standardized uptake value (SUV) index (the ratio of nodule SUVmax to liver SUVmean), were selected to develop a prediction model for distinguishing benign from malignant GGNs. The area under the curve (AUC) of the model was 0.875 in the derivation set, with a sensitivity of 0.702 and a specificity of 0.923. The positive likelihood ratio was 9.131, and the negative likelihood ratio was 0.322. In the validation set, the AUC of the model was 0.874, which was not significantly different from the derivation set (P=0.989).

Conclusions

This study developed and validated a prediction model based on ¹⁸F-FDG PET/CT imaging and clinical characteristics for distinguishing malignant from benign GGNs. The model showed good diagnostic efficacy and high specificity, which can improve the preoperative diagnosis of high-risk GGNs.

Establishment and validation of a prediction model for the probability of malignancy in solid solitary pulmonary nodules in northwest China

BACKGROUND AND OBJECTIVES

To construct a prediction model of solitary pulmonary nodules (SPNs), to predict the possibility of malignant SPNs in patients aged 15-85 years in northwest China for clinical diagnostic and therapeutic decision-making.

METHODS

The features of SPNs were assessed by multivariate logistic regression, followed by visualization using a nomogram. Hosmer lemeshow was applied to evaluate the fitting degree of the model. The area under the receiver operating characteristic (ROC) curve was identified to determine the discriminative ability of the model.

RESULTS

Lobulation, spiculation, pleural-tag, carcinoembryonic antigen, neuron-specific enolase, and total serum protein were independent predictors of malignant pulmonary nodules (p < .05). Lobulation (100 points) scored the highest in the nomogram, and the Hosmer-Lemeshow goodness-of-fit statistic was 0.805 (p > .05). The area under curve (AUC) of the modeling and validation groups using logistic regression were 0.859 (95% CI, 0.805-0.903) and 0.823 (95% CI, 0.738-0.890), respectively. Moreover, the AUC of our model was higher than that of the Mayo model, VA model, and Peking University (AUC 0.823 vs. 0.655 vs. 0.603 vs. 0.521).

CONCLUSION

Our prediction model is more suitable for predicting the possibility of malignant SPNs in northwest China, and can be calculated using a nomogram to determine further treatments.

Establishment and validation of a mathematical diagnosis model to distinguish benign pulmonary nodules from early non-small cell lung cancer in Chinese people

Background

In this study, we aimed to establish and validate a mathematical diagnosis model to distinguish benign pulmonary nodules (BPNs) from early non-small cell lung cancer (eNSCLC) based on clinical characteristics, radiomics features, and hematological biomarkers.

Methods

Medical records from 81 patients (27 BPNs, 54 eNSCLC) were used to establish a novel mathematical diagnosis model and an additional 61 patients (21 BPNs, 40 eNSCLC) were used to validate this new model. To establish a clinical diagnosis model, a least absolute shrinkage and selection operator (LASSO) regression was applied to select predictors for eNSCLC, then multivariate logistic regression analysis was performed to determine independent predictors of the probability of eNSCLC, and to establish a clinical diagnosis model. The diagnostic accuracy and discriminative ability of our model were compared with the PKUPH and Mayo models using the following 4 indices: area under the receiver-operating characteristics curve (ROC), net reclassification improvement index (NRI), integrated discrimination improvement index (IDI), and decision curve analysis (DCA).

Results

Multivariate logistic regression analysis identified age, border, and albumin (ALB) as independent diagnostic markers of eNSCLC. In the training cohort, the AUC of our model was 0.740, which was larger than the AUCs for the PKUPH model (0.717, P=0.755) and the Mayo model (0.652, P=0.275). Compared with the PKUPH and Mayo models, the NRI of our model increased by 3.7% (P=0.731) and 27.78% (P=0.008), respectively, while the IDI changed −4.77% (P=0.437) and 11.67% (P=0.015), respectively. Moreover, the DCA demonstrated that our model had a higher overall net benefit compared to previously published models. Importantly, similar findings were confirmed in the validation cohort.

Conclusions

Age, border, and serum ALB levels were independent diagnostic markers of eNSCLC. Thus, our model could more accurately distinguish BPNs from eNSCLC and outperformed previously published models.

Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes

Objective

Malignancy prediction models for pulmonary nodules are most accurate when used within nodules similar to those in which they were developed. This study was to establish models that respectively predict malignancy risk of incidental solid and subsolid pulmonary nodules of different size.

Materials and Methods

This retrospective study enrolled patients with 5-30 mm pulmonary nodules who had a histopathologic diagnosis of benign or malignant. The median time to lung cancer diagnosis was 25 days. Four training/validation datasets were assembled based on nodule texture and size: subsolid nodules (SSNs) ≤15 mm, SSNs between 15 and 30 mm, solid nodules ≤15 mm and those between 15 and 30 mm. Univariate logistic regression was used to identify potential predictors, and multivariate analysis was used to build four models.

Results

The study identified 1008 benign and 1813 malignant nodules from a single hospital, and by random selection 1008 malignant nodules were enrolled for further analysis. There was a much higher malignancy rate among SSNs than solid nodules (rate, 75% vs 39%, P<0.001). Four distinguishing models were respectively developed and the areas under the curve (AUC) in training sets and validation sets were 0.83 (0.78-0.88) and 0.70 (0.61-0.80) for SSNs ≤15 mm, 0.84 (0.74-0.93) and 0.72 (0.57-0.87) for SSNs between 15 and 30 mm, 0.82 (0.77-0.87) and 0.71 (0.61-0.80) for solid nodules ≤15 mm, 0.82 (0.79-0.85) and 0.81 (0.76-0.86) for solid nodules between 15 and 30 mm. Each model showed good calibration and potential clinical applications. Different independent predictors were identified for solid nodules and SSNs of different size.

Conclusion

We developed four models to help characterize subsolid and solid pulmonary nodules of different sizes. The established models may provide decision-making information for thoracic radiologists and clinicians.

Computed tomography-based spiculated sign for prediction of malignancy in lung nodules: A meta-analysis

BACKGROUND

Computed tomography (CT)-based spiculated sign is a risk factor for malignancy in patients with lung nodules (LNs). The present meta-analysis aimed to evaluate the diagnostic utility of CT-based spiculated sign as a means of differentiating between malignant and benign LNs.

METHODS

PubMed, Cochrane Library and Embase were reviewed from January 2000 to March 2020 for eligible studies. Stata v12.0 was used to conduct this meta-analysis.

RESULTS

We identified 19 retrospective studies for inclusion in this meta-analysis. These studies compiled data pertaining to 8549 LNs (5547 malignant and 3003 benign). Pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratios (DOR) were 0.51 (95% CI: 0.36-0.65), 0.84 (95% CI: 0.74-0.91), 3.15 (95% CI: 2.34-4.23), 0.59 (95% CI: 0.47-0.73) and 5.36 (95% CI: 3.93-7.31), respectively. The area under curve (AUC) was 0.76. Significant heterogeneity was detected among these studies with respect to sensitivity (I² = 98.4%, P = .00), specificity (I² = 95.8%, P = .00), PLR (I² = 78.9%, P = .00), NLR (I² = 99.3%, P = .00) and DOR (I² = 100%, P = .00). A meta-regression analysis revealed that the country in which a study was conducted (China vs Not China) had a strong influence on reported sensitivity and specificity. No significant publication bias was detected via Deeks' funnel plot asymmetry test (P = .191).

CONCLUSIONS

CT-based spiculated sign can achieve moderate diagnostic performance as a means of differentiating between malignant and benign LNs.

Predicting lung nodules malignancy

BACKGROUND

It is critical to developing an accurate method for differentiating between malignant and benign solitary pulmonary nodules. This study aimed was to establish a predicting model of lung nodules malignancy in a real-world setting.

METHODS

The authors retrospectively analysed the clinical and computed tomography (CT) data of 121 patients with lung nodules, submitted to percutaneous CT-guided transthoracic biopsy, between 2014 and 2015. Multiple logistic regression was used to screen independent predictors for malignancy and to establish a clinical prediction model to evaluate the probability of malignancy.

RESULTS

From a total of 121 patients, 75 (62%) were men and with a mean age of 64.7 years old. Multivariate logistic regression analysis identified six independent predictors of malignancy: age, gender, smoking status, current extra-pulmonary cancer, air bronchogram and nodule size (p<0.05). The area under the curve (AUC) was 0.8573.

CONCLUSIONS

The prediction model established in this study can be used to assess the probability of malignancy in the Portuguese population, thereby providing help for the diagnosis of lung nodules and the selection of follow-up interventions.

Computed tomography-guided biopsy of small lung nodules: diagnostic accuracy and analysis for true negatives

Objective

We evaluated the diagnostic accuracy of computed tomography (CT)-guided transthoracic core needle biopsy (TCNB) for small (≤20-mm) lung nodules and identified predictive factors for true negatives among benign biopsy results.

Methods

From March 2010 to June 2015, 222 patients with small lung nodules underwent CT-guided TCNB. We retrospectively analysed data regarding technical success, diagnostic accuracy, and predictors of true negatives.

Results

The technical success rate was 100%. The TCNB results of the 222 lung nodules included malignancy (n = 136), suspected malignancy (n = 8), specific benign lesion (n = 17), and nonspecific benign lesion (n = 61). The final diagnosis of 222 lung nodules included malignant (n = 160), benign (n = 60), and nondiagnostic lesions (n = 2). The sensitivity, specificity, and overall diagnostic accuracy of CT-guided TCNB for small lung nodules were 90.0%, 100%, and 92.7%, respectively. Pneumothorax and haemoptysis occurred in 23 and 41 patients, respectively. Based on the Cox regression analysis, the significant independent predictive factor for true negatives was a biopsy result of chronic inflammation with fibroplasia.

Conclusions

CT-guided TCNB offers high diagnostic accuracy for small lung nodules, and a biopsy result of chronic inflammation with fibroplasia can predict a true-negative result.

Implementation planning for lung cancer screening in China

Lung cancer is the leading cause of cancer-related deaths in China, with over 690 000 lung cancer deaths estimated in 2018. The mortality has increased about five-fold from the mid-1970s to the 2000s. Lung cancer low-dose computerized tomography (LDCT) screening in smokers was shown to improve survival in the US National Lung Screening Trial, and more recently in the European NELSON trial. However, although the predominant risk factor, smoking contributes to a lower fraction of lung cancers in China than in the UK and USA. Therefore, it is necessary to establish Chinese-specific screening strategies. There have been 23 associated programmes completed or still ongoing in China since the 1980s, mainly after 2000; and one has recently been planned. Generally, their entry criteria are not smoking-stringent. Most of the Chinese programmes have reported preliminary results only, which demonstrated a different high-risk subpopulation of lung cancer in China. Evidence concerning LDCT screening implementation is based on results of randomized controlled trials outside China. LDCT screening programmes combining tobacco control would produce more benefits. Population recruitment (e.g. risk-based selection), screening protocol, nodule management and cost-effectiveness are discussed in detail. In China, the high-risk subpopulation eligible for lung cancer screening has not as yet been confirmed, as all the risk parameters have not as yet been determined. Although evidence on best practice for implementation of lung cancer screening has been accumulating in other countries, further research in China is urgently required, as China is now facing a lung cancer epidemic.

[Establishment and Verification of A Novel Predictive Model of Malignancy for Non-solid Pulmonary Nodules]

背景与目的

数学预测模型是判断肺小结节恶性概率的有效工具。伴随肺癌流行病学趋势的改变，以非实性肺小结节为影像学表现的早期肺癌检出率逐年升高，准确鉴别并及时治疗干预可有效改善预后。本研究旨在专门针对非实性肺小结节构建新型恶性概率预测模型，为有创诊疗提供客观依据，并尽量避免不必要的侵袭性操作及其可能造成的严重后果。

方法

回顾性分析自2013年1月-2018年4月，单中心经穿刺活检或手术切除获得明确病理诊断的362例非实性肺小结节病例资料，包括临床基本资料、血清肿瘤标记物和影像学特征等。病例分两组，应用建模组数据做单因素分析和二分类Logistic回归，判定独立危险因素，建立预测模型；应用验证组数据验证模型预测价值并与其他模型比较。

结果

362例非实性肺小结节病例中，313例（86.5%）确诊为非典型腺瘤样增生（atypical adenomatous hyperplasia, AAH）/原位腺癌（adenocarcinoma in situ, AIS）、微浸润腺癌（minimally invasive adenocarcinoma, MIA）或浸润性腺癌，49例诊断为良性病变。年龄、血清肿瘤标记物癌胚抗原（carcino-embryonic antigen, CEA）和Cyfra21-1、肿瘤实性成分比值（consolidation tumor ratio, CTR）、分叶征和钙化被确定为独立危险因素。模型受试者工作曲线下面积为0.894。预测灵敏度为87.6%，特异度为69.7%，阳性预测94.8%，阴性预测值为46.9%。经验证模型预测价值显著优于VA、Brock和GMUFH模型。

结论

本研究建立的新型非实性肺小结节恶性概率预测模型具备较高的诊断灵敏度和阳性预测值。经初步验证，其预测价值优于传统模型。未来经大样本验证后，可用作高危非实性肺小结节活检或手术切除前的初筛方法，具备临床应用价值。

The value of the air bronchogram sign on CT image in the identification of different solitary pulmonary consolidation lesions

The aim of the present study is to investigate the value of air bronchogram sign on computed tomography (CT) image in the differential diagnosis of solitary pulmonary consolidation lesions (SPLs).A total of 105 patients (including 39 cases of lung cancer, 43 cases of tuberculosis, and 23 cases of pneumonia) with SPLs were evaluated for the CT features of air bronchogram sign in this retrospective study. The shape and lumen of the bronchi with air bronchogram sign, the length of the involved bronchus with air bronchogram sign, the length of lesion on the same plane and direction, and the ratio between the length of the involved bronchus and that of the lesion were evaluated.In total, there were 172 segmental and subsegmental bronchi involved. There were 62 segmental and subsegmental bronchi involved among 39 lung cancer cases, 77 segmental and subsegmental bronchi involved among 43 tuberculosis cases, and 33 segmental and subsegmental bronchi involved among 23 pneumonia cases. The shape of the bronchi with air bronchogram sign was significantly different among lung cancer, tuberculosis, and pneumonia (P < .05). The lumen of the bronchi with air bronchogram sign was also significantly different among the 3 SPLs (P < .05). The length of the involved bronchus with air bronchogram sign and the ratio between the length of the involved bronchus and that of the lesion were significantly different between lung cancer and tuberculosis (P < .05), or between lung cancer and pneumonia (P < .05), but not between tuberculosis and pneumonia (P > .05). No significant difference was found in the length of lesion among the 3 SPLs (P > .05).The shape and lumen of the bronchi with air bronchogram sign can be used to distinguish lung cancer, tuberculosis, and pneumonia. The length of the involved bronchus with air bronchogram sign and the ratio between the length of the involved bronchus and that of the lesion can be used to distinguish lung cancer from tuberculosis and pneumonia.

Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules

Pulmonary nodule is one of the important lesions of lung cancer, mainly divided into two categories of solid nodules and ground glass nodules. The improvement of diagnosis of lung cancer has significant clinical significance, which could be realized by machine learning techniques. At present, there have been a lot of researches focusing on solid nodules. But the research on ground glass nodules started late, and lacked research results. This paper summarizes the research progress of the method of intelligent diagnosis for pulmonary nodules since 2014. It is described in details from four aspects: nodular signs, data analysis methods, prediction models and system evaluation. This paper aims to provide the research material for researchers of the clinical diagnosis and intelligent analysis of lung cancer, and further improve the precision of pulmonary ground glass nodule diagnosis.

Computed tomography-guided percutaneous cutting needle biopsy for small (≤ 20 mm) lung nodules

The goal of this study is to determine the feasibility, diagnostic accuracy, and risk factor of complications of computed tomography (CT)-guided percutaneous cutting needle biopsy (PCNB) for small lung nodules.From January 2014 to May 2015, 141 patients with small lung nodule were performed with CT-guided PCNB procedure. Data on technical success, diagnostic accuracy, and complication were collected and analyzed.Technical success of CT-guided PCNB for small lung nodules was 100%. A total of 141 nodules were punctured. The mean time of the procedure was 15.7 ± 4.3 minutes. The PCNB results included malignancy (n = 79), suspected malignancy (n = 6), specific benign lesion (n = 8), nonspecific benign lesion (n = 47), and invalid diagnosis (n = 1). The final diagnosis of the 141 nodules included malignancy (n = 90), benign (n = 37), and nondiagnostic lesion (n = 14). The nondiagnostic nodules were not included for calculating the diagnostic accuracy. The sensitivity, specificity, and overall diagnostic accuracy of CT-guided PCNB for small lung nodule were 94.4% (85/90), 100% (37/37), and 96.1% (122/127), respectively. Pneumothorax and lung hemorrhage (≥ grade 2) occurred in 17 (12.1%) and 22 (15.6%) patients, respectively. Based on the univariate and multivariate logistic analyses, the risk factors of pneumothorax included nonprone position (P = .019) and longer procedure time (P = .018). The independent risk factor of lung hemorrhage (≥ grade 2) was deeper lesion distance from pleura along needle path (P = .024).This study demonstrates that CT-guided PCNB can provide a high diagnostic accuracy for small lung nodule with acceptable complications.

Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions

Purpose of review

Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice.

Recent findings

Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening.

Summary

Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.

[Establishment of A Clinical Prediction Model of Solid Solitary Pulmonary Nodules]

BACKGROUND

The solitary pulmonary nodule (SPN) is a common and challenging clinical problem, especially solid SPN. The object of this study was to explore the predictive factors of SPN appearing as pure solid with malignance and to establish a clinical prediction model of solid SPNs.

METHODS

We had a retrospective review of 317 solid SPNs (group A) having a final diagnosis in the department of thoracic surgery, Shanghai Chest Hospital from January 2015 to December 2015, and analyzed their clinical data and computed tomography (CT) images, including age, gender, smoking history, family history of cancer, previous cancer history, diameter of nodule, nodule location (upper lobe or non-upper lobe, left or right), clear border, smooth margin, lobulation, spiculation, vascular convergence, pleural retraction sign, air bronchogram sign, vocule sign, cavity and calcification. By using univariate and multivariate analysis, we found the independent predictors of malignancy of solid SPNs and subsequently established a clinical prediction model. Then, another 139 solid SPNs with a final diagnosis were chosen in department of Cardiothoracic Surgery, Affiliated Zhoushan Hospital of Wenzhou Medical University as group B, and used to verify the accuracy of the prediction model. Receiver-operating characteristic (ROC) curves were constructed using the prediction model.

RESULTS

Multivariate Logistic regression analysis was used to identify eight clinical characteristics (age, family history of cancer, previous cancer history, clear border, lobulation, spiculation, air bronchogram sign, calcification) as independent predictors of malignancy of in solid SPNs. The area under the ROC curve for our model (0.922; 95%CI: 0.865-0.961). In our model, diagnosis accuration rate was 84.89%. Sensitivity was 90.41%, and specificity was 78.79%, and positive predictive value was 80.50%, and negative predictive value was 88.14%.

CONCLUSIONS

Our prediction model could accurately identify malignancy in patients with solid SPNs, thereby it can provide help for diagnosis of solid SPNs.

Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule

The Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule (SPN) remains unclear. 240 patients with SPNs (malignant, n = 180; benign, n = 60) underwent non-contrast CT (NECT) and contrast-enhanced CT (CECT) which were reconstructed with different slice thickness and convolution kernel. 150 radiomics features were extracted separately from each set of CT and diagnostic performance of each feature were assessed. After feature selection and radiomics signature construction, diagnostic performance of radiomics signature for discriminating benign and malignant SPN was also assessed with respect to the discrimination and classification and compared with net reclassification improvement (NRI). Our results showed NECT-based radiomics signature demonstrated better discrimination and classification capability than CECT in both primary (AUC: 0.862 vs. 0.829, p = 0.032; NRI = 0.578) and validation cohort (AUC: 0.750 vs. 0.735, p = 0.014; NRI = 0.023). Thin-slice (1.25 mm) CT-based radiomics signature had better diagnostic performance than thick-slice CT (5 mm) in both primary (AUC: 0.862 vs. 0.785, p = 0.015; NRI = 0.867) and validation cohort (AUC: 0.750 vs. 0.725, p = 0.025; NRI = 0.467). Standard convolution kernel-based radiomics signature had better diagnostic performance than lung convolution kernel-based CT in both primary (AUC: 0.785 vs. 0.770, p = 0.015; NRI = 0.156) and validation cohort (AUC: 0.725 vs.0.686, p = 0.039; NRI = 0.467). Therefore, this study indicates that the contrast-enhancement, reconstruction slice thickness and convolution kernel can affect the diagnostic performance of radiomics signature in SPN, of which non-contrast, thin-slice and standard convolution kernel-based CT is more informative.

Differential diagnosis of solitary pulmonary nodules with dual-source spiral computed tomography

The aim of the present study was to analyze the value of applying dual-source 64-layer spiral computed tomography (CT) in the differential diagnosis of solitary pulmonary nodules (SPNs). Mediastinal windows from 45 cases were selected to study SPNs (maximum diameter, ≤3 cm), and the pathological nature of lesions was determined by clinical and pathological diagnosis. Conventional 64-layer spiral CT scanning, local enhancement and 3D recombination technologies were used to determine the occurrence rate, lesion diameter, degree of enhancement, lobular sign, spicule sign, pleural indentation sign, vessel convergence sign and bronchus sign. The final diagnoses indicated 34 cases of malignant SPNs (75.6%) and 11 benign cases (24.4%). When the nodule diameter in the malignant group was compared with that of the benign group, the difference was not statistically significant (P>0.05). Nodules in the malignant group showed inhomogeneous enhancement while nodules in the benign group showed homogeneous enhancement. The enhanced CT values in the malignant group were higher than those in the benign group, and the difference was statistically significant (P<0.05). The proportion of nodules with lobular sign in the malignant group was significantly higher than that in the benign group (P<0.05). The proportion of nodules with calcification, vessel convergence sign and bronchus sign in the malignant group were significantly higher than those in the benign group, and the differences were statistically significant (P<0.05). A comparison of vacuole sign, pleural indentation sign, spiculate protuberance and fat occurrence between the two groups yielded no statistically significant differences (P>0.05). The sensitivity of CT enhancement was 85.6%, specificity was 79.6%, positive predicated value was 92.3%, and the negative predicted value was 85.2%. In conclusion, SPNs diagnosed by CT enhancement manifested with enhancement degree, lobular sign, calcification, vessel convergence sign and bronchus sign with high diagnostic accuracy.