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European Journal of Medical Research Jun 2024The 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... (Review)
Review
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.
Topics: Humans; Lung Neoplasms; Multiple Pulmonary Nodules; Tomography, X-Ray Computed; Lung
PubMed: 38824558
DOI: 10.1186/s40001-024-01904-6 -
Journal of Cancer Research and... Dec 2023To retrospectively examine the imaging characteristics of chest-computed tomography (CT) following percutaneous microwave ablation (MWA) of the ground-glass nodule...
PURPOSE
To retrospectively examine the imaging characteristics of chest-computed tomography (CT) following percutaneous microwave ablation (MWA) of the ground-glass nodule (GGN)-like lung cancer and its dynamic evolution over time.
MATERIALS AND METHODS
From June 2020 to May 2021, 147 patients with 152 GGNs (51 pure GGNs and 101 mixed GGNs, mean size 15.0 ± 6.3 mm) were enrolled in this study. One hundred and forty-seven patients underwent MWA procedures. The imaging characteristics were evaluated at predetermined time intervals: immediately after the procedure, 24-48 h, 1, 3, 6, 12, and ≥18 months (47 GGNs).
RESULTS
This study population included 147 patients with 152 GGNs, as indicated by the results: 43.5% (66/152) adenocarcinoma in situ, 41.4% (63/152) minimally invasive adenocarcinoma, and 15.1% (23/152) invasive adenocarcinoma. Immediate post-procedure tumor-level analysis revealed that the most common CT features were ground-glass opacities (93.4%, 142/152), hyperdensity within the nodule (90.7%, 138/152), and fried egg sign or reversed halo sign (46.7%, 71/152). Subsequently, 24-48 h post-procedure, ground-glass attenuations, hyperdensity, and the fried egg sign remained the most frequent CT findings, with incidence rates of 75.0% (114/152), 71.0% (108/152), and 54.0% (82/152), respectively. Cavitation, pleural thickening, and consolidation were less frequent findings. At 1 month after the procedure, consolidation of the ablation region was the most common imaging feature. From 3 to 12 months after the procedure, the most common imaging characteristics were consolidation, involutional parenchymal bands and pleural thickening. At ≥18 months after the procedure, imaging features of the ablation zone revealed three changes: involuting fibrosis (80.8%, 38/47), consolidation nodules (12.8%, 6/47), and disappearance (6.4%, 3/47).
CONCLUSIONS
This study outlined the anticipated CT imaging characteristics of GGN-like lung cancer following MWA. Diagnostic and interventional radiologists should be familiar with the expected imaging characteristics and dynamic evolution post-MWA in order to interpret imaging changes with a reference image.
Topics: Humans; Lung Neoplasms; Retrospective Studies; Microwaves; Lung; Adenocarcinoma; Precancerous Conditions
PubMed: 38156934
DOI: 10.4103/jcrt.jcrt_837_23 -
Zhongguo Yi Liao Qi Xie Za Zhi =... Mar 2024With the widespread adoption of low-dose computed tomography (LDCT) and advancements in computed tomography image resolution, the detection rate of pulmonary nodules,... (Review)
Review
With the widespread adoption of low-dose computed tomography (LDCT) and advancements in computed tomography image resolution, the detection rate of pulmonary nodules, especially smaller ones, has significantly improved. The risk of developing malignant tumors increases with the pulmonary nodule diameter. Video-assisted thoracoscopic surgery (VATS) stands out as the preferred surgical method. The accurate localization of pulmonary nodules is crucial for the success of VATS and remains a significant challenge for thoracic surgeons. Currently, commonly employed localization methods include CT-guided percutaneous positioning, bronchoscope-guided positioning, intraoperative ultrasound positioning, augmented reality (AR), and 3D print-assisted positioning. This review explores recent research progress, highlights the strengths and weaknesses of various pulmonary nodule localization methods. The aim is to provide valuable insights for clinical applications and guide future developments in this field.
Topics: Humans; Lung Neoplasms; Tomography, X-Ray Computed; Thoracic Surgery, Video-Assisted; Retrospective Studies
PubMed: 38605620
DOI: 10.12455/j.issn.1671-7104.230226 -
Cancer Medicine Apr 2024The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various... (Review)
Review
BACKGROUND
The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false-positive reduction, nodule classification, and prognosis.
METHODOLOGY
This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false-positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening.
RESULTS
AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false-positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing.
CONCLUSIONS
AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false-positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large-scale validation of new deep learning-based algorithms and multi-center studies to improve the efficacy of AI in lung cancer screening.
Topics: Humans; Artificial Intelligence; Lung Neoplasms; Early Detection of Cancer; Tomography, X-Ray Computed; Lung; Prognosis
PubMed: 38581113
DOI: 10.1002/cam4.7140 -
BMC Cancer Aug 2023This project aimed to research the significance of THRIL in the diagnosis of benign and malignant solitary pulmonary nodules (SPNs) and to investigate the role of...
BACKGROUND
This project aimed to research the significance of THRIL in the diagnosis of benign and malignant solitary pulmonary nodules (SPNs) and to investigate the role of THRIL/miR-99a in malignant SPNs.
METHODS
The study groups consisted of 169 patients with SPN and 74 healthy subjects. The differences in THRIL levels were compared between the two groups and the healthy group. The receiver operating characteristic curve (ROC) was utilized to analyze the THRIL's significance in detecting benign and malignant SPN. Pearson correlation and binary regression coefficients represented the association between THRIL and SPN. CCK-8 assay, Transwell assay, and flow cytometry were utilized to detect the regulatory effect of THRIL silencing. The interaction between THRIL, miR-99a, and IGF1R was confirmed by the double luciferase reporter gene.
RESULTS
There were differences in THRIL expression in the healthy group, benign SPN group, and malignant SPN group. High accuracy of THRIL in the diagnosis of benign SPN and malignant SPN was observed. THRIL was associated with the development of SPN. The expression of THRIL was upregulated and miR-99a was downregulated in lung cancer cells. The double luciferase report experiment confirmed the connections between THRIL/miR-99a/IGF1R. Silencing THRIL could suppress cell proliferation, migration, and invasion and promote cell apoptosis by binding miR-99a.
CONCLUSION
The detection of THRIL in serum is useful for the assessment of malignant SPN. THRIL can regulate the expression of IGF1R through miR-99a, thereby promoting the growth of lung cancer cells and inhibiting apoptosis.
Topics: Humans; RNA, Long Noncoding; Lung Neoplasms; Lung; Solitary Pulmonary Nodule; Multiple Pulmonary Nodules; MicroRNAs
PubMed: 37582734
DOI: 10.1186/s12885-023-11264-9 -
The Journal of Gene Medicine Sep 2023Although many prediction models in diagnosis of solitary pulmonary nodules (SPNs) have been developed, few are widely used in clinical practice. It is therefore... (Randomized Controlled Trial)
Randomized Controlled Trial
BACKGROUND
Although many prediction models in diagnosis of solitary pulmonary nodules (SPNs) have been developed, few are widely used in clinical practice. It is therefore imperative to identify novel biomarkers and prediction models supporting early diagnosis of SPNs. This study combined folate receptor-positive circulating tumor cells (FR CTC) with serum tumor biomarkers, patient demographics and clinical characteristics to develop a prediction model.
METHODS
A total of 898 patients with a solitary pulmonary nodule who received FR CTC detection were randomly assigned to a training set and a validation set in a 2:1 ratio. Multivariate logistic regression was used to establish a diagnostic model to differentiate malignant and benign nodules. The receiver operating curve (ROC) and the area under the curve (AUC) were calculated to assess the diagnostic efficiency of the model.
RESULTS
The positive rate of FR CTC between patients with non-small cell lung cancer (NSCLC) and benign lung disease was significantly different in both the training and the validation dataset (p < 0.001). The FR CTC level was significantly higher in the NSCLC group compared with that of the benign group (p < 0.001). FR CTC (odds ratio, OR, 95% confidence interval, CI: 1.13, 1.07-1.19, p < 0.0001), age (OR, 95% CI: 1.06, 1.01-1.12, p = 0.03) and sex (OR, 95% CI: 1.07, 1.01-1.13, p = 0.01) were independent risk factors of NSCLC in patients with a solitary pulmonary nodule. The area under the curve (AUC) of FR CTC in diagnosing NSCLC was 0.650 (95% CI, 0.587-0.713) in the training set and 0.700 (95% CI, 0.603-0.796) in the validation set, respectively. The AUC of the combined model was 0.725 (95% CI, 0.659-0.791) in the training set and 0.828 (95% CI, 0.754-0.902) in the validation set, respectively.
CONCLUSIONS
We confirmed the value of FR CTC in diagnosing SPNs and developed a prediction model based on FR CTC, demographic characteristics, and serum biomarkers for differential diagnosis of solitary pulmonary nodules.
Topics: Humans; Carcinoma, Non-Small-Cell Lung; Lung Neoplasms; Solitary Pulmonary Nodule; Neoplastic Cells, Circulating; Biomarkers, Tumor
PubMed: 37194408
DOI: 10.1002/jgm.3529 -
Insights Into Imaging Sep 2023The deep learning-based nodule detection (DLD) system improves nodule detection performance of observers on chest radiographs (CXRs). However, its performance in...
BACKGROUND
The deep learning-based nodule detection (DLD) system improves nodule detection performance of observers on chest radiographs (CXRs). However, its performance in different pulmonary nodule (PN) locations remains unknown.
METHODS
We divided the CXR intrathoracic region into non-danger zone (NDZ) and danger zone (DZ). The DZ included the lung apices, paramediastinal areas, and retrodiaphragmatic areas, where nodules could be missed. We used a dataset of 300 CXRs (100 normal and 200 abnormal images with 216 PNs [107 NDZ and 109 DZ nodules]). Eight observers (two thoracic radiologists [TRs], two non-thoracic radiologists [NTRs], and four radiology residents [RRs]) interpreted each radiograph with and without the DLD system. The metric of lesion localization fraction (LLF; the number of correctly localized lesions divided by the total number of true lesions) was used to evaluate the diagnostic performance according to the nodule location.
RESULTS
The DLD system demonstrated a lower LLF for the detection of DZ nodules (64.2) than that of NDZ nodules (83.2, p = 0.008). For DZ nodule detection, the LLF of the DLD system (64.2) was lower than that of TRs (81.7, p < 0.001), which was comparable to that of NTRs (56.4, p = 0.531) and RRs (56.7, p = 0.459). Nonetheless, the LLF of RRs significantly improved from 56.7 to 65.6 using the DLD system (p = 0.021) for DZ nodule detection.
CONCLUSION
The performance of the DLD system was lower in the detection of DZ nodules compared to that of NDZ nodules. Nonetheless, RR performance in detecting DZ nodules improved upon using the DLD system.
CRITICAL RELEVANCE STATEMENT
Despite the deep learning-based nodule detection system's limitations in detecting danger zone nodules, it proves beneficial for less-experienced observers by providing valuable assistance in identifying these nodules, thereby advancing nodule detection in clinical practice.
KEY POINTS
• The deep learning-based nodule detection (DLD) system can improve the diagnostic performance of observers in nodule detection. • The DLD system shows poor diagnostic performance in detecting danger zone nodules. • For less-experienced observers, the DLD system is helpful in detecting danger zone nodules.
PubMed: 37726452
DOI: 10.1186/s13244-023-01497-4 -
IEEE Transactions on Medical Imaging Dec 2023The growth rate of pulmonary nodules is a critical clue to the cancerous diagnosis. It is essential to monitor their dynamic progressions during pulmonary nodule...
The growth rate of pulmonary nodules is a critical clue to the cancerous diagnosis. It is essential to monitor their dynamic progressions during pulmonary nodule management. To facilitate the prosperity of research on nodule growth prediction, we organized and published a temporal dataset called NLSTt with consecutive computed tomography (CT) scans. Based on the self-built dataset, we develop a visual learner to predict the growth for the following CT scan qualitatively and further propose a model to predict the growth rate of pulmonary nodules quantitatively, so that better diagnosis can be achieved with the help of our predicted results. To this end, in this work, we propose a parameterized Gempertz-guided morphological autoencoder (GM-AE) to generate any future-time-span high-quality visual appearances of pulmonary nodules from the baseline CT scan. Specifically, we parameterize a popular mathematical model for tumor growth kinetics, Gompertz, to predict future masses and volumes of pulmonary nodules. Then, we exploit the expected growth rate on the mass and volume to guide decoders generating future shape and texture of pulmonary nodules. We introduce two branches in an autoencoder to encourage shape-aware and textural-aware representation learning and integrate the generated shape into the textural-aware branch to simulate the future morphology of pulmonary nodules. We conduct extensive experiments on the self-built NLSTt dataset to demonstrate the superiority of our GM-AE to its competitive counterparts. Experiment results also reveal the learnable Gompertz function enjoys promising descriptive power in accounting for inter-subject variability of the growth rate for pulmonary nodules. Besides, we evaluate our GM-AE model on an in-house dataset to validate its generalizability and practicality. We make its code publicly available along with the published NLSTt dataset.
Topics: Humans; Lung Neoplasms; Tomography, X-Ray Computed; Radiographic Image Interpretation, Computer-Assisted; Solitary Pulmonary Nodule
PubMed: 37471191
DOI: 10.1109/TMI.2023.3297209 -
Journal of Thoracic Disease Dec 2023The migration of hook wire used for lung nodule localization to the pulmonary artery is an extremely rare complication. We report a case of migration of hook wire used...
BACKGROUND
The migration of hook wire used for lung nodule localization to the pulmonary artery is an extremely rare complication. We report a case of migration of hook wire used for lung nodule localization to the main pulmonary artery and discuss the management.
CASE DESCRIPTION
The patient was a 50-year-old female with multiple pulmonary nodules, the largest of which was 7 mm and located in right lower lob. Since the size of the nodules were very small, three computed tomography (CT)-guided percutaneous hook wires were placed to localize the nodules prior to surgery. After entering the thorax, the wires were unable to be located in the right lower lobe and an intraoperative urgent chest CT demonstrated that the markers had migrated to the pulmonary artery. Therefore, the original surgical incision was extended and the superior tip subsegment of the pulmonary artery of the right lung was dissected open and the positioning needle was successfully removed. The patient was recovered without further complication and discharged 5 days later.
CONCLUSIONS
When the exact location of a hook wire utilized for lung nodule localization cannot be determined, an exhaustive radiographic evaluation is required to determine the wire's specific location. If conditions permit, it is best to remove the hook wire directly using video-assisted thoracoscopic surgery (VATS). With careful perioperative assessment, surgeons can avoid additional complications and further surgery if they encounter a migrated nodule localization wire.
PubMed: 38249911
DOI: 10.21037/jtd-23-1643 -
Chest Nov 2023Appropriate risk stratification of indeterminate pulmonary nodules (IPNs) is necessary to direct diagnostic evaluation. Currently available models were developed in...
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.
Topics: Humans; Lung Neoplasms; Retrospective Studies; Solitary Pulmonary Nodule; Lung; Multiple Pulmonary Nodules
PubMed: 37421973
DOI: 10.1016/j.chest.2023.06.009