Solitary pulmonary nodules: clinical prediction model versus physicians

Nomogram for predicting invasive lung adenocarcinoma in small solitary pulmonary nodules

Background

This study aimed to construct a clinical prediction model and nomogram to differentiate invasive from non-invasive pulmonary adenocarcinoma in solitary pulmonary nodules (SPNs).

Method

We analyzed computed tomography and clinical features as well as preoperative biomarkers in 1,106 patients with SPN who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University between January 2020 and December 2021. Clinical parameters and imaging characteristics were analyzed using univariate and multivariate logistic regression analyses. Predictive models and nomograms were developed and their recognition abilities were evaluated using receiver operating characteristic (ROC) curves. The clinical utility of the nomogram was evaluated using decision curve analysis (DCA).

Result

The final regression analysis selected age, carcinoembryonic antigen, bronchus sign, lobulation, pleural adhesion, maximum diameter, and the consolidation-to-tumor ratio as associated factors. The areas under the ROC curves were 0.844 (95% confidence interval [CI], 0.817-0.871) and 0.812 (95% CI, 0.766-0.857) for patients in the training and validation cohorts, respectively. The predictive model calibration curve revealed good calibration for both cohorts. The DCA results confirmed that the clinical prediction model was useful in clinical practice. Bias-corrected C-indices for the training and validation cohorts were 0.844 and 0.814, respectively.

Conclusion

Our predictive model and nomogram might be useful for guiding clinical decisions regarding personalized surgical intervention and treatment options.

The use of diagnostic complex robotic-assisted segmentectomy in the management of incidental and screen-detected pulmonary nodules

OBJECTIVES

Robotic-assisted thoracoscopic surgery (RATS) facilitates complex pulmonary segmentectomy which offers one-stage diagnostic and therapeutic management of small pulmonary nodules. We aimed to explore the potential advantages of a faster, simplified pathway and earlier diagnosis against the disadvantages of unnecessary morbidity in benign cases.

METHODS

In an observational study, patients with small, solitary pulmonary nodules deemed suspicious of malignancy by a multidisciplinary team were offered surgery without a pre or intraoperative biopsy. We report our initial experience with RATS complex segmentectomy (using >1 parenchymal staple line) to preserve as much functioning lung tissue as possible.

RESULTS

Over a 4-year period, 245 RATS complex segmentectomies were performed; 140 right: 105 left. A median of 2 (1-4) segments was removed. There was no in-hospital mortality and no requirement for postoperative ventilation. Complications were reported in 63 (25.7%) cases, of which 36 (57.1%) were hospital-acquired pneumonia. A malignant diagnosis was found in 198 (81%) patients and a benign diagnosis in 47 (19%). The malignant diagnoses included: adenocarcinoma in 136, squamous carcinoma in 31 and carcinoid tumour in 15. The most frequent benign diagnosis was granulomatous inflammation in 18 cases.

CONCLUSIONS

RATS complex segmentectomy offers a precise, safe and effective one-stop therapeutic biopsy in incidental and screen-detected pulmonary nodules.

18F-FDG PET/CT based model for predicting malignancy in pulmonary nodules: a meta-analysis

BACKGROUND

Several studies to date have reported on the development of positron emission tomography (PET)/computed tomography (CT)-based models intended to effectively distinguish between benign and malignant pulmonary nodules (PNs). This meta-analysis was designed with the goal of clarifying the utility of these PET/CT-based conventional parameter models as diagnostic tools in the context of the differential diagnosis of PNs.

METHODS

Relevant studies published through September 2023 were identified by searching the Web of Science, PubMed, and Wanfang databases, after which Stata v 12.0 was used to conduct pooled analyses of the resultant data.

RESULTS

This meta-analysis included a total of 13 retrospective studies that analyzed 1,731 and 693 malignant and benign PNs, respectively. The respective pooled sensitivity, specificity, PLR, and NLR values for the PET/CT-based studies developed in these models were 88% (95%CI: 0.86-0.91), 78% (95%CI: 0.71-0.85), 4.10 (95%CI: 2.98-5.64), and 0.15 (95%CI: 0.12-0.19). Of these endpoints, the pooled analyses of model sensitivity (I2 = 69.25%), specificity (I2 = 78.44%), PLR (I2 = 71.42%), and NLR (I2 = 67.18%) were all subject to significant heterogeneity. The overall area under the curve value (AUC) value for these models was 0.91 (95%CI: 0.88-0.93). When differential diagnosis was instead performed based on PET results only, the corresponding pooled sensitivity, specificity, PLR, and NLR values were 92% (95%CI: 0.85-0.96), 51% (95%CI: 0.37-0.66), 1.89 (95%CI: 1.36-2.62), and 0.16 (95%CI: 0.07-0.35), with all four being subject to significant heterogeneity (I2 = 88.08%, 82.63%, 80.19%, and 86.38%). The AUC for these pooled analyses was 0.82 (95%CI: 0.79-0.85).

CONCLUSIONS

These results suggest that PET/CT-based models may offer diagnostic performance superior to that of PET results alone when distinguishing between benign and malignant PNs.

A clinical-radiological predictive model for solitary pulmonary nodules and the relationship between radiological features and pathological subtype

AIM

To develop a clinical-radiological model to predict the malignancy of solitary pulmonary nodules (SPNs) and to evaluate the accuracy of chest computed tomography imaging characteristics of SPN in diagnosing pathological type.

MATERIALS AND METHODS

The predictive model was developed using a retrospective cohort of 601 SPN patients (Group A) between July 2015 and July 2020. The established model was tested using a second retrospective cohort of 124 patients between August 2020 and August 2021 (Group B). The radiological characteristics of all adenocarcinomas in two groups were analysed to determine the correlation between radiological and pathological characteristics.

RESULTS

Malignant nodules were found in 78.87% of cases and benign in 21.13%. Two clinical characteristics (age and gender) and four radiological characteristics (calcification, vascular convergence, pleural retraction sign, and density) were identified as independent predictors of malignancy in patients with SPN using logistic regression analysis. The area under the receiver operating characteristic curve (0.748) of the present model was greater than the other two reported models. Diameter, spiculation, lobulation, vascular convergence, and pleural retraction signs differed significantly among pre-invasive lesions, minimally invasive adenocarcinoma, and invasive adenocarcinoma. Only diameter and density were significantly different among invasive adenocarcinoma subtypes.

CONCLUSIONS

Older age, male gender, no calcification, vascular convergence, pleural contraction sign, and lower density were independent malignancy predictors of SPNs. Furthermore, the pathological classification can be clarified based on the radiological characteristics of SPN, providing a new option for the prevention and treatment of early lung cancer.

Impact of Voxel Normalization on a Machine Learning-Based Method: A Study on Pulmonary Nodule Malignancy Diagnosis Using Low-Dose Computed Tomography (LDCT)

Lung cancer (LC) stands as the foremost cause of cancer-related fatality rates worldwide. Early diagnosis significantly enhances patient survival rate. Nowadays, low-dose computed tomography (LDCT) is widely employed on the chest as a tool for large-scale lung cancer screening. Nonetheless, a large amount of chest radiographs creates an onerous burden for radiologists. Some computer-aided diagnostic (CAD) tools can provide insight to the use of medical images for diagnosis and can augment diagnostic speed. However, due to the variation in the parameter settings across different patients, substantial discrepancies in image voxels persist. We found that different voxel sizes can create a compromise between model generalization and diagnostic efficacy. This study investigates the performance disparities of diagnostic models trained on original images and LDCT images reconstructed to different voxel sizes while making isotropic. We examined the ability of our method to differentiate between benign and malignant nodules. Using 11 features, a support vector machine (SVM) was trained on LDCT images using an isotropic voxel with a side length of 1.5 mm for 225 patients in-house. The result yields a favorable model performance with an accuracy of 0.9596 and an area under the receiver operating characteristic curve (ROC/AUC) of 0.9855. In addition, to furnish CAD tools for clinical application, future research including LDCT images from multi-centers is encouraged.

Comparison of long-term outcomes of stereotactic body radiotherapy (SBRT) via Helical tomotherapy for early-stage lung cancer with or without pathological proof

BACKGROUND

Stereotactic body radio therapy (SBRT) has emerged as a standard treatment option for nonsurgical candidates with early-stage non-small cell lung cancer (NSCLC). Pathological proof is sometimes difficult to obtain in patients with solitary pulmonary nodules (SPNs). We aimed to compare the clinical outcomes of stereotactic body radiotherapy via helical tomotherapy (HT-SBRT) for early-stage lung cancer patients with or without a pathological diagnosis.

METHODS

Between June 2011 and December 2016, we treated 119 lung cancer patients with HT-SBRT, including 55 with a clinical diagnosis and 64 with a pathological diagnosis. Survival outcomes, including local control (LC), progression-free survival (PFS), cancer-specific survival (CSS), and overall survival (OS), were compared between two cohorts with and without a pathological diagnosis.

RESULTS

The median follow-up for the whole group was 69 months. Patients with a clinical diagnosis were significantly older (p = 0.002). No significant differences were observed between the clinical and pathological diagnosis cohorts in terms of the long-term outcome, with 5-year LC, PFS, CSS, and OS of 87% versus 83% (p = 0.58), 48% versus 45% (p = 0.82), 87% versus 84% (p = 0.65), and 60% versus 63% (p = 0.79), respectively. Recurrence patterns and toxicity were also similar.

CONCLUSIONS

Empiric SBRT appears to be a safe and effective treatment option in a multidisciplinary setting when patients with SPNs highly suggestive of malignancy are unable/refuse to obtain a definitive pathological diagnosis.

Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges?

As most lung cancer (LC) cases are still detected at advanced and incurable stages, there are increasing efforts to foster detection at earlier stages by low dose computed tomography (LDCT) based LC screening. In this scoping review, we describe current advances in candidate selection for screening (selection phase), technical aspects (screening), and probability evaluation of malignancy of CT-detected pulmonary nodules (PN management). Literature was non-systematically assessed and reviewed for suitability by the authors. For the selection phase, we describe current eligibility criteria for screening, along with their limitations and potential refinements through advanced clinical scores and biomarker assessments. For LC screening, we discuss how the accuracy of computerized tomography (CT) scan reading might be augmented by IT tools, helping radiologists to cope with increasing workloads. For PN management, we evaluate the precision of follow-up scans by semi-automatic volume measurements of CT-detected PN. Moreover, we present an integrative approach to evaluate the probability of PN malignancy to enable safe decisions on further management. As a clear limitation, additional validation studies are required for most innovative diagnostic approaches presented in this article, but the integration of clinical risk models, current imaging techniques, and advancing biomarker research has the potential to improve the LC screening performance generally.

Prevalence and clinical characteristics of malignant lung nodules in tuberculosis endemic area in a single tertiary centre

BACKGROUND

Lung nodule management remains a challenge to clinicians, especially in endemic tuberculosis areas. Different guidelines are available with various recommendations; however, the suitability of these guidelines for the Asian population is still unclear. Our study described the prevalence of malignant lung nodules among nodules measuring 2-30 mm, the demographic and characteristics of lung nodules between benign and malignant groups, and the clinician's clinical practice in managing lung nodules.

METHOD

Retrospective review of lung nodules from the computed tomography archiving and communication system (PACS) database and clinical data from January 2019 to January 2022. The data was analysed by using chi square, mann whitney test and simple logistic regression.

RESULTS

There were 288 nodules measuring 2-30 mm identified; 49 nodules underwent biopsy. Twenty-seven (55%) biopsied nodules were malignant, (prevalence of 9.4%). Among the malignant lung nodules, 74% were adenocarcinoma (n = 20). The commonest benign nodules were granuloma n = 12 (55%). In nodules > 8 mm, the median age of malignant and benign was 72 ± 12 years and 66 ± 16 years, respectively (p = 0.024). There was a significant association of benign nodules (> 8 mm) in subjects with previous or concurrent tuberculosis (p = 0.008). Benign nodules are also associated with nodule size ≤ 8 mm, without spiculation (p < 0.001) and absence of emphysema (p = 0.007). The nodule size and the presence of spiculation are factors to make the clinicians proceed with tissue biopsy. Spiculated nodules and increased nodule size had 11 and 13 times higher chances of undergoing biopsy respectively (p < 0.001).) Previous history of tuberculosis had a 0.874 reduced risk of progression to malignant lung nodules (p = 0.013). These findings implied that these three factors are important risk factors for malignant lung nodules. There was no mortality association between benign and malignant. Using Brock's probability of malignancy, nodules ≤ 8 mm had a low probability of malignancy.

CONCLUSION

The prevalence of malignant lung nodules in our centre was comparatively lower than non-Asian countries. Older age, the presence of emphysema, and spiculation are associated with malignancy. Clinical judgment is of utmost importance in managing these patients. Fleishner guideline is still being used as a reference by our clinician.

Controversies and challenges in lung cancer screening

Two large randomized controlled trials have shown mortality benefit from lung cancer screening (LCS) in high-risk groups. Updated guidelines by the United State Preventative Service Task Force in 2020 will allow for inclusion of more patients who are at high risk of developing lung cancer and benefit from screening. As medical clinics and lung cancer screening programs around the country continue to work on perfecting the LCS workflow, it is important to understand some controversial issues surrounding LCS that should be addressed. In this article, we identify some of these issues, including false positive rates of low-dose CT, over-diagnosis, cost expenditure, LCS disparities in minorities, and utility of biomarkers. We hope to provide clarity, potential solutions, and future directions on how to address these controversies.

Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis

BACKGROUND

To date, multiple predictive models have been developed with the goal of reliably differentiating between solitary pulmonary nodules (SPNs) that are malignant and those that are benign. The present meta-analysis was conducted to assess the diagnostic utility of these predictive models in the context of SPN differential diagnosis.

METHODS

The PubMed, Embase, Cochrane Library, CNKI, Wanfang, and VIP databases were searched for relevant studies published through August 31, 2021. Pooled data analyses were conducted using Stata v12.0.

RESULTS

In total, 20 retrospective studies that included 5171 SPNs (malignant/benign: 3662/1509) were incorporated into this meta-analysis. Respective pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic score values were 88% (95CI%: 0.84-0.91), 78% (95CI%: 0.74-0.80), 3.91 (95CI%: 3.42-4.46), 0.16 (95CI%: 0.12-0.21), and 3.21 (95CI%: 2.87-3.55), with an area under the summary receiver operating characteristic curve value of 86% (95CI%: 0.83-0.89). Significant heterogeneity among studies was detected with respect to sensitivity (I² = 89.07%), NLR (I² = 87.29%), and diagnostic score (I² = 72.28%). In a meta-regression analysis, sensitivity was found to be impacted by the standard reference in a given study (surgery and biopsy vs. surgery only, P = 0.02), while specificity was impacted by whether studies were blinded (yes vs. unclear, P = 0.01). Sensitivity values were higher when surgery and biopsy samples were used as a standard reference, while unclear blinding status was associated with increased specificity. No significant evidence of publication bias was detected for the present meta-analysis (P = 0.539).

CONCLUSIONS

The results of this meta-analysis demonstrate that predictive models can offer significant diagnostic utility when establishing whether SPNs are malignant or benign.

Outcomes of Incidental Lung Nodules With Structured Recommendations and Electronic Tracking

OBJECTIVE

To evaluate the impact of structured recommendations on follow-up completion for incidental lung nodules (ILNs).

METHODS

Patients with ILNs before and after implementation of structured Fleischner recommendations and electronic tracking were sampled randomly. The cohorts were compared for imaging follow-up. Multivariable logistic regression was used to assess appropriate follow-up and loss to follow-up, with independent variables including use of structured recommendations or tracking, age, gender, race, ethnicity, setting of the index test (inpatient, outpatient, emergency department), smoking history, and nodule features.

RESULTS

In all, 1,301 patients met final inclusion criteria, including 255 patients before and 1,046 patients after structured recommendations or tracking. Baseline differences were found in the pre- and postintervention groups, with smaller ILNs and younger age after implementing structured recommendations. Comparing pre- versus postintervention outcomes, 40.0% (100 of 250) versus 29.5% (309 of 1,046) of patients had no follow-up despite Fleischner indications for imaging (P = .002), and among the remaining patients, 56.6% (82 of 145) versus 75.0% (553 of 737) followed up on time (P < .001). Delayed follow-up was more frequent before intervention. Differences postintervention were mostly accounted for by nodules ≤ 8 mm in the outpatient setting (P < .001). In multivariable analysis, younger age, White race, outpatient setting, and larger nodule size showed significant association with appropriate follow-up completion (P < .015), but structured recommendations did not. Similar results applied for loss to follow-up.

DISCUSSION

Consistent use of structured reporting is likely key to mitigate selection bias when benchmarking rates of appropriate follow-up of ILN. Emergency department patients and inpatients are at high risk of missed or delayed follow-up despite structured recommendations.

Undiagnosed solitary caseating granulomas: Is lung resection surgery a feasible method for diagnosis and treatment?

BACKGROUND

In many patients, low-dose computed tomography (CT) screening for lung cancer reveals asymptomatic pulmonary nodules. Lung resection surgery may be indicated in these patients; however, distinguishing malignancies from benign lesions preoperatively can be challenging.

METHODS

From 2013 to 2018, 4181 patients undergoing surgery for pulmonary nodules were reviewed at National Taiwan University Hospital, and 837 were diagnosed with benign pathologies. Only patients with pathological diagnosis as caseating granulomatous inflammation were included, sixty-nine patients were then analyzed for preoperative clinical and imaging characteristics, surgical methods and complications, pathogens, medical treatment and outcomes. Mycobacterial evidence was obtained from the culture of respiratory or surgical specimen.

RESULTS

Overall, 68% of the patients were asymptomatic before surgery. More than half of the nodules were in the upper lobes, and all patients underwent video-assisted thoracoscopic surgery (VATS). Some patients (14.5%) developed grade I complications, and the mean postoperative hospital stay was 4 days. The final pathology reports of 20% benign entities postoperatively, and caseating granulomatous inflammation accounted for a significant part. MTB and NTM were cultured from one-fourth of the patients respectively. All patients with confirmed MTB infection received antimycobacterial treatment, while the medical treatment in NTM-infected patients was decided by the infectious disease specialists. The mean follow-up period was 736 days, and no recurrence was found.

CONCLUSION

Lung resection surgery is an aggressive but safe and feasible method for diagnosing MTB- or NTM-associated pulmonary nodules, and, potentially, an effective therapeutic tool for patients with undiagnosed MTB- or NTM-associated pulmonary nodules.

Clinical-radiological predictive model in differential diagnosis of small (≤ 20 mm) solitary pulmonary nodules

BACKGROUND

There is a lack of clinical-radiological predictive models for the small (≤ 20 mm) solitary pulmonary nodules (SPNs). We aim to establish a clinical-radiological predictive model for differentiating malignant and benign small SPNs.

MATERIALS AND METHODS

Between January 2013 and December 2018, a retrospective cohort of 250 patients with small SPNs was used to construct the predictive model. A second retrospective cohort of 101 patients treated between January 2019 and December 2020 was used to independently test the model. The model was also compared to two other models that had previously been identified.

RESULTS

In the training group, 250 patients with small SPNs including 156 (62.4%) malignant SPNs and 94 (37.6%) benign SPNs patients were included. Multivariate logistic regression analysis indicated that older age, pleural retraction sign, CT bronchus sign, and higher CEA level were the risk factors of malignant small SPNs. The predictive model was established as: X = - 10.111 + [0.129 × age (y)] + [1.214 × pleural retraction sign (present = 1; no present = 0)] + [0.985 × CT bronchus sign (present = 1; no present = 0)] + [0.21 × CEA level (ug/L)]. Our model had a significantly higher region under the receiver operating characteristic (ROC) curve (0.870; 50% CI: 0.828-0.913) than the other two models.

CONCLUSIONS

We established and validated a predictive model for estimating the pre-test probability of malignant small SPNs, that can help physicians to choose and interpret the outcomes of subsequent diagnostic tests.

A simple assessment of lung nodule location for reduction in unnecessary invasive procedures

Background

CT screening for lung cancer results in a significant mortality reduction but is complicated by invasive procedures performed for evaluation of the many detected benign nodules. The purpose of this study was to evaluate measures of nodule location within the lung as predictors of malignancy.

Methods

We analyzed images and data from 3,483 participants in the National Lung Screening Trial (NLST). All nodules (4–20 mm) were characterized by 3D geospatial location using a Cartesian coordinate system and evaluated in logistic regression analysis. Model development and probability cutpoint selection was performed in the NLST testing set. The Geospatial test was then validated in the NLST testing set, and subsequently replicated in a new cohort of 147 participants from The Detection of Early Lung Cancer Among Military Personnel (DECAMP) Consortium.

Results

The Geospatial Test, consisting of the superior-inferior distance (Z distance), nodule diameter, and radial distance (carina to nodule) performed well in both the NLST validation set (AUC 0.85) and the DECAMP replication cohort (AUC 0.75). A negative Geospatial Test resulted in a less than 2% risk of cancer across all nodule diameters. The Geospatial Test correctly reclassified 19.7% of indeterminate nodules with a diameter over 6mm as benign, while only incorrectly classifying 1% of cancerous nodules as benign. In contrast, the parsimonious Brock Model applied to the same group of nodules correctly reclassified 64.5% of indeterminate nodules as benign but resulted in misclassification of a cancer as benign in 18.2% of the cases. Applying the Geospatial test would result in reducing invasive procedures performed for benign lesions by 11.3% with a low rate of misclassification (1.3%). In contrast, the Brock model applied to the same group of patients results in decreasing invasive procedures for benign lesion by 39.0% but misclassifying 21.1% of cancers as benign.

Conclusions

Utilizing information about geospatial location within the lung improves risk assessment for indeterminate lung nodules and may reduce unnecessary procedures.

Trial Registration

NCT00047385, NCT01785342.

Is the Yedikule-solitary pulmonary nodule malignancy risk score sufficient to predict malignancy? An internal validation study

OBJECTIVES

We aimed to develop a malignancy risk score model for solitary pulmonary nodules (SPNs) using the demographic, radiological and clinical characteristics of patients in our centre. The model was then internally validated for malignancy risk estimation.

METHODS

A total of 270 consecutive patients who underwent surgery for SPN between June 2017 and May 2019 were retrospectively analysed. Using the receiver operating characteristic curve analysis, cut-off values were determined for radiological tumour diameter, maximum standardized uptake value and the Brock University probability of malignancy (BU-PM) model. The Yedikule-SPN malignancy risk model was developed using these cut-off values and demographic, radiological and clinical criteria in the first 180 patients (study cohort) and internally validated with the next 90 patients (validation cohort). The Yedikule-SPN model was then compared with the BU-PM model in terms of malignancy prediction.

RESULTS

Malignancy was reported in 171 patients (63.3%). Maximum standardized uptake value and BU-PM scores were sufficient to predict malignancy (P < 0.001 for both), while the effectiveness of nodule size determined on thoracic computed tomography did not reach statistical significance (P = 0.09). When the Yedikule-SPN model developed with the study cohort was applied to the validation cohort, it significantly predicted malignancy (area under the receiver operating characteristic curve: 0.883, 95% confidence interval: 0.827-0.957, P < 0.001). Comparison of patients in the validation group with Yedikule-SPN scores above (n = 53) and below (n = 37) the cut-off value of 65.75 showed that the malignancy rate was significantly higher among patients with Yedikule-SPN score over 65.75 (86.8% vs 21.6%, P < 0.001, odds ratio = 23.821, 95% confidence interval: 7.805-72.701). When compared with the BU-PM model in all patients, the Yedikule-SPN model tended to be a better predictor of malignancy (P = 0.06).

CONCLUSIONS

The internally validated Yedikule-SPN model is also a good predictor of the malignancy of SPN(s). Prospective and multicentre external validation studies with large patients' cohorts are needed.

A novel clinical model for predicting malignancy of solitary pulmonary nodules: a multicenter study in chinese population

Background

This study aimed to establish and validate a novel clinical model to differentiate between benign and malignant solitary pulmonary nodules (SPNs).

Methods

Records from 295 patients with SPNs in Sun Yat-sen University Cancer Center were retrospectively reviewed. The novel prediction model was established using LASSO logistic regression analysis by integrating clinical features, radiologic characteristics and laboratory test data, the calibration of model was analyzed using the Hosmer-Lemeshow test (HL test). Subsequently, the model was compared with PKUPH, Shanghai and Mayo models using receiver-operating characteristics curve (ROC), decision curve analysis (DCA), net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI) with the same data. Other 101 SPNs patients in Henan Tumor Hospital were used for external validation cohort.

Results

A total of 11 variables were screened out and then aggregated to generate new prediction model. The model showed good calibration with the HL test (P = 0.964). The AUC for our model was 0.768, which was higher than other three reported models. DCA also showed our model was superior to the other three reported models. In our model, sensitivity = 78.84%, specificity = 61.32%. Compared with the PKUPH, Shanghai and Mayo models, the NRI of our model increased by 0.177, 0.127, and 0.396 respectively, and the IDI changed − 0.019, -0.076, and 0.112, respectively. Furthermore, the model was significant positive correlation with PKUPH, Shanghai and Mayo models.

Conclusions

The novel model in our study had a high clinical value in diagnose of MSPNs.

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.

Biomarkers for Lung Cancer Screening and Detection

Lung cancer is the leading worldwide cause of cancer mortality, as it is often detected at an advanced stage. Since 2011, low-dose CT scan-based screening has promised a 20% reduction in lung cancer mortality. However, effectiveness of screening has been limited by eligibility only for a high-risk population of heavy smokers and a large number of false positives generated by CT. Biomarkers have tremendous potential to improve early detection of lung cancer by refining lung cancer risk, stratifying positive CT scans, and categorizing intermediate-risk pulmonary nodules. Three biomarker tests (Early CDT-Lung, Nodify XL2, Percepta) have undergone extensive validation and are available to the clinician. The authors discuss these tests, with their clinical applicability and limitations, current ongoing evaluation, and future directions for biomarkers in lung cancer screening and detection.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Role of Computed Tomography-guided Biopsies in the Era of Electromagnetic Navigational Bronchoscopy: A Retrospective Study of Factors Predicting Diagnostic Yield in Electromagnetic Navigational Bronchoscopy and Computed Tomography Biopsies

Objectives:

Over 25% of the high-risk population screened for lung cancer have an abnormal computed tomography (CT) scan. Conventionally, these lesions have been biopsied with CT guidance with a high diagnostic yield. Electromagnetic navigational bronchoscopy (ENB) with transbronchial biopsy has emerged as a technology that improves the diagnostic sensitivity of conventional bronchoscopic biopsy. It has been used to biopsy lung lesions, due to the low risk of pneumothorax. It is, however, a new technology that is expensive and its role in the diagnosis of the solitary pulmonary nodule (SPN) is yet to be determined. The purpose of this study was to evaluate the diagnostic yield of CT-guided biopsy (CTB) following non-diagnostic ENB biopsy and identify characteristics of the lesion that predicts a low diagnostic yield with ENB, to ensure appropriate use of ENB in the evaluation of SPN.

Materials and Methods:

One hundred and thirty-five lung lesions were biopsied with ENB from January 2017 to August 2019. Biopsies were considered diagnostic if pathology confirmed malignancy or inflammation in the appropriate clinical and imaging setting. We evaluated lesions for several characteristics including size, lobe, and central/peripheral distribution. The diagnostic yield of CTB in patients who failed ENB biopsies was also evaluated. Logistic regression was used to identify factors likely to predict a non-diagnostic ENB biopsy.

Result:

Overall, ENB biopsies were performed in 135 patients with solitary lung lesions. ENB biopsies were diagnostic in 52% (70/135) of the patients. In 23 patients with solitary lung lesions, CTBs were performed following a non-diagnostic ENB biopsy. The CTBs were diagnostic in 87% of the patients (20/23). ENB biopsies of lesions <21.5 mm were non-diagnostic in 71% of cases (42/59); 14 of these patients with non-diagnostic ENB biopsies had CTBs, and 86% of them were diagnostic (12/14). ENB biopsies of lesions in the lower lobes were non- diagnostic in 59% of cases (35/59); 12 of these patients with non-diagnostic ENB biopsies had CTBs, and 83% were diagnostic (10/12). ENB biopsies of lesions in the outer 2/3 were non-diagnostic in 57% of cases (50/87); 21 of these patients with non-diagnostic ENB biopsies had CTBs, and 86% were diagnostic (18/21).

Conclusion:

CTBs have a high diagnostic yield even following non-diagnostic ENB biopsies. Lesions <21.5 mm, in the outer 2/3 of the lung, and in the lower lung have the lowest likelihood of a diagnostic yield with ENB biopsies. Although CTBs have a slightly higher pneumothorax rate, these lesions would be more successfully diagnosed with CTB as opposed to ENB biopsy, in the process expediting the diagnosis and saving valuable medical resources.

Diagnosing peripheral lung lesions using endobronchial ultrasonography with guide sheath: A prospective registry study to assess the effect of virtual bronchoscopic navigation using a computed tomography workstation

Bronchoscopy has a lower diagnostic yield for peripheral lung lesions (PLL). Endobronchial ultrasound guide sheath transbronchial lung biopsy (EBUS GS TBLB) has been used to overcome such limitation. Recent studies revealed that combined methods (e.g., EBUS GS TBLB plus electromagnetic navigation [EMN] or virtual bronchoscopic navigation [VBN]) further improve the diagnostic yield. However, those systems are associated with a high cost burden. Accordingly, we attempted to use VBN by computed tomography (CT) workstation (Aquarius iNtuition, TeraRecon) not dedicated only for VBN as an adjunctive tool for EBUS GS TBLB. We performed a prospective registry study to investigate whether VBN by CT workstation could improve the diagnostic yield of PLL.Between February 2017 and February 2018, 128 patients with PLL were divided into 2 groups (VBN and non-VBN [NVBN]). In NVBN group (n = 64), EBUS GS TBLB was performed using a hand-drawn bronchial map based on CT images. VBN group (n = 64) underwent EBUS GS TBLB using VBN images.VBN using CT workstation did not improve the diagnostic yield of EBUS GS TBLB for PLL (VBN vs NVBN, 72% vs 80%, P = .284). VBN slightly reduced procedure time (minute [mean ± SD], 25.31 ± 10.33 vs 25.81 ± 9.22), navigation time (time to find the lesion) (9.10 ± 7.88 vs 9.50 ± 7.14), and fluoroscopy time (2.23 ± 2.39 vs 2.86 ± 4.61), while these differences were not statistically significant.The diagnostic yield of EBUS GS TBLB was not improved with VBN (compared with using a hand-drawn bronchial map). Although VBN slightly shortened the procedure-related times, which were not significantly different.

Diagnosis and management of peripheral lung nodule

A solitary pulmonary nodule (SPN) is a well-defined radiographic opacity up to 3 cm in diameter that is surrounded by unaltered aerated lung. Frequently, it is an incidental finding on chest radiographs and chest CT scans. Determining the probability of malignancy is the first step in the evaluation of SPN. This can be done by looking at specific risk factors and the rate of radiographic progression. Subsequent management is guided by the type of the nodule. Patients with solid nodules and low pretest probability can be followed radiographically; those with high probability, who are good surgical candidates, can be referred for surgical resection. When the pretest probability is in the intermediate range additional testing such as biopsy should be done. Various modalities are now available to obtain tissue diagnosis. These modalities differ in their yield and complication rate. Patients with SPN should be well informed of each approach's risks and benefits and should be able to make an informed decision regarding the different diagnostic and therapeutic modalities.

Management of pulmonary nodules

Pulmonary nodules are frequently detected during clinical practice and require a structured approach in their management in order to identify early lung cancers and avoid harm from over investigation. The article reviews the 2015 British Thoracic Society guidelines for the management of pulmonary nodules and the evidence behind them.

Post-imaging pulmonary nodule mathematical prediction models: are they clinically relevant?

OBJECTIVES

Post-imaging mathematical prediction models (MPMs) provide guidance for the management of solid pulmonary nodules by providing a lung cancer risk score from demographic and radiologists-indicated imaging characteristics. We hypothesized calibrating the MPM risk score threshold to a local study cohort would result in improved performance over the original recommended MPM thresholds. We compared the pre- and post-calibration performance of four MPM models and determined if improvement in MPM prediction occurs as nodules are imaged longitudinally.

MATERIALS AND METHODS

A common cohort of 317 individuals with computed tomography-detected, solid nodules (80 malignant, 237 benign) were used to evaluate the MPM performance. We created a web-based application for this study that allows others to easily calibrate thresholds and analyze the performance of MPMs on their local cohort. Thirty patients with repeated imaging were tested for improved performance longitudinally.

RESULTS

Using calibrated thresholds, Mayo Clinic and Brock University (BU) MPMs performed the best (AUC = 0.63, 0.61) compared to the Veteran's Affairs (0.51) and Peking University (0.55). Only BU had consensus with the original MPM threshold; the other calibrated thresholds improved MPM accuracy. No significant improvements in accuracy were found longitudinally between time points.

CONCLUSIONS

Calibration to a common cohort can select the best-performing MPM for your institution. Without calibration, BU has the most stable performance in solid nodules ≥ 8 mm but has only moderate potential to refine subjects into appropriate workup. Application of MPM is recommended only at initial evaluation as no increase in accuracy was achieved over time.

KEY POINTS

• Post-imaging lung cancer risk mathematical predication models (MPMs) perform poorly on local populations without calibration. • An application is provided to facilitate calibration to new study cohorts: the Mayo Clinic model, the U.S. Department of Veteran's Affairs model, the Brock University model, and the Peking University model. • No significant improvement in risk prediction occurred in nodules with repeated imaging sessions, indicating the potential value of risk prediction application is limited to the initial evaluation.

Empiric Radiotherapy for Lung Cancer Collaborative Group multi-institutional evidence-based guidelines for the use of empiric stereotactic body radiation therapy for non-small cell lung cancer without pathologic confirmation

The standard of care for managing early stage non-small cell lung cancer (NSCLC) is definitive surgical resection. Stereotactic body radiation therapy (SBRT) has become the standard treatment for patient who are medically inoperable, and it is increasingly being considered as an option in operable patients. With the growing use of screening thoracic CT scans for patients with a history of heavy smoking, as well as improved imaging capabilities, the discovery of small lung nodes has become a common dilemma. As a result, clinicians are increasingly faced with managing lung nodules in patients in whom diagnostic biopsy is not safe or feasible. Herein, we describe the scope of the problem, tools available for predicting the probability that a lung nodule is a malignancy, staging procedures, benefits of pathology-proven and empiric SBRT, considerations of safety based on location of the lesion of concern, and overall efficacy of SBRT.

Survival in early lung cancer patients treated with high dose radiotherapy is independent of pathological confirmation

BACKGROUND

Approximately 15% of lung cancer patients are diagnosed in early stages. Microscopic proof of disease cannot always be obtained because of comorbidity or reluctance to undergo invasive diagnostic procedures. In the current study, survival data of patients with and without pathology are compared.

METHODS

One hundred and sixty three patients with NSCLC I-IIb (T3 N0) treated between 2002 and 2016 were eligible: 123 (75%) had pathological confirmation of disease, whereas 40 (25%) did not. In accordance with international guidelines, both groups received radiotherapy. Comorbidity was assessed with the Charlson Comorbidity Index (CCI).

RESULTS

The median follow-up was 28.6 months (range: 0.3-162): 66 (40%) patients are still alive, while 97 (59%) patients died: 48 (29%) cancer-related deaths and 49 (30%) from causes other than cancer. Median overall survival (OS) in patients without pathological confirmation was 58.6 months (range: 0.5-162), which did not differ from those with microscopic proof of disease (39.4 months, range: 0.3-147.5; logrank P = 0.481). Median cancer-specific survival (CSS) also did not differ at 113.4 months (range: 0.5-162) in the non-confirmation group (logrank P = 0.763) versus 51.5 months (range: 3.7-129.5) in patients with pathology. In Cox regression, a CCI of ≥ 3 was associated with poor OS (hazard ratio 2.0; range 1.2-3.4; P = 0.010) and CSS (hazard ratio 2.0; 1.0-4.0; P = 0.043).

CONCLUSION

OS and CSS in early lung cancer patients depend on comorbidity rather than on pathological confirmation of disease.

Lung nodules: A comprehensive review on current approach and management

In daily clinical practice, radiologists and pulmonologists are faced with incidental radiographic findings of pulmonary nodules. Deciding how to manage these findings is very important as many of them may be benign and require no further action, but others may represent early disease and importantly early-stage lung cancer and require prompt diagnosis and definitive treatment. As the diagnosis of pulmonary nodules includes invasive procedures which can be relatively minimal, such as bronchoscopy or transthoracic aspiration or biopsy, but also more invasive procedures such as thoracic surgical biopsies, and as these procedures are linked to anxiety and to cost, it is important to have clearly defined algorithms for the description, management, and follow-up of these nodules. Clear algorithms for the imaging protocols and the management of positive findings should also exist in lung cancer screening programs, which are already established in the USA and which will hopefully be established worldwide. This article reviews current knowledge on nodule definition, diagnostic evaluation, and management based on literature data and mainly recent guidelines.

Lung cancer screening: nodule identification and characterization

The accurate identification and characterization of small pulmonary nodules at low-dose CT is an essential requirement for the implementation of effective lung cancer screening. Individual reader detection performance is influenced by nodule characteristics and technical CT parameters but can be improved by training, the application of CT techniques, and by computer-aided techniques. However, the evaluation of nodule detection in lung cancer screening trials differs from the assessment of individual readers as it incorporates multiple readers, their inter-observer variability, reporting thresholds, and reflects the program accuracy in identifying lung cancer. Understanding detection and interpretation errors in screening trials aids in the implementation of lung cancer screening in clinical practice. Indeed, as CT screening moves to ever lower radiation doses, radiologists must be cognisant of new technical challenges in nodule assessment. Screen detected lung cancers demonstrate distinct morphological features from incidentally or symptomatically detected lung cancers. Hence characterization of screen detected nodules requires an awareness of emerging concepts in early lung cancer appearances and their impact on radiological assessment and malignancy prediction models. Ultimately many nodules remain indeterminate, but further imaging evaluation can be appropriate with judicious utilization of contrast enhanced CT or MRI techniques or functional evaluation by PET-CT.

Novel high-resolution computed tomography-based radiomic classifier for screen-identified pulmonary nodules in the National Lung Screening Trial

Purpose

Optimization of the clinical management of screen-detected lung nodules is needed to avoid unnecessary diagnostic interventions. Herein we demonstrate the potential value of a novel radiomics-based approach for the classification of screen-detected indeterminate nodules.

Material and methods

Independent quantitative variables assessing various radiologic nodule features such as sphericity, flatness, elongation, spiculation, lobulation and curvature were developed from the NLST dataset using 726 indeterminate nodules (all ≥ 7 mm, benign, n = 318 and malignant, n = 408). Multivariate analysis was performed using least absolute shrinkage and selection operator (LASSO) method for variable selection and regularization in order to enhance the prediction accuracy and interpretability of the multivariate model. The bootstrapping method was then applied for the internal validation and the optimism-corrected AUC was reported for the final model.

Results

Eight of the originally considered 57 quantitative radiologic features were selected by LASSO multivariate modeling. These 8 features include variables capturing Location: vertical location (Offset carina centroid z), Size: volume estimate (Minimum enclosing brick), Shape: flatness, Density: texture analysis (Score Indicative of Lesion/Lung Aggression/Abnormality (SILA) texture), and surface characteristics: surface complexity (Maximum shape index and Average shape index), and estimates of surface curvature (Average positive mean curvature and Minimum mean curvature), all with P<0.01. The optimism-corrected AUC for these 8 features is 0.939.

Conclusions

Our novel radiomic LDCT-based approach for indeterminate screen-detected nodule characterization appears extremely promising however independent external validation is needed.

A Prediction Model to Help with the Assessment of Adenopathy in Lung Cancer: HAL

RATIONALE

Estimating the probability of finding N2 or N3 (prN2/3) malignant nodal disease on endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) in patients with non-small cell lung cancer (NSCLC) can facilitate the selection of subsequent management strategies.

OBJECTIVES

To develop a clinical prediction model for estimating the prN2/3.

METHODS

We used the AQuIRE (American College of Chest Physicians Quality Improvement Registry, Evaluation, and Education) registry to identify patients with NSCLC with clinical radiographic stage T1-3, N0-3, M0 disease that had EBUS-TBNA for staging. The dependent variable was the presence of N2 or N3 disease (vs. N0 or N1) as assessed by EBUS-TBNA. Univariate followed by multivariable logistic regression analysis was used to develop a parsimonious clinical prediction model to estimate prN2/3. External validation was performed using data from three other hospitals.

MEASUREMENTS AND MAIN RESULTS

The model derivation cohort (n = 633) had a 25% prevalence of malignant N2 or N3 disease. Younger age, central location, adenocarcinoma histology, and higher positron emission tomography-computed tomography N stage were associated with a higher prN2/3. Area under the receiver operating characteristic curve was 0.85 (95% confidence interval, 0.82-0.89), model fit was acceptable (Hosmer-Lemeshow, P = 0.62; Brier score, 0.125). We externally validated the model in 722 patients. Area under the receiver operating characteristic curve was 0.88 (95% confidence interval, 0.85-0.90). Calibration using the general calibration model method resulted in acceptable goodness of fit (Hosmer-Lemeshow test, P = 0.54; Brier score, 0.132).

CONCLUSIONS

Our prediction rule can be used to estimate prN2/3 in patients with NSCLC. The model has the potential to facilitate clinical decision making in the staging of NSCLC.

Comparison of plasma to tissue DNA mutations in surgical patients with non-small cell lung cancer

OBJECTIVE

Noninvasive liquid biopsies of circulating tumor DNA (ctDNA) can be used to assess non-small cell lung cancer (NSCLC), but previous work focused on patients with advanced-stage cancer. Thus, we evaluated the feasibility and their potential clinical application of circulating tumor DNA approached for surgical patients with NSCLC.

METHOD

Consecutive patients with suspected lung cancer who underwent curative-intent lung resection were enrolled prospectively in this study. Targeted DNA sequencing with a next-generation sequencing platform was used to identify a series of somatic mutations in matched tumor tissue DNA (tDNA) and plasma ctDNA samples. Plasma was collected before, during, and after surgery. Concordance was defined as matched tDNA and ctDNA with the same identified mutations or with no mutations.

RESULTS

In the enrolled 76 patients with lung cancer who were included, 31 had concordant mutations and 21 had no mutation in both ctDNA and tDNA, yielding an overall concordance of 68.4%. ctDNA samples obtained before and during surgery had the same mutations with a low variance in mutation frequency (1.2%) that was reduced to an average of 0.28% after surgery (P < .001). More patients were positive as assayed by ctDNA (48; 63.2%) than with serum tumor protein markers (36; 49.3%). The area under the curve was greater in ctDNA (0.887, 95% confidence interval [CI], 0.788-0.986) than for the 2 prediction models (0.803, 95% CI, 0.647-0.959; 0.69, 95% CI, 0.512-0.869) for estimating malignancy of solitary pulmonary nodules.

CONCLUSION

ctDNA mutation analysis for stage I-III surgical patients with NSCLC is feasible. More studies are needed to investigate its clinical application.

Physician Assessment of Pretest Probability of Malignancy and Adherence With Guidelines for Pulmonary Nodule Evaluation

BACKGROUND

The annual incidence of pulmonary nodules is estimated at 1.57 million. Guidelines recommend using an initial assessment of nodule probability of malignancy (pCA). A previous study found that despite this recommendation, physicians did not follow guidelines.

METHODS

Physician assessments (N = 337) and two previously validated risk model assessments of pretest probability of cancer were evaluated for performance in 337 patients with pulmonary nodules based on final diagnosis and compared. Physician-assessed pCA was categorized into low, intermediate, and high risk, and the next test ordered was evaluated.

RESULTS

The prevalence of malignancy was 47% (n = 158) at 1 year. Physician-assessed pCA performed better than nodule prediction calculators (area under the curve, 0.85 vs 0.75; P < .001 and .78; P = .0001). Physicians did not follow indicated guidelines when selecting the next test in 61% of cases (n = 205). Despite recommendations for serial CT imaging in those with low pCA, 52% (n = 13) were managed more aggressively with PET imaging or biopsy; 12% (n = 3) underwent biopsy procedures for benign disease. Alternatively, in the high-risk category, the majority (n = 103 [75%]) were managed more conservatively. Stratified by diagnosis, 92% (n = 22) with benign disease underwent more conservative management with CT imaging (20%), PET scanning (15%), or biopsy (8%), although three had surgery (8%).

CONCLUSIONS

Physician assessment as a means for predicting malignancy in pulmonary nodules is more accurate than previously validated nodule prediction calculators. Despite the accuracy of clinical intuition, physicians did not follow guideline-based recommendations when selecting the next diagnostic test. To provide optimal patient care, focus in the areas of guideline refinement, implementation, and dissemination is needed.

A retrospective validation study of three models to estimate the probability of malignancy in patients with small pulmonary nodules from a tertiary oncology follow-up centre

AIM

To estimate the probability of malignancy in small pulmonary nodules (PNs) based on clinical and radiological characteristics in a non-screening population that includes patients with a prior history of malignancy using three validated models.

MATERIALS AND METHODS

Retrospective data on clinical and radiological characteristics was collected from the medical records of 702 patients (379 men, 323 women; range 19-94 years) with PNs ≤12 mm in diameter at a single centre. The final diagnosis was compared to the probability of malignancy calculated by one of three models (Mayo, VA, and McWilliams). Model accuracy was assessed by receiver operating characteristics (ROC). The models were calibrated by comparing predicted and observed rates of malignancy.

RESULTS

The area under the ROC curve (AUC) was highest for the McWilliams model (0.82; 95% confidence interval [CI]: 0.78-0.91) and lowest for the Mayo model (0.58; 95% CI: 0.55-0.59). The VA model had an AUC of (0.62; 95% CI: 0.47-0.64). Performance of the models was significantly lower than that in the published literature.

CONCLUSIONS

The accuracy of the three models is lower in a non-screening population with a high prevalence of prior malignancy compared to the papers that describe their development. To the authors' knowledge, this is the largest study to validate predictive models for PNs in a non-screening clinically referred patient population, and has potential implications for the implementation of predictive models.

[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.

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

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

The Fate of Patients with Solitary Pulmonary Nodules: Clinical Management and Radiation Exposure Associated

Background

The appropriate management of the large number of lung nodules detected during the course of routine medical care presents a challenge. We aimed to evaluate the usual clinical practice in solitary pulmonary nodule (SPN) management and associated radiation exposure.

Methods

We examined 893 radiology reports of consecutive patients undergoing chest computed tomography (CT) and radiography at two public hospitals in Spain. Information on diagnostic procedures from SPN detection and lung cancer diagnosis was collected prospectively for 18 months.

Results

More than 20% of patients with SPN detected on either chest radiograph (19.8%) or CT (26.1%) underwent no additional interventions and none developed lung cancer (100% negative predictive value). 346 (72.0%) patients with SPN detected on chest radiograph and 254 (61.5%) patients with SPN detected on CT had additional diagnostic tests and were not diagnosed with lung cancer. In patients undergoing follow-up imaging for SPNs detected on CT median number of additional imaging tests was 3.5 and the mean cumulative effective dose was 24.4 mSv; for those detected on chest radiograph the median number of additional imaging tests was 2.8 and the mean cumulative effective dose was 10.3 mSv.

Conclusions

Patients who did not have additional interventions were not diagnosed of lung cancer. There was an excessive amount of interventions in a high percentage of patients presenting SPN, which was associated with an excess of radiation exposure.

The Solitary Pulmonary Nodule

Due to the high etiological diversity and the potential for malignancy, pulmonary nodules represent a clinical challenge, becoming increasingly frequent as the number of CT examinations rises. The topic gains even more importance as clear evidence for the effectiveness of CT screening was provided by the National Lung Screening Trial (NLST). Yet, the results were tempered by the high false-positive rate and the requirement of performing further diagnostic procedures. The management of those detected solitary pulmonary nodules is currently based on the individuals' risk of developing lung cancer, the pulmonary nodule characteristics and the capability of diagnostic and therapeutic approaches.

Nonintubated video-assisted thoracoscopic surgery for management of indeterminate pulmonary nodules

Indeterminate pulmonary nodules are common findings in clinical practice, especially after widespread use of high-resolution computed tomographic scans for cancer screening. To determine whether the nodule is malignant or not, surgery is usually required for either diagnostic or therapeutic purposes in the early stages. Current development in minimally invasive surgery and anesthesia using video-assisted thoracoscopic surgery without tracheal intubation (nonintubated VATS) are feasible and safe for resection of peripheral lung nodules, including nonintubated needlescopic or uniportal approaches. In addition, nonintubated VATS may offer high-risk patients for intubated general anesthesia opportunities to receive surgery. Therefore, nonintubated VATS can provide an attractive alternative for early diagnosis and treatment of indeterminate lung nodules.

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

BACKGROUND

With the recent widespread use of computed tomography, interest in ground glass opacity pulmonary lesions has increased. We aimed to develop a model for predicting the probability of malignancy in solitary pulmonary nodules.

METHODS

We assessed 846 patients with newly discovered solitary pulmonary nodules referred to Fujian Medical University Union Hospital. Data on 18 clinical and 13 radiologic variables were collected. Two thirds of the patients were randomly selected to derive the prediction model (derivation set); the remaining one third provided a validation set. The lesions were divided according to proportion of ground glass opacity (less than 50% or 50% or greater). Univariate analysis of significant covariates for their relationship to the presence of malignancy was performed. An equation expressing the probability of malignancy was derived from these findings and tested on data from the validation group. Receiver-operating characteristic curves were constructed using the prediction model and the Mayo Clinic model.

RESULTS

In lesions with less than 50% ground glass opacity, three clinical characteristics (age, presence of symptoms, total protein) and three radiologic characteristics (diameter, lobulation, calcified nodes) were independent predictors of malignancy. In lesions with 50% or more ground glass opacity, two clinical characteristics (sex, percent of forced expiratory volume in 1 second accounting for expected value) and two radiologic characteristics (diameter, calcified nodes) were independent predictors of malignancy. Our prediction model was better than the Mayo Clinic model to distinguish between benign and malignant solitary pulmonary nodules (p < 0.05).

CONCLUSIONS

Our prediction model could accurately identify malignancy in patients with solitary pulmonary nodules, especially in lesions with 50% or more ground glass opacity.

Natural history of incidental pulmonary nodules in children

RATIONALE

As there are no evidence based guidelines for the diagnosis and/or management of pulmonary nodules in children, there is an over reliance on the adult based algorithms when dealing with pulmonary nodules in children. We present our experience of pediatric patients evaluated for incidentally found pulmonary nodules.

METHODS

Retrospective chart review of patients diagnosed with a pulmonary nodule and evaluated at Goryeb Children's Hospital between January 2000 and December 2012.

PRIMARY OUTCOME

change in the size of the pulmonary nodule between the initial and follow-up imaging.

RESULTS

Thirty six patients with pulmonary nodule (21 male/15 female; Median [range] age 15 [5-20] years.) were included in the study. Chest CT was obtained for respiratory symptoms and/or abnormal chest radiograph in 19 (52%). Nine pulmonary nodules (25%) were identified on abdominal CT obtained for abdominal symptoms. A total of 46 nodules were identified in 36 patients. Nine of the pulmonary nodules (9 patients) were ≤4 mm in size, 37 of the pulmonary nodules (27 patients) were >4 mm in size. Twenty-two of the 27 (81%) patients with nodule size >4 mm had follow-up CT: 14 nodules (54%) remained unchanged in size, 5 nodules (19%) decreased in size, and 7 nodules (27%) were not detected.

CONCLUSION

Our review of 36 patients with pulmonary nodules shows no obvious growth of the nodules over the study period, suggesting low risk of malignancy. Routine follow-up chest computer tomography using ACCP/Fleischner Society guidelines may not apply in children without known malignancy.

Using Clinical Risk Models for Lung Nodule Classification

Evaluation and diagnosis of indeterminate pulmonary nodules is a significant and increasing burden on our health care system. The advent of lung cancer screening with low-dose computed tomography only exacerbates this problem, and more surgeons will be evaluating smaller and screening discovered nodules. Multiple calculators exist that can help the clinician diagnose lung cancer at the bedside. The Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) model helps to determine who needs lung cancer screening, and the McWilliams and Mayo models help to guide the primary care clinician or pulmonologist with diagnosis by estimating the probability of cancer in patients with indeterminate pulmonary nodules. The Thoracic Research Evaluation And Treatment (TREAT) model assists surgeons to determine who needs a surgical biopsy among patients referred for suspicious lesions. Additional work is needed to develop decision support tools that will facilitate the use of these models in clinical practice, to complement the clinician's judgment and enhance shared decision making with the patient at the bedside.

[The importance of risk models for management of pulmonary nodules]

CLINICAL/METHODICAL ISSUE

Pulmonary nodules are a frequent finding in computed tomography (CT) investigations.

STANDARD RADIOLOGICAL METHODS

Further diagnostic work-up of detected nodules mainly depends on the so-called pre-test probability, i.e. the probability that the nodule is malignant or benign.

METHODICAL INNOVATIONS

The pre-test probability can be calculated by combining all relevant information, such as the age and the sex of the patient, the smoking history, and history of previous malignancies, as well as the size and CT morphology of the nodule.

PERFORMANCE

If additional investigations are performed to further investigate the nodules, all results must be interpreted taking into account the pre-test probability and the test performance of the investigation in order to estimate the post-test probability.

ACHIEVEMENTS

In cases with a low pre-test probability, a negative result from an exact test can exclude malignancies but a positive test cannot prove malignancy in such a setting. In cases with a high pre-test probability, a positive test result can be considered as proof of malignancy but a negative test result does not exclude malignancy.

Accuracy of clinicians and models for estimating the probability that a pulmonary nodule is malignant

RATIONALE

Management of pulmonary nodules depends critically on the probability of malignancy. Models to estimate probability have been developed and validated, but most clinicians rely on judgment.

OBJECTIVES

The aim of this study was to compare the accuracy of clinical judgment with that of two prediction models.

METHODS

Physician participants reviewed up to five clinical vignettes, selected at random from a larger pool of 35 vignettes, all based on actual patients with lung nodules of known final diagnosis. Vignettes included clinical information and a representative slice from computed tomography. Clinicians estimated the probability of malignancy for each vignette. To examine agreement with models, we calculated intraclass correlation coefficients (ICC) and kappa statistics. To examine accuracy, we compared areas under the receiver operator characteristic curve (AUC).

MEASUREMENTS AND MAIN RESULTS

Thirty-six participants completed 179 vignettes, 47% of which described patients with malignant nodules. Agreement between participants and models was fair for the Mayo Clinic model (ICC, 0.37; 95% confidence interval [CI], 0.23-0.50) and moderate for the Veterans Affairs model (ICC, 0.46; 95% CI, 0.34-0.57). There was no difference in accuracy between participants (AUC, 0.70; 95% CI, 0.62-0.77) and the Mayo Clinic model (AUC, 0.71; 95% CI, 0.62-0.80; P = 0.90) or the Veterans Affairs model (AUC, 0.72; 95% CI, 0.64-0.80; P = 0.54).

CONCLUSIONS

In this vignette-based study, clinical judgment and models appeared to have similar accuracy for lung nodule characterization, but agreement between judgment and the models was modest, suggesting that qualitative and quantitative approaches may provide complementary information.