A nomogram combining CT-based radiomic features with clinical features for the differentiation of benign and malignant cystic pulmonary nodules.

BACKGROUND: Currently, the differentiation between benign and malignant cystic pulmonary nodules poses a significant challenge for clinicians. The objective of this retrospective study was to construct a predictive model for determining the likelihood of malignancy in patients with cystic pulmonary nodules. METHODS: The current study involved 129 patients diagnosed with cystic pulmonary nodules between January 2017 and June 2023 at the Neijiang First People's Hospital. The study gathered the clinical data, preoperative imaging features of chest CT, and postoperative histopathological results for both cohorts. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model's performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA). RESULTS: A cohort of 129 patients presenting with cystic pulmonary nodules, consisting of 92 malignant and 37 benign lesions, was examined. Logistic data analysis identified a cystic airspace with a mural nodule, spiculation, mural morphology, and the number of cystic cavities as significant independent predictors for discriminating between benign and malignant cystic lung nodules. The nomogram prediction model demonstrated a high level of predictive accuracy, as evidenced by an area under the ROC curve (AUC) of 0.874 (95% CI: 0.804-0.944). Furthermore, the calibration curve of the model displayed satisfactory calibration. DCA proved that the prediction model was useful for clinical application. CONCLUSION: In summary, the risk prediction model for benign and malignant cystic pulmonary nodules has the potential to assist clinicians in the diagnosis of such nodules and enhance clinical decision-making processes.

Advances in the localization of pulmonary nodules: a comprehensive review.

In recent years, with the widespread use of chest CT, the detection rate of pulmonary nodules has significantly increased (Abtin and Brown, J Clin Oncol 31:1002-8, 2013). Video-assisted thoracoscopic surgery (VATS) is the most commonly used method for suspected malignant nodules. However, for nodules with a diameter less than 1 cm, or located more than 1.5 cm from the pleural edge, especially ground-glass nodules, it is challenging to achieve precise intraoperative localization by manual palpation (Ciriaco et al., Eur J Cardiothorac Surg 25:429-33, 2004). Therefore, preoperative accurate localization of such nodules becomes a necessary condition for precise resection. This article provides a comprehensive review and analysis of the research progress in pulmonary nodule localization, focusing on four major localization techniques: Percutaneous puncture-assisted localization, Bronchoscopic preoperative pulmonary nodule localization, 3D Printing-Assisted Localization, and intraoperative ultrasound-guided pulmonary nodule localization.

Non-malignant pathological results from CT-guided biopsy for pulmonary nodules: a predictive model for identifying false-negative results.

BACKGROUND: Computed tomography (CT)-guided biopsy (CTB) procedures are commonly used to aid in the diagnosis of pulmonary nodules (PNs). When CTB findings indicate a non-malignant lesion, it is critical to correctly determine false-negative results. Therefore, the current study was designed to construct a predictive model for predicting false-negative cases among patients receiving CTB for PNs who receive non-malignant results. MATERIALS AND METHODS: From January 2016 to December 2020, consecutive patients from two centers who received CTB-based non-malignant pathology results while undergoing evaluation for PNs were examined retrospectively. A training cohort was used to discover characteristics that predicted false negative results, allowing the development of a predictive model. The remaining patients were used to establish a testing cohort that served to validate predictive model accuracy. RESULTS: The training cohort included 102 patients with PNs who showed non-malignant pathology results based on CTB. Each patient underwent CTB for a single nodule. Among these patients, 85 and 17 patients, respectively, showed true negative and false negative PNs. Through univariate and multivariate analyses, higher standardized maximum uptake values (SUVmax, P = 0.001) and CTB-based findings of suspected malignant cells (P = 0.043) were identified as being predictive of false negative results. Following that, these two predictors were combined to produce a predictive model. The model achieved an area under the receiver operating characteristic curve (AUC) of 0.945. Furthermore, it demonstrated sensitivity and specificity values of 88.2% and 87.1% respectively. The testing cohort included 62 patients, each of whom had a single PN. When the developed model was used to evaluate this testing cohort, this yielded an AUC value of 0.851. CONCLUSIONS: In patients with PNs, the predictive model developed herein demonstrated good diagnostic effectiveness for identifying false-negative CTB-based non-malignant pathology data.

The correlation between KRAS and TP53 gene mutations and early growth of pulmonary nodules.

PURPOSE: The purpose of this study is to investigate whether gene mutations can lead to the growth of malignant pulmonary nodules. METHODS: Retrospective analysis was conducted on patients with pulmonary nodules at Hebei Provincial People's Hospital, collecting basic clinical information such as gender, age, BMI, and hematological indicators. According to the inclusion and exclusion criteria, 85 patients with malignant pulmonary nodules were selected for screening, and gene mutation testing was performed on all patient tissues to explore the relationship between gene mutations and the growth of malignant pulmonary nodules. RESULTS: There is a correlation between KRAS and TP53 gene mutations and the growth of pulmonary nodules (P < 0.05), while there is a correlation between KRAS and TP53 gene mutations and the growth of pulmonary nodules in the subgroup of invasive malignant pulmonary nodules (P < 0.05). CONCLUSION: Mutations in the TP53 gene can lead to the growth of malignant pulmonary nodules and are correlated with the degree of invasion of malignant pulmonary nodules.

How will lung cancer screening and lung nodule management change the diagnostic and surgical lung cancer landscape?

INTRODUCTION: Implementation of lung cancer screening, with its subsequent findings, is anticipated to change the current diagnostic and surgical lung cancer landscape. This review aimed to identify and present the most updated expert opinion and discuss relevant evidence regarding the impact of lung cancer screening and lung nodule management on the diagnostic and surgical landscape of lung cancer, as well as summarise points for clinical practice. METHODS: This article is based on relevant lectures and talks delivered during the European Society of Thoracic Surgeons-European Respiratory Society Collaborative Course on Thoracic Oncology (February 2023). Original lectures and talks and their relevant references were included. An additional literature search was conducted and peer-reviewed studies in English (December 2022 to June 2023) from the PubMed/Medline databases were evaluated with regards to immediate affinity of the published papers to the original talks presented at the course. An updated literature search was conducted (June 2023 to December 2023) to ensure that updated literature is included within this article. RESULTS: Lung cancer screening suspicious findings are expected to increase the number of diagnostic investigations required therefore impacting on current capacity and resources. Healthcare systems already face a shortage of imaging and diagnostic slots and they are also challenged by the shortage of interventional radiologists. Thoracic surgery will be impacted by the wider lung cancer screening implementation with increased volume and earlier stages of lung cancer. Nonsuspicious findings reported at lung cancer screening will need attention and subsequent referrals where required to ensure participants are appropriately diagnosed and managed and that they are not lost within healthcare systems. CONCLUSIONS: Implementation of lung cancer screening requires appropriate mapping of existing resources and infrastructure to ensure a tailored restructuring strategy to ensure that healthcare systems can meet the new needs.

Development and validation of a risk prediction model for invasiveness of pure ground-glass nodules based on a systematic review and meta-analysis.

BACKGROUND: Assessing the aggressiveness of pure ground glass nodules early on significantly aids in making informed clinical decisions. OBJECTIVE: Developing a predictive model to assess the aggressiveness of pure ground glass nodules in lung adenocarcinoma is the study's goal. METHODS: A comprehensive search for studies on the relationship between computed tomography(CT) characteristics and the aggressiveness of pure ground glass nodules was conducted using databases such as PubMed, Embase, Web of Science, Cochrane Library, Scopus, Wanfang, CNKI, VIP, and CBM, up to December 20, 2023. Two independent researchers were responsible for screening literature, extracting data, and assessing the quality of the studies. Meta-analysis was performed using Stata 16.0, with the training data derived from this analysis. To identify publication bias, Funnel plots and Egger tests and Begg test were employed. This meta-analysis facilitated the creation of a risk prediction model for invasive adenocarcinoma in pure ground glass nodules. Data on clinical presentation and CT imaging features of patients treated surgically for these nodules at the Third Affiliated Hospital of Kunming Medical University, from September 2020 to September 2023, were compiled and scrutinized using specific inclusion and exclusion criteria. The model's effectiveness for predicting invasive adenocarcinoma risk in pure ground glass nodules was validated using ROC curves, calibration curves, and decision analysis curves. RESULTS: In this analysis, 17 studies were incorporated. Key variables included in the model were the largest diameter of the lesion, average CT value, presence of pleural traction, and spiculation. The derived formula from the meta-analysis was: 1.16×the largest lesion diameter + 0.01 × the average CT value + 0.66 × pleural traction + 0.44 × spiculation. This model underwent validation using an external set of 512 pure ground glass nodules, demonstrating good diagnostic performance with an ROC curve area of 0.880 (95% CI: 0.852-0.909). The calibration curve indicated accurate predictions, and the decision analysis curve suggested high clinical applicability of the model. CONCLUSION: We established a predictive model for determining the invasiveness of pure ground-glass nodules, incorporating four key radiological indicators. This model is both straightforward and effective for identifying patients with a high likelihood of invasive adenocarcinoma.

Is There a Baby in the Lung? A Case of Placental Transmogrification of the Lung.

Pulmonary nodules are commonly encountered in pulmonary practice. Etiologies could include infectious, inflammatory, and malignant. Placental transmogrification of the lung is an extremely rare etiology of pulmonary nodules. Such condition often presents as unilateral lesions in asymptomatic men. In general, such nodules are generally stable and grow extremely slowly. We highlight an unusual case of placental transmogrification of the lung (PLC) identified in a young female. The patient's bilateral nodules were larger than what has been previously cited in the literature and exhibited growth over an 8-year follow-up period.

The diagnosis and management of multiple ground-glass nodules in the lung.

The prevalence of low-dose CT (LDCT) in lung cancer screening has gradually increased, and more and more lung ground glass nodules (GGNs) have been detected. So far, a consensus has been reached on the treatment of single pulmonary ground glass nodules, and there have been many guidelines that can be widely accepted. However, at present, more than half of the patients have more than one nodule when pulmonary ground glass nodules are found, which means that different treatment methods for nodules may have different effects on the prognosis or quality of life of patients. This article reviews the research progress in the diagnosis and treatment strategies of pulmonary multiple lesions manifested as GGNs.

[A case of hypereosinophilia with diffuse pulmonary nodules].

We reported a case of a 36-year-old woman who presented with cough, dyspnea, hypereosinophilia, multiple pulmonary nodules and mediastinal lymphadenopathy. The percentage of eosinophils in bronchoalveolar lavage fluid (BALF) was as high as 65%. Pathogenic tests and cytologic examination of BALF were negative. Transbronchial lung biopsy and endobronchial ultrasound-guided transbronchial needle aspiration revealed only eosinophil infiltration. As the patient responded poorly to high-dose corticosteroids, a surgical lung biopsy was performed. The pathological diagnosis was angioimmunoblastic T-cell lymphoma. The patient received chemotherapy and achieved a partial response. Her eosinophil count returned to the normal range, and the pulmonary nodules on chest CT partially resolved.

[Artificial intelligence research advances in discrimination and diagnosis of pulmonary ground-glass nodules].

Lung cancer, which accounts for about 18% of all cancer-related deaths worldwide, has a dismal 5-year survival rate of less than 20%. Survival rates for early-stage lung cancers (stages IA1, IA2, IA3, and IB, according to the TNM staging system) are significantly higher, underscoring the critical importance of early detection, diagnosis, and treatment. Ground-glass nodules (GGNs), which are commonly seen on lung imaging, can be indicative of both benign and malignant lesions. For clinicians, accurately characterizing GGNs and choosing the right management strategies present significant challenges. Artificial intelligence (AI), specifically deep learning algorithms, has shown promise in the evaluation of GGNs by analyzing complex imaging data and predicting the nature of GGNs, including their benign or malignant status, pathological subtypes, and genetic mutations such as epidermal growth factor receptor (EGFR) mutations. By integrating imaging features and clinical data, AI models have demonstrated high accuracy in distinguishing between benign and malignant GGNs and in predicting specific pathological subtypes. In addition, AI has shown promise in predicting genetic mutations such as EGFR mutations, which are critical for personalized treatment decisions in lung cancer. While AI offers significant potential to improve the accuracy and efficiency of GGN assessment, challenges remain, such as the need for extensive validation studies, standardization of imaging protocols, and improving the interpretability of AI algorithms. In summary, AI has the potential to revolutionise the management of GGNs by providing clinicians with more accurate and timely information for diagnosis and treatment decisions. However, further research and validation are needed to fully realize the benefits of AI in clinical practice.

Comparative Analysis of Clinical Characteristics and Prognosis Between Primary Lung Cancers Identified as Screening Detected and Incidentally Detected Pulmonary Nodules.

BACKGROUND/AIM: Although the importance of low-dose computed tomography (LDCT) screening is increasingly emphasized and implemented, many lung cancers continue to be incidentally detected during routine medical practices, and data on incidentally detected lung cancer (IDLC) remain scarce. This study aimed to investigate the clinical characteristics and prognosis of IDLCs by comparing them with screening-detected lung cancers (SDLCs). PATIENTS AND METHODS: In this retrospective study, subjects with cT1 (≤3 cm) pulmonary nodules detected on baseline computed tomography (CT), later pathologically confirmed as primary lung cancer in 2015, were included. Patients were categorized into IDLC and SDLC groups based on the setting of the first pulmonary nodule detection. RESULTS: Out of 457 subjects, 129 (28.2%) were IDLCs and 328 (71.8%) were SDLCs. The IDLC group, consisted of older individuals with a higher prevalence of smokers and underlying pulmonary disease, compared to the SDLC group. Adenocarcinomas were more frequently detected in SDLCs (87.5%) than in IDLCs (76.7%, p<0.001). The time to treatment initiation (TTI) and 5-year overall survival (OS) rates were similar. Multivariate analyses revealed underlying interstitial lung disease, DLCO, solidity of nodules and TNM stage as independent risk factors associated with mortality. Less than 30% of study participants would have been eligible for the current lung cancer screening program. CONCLUSION: The IDLC group was associated with older age, higher rate of smokers, underlying pulmonary disease, and non-adenocarcinoma histology. However, prognosis was similar to that of the SDLC group, attributable to the similarity in TNM stage, strict adherence to guidelines, and short TTI. Furthermore, less than 30% of the participants would have been suitable for the existing lung cancer screening program, indicating a potential need to reconsider the scope for screening candidates.

[A design of interactive review for computer aided diagnosis of pulmonary nodules based on active learning].

Automatic detection of pulmonary nodule based on computer tomography (CT) images can significantly improve the diagnosis and treatment of lung cancer. However, there is a lack of effective interactive tools to record the marked results of radiologists in real time and feed them back to the algorithm model for iterative optimization. This paper designed and developed an online interactive review system supporting the assisted diagnosis of lung nodules in CT images. Lung nodules were detected by the preset model and presented to doctors, who marked or corrected the lung nodules detected by the system with their professional knowledge, and then iteratively optimized the AI model with active learning strategy according to the marked results of radiologists to continuously improve the accuracy of the model. The subset 5-9 dataset of the lung nodule analysis 2016(LUNA16) was used for iteration experiments. The precision, F1-score and MioU indexes were steadily improved with the increase of the number of iterations, and the precision increased from 0.213 9 to 0.565 6. The results in this paper show that the system not only uses deep segmentation model to assist radiologists, but also optimizes the model by using radiologists' feedback information to the maximum extent, iteratively improving the accuracy of the model and better assisting radiologists.

AI-driven Characterization of Solid Pulmonary Nodules on CT Imaging for Enhanced Malignancy Prediction in Small-sized Lung Adenocarcinoma.

OBJECTIVES: Distinguishing solid nodules from nodules with ground-glass lesions in lung cancer is a critical diagnostic challenge, especially for tumors ≤2 cm. Human assessment of these nodules is associated with high inter-observer variability, which is why an objective and reliable diagnostic tool is necessary. This study focuses on artificial intelligence (AI) to automatically analyze such tumors and to develop prospective AI systems that can independently differentiate highly malignant nodules. MATERIALS AND METHODS: Our retrospective study analyzed 246 patients who were diagnosed with negative clinical lymph node metastases (cN0) using positron emission tomography-computed tomography (PET/CT) imaging and underwent surgical resection for lung adenocarcinoma. AI detected tumor sizes ≤2 cm in these patients. By utilizing AI to classify these nodules as solid (AI_solid) or non-solid (non-AI_solid) based on confidence scores, we aim to correlate AI determinations with pathological findings, thereby advancing the precision of preoperative assessments. RESULTS: Solid nodules identified by AI with a confidence score ≥0.87 showed significantly higher solid component volumes and proportions in patients with AI_solid than in those with non-AI_solid, with no differences in overall diameter or total volume of the tumors. Among patients with AI_solid, 16% demonstrated lymph node metastasis, and a significant 94% harbored invasive adenocarcinoma. Additionally, 44% were upstaging postoperatively. These AI_solid nodules represented high-grade malignancies. CONCLUSION: In small-sized lung cancer diagnosed as cN0, AI automatically identifies tumors as solid nodules ≤2 cm and evaluates their malignancy preoperatively. The AI classification can inform lymph node assessment necessity in sublobar resections, reflecting metastatic potential.

Pathological T3 Non-Small Cell Lung Cancer with satellite nodules: Number or size, what does matter?

BACKGROUND: Non-small Cell Lung Cancer (NSCLC) with intralobar satellite nodule are defined as T3 (T3SN). We investigated the main features of these tumors and analyzed their impact on Overall Survival (OS). METHODS: This was a retrospective multicentric study including all pT3SN NSCLC operated on between 2005 and 2020, excluding patients with multifocal ground-glass opacities; who received induction therapies; N3 or stage IV. The diameter of largest (LgN) and smallest nodule (SmN), the total diameter (sum of diameter of all nodules, TS), and the number of SN were measured. RESULTS: Among 102 patients, 64.7 % were male. 84.3 % of patients had one SN (84.3 %), 9.8 % two SN while 5.9 % more than 2 SN. 63 patients were pN0. LgN (p = 0.001), SN (p = 0.005) and TS (p = 0.014) were significantly related to lymph-node metastasis; the LgN and TS were related to visceral pleural invasion (p < 0.001). Five-year OS was 65.1 %; at univariable analysis more than 2 satellite nodules, LgN and TS were significantly related to worse OS; at multivariable analysis, TS (Hazard Ratio [HR] 1.116 95 % Confidence Interval [CI] 1.008-1.235, p = 0.034) was an independent prognostic factors for OS. No significant prognostic factors were found for DFS at multivariable analysis. In pN0 patients, LgN (HR 1.051, 95 % CI 1.066-1.099, p = 0.027) and non-adenocarcinoma (HR 5.315 CI 95 % 1.494-18.910, p = 0.010) influenced OS. CONCLUSIONS: Tumor size is related to tumor's local invasiveness. TS is an independent prognostic factor for OS. Patients with more than 2 SN seem to be at higher risk for death and recurrence.

[Value of peripheral blood rare cell EGFR gene amplification detection in the evaluation of benign and malignant pulmonary nodules].

Objective: To explore the value of detection of epidermal growth factor receptor (EGFR) gene amplification in peripheral blood rare cells in the assessment of benign and malignant pulmonary nodules. Methods: A total of 262 patients with pulmonary nodules were selected as the retrospectively study subjects from the Second Affiliated Hospital of Army Military Medical University and Peking Union Medical College Hospital from July 2022 to August 2023. There were 98 males and 164 females, with the age range from 16 to 79 (52.1±12.1) years. The EGFR gene amplification testing was performed on the rare cells enriched from patients' peripheral blood, and the clinical manifestations, CT imaging features, histopathological and/or pathological cytological confirmed results of patients were collected. The receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of the method of detection of EGFR gene amplification in peripheral blood rare cells, and its diagnostic efficacy was evaluated. Results: Among the 262 patients, 143 were malignant pulmonary nodules and 119 were benign pulmonary nodules. The differences between malignant pulmonary nodules and benign pulmonary nodules in nodule diameter and nodule density were statistically significant (both P<0.001), while the differences in age, gender and nodule number were not statistically significant (all P>0.05). The number [M (Q1, Q3)] of EGFR gene amplification positive rare cells in patients with malignant pulmonary nodule was 8 (6, 11), which was higher than that in patients with benign pulmonary nodule [2 (1, 4), P<0.001]. The ROC curve results showed that when the optimal cut-off value was 5 (that was, the number of EGFR gene amplification positive rare cells was>5), the area under the curve (AUC) of the detection of EGFR gene amplification in peripheral blood rare cells for discrimination of benign and malignant pulmonary lesions was 0.816 (95%CI: 0.761-0.870), with a sensitivity of 83.2%, a specificity of 80.7%, and an accuracy of 82.1%. Based on the analysis of the diameter of the nodules, the AUC for distinguishing between benign and malignant pulmonary nodules with diameter 5-9 mm and 10-30 mm was 0.797 (95%CI: 0.707-0.887) and 0.809 (95%CI: 0.669-0.949), respectively, with sensitivity, specificity and accuracy reached 75% or above. Based on the analysis of nodule density, the AUC for distinguishing between benign and malignant solid nodule and subsolid nodule was 0.845 (95%CI: 0.751-0.939) and 0.790 (95%CI: 0.701-0.880), respectively, with sensitivity, specificity and accuracy reached 75% or above. Based on the analysis of nodule number, the AUC for distinguishing between benign and malignant solitary pulmonary nodule and multiple pulmonary nodule was 0.830 (95%CI: 0.696-0.965) and 0.817 (95%CI: 0.758-0.877), respectively, with sensitivity, specificity and accuracy reached 80% or above. Conclusion: The detection of EGFR gene amplification in peripheral blood rare cells contributes to the evaluation of benign and malignant pulmonary nodules, and can be used in the auxiliary diagnosis of benign and malignant pulmonary nodules.

Who is at risk of lung nodules on low-dose CT in a Western country? A population-based approach.

BACKGROUND: This population-based study aimed to identify the risk factors for lung nodules in a Western European general population. METHODS: We quantified the presence or absence of lung nodules among 12 055 participants of the Dutch population-based ImaLife (Imaging in Lifelines) study (age ≥45 years) who underwent low-dose chest computed tomography. Outcomes included the presence of 1) at least one solid lung nodule (volume ≥30 mm3) and 2) a clinically relevant lung nodule (volume ≥100 mm3). Fully adjusted multivariable logistic regression models were applied overall and stratified by smoking status to identify independent risk factors for the presence of nodules. RESULTS: Among the 12 055 participants (44.1% male; median age 60 years; 39.9% never-smokers; 98.7% White), we found lung nodules in 41.8% (5045 out of 12 055) and clinically relevant nodules in 11.4% (1377 out of 12 055); the corresponding figures among never-smokers were 38.8% and 9.5%, respectively. Factors independently associated with increased odds of having any lung nodule included male sex, older age, low educational level, former smoking, asbestos exposure and COPD. Among never-smokers, a family history of lung cancer increased the odds of both lung nodules and clinically relevant nodules. Among former and current smokers, low educational level was positively associated with lung nodules, whereas being overweight was negatively associated. Among current smokers, asbestos exposure and low physical activity were associated with clinically relevant nodules. CONCLUSIONS: The study provides a large-scale evaluation of lung nodules and associated risk factors in a Western European general population: lung nodules and clinically relevant nodules were prevalent, and never-smokers with a family history of lung cancer were a non-negligible group.

Imaging of Solid Pulmonary Nodules.

Early detection with accurate classification of solid pulmonary nodules is critical in reducing lung cancer morbidity and mortality. Computed tomography (CT) remains the most widely used imaging examination for pulmonary nodule evaluation; however, other imaging modalities, such as PET/CT and MRI, are increasingly used for nodule characterization. Current advances in solid nodule imaging are largely due to developments in machine learning, including automated nodule segmentation and computer-aided detection. This review explores current multi-modality solid pulmonary nodule detection and characterization with discussion of radiomics and risk prediction models.

Subsolid Nodules: Significance and Current Understanding.

Subsolid nodules are heterogeneously appearing and behaving entities, commonly encountered incidentally and in high-risk populations. Accurate characterization of subsolid nodules, and application of evolving surveillance guidelines, facilitates evidence-based and multidisciplinary patient-centered management.