Subsolid Pulmonary Nodules Research Articles

Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography.

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) helps early lung cancer detection, commonly presenting as small pulmonary nodules. Artificial intelligence (AI)-based vessel suppression (AI-VS) and automatic detection (AI-AD) algorithm can improve detection of subsolid nodules (SSNs) on LDCT. We assessed the impact of AI-VS and AI-AD in detection and classification of SSNs [ground-glass nodules (GGNs) and part-solid nodules (PSNs)], on LDCT performed for LCS. Following regulatory approval, 123 LDCT examinations with sub-solid pulmonary nodules (average diameter ≥6 mm) were processed to generate three image series for each examination-unprocessed, AI-VS, and AI-AD series with annotated lung nodules. Two thoracic radiologists in consensus formed the standard of reference (SOR) for this study. Two other thoracic radiologists (R1 and R2; 5 and 10 years of experience in thoracic CT image interpretation) independently assessed the unprocessed images alone, then together with AI-VS series, and finally with AI-AD for detecting all ≥6 mm GGN and PSN. We performed receiver operator characteristics (ROC) and Cohen's Kappa analyses for statistical analyses. On unprocessed images, R1 and R2 detected 232/310 nodules (R1: 114 GGN, 118 PSN) and 255/310 nodules (R2: 122 GGN, 133 PSN), respectively (P>0.05). On AI-VS images, they detected 249/310 nodules (119 GGN, 130 PSN) and 277/310 nodules (128 GGN, 149 PSN), respectively (P≥0.12). When compared to the SOR, accuracy (AUC) for detection of PSN on the AI-VS images (AUC 0.80-0.81) was greater than on the unprocessed images (AUC 0.70-0.76). AI-VS images enabled detection of solid components in five nodules deemed as GGN on the unprocessed images. Accuracy of AI-AD was lower than both the radiologists (AUC 0.60-0.72). AI-VS improved the detection and classification of SSN into GGN and PSN on LDCT of the chest for the two radiologist (R1 and R2) readers.

Solid Components in the Mediastinal Window of Computed Tomography Define a Distinct Subtype of Subsolid Nodules in Clinical Stage I Lung Cancers

BackgroundWe aimed to validate the clinicopathologic characteristics and prognostic value of the presence of solid components in the mediastinal window of computed tomography scan in clinical stage I pulmonary subsolid nodules (SSNs). MethodsWe retrospectively evaluated patients with pulmonary SSNs resected between 2011 and 2016. We classified SSNs into heterogeneous ground-glass nodules (HGGNs) (solid component detected only in lung window) and part-solid nodules (PSNs) (solid component detected both in lung/mediastinal windows). ResultsA total of 487 patients (216 PSNs) were included. PSNs were associated with higher frequencies of micropapillary or solid pathologic patterns (18.1% vs. 3.3%; P < .001), epidermal growth factor receptor gene mutation (39.4% vs. 32.8%), and other types of gene mutations (2.3% vs. 1.1%; P = .043). Logistic regression analysis revealed that male sex (odds ratio [OR], 2.58; 95% confidence interval [CI], 1.20-5.57; P = .016) and higher consolidation tumor ratio (CTR) (OR, 110.04; 95% CI, 8.56-1414.39; P < .001) remained independent for invasive adenocarcinomas with poor differentiation. Receiver operating characteristic analyses revealed that solid component size in the mediastinal window (area under the curve [AUC], 0.731; 95% CI, 0.653-0.808; P < .0001) showed a better predictive ability to poor differentiation compared with solid component size in the lung window and CTR. The 5-year recurrence-free survival (RFS) rate of PSNs was worse than that of HGGNs (94.6% vs. 99.1%; P = .019). Multivariate Cox regression revealed that positive lymph node status (hazard ratio, 22.99; 95% CI, 4.52-116.86; P < .001) indicated worse RFS for PSNs. ConclusionSSNs with solid components in mediastinal window demonstrated clinicopathologic and prognostic features different from those without in clinical stage I lung cancer. Solid components in mediastinal window was a strong predictor of poor differentiation.

Inter-Reader Variability of Volumetric Subsolid Pulmonary Nodule Radiomic Features

To evaluate the inter-observer consistency for subsolid pulmonary nodule radiomic features. Subsolid nodules were selected by reviewing radiology reports of CT examinations performed December 1, 2015 to April 1, 2016. Patients with CTs at two time points were included in this study. There were 55 patients with subsolid nodules, of whom 14 had two nodules. Of 69 subsolid nodules, 66 were persistent at the second time point, yielding 135 lesions for segmentation. Two thoracic radiologists and an imaging fellow segmented the lesions using a semi-automated volumetry algorithm (Syngo.via Vb20, Siemens). Coefficient of variation (CV) was used to assess consistency of 91 quantitative measures extracted from the subsolid nodule segmentations, including first and higher order texture features. The accuracy of segmentation was visually graded by an experienced thoracic radiologist. Influencing factors on radiomic feature consistency and segmentation accuracy were assessed using generalized estimating equation analyses and the Exact Mann-Whitney test. Mean patient age was 71 (38-93 years), with 39 women and 16 men. Mean nodule volume was 1.39mL, range .03-48.2mL, for 135 nodules. Several radiomic features showed high inter-reader consistency (CV<5%), including entropy, uniformity, sphericity, and spherical disproportion. Descriptors such as surface area and energy had low consistency across inter-reader segmentations (CV>10%). Nodule percent solid component and attenuation influenced inter-reader variability of some radiomic features. The presence of contrast did not significantly affect the consistency of subsolid nodule radiomic features. Near perfect segmentation, within 5% of actual nodule size, was achieved in 68% of segmentations, and very good segmentation, within 25% of actual nodule size, in 94%. Morphologic features including nodule margin and shape (each p <0.01), and presence of air bronchograms (p = 0.004), bubble lucencies (p = 0.02) and broad pleural contact (p < 0.01) significantly affected the probability of near perfect segmentation. Stroke angle (p = 0.001) and length (p < 0.001) also significantly influenced probability of near perfect segmentation. The inter-observer consistency of radiomic features for subsolid pulmonary nodules varies, with high consistency for several features, including sphericity, spherical disproportion, and first and higher order entropy, and normalized non-uniformity. Nodule morphology influences the consistency of subsolid nodule radiomic features, and the accuracy of subsolid nodule segmentation.

Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules.

To evaluate a deep learning-based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions or minimally invasive adenocarcinoma (MIA) on CT, making comparisons with radiologist-derived measurements of solid portion size. Four hundred eleven subsolid nodules (SSNs) (120 preinvasive lesions or MIAs and 291 IPAs) in 333 patients who underwent surgery between June 2010 and August 2016 were retrospectively included to develop the model (370 SSNs in 293 patients for training and 41 SSNs in 40 patients for tuning). Ninety SSNs of 2cm or smaller (45 preinvasive lesions or MIAs and 45 IPAs) resected in 2018 formed a validation set. Six radiologists measured the solid portion of each nodule. Performances of the model and radiologists were assessed using receiver operating characteristics curve analysis. The deep learning model differentiated IPA from preinvasive lesions or MIA with areas under the curve (AUCs) of 0.914, 0.956, and 0.833 for the training, tuning, and validation sets, respectively. The mean AUC of the radiologists was 0.835 in the validation set, without significant differences between radiologists and the model (p = 0.97). The sensitivity, specificity, and accuracy of the model were 71% (32/45), 87% (39/45), and 79% (71/90), respectively, whereas the corresponding values of the radiologists were 75.2% (203/270), 76.7% (207/270), and 75.9% (410/540) with a 5-mm threshold for the solid portion size. The performance of the model for differentiating IPA from preinvasive lesions or MIA was comparable to that of the radiologists' measurements of solid portion size. • A deep learning-based model differentiated IPA from preinvasive lesions or MIA with AUCs of 0.914 and 0.956 for the training and tuning sets, respectively. • In the validation set including subsolid nodules of 2cm or smaller, the model showed an AUC of 0.833, being on par with the performance of the solid portion size measurements made by the radiologists (AUC, 0.835; p = 0.97). • SSNs with a solid portion measuring > 10mm on CT showed a high probability of being IPA (positive predictive value, 93.5-100.0%).

Decoding the multicellular ecosystem of lung adenocarcinoma manifested as pulmonary subsolid nodules by single-cell RNA sequencing.

Lung adenocarcinomas (LUAD) that radiologically display as subsolid nodules (SSNs) exhibit more indolent biological behavior than solid LUAD. The transcriptomic features and tumor microenvironment (TME) of SSN remain poorly understood. Here, we performed single-cell RNA sequencing analyses of 16 SSN samples, 6 adjacent normal lung tissues (nLung), and 9 primary LUAD with lymph node metastasis (mLUAD). Approximately 0.6 billion unique transcripts were obtained from 118,293 cells. We found that cytotoxic natural killer/T cells were dominant in the TME of SSN, and malignant cells in SSN undergo a strong metabolic reprogram and immune stress. In SSN, the subtype composition of endothelial cells was similar to that in mLUAD, while the subtype distribution of fibroblasts was more like that in nLung. Our study provides single-cell transcriptomic profiling of SSN and their TME. This resource provides deeper insight into the indolent nature of SSN and will be helpful in advancing lung cancer immunotherapy.

Electromagnetic Navigation Bronchoscopy Combined Endobronchial Ultrasound in the Diagnosis of Lung Nodules.

Electromagnetic navigational bronchoscopy (ENB) combined with a radial endobronchial ultrasound probe realizes a combination of magnetic navigation and ultrasound imaging, allowing for the accurate navigation of peripheral lung lesions in real time during surgery. ENB has been evaluated in many studies. However, a comparative report on the feasibility of ENB combined radial endobronchial ultrasound diagnosis in different density lung nodules was small, and few of these studies have reported long-term follow-up results to exclude false negative results. The aim of this study is to explore the applicability of ENB combined radial endobronchial ultrasound in the diagnosis of lung nodules with different densities.Patients underwent biopsy in our medical center from 2016-09 to 2019-03 were divided into 2 groups: the solid nodule group and the subsolid pulmonary nodule group. We collected and analyzed the diagnostic accuracy, the diagnostic yield, the false negative rate and the incidence of complications between these 2 groups.A total of 37 lesions in 25 patients were biopsied, 14 lesions were subsolid pulmonary nodules and 23 were solid nodules. The diagnostic accuracy (success rate to obtain meaningful pathology tissues) was 34/37 (91.8%). Lost to follow-up in 1 case and three cases were undiagnosed. After at least 12 months of follow-up, the total diagnostic yield (true positive rate+ true negative rate) was 27/36 (75%) (P = .006). The false negative rate was 9/19 (47.3%) (P = .26). Complications occurred in 1/36 (2.7%) lesions. For the subsolid pulmonary nodule group, the diagnostic accuracy was 13/14 (92.8%) and the diagnostic yield was 7/14 (50%). For the solid nodule group, the diagnostic accuracy was 21/23 (91.3%), and the diagnostic yield was 20/22 (90.9%).Electromagnetic navigational bronchoscopy combined with radial endobronchial ultrasound in peripheral lung nodule biopsies is safe and effective, especially for solid nodules, but the diagnostic yield in subsolid nodule biopsies remains to be improved.

Semi-Quantitative Analysis for Determining the Optimal Threshold Value on CT to Measure the Solid Portion of Pulmonary Subsolid Nodules.

PurposeThis study aimed to investigate the optimal threshold value in Hounsfield units (HU) on CT to detect the solid components of pulmonary subsolid nodules using pathologic invasive foci as reference.Materials and MethodsThin-section non-enhanced chest CT scans of 25 patients with pathologically confirmed minimally invasive adenocarcinoma were retrospectively reviewed. On CT images, the solid portion was defined as the area with higher attenuation than various HU thresholds ranging from −600 to −100 HU in 50-HU intervals. The solid portion was measured as the largest diameter on axial images and as the maximum diameter on multiplanar reconstruction images. A linear mixed model was used to evaluate bias in each threshold by using the pathological size of invasive foci as reference.ResultsAt a threshold of −400 HU, the biases were lowest between the largest/maximum diameter of the solid portion of subsolid nodule and the size of invasive foci of the pathological specimen, with 0.388 and −0.0176, respectively. They showed insignificant difference (p = 0.2682, p = 0.963, respectively) at a threshold of −400 HU.ConclusionFor quantitative analysis, −400 HU may be the optimal threshold to define the solid portion of subsolid nodules as a surrogate marker of invasive foci.

The Effect of Lung Volume on the Size and Volume of Pulmonary Subsolid Nodules on CT: Intraindividual Comparison between Total Lung Capacity and Tidal Volume.

PurposeTo examine the effect of lung volume on the size and volume of pulmonary subsolid nodules (SSNs) measured on CT.Materials and MethodsA total of 42 SSNs from 31 patients were included. CT examination was first performed at total lung capacity (TLC), and a section containing the nodule was additionally scanned at tidal volume (TV). The diameter and volume of each SSN, as well as the cross-sectional lung area containing the nodule, were measured. The significance of the changes in measurements between TLC and TV within the same individuals was evaluated.ResultsThe lung area and the diameter and volume of SSNs decreased significantly at TV by 12.7 cm2, 0.5 mm, and 46.4 mm3 on average, respectively (p < 0.001), compared to those at TLC. Changes in lung area between TV and TLC were positively correlated with the change in SSN diameter (p = 0.027) and volume (p = 0.014). However, after correction (by considering the change in lung area), the changes in SSN diameter (p = 0.124) and volume (p = 0.062) were not significantly different.ConclusionSSN size and volume can be significantly affected by lung volume during CT scans of the same individuals.

Use of relative CT values to evaluate the invasiveness of pulmonary subsolid nodules in patients with emphysema.

Lung cancer is a major cause of death, and adenocarcinoma is the most common histologic subtype. Precise diagnosis and treatment of invasive adenocarcinoma (IAC) can substantially improve the survival of patients. However, early-stage adenocarcinomas frequently appear as subsolid nodules (SSN) on computed tomography (CT), and the optimal cut-off CT value for differentiating the invasiveness of SSNs in emphysematous patients is unclear. High-resolution CT targeted scans of 187 pulmonary SSNs in 175 patients with emphysema as confirmed by surgery and histology were retrospectively reviewed. The mean CT value, the relative CT (rCT) values of 1 (nodule CT value - lung CT value), and 2 (nodule CT value/lung CT value), and the size of the SSNs were measured and calculated. The differentiating performance of the CT values between pre-invasive and invasive tumors was evaluated using a receiver operating characteristic (ROC) curve. Significant differences were found in the rCT values of 1 and 2 among pure ground-glass nodules (GGNs) with different levels of invasiveness, in the rCT values of 1 and 2 for the ground-glass component (GGC) and the mean CT value of the solid component (SC) of part-solid nodules (PSNs) between minimally invasive adenocarcinoma (MIA) and IAC (all P<<0.05). The size was significantly different among pure GGNs with different invasiveness (P<0.05). The cut-off rCT values of 1, 2 and nodule size for differentiating between pre-invasive and invasive pure GGNs were 293.82 [sensitivity 58.0%, specificity 94.7%; area under the curve (AUC) 0.783], 0.68 (sensitivity 89.5%, specificity 58.0%, AUC 0.742) and 1.10 cm (sensitivity 74.0%, specificity 79.0%, AUC 0.796), respectively. The AUCs of combining rCT values 1 and 2 with the size of nodule were 0.795 (sensitivity 62.5%, specificity 89.5%) and 0.845 (sensitivity 71.6%, specificity 89.5%) respectively. There were no significant differences in the mean CT values between pure GGNs with different levels of invasiveness and between the GGC of PSNs of MIA and IAC. In patients with emphysema, the rCT values are more useful than the mean CT values for differentiating between SSNs with different invasiveness and can be valuable for patient management.

Natural history of pathologically confirmed pulmonary subsolid nodules with deep learning-assisted nodule segmentation.

To explore the natural history of pulmonary subsolid nodules (SSNs) with different pathological types by deep learning-assisted nodule segmentation. Between June 2012 and June 2019, 95 resected SSNs with preoperative long-term follow-up were enrolled in this retrospective study. SSN detection and segmentation were performed on preoperative follow-up CTs using the deep learning-based Dr. Wise system. SSNs were categorized into invasive adenocarcinoma (IAC, n = 47) and non-IAC (n = 48) groups; according to the interval change during the preoperative follow-up, SSNs were divided into growth (n = 68), nongrowth (n = 22), and new emergence (n = 5) groups. We analyzed the cumulative percentages and pattern of SSN growth and identified significant factors for IAC diagnosis and SSN growth. The mean preoperative follow-up was 42.1 ± 17.0 months. More SSNs showed growth or new emergence in the IAC than in the non-IAC group (89.4% vs. 64.6%, p = 0.009). Volume doubling time was non-significantly shorter for IACs than for non-IACs (1436.0 ± 1188.2 vs. 2087.5 ± 1799.7 days, p = 0.077). Median mass doubling time was significantly shorter for IACs than for non-IACs (821.7 vs. 1944.1 days, p = 0.001). Lobulated sign (p = 0.002) and SSN mass (p = 0.004) were significant factors for differentiating IACs. IACs showed significantly higher cumulative growth percentages than non-IACs in the first 70 months of follow-up. The growth pattern of SSNs may conform to the exponential model. The initial volume (p = 0.042) was a predictor for SSN growth. IACs appearing as SSNs showed an indolent course. The mean growth rate was larger for IACs than for non-IACs. SSNs with larger initial volume are more likely to grow. • Invasive adenocarcinomas (IACs) appearing as subsolid nodules (SSNs), with a mean volume doubling time (VDT) of 1436.0 ± 1188.2 days and median mass doubling time (MDT) of 821.7 days, showed an indolent course. • The VDT was shorter for IACs than for non-IACs (1436.0 ± 1188.2 vs. 2087.5 ± 1799.7 days), but the difference was not significant (p = 0.077). The median MDT was significantly shorter for IACs than for non-IACs (821.7 vs. 1944.1 days, p = 0.001). • SSNs with lobulated sign and larger mass (> 390.5 mg) may very likely be IACs. SSNs with larger initial volume are more likely to grow.

LIPOID PNEUMONIA: A RARE CASE

SESSION TITLE: Fellows Lung Pathology Posters SESSION TYPE: Fellow Case Report Posters PRESENTED ON: October 18-21, 2020 INTRODUCTION: Lipoid pneumonia is an uncommon entity without specific imaging features or clinical presentation. On chest computerized tomography (CT) it may appear as consolidation, ground-glass opacities (GGO), "crazy-paving” pattern, or a mass. Management requires sampling for histopathologic diagnosis. CASE PRESENTATION: An 87-year old asymptomatic man with remote history of smoking 47 pk/yr, enrolled in the lung nodule clinic for lung surveillance, had redemonstrations of right-sided subsolid pulmonary nodules. The largest of these nodules in the right upper lobe measured 3.4 cm and was suspicious for a low-grade cystic adenocarcinoma. His health history includes history of prostate carcinoma, positive for fecal blood, essential, benign hypertension, hyperlipidemia, GERD and eczema. Review of systems was negative. He underwent bronchial brush, BAL and transbronchial biopsy lesion. The cytology and surgical pathology specimens consistently found lipoid pneumonia. NO further sampling was indicated DISCUSSION: Lipoid pneumonia may be endogenous (EnLP) or exogenous (ExLP). While EnLP is typically due to an accumulation of lipid-containing macrophages distal to an area of obstruction, infection, or lipid storage disease, ExLP results from ingestion and aspiration of foreign material, with mineral oil, often used as a laxative . Patients with chronic lipoid pneumonia (CLP) are usually asymptomatic. Radiologically CLP frequently displays GGO or consolidative opacities involving one or more segments with a peribronchovascular distribution and predominant involvement of the lower lobes. Architectural distortion with the consolidative opacities has been reported and thickening of the interlobular septa or fibrosis in the adjacent lungs can occur due to the transportation of oils from the alveoli into the lung interstitium. GGO with associated interlobular septal thickening with a basilar predominance have also been described. The morphology of ExLP, in the case of mineral oil, is attributed to the inability of enzymes to metabolize the non-saponifiable substances that are absorbed by macrophage. Cytologically, lipoid pneumonia is characterized by proliferation of foamy histiocytes that show innumerable intracytoplasmic microvesicles. Imaging of cystic infiltrates in the RUL was concerning for adenocarcinoma and sampling via tip-tracking with bronchial brush and biopsy obtained cytology and surgical material. BAL was also performed. The cellular cytology smears and cellblock showed an abundance of multivacuolated histiocytes, also seen in the BAL specimen. The background consisted of reactive bronchial cells with mild atypia, and tridimensional bronchial cell clusters. The cyto/histology appearance is consistent with ExLP. CONCLUSIONS: In this patient's case, the etiology may be related to laxative use. Histologic and cytologic sampling was necessary to rule out malignancy, arrive to the correct diagnosis. Reference #1: 1. Sung S, Tazelaar HD, Crapanzano JP, Nassar A, Saqi A. Adult exogenous lipoid pneumonia: A rare and underrecognized entity in cytology - A case series. Cytojournal. 2018;15:17. Published 2018 Jul 13. https://doi.org/10.4103/cytojournal.cytojournal_29_17 Reference #2: 2. Betancourt SL, Martinez-Jimenez S, Rossi SE, Truong MT, Carrillo J, Erasmus JJ. Lipoid Pneumonia: Spectrum of Clinical and Radiologic Manifestations. American Journal of Roentgenology 2010 194:1, 103-109 Reference #3: 3. Baron SE, Haramati LB, Rivera VT. Radiological and clinical findings in acute and chronic exogenous lipoid pneumonia. J Thorac Imaging. 2003;18(4):217-224. https://doi.org/10.1097/00005382-200310000-00002 DISCLOSURES: No relevant relationships by Yazeed Alwelaie, source=Web Response No relevant relationships by Eric Flenaugh, source=Web Response No relevant relationships by Gabriela Oprea-Ilies, source=Web Response No relevant relationships by James Roberts, source=Web Response

Predicting Lung Cancer Risk of Incidental Solid and Subsolid Pulmonary Nodules in Different Sizes.

ObjectiveMalignancy prediction models for pulmonary nodules are most accurate when used within nodules similar to those in which they were developed. This study was to establish models that respectively predict malignancy risk of incidental solid and subsolid pulmonary nodules of different size.Materials and MethodsThis retrospective study enrolled patients with 5–30 mm pulmonary nodules who had a histopathologic diagnosis of benign or malignant. The median time to lung cancer diagnosis was 25 days. Four training/validation datasets were assembled based on nodule texture and size: subsolid nodules (SSNs) ≤15 mm, SSNs between 15 and 30 mm, solid nodules ≤15 mm and those between 15 and 30 mm. Univariate logistic regression was used to identify potential predictors, and multivariate analysis was used to build four models.ResultsThe study identified 1008 benign and 1813 malignant nodules from a single hospital, and by random selection 1008 malignant nodules were enrolled for further analysis. There was a much higher malignancy rate among SSNs than solid nodules (rate, 75% vs 39%, P<0.001). Four distinguishing models were respectively developed and the areas under the curve (AUC) in training sets and validation sets were 0.83 (0.78–0.88) and 0.70 (0.61–0.80) for SSNs ≤15 mm, 0.84 (0.74–0.93) and 0.72 (0.57–0.87) for SSNs between 15 and 30 mm, 0.82 (0.77–0.87) and 0.71 (0.61–0.80) for solid nodules ≤15 mm, 0.82 (0.79–0.85) and 0.81 (0.76–0.86) for solid nodules between 15 and 30 mm. Each model showed good calibration and potential clinical applications. Different independent predictors were identified for solid nodules and SSNs of different size.ConclusionWe developed four models to help characterize subsolid and solid pulmonary nodules of different sizes. The established models may provide decision-making information for thoracic radiologists and clinicians.

Persistent pulmonary subsolid nodules with a solid component smaller than 6 mm: what do we know?

Persistent pulmonary subsolid nodules with a solid component smaller than 6 mm: what do we know?

Percutaneous computed tomography guided biopsy of sub-solid pulmonary nodules: differentiating solid from ground glass components at the time of biopsy

IntroductionThis study assessed (i) the ability to identify the solid components of part-solid nodules (PSN) during computed tomography (CT) guided lung biopsy (CTGLB), (ii) the ability of CTGLB to assess the invasive nature of a nodule on pathology. Materials and methodsSixty-nine nodules were studied in 68 patients who underwent CTGLB between 1/1/2014 and 10/31/2015. Diagnostic CT images and CTGLB images were reviewed. On diagnostic CT images, nodules were classified as ground glass nodules (GGN) or PSNs. Nodule size, location, and percentage of solid component were recorded. At the time of biopsy, the ability to visualize the solid component of a PSN, depth of lesion from skin, and ability to identify the needle within the solid component were recorded. ResultsThere were 42 (61%) part-solid nodules and 27 (39%) GGNs. During biopsy, it was possible to differentiate the solid from the ground glass components in 35 (83%) PSNs. Fifty-nine (86%) nodules were neoplastic based on biopsy pathology (all non-small cell lung carcinoma). Thirty-nine (66%) were resected. In all cases biopsy pathology and surgical pathology agreed regarding the presence of lung carcinoma. In 6 (15%) cases biopsy pathology demonstrated purely lepidic growth but had some non-lepidic growth on surgical pathology, including 2 cases with acinar growth as a dominant pattern. ConclusionIn most patients, the solid and ground glass components of a PSN were distinguishable when performing a CTGLB. In a minority of patients, discrepancy was noted between biopsy pathology and surgical pathology regarding the invasive nature of a nodule.

Use of the hybrid room for thoracic surgery procedures: single-stage localization and removal of non-palpable nodules.

With the widespread availability of lung cancer screening programs, the number of small lung nodules requiring histological characterization has dramatically increased. Because computed tomography-guided fine-needle aspiration may frequently yield false-negative results, excisional biopsy using thoracoscopy is frequently required. Although thoracoscopic procedure has been known to be ideal for nodule resection, the identification of very small, subsolid and deep pulmonary nodules may still be challenging. Precise lesion localization is a key prerequisite to avoid conversion to an unplanned thoracotomy. In the traditional workflow, the localization procedure is performed in the radiology suite, after which the patient is moved to an operating room. With the availability of hybrid operating rooms, a new approach encompassing simultaneous localization and removal of non-palpable lung nodules has become feasible. In this article, we review the procedural workflow of this new technique and discuss its indications and results.

Discrimination between transient and persistent subsolid pulmonary nodules on baseline CT using deep transfer learning

To develop and validate a deep learning model to discriminate transient from persistent subsolid nodules (SSNs) on baseline CT. A cohort of 1414 SSNs, consisting of 319 transient SSNs in 168 individuals and 1095 persistent SSNs in 816 individuals, were identified on chest CT. The cohort was assigned by examination date into a development set of 996 SSNs, a tuning set of 212 SSNs, and a validation set of 206 SSNs. Our model was built by transfer learning, which was transferred from a well-performed deep learning model for pulmonary nodule classification. The performance of the model was compared with that of two experienced radiologists. Each nodule was categorized by Lung CT Screening Reporting and Data System (Lung-RADS) to further evaluate the performance and the potential clinical benefit of the model. Two methods were employed to visualize the learned features. Our model achieved an AUC of 0.926 on the validation set with an accuracy of 0.859, a sensitivity of 0.863, and a specificity of 0.858, and outperformed the radiologists. The model performed the best among Lung-RADS 2 nodules and maintained well performance among Lung-RADS 4 nodules. Feature visualization demonstrated the model's effectiveness in extracting features from images. The transfer learning model presented good performance on the discrimination between transient and persistent SSNs. A reliable diagnosis on nodule persistence can be achieved at baseline CT; thus, an early diagnosis as well as better patient care is available. • Deep learning can be used for the discrimination between transient and persistent subsolid nodules. • A transfer learning model can achieve good performance when it is transferred from a model with a similar task. • With the assistance of deep learning model, a reliable diagnosis on nodule persistence can be achieved at baseline CT, which can bring a better patient care strategy.

Radiological-pathological correlation of subsolid pulmonary nodules: A single centre retrospective evaluation of the 2011 IASLC adenocarcinoma classification system

IntroductionThe 2011 IASLC classification system proposes guidelines for radiologists and pathologists to classify adenocarcinomas spectrum lesions as preinvasive, minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA). IA portends the worst clinical prognosis, and the imaging distinction between MIA and IA is controversial. Materials and methodsSubsolid pulmonary nodules resected by microcoil localization over a three-year period were retrospectively reviewed by three chest radiologists and a pulmonary pathologist. Nodules were classified radiologically based on preoperative computed tomography (CT), with the solid nodule component measured on mediastinal windows applied to high-frequency lung kernel reconstructions, and pathologically according to 2011 IASLC criteria. Radiology interobserver and radiological-pathological variability of nodule classification, and potential reasons for nodule classification discordance were assessed. ResultsSeventy-one subsolid nodules in 67 patients were included. The average size of invasive disease focus at histopathology was 5 mm (standard deviation 5 mm). Radiology interobserver agreement of nodule classification was good (Cohen’s Kappa = 0.604, 95 % CI: 0.447 to 0.761). Agreement between consensus radiological interpretation and pathological category was fair (Cohen’s Kappa = 0.236, 95 % CI: 0.054−0.421). Radiological and pathological nodule classification were concordant in 52 % (37 of 71) of nodules. The IASLC proposed CT solid component cut-off of 5 mm to distinguish MIA and IA yielded a sensitivity of 59 % and specificity of 80 %. Common reasons for nodule classification discordance included multiple solid components within a nodule on CT, scar and stromal collapse at pathology, and measurement variability. ConclusionSolid component(s) within persistent part-solid pulmonary nodules raise suspicion for invasive adenocarcinoma. Preoperative imaging classification is frequently discordant from final pathology, reflecting interpretive and technical challenges in radiological and pathological analysis.

Artificial intelligence-assisted CT characterizations and quantitative analysis for differentiating pre-invasive lesions from invasive adenocarcinomas in pulmonary subsolid nodules ≤ 2cm.

e21614 Background: The use of artificial intelligence (AI) in medical imaging has dramatically improved the quality of segmentation including accuracy, efficiency and reproducibility. This study sought to determine whether AI-assisted computed tomography (CT) features and quantitative analysis of pulmonary subsolid nodules (SSNs) under 2cm could be used to differentiate preinvasive lesions from invasive adenocarcinomas. Methods: Clinical data and CT images of 297 preinvasive lesions and early invasive lung adenocarcinomas confirmed by surgery pathology with CT manifestations of SSNs under 2cm were retrospectively analysed. The nodules were divided into two groups: the preinvasive lesions (PILs, N = 115) including 7 cases of atypical adenomatous hyperplasia (AAH), 30 cases of adenocarcinoma in situ (AIS) and 78 cases of minimally invasive adenocarcinoma (MIA), and the invasive adenocarcinomas (IACs, N = 182). All CTs were processed by AI and the volume, mean CT value, consolidation-to-tumor ratio (CTR), mass and maximum diameter of each SSN were obtained. Results: The volume, mean CT value, CTR, maximum diameter and mass of nodules showed significant difference between the two groups (Table). Multivariate analysis was determined by logistic regression. The regression model between the two groups was logit(p) = -1.439-2.927Volume +0.0005(mean CT value)-0.463(CTR &gt; 0.5) +0.238(maximum diameter)+6.298(mass).The receiver operating characteristic curve (ROC) showed that the mass can do the best prediction among all the independent factors with the areas under the curve(AUC) 0.748 at a cut-off value of 0.154, with the sensitivity of 70.9% and specificity of 70.4% .The AUC of the ROC using the regression probabilities of regression model was 0.769. Conclusions: AI-assisted CT characterizations may be promising tools to predict if SSNs under 2 cm have invaded. [Table: see text]

CT-Guided Core Biopsy for Peripheral Sub-solid Pulmonary Nodules to Predict Predominant Histological and Aggressive Subtypes of Lung Adenocarcinoma.

Adenocarcinoma is the most common type of lung cancer, and pre-operative biopsy plays an important role to determine its major subtypes. As proposed by the International Association for the Study of Lung Cancer (IASLC), American Thoracic Society (ATS), and European Respiratory Society (ERS) in 2011, the predominant histological subtype of adenocarcinoma is an indicator of outcomes and recurrence rate. However, the value of CT-guided core biopsy in predicting the predominant subtype and detecting the presence of an aggressive subtype of adenocarcinoma, peripheral sub-solid nodule, has less been discussed. We retrospectively reviewed 318 consecutive peripheral sub-solid nodules that underwent percutaneous CT-guided lung biopsy and surgical resection, between October 2015 and December 2018 and were diagnosed as adenocarcinoma with histological subtype. The subtyping results from biopsy and surgical pathology were compared to evaluate the concordance rate. The overall concordance rate between biopsy and surgical pathology in determining the predominant histological subtype was 64%. Better concordance was found in small tumors (≤ 2cm), in predicting either predominant histology (χ2 = 7.091, P = 0.008) or high grade adenocarcinoma, micropapillary and/or solid subtype, MIP-SOL (χ2 = 22.301, P < 0.001). The analysis of ground glass opacity (GGO) component (C/T ratio) obtained significantly higher accuracy in the pure GGO group than in the other two groups in predicting predominant histology or high grade adenocarcinoma (χ2 = 17.560, P < 0.001 and χ2 = 61.938, P < 0.001, respectively). CT-guided core biopsies provide additional value in predicting the histological subtype of lung adenocarcinoma after surgical resection, especially in small tumors (≤ 2cm) or an initially pure GGO group.

Growth and Clinical Impact of 6-mm or Larger Subsolid Nodules after 5 Years of Stability at Chest CT.

Background It remains unclear whether 5 years of stability is sufficient to establish the benign behavior of subsolid nodules (SSNs) of the lung. There are no guidelines for the length of follow-up needed for these SSNs. Purpose To investigate the incidence of interval growth of pulmonary SSNs 6 mm or greater in diameter after 5 years of stability and their clinical outcome. Materials and Methods This retrospective study assessed SSNs 6 mm or greater that were stable for 5 years after detection (January 2002 to December 2018). The incidence of interval growth after 5 years of stability and the clinical and radiologic features of these SSNs were investigated. Clinical stage shifts of growing SSNs, presence of metastasis, and overall survival were assessed during the follow-up period. Subgroup analysis was performed in patients with nonenhanced thin-section (section thickness ≤1.5 mm) CT for interval growth after 5 years of stability. Results A total of 235 SSNs in 235 patients (mean age, 64 years ± 10 [standard deviation]; 132 women) were evaluated. There were 212 pure ground-glass nodules and 24 part-solid nodules. During follow-up (median, 112 months; range, 84-208 months), five of the 235 SSNs (2%; three primary ground-glass nodules and two part-solid nodules) showed interval growth. Three of these five growing SSNs were 10 mm or greater. Three of the five SSNs with interval growth had clinical stage shifts after growth (from Tis [in situ] to T1mi [minimally invasive] in one lesion; from T1mi to T1a in two lesions). There were no deaths or metastases from lung cancer during follow-up. Of 160 SSNs imaged with section thickness of 1.5 mm or less, two (1%) grew; both lesions were 10 mm or greater. Conclusion Only 2% of subsolid pulmonary nodules greater than or equal to 6 mm that had been stable for 5 years showed subsequent growth. At median follow-up of 9 years (after the initial 5-year period of stability), growth of those lung nodules had no clinical effect. © RSNA, 2020 See also the editorial by Naidich and Azour in this issue.