Comparison of laser guidance and freehand hook-wire for CT-guided preoperative localization of pulmonary nodules

PURPOSE

In VATS surgery, precise preoperative localization is particularly crucial when dealing with small-diameter pulmonary nodules located deep within the lung parenchyma. The purpose of this study was to compare the efficacy and safety of laser guidance and freehand hook-wire for CT-guided preoperative localization of pulmonary nodules.

METHODS

This retrospective study was conducted on 164 patients who received either laser guidance or freehand hook-wire localization prior to Uni-port VATS from September 1st, 2022 to September 30th, 2023 at The First Affiliated Hospital of Soochow University. Patients were divided into laser guidance group and freehand group based on which technology was used. Preoperative localization data from all patients were compiled. The localization success and complication rates associated with the two groups were compared. The risk factors for common complications were analyzed.

RESULTS

The average time of the localization duration in the laser guidance group was shorter than the freehand group (p<0.001), and the average CT scan times in the laser guidance group was less than that in the freehand group (p<0.001). The hook-wire was closer to the nodule in the laser guidance group (p<0.001). After the localization of pulmonary nodules, a CT scan showed 14 cases of minor pneumothorax (22.58%) in the laser guidance group and 21 cases (20.59%) in the freehand group, indicating no statistical difference between the two groups (p=0.763). CT scans in the laser guidance group showed pulmonary minor hemorrhage in 8 cases (12.90%) and 6 cases (5.88%) in the freehand group, indicating no statistically significant difference between the two groups (p=0.119). Three patients (4.84%) in the laser guidance group and six patients (5.88%) in the freehand group had hook-wire dislodgement, showing no statistical difference between the two groups (p=0.776).

CONCLUSION

The laser guidance localization method possessed a greater precision and less localization duration and CT scan times compared to the freehand method. However, laser guidance group and freehand group do not differ in the appearance of complications such as pulmonary hemorrhage, pneumothorax and hook-wire dislodgement.

Bubble-like lucency in pulmonary ground glass nodules on computed tomography: a specific pattern of air-containing space for diagnosing neoplastic lesions

PURPOSE

To investigate the computed tomography (CT) characteristics of air-containing space and its specific patterns in neoplastic and non-neoplastic ground glass nodules (GGNs) for clarifying their significance in differential diagnosis.

MATERIALS AND METHODS

From January 2015 to October 2022, 1328 patients with 1,350 neoplastic GGNs and 462 patients with 465 non-neoplastic GGNs were retrospectively enrolled. Their clinical and CT data were analyzed and compared with emphasis on revealing the differences of air-containing space and its specific patterns (air bronchogram and bubble-like lucency [BLL]) between neoplastic and non-neoplastic GGNs and their significance in differentiating them.

RESULTS

Compared with patients with non-neoplastic GGNs, female was more common (P < 0.001) and lesions were larger (P < 0.001) in those with neoplastic ones. Air bronchogram (30.1% vs. 17.2%), and BLL (13.0% vs. 2.6%) were all more frequent in neoplastic GGNs than in non-neoplastic ones (each P < 0.001), and the BLL had the highest specificity (93.6%) in differentiation. Among neoplastic GGNs, the BLL was more frequently detected in the larger (14.9 ± 6.0 mm vs. 11.4 ± 4.9 mm, P < 0.001) and part-solid (15.3% vs. 10.7%, P = 0.011) ones, and its incidence significantly increased along with the invasiveness (9.5-18.0%, P = 0.001), whereas no significant correlation was observed between the occurrence of BLL and lesion size, attenuation, or invasiveness.

CONCLUSION

The air containing space and its specific patterns are of great value in differentiating GGNs, while BLL is a more specific and independent sign of neoplasms.

[A Growth Prediction Model of Pulmonary Ground-Glass Nodules Based on Clinical Visualization Parameters]

Objective To establish a model for predicting the growth of pulmonary ground-glass nodules (GGN) based on the clinical visualization parameters extracted by the 3D reconstruction technique and to verify the prediction performance of the model. Methods A retrospective analysis was carried out for 354 cases of pulmonary GGN followed up regularly in the outpatient of pulmonary nodules in Zhoushan Hospital of Zhejiang Province from March 2015 to December 2022.The semi-automatic segmentation method of 3D Slicer was employed to extract the quantitative imaging features of nodules.According to the follow-up results,the nodules were classified into a resting group and a growing group.Furthermore,the nodules were classified into a training set and a test set by the simple random method at a ratio of 7∶3.Clinical and imaging parameters were used to establish a prediction model,and the prediction performance of the model was tested on the validation set. Results A total of 119 males and 235 females were included,with a median age of 55.0 (47.0,63.0) years and the mean follow-up of (48.4±16.3) months.There were 247 cases in the training set and 107 cases in the test set.The binary Logistic regression analysis showed that age (95%CI=1.010-1.092,P=0.015) and mass (95%CI=1.002-1.067,P=0.035) were independent predictors of nodular growth.The mass (M) of nodules was calculated according to the formula M=V×(CTmean+1000)×0.001 (where V is the volume,V=3/4πR3,R:radius).Therefore,the logit prediction model was established as ln[P/(1-P)]=-1.300+0.043×age+0.257×two-dimensional diameter+0.007×CTmean.The Hosmer-Lemeshow goodness of fit test was performed to test the fitting degree of the model for the measured data in the validation set (χ2=4.515,P=0.808).The check plot was established for the prediction model,which showed the area under receiver-operating characteristic curve being 0.702. Conclusions The results of this study indicate that patient age and nodule mass are independent risk factors for promoting the growth of pulmonary GGN.A model for predicting the growth possibility of GGN is established and evaluated,which provides a basis for the formulation of GGN management strategies.

Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography

RATIONALE AND OBJECTIVES

To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with clinical features, radiomics features, and deep transfer learning features to predict high-risk pathological pulmonary nodules.

MATERIALS AND METHODS

The study cohort consisted of 469 cases of lung adenocarcinoma patients, divided into a training cohort (n = 400) and an external validation cohort (n = 69). We obtained computed tomography (CT) semantic features and clinical characteristics, as well as extracted radiomics and deep transfer learning (DTL) features from the CT images. Selected features were used for constructing prediction models using the logistic regression (LR) algorithm. The performance of the models was evaluated through metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis.

RESULTS

The clinical model achieved an AUC of 0.774 (95% CI: 0.728-0.821) in the training cohort and 0.762 (95% confidence interval [CI]: 0.650-0.873) in the external validation cohort. The radiomics model demonstrated an AUC of 0.847 (95% CI: 0.810-0.884) in the training cohort and 0.800 (95% CI: 0.693-0.907) in the external validation cohort. The radiomics-DTL (Rad-DTL) model showed an AUC of 0.871 (95% CI: 0.838-0.905) in the training cohort and 0.806 (95% CI: 0.698-0.914) in the external validation cohort. The proposed combined model yielded AUC values of 0.872 and 0.814 in the training and external validation cohorts, respectively. The combined model demonstrated superiority over both the clinical model and the Rad-DTL model. There were no statistically significant differences observed in the comparison between the combined model incorporating clinical features and the Rad-DTL model. Decision curve analysis (DCA) indicated that the models provided a net benefit in predicting high-risk pathologic pulmonary nodules.

CONCLUSION

Rad-DTL signature is a potential biomarker for predicting high-risk pathologic pulmonary nodules using preoperative CT, determining the appropriate surgical strategy, and guiding the extent of resection.

Chemical Localization With Robotic Bronchoscopy: Can It Aid Resection of Subsolid Lung Nodules?

INTRODUCTION

Subsolid nodules or those located deep in lung parenchyma are difficult to localize using minimally invasive thoracic surgery. While image-guided percutaneous needle localization has been performed, it is inconvenient and has potential complications. In this study, the role of chemical localization using robotic bronchoscopy to facilitate resection was evaluated.

METHODS

Consecutive patients undergoing surgical resection for lung nodules between 8/2019-3/2022 were included. Patients with subsolid lung nodules, or small nodules deep in lung parenchyma that were deemed difficult to localize, were chemically localized (CL) using robotic bronchoscopy before resection. Clinico-demographic data were obtained retrospectively using a prospectively maintained database.

RESULTS

Localization of lung nodules before resection was performed in 139 patients while 110 patients were not localized. Daily activity score was higher for localized patients. Nodules in the localized group were smaller (P < 0.001) and had similar solid:ground glass ratio. In the localized group, larger margins were observed, and no re-resection of the parenchymal margin was required. Twenty patients in the non-localized group required re-resection intraoperatively due to close pathological margins or inability to locate the nodule in the resected specimen. Operative time was a median of 10-15 min longer for localized patients, P < 0.001. Length of stay was shorter in the localized group (P < 0.05).

CONCLUSIONS

Chemical localization of lung nodules using robotic bronchoscopy appears to be a safe and effective method of identifying the location of nodules with small size and less density and aids increased tumor margins intraoperatively.

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

BACKGROUND

Diagnosis of malignant pulmonary nodules can greatly reduce the occurrence of lung cancer death, and computed tomography (CT) is commonly used in diagnosis. In addition, tumor-associated autoantibodies (TAAbs) show high specificity and stability. We aim to establish a computable risk model of pulmonary nodules by combining CT with TAAb detection.

METHODS

The concentrations of 7 TAAbs (p53, PGP9.5, SOX2, GAGE7, GBU4-5, CAGE, MAGEA1, and CAGE) were assayed using the enzyme-linked immunosorbent assay in 136 patients with pulmonary nodules (84 with newly diagnosed lung adenocarcinoma, 21 with squamous cell carcinoma, and 31 with benign nodules) and 42 control subjects without pulmonary nodules. We then drew receiver operating characteristic curves and conducted logistic regression to analyze the diagnostic efficiency of our method in the detection of lung cancer.

RESULTS

The positivity rate of the 7 TAAbs was 49.5%, and the specificity was 83.6%. Our regression results indicated 65% overall accuracy, 44.76% sensitivity, and 76.71% specificity. Notably, when combined with CT imaging and the demographic characteristics, diagnostic accuracy increased to 73.4%, sensitivity to 61.5%, and specificity to 87.1%. The positive predictive value and negative predictive value were 93% and 41%, respectively.

CONCLUSION

Our study provides a method that combines 7 serum TAAbs with imaging and demographic characteristics to diagnose malignant pulmonary nodules more accurately than existing methods.

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.

Lung Nodule Detection With Modern Low-Field MRI (0.55 T) in Comparison to CT

OBJECTIVES

The aim of this study was to evaluate the accuracy of modern low-field magnetic resonance imaging (MRI) for lung nodule detection and to correlate nodule size measurement with computed tomography (CT) as reference.

MATERIALS AND METHODS

Between November 2020 and July 2021, a prospective clinical trial using low-field MRI at 0.55 T was performed in patients with known pulmonary nodules from a single academic medical center. Every patient underwent MRI and CT imaging on the same day. The primary aim was to evaluate the detection accuracy of pulmonary nodules using MRI with transversal periodically rotated overlapping parallel lines with enhanced reconstruction in combination with coronal half-Fourier acquired single-shot turbo spin-echo MRI sequences. The secondary outcome was the correlation of the mean lung nodule diameter with CT as reference according to the Lung Imaging Reporting and Data System. Nonparametric Mann-Whitney U test, Spearman rank correlation coefficient, and Bland-Altman analysis were applied to analyze the results.

RESULTS

A total of 46 participants (mean age ± SD, 66 ± 11 years; 26 women) were included. In a blinded analysis of 964 lung nodules, the detection accuracy was 100% for those ≥6 mm (126/126), 80% (159/200) for those ≥4-<6 mm, and 23% (147/638) for those <4 mm in MRI compared with reference CT. Spearman correlation coefficient of MRI and CT size measurement was r = 0.87 ( P < 0.001), and the mean difference was 0.16 ± 0.9 mm.

CONCLUSIONS

Modern low-field MRI shows excellent accuracy in lesion detection for lung nodules ≥6 mm and a very strong correlation with CT imaging for size measurement, but could not compete with CT in the detection of small nodules.

A Dual-Branch Framework With Prior Knowledge for Precise Segmentation of Lung Nodules in Challenging CT Scans

Lung cancer is one of the deadliest cancers globally, and early diagnosis is crucial for patient survival. Pulmonary nodules are the main manifestation of early lung cancer, usually assessed using CT scans. Nowadays, computer-aided diagnostic systems are widely used to assist physicians in disease diagnosis. The accurate segmentation of pulmonary nodules is affected by internal heterogeneity and external data factors. In order to overcome the segmentation challenges of subtle, mixed, adhesion-type, benign, and uncertain categories of nodules, a new mixed manual feature network that enhances sensitivity and accuracy is proposed. This method integrates feature information through a dual-branch network framework and multi-dimensional fusion module. By training and validating with multiple data sources and different data qualities, our method demonstrates leading performance on the LUNA16, Multi-thickness Slice Image dataset, LIDC, and UniToChest, with Dice similarity coefficients reaching 86.89%, 75.72%, 84.12%, and 80.74% respectively, surpassing most current methods for pulmonary nodule segmentation. Our method further improved the accuracy, reliability, and stability of lung nodule segmentation tasks even on challenging CT scans.

A novel image deep learning-based sub-centimeter pulmonary nodule management algorithm to expedite resection of the malignant and avoid over-diagnosis of the benign

OBJECTIVES

With the popularization of chest computed tomography (CT) screening, there are more sub-centimeter (≤ 1 cm) pulmonary nodules (SCPNs) requiring further diagnostic workup. This area represents an important opportunity to optimize the SCPN management algorithm avoiding "one-size fits all" approach. One critical problem is how to learn the discriminative multi-view characteristics and the unique context of each SCPN.

METHODS

Here, we propose a multi-view coupled self-attention module (MVCS) to capture the global spatial context of the CT image through modeling the association order of space and dimension. Compared with existing self-attention methods, MVCS uses less memory consumption and computational complexity, unearths dimension correlations that previous methods have not found, and is easy to integrate with other frameworks.

RESULTS

In total, a public dataset LUNA16 from LIDC-IDRI, 1319 SCPNs from 1069 patients presenting to a major referral center, and 160 SCPNs from 137 patients from three other major centers were analyzed to pre-train, train, and validate the model. Experimental results showed that performance outperforms the state-of-the-art models in terms of accuracy and stability and is comparable to that of human experts in classifying precancerous lesions and invasive adenocarcinoma. We also provide a fusion MVCS network (MVCSN) by combining the CT image with the clinical characteristics and radiographic features of patients.

CONCLUSION

This tool may ultimately aid in expediting resection of the malignant SCPNs and avoid over-diagnosis of the benign ones, resulting in improved management outcomes.

CLINICAL RELEVANCE STATEMENT

In the diagnosis of sub-centimeter lung adenocarcinoma, fusion MVCSN can help doctors improve work efficiency and guide their treatment decisions to a certain extent.

KEY POINTS

• Advances in computed tomography (CT) not only increase the number of nodules detected, but also the nodules that are identified are smaller, such as sub-centimeter pulmonary nodules (SCPNs). • We propose a multi-view coupled self-attention module (MVCS), which could model spatial and dimensional correlations sequentially for learning global spatial contexts, which is better than other attention mechanisms. • MVCS uses fewer huge memory consumption and computational complexity than the existing self-attention methods when dealing with 3D medical image data. Additionally, it reaches promising accuracy for SCPNs' malignancy evaluation and has lower training cost than other models.

CT-guided microcoil localization of pulmonary nodules before VATS: clinical experience in 1059 patients

OBJECTIVE

To retrospectively evaluate the efficacy and safety of CT-guided microcoil localization of pulmonary nodules before video-assisted thoracoscopic surgery (VATS).

METHODS

A total of 1059 consecutive patients with 1331 pulmonary nodules treated between July 2018 and April 2021 were included in this study. Of the 1331 nodules, 1318 were localized using the tailed method and 13 were localized using the non-tailed method. The localization technical success rate and complications of the microcoil localization procedure were assessed. Univariate and multivariate logistic regression analyses were used to determine potential risk factors for technical failure, pneumothorax, and pulmonary hemorrhage.

RESULTS

The technical success rate of the localization procedure was 98.4% (1310/1331 nodules). Nodule location in the lower lobes (p = 0.015) and need for a longer needle path (p < 0.001) were independent predictors of technical failure. All localization procedure-related complications were minor (grade 1 or 2) adverse events, with the exception of one grade 3 complication. The most common complications were pneumothorax (302/1331 nodules [22.7%]) and pulmonary hemorrhage (328/1331 nodules [24.6%]). Male sex (p = 0.001), nodule location in the middle (p = 0.003) and lower lobes (p = 0.025), need for a longer needle path (p < 0.001), use of transfissural puncture (p = 0.042), and simultaneous multiple localizations (p < 0.001) were independent risk factors for pneumothorax. Female sex (p = 0.015), younger age (p = 0.023), nodules location in the upper lobes (p = 0.011), and longer needle path (p < 0.001) were independent risk factors for pulmonary hemorrhage.

CONCLUSIONS

CT-guided microcoil localization of pulmonary nodules before VATS using either the tailed or non-tailed method is effective and safe.

CLINICAL RELEVANCE STATEMENT

CT-guided microcoil localization of pulmonary nodules before VATS resection is effective and safe when using either the tailed or non-tailed method. Nodules requiring transfissural puncture and multiple nodules requiring simultaneous localizations can also be successfully localized with this method.

KEY POINTS

• Pre-VATS CT-guided microcoil localization of pulmonary nodules by tailed or non-tailed method was effective and safe. • When the feasible puncture path was beyond the scope of wedge resection, localization could be performed using the non-tailed method. • Although transfissural puncture and simultaneous multiple localization were independent risk factors for pneumothorax, they remained clinically feasible.

Intravascular Large B-cell Lymphoma Presenting as Pulmonary Ground-glass Nodules That Progressed Slowly over Several Months with No Overt Symptoms

A 74-year-old man with no overt symptoms was referred for a chest computed tomography (CT) that revealed multiple bilaterally pulmonary ground-glass nodules (GGNs) with subtle changes in size over eight months. Surgical lung biopsies were performed in the left upper lobe. A pathologic study confirmed the intravascular large B-cell lymphoma (IVLBCL). This lesion was a nodule-like cluster of atypical cells, meaning that it had been localized for several months. Pulmonary IVLBCL may form focal lesions presenting as GGN on chest CT and progress slowly without apparent symptoms.

Presurgical computed tomography-guided localization of lung ground glass nodules: comparing hook-wire and indocyanine green

BACKGROUND

Presurgical computed tomography (CT)-guided localization is frequently employed to reduce the thoracotomy conversion rate, while increasing the rate of successful sublobar resection of ground glass nodules (GGNs) via video-assisted thoracoscopic surgery (VATS). In this study, we compared the clinical efficacies of presurgical CT-guided hook-wire and indocyanine green (IG)-based localization of GGNs.

METHODS

Between January 2018 and December 2021, we recruited 86 patients who underwent CT-guided hook-wire or IG-based GGN localization before VATS resection in our hospital, and compared the clinical efficiency and safety of both techniques.

RESULTS

A total of 38 patients with 39 GGNs were included in the hook-wire group, whereas 48 patients with 50 GGNs were included in the IG group. There were no significant disparities in the baseline data between the two groups of patients. According to our investigation, the technical success rates of CT-based hook-wire- and IG-based localization procedures were 97.4% and 100%, respectively (P = 1.000). Moreover, the significantly longer localization duration (15.3 ± 6.3 min vs. 11.2 ± 5.3 min, P = 0.002) and higher visual analog scale (4.5 ± 0.6 vs. 3.0 ± 0.5, P = 0.001) were observed in the hook-wire patients, than in the IG patients. Occurrence of pneumothorax was significantly higher in hook-wire patients (27.3% vs. 6.3%, P = 0.048). Lung hemorrhage seemed higher in hook-wire patients (28.9% vs. 12.5%, P = 0.057) but did not reach statistical significance. Lastly, the technical success rates of VATS sublobar resection were 97.4% and 100% in hook-wire and IG patients, respectively (P = 1.000).

CONCLUSIONS

Both hook-wire- and IG-based localization methods can effectively identified GGNs before VATS resection. Furthermore, IG-based localization resulted in fewer complications, lower pain scores, and a shorter duration of localization.

Randomized comparison of the four-hook anchor device and hook-wire use for the preoperative localization of pulmonary nodules

OBJECTIVE

To compare the efficacy and safety of preoperative localization of small pulmonary nodules (SPNs) with 4-hook anchor device and hook-wire before video-assisted thoracoscopic surgery.

METHODS

Patients with SPNs scheduled for computed tomography-guided nodule localization before video-assisted thoracoscopic surgery between May 2021 and June 2021 at our center were randomized to either 4-hook anchor group or hook-wire group. The primary end point was intraoperative localization success.

RESULTS

After randomization, 28 patients with 34 SPNs were assigned to the 4-hook anchor group and 28 patients with 34 SPNs to the hook-wire group. The operative localization success rate was significantly greater in the 4-hook anchor group than in the hook-wire group (94.1% [32/34] vs 64.7% [22/34]; P = .007). All lesions in the 2 groups were successfully resected under thoracoscopy, but 4 patients in the hook-wire group who required transition from wedge resection to segmentectomy or lobectomy because of unsuccessful localization. Total localization-related complication rate was significantly lower in the 4-hook anchor group than in the hook-wire group (10.3% [3/28] vs 50.0% [14/28]; P = .004). The rate of chest pain requiring analgesia after the localization procedure was significantly lower in the 4-hook anchor group than in the hook-wire group (0 vs 5/28, 17.9%; P = .026). There were no significant differences in localization technical success rate, operative blood loss, hospital stay length and hospital cost between the 2 groups (all P > .05).

CONCLUSIONS

The use of the 4-hook anchor device for SPN localization offers advantages over the traditional hook-wire technique.

Machine learning-based radiomics to distinguish pulmonary nodules between lung adenocarcinoma and tuberculosis

BACKGROUND

Radiomics is increasingly utilized to distinguish pulmonary nodules between lung adenocarcinoma (LUAD) and tuberculosis (TB). However, it remains unclear whether different segmentation criteria, such as the inclusion or exclusion of the cavity region within nodules, affect the results.

METHODS

A total of 525 patients from two medical centers were retrospectively enrolled. The radiomics features were extracted according to two regions of interest (ROI) segmentation criteria. Multiple logistic regression models were trained to predict the pathology: (1) The clinical model relied on clinical-radiological semantic features; (2) The radiomics models (radiomics+ and radiomics-) utilized radiomics features from different ROIs (including or excluding cavities); (3) the composite models (composite+ and composite-) incorporated both above.

RESULTS

In the testing set, the radiomics+/- models and the composite+/- models still possessed efficient prediction performance (AUC ≥ 0.94), while the AUC of the clinical model was 0.881. In the validation set, the AUC of the clinical model was only 0.717, while that of the radiomics+/- models and the composite+/- models ranged from 0.801 to 0.825. The prediction performance of all the radiomics+/- and composite+/- models were significantly superior to that of the clinical model (p < 0.05). Whether the ROI segmentation included or excluded the cavity had no significant effect on these models (radiomics+ vs. radiomics-, composite+ model vs. composite-) (p > 0.05).

CONCLUSIONS

The present study established a machine learning-based radiomics strategy for differentiating LUAD from TB lesions. The ROI segmentation including or excluding the cavity region may exert no significant effect on the predictive ability.

Comparison of the safety and effectiveness of the four-hook needle and hook wire for the preoperative positioning of localization ground glass nodules

BACKGROUND

With the implementation of lung cancer screening programs, an increasing number of pulmonary nodules have been detected.Video-assisted thoracoscopic surgery (VATS) could provide adequate tissue specimens for pathological analysis, and has few postoperative complications.However, locating the nodules intraoperatively by palpation can be difficult for thoracic surgeons. The preoperative pulmonary nodule localization technique is a very effective method.We compared the safety and effectiveness of two methods for the preoperative localization of pulmonary ground glass nodules.

METHODS

From October 2020 to April 2021, 133 patients who underwent CT-guided single pulmonary nodule localization were retrospectively reviewed. All patients underwent video-assisted thoracoscopic surgery (VATS) after successful localization. Statistical analysis was used to evaluate the localization accuracy, safety, information related to surgery and postoperative pathology information. The aim of this study was to evaluate the clinical effects of the two localization needles.

RESULTS

The mean maximal transverse nodule diameters in the four-hook needle and hook wire groups were 8.97 ± 3.85 mm and 9.00 ± 3.19 mm, respectively (P = 0.967). The localization times in the four-hook needle and hook wire groups were 20.58 ± 2.65 min and 21.43 ± 3.06 min, respectively (P = 0.09). The dislodgement rate was significantly higher in the hook wire group than in the four-hook needle group (7.46% vs. 0, P = 0.024). The mean patient pain scores based on the visual analog scale in the four-hook needle and hook wire groups were 2.87 ± 0.67 and 6.10 ± 2.39, respectively (P = 0.000). All ground glass nodules (GGNs) were successfully resected by VATS.

CONCLUSIONS

Preoperative pulmonary nodule localization with both a four-hook needle and hook wire is safe, convenient and effective.

Comparison of different classification systems for pulmonary nodules: a multicenter retrospective study in China

BACKGROUND

To compare the diagnostic performance of Lung-RADS (lung imaging-reporting and data system) 2022 and PNI-GARS (pulmonary node imaging-grading and reporting system).

METHODS

Pulmonary nodules (PNs) were selected at four centers, namely, CQ Center (January 1, 2018-December 31, 2021), HB Center (January 1, 2021-June 30, 2022), SC Center (September 1, 2021-December 31, 2021), and SX Center (January 1, 2021-December 31, 2021). PNs were divided into solid nodules (SNs), partial solid nodules (PSNs) and ground-glass nodules (GGNs), and they were then classified by the Lung-RADS and PNI-GARS. The sensitivity, specificity and agreement rate were compared between the two systems by the χ2 test.

RESULTS

For SN and PSN, the sensitivity of PNI-GARS and Lung-RADS was close (SN 99.8% vs. 99.4%, P < 0.001; PSN 99.9% vs. 98.4%, P = 0.015), but the specificity (SN 51.2% > 35.1%, PSN 13.3% > 5.7%, all P < 0.001) and agreement rate (SN 81.1% > 74.5%, P < 0.001, PSN 94.6% > 92.7%, all P < 0.05) of PNI-GARS were superior to those of Lung-RADS. For GGN, the sensitivity (96.5%) and agreement rate (88.6%) of PNI-GARS were better than those of Lung-RADS (0, 18.5%, P < 0.001). For the whole sample, the sensitivity (98.5%) and agreement rate (87.0%) of PNI-GARS were better than Lung-RADS (57.5%, 56.5%, all P < 0.001), whereas the specificity was slightly lower (49.8% < 53.4%, P = 0.003).

CONCLUSION

PNI-GARS was superior to Lung-RADS in diagnostic performance, especially for GGN.

Consequences of Losing Incidental Pulmonary Nodules to Follow-Up: Unmonitored Nodules Progressing to Stage IV Lung Cancer

INTRODUCTION

Lung cancer is the leading cause of cancer-related death globally. Incidental pulmonary nodules represent a golden opportunity for early diagnosis, which is critical for improving survival rates. This study explores the impact of missed pulmonary nodules on the progression of lung cancer.

METHODS

A total of 4,066 stage IV lung cancer cases from 2019 to 2021 in Danish hospitals were investigated to determine whether a chest computed tomography (CT) had been performed within 2 years before diagnosis. CT reports and images were reviewed to identify nodules that had been missed by radiologists or were not appropriately monitored, despite being mentioned by the radiologist, and to assess whether these nodules had progressed to stage IV lung cancer.

RESULTS

Among stage IV lung cancer patients, 13.6% had undergone a chest CT scan before their diagnosis; of these, 44.4% had nodules mentioned. Radiologists missed a nodule in 7.6% of cases. In total, 45.3% of nodules were not appropriately monitored. An estimated 2.5% of stage IV cases could have been detected earlier with proper surveillance.

CONCLUSION

This study underlines the significance of monitoring pulmonary nodules and proposes strategies for enhancing detection and surveillance. These strategies include centralized monitoring and the implementation of automated registries to prevent gaps in follow-up.

Detection of subsolid nodules on chest CT scans during the COVID-19 pandemic

OBJECTIVE

To investigate the detection of subsolid nodules (SSNs) on chest CT scans of outpatients before and during the COVID-19 pandemic, as well as to correlate the imaging findings with epidemiological data. We hypothesized that (pre)malignant nonsolid nodules were underdiagnosed during the COVID-19 pandemic because of an overlap of imaging findings between SSNs and COVID-19 pneumonia.

METHODS

This was a retrospective study including all chest CT scans performed in adult outpatients (> 18 years of age) in September of 2019 (i.e., before the COVID-19 pandemic) and in September of 2020 (i.e., during the COVID-19 pandemic). The images were reviewed by a thoracic radiologist, and epidemiological data were collected from patient-filled questionnaires and clinical referrals. Regression models were used in order to control for confounding factors.

RESULTS

A total of 650 and 760 chest CT scans were reviewed for the 2019 and 2020 samples, respectively. SSNs were found in 10.6% of the patients in the 2019 sample and in 7.9% of those in the 2020 sample (p = 0.10). Multiple SSNs were found in 23 and 11 of the patients in the 2019 and 2020 samples, respectively. Women constituted the majority of the study population. The mean age was 62.8 ± 14.8 years in the 2019 sample and 59.5 ± 15.1 years in the 2020 sample (p < 0.01). COVID-19 accounted for 24% of all referrals for CT examination in 2020.

CONCLUSIONS

Fewer SSNs were detected on chest CT scans of outpatients during the COVID-19 pandemic than before the pandemic, although the difference was not significant. In addition to COVID-19, the major difference between the 2019 and 2020 samples was the younger age in the 2020 sample. We can assume that fewer SSNs will be detected in a population with a higher proportion of COVID-19 suspicion or diagnosis.

Distress and its influencing factors among Chinese patients with incidental pulmonary nodules: a cross-sectional study

The study aims to investigate the distress level and its influencing factors in Chinese pulmonary nodules patients. A total of 163 outpatients in a tertiary hospital in Xi'an, China, were recruited and investigated by using the Impact of Event Scale, Decision Conflict Scale, Consultation Care Measure, Lung Cancer Worry Scale and a demographic questionnaire. The logistic regression model was used to identify the factors of distress. The mean IES score was 37.35 ± 16.65, which was a moderate level. Patients aged 50-60 years, with higher decision conflicts scores, lower physician-patient communication quality score, and who are anxious about the results of future tests or treatments had higher distress score. Distress levels were moderate in patients with pulmonary nodules. Communication between medical staff and patients is extremely important for the management of pulmonary nodules, which affects the quality of the patient's decision-making and his level of distress.

Assessing the effect of scanning parameter on the size and density of pulmonary nodules: a phantom study

BACKGROUND

Lung cancer remains a leading cause of death among cancer patients. Computed tomography (CT) plays a key role in lung cancer screening. Previous studies have not adequately quantified the effect of scanning protocols on the detected tumor size. The aim of this study was to assess the effect of various CT scanning parameters on tumor size and densitometry based on a phantom study and to investigate the optimal energy and mA image quality for screening assessment.

METHODS

We proposed a new model using the LUNGMAN N1 phantom multipurpose anthropomorphic chest phantom (diameters: 8, 10, and 12 mm; CT values: - 100, - 630, and - 800 HU) to evaluate the influence of changes in tube voltage and tube current on the size and density of pulmonary nodules. In the LUNGMAN N1 model, three types of simulated lung nodules representing solid tumors of different sizes were used. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were used to evaluate the image quality of each scanning combination. The consistency between the calculated results based on segmentation from two physicists was evaluated using the interclass correlation coefficient (ICC).

RESULTS

In terms of nodule size, the longest diameters of ground-glass nodules (GGNs) were closest to the ground truth on the images measured at 100 kVp tube voltage, and the longest diameters of solid nodules were closest to the ground truth on the images measured at 80 kVp tube voltage. In respect to density, the CT values of GGNs and solid nodules were closest to the ground truth when measured at 80 kVp and 100 kVp tube voltage, respectively. The overall agreement demonstrates that the measurements were consistent between the two physicists.

CONCLUSIONS

Our proposed model demonstrated that a combination of 80 kVp and 140 mA scans was preferred for measuring the size of the solid nodules, and a combination of 100 kVp and 100 mA scans was preferred for measuring the size of the GGNs when performing lung cancer screening. The CT values at 80 kVp and 100 kVp were preferred for the measurement of GGNs and solid nodules, respectively, which were closest to the true CT values of the nodules. Therefore, the combination of scanning parameters should be selected for different types of nodules to obtain more accurate nodal data.

Determining the impact of an artificial intelligence tool on the management of pulmonary nodules detected incidentally on CT (DOLCE) study protocol: a prospective, non-interventional multicentre UK study

INTRODUCTION

In a small percentage of patients, pulmonary nodules found on CT scans are early lung cancers. Lung cancer detected at an early stage has a much better prognosis. The British Thoracic Society guideline on managing pulmonary nodules recommends using multivariable malignancy risk prediction models to assist in management. While these guidelines seem to be effective in clinical practice, recent data suggest that artificial intelligence (AI)-based malignant-nodule prediction solutions might outperform existing models.

METHODS AND ANALYSIS

This study is a prospective, observational multicentre study to assess the clinical utility of an AI-assisted CT-based lung cancer prediction tool (LCP) for managing incidental solid and part solid pulmonary nodule patients vs standard care. Two thousand patients will be recruited from 12 different UK hospitals. The primary outcome is the difference between standard care and LCP-guided care in terms of the rate of benign nodules and patients with cancer discharged straight after the assessment of the baseline CT scan. Secondary outcomes investigate adherence to clinical guidelines, other measures of changes to clinical management, patient outcomes and cost-effectiveness.

ETHICS AND DISSEMINATION

This study has been reviewed and given a favourable opinion by the South Central-Oxford C Research Ethics Committee in UK (REC reference number: 22/SC/0142).Study results will be available publicly following peer-reviewed publication in open-access journals. A patient and public involvement group workshop is planned before the study results are available to discuss best methods to disseminate the results. Study results will also be fed back to participating organisations to inform training and procurement activities.

TRIAL REGISTRATION NUMBER

NCT05389774.

Diagnostic value of artificial intelligence based on computed tomography (CT) density in benign and malignant pulmonary nodules: a retrospective investigation

Objective

To evaluate the diagnostic value of artificial intelligence (AI) in the detection and management of benign and malignant pulmonary nodules (PNs) using computed tomography (CT) density.

Methods

A retrospective analysis was conducted on the clinical data of 130 individuals diagnosed with PNs based on pathological confirmation. The utilization of AI and physicians has been employed in the diagnostic process of distinguishing benign and malignant PNs. The CT images depicting PNs were integrated into AI-based software. The gold standard for evaluating the accuracy of AI diagnosis software and physician interpretation was the pathological diagnosis.

Results

Out of 226 PNs screened from 130 patients diagnosed by AI and physician reading based on CT, 147 were confirmed by pathology. AI had a sensitivity of 94.69% and radiologists had a sensitivity of 85.40% in identifying PNs. The chi-square analysis indicated that the screening capacity of AI was superior to that of physician reading, with statistical significance (p < 0.05). 195 of the 214 PNs suggested by AI were confirmed pathologically as malignant, and 19 were identified as benign; among the 29 PNs suggested by AI as low risk, 13 were confirmed pathologically as malignant, and 16 were identified as benign. From the physician reading, 193 PNs were identified as malignant, 183 were confirmed malignant by pathology, and 10 appeared benign. Physician reading also identified 30 low-risk PNs, 19 of which were pathologically malignant and 11 benign. The physician readings and AI had kappa values of 0.432 and 0.547, respectively. The physician reading and AI area under curves (AUCs) were 0.814 and 0.798, respectively. Both of the diagnostic techniques had worthy diagnostic value, as indicated by their AUCs of >0.7.

Conclusion

It is anticipated that the use of AI-based CT diagnosis in the detection of PNs would increase the precision in early detection of lung carcinoma, as well as yield more precise evidence for clinical management.

A large pulmonary nodule in a rheumatoid arthritis patient treated with tofacitinib

Pulmonary rheumatoid nodules are rare extra-articular manifestations of rheumatoid arthritis (RA). They are usually asymptomatic but may form cavities and cause clinical symptoms. These nodules are difficult to differentiate clinically and radiologically from tuberculosis, fungal infection, or lung malignancies. Histopathological studies help in the differential diagnosis of pulmonary nodules in patients with RA; however, an effective treatment for rheumatoid lung nodules has not yet been established. This study reports a case of active RA with interstitial lung disease and a large inflammatory lung nodule that was improved with tofacitinib treatment.

Cone-Beam Computed Tomography-Guided Cryobiopsy Combined with Conventional Biopsy for Ground Glass Opacity-Predominant Pulmonary Nodules

INTRODUCTION

Cryobiopsy (CB) using a 1.1-mm cryoprobe under fluoroscopic guidance is feasible and safe for diagnosis of ground glass opacity (GGO) lesions. However, the efficacy of CB combined with cone-beam CT (CBCT) for GGO-predominant pulmonary nodules remains elusive.

METHODS

We retrospectively studied patients who underwent CB combined with conventional biopsy under CBCT guidance for GGO-predominant pulmonary nodules with a consolidation-to-tumour ratio <50.0%.

RESULTS

A total of 32 patients with GGO-predominant pulmonary nodules were enrolled: 17 pure GGOs and 15 mixed GGOs. The mean lesion diameter was 15.81 ± 5.52 mm and the overall diagnostic yield was 71.9%. Seven lesions were diagnosed by CB alone, which increased the diagnostic outcomes by 21.9%. Diagnostic yields for CB, forceps biopsy (FB), brushing, and guide sheath flushing were 65.6%, 46.9%, 15.6%, and 14.3%, respectively. Univariate analysis revealed that positive computed tomography (CT) bronchus sign (p = 0.035), positive CBCT sign (p < 0.01), and CB-first biopsy sequence (p = 0.036) were significant predictive factors for higher diagnostic yield. Specimens obtained by CB had larger mean sample size (p < 0.01), lower blood cell area (p < 0.01), and fewer crush artefacts (p < 0.01) than specimens from FB. No severe bleeding or other complications occurred.

CONCLUSION

CB using a 1.1-mm cryoprobe under CBCT guidance increased diagnostic yield for GGO-predominant pulmonary nodules based on conventional biopsy. Further, it provided larger and nearly intact samples compared with forceps.

Computed tomography imaging and clinical characteristics of pulmonary ground-glass nodules ≤2 cm with micropapillary pattern

BACKGROUND

The aim of this study was to investigate the imaging features, lymph node metastasis, and genetic mutations in micropapillary lung adenocarcinoma (imaging with mixed ground-glass nodules) ≤2 cm, to provide a more precise and refined basis for the selection of lung segment resection.

METHODS

A retrospective analysis of 162 patients with surgically resected pathologically confirmed cancers ≤2.0 cm in diameter (50 cases of micropapillary mixed ground-glass nodules [mGGNs], 50 cases of nonmicropapillary mGGNs, and 62 cases of micropapillary SNs [solid nodules]) was performed. mGGNs were classified into five categories according to imaging features. The distribution of these five morphologies in micropapillary with mGGN and nonmicropapillary with mGGN was analyzed. The postoperative pathology and prognosis of lymph node metastasis were also compared between micropapillary mGGNs and micropapillary with SNs. After searching the TCGA database, we demonstrated heterogeneity, high malignancy and high risk of microcapillary lung cancer cancers.

RESULTS

Different pathological subtypes of mGGN differed in morphological features (p < 0.05). The rate of lymph node metastasis was significantly higher in micropapillary mGGNs than in nonmicropapillary mGGNs. In the TCGA database samples, lactate transmembrane protein activity, collagen transcription score, and fibroblast EMT score were remarkably higher in micropapillary adenocarcinoma. Other pathological subtypes had a better survival prognosis and longer disease-free survival compared with micropapillary adenocarcinoma.

CONCLUSION

mGGNs ≤2 cm with a micropapillary pattern have a higher risk of lymph node metastasis compared with SNs, and computed tomography (CT) imaging features can assist in their diagnosis.

An Effective Malignancy Prediction Model for Incidentally Detected Pulmonary Subsolid Nodules Based on Current and Prior CT Scans

INTRODUCTION

It is challenging to diagnose and manage incidentally detected pulmonary subsolid nodules due to their indolent nature and heterogeneity. The objective of this study is to construct a decision tree-based model to predict malignancy of a subsolid nodule based on radiomics features and evolution over time.

MATERIALS AND METHODS

We derived a training set (2947 subsolid nodules), a test set (280 subsolid nodules) from a cohort of outpatient CT scans, and a second test set (5171 subsolid nodules) from the National Lung Cancer Screening Trial (NLST). A Computer-Aided Diagnosis system (CADs) automatically extracted 28 preselected radiomics features, and we calculated the feature change rates as the change of the quantitative measure per time unit between the prior and current CT scans. We built classification models based on XGBoost and employed 5-fold cross validation to optimize the parameters.

RESULTS

The model that combined radiomics features with their change rates performed the best. The Areas Under Curve (AUCs) on the outpatient test set and on the NLST test set were 0.977 (95% CI, 0.958-0.996) and 0.955 (95% CI, 0.930-0.980), respectively. The model performed consistently well on subgroups stratified by nodule diameters, solid components, and CT scan intervals.

CONCLUSION

This decision tree-based model trained with the outpatient dataset gives promising predictive performance on the malignancy of pulmonary subsolid nodules. Additionally, it can assist clinicians to deliver more accurate diagnoses and formulate more in-depth follow-up strategies.

CT-based radiomics for prediction of pulmonary haemorrhage after percutaneous CT-guided transthoracic lung biopsy of pulmonary nodules

AIM

To evaluate the feasibility of intranodular and perinodular computed tomography (CT) radiomics features for predicting the occurrence of pulmonary haemorrhage after percutaneous CT-guided transthoracic lung biopsy (PCTLB) in pulmonary nodules.

MATERIALS AND METHODS

The data for 332 patients with pulmonary nodules who underwent PCTLB were reviewed retrospectively. Pulmonary haemorrhage after PCTLB was evaluated using CT (144 cases occurred). Radiomics features based on gross nodular (GNV) and perinodular volumes (PNV) were extracted from pre-biopsy CT images and features selection using least absolute shrinkage and selection operator (LASSO) regression, and three radiomics scores (rad-scores) were built. Rad-scores, clinical, and clinical-radiomic models were developed and evaluated to predict the occurrence of pulmonary haemorrhage.

RESULTS

Five, five, and six significant features were selected for prediction of pulmonary haemorrhage based on GNV, PNV, and GNV + PNV, respectively. Lesion depth was the only clinical characteristics related to pulmonary haemorrhage. Lesion depth and rad-score based on GNV, PNV, and GNV + PNV for predicting the pulmonary haemorrhage achieved areas under the curves (AUCs) of 0.656, 0.645, 0.651, and 0.635 in the validation group, respectively. Three clinical-radiomic models improved the AUCs to 0.743, 0.723, and 0.748. The performance of rad-score_GNV + PNV combined with lesion depth outperformed the clinical model (p=0.024) and the radiomics signature (p=0.038). In addition, the radiomics signatures were significantly associated with higher-grade pulmonary haemorrhage (p<0.05).

CONCLUSIONS

Radiomics features from intranodular and perinodular regions of pulmonary nodules have good predictive ability for pulmonary haemorrhage after PCTLB, which may provide additional predictive value for clinical practice.

Noninvasive diagnosis of pulmonary nodules using a circulating tsRNA-based nomogram

Evaluating the accuracy of pulmonary nodule diagnosis avoids repeated low-dose computed tomography (LDCT)/CT scans or invasive examination, yet remains a main clinical challenge. Screening for new diagnostic tools is urgent. Herein, we established a nomogram based on the diagnostic signature of five circulating tsRNAs and CT information to predict malignant pulmonary nodules. In total, 249 blood samples of patients with pulmonary nodules were selected from three different lung cancer centers. Five tsRNAs were identified in the discovery and training cohorts and the diagnostic signature was established by the randomForest algorithm (tRF-Ser-TGA-003, tRF-Val-CAC-005, tRF-Ala-AGC-060, tRF-Val-CAC-024, and tiRNA-Gln-TTG-001). A nomogram was developed by combining tsRNA signature and CT information. The high level of accuracy was identified in an internal validation cohort (n = 83, area under the receiver operating characteristic curve [AUC] = 0.930, sensitivity 100.0%, specificity 73.8%) and external validation cohort (n = 66, AUC = 0.943, sensitivity 100.0%, specificity 86.8%). Furthermore, the diagnostic ability of our model discriminating invasive malignant ones from noninvasive lesions was assessed. A robust performance was achieved in the diagnosis of invasive malignant lesions in both training and validation cohorts (discovery cohort: AUC = 0.850, sensitivity 86.0%, specificity 81.4%; internal validation cohort: AUC = 0.784, sensitivity 78.8%, specificity 78.1%; and external validation cohort: AUC = 0.837, sensitivity 85.7%, specificity 84.0%). This novel circulating tsRNA-based diagnostic model has potential significance in predicting malignant pulmonary nodules. Application of the model could improve the accuracy of pulmonary nodule diagnosis and optimize surgical plans.

CT-Based Radiomics Predicts the Malignancy of Pulmonary Nodules: A Systematic Review and Meta-Analysis

RATIONALE AND OBJECTIVES

More pulmonary nodules (PNs) have been detected with the wide application of computed tomography (CT) in lung cancer screening. Radiomics is a noninvasive approach to predict the malignancy of PNs. We aimed to systematically evaluate the methodological quality of the eligible studies regarding CT-based radiomics models in predicting the malignancy of PNs and evaluate the model performance of the available studies.

MATERIALS AND METHODS

PubMed, Embase, and Web of Science were searched to retrieve relevant studies. The methodological quality of the included studies was assessed using the Radiomics Quality Score (RQS) and Prediction model Risk of Bias Assessment Tool. A meta-analysis was conducted to evaluate the performance of CT-based radiomics model. Meta-regression and subgroup analyses were employed to investigate the source of heterogeneity.

RESULTS

In total, 49 studies were eligible for qualitative analysis and 27 studies were included in quantitative synthesis. The median RQS of 49 studies was 13 (range -2 to 20). The overall risk of bias was found to be high, and the overall applicability was of low concern in all included studies. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.86 95% confidence interval (CI): 0.79-0.91, 0.84 95% CI: 0.78-0.88, and 31.55 95% CI: 21.31-46.70, respectively. The overall area under the curve was 0.91 95% CI: 0.89-0.94. Meta-regression showed the type of PNs on heterogeneity. CT-based radiomics models performed better in studies including only solid PNs.

CONCLUSION

CT-based radiomics models exhibited excellent diagnostic performance in predicting the malignancy of PNs. Prospective, large sample size, and well-devised studies are desired to verify the prediction capabilities of CT-based radiomics model.

The diagnosis performance of convolutional neural network in the detection of pulmonary nodules: a systematic review and meta-analysis

BACKGROUND

Pulmonary nodules are an early imaging indication of lung cancer, and early detection of pulmonary nodules can improve the prognosis of lung cancer. As one of the applications of machine learning, the convolutional neural network (CNN) applied to computed tomography (CT) imaging data improves the accuracy of diagnosis, but the results could be more consistent.

PURPOSE

To evaluate the diagnostic performance of CNN in assisting in detecting pulmonary nodules in CT images.

MATERIAL AND METHODS

PubMed, Cochrane Library, Web of Science, Elsevier, CNKI and Wanfang databases were systematically retrieved before 30 April 2023. Two reviewers searched and checked the full text of articles that might meet the criteria. The reference criteria are joint diagnoses by experienced physicians. The pooled sensitivity, specificity and the area under the summary receiver operating characteristic curve (AUC) were calculated by a random-effects model. Meta-regression analysis was performed to explore potential sources of heterogeneity.

RESULTS

Twenty-six studies were included in this meta-analysis, involving 2,391,702 regions of interest, comprising segmented images with a few wide pixels. The combined sensitivity and specificity values of the CNN model in detecting pulmonary nodules were 0.93 and 0.95, respectively. The pooled diagnostic odds ratio was 291. The AUC was 0.98. There was heterogeneity in sensitivity and specificity among the studies. The results suggested that data sources, pretreatment methods, reconstruction slice thickness, population source and locality might contribute to the heterogeneity of these eligible studies.

CONCLUSION

The CNN model can be a valuable diagnostic tool with high accuracy in detecting pulmonary nodules.

Emotional Distress, Anxiety, and General Health Status in Patients With Newly Identified Small Pulmonary Nodules: Results From the Watch the Spot Trial

BACKGROUND

Anxiety and emotional distress have not been studied in large, diverse samples of patients with pulmonary nodules.

RESEARCH QUESTION

How common are anxiety and distress in patients with newly identified pulmonary nodules, and what factors are associated with these outcomes?

STUDY DESIGN AND METHODS

This study surveyed participants in the Watch the Spot Trial, a large, pragmatic clinical trial of more vs less intensive strategies for radiographic surveillance of patients with small pulmonary nodules. The survey included validated instruments to measure patient-centered outcomes such as nodule-related emotional distress (Impact of Event Scale-Revised) and anxiety (Six-Item State Anxiety Inventory) 6 to 8 weeks following nodule identification. Mixed-effects models were used to compare outcomes between study arms following adjustment for potential confounders and clustering within enrollment site, while also examining a limited number of prespecified explanatory factors, including nodule size, mode of detection, type of ordering clinician, and lack of timely notification prior to contact by the study team.

RESULTS

The trial enrolled 34,699 patients; 2,049 individuals completed the baseline survey (5.9%). Respondents and nonrespondents had similar demographic and nodule characteristics, although more respondents were non-Hispanic and White. Impact of Event Scale-Revised scores indicated mild, moderate, or severe distress in 32.2%, 9.4%, and 7.2% of respondents, respectively, with no difference in scores between study arms. Following adjustment, greater emotional distress was associated with larger nodule size and lack of timely notification by a clinician; distress was also associated with younger age, female sex, ever smoking, Black race, and Hispanic ethnicity. Anxiety was associated with lack of timely notification, ever smoking, and female sex.

INTERPRETATION

Almost one-half of respondents experienced emotional distress 6 to 8 weeks following pulmonary nodule identification. Strategies are needed to mitigate the burden of distress, especially in younger, female, ever smoking, and minoritized patients, and those with larger nodules.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov; No.: NCT02623712; URL: www.

CLINICALTRIALS

gov.

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

OBJECTIVE

To establish a preoperative model for the differential diagnosis of benign and malignant pulmonary nodules (PNs), and to evaluate the related factors of overdiagnosis of benign PNs at the time of imaging assessments.

MATERIALS AND METHODS

In this retrospective study, 357 patients (median age, 52 years; interquartile range, 46-59 years) with 407 PNs were included, who underwent surgical histopathologic evaluation between January 2020 and December 2020. Patients were divided into a training set (n = 285) and a validation set (n = 122) to develop a preoperative model to identify benign PNs. CT scan features were reviewed by two chest radiologists, and imaging findings were categorized. The overdiagnosis rate of benign PNs was calculated, and bivariate and multivariable logistic regression analyses were used to evaluate factors associated with benign PNs that were over-diagnosed as malignant PNs.

RESULTS

The preoperative model identified features such as the absence of part-solid and non-solid nodules, absence of spiculation, absence of vascular convergence, larger lesion size, and CYFRA21-1 positivity as features for identifying benign PNs on imaging, with a high area under the receiver operating characteristic curve of 0.88 in the validation set. The overdiagnosis rate of benign PNs was found to be 50%. Independent risk factors for overdiagnosis included diagnosis as non-solid nodules, pleural retraction, vascular convergence, and larger lesion size at imaging.

CONCLUSION

We developed a preoperative model for identifying benign and malignant PNs and evaluating factors that led to the overdiagnosis of benign PNs. This preoperative model and result may help clinicians and imaging physicians reduce unnecessary surgery.

Development of a novel artificial intelligence algorithm to detect pulmonary nodules on chest radiography

BACKGROUND

In this study, we aimed to develop a novel artificial intelligence (AI) algorithm to support pulmonary nodule detection, which will enable physicians to efficiently interpret chest radiographs for lung cancer diagnosis.

METHODS

We analyzed chest X-ray images obtained from a health examination center in Fukushima and the National Institutes of Health (NIH) Chest X-ray 14 dataset. We categorized these data into two types: type A included both Fukushima and NIH datasets, and type B included only the Fukushima dataset. We also demonstrated pulmonary nodules in the form of a heatmap display on each chest radiograph and calculated the positive probability score as an index value.

RESULTS

Our novel AI algorithms had a receiver operating characteristic (ROC) area under the curve (AUC) of 0.74, a sensitivity of 0.75, and a specificity of 0.60 for the type A dataset. For the type B dataset, the respective values were 0.79, 0.72, and 0.74. The algorithms in both the type A and B datasets were superior to the accuracy of radiologists and similar to previous studies.

CONCLUSIONS

The proprietary AI algorithms had a similar accuracy for interpreting chest radiographs when compared with previous studies and radiologists. Especially, we could train a high quality AI algorithm, even with our small type B data set. However, further studies are needed to improve and further validate the accuracy of our AI algorithm.

A novel preoperative image-guided localization for small pulmonary nodule resection using a claw-suture device

Video-assisted thoracoscopic surgery (VATS) provides better option concerning pathological diagnosis and curative intention of small pulmonary nodules (SPNs) that are sometimes challenging to localize. We assess the safety and feasibility of a new localization technique for SPNs, and report experience accumulated over time. A retrospective review of the new claw-suture localization cases between February 2018 and May 2023 was performed. Nodules were localized by a novel system that has an anchor claw and a tri-colored suture, guided by computed tomography (CT). Localization and operative procedure outcomes were then assessed. A total of 590 SPNs were localized from 568 patients before operation. The median nodule size was 0.70 cm (range, 0.3-2.0 cm). The claw-suture localization was successful without dislodgment or device fracture in 574 of 590 lesions (97.3%). Failures included not meeting target distance between claw and lesion (n = 13 [2.2%]), and device displacement (n = 3 [0.5%]). Complications requiring no further medical intervention included asymptomatic pneumothorax (n = 68 [11.5%]), parenchymal hemorrhage (n = 51 [8.6%]), and hemothorax (n = 1 [0.2%]) with the exception of pleural reaction observed in 2 cases (0.3%). Additionally, the depth of pulmonary nodules was significantly associated with the occurrence of pneumothorax (P = 0.036) and parenchymal hemorrhage (P = 0.000). The median duration of the localization was 12 min (range, 7-25 min). No patient complained of remarkable pain during the entire procedure. Retrieve of device after operation was 100%. The new localization technique is a safe, feasible, and well-tolerated method to localize SPNs for VATS resection.

A 51-Year-Old Woman With Progressive Dyspnea and Diffuse Bilateral Pulmonary Nodules

CASE PRESENTATION

A 51-year-old woman was referred to our hospital with progressive dyspnea on exertion for 2 months after COVID-19 vaccination (ChAdOx1-S [recombinant] vaccine). She did not have a cough, fever, hemoptysis, weight loss, or night sweats. She had no history of arthritis, rash, photosensitivity, or other signs of autoimmune disease. Chest radiograph revealed diffuse ground-glass opacities and bilateral pulmonary nodules. She denied any history of smoking, contact with individuals infected with TB, relevant hobbies, or exposure to domestic animals. She had no relevant medical history, was previously healthy, and worked as a chef.

Trends in the incidence of pulmonary nodules in chest computed tomography: 10-year results from two Dutch hospitals

OBJECTIVE

To study trends in the incidence of reported pulmonary nodules and stage I lung cancer in chest CT.

METHODS

We analyzed the trends in the incidence of detected pulmonary nodules and stage I lung cancer in chest CT scans in the period between 2008 and 2019. Imaging metadata and radiology reports from all chest CT studies were collected from two large Dutch hospitals. A natural language processing algorithm was developed to identify studies with any reported pulmonary nodule.

RESULTS

Between 2008 and 2019, a total of 74,803 patients underwent 166,688 chest CT examinations at both hospitals combined. During this period, the annual number of chest CT scans increased from 9955 scans in 6845 patients in 2008 to 20,476 scans in 13,286 patients in 2019. The proportion of patients in whom nodules (old or new) were reported increased from 38% (2595/6845) in 2008 to 50% (6654/13,286) in 2019. The proportion of patients in whom significant new nodules (≥ 5 mm) were reported increased from 9% (608/6954) in 2010 to 17% (1660/9883) in 2017. The number of patients with new nodules and corresponding stage I lung cancer diagnosis tripled and their proportion doubled, from 0.4% (26/6954) in 2010 to 0.8% (78/9883) in 2017.

CONCLUSION

The identification of incidental pulmonary nodules in chest CT has steadily increased over the past decade and has been accompanied by more stage I lung cancer diagnoses.

CLINICAL RELEVANCE STATEMENT

These findings stress the importance of identifying and efficiently managing incidental pulmonary nodules in routine clinical practice.

KEY POINTS

• The number of patients who underwent chest CT examinations substantially increased over the past decade, as did the number of patients in whom pulmonary nodules were identified. • The increased use of chest CT and more frequently identified pulmonary nodules were associated with more stage I lung cancer diagnoses.

ACR Appropriateness Criteria® Incidentally Detected Indeterminate Pulmonary Nodule

Incidental pulmonary nodules are common. Although the majority are benign, most are indeterminate for malignancy when first encountered making their management challenging. CT remains the primary imaging modality to first characterize and follow-up incidental lung nodules. This document reviews available literature on various imaging modalities and summarizes management of indeterminate pulmonary nodules detected incidentally. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Diagnostic performance of artificial intelligence for pediatric pulmonary nodule detection on chest computed tomography: comparison of simulated lower radiation doses

The combination of low dose CT and AI performance in the pediatric population has not been explored. Understanding this relationship is relevant for pediatric patients given the potential radiation risks. Here, the objective was to determine the diagnostic performance of commercially available Computer Aided Detection (CAD) for pulmonary nodules in pediatric patients at simulated lower radiation doses. Retrospective chart review of 30 sequential patients between 12-18 years old who underwent a chest CT on the Siemens SOMATOM Force from December 20, 2021, to April 12, 2022. Simulated lower doses at 75%, 50%, and 25% were reconstructed in lung kernel at 3 mm slice thickness using ReconCT and imported to Syngo CT Lung CAD software for analysis. Two pediatric radiologists reviewed the full dose CTs to determine the reference read. Two other pediatric radiologists compared the Lung CAD results at 100% dose and each simulated lower dose level to the reference on a nodule by nodule basis. The sensitivity (Sn), positive predictive value (PPV), and McNemar test were used for comparison of Lung CAD performance based on dose. As reference standard, 109 nodules were identified by the two radiologists. At 100%, and simulated 75%, 50%, and 25% doses, lung CAD detected 60, 62, 58, and 62 nodules, respectively; 28, 28, 29, and 26 were true positive (Sn = 26%, 26%, 27%, 24%), 30, 32, 27, and 34 were false positive (PPV = 48%, 47%, 52%, 43%). No statistically significance difference of Lung CAD performance at different doses was found, with p-values of 1.0, 1.0, and 0.7 at simulated 75%, 50%, and 25% doses compared to standard dose.

CONCLUSION

The Lung CAD shows low sensitivity at all simulated lower doses for the detection of pulmonary nodules in this pediatric population. However, radiation dose may be reduced from standard without further compromise to the Lung CAD performance.

WHAT IS KNOWN

• High diagnostic performance of Lung CAD for detection of pulmonary nodules in adults. • Several imaging techniques are applied to reduce pediatric radiation dose.

WHAT IS NEW

• Low sensitivity at all simulated lower doses for the detection of pulmonary nodules in our pediatric population. • Radiation dose may be reduced from standard without further compromise to the Lung CAD performance.

The Thoracic Research Evaluation and Treatment 2.0 Model: A Lung Cancer Prediction Model for Indeterminate Nodules Referred for Specialist Evaluation

BACKGROUND

Appropriate risk stratification of indeterminate pulmonary nodules (IPNs) is necessary to direct diagnostic evaluation. Currently available models were developed in populations with lower cancer prevalence than that seen in thoracic surgery and pulmonology clinics and usually do not allow for missing data. We updated and expanded the Thoracic Research Evaluation and Treatment (TREAT) model into a more generalized, robust approach for lung cancer prediction in patients referred for specialty evaluation.

RESEARCH QUESTION

Can clinic-level differences in nodule evaluation be incorporated to improve lung cancer prediction accuracy in patients seeking immediate specialty evaluation compared with currently available models?

STUDY DESIGN AND METHODS

Clinical and radiographic data on patients with IPNs from six sites (N = 1,401) were collected retrospectively and divided into groups by clinical setting: pulmonary nodule clinic (n = 374; cancer prevalence, 42%), outpatient thoracic surgery clinic (n = 553; cancer prevalence, 73%), or inpatient surgical resection (n = 474; cancer prevalence, 90%). A new prediction model was developed using a missing data-driven pattern submodel approach. Discrimination and calibration were estimated with cross-validation and were compared with the original TREAT, Mayo Clinic, Herder, and Brock models. Reclassification was assessed with bias-corrected clinical net reclassification index and reclassification plots.

RESULTS

Two-thirds of patients had missing data; nodule growth and fluorodeoxyglucose-PET scan avidity were missing most frequently. The TREAT version 2.0 mean area under the receiver operating characteristic curve across missingness patterns was 0.85 compared with that of the original TREAT (0.80), Herder (0.73), Mayo Clinic (0.72), and Brock (0.68) models with improved calibration. The bias-corrected clinical net reclassification index was 0.23.

INTERPRETATION

The TREAT 2.0 model is more accurate and better calibrated for predicting lung cancer in high-risk IPNs than the Mayo, Herder, or Brock models. Nodule calculators such as TREAT 2.0 that account for varied lung cancer prevalence and that consider missing data may provide more accurate risk stratification for patients seeking evaluation at specialty nodule evaluation clinics.

Semantic characteristic grading of pulmonary nodules based on deep neural networks

BACKGROUND

Accurate grading of semantic characteristics is helpful for radiologists to determine the probabilities of the likelihood of malignancy of a pulmonary nodule. Nevertheless, because of the complex and varied properties of pulmonary nodules, assessing semantic characteristics (SC) is a difficult task.

METHOD

In this paper, we first analyze a set of important semantic characteristics of pulmonary nodules and extract the important SCs relating to pulmonary nodule malignancy by Pearson's correlation approach. Then, we propose three automatic SC grading models based on deep belief network (DBN) and a multi-branch convolutional neural network (CNN) classifier, MBCNN. The first DBN model takes grayscale and binary nodule images as the input, and the second DBN model takes grayscale nodule images and 72 features extracted from pulmonary nodules as the input.

RESULTS

Experimental results indicate that our algorithms can achieve satisfying results on semantic characteristic grading. Especially, the MBCNN can obtain higher semantic characteristic grading results with an average accuracy of 89.37%.

CONCLUSIONS

Quantitative and automatic grading of semantic characteristics proposed in this paper can assist radiologists effectively assess the likelihood of pulmonary nodules being malignant and further promote the early expectant treatment of malignant nodules.

PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images

OBJECTIVE

To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning.

METHODS

We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 6:2:2. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility.

RESULTS

Four hundred seventy-seven patients were included and the nodules were divided into six groups: benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89.

CONCLUSIONS

In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.