Nonsolid Nodules Research Articles

Surveillance of subsolid nodules avoids unnecessary resections in lung cancer screening: long-term results of the prospective BioMILD trial.

The management of subsolid nodules (SSNs) in lung cancer screening (LCS) is still a topic of debate, with no current uniform strategy to deal with these lesions at risk of overdiagnosis and overtreatment. The BioMILD LCS trial has implemented a prospective conservative approach for SSNs, managing with annual low-dose computed tomography nonsolid nodules (NSNs) and part-solid nodules (PSNs) with a solid component <5 mm, regardless of the size of the nonsolid component. The present study aims to determine the lung cancer (LC) detection and survival in BioMILD volunteers with SSNs. Eligible participants were 758 out of 4071 (18.6%) BioMILD volunteers without baseline LC and at least one SSN detected at the baseline or further low-dose computed tomography rounds. The outcomes of the study were LC detection and long-term survival. A total of 844 NSNs and 241 PSNs were included. LC detection was 3.7% (31 out of 844) in NSNs and 7.1% (17 out of 241) in PSNs, being significantly greater in prevalent than incident nodules (8.4% versus 1.3% in NSNs; 14.1% versus 2.1% in PSNs; p-value for both nodule types p<0.01). Most LCs from SSNs were stage I (42/48, 87.5%), resectable (47/48, 97.9%), and caused no deaths. The 8-year cumulative survival of volunteers with LC derived from SSNs and not derived from SSNs was 93.8% and 74.9%, respectively. Conservative management of SSNs in LCS enables timely diagnosis and treatment of LCs arising from SSNs while ensuring the resection of more aggressive LCs detected away from SSNs.

Enhancing a deep learning model for pulmonary nodule malignancy risk estimation in chest CT with uncertainty estimation

ObjectiveTo investigate the effect of uncertainty estimation on the performance of a Deep Learning (DL) algorithm for estimating malignancy risk of pulmonary nodules.Methods and materialsIn this retrospective study, we integrated an uncertainty estimation method into a previously developed DL algorithm for nodule malignancy risk estimation. Uncertainty thresholds were developed using CT data from the Danish Lung Cancer Screening Trial (DLCST), containing 883 nodules (65 malignant) collected between 2004 and 2010. We used thresholds on the 90th and 95th percentiles of the uncertainty score distribution to categorize nodules into certain and uncertain groups. External validation was performed on clinical CT data from a tertiary academic center containing 374 nodules (207 malignant) collected between 2004 and 2012. DL performance was measured using area under the ROC curve (AUC) for the full set of nodules, for the certain cases and for the uncertain cases. Additionally, nodule characteristics were compared to identify trends for inducing uncertainty.ResultsThe DL algorithm performed significantly worse in the uncertain group compared to the certain group of DLCST (AUC 0.62 (95% CI: 0.49, 0.76) vs 0.93 (95% CI: 0.88, 0.97); p < .001) and the clinical dataset (AUC 0.62 (95% CI: 0.50, 0.73) vs 0.90 (95% CI: 0.86, 0.94); p < .001). The uncertain group included larger benign nodules as well as more part-solid and non-solid nodules than the certain group.ConclusionThe integrated uncertainty estimation showed excellent performance for identifying uncertain cases in which the DL-based nodule malignancy risk estimation algorithm had significantly worse performance.Clinical relevance statementDeep Learning algorithms often lack the ability to gauge and communicate uncertainty. For safe clinical implementation, uncertainty estimation is of pivotal importance to identify cases where the deep learning algorithm harbors doubt in its prediction.Key Points• Deep learning (DL) algorithms often lack uncertainty estimation, which potentially reduce the risk of errors and improve safety during clinical adoption of the DL algorithm.• Uncertainty estimation identifies pulmonary nodules in which the discriminative performance of the DL algorithm is significantly worse.• Uncertainty estimation can further enhance the benefits of the DL algorithm and improve its safety and trustworthiness.

Combination of electromagnetic navigation and probe-based LASER endomicroscopy to guide non-solid nodules sampling: Results from the CELTICS 2 study

Combination of electromagnetic navigation and probe-based LASER endomicroscopy to guide non-solid nodules sampling: Results from the CELTICS 2 study

Introduction of the Danish Lung Nodule Registry: A part of the Danish Lung Cancer Registry

BackgroundThe majority of lung cancer cases are diagnosed late, resulting in poor prognosis and high mortality rates. Early detection and management of lung cancer can improve patient outcomes and reduce mortality rates. Pulmonary nodules are key factors in the early detection of lung cancer, they are common in high-risk populations and require correct classification to determine whether they are benign or malignant. Over the last decade a steep increase in the number of thoracic CT scans has been seen in Denmark, resulting in substantial resources allocated to CT follow-up of incidentally detected pulmonary nodules. The implementation of a nationwide Danish prospective pulmonary nodule registry is to methodically record pulmonary nodules and thereby evaluate the scope of pulmonary nodule follow-up, the nature of the nodules, and the clinical progression of patients with pulmonary nodules. MethodsA prospective pulmonary nodule registry (Danish Lung Nodule Registry) will be a natural appendix to the Danish Lung Cancer Registry. Three new ICD-10 classification codes will be introduced, defining the type of nodule: /DR91.1/ Solid nodule /DR91.2/ Part-solid nodule; /DR91.3/ Non-solid nodule. Furthermore, an additional letter will describe whether the imaging exam is performed on suspicion of lung cancer (A), or the finding is incidental (B). Registration of the nodules will be performed by the departments of respiratory medicine who manage follow-up of pulmonary nodules. It is estimated that around 7000 nodules will be registered annually. DiscussionThe registration of patients in the lung nodule registry complies with current Danish legislation. The registry will be seamlessly integrated with other nationwide Danish registries, including the Danish Lung Cancer Registry, to collect additional patient data and improve the quality and scope of the data acquired. The results from these comprehensive epidemiological studies will be of significant interest and offer valuable research opportunities.

ACR Lung-RADS v2022: Assessment Categories and Management Recommendations

The American College of Radiology created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.

Possible overdiagnosis of early-stage lung adenocarcinoma among never-smokers in Taiwan.

10-year survival for never-smokers with >1 cm but ≤3 cm AIS/BAC/MIA was not inferior to that of the matched referents, pointing to possible overdiagnosis. Clinicians might consider adhering to Lung-RADS and watchful waiting for these non-solid nodules. https://bit.ly/41U6kxs.

Detection of subsolid nodules on chest CT scans during the COVID-19 pandemic.

To investigate the detection of subsolid nodules (SSNs) on chest CT scans of outpatients before and during the COVID-19 pandemic, as well as to correlate the imaging findings with epidemiological data. We hypothesized that (pre)malignant nonsolid nodules were underdiagnosed during the COVID-19 pandemic because of an overlap of imaging findings between SSNs and COVID-19 pneumonia. This was a retrospective study including all chest CT scans performed in adult outpatients (> 18 years of age) in September of 2019 (i.e., before the COVID-19 pandemic) and in September of 2020 (i.e., during the COVID-19 pandemic). The images were reviewed by a thoracic radiologist, and epidemiological data were collected from patient-filled questionnaires and clinical referrals. Regression models were used in order to control for confounding factors. A total of 650 and 760 chest CT scans were reviewed for the 2019 and 2020 samples, respectively. SSNs were found in 10.6% of the patients in the 2019 sample and in 7.9% of those in the 2020 sample (p = 0.10). Multiple SSNs were found in 23 and 11 of the patients in the 2019 and 2020 samples, respectively. Women constituted the majority of the study population. The mean age was 62.8 ± 14.8 years in the 2019 sample and 59.5 ± 15.1 years in the 2020 sample (p < 0.01). COVID-19 accounted for 24% of all referrals for CT examination in 2020. Fewer SSNs were detected on chest CT scans of outpatients during the COVID-19 pandemic than before the pandemic, although the difference was not significant. In addition to COVID-19, the major difference between the 2019 and 2020 samples was the younger age in the 2020 sample. We can assume that fewer SSNs will be detected in a population with a higher proportion of COVID-19 suspicion or diagnosis.

Factors associated with overdiagnosis of benign pulmonary nodules as malignancy: a retrospective cohort study

ObjectiveTo establish a preoperative model for the differential diagnosis of benign and malignant pulmonary nodules (PNs), and to evaluate the related factors of overdiagnosis of benign PNs at the time of imaging assessments.Materials and methodsIn this retrospective study, 357 patients (median age, 52 years; interquartile range, 46–59 years) with 407 PNs were included, who underwent surgical histopathologic evaluation between January 2020 and December 2020. Patients were divided into a training set (n = 285) and a validation set (n = 122) to develop a preoperative model to identify benign PNs. CT scan features were reviewed by two chest radiologists, and imaging findings were categorized. The overdiagnosis rate of benign PNs was calculated, and bivariate and multivariable logistic regression analyses were used to evaluate factors associated with benign PNs that were over-diagnosed as malignant PNs.ResultsThe preoperative model identified features such as the absence of part-solid and non-solid nodules, absence of spiculation, absence of vascular convergence, larger lesion size, and CYFRA21-1 positivity as features for identifying benign PNs on imaging, with a high area under the receiver operating characteristic curve of 0.88 in the validation set. The overdiagnosis rate of benign PNs was found to be 50%. Independent risk factors for overdiagnosis included diagnosis as non-solid nodules, pleural retraction, vascular convergence, and larger lesion size at imaging.ConclusionWe developed a preoperative model for identifying benign and malignant PNs and evaluating factors that led to the overdiagnosis of benign PNs. This preoperative model and result may help clinicians and imaging physicians reduce unnecessary surgery.

Discrimination of invasive lung adenocarcinoma from Lung-RADS category 2 nonsolid nodules through visual assessment: a retrospective study.

Invasive adenocarcinomas (IADs) have been identified among nonsolid nodules (NSNs) assigned as Lung Imaging Reporting and Data System (Lung-RADS) category 2. This study used visual assessment for differentiating IADs from noninvasive lesions (NILs) in this category. This retrospective study included 222 patients with 242 NSNs, which were resected after preoperative computed tomography (CT)-guided dye localization. Visual assessment was performed by using the lung and bone window (BW) settings to classify NSNs into BW-visible (BWV) and BW-invisible (BWI) NSNs. In addition, nodule size, shape, border, CT attenuation, and location were evaluated and correlated with histopathological results. Logistic regression was performed for multivariate analysis. A p value of < 0.05 was considered statistically significant. A total of 242 NSNs (mean diameter, 7.6 ± 2.8mm), including 166 (68.6%) BWV and 76 (31.4%) BWI NSNs, were included. IADs accounted for 31% (75) of the nodules. Only 4 (5.3%) IADs were identified in the BWI group and belonged to the lepidic-predominant (n = 3) and acinar-predominant (n = 1) subtypes. In univariate analysis for differentiating IADs from NILs, the nodule size, shape, CT attenuation, and visual classification exhibited statistical significance. Nodule size and visual classification were the significant predictors for IAD in multivariate analysis with logistic regression (p < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value of visual classification in IAD prediction were 94.7%, 43.1%, 42.8%, and 94.7%, respectively. The window-based visual classification of NSNs is a simple and objective method to discriminate IADs from NILs. The present study shows that using the bone window to classify nonsolid nodules helps discriminate invasive adenocarcinoma from noninvasive lesions. • Evidence has shown the presence of lung adenocarcinoma in Lung-RADS category 2 nonsolid nodules. • Nonsolid nodules are classified into the bone window-visible and the bone window-invisible nonsolid nodules, and this classification differentiates invasive adenocarcinoma from noninvasive lesions. • The Lung-RADS category 2 nonsolid nodules are unlikely invasive adenocarcinoma if they show nonvisualization in the bone window.

ACR Lung-RADS v2022: Assessment Categories and Management Recommendations

The ACR created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.

A novel, simple near-infrared thoracoscopic technique by a particular route for locating lung nodules.

The localization of pulmonary nodules prior to thoracoscopic surgery remains challenging for thoracic surgeons, especially for those nodules that are not visible or palpable on the lung surface. Our study is a simple and effective minimally invasive method using indocyanine green through a special pathway to locate pulmonary nodules and fluorescence thoracoscopic surgery. Thoracoscopic surgery was performed for 18 undiagnosed peripheral non-solid nodules no larger than 2 cm after location. After 0.3 mg/kg indocyanine green was injected through the peripheral vein, the puncture needle was pulled out after it reached approximately 1 cm of the pulmonary parenchyma near the nodules. This was followed by transfer to the operating room. The nodule was initially localized by using a near-infrared thoracoscope to visualize indocyanine green fluorescence. Then, thoracoscopic resection was performed. Eighteen patients received this special and simple localization method, and underwent near-infrared, image-guided, video-assisted thoracoscopic surgery resection. Median computed tomography (CT) tumor size was 1.2 cm. Median depth from the pleural surface is 1.6 cm (range, 0.1-4.6 cm). The median time of CT-guided intervention was 12 min. The duration of thoracoscopic surgery was 67 min. Indocyanine green fluorescence was clearly identified in 17 of 18 patients (94.4%). The surgical margins were all negative on final pathology. The final diagnoses included 17 primary lung cancers, and 1 benign lung tumor. CT-guided single puncture of indocyanine green after peripheral intravenous injection is a simple, effective, and safe method to locate the nodule. This offers surgeons the ease of localization through direct indocyanine green fluorescence imaging, and it can be used as an effective alternative to other placement methods of locating pulmonary nodules.

Mediastinal lymph node dissection versus spared mediastinal lymph node dissection in stage IA non-small cell lung cancer presented as ground glass nodules: study protocol of a phase III, randomised, multicentre trial (MELDSIG) in China

IntroductionRadical surgery including mediastinal lymph node dissection is the standard treatment for early-stage non-small cell lung cancer (NSCLC). About 50% lung nodules are pure ground glass or part-solid nodules, which...

A dual-task region-boundary aware neural network for accurate pulmonary nodule segmentation

Recently, the performance of pulmonary nodule segmentation has improved fast because of the development of deep learning-based methods. However, existing CNN-based methods still don’t solve the two issues, leading to poor segmentation: (1) The information of small nodules lost in continuous down-sampling operation; (2) Discriminative features can’t be extracted from non-solid nodules because whose gray values are close to the surrounding environment. This study proposes a novel dual-task region-boundary aware deep convolutional neural network to solve the problems above. A hierarchical feature module is proposed to capture multi-scale information, improving small nodules' segmentation accuracy. Boundary guided module is introduced to extract edge features and predict nodule boundaries directly. In addition, a feature aggregation module is proposed to aggregate features at different levels. Besides, to better optimize the segment results and reduce overfitting, the region-boundary aware loss function is proposed. The method is trained and tested on the LIDC-IDRI dataset and the LUNA16 dataset, and the results demonstrate that our method is efficient. More importantly, our method is suitable for the segmentation of various nodules, especially small nodules, GGO nodules, and part-solid nodules.

Software-based quantitative CT analysis to predict the growth trend of persistent nonsolid pulmonary nodules: a retrospective study

PurposePersistent nonsolid nodules (NSNs) usually exhibit an indolent course and may remain stable for several years; however, some NSNs grow quickly and require surgical excision. Therefore, identifying quantitative features capable of early discrimination between growing and nongrowing NSNs is becoming a crucial aspect of radiological analysis. The main purpose of this study was to evaluate the performance of an open-source software (ImageJ) to predict the future growth of NSNs detected in a Caucasian (Italian) population.Material and methodsWe retrospectively selected 60 NSNs with an axial diameter of 6–30 mm scanned with the same acquisition-reconstruction parameters and the same computed tomography (CT) scanner. Software-based analysis was performed on thin-section CT images using ImageJ. For each NSNs, several quantitative features were extracted from the baseline CT images. The relationships of NSN growth with quantitative CT features and other categorical variables were analyzed using univariate and multivariable logistic regression analyses.ResultsIn multivariable analysis, only the skewness and linear mass density (LMD) were significantly associated with NSN growth, and the skewness was the strongest predictor of growth. In receiver operating characteristic curve analyses, the optimal cutoff values of skewness and LMD were 0.90 and 19.16 mg/mm, respectively. The two predictive models that included the skewness, with or without LMD, exhibited an excellent power for predicting NSN growth.ConclusionAccording to our results, NSNs with a skewness value > 0.90, specifically those with a LMD > 19.16 mg/mm, should require closer follow-up due to their higher growth potential, and higher risk of becoming an active cancer.

Assessment of diagnostic accuracy and complication rates of CT-guided percutaneous core-needle biopsy for lung lesion: difference between solid and sub-solid nodules based on propensity score matching analysis

To compare the success and complication rates of computed tomography (CT)-guided percutaneous core-needle biopsy (PCNB) based on the density of lung nodules, using propensity score matching (PSM). This single-centre retrospective study included 1,312 PCNB cases of lung lesions, including solid (n=1,120), part-solid (n=115), and non-solid nodules (n=77), that were detected between March 2013 and March 2021. The diagnostic accuracy and complication rates of pneumothorax and pulmonary haemorrhage were analysed before PSM. To perform PSM, part-solid and non-solid nodules were combined and newly defined as sub-solid nodules. The diagnostic accuracy and complication rates of pneumothorax and pulmonary haemorrhage were then compared between solid and sub-solid nodules after PSM. Among the 1,312 included cases, the success rate and incidence of pneumothorax after CT-guided PCNB for solid, part-solid, and non-solid nodules were not statistically different (p=0.080 and 0.410). However, the rates of overall pulmonary haemorrhage showed statistical differences (p<0.001), particularly between solid and part-solid nodules (p<0.001) and between solid and non-solid nodules (p<0.001). After PSM, the incidence rates of overall pulmonary haemorrhage in solid and sub-solid nodules were 8.9% (17/192) and 29.7% (44/182), respectively, showing a statistically significant difference (p<0.001). There is increased risk of haemorrhage in CT-guided needle biopsy of sub-solid nodules compared to solid nodules. Increased emphasis should be placed on the risk of pulmonary haemorrhage when consenting these patients.

Non‑diagnostic electromagnetic navigation bronchoscopy biopsy: Predictive factors and final diagnoses.

The present study aimed to explore the final diagnosis of pulmonary nodules with an initial non-diagnostic result on electromagnetic navigation bronchoscopy (ENB) biopsy and the predictive factors for a non-diagnostic result. A total of 198 nodules from 194 patients that were suspected to be malignant tumors were included in the present study. The initial biopsy pathology results were divided into two groups: The diagnostic group and the non-diagnostic group. The diagnostic group was defined as a successful initial biopsy to obtain a diagnosis, including malignant and benign diagnoses. The non-diagnostic group was defined as a non-specific benign diagnosis, normal lung tissue or an unsuccessful biopsy. Among the 198 nodules, 139 (70.2%) were in the diagnostic group and 59 (29.8%) were in the non-diagnostic group. Predictive factors for a non-diagnostic biopsy included nodule size ≤1.5 cm [odds ratio (OR), 2.05; 95% confidence interval (CI), 1.03-4.09], non-solid nodules (OR, 2.71; 95% CI, 1.33-5.64) and nodules in the left lung (OR, 2.50; 95% CI, 1.27-4.92). Of the 59 non-diagnostic biopsies, 46 were finally confirmed to be malignant by surgery. Notably, non-diagnostic biopsies with non-solid nodules (OR, 7.64; 95% CI, 3.11-18.76) were more likely to be malignant. In conclusion, the predictive factors for a non-diagnostic biopsy were nodule size ≤1.5 cm and non-solid nodules. It was not rare for patients to finally be diagnosed with a malignancy in the non-diagnostic group. Therefore, care should be taken when the results of an ENB are non-diagnostic to prevent misdiagnosis.

Prospective Radiologic-Pathologic Correlation of Macroscopic Volume and Microscopic Extension of Nonsolid Lung Nodules on Thin-section CT Images for Sublobar Resection and Stereotactic Radiotherapy Planning

IntroductionThe objective of this study was to determine whether computed tomography (CT) could be a useful tool for nonsolid lung nodule (NSN) treatment planning, surgery or stereotactic body radiation therapy (SBRT), by assessing the macroscopic and microscopic extension of these nodules. MethodsThe study prospectively included 23 patients undergoing anatomic resection at the Foch Hospital in 2020/2021 for NSN with a ground-glass component of more than 50%. Firstly, for each patient, both the macroscopic dimensions of the NSN were assessed on CT and during pathologic analysis. Secondly, the microscopic extension was assessed during pathologic examination. Wilcoxon sign rank tests were used to compare these dimensions. Spearman correlation test and Bland-Altman analysis were used to evaluate the agreement between radiological and pathologic measurements. ResultsOn CT, the median largest diameter and volume of NSN were 21 mm and 3780 cc, while on pathologic analysis, they were 15 mm and 1800 cc, respectively. Therefore, the largest diameter and volume of the NSN were significantly higher on CT than on pathological analysis. For microscopic extension, the median largest diameter and volume of NSN were 17 mm and 2040 cc, respectively. No significant difference was observed between the macroscopic size and the microscopic extension assessed during pathologic analysis. Moreover, correlation analysis and Bland-Altman plots showed that radiological and pathologic measurements could provide equivalent precision. ConclusionOur study showed that CT did not underestimate the macroscopic size and microscopic extension of NSN and confirmed that CT can be used for NSN treatment planning.

Lung cancer screening study from a smoking population in Kunming.

Yunnan, China, is a central tobacco-producing region with a large smoking population and an increasing incidence of lung cancer in recent years. This study aimed to understand the incidence of lung cancer and the characteristics of lung nodules on low-dose computed tomography (LDCT) scans of the chest in a long-term smoking population in Kunming. Long-term smokers in Kunming who were not at risk of evident lung disease symptoms were recruited through recommendation and publicity by the Kunming University of Science and Technology. Among 375 cases eligible for inclusion,14 cases of lung cancer were detected with a detection rate of 3.73% (95% CI: 2.55%-4.27%), including one case of squamous carcinoma, one case of small cell lung cancer, seven cases of adenocarcinoma of the lung and five cases of early-stage lung cancer (35.71%). In the group of < 6 mm solid nodules and < 5 mm non-solid nodules, no lung cancer was detected in 201 cases; lung cancer was detected in 14 cases in 61 cases, and there was a statistical difference between the two groups (p < 0.05). The lung cancer detection rate in long-term smokers was high, with the type predominantly adenocarcinoma and a high incidence of lung nodules, and increased when solid nodules≥6 mm or non-solid nodules ≥ 5 mm were present. It is recommended that screening for lung cancer by LDCT of the chest be introduced in the male smoking population who meet the risk factors and that screening for lung cancer in women should be redefined as a high-risk factor.

Analysis of pulmonary nodules detected by annual low-dose computed tomography in the elderly during a 10-year follow-up.

To describe pulmonary nodules detected by annual low-dose computed tomography (LDCT) in the elderly during a 10-year follow-up, and to provide a basis for clinical decision-making in the elderly. In this retrospective study, patients who completed at least a 3-year follow-up visit with annual LDCT imaging data were eligible for inclusion. The evolution of pulmonary nodules was evaluated, including malignant, suspicious malignant, benign and undetermined nodules. Additionally, the nature and outcome of new nodules during the follow-up were analyzed. For the 365 subjects included, 899 positive pulmonary nodules were detected in 286 patients. Among these there were 788 solid nodules, 20 part-solid nodules and 91 nonsolid nodules. The detection rate of positive nodules and of lung cancer was 78.4% and 5.5%, respectively. 99.7% (786/788) of solid nodules were benign, and 75% (15/20) of part-solid nodules and 28.6% (26/91) of nonsolid nodules were malignant or suspected malignant. 124 new positive nodules appeared during the annual follow-up, but 58.9% of them subsequently disappeared. Significant higher detection rates of 10-20-mm nodules (P=0.0485) and suspicious malignant nodules (P=0.017) were observed in subjects over 75 years old as compared with those under 75 years old. Solid nodules accounted for the highest proportion of lung nodules screened at baseline, and most of them were benign. The malignant probability of part-solid nodules was the highest. Most newly appeared nodules disappeared during subsequent follow-up. The proportions of suspicious malignant nodules and 10-20-mm nodules in subjects over 75 years old were higher than in those under 75 years old. Geriatr Gerontol Int 2022; 22: 865-869.

EP01.06-009 Frequency of Lung Adenocarcinoma in Patients with Growing Nonsolid and Part-Solid Nodules with Solid Component ≤5 mm

The 2011 Classification of adenocarcinoma of the lung added terms “preinvasive” terminology for pathological diagnosis of resection specimens; Atypical adenomatous hyperplasia (AAH), Adenocarcinoma in situ (AIS) which on CT usually present as nonsolid nodules and minimally invasive adenocarcinoma, which at CT usually present as nonsolid nodules but may have a solid component of up to 5 mm. Previous studies has reported that subsolid nodule have a high likelihood of pathological diagnosis of malignancy varying between 83.6% -100%.