Growth Of Lung Nodules Research Articles

MA08.12 Semi-Sold Lung Nodule Growth Rates, Need for Treatment, and Cancer-Specific Verses All-Cause Mortality in a Large North American Population

MA08.12 Semi-Sold Lung Nodule Growth Rates, Need for Treatment, and Cancer-Specific Verses All-Cause Mortality in a Large North American Population

Lung nodule growth measurement and prediction using Multi scale - 3 D- UNet segmentation and shape variance analysis

In this work, a statistical model is constructed to forecast the possibility of lung nodules that may grow in the future. This study segments all potential lung nodule candidates using the Multi-scale 3D UNet (M-3D-UNet) method. 34 patients' CT scan series yielded an average of approximately 600 nodule candidates larger than 3 mm, which were then segmented. After removing the arteries, non-nodules and 3D shape variation analysis, 34 actual nodules remained. On actual nodules, the nodule growth Rate (NGR) was calculated in terms of 3D-volume change. Three of the 34 actual nodules had RNG values greater than one, indicating that they were malignant. Compactness, Tissue deficit, Tissue excess, Isotropic Factor and Edge gradient were used to develop the nodule growth predictive measure.

Impact of Duration of Diagnostic Workup on Prognosis for Early Lung Cancer.

Growth assessment for pulmonary nodules is an important diagnostic tool; however, the impact on prognosis due to time delay for follow-up diagnostic scans needs to be considered. Using the data between 2003 and 2019 from the International Early Lung Cancer Action Program, a prospective cohort study, we determined the size-specific, 10-year Kaplan-Meier lung cancer (LC) survival rates as surrogates for cure rates. We estimated the change in LC diameter after delays of 90, 180, and 365 days using three representative LC volume doubling times (VDTs) of 60 (fast), 120 (moderate), and 240 (slow). We then estimated the decrease in the LC cure rate resulting from time between computed tomography scans to assess for growth during the diagnostic workup. Using a regression model of the 10-year LC survival rates on LC diameter, the estimated LC cure rate of a 4.0 mm LC with fast (60-d) VDT is 96.0% (95% confidence interval [CI]: 95.2%-96.7%) initially, but it would decrease to 94.3% (95% CI: 93.2%-95.0%), 92.0% (95% CI: 90.5%-93.4%), and 83.6%(95% CI: 80.6%-86.6%) after delays of 90, 180, and 365 days, respectively. A 20.0-mm LC with the same VDTs has a lower LC cure rate of 79.9% (95% CI: 76.2%-83.5%) initially and decreases more rapidly to 71.5% (95% CI: 66.4%-76.7%), 59.8% (95% CI: 52.4%-67.1%), and 17.9% (95% CI: 3.0%-32.8%) after the same delays of 90, 180, and 365 days, respectively. Time between scans required to measure growth of lung nodules affects prognosis with the effect being greater for fast growing and larger cancers. Quantifying the extent of change in prognosis is required to understand efficiencies of different management protocols.

Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study.

BackgroundRisk prediction models of lung nodules have been built to alleviate the heavy interpretative burden on clinicians. However, the malignancy scores output by those models can be difficult to interpret in a clinically meaningful manner. In contrast, the modeling of lung nodule growth may be more readily useful. This study developed a CT-based visual forecasting system that can visualize and quantify a nodule in three dimensions (3D) in any future time point using follow-up CT scans.MethodsWe retrospectively included 246 patients with 313 lung nodules with at least 1 follow-up CT scan. For the manually segmented nodules, we calculated geometric properties including CT value, diameter, volume, and mass, as well as growth properties including volume doubling time (VDT), and consolidation-to-tumor ratio (CTR) at follow-ups. These nodules were divided into growth and non-growth groups by thresholding their VDTs. We then developed a convolutional neural network (CNN) to model the imagery change of the nodules from baseline CT image (combined with the nodule mask) to follow-up CT image with a particular time interval. The model was evaluated on the geometric and radiological properties using either logistic regression or receiver operating characteristic (ROC) curve.ResultsThe lung nodules consisted of 115 ground glass nodules (GGN) and 198 solid nodules and were followed up for an average of 354 days with 2 to 11 scans. The 2 groups differed significantly in most properties. The prediction of our forecasting system was highly correlated with the ground truth with small relative errors regarding the four geometric properties. The prediction-derived VDTs had an area under the curve (AUC) of 0.857 and 0.843 in differentiating growth and non-growth nodules for GGN and solid nodules, respectively. The prediction-derived CTRs had an AUC of 0.892 in classifying high- and low-risk nodules.ConclusionsThis proof-of-concept study demonstrated that the deep learning-based model can accurately forecast the imagery of a nodule in a given future for both GGNs and solid nodules and is worthy of further investigation. With a larger dataset and more validation, such a system has the potential to become a prognostication tool for assessing lung nodules.

Abstract 2936: Ago2 induces Kras signaling and promotes tumor progression in mouse models of lung adenocarcinoma

Abstract Lung cancer is the deadliest malignancy in the United States. Non-small cell lung cancer (NSCLC) accounts for 85% of cases, and is frequently driven by activating mutations in the gene encoding the KRAS GTPase (e.g. KRASG12D). Our previous work demonstrated that Argonaute 2 (AGO2)—a component of the RNA induced silencing complex (RISC)—physically interacts with KRAS and stabilizes it at the protein level. In multiple cell lines, AGO2 knockdown reduces KRAS protein level and attenuates cell proliferation. We therefore hypothesized that AGO2 could promote KRASG12D-dependent NSCLC in vivo. To test the hypothesis, we evaluated the impact of Ago2 knockout in the KPC mouse model of NSCLC. In KPC mice, intratracheal delivery of adenoviral Cre drives lung-specific expression of a stop-floxed KRASG12D allele and biallelic ablation of p53. Simultaneous biallelic ablation of floxed Ago2 inhibited KPC lung nodule growth while reducing proliferative index and improving pathological grade. We next applied the KPHetC model, in which the clara cell-specific CCSP-driven Cre activates KRASG12D and ablates a single p53 allele. In these mice, Ago2 ablation also reduced tumor size and grade. In both models, Ago2 knockout inhibited ERK phosphorylation in tumor nodules, indicating impaired KRAS signaling. Furthermore, RNA-seq analysis of KPC nodules demonstrated broadly reduced expression of Kras-related genes. Taken together, our data demonstrate a novel pathogenic role for AGO2 in KRAS-dependent NSCLC. Given the prevalence of this malignancy and current difficulties in therapeutically targeting KRAS signaling, our work has future translational relevance. Citation Format: Jean C. Tien, Seema Chugh, Andrew E. Goodrum, Yunhui Cheng, Lisha Wang, Rahul Mannan, Xiaoming Wang, Vijaya L. Dommeti, Yuping Zhang, Alice Xu, Fengyu Su, Xuhong Cao, Sunita Shankar, Arul M. Chinnaiyan. Ago2 induces Kras signaling and promotes tumor progression in mouse models of lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2936.

AGO2 promotes tumor progression in KRAS-driven mouse models of non–small cell lung cancer

Lung cancer is the deadliest malignancy in the United States. Non-small cell lung cancer (NSCLC) accounts for 85% of cases and is frequently driven by activating mutations in the gene encoding the KRAS GTPase (e.g., KRASG12D). Our previous work demonstrated that Argonaute 2 (AGO2)-a component of the RNA-induced silencing complex (RISC)-physically interacts with RAS and promotes its downstream signaling. We therefore hypothesized that AGO2 could promote KRASG12D-dependent NSCLC in vivo. To test the hypothesis, we evaluated the impact of Ago2 knockout in the KPC (LSL-KrasG12D/+;p53f/f;Cre) mouse model of NSCLC. In KPC mice, intratracheal delivery of adenoviral Cre drives lung-specific expression of a stop-floxed KRASG12D allele and biallelic ablation of p53 Simultaneous biallelic ablation of floxed Ago2 inhibited KPC lung nodule growth while reducing proliferative index and improving pathological grade. We next applied the KPHetC model, in which the Clara cell-specific CCSP-driven Cre activates KRASG12D and ablates a single p53 allele. In these mice, Ago2 ablation also reduced tumor size and grade. In both models, Ago2 knockout inhibited ERK phosphorylation (pERK) in tumor cells, indicating impaired KRAS signaling. RNA sequencing (RNA-seq) of KPC nodules and nodule-derived organoids demonstrated impaired canonical KRAS signaling with Ago2 ablation. Strikingly, accumulation of pERK in KPC organoids depended on physical interaction of AGO2 and KRAS. Taken together, our data demonstrate a pathogenic role for AGO2 in KRAS-dependent NSCLC. Given the prevalence of this malignancy and current difficulties in therapeutically targeting KRAS signaling, our work may have future translational relevance.

Screening for Early Lung Cancer, Chronic Obstructive Pulmonary Disease, and Cardiovascular Disease (the Big-3) Using Low-dose Chest Computed Tomography: Current Evidence and Technical Considerations.

Lung cancer, chronic obstructive pulmonary disease, and cardiovascular disease are highly prevalent in the general population and expected to cause most deaths by 2050. For these "Big-3," treatment might cure, delay, or stop the progression of disease at a very early stage. Lung nodule growth rate (a biomarker for lung cancer), emphysema/air trapping (a biomarker for chronic obstructive pulmonary disease), and coronary artery calcification (a biomarker for cardiovascular disease) are imaging biomarkers of early stages of the Big-3 that can be acquired with low-dose computed tomography (CT). We hypothesize that a (combined) low-dose CT examination for detection of all 3 diseases may significantly improve the cost-effectiveness of screening in the future. We review the current evidence of the imaging biomarkers for the detection of the Big-3 diseases and present the potential health economic potential of Big-3 screening. Furthermore, we review the low-dose CT protocols to acquire these biomarkers and describe the technical considerations when combining the CT protocols for the different biomarkers.

Quantitative features can predict further growth of persistent pure ground-glass nodule.

To evaluate whether quantitative features of persistent pure ground-glass nodules (PGGN) on the initial computed tomography (CT) scans can predict further nodule growth. This retrospective study included 59 patients with 101 PGGNs from 2011 to 2012, who received regular CT follow-up for lung nodule surveillance. Nineteen quantitative image features consisting of 8 volumetric and 11 histogram parameters were calculated to detect lung nodule growth. For the extraction of the quantitative features, semi-automatic GrowCut segmentation was implemented on chest CT images in 3D slicer platform. Univariate and multivariate analyses were performed to identify risk factors for nodule growth. With a median follow-up of 52 months, nodule growth was detected in 10 nodules by radiological assessment and in 16 nodules by quantitative features. In univariate analysis, 3D maximum diameter (MD), volume, mass, surface area, 90% percentile, and standard deviation value (SD) of PGGN on the initial CT scan were significantly different between stable nodules and nodules with further growth. In multivariate analysis, MD [hazard ratio (HR), 3.75; 95% confidence interval (CI), 2.14-6.55] and SD (HR, 2.06; 95% CI, 1.35-3.14) were independent predictors of further nodule growth. Also, the area under the curve was 0.896 (95% CI: 0.820-0.948) and 0.813 (95% CI: 0.723-0.883) for MD with a cut-off value of 10.2mm and SD of 50.0 Hounsfield Unit (HU). Besides, the growth rate was 55.6% (n=15) of PGGNs with MD >10.2 mm and SD >50.0 HU. Based on the initial CT scan, the quantitative features can predict PGGN growth more precisely. PGGN with MD >10.2 mm and SD >50.0 HU may require close follow-up or surgical intervention for the high incidence of growth.

Influence of lung nodule margin on volume- and diameter-based reader variability in CT lung cancer screening.

To evaluate the influence of nodule margin on inter- and intrareader variability in manual diameter measurements and semi-automatic volume measurements of solid nodules detected in low-dose CT lung cancer screening. 25 nodules of each morphological category (smooth, lobulated, spiculated and irregular) were randomly selected from 93 participants of the Dutch-Belgian Randomized Lung Cancer Screening Trial (NELSON). Semi-automatic volume measurements were performed using Syngo LungCARE® software (Version Somaris/5 VB10A-W, Siemens, Forchheim, Germany). Three radiologists independently measured mean diameters manually. Impact of nodule margin on interreader variability was evaluated based on systematic error and 95% limits of agreement. Interreader variability was compared with the nodule growth cut-off as used in Lung CT Screening Reporting and Data System (LungRADS; +1.5-mm diameter) and the Dutch-Belgian Randomized Lung Cancer Screening Trial(acronym: NELSON) /British Thoracic Society (+25% volume). For manual diameter measurements, a significant systematic error (up to 1.2 mm) between readers was found in all morphological categories. For semi-automatic volume measurements, no statistically significant systematic error was found. The interreader variability in mean diameter measurements exceeded the 1.5-mm cut-off for nodule growth for all morphological categories [smooth: ±1.9 mm (+27%), lobulated: ±2.0 mm (+33%), spiculated: ±3.5 mm (+133%), irregular: ±4.5 mm (+200%)]. The 25% vol growth cut-off was exceeded slightly for spiculated [28% (+12%)] and irregular [27% (+8%)] nodules. Lung nodule sizing based on manual diameter measurement is affected by nodule margin. Interreader variability increases especially for nodules with spiculated and irregular margins, and causes substantial misclassification of nodule growth. This effect is almost neglectable for semi-automated volume measurements. Semi-automatic volume measurements are superior for both size and growth determination of pulmonary nodules. Nodule assessment based on manual diameter measurements is susceptible to nodule margin. This effect is almost neglectable for semi-automated volume measurements. The larger interreader variability for manual diameter measurement results in inaccurate lung nodule growth detection and size classification.

Lung nodule growth measurement and prediction using auto cluster seed K-means morphological segmentation and shape variance analysis

Lung nodule growth measurement and prediction using auto cluster seed K-means morphological segmentation and shape variance analysis

Lung nodule growth measurement and prediction using auto cluster seed K-means morphological segmentation and shape variance analysis

A quantitative model is developed in this work to predict the lung nodules which have the potential to grow in future. An Auto Cluster Seed K-means Morphological segmentation (ACSKMM) algorithm was implemented in this work to segment all the possible lung nodule candidates. An average of around 600 nodule candidates of size >3mm were segmented from each CT scan series of 34 patients. Finally, in total 34 real nodules were remained after eliminating the vessels, non-nodules and calcifications using centroid shift and 3D shape variance analysis. The rate of nodule growth (RNG) was computed on real nodules in terms of 3D-volume change. Out of the 34 real nodules, 3 nodules had RNG value>1, confirming their malignant nature. The nodule growth predictive measure was modelled through compactness, mass deficit, mass excess and isotropic factor.

Disulfiram induces anoikis and suppresses lung colonization in triple-negative breast cancer via calpain activation

Triple-negative breast cancers (TNBC) often exhibit an aggressive phenotype. Disulfiram (DSF) is an approved drug for the treatment of alcohol dependence, but has also been shown to kill TNBC cells in a copper (Cu)-dependent manner. Exactly how this occurs has not been clearly elucidated. We sought to investigate the mechanisms responsible for DSF/Cu-dependent induction of apoptosis and suppression of lung colonization by TNBC cells. DSF/Cu induced anoikis and significantly suppressed cell migration and invasion with negative effects on focal adhesions, coinciding with vimentin breakdown and calpain activation in TNBC cells. In a xenograft tumor model, DSF suppressed tumor growth and lung nodule growth, which was also associated with calpain activation. These findings warrant further investigation of disulfiram as a potential treatment for metastatic TNBC.

Three-dimensional analysis of pulmonary nodules: variability of semiautomated volume measurements between different versions of the same software

This study was done to evaluate the variability of semiautomated volume measurements of solid pulmonary nodules between two different versions of the same volumetric software. The volumes of 100 solid intraparenchymal nodules (mean volume 88.10 mm(3); range 7.36-595.25 mm(3)) studied with the same multidetector computed tomography (MDCT) protocol were determined using two different versions of the same volumetric software (LungCARE 2006G and LungCARE 2007S). The 2006G version is based on a single-segmentation algorithm, whereas the newer version features two algorithms: SmallSizeNodule and AllSizeNodule. The results obtained with the 2006G version were compared with those of the 2007S version with the SmallSizeNodule algorithm, as recommended by the user manual. In addition, we compared the volumetric measurements obtained by the two different algorithms of the 2007S version. The 2006G version and the 2007S version with the SmallSizeNodule algorithm agreed in only two of 100 cases and showed a mean variability of 1.66% (range 0%-8.78%). A more significant volumetric discrepancy was observed between the two different algorithms of the 2007S version, with the AllSizeNodule algorithm providing on average larger volumes (mean variability 71.08%; range 6.02%-218.80%) than SmallSizeNodule. Volume discrepancies were more pronounced in the subgroups of smaller nodules in all comparisons. There is variability also in the results provided by different versions of the same volumetric software, and this may affect the calculation of the nodule-doubling time. Computer-aided assessment of the growth of lung nodules should always be performed using the same version of volumetric software and the same segmentation algorithm.

Rheumatoid lung nodulosis and osteopathy associated with leflunomide therapy

Leflunomide (LEF) is indicated in adults for the treatment of active rheumatoid arthritis (RA). LEF inhibits dehydroorotate dehydrogenase, a key enzyme of the pyrimidine synthesis in activated lymphocytes. Among rare adverse effects, fatal interstitial lung disease has been recently reported during treatment of RA with LEF in Japan. Clinical trials outside Japan do not suggest that LEF causes an excess of pulmonary adverse effects. Development and increase of peripheral rheumatoid nodules in typical sites of RA patients following LEF therapy has been recently reported. Two cases with new and accelerated development of rheumatoid lung nodulosis during LEF therapy were described in this study. LEF treatment was administered to two male patients (77 and 66 years old) with long-standing active seropositive nodular RA with failure of multiple second line drugs and without lung involvement. Clinical and laboratory assessment using the American College of Rheumatology response criteria, chest computed tomography (CT), quantification of serum rheumatoid factor (RF), and monocyte count of peripheral blood along with routine laboratory follow up were performed on both patients before and during therapy. In case 1, a bone scan was performed due to sustained limbs pain. Open lung biopsy was performed in case 1 and core lung biopsy in case 2. Both patients achieved full clinical remission during 2 months of LEF therapy. In case 1, the first complaints were limbs pain after 10 months of treatment associated with intensive bone uptake on a bone scan consistent with hypertrophic pulmonary osteopathy. Productive cough developed after 3 months of the therapy in case 2. Initially, these complaints were not attributed to therapy. New lung disease was present on CT with cherry-like progressive cavitary nodules, predominantly involving the basal segments of the right lung. The first lung lesions were found by CT 13 months (case 1) and 7 months (case 2) after the beginning of therapy and were erroneously related to bronchiectasia in case 2. In both cases, the lung biopsy showed necrosis surrounded by epithelioid mononuclear inflammation with giant cells, consistent with rheumatoid lung node. The time that elapsed between the beginning of the first symptoms to LEF discontinuation was very long: 13 months in case 1 and 24 months in case 2. Discontinuation of LEF therapy was followed by an arrest in growth of lung nodules, resolution of limb pain, and gradual improvement of bone scan. A significant decrease of monocyte count and RF level in peripheral blood was observed during LEF therapy in both cases. For the first time, we described rheumatoid lung nodulosis as complication of successful LEF therapy for RA. Hypertrophic pulmonary osteopathy with severe limbs pain and dry cough were the first manifestations of the lung nodulosis. Monocytopenia during LEF therapy is proposed to be involved in pathogenesis of this rare complication of LEF therapy.

The mechanism of inhibition by Corynebacterium parvum of the growth of lung nodules from intravenously injected tumour cells.

International Journal of CancerVolume 14, Issue 2 p. 226-235 Article The mechanism of inhibition by Corynebacterium parvum of the growth of lung nodules from intravenously-injected tumor cells Bomford, BomfordSearch for more papers by this authorM. Olivotto, M. OlivottoSearch for more papers by this author Bomford, BomfordSearch for more papers by this authorM. Olivotto, M. OlivottoSearch for more papers by this author First published: 15 August 1974 https://doi.org/10.1002/ijc.2910140211Citations: 40AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume14, Issue215 August 1974Pages 226-235 RelatedInformation