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Cureus May 2024Neuroendocrine tumors (NETs) represent a heterogeneous group of neoplasms with diverse clinical presentations and prognoses. Accurate and timely diagnosis of these... (Review)
Review
Neuroendocrine tumors (NETs) represent a heterogeneous group of neoplasms with diverse clinical presentations and prognoses. Accurate and timely diagnosis of these tumors is crucial for appropriate management and improved patient outcomes. In recent years, exciting advancements in artificial intelligence (AI) technologies have been revolutionizing medical diagnostics, particularly in the realm of detecting and characterizing pulmonary NETs, offering promising avenues for improved patient care. This article aims to provide a comprehensive overview of the role of AI in diagnosing lung NETs. We discuss the current challenges associated with conventional diagnostic approaches, including histopathological examination and imaging modalities. Despite advancements in these techniques, accurate diagnosis remains challenging due to the overlapping features with other pulmonary lesions and the subjective interpretation of imaging findings. AI-based approaches, including machine learning and deep learning algorithms, have demonstrated remarkable potential in addressing these challenges. By leveraging large datasets of radiological images, histopathological samples, and clinical data, AI models can extract complex patterns and features that may not be readily discernible to human observers. Moreover, AI algorithms can continuously learn and improve from new data, leading to enhanced diagnostic accuracy and efficiency over time. Specific AI applications in the diagnosis of lung NETs include computer-aided detection and classification of pulmonary nodules on CT scans, quantitative analysis of PET imaging for tumor characterization, and integration of multi-modal data for comprehensive diagnostic assessments. These AI-driven tools hold promise for facilitating early detection, risk stratification, and personalized treatment planning in patients with lung NETs.
PubMed: 38910787
DOI: 10.7759/cureus.61012 -
BMC Medical Imaging Jun 2024Assessing the aggressiveness of pure ground glass nodules early on significantly aids in making informed clinical decisions. (Meta-Analysis)
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.
Topics: Humans; Lung Neoplasms; Tomography, X-Ray Computed; Risk Assessment; Neoplasm Invasiveness; Adenocarcinoma of Lung; Multiple Pulmonary Nodules
PubMed: 38886695
DOI: 10.1186/s12880-024-01313-5 -
Clinical & Translational Oncology :... Jun 2024This study aims to assess the diagnostic utility of circulating tumor cells (CTCs) in conjunction with low-dose computed tomography (LDCT) for differentiating between... (Review)
Review
Value of circulating tumor cell assisting low-dose computed tomography in screening pulmonary nodules based on existing liquid biopsy techniques: a systematic review with meta-analysis and trial sequential analysis.
OBJECTIVE
This study aims to assess the diagnostic utility of circulating tumor cells (CTCs) in conjunction with low-dose computed tomography (LDCT) for differentiating between benign and malignant pulmonary nodules and to substantiate the foundation for their integration into clinical practice.
METHODS
A systematic literature review was performed independently by two researchers utilizing databases including PubMed, Web of Science, The Cochrane Library, Embase, and Medline, to collate studies up to September 15, 2023, that investigated the application of CTCs in diagnosing pulmonary nodules. A meta-analysis was executed employing Stata 15.0 and Revman 5.4 to calculate the pooled sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC). Additionally, trial sequential analysis was conducted using dedicated TSA software.
RESULTS
The selection criteria identified 16 studies, encompassing a total of 3409 patients. The meta-analysis revealed that CTCs achieved a pooled sensitivity of 0.84 (95% CI 0.80 to 0.87), specificity of 0.80 (95% CI 0.73 to 0.86), PLR of 4.23 (95% CI 3.12 to 5.72), NLR of 0.20 (95% CI 0.16 to 0.25), DOR of 20.92 (95% CI 13.52 to 32.36), and AUC of 0.89 (95% CI 0.86 to 0.93).
CONCLUSIONS
Circulating tumor cells demonstrate substantial diagnostic accuracy in distinguishing benign from malignant pulmonary nodules. The incorporation of CTCs into the diagnostic protocol can significantly augment the diagnostic efficacy of LDCT in screening for malignant lung diseases.
PubMed: 38869739
DOI: 10.1007/s12094-024-03556-8 -
Radiotherapy and Oncology : Journal of... May 2024Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as... (Review)
Review
BACKGROUND
Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable.
METHODS
We conducted a comprehensive search of PubMed, Embase, and Web of Science until November 7, 2023. We assessed the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. This analysis included data from various clinical settings and stages of lung cancer. Key performance metrics, such as the Dice similarity coefficient, were pooled, and factors affecting algorithm performance, such as clinical setting, algorithm type, and image processing techniques, were examined.
RESULTS
Our analysis of 37 studies revealed a pooled Dice score of 79 % (95 % CI: 76 %-83 %), indicating moderate accuracy. Radiotherapy studies had a slightly lower score of 78 % (95 % CI: 74 %-82 %). A temporal increase was noted, with recent studies (post-2022) showing improvement from 75 % (95 % CI: 70 %-81 %). to 82 % (95 % CI: 81 %-84 %). Key factors affecting performance included algorithm type, resolution adjustment, and image cropping. QUADAS-2 assessments identified ambiguous risks in 78 % of studies due to data interval omissions and concerns about generalizability in 8 % due to nodule size exclusions, and CLAIM criteria highlighted areas for improvement, with an average score of 27.24 out of 42.
CONCLUSION
This meta-analysis demonstrates DL algorithms' promising but varied efficacy in lung cancer segmentation, particularly higher efficacy noted in early stages. The results highlight the critical need for continued development of tailored DL models to improve segmentation accuracy across diverse clinical settings, especially in advanced cancer stages with greater challenges. As recent studies demonstrate, ongoing advancements in algorithmic approaches are crucial for future applications.
PubMed: 38806113
DOI: 10.1016/j.radonc.2024.110344 -
Lung May 2024There has been growing interest in using artificial intelligence/deep learning (DL) to help diagnose prevalent diseases earlier. In this study we sought to survey the...
BACKGROUND
There has been growing interest in using artificial intelligence/deep learning (DL) to help diagnose prevalent diseases earlier. In this study we sought to survey the landscape of externally validated DL-based computer-aided diagnostic (CADx) models, and assess their diagnostic performance for predicting the risk of malignancy in computed tomography (CT)-detected pulmonary nodules.
METHODS
An electronic search was performed in four databases (from inception to 10 August 2023). Studies were eligible if they were peer-reviewed experimental or observational articles comparing the diagnostic performance of externally validated DL-based CADx models with models widely used in clinical practice to predict the risk of malignancy. A bivariate random-effect approach for the meta-analysis on the included studies was used.
RESULTS
Seventeen studies were included, comprising 8553 participants and 9884 nodules. Pooled analyses showed DL-based CADx models were 11.6% more sensitive than physician judgement alone, and 14.5% more than clinical risk models alone. They had a similar pooled specificity to physician judgement alone [0.77 (95% CI 0.68-0.84) v 0.81 (95% CI 0.71-0.88)], and were 7.4% more specific than clinical risk models alone. They had superior pooled areas under the receiver operating curve (AUC), with relative pooled AUCs of 1.03 (95% CI 1.00-1.07) and 1.10 (95% CI 1.07-1.13) versus physician judgement and clinical risk models alone, respectively.
CONCLUSION
DL-based models are already used in clinical practice in certain settings for nodule management. Our results show their diagnostic performance potentially justifies wider, more routine deployment alongside experienced physician readers to help inform multidisciplinary team decision-making.
PubMed: 38782779
DOI: 10.1007/s00408-024-00706-1 -
Rheumatology International May 2024Immunoglobulin G4-related disease (IgG4-RD) is a multisystem fibroinflammatory condition. A consistent feature of many cases is pulmonary infiltrates, or respiratory... (Review)
Review
Immunoglobulin G4-related disease (IgG4-RD) is a multisystem fibroinflammatory condition. A consistent feature of many cases is pulmonary infiltrates, or respiratory failure. This systematic literature review aims to summarise the pulmonary manifestations of IgG4-RD, including clinical outcomes and treatment. This review was registered on PROSPERO (CRD42023416410). Medline, Embase and Cochrane databases were searched for articles discussing IgG4-RD syndrome. Information was extracted on demographics, type and prevalence of pulmonary manifestations, treatment and clinical outcomes. Initially, after deduplication, 3123 articles were retrieved with 18 ultimately included. A pooled total of 724 patients with IgG4-RD were included, 68.6% male, mean age 59.4 years (SD 5.8) at disease onset. The most frequently described pulmonary manifestation was mediastinal lymphadenopathy (n = 186, 48.8%), followed by pulmonary nodules (n = 151, 39.6%) and broncho-vascular thickening (n = 85, 22.3%). Where treatment was reported, the majority of patients received glucocorticoids (n = 211, 93.4%). Other immunosuppressive therapy included cyclophosphamide (n = 31), azathioprine (n = 18), with mycophenolate mofetil (n = 6), rituximab (n = 6), methotrexate (n = 5) and other unspecified immunomodulators (50). Clinical outcomes were reported in 263 patients, where 196 patients had remission of their disease, 20 had relapse, 35 had stable disease, four had progression and eight patients died from complications of IgG4-RD. This systematic review summarises pulmonary manifestations, treatments and outcomes in patients with IgG4-RD. Pulmonary involvement in IgG4-RD is relatively common, leading to high levels of morbidity and mortality. Glucocorticoids remain the mainstay of treatment, but further work is required to explore the management of patients with pulmonary manifestations in association with IgG4-RD.
PubMed: 38769126
DOI: 10.1007/s00296-024-05611-7 -
Radiology. Cardiothoracic Imaging Apr 2024Purpose To perform a meta-analysis of the diagnostic performance of MRI for the detection of pulmonary nodules, with use of CT as the reference standard. Materials and... (Meta-Analysis)
Meta-Analysis
Purpose To perform a meta-analysis of the diagnostic performance of MRI for the detection of pulmonary nodules, with use of CT as the reference standard. Materials and Methods PubMed, Embase, Scopus, and other databases were systematically searched for studies published from January 2000 to March 2023 evaluating the performance of MRI for diagnosis of lung nodules measuring 4 mm or larger, with CT as reference. Studies including micronodules, nodules without size stratification, or those from which data for contingency tables could not be extracted were excluded. Primary outcomes were the per-lesion sensitivity of MRI and the rate of false-positive nodules per patient (FPP). Subgroup analysis by size and meta-regression with other covariates were performed. The study protocol was registered in the International Prospective Register of Systematic Reviews, or PROSPERO (no. CRD42023437509). Results Ten studies met inclusion criteria (1354 patients and 2062 CT-detected nodules). Overall, per-lesion sensitivity of MRI for nodules measuring 4 mm or larger was 87.7% (95% CI: 81.1, 92.2), while the FPP rate was 12.4% (95% CI: 7.0, 21.1). Subgroup analyses demonstrated that MRI sensitivity was 98.5% (95% CI: 90.4, 99.8) for nodules measuring at least 8-10 mm and 80.5% (95% CI: 71.5, 87.1) for nodules less than 8 mm. Conclusion MRI demonstrated a good overall performance for detection of pulmonary nodules measuring 4 mm or larger and almost equal performance to CT for nodules measuring at least 8-10 mm, with a low rate of FPP. Systematic review registry no. CRD42023437509 Lung Nodule, Lung Cancer, Lung Cancer Screening, MRI, CT © RSNA, 2024.
Topics: Humans; Lung Neoplasms; Early Detection of Cancer; Multiple Pulmonary Nodules; Asparagales; Magnetic Resonance Imaging
PubMed: 38634743
DOI: 10.1148/ryct.230241 -
Annals of Medicine Dec 2024Interstitial lung disease (ILD) is the most widespread and fatal pulmonary complication of rheumatoid arthritis (RA). Existing knowledge on the prevalence and risk... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Interstitial lung disease (ILD) is the most widespread and fatal pulmonary complication of rheumatoid arthritis (RA). Existing knowledge on the prevalence and risk factors of rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is inconclusive. Therefore, we designed this review to address this gap.
MATERIALS AND METHODS
To find relevant observational studies discussing the prevalence and/or risk factors of RA-ILD, EMBASE, Web of Science, PubMed, and the Cochrane Library were explored. The pooled odds ratios (ORs) / hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated with a fixed/ random effects model. While subgroup analysis, meta-regression analysis and sensitivity analysis were carried out to determine the sources of heterogeneity, the statistic was utilized to assess between-studies heterogeneity. Funnel plots and Egger's test were employed to assess publication bias. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines, our review was conducted.
RESULTS
A total of 56 studies with 11,851 RA-ILD patients were included in this meta-analysis. The pooled prevalence of RA-ILD was 18.7% (95% CI 15.8-21.6) with significant heterogeneity ( = 96.4%). The prevalence of RA-ILD was found to be more likely as a result of several identified factors, including male sex (ORs = 1.92 95% CI 1.70-2.16), older age (WMDs = 6.89, 95% CI 3.10-10.67), having a smoking history (ORs =1.91, 95% CI 1.48-2.47), pulmonary comorbidities predicted (HRs = 2.08, 95% CI 1.89-2.30), longer RA duration (ORs = 1.03, 95% CI 1.01-1.05), older age of RA onset (WMDs =4.46, 95% CI 0.63-8.29), positive RF (HRs = 1.15, 95%CI 0.75-1.77; ORs = 2.11, 95%CI 1.65-2.68), positive ACPA (ORs = 2.11, 95%CI 1.65-2.68), higher ESR (ORs = 1.008, 95%CI 1.002-1.014), moderate and high DAS28 (≥3.2) (ORs = 1.87, 95%CI 1.36-2.58), rheumatoid nodules (ORs = 1.87, 95% CI 1.18-2.98), LEF use (ORs = 1.42, 95%CI 1.08-1.87) and steroid use (HRs= 1.70, 1.13-2.55). The use of biological agents was a protective factor (HRs = 0.77, 95% CI 0.69-0.87).
CONCLUSION(S)
The pooled prevalence of RA-ILD in our study was approximately 18.7%. Furthermore, we identified 13 risk factors for RA-ILD, including male sex, older age, having a smoking history, pulmonary comorbidities, older age of RA onset, longer RA duration, positive RF, positive ACPA, higher ESR, moderate and high DAS28 (≥3.2), rheumatoid nodules, LEF use and steroid use. Additionally, biological agents use was a protective factor.
Topics: Humans; Male; Rheumatoid Nodule; Prevalence; Arthritis, Rheumatoid; Risk Factors; Lung Diseases, Interstitial; Steroids
PubMed: 38547537
DOI: 10.1080/07853890.2024.2332406 -
Frontiers in Oncology 2024Due to the widespread use of imaging techniques, the detection rate of early-stage lung cancer has increased. Video-assisted thoracoscopic surgery (VATS) sublobectomy...
BACKGROUND
Due to the widespread use of imaging techniques, the detection rate of early-stage lung cancer has increased. Video-assisted thoracoscopic surgery (VATS) sublobectomy has emerged as a prominent alternative to lobectomy, offering advantages like reduced resection range, better preservation of lung function, and enhanced postoperative quality of life. However, sublobectomy is more intricate than lobectomy, necessitating a higher level of surgical proficiency and anatomical understanding.
METHODS
Three electronic databases were searched to capture relevant studies from January 2016 to March 2023, which related to the application of three-dimensional(3D) technology in VATS sublobectomy.
RESULTS
Currently, clinical departments such as orthopedics, hepatobiliary surgery, and urology have started using 3D technology. This technology is expected to be widely used in thoracic surgery in future. Now 3D technology assists in preoperative planning, intraoperative navigation and doctor-patient communication.
CONCLUSION
3D technologies, instrumental in locating pulmonary nodules and identifying variations in target lung segmental vessels and bronchi, play pivotal roles in VATS sublobectomy, especially in preoperative planning, intraoperative navigation, and doctor-patient communication. The limitations of 3D technology in clinical application are analyzed, and the future direction of existing 3D technology development is prospected.
PubMed: 38525423
DOI: 10.3389/fonc.2024.1280075 -
Journal of Thoracic Disease Feb 2024Pure ground glass nodules (GGNs) have been increasingly detected through lung cancer screening programs. However, there were limited reports about pathologic...
BACKGROUND
Pure ground glass nodules (GGNs) have been increasingly detected through lung cancer screening programs. However, there were limited reports about pathologic characteristics of pure GGN. Here we presented a meta-analysis of the histologic outcome and proportion analysis of pure GGN.
METHODS
This study included previous pathological reports of pure GGN published until June 14, 2022 following a systematic search. A meta-analysis estimated the summary effects and between-study heterogeneity for pathologic diagnosis of invasive adenocarcinoma (IA), minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and atypical adenomatous hyperplasia (AAH).
RESULTS
This study incorporated 24 studies with 3,845 cases of pure GGN that underwent surgery. Among them, sublobar resection was undertaken in 60% of the patients [95% confidence interval (CI): 38-78%, I=95%]. The proportion of IA in cases of resected pure GGN was 27% (95% CI: 18-37%, I=95%), and 50% of IA had non-lepidic predominant patterns (95% CI: 35-65%, I=91%). The pooled proportions of MIA, AIS, and AAH were 24%, 36%, and 11%, respectively. Among nine studies with available clinical outcomes, no recurrences or metastases was observed other than one study.
CONCLUSIONS
The portion of IA in cases of pure GGN is significantly larger that expected. More than half of them owned invasiveness components if MIA and IA were combined. Furthermore, there were quite number of lesions with aggressive histologic patterns other than the lepidic subtype. Therefore, further attempts are necessary to differentiate advanced histologic subtype among radiologically favorable pure GGN.
PubMed: 38505083
DOI: 10.21037/jtd-23-1089