Subsolid Nodules Research Articles

Comparison of Comprehensive Morphological and Radiomics Features of Subsolid Pulmonary Nodules to Distinguish Minimally Invasive Adenocarcinomas and Invasive Adenocarcinomas in CT Scan.

ObjectiveTo investigative the diagnostic performance of the morphological model, radiomics model, and combined model in differentiating invasive adenocarcinomas (IACs) from minimally invasive adenocarcinomas (MIAs).MethodsThis study retrospectively involved 307 patients who underwent chest computed tomography (CT) examination and presented as subsolid pulmonary nodules whose pathological findings were MIAs or IACs from January 2010 to May 2018. These patients were randomly assigned to training and validation groups in a ratio of 4:1 for 10 times. Eighteen categories of morphological features of pulmonary nodules including internal and surrounding structure were labeled. The following radiomics features are extracted: first-order features, shape-based features, gray-level co-occurrence matrix (GLCM) features, gray-level size zone matrix (GLSZM) features, gray-level run length matrix (GLRLM) features, and gray-level dependence matrix (GLDM) features. The chi-square test and F1 test selected morphology features, and LASSO selected radiomics features. Logistic regression was used to establish models. Receiver operating characteristic (ROC) curves evaluated the effectiveness, and Delong analysis compared ROC statistic difference among three models.ResultsIn validation cohorts, areas under the curve (AUC) of the morphological model, radiomics model, and combined model of distinguishing MIAs from IACs were 0.88, 0.87, and 0.89; the sensitivity (SE) was 0.68, 0.81, and 0.83; and the specificity (SP) was 0.93, 0.79, and 0.87. There was no statistically significant difference in AUC between three models (p > 0.05).ConclusionThe morphological model, radiomics model, and combined model all have a high efficiency in the differentiation between MIAs and IACs and have potential to provide non-invasive assistant information for clinical decision-making.

Impact of dose reduction and iterative reconstruction algorithm on the detectability of pulmonary nodules by artificial intelligence

PurposeThe purpose of this study was to assess whether the performances of an automated software for lung nodule detection with computed tomography (CT) are affected by radiation dose and the use of iterative reconstruction algorithm. Materials and methodsA chest phantom (Multipurpose Chest Phantom N1; Kyoto Kagaku Co. Ltd, Kyoto, Japan) with 15 pulmonary nodules was scanned with a total of five CT protocol settings with up to 20-fold dose reduction. All CT examinations were reconstructed with iterative reconstruction algorithms ADMIRE 3 and ADMIRE 5 and were then analyzed for the presence of pulmonary nodules with a fully automated computer aided detection software system (InferReadTM CT Lung, Infervision), which is based on deep neural networks. ResultsThe sensitivity of fully automated pulmonary nodule detection for ground-glass nodules at standard dose CT was greater (70.0%; 14/20; 95% CI: 51.6-88.4%) than at 10-fold and 20-fold dose reduction (30.0%; 6/20; 95% CI: 0.0%-62.5%). There were less false positive findings when ADMIRE 5 reconstruction was used (4.0 ± 2.8 [SD]; range: 2–6) instead of ADMIRE 3 reconstruction (25.0 ± 15.6 [SD]; range: 14–36). There was no difference in the sensitivity of detection of solid and subsolid nodules between standard dose (100%; 95% CI: 100–100%) and 10- and 20-fold reduced dose CT (92.5%; 95% CI: 83.8–100.0%). Image noise was significantly greater with ADMIRE 3 (81 ± 2 [SD] [range: 79–84]; 104 ± 3 [SD] [range: 101–107]; 114 ± 5 [SD] [range: 110–119]; 193 ± 10 [SD] [range: 183-203]; 220 ± 16 [SD] [range: 210–238]) compared to ADMIRE 5 (44 ± 2 [SD] [range: 42–46]; 60 ± 2 [SD] [range: 57–61]; 66 ± 1 [SD] [range: 65–67]; 103 ± 4 [SD] [range: 98–106]; 110 ± 1 [SD] [range: 109–111]), respectively in each of the five CT protocols. ConclusionThis phantom study suggests that dose reduction and iterative reconstruction settings have an impact on detectability of pulmonary nodules by artificial intelligence software and we therefore encourage adaption of dose levels and reconstruction methods prior to widespread implementation of fully automatic nodule detection software for lung cancer screening purposes.

Predicting the Ki-67 proliferation index in pulmonary adenocarcinoma patients presenting with subsolid nodules: construction of a nomogram based on CT images.

The Ki-67 proliferation index (PI) reflects the proliferation of cells. However, the conventional methods for the acquisition of the Ki-67 PI, such as surgery and biopsy, are generally invasive. This study investigated a potential noninvasive method of predicting the Ki-67 PI in patients with lung adenocarcinoma presenting with subsolid nodules. This retrospective study enrolled 153 patients who presented with pulmonary adenocarcinoma appearing as subsolid nodules (SSNs) on computed tomography (CT) images between January 2015 and December 2018. Presence of LUAD with SSNs was confirmed by histopathology. Of these participants, 107 patients were from institution 1 and were divided into a training cohort and an internal validation cohort in a 7:3 ratio. The other 46 patients were from institution 2 and were enrolled as an external validation cohort. All patients underwent conventional CT scans with thin-slice (≤1.25 mm) reconstruction, and 1,316 quantitative radiomic features were extracted from the CT images for each nodule. The minimum redundancy maximum relevance and the least absolute shrinkage and selection operator were used for feature selection, and the radiomics signature was constructed based on these selected features. Clinical features were examined using univariate logistic regression analysis. The nomogram was developed based on the radiomics signature and the independent clinical risk factors. The Delong test and t test were employed for statistical analysis. The performance of different models was assessed by the receiver operating characteristic (ROC) curve. The diameter of the nodules [odds ratio (OR) =1.17; P=0.003] was identified as an independent predictive parameter. Both the radiomics signature and the nomogram suggested a good predictive probability for Ki-67 expression. For the radiomics signature, the area under the ROC curve (AUC) for the training cohort, the internal validation cohort, and the external validation cohort was 0.86 [95% confidence interval (CI): 0.77 to 0.95], 0.81 (95% CI: 0.64 to 0.98), and 0.77 (95% CI: 0.62 to 0.91), respectively. For the nomogram, the AUC for the training cohort, the internal validation cohort, and the external validation cohort was 0.86 (95% CI: 0.77 to 0.95), 0.80 (95% CI: 0.64 to 0.97), and 0.79 (95% CI: 0.65 to 0.94), respectively. There were no statistical differences in the AUCs between the radiomics signature and the radiomic nomogram in the training cohort or the validation cohorts (all P>0.05). The nomogram provides a novel strategy for determining the Ki-67 PI in predicting the proliferation of subsolid nodules, which may be beneficial for the management of patients with SSNs.

Preoperative electromagnetic navigation bronchoscopy-guided one-stage multiple-dye localization for resection of subsolid nodules: A single-center pilot study.

BackgroundElectromagnetic navigation bronchoscopy (ENB)‐guided transbronchial dye marking and video‐assisted thoracoscopic surgery (VATS) is an emerging technique that enables successful resection of multiple small subsolid pulmonary nodules. The aim of this study was to evaluate the accuracy and safety of preoperative ENB‐guided transbronchial multiple dye localization for VATS resection of subsolid pulmonary nodules.MethodsAs a single‐center pilot study, we recruited patients with at least two small or subsolid pulmonary nodules. Multiple‐dye localization was performed by intraoperative ENB‐guided transbronchial injection of an indigo carmine dye. The patients underwent VATS for sublobar resection immediately after localization. The accuracy of ENB‐guided dye marking was checked.ResultsENB‐guided one‐stage multiple dye localization was conducted for 18 pulmonary nodules in seven patients between September 2018 and December 2019. The mean diameter of the pulmonary nodules was 9.3 mm (range, 4–18) and the mean distance from the pleura to pulmonary nodule was 6 mm (range, 1–17 mm). ENB‐guided transbronchial multiple dye localization was successfully performed in 94.4% (17/18), and the accuracy of ENB‐guided dye marking was 88.2% (15/17). When two nodules were not seen in intraoperative fields, anatomical sublobar resection was performed. There was no conversion to thoracotomy and operative mortalities. Among the seven patients, only one patient showed mild intrabronchial bleeding but stopped spontaneously. The changes in lung function after multiple wedge resections (−1.6% to 24.8%) were tolerable level.ConclusionsENB‐guided one‐stage transbronchial dye localization showed accurate and safe intraoperative identification of multiple subsolid pulmonary nodules. A large scale prospective clinical study is warranted.

Implementation of artificial intelligence in the histological assessment of pulmonary subsolid nodules.

BackgroundClinical management of subsolid nodules (SSNs) is defined by the suspicion of tumor invasiveness. We sought to develop an artificial intelligent (AI) algorithm for invasiveness assessment of lung adenocarcinoma manifesting as radiological SSNs. We investigated the performance of this algorithm in classification of SSNs related to invasiveness.MethodsA retrospective chest computed tomography (CT) dataset of 1,589 SSNs was constructed to develop (85%) and internally test (15%) the proposed AI diagnostic tool, SSNet. Diagnostic performance was evaluated in the hold-out test set and was further tested in an external cohort of 102 SSNs. Three thoracic surgeons and three radiologists were required to evaluate the invasiveness of SSNs on both test datasets to investigate the clinical utility of the proposed SSNet.ResultsIn the differentiation of invasive adenocarcinoma (IA), SSNet achieved a similar area under the curve [AUC; 0.914, 95% confidence interval (CI): 0.813–0.987] with that of the 6 doctors (0.900, 95% CI: 0.867–0.922). When interpreting with the assistance of SSNet, the sensitivity of junior doctors, specificity of senior doctor, and their accuracy were significantly improved. In the external test, SSNet (AUC: 0.949, 95% CI: 0.884–1.000) achieved a better AUC than doctors (AUC: 0.883, 95% CI: 0.826–0.939) whose AUC increased (AUC: 0.908, 95% CI: 0.847–0.982) with SSNet assistance. In the histological subtype classifications, SSNet achieved better performance than practicing doctors. The AUCs of doctors were significantly improved with the assistance of SSNet in both 4-category and 3-category classifications to 0.836 (95% CI: 0.811–0.862) and 0.852 (95% CI: 0.825–0.882), respectively.ConclusionsThe AI diagnostic system achieved non-inferior performance to doctors, and will potentially improve diagnostic performance and efficiency in SSN evaluation.

Thermal Ablation for Pulmonary Subsolid Nodules

Thermal Ablation for Pulmonary Subsolid Nodules

A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma

ObjectiveTo develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminating minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in patients with subsolid pulmonary nodules (SSPNs). Materials and MethodsIn total, 365 consecutive patients who presented with SSPNs and were pathologically diagnosed with MIA or IAC after surgery, were recruited from two medical institutions from 2016 to 2019. Deep learning features were selected from preoperative CT images using convolutional neural network. Deep learning signature (DLS) was developed via the least absolute shrinkage and selection operator (LASSO). New DLN integrating clinical variables, subjective CT findings, and DLS was constructed. The diagnostic efficiency and discriminative capability were analyzed using the receiver operating characteristic method and decision curve analysis (DCA). ResultsIn total, 18 deep learning features with non-zero coefficients were enrolled to develop the DLS, which was statistically different between the MIA and IAC groups. Independent predictors of DLS and lobulated sharp were used to build the DLN. The areas under the curves of the DLN were 0.889 (95% confidence interval (CI): 0.824–0.936), 0.915 (95% CI: 0.846–0.959), and 0.914 (95% CI: 0.848–0.958) in the training, internal validation, and external validation cohorts, respectively. After stratification analysis and DCA, the DLN showed potential generalization ability. ConclusionThe DLN incorporating the DLS and subjective CT findings have strong potential to distinguish MIA from IAC in patients with SSPNs, and will facilitate the suitable treatment method selection for the management of SSPNs.

The Impact of EGFR Tyrosine Kinase Inhibitor on the Natural Course of Concurrent Subsolid Nodules in Patients with Non-Small Cell Lung Cancer.

PurposeThe role of epidermal growth factor receptor–tyrosine kinase inhibitors (EGFR-TKIs) in the management of persistent subsolid nodules (SSNs) is unclear. This study aimed to investigate the impact of EGFR-TKIs on concurrent SSNs in patients with stage IV non–small cell lung cancer (NSCLC).Materials and MethodsPatients who received an EGFR-TKI for at least 1 month for stage IV NSCLC and had concurrent SSN(s) that had existed for at least 3 months on chest computed tomography were included in this retrospective study. Size change of each nodule before and after EGFR-TKI therapies were evaluated using a cutoff value of 2 mm; increase (≥ 2 mm), decrease (≤ −2 mm), and no change (−2 mm < size change < +2 mm).ResultsA total of 77 SSNs, 51 pure ground-glass (66.2%) and 26 part-solid nodules (33.8%), were identified in 59 patients who received gefitinib (n=45) and erlotinib (n=14). Among 58 EGFR mutation analysis performed for primary lung cancer, 45 (77.6%) were EGFR mutant. The proportions of decrease group were 19.5% (15/77) on per-nodule basis and 25.4% (15/59) on per-patient basis. Four SSNs (5.2%) disappeared completely. On per-patient based multivariable analysis, EGFR exon 19 deletion positivity for primary lung cancer was associated with a decrease after initial EGFR-TKI therapy (adjusted odds ratio, 4.29; 95% confidence interval, 1.21 to 15.29; p=0.025).ConclusionApproximately 20% of the concurrent SSNs decreased after the initial EGFR-TKI therapy. EGFR exon 19 deletion positivity for primary lung cancer was significantly associated with the size change of concurrent SSNs.

First case of chronic cell leukemia discovered incidentally in extra-saccular inguinal lymph node during laparoscopic bilateral inguinal hernia repair. Case report and literature review.

IntroductionChronic cell leukemia discovered incidentally in extra-saccular inguinal lymph node during laparoscopic bilateral inguinal hernia repair is extremely rare.Presentation of case62-year-old Romanian male presented at the outpatient general surgery clinic in April 2019 complaining of bilateral inguinal swelling that gradually increased in size mainly on right side and was diagnosed with bilateral inguinal hernia. During the laparoscopic repair of the hernia, a large lymph node in the left femoral canal was incidentally observed. Histopathologic, immunohistochemical, and flowcytometric evaluation of the excised specimen confirmed chronic lymphocytic leukemia/small lymphocytic lymphoma.DiscussionWhole body CT showed supra and infra-diaphragmatic lymphadenopathy, and few small subsolid pulmonary nodules, possibly metastatic. Splenomegaly and pancreatomegaly were also noted, suggesting lymphomatoid infiltration.ConclusionThere is need for cautious inspection and meticulous palpation of the inguinal area for any lymphadenopathy during routine inguinal hernia repair.

Expert consensus on thermal ablation therapy of pulmonary subsolid nodules (2021 Edition)

The Expert Consensus reviews current literatures and provides clinical practice guidelines for thermal ablation of pulmonary subsolid nodules or ground-glass nodule (GGN). The main contents include the following: (1) clinical evaluation of GGN; (2) procedures, indications, contraindications, outcomes evaluation, and related complications of thermal ablation for GGN; and (3) future development directions.

Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung Nodule and Treatment Effect of Radiofrequency Ablation.

This study aimed to detect and diagnose the lung nodules as early as possible to effectively treat them, thereby reducing the burden on the medical system and patients. A lung computed tomography (CT) image segmentation algorithm was constructed based on the deep learning convolutional neural network (CNN). The clinical data of 69 patients with lung nodules diagnosed by needle biopsy and pathological comprehensive diagnosis at hospital were collected for specific analysis. The CT image segmentation algorithm was used to distinguish the nature and volume of lung nodules and compared with other computer aided design (CAD) software (Philips ISP). 69 patients with lung nodules were treated by radiofrequency ablation (RFA). The results showed that the diagnostic sensitivity of the CT image segmentation algorithm based on the CNN was obviously higher than that of the Philips ISP for solid nodules <5 mm (63 cases vs. 33 cases) (P < 0.05); it was the same result for the subsolid nodule <5 mm (33 case vs. 5 cases) (P < 0.05) that was slightly higher for solid and subsolid nodules with a diameter of 5–10 mm (37 cases vs. 28 cases) (P < 0.05). In addition, the CNN algorithm can reach all detection for calcified nodules and pleural nodules (7 cases; 5 cases), and the diagnostic sensitivities were much better than those of Philips ISP (2 cases; 3 cases) (P < 0.05). Patients with pulmonary nodules treated by RFA were in good postoperative condition, with a half-year survival rate of 100% and a one-year survival rate of 72.4%. Therefore, it could be concluded that the CT image segmentation algorithm based on the CNN could effectively detect and diagnose the lung nodules early, and the RFA could effectively treat the lung nodules.

Diagnostic accuracy and complication rate of image-guided percutaneous transthoracic needle lung biopsy for subsolid pulmonary nodules: a systematic review and meta-analysis.

To determine the diagnostic accuracy and complication rate of percutaneous transthoracic needle biopsy (PTNB) for subsolid pulmonary nodules and sources of heterogeneity among reported results. We searched PubMed, EMBASE, and Cochrane libraries (until November 7, 2020) for studies measuring the diagnostic accuracy of PTNB for subsolid pulmonary nodules. Pooled sensitivity and specificity of PTNB were calculated using a bivariate random-effects model. Bivariate meta-regression analyses were performed to identify sources of heterogeneity. Pooled overall and major complication rates were calculated. We included 744 biopsies from 685 patients (12 studies). The pooled sensitivity and specificity of PTNB for subsolid nodules were 90% (95% confidence interval [CI]: 85-94%) and 99% (95% CI: 92-100%), respectively. Mean age above 65 years was the only covariate significantly associated with higher sensitivity (93% vs 85%, p = 0.04). Core needle biopsy showed marginally higher sensitivity than fine-needle aspiration (93% vs 83%, p = 0.07). Pooled overall and major complication rate of PTNB were 43% (95% CI: 25-62%) and 0.1% (95% CI: 0-0.4%), respectively. Major complication rate was not different between fine-needle aspiration and core needle biopsy groups (p = 0.25). PTNB had acceptable performance and a low major complication rate in diagnosing subsolid pulmonary nodules. The only significant source of heterogeneity in reported sensitivities was a mean age above 65 years. This is the first meta-analysis attempting to systemically determine the cause of heterogeneity in the diagnostic accuracy and complication rate of PTNB for subsolid pulmonary nodules.

P62.04 Neoteric Small Extracellular Vesicles Based Biomarker for Predicting Cancerous Sub-Solid Nodules of Lung

Sub-solid nodule (SSN) is a common radiographic finding, and due to the possibility of malignancy, further evaluation is urgently needed for the prevention and management of lung cancer (LC). The study aims to identify small extracellular vesicles (sEVs) based biomarker integrated into radiomics-clinical features through cross-scale to differentiate the suspicious SSN and predict the risk of LC. The study enrolled patients with SSN including LC and Benign nodules (BN) and Healthy persons as a control to discover sEVs differentials expressed miRNAs (DEMs) as biomarker by next-generation sequencing (NGS) (n=9) and validation (n=103) by RT-qPCR. Through cross-scale integration of small-molecule biomarker and macro-imaging, the prediction model was developed by Logit and Logistic algorithms and further interpreted into an easy-to-use nomogram by Cox-proportional hazards modeling. Present study has discovered various sEVs DEMs, and sEVs-miR-424-5p was selected as a novel potential biomarker for validating cohort. The results depicted that sEVs-miR-424-5p represented higher efficacy by the area under the curve (AUC) ranged from (0.7991 to 0.9553, p<0.01) in between groups. Furthermore, the 10 radiomics signs and 4 clinical features of SSN were merged with sEVs-miR-424-5p and obtained the correlation matrix of each sign. Afterward, the significant features were proceeded in multivariate logistic regression analysis to develop the cross-scale integrated modeling, which yielded a significantly higher AUC of 0.931 (p<0.0001). sEVs-miR-424-5p could be a novel biomarker for distinguishing SSN of LC and BN populations, and its association with the cross-scale fusion of radiomics-clinical features will provide great potential to be an errorless prediction of malignant SSN.

Detectability of Lung Nodules in Ultra-low Dose CT.

Investigation of the influence of different ultra-low dose computed tomography (ULDCT) protocols on the detection of solid and subsolid nodules in a phantom study. A chest phantom with pulmonary nodules was scanned with different CT protocols ranging from ultra-low dose settings with spectral shaping to a standard low dose lung cancer screening protocol. Image analysis was performed with different reconstruction algorithms and dedicated computer aided detection (CAD), which was compared to manual readout. The highest sensitivity rates (83%) were achieved for the 90 mAs and 120 mAs protocols when reconstructed with ADMIRE 3 or 5 and manual readout. The only statistically significant difference was found for subsolid nodules with preference of manual readout compared to CAD (p<0.05). Dose levels for the mAs settings ranged from 0.029 to 0.2 mSv. Reliable detectability rates for solid nodules were achieved; CAD software did not prove reliable for subsolid nodules.

Lung cancer probability and clinical outcomes of baseline and new subsolid nodules detected on low-dose CT screening

BackgroundLimited data are available regarding the management of subsolid nodules detected on lung cancer screening with low-dose CT (LDCT). We aimed to determine the characteristics of screen-detected subsolid nodules, and...

Predicting Malignancy and Invasiveness of Pulmonary Subsolid Nodules on CT Images Using Deep Learning.

BackgroundTo develop and validate a deep learning–based model on CT images for the malignancy and invasiveness prediction of pulmonary subsolid nodules (SSNs).Materials and MethodsThis study retrospectively collected patients with pulmonary SSNs treated by surgery in our hospital from 2012 to 2018. Postoperative pathology was used as the diagnostic reference standard. Three-dimensional convolutional neural network (3D CNN) models were constructed using preoperative CT images to predict the malignancy and invasiveness of SSNs. Then, an observer reader study conducted by two thoracic radiologists was used to compare with the CNN model. The diagnostic power of the models was evaluated with receiver operating characteristic curve (ROC) analysis.ResultsA total of 2,614 patients were finally included and randomly divided for training (60.9%), validation (19.1%), and testing (20%). For the benign and malignant classification, the best 3D CNN model achieved a satisfactory AUC of 0.913 (95% CI: 0.885–0.940), sensitivity of 86.1%, and specificity of 83.8% at the optimal decision point, which outperformed all observer readers’ performance (AUC: 0.846±0.031). For pre-invasive and invasive classification of malignant SSNs, the 3D CNN also achieved satisfactory AUC of 0.908 (95% CI: 0.877–0.939), sensitivity of 87.4%, and specificity of 80.8%.ConclusionThe deep-learning model showed its potential to accurately identify the malignancy and invasiveness of SSNs and thus can help surgeons make treatment decisions.

Developing of risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features.

BackgroundAccurate evaluation of pulmonary nodule malignancy is important for lung cancer management. This current study aimed to develop risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features.MethodsThis study enrolled 5–20 mm pulmonary nodules detected on thoracic high-resolution computed tomography (HRCT), which were all confirmed pathologically. There were 548 solid nodules (242 malignant vs. 306 benign) and 623 subsolid nodules (SSNs 519 malignant vs. 104 benign). Relevant clinical characteristics were recorded. The CT image prior to the initial treatment was chosen for manual segmentation of the targeted nodule using the ITK-SNAP software. Subsequently, the marked image was processed to quantitatively extract 1218 radiomics features using PyRadiomics. We performed five-fold cross-validation to select potential predictors from clinical and radiomics features using the LASSO method and to evaluate the performance of the established models. In total, four types of models were tried: random forest, XGBOOST, SVM, and logistic models. The established models were compared with the Mayo model.ResultsLung cancer risk models were developed among four nodule groups: all nodules (410 benign vs. 761 malignant; 1:1.86), nodules ≤10 mm (185 benign vs. 224 malignant; 1:1.21), solid nodules (306 benign vs. 242 malignant; 1.26:1), and SSNs (104 benign vs. 104 malignant; 1:1 matched). Significant clinical and radiomics predictors were selected for each group. The accuracy, area under the ROC curve, sensitivity, and specificity of the best model on validation dataset was 0.86, 0.91, 0.93, 0.73 for all nodules (XGBOOST), 0.82, 0.90, 0.86, 0.76 for nodules ≤10 mm (XGBOOST), 0.80, 0.89, 0.78, 0.82 for solid nodules (XGBOOST) and 0.70, 0.73, 0.73, 0.67 for SSNs (Random Forest). Except for the SSN models, the established clinical-radiomics models were superior to the Mayo model.ConclusionsPredictive models based on both clinical and radiomics features can be used to assess the malignancy of small solid and subsolid pulmonary nodules, even for nodules that are 10 mm or smaller.

Cost-Effectiveness of Treatment Thresholds for Subsolid Pulmonary Nodules in CT Lung Cancer Screening.

Background Guidelines such as the Lung CT Screening Reporting and Data System (Lung-RADS) are available for determining when subsolid nodules should be treated within lung cancer screening programs, but they are based on expert opinion. Purpose To evaluate the cost-effectiveness of varying treatment thresholds for subsolid nodules within a lung cancer screening setting by using a simulation model. Materials and Methods A previously developed model simulated 10 million current and former smokers undergoing CT lung cancer screening who were assumed to have a ground-glass nodule (GGN) at baseline. Nodules were allowed to grow and to develop solid components over time according to a monthly cycle and lifetime horizon. Management strategies generated by varying treatment thresholds, including the solid component size and use of the Brock risk calculator, were tested. For each strategy, average U.S. costs and quality-adjusted life years (QALYs) gained per patient were computed, and the incremental cost-effectiveness ratios (ICERs) of those on the efficient frontier were calculated. One-way and probabilistic sensitivity analyses of results were performed by varying several relevant parameters, such as treatment costs or malignancy growth rates. Results Variants of the Lung-RADS guidelines that did not treat pure GGNs were cost-effective. Strategies based on the Brock risk calculator did not reach the efficient frontier. The strategy with the highest QALYs under a willingness-to-pay threshold of $100 000 per QALY included no treatment of GGNs and a threshold of 4-mm solid component size for treatment of subsolid nodules. This strategy yielded an ICER of $52 993 per QALY (95% CI: 44 407, 64 372). Probabilistic sensitivity analysis showed this was the optimal strategy under a range of parameter variations. Conclusion Treatment of pure ground-glass nodules was not cost-effective. Strategies that use modifications of the Lung CT Screening Reporting and Data System guidelines were cost-effective for treating part-solid nodules; an optimal threshold of 4 mm for the solid component yielded the most quality-adjusted life years. © RSNA, 2021 Online supplemental material is available for this article.

CT-Detected Subsolid Nodules: A Predictor of Lung Cancer Development at Another Location?

Simple SummaryThe risk assessment of pulmonary nodules on chest computed tomography (CT) is key to the early detection of lung cancer. Whereas a nodule’s morphological characteristics are strong predictors of malignancy for that individual nodule, it remains unclear whether the frequency and CT features of pulmonary nodules contribute to the prediction accuracy of future development of LC in another location. This was found by performing risk prediction modelling using age, sex, and CT measures from lung-cancer-free scans as predictors. We found that a greater number of part-solid and ground-glass nodules in the earliest scan was linearly associated with a higher risk of lung cancer developing in another location in the future. There were also indications that CT biomarkers of other pulmonary and heart diseases are risk factors for lung cancer. Our findings endorse the utilization of information from the entire scan to improve the accuracy of lung risk assessment.The purpose of this case–cohort study was to investigate whether the frequency and computed tomography (CT) features of pulmonary nodules posed a risk for the future development of lung cancer (LC) at a different location. Patients scanned between 2004 and 2012 at two Dutch academic hospitals were cross-linked with the Dutch Cancer Registry. All patients who were diagnosed with LC by 2014 and a random selection of LC-free patients were considered. LC patients who were determined to be LC-free at the time of the scan and all LC-free patients with an adequate scan were included. The nodule count and types (solid, part-solid, ground-glass, and perifissural) were recorded per scan. Age, sex, and other CT measures were included to control for confounding factors. The cohort included 163 LC patients and 1178 LC-free patients. Cox regression revealed that the number of ground-glass nodules and part-solid nodules present were positively correlated to future LC risk. The area under the receiver operating curve of parsimonious models with and without nodule type information were 0.827 and 0.802, respectively. The presence of subsolid nodules in a clinical setting may be a risk factor for future LC development in another pulmonary location in a dose-dependent manner. Replication of the results in screening cohorts is required for maximum utility of these findings.

Subsolid Lung Adenocarcinomas: Radiological, Clinical and Pathological Features and Outcomes

Lung adenocarcinomas manifesting as subsolid nodules usually have a favorable prognosis. This study aimed to have a comprehensive investigation of the radiological and clinicopathologic features and oncological outcomes of subsolid nodules. Between March 2010 and December 2015, 865 patients with surgically resected clinical IA subsolid lung adenocarcinoma were retrospectively reviewed. Patients were classified into the pure ground-glass nodules (GGN) (pGGN [n=358], without solid component on lung and mediastinal windows), heterogeneous GGN (hGGN [n=65], only with solid components on lung window), and real part-solid nodule (rPSN [n=442], with solid component on both lung and mediastinal windows) groups. The clinicopathological features and survival time of the three groups were compared between groups. There was a significant increase in median tumor size (P < 0.001), solid component size measured at lung window (LW-SCS) (P < 0.001), and the proportion of invasive adenocarcinoma subtypes (P < 0.001) from pGGNs to hGGNs to rPSNs. After adjustment for LW-SCS, adenocarcinomas with predominant lepidic patterns were still more common in hGGNs than in rPSNs (P=0.009). Patients with rPSNs had a significantly worse recurrence-free survival (RFS) than those with pGGNs and hGGNs (5-year: 91.9% versus 100% versus 100%, P < 0.001). Multivariate Cox analyses revealed that gender (both P < 0.05) and clinical T category (based on lung window [LW-cT] [P=0.002] or mediastinal window [MW-cT] [P < 0.001]) were independent prognostic factors of RFS in the rPSN group. HGGNs represented as an intermediate subtype between pGGNs and rPSNs. Both pGGNs and hGGNs had excellent outcomes, while rPSNs exhibited a worse prognosis than them. Clinical T category and gender had prognostic implications for rPSNs.