Perifissural nodules: ready for application into lung cancer CT screening?
Ontology highlight
ABSTRACT:
SUBMITTER: Schreuder A
PROVIDER: S-EPMC7607092 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
Publications
Perifissural nodules: ready for application into lung cancer CT screening?
Schreuder Anton A Schaefer-Prokop Cornelia M CM
Annals of translational medicine 20201001 19
Similar Datasets
Project description:Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. With various countries currently considering the implementation of lung cancer screening, recurring discussion points are, among others, the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening. We discuss the performance levels of AI algorithms for various tasks related to the interpretation of lung screening CT scans, how they compare to human experts, and how AI and humans may complement each other. We discuss how AI may be used in the lung cancer CT screening workflow according to the current evidence and describe the additional research that will be required before AI can take a more prominent role in the analysis of lung screening CT scans.
| S-EPMC8182724 | biostudies-literature
Project description:Current clinical management of lung nodule patients is inefficient and therefore causes patient misclassification, which increases healthcare expenses. A precise and robust lung nodule classifier could minimise healthcare costs and discomfort for patients. http://bit.ly/2oMIEwQ.
| S-EPMC6885334 | biostudies-literature
Project description:Lung cancer screening based on low-dose CT (LDCT) has now been widely applied because of its effectiveness and ease of performance. Radiologists who evaluate a large LDCT screening images face enormous challenges, including mechanical repetition and boring work, the easy omission of small nodules, lack of consistent criteria, etc. It requires an efficient method for helping radiologists improve nodule detection accuracy with efficiency and cost-effectiveness. Many novel deep neural network-based systems have demonstrated the potential for use in the proposed technique to detect lung nodules. However, the effectiveness of clinical practice has not been fully recognized or proven. Therefore, the aim of this study to develop and assess a deep learning (DL) algorithm in identifying pulmonary nodules (PNs) on LDCT and investigate the prevalence of the PNs in China. Radiologists and algorithm performance were assessed using the FROC score, ROC-AUC, and average time consumption. Agreement between the reference standard and the DL algorithm in detecting positive nodules was assessed per-study by Bland-Altman analysis. The Lung Nodule Analysis (LUNA) public database was used as the external test. The prevalence of NCPNs was investigated as well as other detailed information regarding the number of pulmonary nodules, their location, and characteristics, as interpreted by two radiologists.
| S-EPMC7423892 | biostudies-literature
Project description:Lung cancer is the leading cause of cancer death in the United States. Non-small cell lung cancer accounts for 75% to 80% of all lung cancers. There is an impetus to find a screening test that can detect non-small cell lung cancer in its early preclinical stages, when surgical resection is most likely to reduce lung cancer mortality. Although earlier randomized controlled trials of lung cancer screening using chest radiography and sputum cytology failed to show reduced lung cancer mortality, CT is a much more sensitive test for detecting small lung nodules, and has generated considerable enthusiasm as a potential contemporary screening tool for lung cancer.
| S-EPMC4307918 | biostudies-literature
Project description:BackgroundMajor issues in the implementation of screening for lung cancer by means of low-dose computed tomography (CT) are the definition of a positive result and the management of lung nodules detected on the scans. We conducted a population-based prospective study to determine factors predicting the probability that lung nodules detected on the first screening low-dose CT scans are malignant or will be found to be malignant on follow-up.MethodsWe analyzed data from two cohorts of participants undergoing low-dose CT screening. The development data set included participants in the Pan-Canadian Early Detection of Lung Cancer Study (PanCan). The validation data set included participants involved in chemoprevention trials at the British Columbia Cancer Agency (BCCA), sponsored by the U.S. National Cancer Institute. The final outcomes of all nodules of any size that were detected on baseline low-dose CT scans were tracked. Parsimonious and fuller multivariable logistic-regression models were prepared to estimate the probability of lung cancer.ResultsIn the PanCan data set, 1871 persons had 7008 nodules, of which 102 were malignant, and in the BCCA data set, 1090 persons had 5021 nodules, of which 42 were malignant. Among persons with nodules, the rates of cancer in the two data sets were 5.5% and 3.7%, respectively. Predictors of cancer in the model included older age, female sex, family history of lung cancer, emphysema, larger nodule size, location of the nodule in the upper lobe, part-solid nodule type, lower nodule count, and spiculation. Our final parsimonious and full models showed excellent discrimination and calibration, with areas under the receiver-operating-characteristic curve of more than 0.90, even for nodules that were 10 mm or smaller in the validation set.ConclusionsPredictive tools based on patient and nodule characteristics can be used to accurately estimate the probability that lung nodules detected on baseline screening low-dose CT scans are malignant. (Funded by the Terry Fox Research Institute and others; ClinicalTrials.gov number, NCT00751660.).
| S-EPMC3951177 | biostudies-literature
Project description:The aim of this study was to determine whether quantitative analyses ("radiomics") of low-dose computed tomography lung cancer screening images at baseline can predict subsequent emergence of cancer.Public data from the National Lung Screening Trial (ACRIN 6684) were assembled into two cohorts of 104 and 92 patients with screen-detected lung cancer and then matched with cohorts of 208 and 196 screening subjects with benign pulmonary nodules. Image features were extracted from each nodule and used to predict the subsequent emergence of cancer.The best models used 23 stable features in a random forests classifier and could predict nodules that would become cancerous 1 and 2 years hence with accuracies of 80% (area under the curve 0.83) and 79% (area under the curve 0.75), respectively. Radiomics outperformed the Lung Imaging Reporting and Data System and volume-only approaches. The performance of the McWilliams risk assessment model was commensurate.The radiomics of lung cancer screening computed tomography scans at baseline can be used to assess risk for development of cancer.
| S-EPMC5545995 | biostudies-literature
Project description:ObjectivesThis study aimed to evaluate the association between visual emphysema and the presence of lung nodules, and Lung-RADS category with low-dose CT (LDCT).MethodsBaseline LDCT scans of 1162 participants from a lung cancer screening study (Nelcin-B3) performed in a Chinese general population were included. The presence, subtypes, and severity of emphysema (at least trace) were visually assessed by one radiologist. The presence, size, and classification of non-calcified lung nodules (≥ 30 mm3) and Lung-RADS category were independently assessed by another two radiologists. Multivariable logistic regression and stratified analyses were performed to estimate the association between emphysema and lung nodules, Lung-RADS category, after adjusting for age, sex, BMI, smoking status, pack-years, and passive smoking.ResultsEmphysema and lung nodules were observed in 674 (58.0%) and 424 (36.5%) participants, respectively. Participants with emphysema had a 71% increased risk of having lung nodules (adjusted odds ratios, aOR: 1.71, 95% CI: 1.26-2.31) and 70% increased risk of positive Lung-RADS category (aOR: 1.70, 95% CI: 1.09-2.66) than those without emphysema. Participants with paraseptal emphysema (n = 47, 4.0%) were at a higher risk for lung nodules than those with centrilobular emphysema (CLE) (aOR: 2.43, 95% CI: 1.32-4.50 and aOR: 1.60, 95% CI: 1.23-2.09, respectively). Only CLE was associated with positive Lung-RADS category (p = 0.02). CLE severity was related to a higher risk of lung nodules (ranges aOR: 1.44-2.61, overall p < 0.01).ConclusionIn a Chinese general population, visual emphysema based on LDCT is independently related to the presence of lung nodules (≥ 30 mm3) and specifically CLE subtype is related to positive Lung-RADS category. The risk of lung nodules increases with CLE severity.Key points• Participants with emphysema had an increased risk of having lung nodules, especially smokers. • Participants with PSE were at a higher risk for lung nodules than those with CLE, but nodules in participants with CLE had a higher risk of positive Lung-RADS category. • The risk of lung nodules increases with CLE severity.
| S-EPMC9705457 | biostudies-literature
Project description:BackgroundLung nodules observed in cancer screening are believed to grow exponentially, and their associated volume doubling time (VDT) has been proposed for nodule classification. This retrospective study aimed to elucidate the growth dynamics of lung nodules and determine the best classification as either benign or malignant.MethodsData were analyzed from 180 participants (73.7% male) enrolled in the I-ELCAP screening program (140 primary lung cancer and 40 benign) with three or more annual CT examinations before resection. Attenuation, volume, mass and growth patterns (decelerated, linear, subexponential, exponential and accelerated) were assessed and compared as classification methods.ResultsMost lung cancers (83/140) and few benign nodules (11/40) exhibited an accelerated, faster than exponential, growth pattern. Half (50%) of the benign nodules versus 26.4% of the malignant ones displayed decelerated growth. Differences in growth patterns allowed nodule malignancy to be classified, the most effective individual variable being the increase in volume between two-year-interval scans (ROC-AUC = 0.871). The same metric on the first two follow-ups yielded an AUC value of 0.769. Further classification into solid, part-solid or non-solid, improved results (ROC-AUC of 0.813 in the first year and 0.897 in the second year).ConclusionsIn our dataset, most lung cancers exhibited accelerated growth in contrast to their benign counterparts. A measure of volumetric growth allowed discrimination between benign and malignant nodules. Its classification power increased when adding information on nodule compactness. The combination of these two meaningful and easily obtained variables could be used to assess malignancy of lung cancer nodules.
| S-EPMC11346294 | biostudies-literature
Project description:Purpose18F-FDG PET/CT is widely used to evaluate indeterminate pulmonary nodules (IPNs). False positive results occur, especially from active granulomatous nodules. A PET-based imaging agent with superior specificity to 18F-FDG for IPNs, is badly needed, especially in areas of endemic granulomatous nodules. Somatostatin receptors (SSTR) are expressed in many malignant cells including small cell and non-small cell lung cancers (NSCLCs). 68Ga-DOTATATE, a positron emitter labeled somatostatin analog, combined with PET/CT imaging, may improve the diagnosis of IPNs over 18F-FDG by reducing false positives. Our study purpose was to test this hypothesis in our region with high endemic granulomatous IPNs.MethodsWe prospectively performed 68Ga-DOTATATE PET/CT and 18F-FDG PET/CT scans in the same 30 patients with newly diagnosed, treatment-naïve lung cancer (N = 14) or IPNs (N = 15) and one metastatic nodule. 68Ga-DOTATATE SUVmax levels at or above 1.5 were considered likely malignant. We analyzed the scan results, correlating with ultimate diagnosis via biopsy or 2-year chest CT follow-up. We also correlated 68Ga-DOTATATE uptake with immunohistochemical (IHC) staining for SSTR subtype 2A (SSTR2A) in pathological specimens.ResultsWe analyzed 31 lesions in 30 individuals, with 14 (45%) being non-neuroendocrine lung cancers and 1 (3%) being metastatic disease. McNemar's result comparing the two radiopharmaceuticals (p = 0.65) indicates that their accuracy of diagnosis in this indication are equivalent. 68Ga-DOTATATE was more specific (94% compared to 81%) and less sensitive 73% compared to 93%) than 18F-FDG. 68Ga-DOTATATE uptake correlated with SSTR2A expression in tumor stroma determined by immunohistochemical (IHC) staining in 5 of 9 (55%) NSCLCs.Conclusion68Ga-DOTATATE and 18F-FDG PET/CT had equivalent accuracy in the diagnosis of non-neuroendocrine lung cancer and 68Ga-DOTATATE was more specific than 18F-FDG for the diagnosis of IPNs. IHC staining for SSTR2A receptor expression correlated with tumor stroma but not tumor cells.
| S-EPMC5300187 | biostudies-literature
Project description:PurposeThis study evaluated thyroid cancer risk in a lung cancer screening population according to the presence of an incidental thyroid nodule (ITN) detected on low-dose chest computed tomography (LDCT).MethodsOf 47,837 subjects who underwent LDCT, a lung cancer screening population according to the National Lung Screening Trial results was retrospectively enrolled. The prevalence of ITN on LDCT was calculated, and the ultrasonography (US)/fine-needle aspiration (FNA)-based risk of thyroid cancer according to the presence of ITN on LDCT was compared using the Fisher exact or Student t-test as appropriate.ResultsOf the 2,329 subjects (female:male=44:2,285; mean age, 60.9±4.9 years), the prevalence of ITN on LDCT was 4.8% (111/2,329). The incidence of thyroid cancer was 0.8% (18/2,329, papillary thyroid microcarcinomas [PTMCs]) and was higher in the ITN-positive group than in the ITN-negative group (3.6% [4/111] vs. 0.6% [14/2,218], P=0.009). Among the 2,011 subjects who underwent both LDCT and thyroid US, all risks were higher (P<0.001) in the ITNpositive group than in the ITN-negative group: presence of thyroid nodule on US, 94.1% (95/101) vs. 48.6% (928/1,910); recommendation of FNA according to the American Thyroid Association guideline and Korean Thyroid Imaging Reporting and Data System guideline, 41.2% (42/101) vs. 2.4% (46/1,910) and 39.6% (40/101) vs. 1.9% (37/1,910), respectively.ConclusionDespite a higher risk of thyroid cancer in the LDCT ITN-positive group than in the ITN-negative group in a lung cancer screening population, all cancers were PTMCs. A heavy smoking history may not necessitate thorough screening US for thyroid incidentalomas.
| S-EPMC10071062 | biostudies-literature