Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features.

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

A clinically applicable model more suitable for predicting malignancy or benignity of pulmonary ground glass nodules in women patients.

BACKGROUND: In recent years, clinicians often encounter patients with multiple pulmonary nodules in their clinical practices. As most of these ground glass nodules (GGNs) are small in volume and show no spicule sign, it is difficult to use Mayo Clinic Model to make early diagnosis of lung cancer accurately, especially in large numbers of nonsmoking women who have no tumor history. Other clinical models are disadvantaged by a relatively high false-positive or false-negative rate. Therefore, there is an urgent need to establish a new model of predicting malignancy or benignity of pulmonary GGNs for the sake of making accurate and early diagnosis of lung cancer. METHODS: Included in this study were GGNs surgically resected from patients who were admitted to Yiwu Central Hospital from January 2018 to March 2024, including both male and female patients, there is no gender specific issue. The nature of all these GGN tissues was confirmed pathologically. The case data were statistically analyzed to establish a mathematical prediction model, the prediction performance of which was verified by the pathological results. RESULTS: Altogether 261 GGN patients met the inclusion criteria. Using the results of logistic regression analysis, a mathematical prediction equation was established as follows: Malignant probability (mP) = ex/ (1 + ex); when mP was > 0.5, the GGN was considered as malignant, and when mP was ≤ 0.5, it was considered as benign. x= -2.46 + 1.032*gender + 1.85*mGGN + 1.40*VCS-0.0027*mean CT value of the nodule + 0.078*maximum diameter of the nodule, where e represents the natural logarithm; if the patient was a female, gender = 1 (otherwise = 0); if the pulmonary nodule was a mixed GGN, mGGN = 1 (otherwise = 0); if the pulmonary nodule had vascular convergence sign, VCS = 1 (otherwise = 0). The prediction performance of the mathematical prediction model was verified as follows: the negative prediction value was 0.97156 and the positive prediction value was 0.3800 in the model group versus 1 and 0.25 in the verification group. CONCLUSION: In this study, we identified female gender, mGGN, VCS, mean CT value and maximum nodule diameter as five key factors for predicting malignancy or benignity of pulmonary nodules, based on which we established a mathematical prediction model. This novel innovation may provide a useful auxiliary tool for predicting malignancy and benignity of pulmonary nodules, especially in women patients.

A Case of Unknown Pulmonary Nodules.

A 54-year-old female with history of underlying asthma and 10 pack-year smoking history was seen in interventional pulmonology clinic for evaluation of multiple scattered pulmonary nodules incidentally found on chest computed tomography (CT). Given the central location of the dominant left upper lobe (LUL) nodule and its proximity to an airway, bronchoscopic biopsy was felt to be the right approach. The IonTM Endoluminal System robotic-assisted navigational bronchoscope (Intuitive Surgical, Sunnyvale, California) was used to sample the LUL nodule under fluoroscopic guidance. Together with clinical and radiological findings, the histological and immunophenotypic findings are supportive for Diffuse Idiopathic Pulmonary Neuroendocrine Cell Hyperplasia (DIPNECH). The DIPNECH is a rare condition first described in a case series published in cancer in 1953. This highly atypical condition highlights the utility of modern navigational bronchoscopy in safely securing a diagnostic bronchoscopic biopsy in locations not previously reachable. This is especially relevant given the challenge and risk to percutaneous CT-guided biopsy. Complications are known to scale with depth from skin site, emphasizing benefits of the bronchoscopic approach in obese patients.

The Value of Topological Radiomics Analysis in Predicting Malignant Risk of Pulmonary Ground-Glass Nodules: A Multi-Center Study.

BACKGROUND: Early detection and accurate differentiation of malignant ground-glass nodules (GGNs) in lung CT scans are crucial for the effective treatment of lung adenocarcinoma. However, existing imaging diagnostic methods often struggle to distinguish between benign and malignant GGNs in the early stages. This study aims to predict the malignancy risk of GGNs observed in lung CT scans by applying two radiomics methods: topological data analysis and texture analysis. METHODS: A retrospective analysis was conducted on 3223 patients from two centers between January 2018 and June2023. The dataset was divided into training, testing, and validation sets to ensure robust model development and validation. We developed topological features applied to GGNs using radiomics analysis based on homology. This innovative approach emphasizes the integration of topological information, capturing complex geometric and spatial relationships within GGNs. By combining machine learning and deep learning algorithms, we established a predictive model that integrates clinical parameters, previous radiomics features, and topological radiomics features. RESULTS: Incorporating topological radiomics into our model significantly enhanced the ability to distinguish between benign and malignant GGNs. The topological radiomics model achieved areas under the curve (AUC) of 0.85 and 0.862 in two independent validation sets, outperforming previous radiomics models. Furthermore, this model demonstrated higher sensitivity compared to models based solely on clinical parameters, with sensitivities of 80.7% in validation set 1 and 82.3% in validation set 2. The most comprehensive model, which combined clinical parameters, previous radiomics features, and topological radiomics features, achieved the highest AUC value of 0.879 across all datasets. CONCLUSION: This study validates the potential of topological radiomics in improving the predictive performance for distinguishing between benign and malignant GGNs. By integrating topological features with previous radiomics and clinical parameters, our comprehensive model provides a more accurate and reliable basis for developing treatment strategies for patients with GGNs.

A retrospective analysis of simultaneous bilateral uniportal thoracoscopic surgeries for multiple primary bilateral pulmonary nodules.

BACKGROUND: There are no standard treatment options for bilateral multiple pulmonary nodules requiring resection. This study aimed to summarize the experience of simultaneous bilateral uniportal video-assisted thoracoscopic surgery for the treatment of bilateral multiple primary pulmonary nodules. METHODS: The clinical data of 65 cases of simultaneous bilateral uniportal thoracoscopic surgery for bilateral multiple primary pulmonary nodules treated were retrospectively analyzed. These cases were treated within The Ninth Medical Center of PLA General Hospital between January 2018 and November 2020. Parameters related to the surgery, perioperative aspects, surgical techniques, pathology results, and postoperative complications were examined. RESULTS: All surgeries were conducted through uniportal video-assisted thoracoscopic surgery, with no instances of intraoperative conversion to thoracotomy. Fifty-three patients further underwent CT-guided Hookwire localization for the localization of pulmonary nodules. A total of 189 nodules were resected using multiple surgical procedures, with a malignancy rate of 86.2%. The average operation time was 226 ± 77.4 min, the average thoracic drainage duration was 3.1 ± 1.5 days, the average 24 h pleural drainage was 385.9 ± 157.4 mL, the average postoperative hospital stay was 8.6 ± 2.4 days, and the average blood loss was 77.2 ± 33.8 mL. Post-surgery, all patients were transferred to the ward safely within 12 h. 15.38% of patients have prolonged drainage time, and 12.31% of patients experience complications such as lung infection, arrhythmia, and venous thrombosis. CONCLUSION: The selected cases undergoing simultaneous bilateral uniportal video-assisted thoracoscopic surgery for the management of bilateral multiple primary pulmonary nodules demonstrated favorable outcomes. Our observations indicate the safety and feasibility of this procedure, providing an individualized and precise treatment approach for affected patients.

CT-guided microcoil versus hook-wire localization of pulmonary nodule prior to video-assisted thoracoscopic surgery without fluoroscopic guidance.

BACKGROUND: Both microcoils and hook-wires are commonly utilized for preoperative pulmonary nodule localization due to their convenience, but it remains unclear which one should be prioritized for recommendation. AIMS: To compare the safety and efficacy of microcoils and hook-wires for pulmonary nodule localization. METHODS: From January 2021 to December 2021, 310 consecutive patients (113 males and 197 females) with 341 pulmonary nodules who underwent CT-guided microcoil or hook-wire localization prior to video-assisted thoracoscopic surgery (VATS) at our center were retrospectively included in this study. There were 161 patients in the microcoil group and 149 patients in the hook-wire group. The successful localization rate, complication rate, radiation exposure, and medical costs were compared between the two groups. RESULTS: A total of 341 pulmonary nodules were localized, with a success rate of 99% (180/184) in the microcoil group and 93% (146/157) in the hook-wire group, respectively. All patients successfully underwent VATS. Multivariate analysis revealed that hook-wire localization, shorter needle depth into the lung tissue and the longer waiting time from localization to VATS were the risk factors for the localization failure. The incidences of pneumothorax in the microcoil group and hook-wire group were 34.8% (56/161) and 34.9% (52/149), respectively (P = 0.983). The incidences of pneumorrhagia were 13% (24/184) and 46.5% (73/157), respectively (P = 0.000). Multivariate analysis revealed that hook-wire localization and greater depth of needle penetration into lung tissue were risk factors for pneumorrhagia. CONCLUSION: Microcoil localization of pulmonary nodules is superior to hook-wire localization in terms of efficacy and safety. This finding provides insight into priority and broader promotion of microcoil localization.

A Case of Nonsmoker Pulmonary Langerhans Cell Histiocytosis With Multiple Pulmonary Nodules Disappeared and Appeared.

We present a non-smoker woman in her 40s with PLCH who presented with atypical imaging findings of multiple pulmonary noncavitary nodules without air cysts with repeated waxing and waning.

An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images.

BACKGROUND: This study aims to assess the performance of an established an AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPIs) to detect pulmonary ground-glass nodules (GGNs) on virtual monochromatic images (VMIs), and to screen the optimal virtual monochromatic energy for the clinical evaluation of GGNs. METHODS: Non-enhanced chest SDCT images of patients with pulmonary GGNs in our clinic from January 2022 to December 2022 were continuously collected: adenocarcinoma in situ (AIS, n = 40); minimally invasive adenocarcinoma (MIA, n = 44) and invasive adenocarcinoma (IAC, n = 46). A commercial CAD system based on deep convolutional neural networks (DL-CAD) was used to process the CPIs, 40, 50, 60, 70, and 80 keV monochromatic images of 130 spectral CT images. AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by the receiver operating characteristic (ROC) curves, and Delong's test was used to compare the CPIs group with the VMIs group. RESULTS: When distinguishing IAC from MIA, the diagnostic efficiency of total mass was obtained at 80 keV, which was superior to those of other energy levels (P < 0.05). And Delong's test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05). CONCLUSION: The AI algorithm trained on CPIs showed consistent diagnostic performance on VMIs. When pulmonary GGNs are encountered in clinical practice, 80 keV could be the optimal virtual monochromatic energy for the identification of preoperative IAC on a non-enhanced chest CT.

Electromagnetic navigation system for computed tomography-guided synchronous percutaneous lung biopsy and microwave ablation of pulmonary nodules: a prospective, single-center, single-arm clinical study.

BACKGROUND: The purpose of this study was to clinically evaluate the safety and effectiveness of the electromagnetic navigation (EMN) system designed for computed tomography (CT)-guided synchronous percutaneous lung biopsy and microwave ablation (MWA) of pulmonary nodules. METHODS: This prospective, single-center, single-arm clinical cohort study was conducted in Beijing Hospital from March 2023 to May 2023. Patients who underwent CT-guided synchronous percutaneous lung biopsy and MWA via the EMN system were prospectively enrolled in our study. All the interventional procedures were performed by the same interventional radiologist. The technical success rate, the technical efficacy rates of biopsy and MWA were assessed as the primary outcomes. Preoperative, intraoperative, and postoperative variables were also recorded and analyzed for each patient. RESULTS: A total of 48 patients were enrolled in the study. The technical success rate was 100%. The technical efficacy rate of biopsy was 95.8% (46/48), and the technical efficacy rate of WMA was 100% (48/48) with no recurrence during follow-up. The total and subpleural needle trajectory length and distance error were 8.3 ± 2.6 cm, 3.6 ± 1.6 cm, and 1.84 ± 1.08 mm, respectively. The median numbers of needle adjustments and CT acquisitions were 1 (range 1-3) and 3 (range 3-5), respectively. The time to reach the target and procedure time were 4.4 ± 1.7 and 19.7 ± 5.2 min, respectively. The dose length product was 748.8 ± 221.8 mGy*cm. The median postoperative hospital stay was 1 (range 1-7) days. No major complications (grade ≥3) occurred and only seven minor complications (14.6%) occurred, including six cases of pneumothorax and one case of hemoptysis. The radiologists achieved high satisfaction scores after surgery. CONCLUSION: The EMN system is feasible, safe and effective for CT-guided synchronous percutaneous lung biopsy and MWA of pulmonary nodules.

Comparison of electromagnetic navigation bronchoscopy localization and CT-guided percutaneous localization in resection of lung nodules: A protocol for systematic review and meta-analysis.

BACKGROUND: This study aimed to evaluate the efficacy and safety between electromagnetic navigational bronchoscopy (ENB) and computed tomography (CT)-guided percutaneous localization before resection of pulmonary nodules. METHODS: Pubmed, Embase, Web of Science, and the Cochrane Library databases were searched from January 1, 2000 to April 30, 2022, for relevant studies. Two reviewers conducted the search, selection, and extraction of data from eligible studies. The risk of bias was assessed using the Newcastle-Ottawa Scale. The primary outcome was the localization success rate, and the secondary outcomes were the pneumothorax incidence and localization time. The meta-analysis was performed by Review Manager 5.4. The protocol for the meta-analysis was registered on PROSPERO (Registration number: CRD42022345972). RESULTS: Five cohort studies comprising 441 patients (ENB group: 185, CT group: 256) were analyzed. Compared with the CT-guided group, the ENB-guided group was associated with lower pneumothorax incidence (relative ratio = 0.16, 95% confidence interval [CI]: 0.04-0.65, P = .01). No significant differences were found in location success rates (relative ratio = 1.01, 95% CI: 0.98-1.05, P = .38) and localization time (mean difference = 0.99, 95% CI: -5.73 to 7.71, P = .77) between the ENB group and CT group. CONCLUSION: Both ENB and CT-guided are valuable technologies in localizing lung nodules before video-assisted thoracoscopic surgery based on current investigations. ENB achieved a lower pneumothorax rate than the CT-guided group. In our opinion, there is no perfect method, and decision-making should be given the actual circumstances of each institute. Future prospective studies in the form of a randomized trial are needed to confirm their clinical value.

H-marker via bronchoscopy under LungPro navigation combined with cone-beam computed tomography for locating multiple pulmonary ground-glass nodules: A case report and literature review.

RATIONALE: Pulmonary ground-glass nodules (GGNs) pose challenges in intraoperative localization due to their primarily nonsolid composition. This report highlights a novel approach using H-marker deployment guided by LungPro navigation combined with cone-beam computed tomography (CBCT) for precise localization of multiple GGNs. PATIENT CONCERNS: A 55-year-old female patient presented at Sir-Run-Run-Shaw Hospital, Zhejiang University School of Medicine, in June 2021, requiring thoracoscopic surgery for the management of multiple GGNs in her right lung. She had a recent history of thoracoscopic wedge resection for a lesion in her lower left lung 3 months prior. DIAGNOSES: Computed tomography scans revealed the presence of 3 mixed GGNs in the right lung, with further confirmation identifying these as solitary pulmonary nodules, necessitating surgical intervention. INTERVENTIONS: The patient underwent thoracoscopic surgery, during which the multiple nodules in her right lung were precisely localized utilizing an H-marker implanted bronchoscopically under the guidance of LungPro navigation technology, with CBCT providing additional confirmation of nodule positioning. This innovative combination of technologies facilitated accurate targeting of the lesions. OUTCOMES: Postoperative histopathological analysis confirmed the nodules to be microinvasive adenocarcinomas. Radiographic examination with chest X-rays demonstrated satisfactory lung expansion, indicating effective lung function preservation following the procedure. Follow-up assessments have shown no evidence of tumor recurrence, suggesting successful treatment. LESSONS: The employment of H-marker implantation guided by the LungPro navigation system with CBCT confirmation presents a feasible and efficacious strategy for localizing multiple pulmonary GGNs. To further validate its clinical utility and safety, large-scale, multicenter, prospective studies are warranted. This approach holds promise in enhancing the precision and outcomes of surgeries involving GGNs.

Evaluation of the invasiveness of pure ground-glass nodules based on dual-head ResNet technique.

OBJECTIVE: To intelligently evaluate the invasiveness of pure ground-glass nodules with multiple classifications using deep learning. METHODS: pGGNs in 1136 patients were pathologically confirmed as lung precursor lesions [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)], minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Four different models [EfficientNet-b0 2D, dual-head ResNet_3D, a 3D model combining three features (3D_3F), and a 3D model combining 19 features (3D_19F)] were constructed to evaluate the invasiveness of pGGNs using the EfficientNet and ResNet networks. The Obuchowski index was used to evaluate the differences in diagnostic efficiency among the four models. RESULTS: The patients with pGGNs (360 men, 776 women; mean age, 54.63 ± 12.36 years) included 235 cases of AAH + AIS, 332 cases of MIA, and 569 cases of IAC. In the validation group, the areas under the curve in detecting the invasiveness of pGGNs as a three-category classification (AAH + AIS, MIA, IAC) were 0.8008, 0.8090, 0.8165, and 0.8158 for EfficientNet-b0 2D, dual-head ResNet_3D, 3D_3F, and 3D_19F, respectively, whereas the accuracies were 0.6422, 0.6158, 0.651, and 0.6364, respectively. The Obuchowski index revealed no significant differences in the diagnostic performance of the four models. CONCLUSIONS: The dual-head ResNet_3D_3F model had the highest diagnostic efficiency for evaluating the invasiveness of pGGNs in the four models.

GC-WIR : 3D global coordinate attention wide inverted ResNet network for pulmonary nodules classification.

PURPOSE: Currently, deep learning methods for the classification of benign and malignant lung nodules encounter challenges encompassing intricate and unstable algorithmic models, limited data adaptability, and an abundance of model parameters.To tackle these concerns, this investigation introduces a novel approach: the 3D Global Coordinated Attention Wide Inverted ResNet Network (GC-WIR). This network aims to achieve precise classification of benign and malignant pulmonary nodules, leveraging its merits of heightened efficiency, parsimonious parameterization, and robust stability. METHODS: Within this framework, a 3D Global Coordinate Attention Mechanism (3D GCA) is designed to compute the features of the input images by converting 3D channel information and multi-dimensional positional cues. By encompassing both global channel details and spatial positional cues, this approach maintains a judicious balance between flexibility and computational efficiency. Furthermore, the GC-WIR architecture incorporates a 3D Wide Inverted Residual Network (3D WIRN), which augments feature computation by expanding input channels. This augmentation mitigates information loss during feature extraction, expedites model convergence, and concurrently enhances performance. The utilization of the inverted residual structure imbues the model with heightened stability. RESULTS: Empirical validation of the GC-WIR method is performed on the LUNA 16 dataset, yielding predictions that surpass those generated by previous models. This novel approach achieves an impressive accuracy rate of 94.32%, coupled with a specificity of 93.69%. Notably, the model's parameter count remains modest at 5.76M, affording optimal classification accuracy. CONCLUSION: Furthermore, experimental results unequivocally demonstrate that, even under stringent computational constraints, GC-WIR outperforms alternative deep learning methodologies, establishing a new benchmark in performance.

Impact of artificial intelligence assistance on pulmonary nodule detection and localization in chest CT: a comparative study among radiologists of varying experience levels.

The study aimed to evaluate the impact of AI assistance on pulmonary nodule detection rates among radiology residents and senior radiologists, along with assessing the effectiveness of two different commercialy available AI software systems in improving detection rates and LungRADS classification in chest CT. The study cohort included 198 participants with 221 pulmonary nodules. Residents' mean detection rate increased significantly from 64 to 77% with AI assist, while seniors' detection rate remained largely unchanged (85% vs. 86%). Residents showed significant improvement in segmental nodule localization with AI assistance, seniors did not. Software 2 slightly outperformed software 1 in increasing detection rates (67-77% vs. 80-86%), but neither significantly affected LungRADS classification. The study suggests that clinical experience mitigates the need for additional AI software, with the combination of CAD with residents being the most beneficial approach. Both software systems performed similarly, with software 2 showing a slightly higher but non-significant increase in detection rates.

Effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction strength level on the detection and characterization of pulmonary nodules in ultra-low-dose chest CT.

OBJECTIVE: To explore the effects of tube voltage, radiation dose and adaptive statistical iterative reconstruction (ASiR-V) strength level on the detection and characterization of pulmonary nodules by an artificial intelligence (AI) software in ultra-low-dose chest CT (ULDCT). MATERIALS AND METHODS: An anthropomorphic thorax phantom containing 12 spherical simulated nodules (Diameter: 12 mm, 10 mm, 8 mm, 5 mm; CT value: -800HU, -630HU, 100HU) was scanned with three ULDCT protocols: Dose-1 (70kVp:0.11mSv, 100kVp:0.10mSv), Dose-2 (70kVp:0.34mSv, 100kVp:0.32mSv), Dose-3 (70kVp:0.53mSv, 100kVp:0.51mSv). All scanning protocols were repeated five times. CT images were reconstructed using four different strength levels of ASiR-V (0%=FBP, 30%, 50%, 70%ASiR-V) with a slice thickness of 1.25 mm. The characteristics of the physical nodules were used as reference standards. All images were analyzed using a commercially available AI software to identify nodules for calculating nodule detection rate (DR) and to obtain their long diameter and short diameter, which were used to calculate the deformation coefficient (DC) and size measurement deviation percentage (SP) of nodules. DR, DC and SP of different imaging groups were statistically compared. RESULTS: Image noise decreased with the increase of ASiR-V strength level, and the 70 kV images had lower noise under the same strength level (mean-value 70 kV: 40.14 ± 7.05 (dose 1), 27.55 ± 7.38 (dose 2), 23.88 ± 6.98 (dose 3); 100 kV: 42.36 ± 7.62 (dose 1); 30.78 ± 6.87 (dose 2); 26.49 ± 6.61 (dose 3)). Under the same dose level, there were no differences in DR between 70 kV and 100 kV (dose 1: 58.76% vs. 58.33%; dose 2: 73.33% vs. 70.83%; dose 3: 75.42% vs. 75.42%, all p > 0.05). The DR of GGNs increased significantly at dose 2 and higher (70 kV: 38.12% (dose 1), 60.63% (dose 2), 64.38% (dose 3); 100 kV: 37.50% (dose 1), 59.38% (dose 2), 66.25% (dose 3)). In general, the use of ASiR-V at higher strength levels (> 50%) and 100 kV provided better (lower) DC and SP. CONCLUSION: Detection rates are similar between 70 kV and 100 kV scans. The 70 kV images have better noise performance under the same ASiR-V level, while images of 100 kV and higher ASiR-V levels are better in preserving the nodule morphology (lower DC and SP); the dose levels above 0.33mSv provide high sensitivity for nodules detection, especially the simulated ground glass nodules.

A clinically effective model based on cell-free DNA methylation and low-dose CT for risk stratification of pulmonary nodules.

Accurate, non-invasive, and cost-effective tools are needed to assist pulmonary nodule diagnosis and management due to increasing detection by low-dose computed tomography (LDCT). We perform genome-wide methylation sequencing on malignant and non-malignant lung tissues and designed a panel of 263 differential DNA methylation regions, which is used for targeted methylation sequencing on blood cell-free DNA (cfDNA) in two prospectively collected and retrospectively analyzed multicenter cohorts. We develop and optimize an integrative model for risk stratification of pulmonary nodules based on 40 cfDNA methylation biomarkers, age, and five simple computed tomography (CT) imaging features using machine learning approaches and validate its good performance in two cohorts. Using the two-threshold strategy can effectively reduce unnecessary invasive surgeries, overtreatment costs, and injury for patients with benign nodules while advising immediate treatment for patients with lung cancer, which can potentially improve the overall diagnosis of lung cancer following LDCT/CT screening.

Value of ultrathin bronchoscope in improving the endobronchial ultrasound localization rate and diagnosing peripheral pulmonary nodules by cryobiopsy.

BACKGROUND: A 3.0-mm ultrathin bronchoscope (UTB) with a 1.7-mm working channel provides better accessibility to peripheral bronchi. A 4.0-mm thin bronchoscope with a larger 2.0-mm working channel facilitates the use of a guide sheath (GS), ensuring repeated sampling from the same location. The 1.1-mm ultrathin cryoprobe has a smaller diameter, overcoming the limitation of the size of biopsy instruments used with UTB. In this study, we compared the endobronchial ultrasound localization rate and diagnostic yield of peripheral lung lesions by cryobiopsy using UTB and thin bronchoscopy combined with GS. METHODS: We retrospectively evaluated 133 patients with peripheral pulmonary lesions with a diameter less than 30 mm who underwent bronchoscopy with either thin bronchoscope or UTB from May 2019 to May 2023. A 3.0-mm UTB combined with rEBUS was used in the UTB group, whereas a 4.0-mm thin bronchoscope combined with rEBUS and GS was used for the thin bronchoscope group. A 1.1-mm ultrathin cryoprobe was used for cryobiopsy in the two groups. RESULTS: Among the 133 patients, peripheral pulmonary nodules in 85 subjects were visualized using r-EBUS. The ultrasound localization rate was significantly higher in the UTB group than in the thin bronchoscope group (96.0% vs. 44.6%, respectively; P < 0.001). The diagnostic yield of cryobiopsy specimens from the UTB group was significantly higher compared to the thin bronchoscope group (54.0% vs. 30.1%, respectively; p = 0.006). Univariate analysis demonstrated that the cryobiopsy diagnostic yields of the UTB group were significantly higher for lesions ≤ 20 mm, benign lesions, upper lobe lesions, lesions located lateral one-third from the hilum, and lesions without bronchus sign. CONCLUSIONS: Ultrathin bronchoscopy combined with cryobiopsy has a superior ultrasound localization rate and diagnostic yield compared to a combination of cryobiopsy and thin bronchoscopy.

The scoring system combined with radiomics and imaging features in predicting the malignant potential of incidental indeterminate small (<20 mm) solid pulmonary nodules.

OBJECTIVE: Develop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm. METHODS: A total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed. RESULTS: Three radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830-0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%. CONCLUSION: The combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system.

Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules.

BACKGROUND: Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients. METHODS: We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) score. We developed logistic regression models to evaluate a well-validated clinical risk prediction model (the Mayo Clinic model) with and without the LCP score (Mayo vs Mayo + LCP) using area under the curve (AUC), risk stratification table, and standardized net benefit analyses. RESULTS: Among the 134 patients undergoing PN biopsy, cancer prevalence was 61%. Addition of the radiomics-based LCP score to the Mayo model was associated with increased predictive accuracy (likelihood ratio test, P = .012). The AUCs for the Mayo and Mayo + LCP models were 0.58 (95% CI = 0.48 to 0.69) and 0.65 (95% CI = 0.56 to 0.75), respectively. At the 65% risk threshold, the Mayo + LCP model was associated with increased sensitivity (56% vs 38%; P = .019), similar false positive rate (33% vs 35%; P = .8), and increased standardized net benefit (18% vs -3.3%) compared with the Mayo model. CONCLUSIONS: Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification.