Assessment of pulmonary vascular anatomy: comparing augmented reality by holograms versus standard CT images/reconstructions using surgical findings as reference standard.

BACKGROUND: We compared computed tomography (CT) images and holograms (HG) to assess the number of arteries of the lung lobes undergoing lobectomy and assessed easiness in interpretation by radiologists and thoracic surgeons with both techniques. METHODS: Patients scheduled for lobectomy for lung cancer were prospectively included and underwent CT for staging. A patient-specific three-dimensional model was generated and visualized in an augmented reality setting. One radiologist and one thoracic surgeon evaluated CT images and holograms to count lobar arteries, having as reference standard the number of arteries recorded at surgery. The easiness of vessel identification was graded according to a Likert scale. Wilcoxon signed-rank test and κ statistics were used. RESULTS: Fifty-two patients were prospectively included. The two doctors detected the same number of arteries in 44/52 images (85%) and in 51/52 holograms (98%). The mean difference between the number of artery branches detected by surgery and CT images was 0.31 ± 0.98, whereas it was 0.09 ± 0.37 between surgery and HGs (p = 0.433). In particular, the mean difference in the number of arteries detected in the upper lobes was 0.67 ± 1.08 between surgery and CT images and 0.17 ± 0.46 between surgery and holograms (p = 0.029). Both radiologist and surgeon showed a higher agreement for holograms (κ = 0.99) than for CT (κ = 0.81) and found holograms easier to evaluate than CTs (p < 0.001). CONCLUSIONS: Augmented reality by holograms is an effective tool for preoperative vascular anatomy assessment of lungs, especially when evaluating the upper lobes, more prone to anatomical variations. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04227444 RELEVANCE STATEMENT: Preoperative evaluation of the lung lobe arteries through augmented reality may help the thoracic surgeons to carefully plan a lobectomy, thus contributing to optimize patients' outcomes. KEY POINTS: • Preoperative assessment of the lung arteries may help surgical planning. • Lung artery detection by augmented reality was more accurate than that by CT images, particularly for the upper lobes. • The assessment of the lung arterial vessels was easier by using holograms than CT images.

Association between Contrast-Enhanced Computed Tomography Radiomic Features, Genomic Alterations and Prognosis in Advanced Lung Adenocarcinoma Patients.

Non-invasive methods to assess mutational status, as well as novel prognostic biomarkers, are warranted to foster therapy personalization of patients with advanced non-small cell lung cancer (NSCLC). This study investigated the association of contrast-enhanced Computed Tomography (CT) radiomic features of lung adenocarcinoma lesions, alone or integrated with clinical parameters, with tumor mutational status (EGFR, KRAS, ALK alterations) and Overall Survival (OS). In total, 261 retrospective and 48 prospective patients were enrolled. A Radiomic Score (RS) was created with LASSO-Logistic regression models to predict mutational status. Radiomic, clinical and clinical-radiomic models were trained on retrospective data and tested (Area Under the Curve, AUC) on prospective data. OS prediction models were trained and tested on retrospective data with internal cross-validation (C-index). RS significantly predicted each alteration at training (radiomic and clinical-radiomic AUC 0.95-0.98); validation performance was good for EGFR (AUC 0.86), moderate for KRAS and ALK (AUC 0.61-0.65). RS was also associated with OS at univariate and multivariable analysis, in the latter with stage and type of treatment. The validation C-index was 0.63, 0.79, and 0.80 for clinical, radiomic, and clinical-radiomic models. The study supports the potential role of CT radiomics for non-invasive identification of gene alterations and prognosis prediction in patients with advanced lung adenocarcinoma, to be confirmed with independent studies.

Integrating Biological and Radiological Data in a Structured Repository: a Data Model Applied to the COSMOS Case Study.

Integrating the information coming from biological samples with digital data, such as medical images, has gained prominence with the advent of precision medicine. Research in this field faces an ever-increasing amount of data to manage and, as a consequence, the need to structure these data in a functional and standardized fashion to promote and facilitate cooperation among institutions. Inspired by the Minimum Information About BIobank data Sharing (MIABIS), we propose an extended data model which aims to standardize data collections where both biological and digital samples are involved. In the proposed model, strong emphasis is given to the cause-effect relationships among factors as these are frequently encountered in clinical workflows. To test the data model in a realistic context, we consider the Continuous Observation of SMOking Subjects (COSMOS) dataset as case study, consisting of 10 consecutive years of lung cancer screening and follow-up on more than 5000 subjects. The structure of the COSMOS database, implemented to facilitate the process of data retrieval, is therefore presented along with a description of data that we hope to share in a public repository for lung cancer screening research.

Low-dose CT for lung cancer screening: position paper from the Italian college of thoracic radiology.

Smoking is the main risk factor for lung cancer (LC), which is the leading cause of cancer-related death worldwide. Independent randomized controlled trials, governmental and inter-governmental task forces, and meta-analyses established that LC screening (LCS) with chest low dose computed tomography (LDCT) decreases the mortality of LC in smokers and former smokers, compared to no-screening, especially in women. Accordingly, several Italian initiatives are offering LCS by LDCT and smoking cessation to about 10,000 high-risk subjects, supported by Private or Public Health Institutions, envisaging a possible population-based screening program. Because LDCT is the backbone of LCS, Italian radiologists with LCS expertise are presenting this position paper that encompasses recommendations for LDCT scan protocol and its reading. Moreover, fundamentals for classification of lung nodules and other findings at LDCT test are detailed along with international guidelines, from the European Society of Thoracic Imaging, the British Thoracic Society, and the American College of Radiology, for their reporting and management in LCS. The Italian College of Thoracic Radiologists produced this document to provide the basics for radiologists who plan to set up or to be involved in LCS, thus fostering homogenous evidence-based approach to the LDCT test over the Italian territory and warrant comparison and analyses throughout National and International practices.

Reproducibility of radiomic features in CT images of NSCLC patients: an integrative analysis on the impact of acquisition and reconstruction parameters.

BACKGROUND: We investigated to what extent tube voltage, scanner model, and reconstruction algorithm affect radiomic feature reproducibility in a single-institution retrospective database of computed tomography images of non-small-cell lung cancer patients. METHODS: This study was approved by the Institutional Review Board (UID 2412). Images of 103 patients were considered, being acquired on either among two scanners, at 100 or 120 kVp. For each patient, images were reconstructed with six iterative blending levels, and 1414 features were extracted from each reconstruction. At univariate analysis, Wilcoxon-Mann-Whitney test was applied to evaluate feature differences within scanners and voltages, whereas the impact of the reconstruction was established with the overall concordance correlation coefficient (OCCC). A multivariable mixed model was also applied to investigate the independent contribution of each acquisition/reconstruction parameter. Univariate and multivariable analyses were combined to analyse feature behaviour. RESULTS: Scanner model and voltage did not affect features significantly. The reconstruction blending level showed a significant impact at both univariate analysis (154/1414 features yielding an OCCC < 0.85) and multivariable analysis, with most features (1042/1414) revealing a systematic trend with the blending level (multiple comparisons adjusted p < 0.05). Reproducibility increased in association to image processing with smooth filters, nonetheless specific investigation in relation to clinical endpoints should be performed to ensure that textural information is not removed. CONCLUSIONS: Combining univariate and multivariable models is allowed to identify features for which corrections may be applied to reduce the trend with the algorithm and increase reproducibility. Subsequent clustering may be applied to eliminate residual redundancy.

A methodological quality evaluation of the published guidelines and recommendations about the lung cancer screening.

BACKGROUND: A project to assess the existing literature and to benchmark the quality of past guidelines and recommendations on lung cancer screening projects was developed with a particular focus on the assessment of the methodology used in producing them. METHODS: Each guideline was assessed in the different items and domains with the Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument and scored on a seven-point scale. RESULTS: Eight guidelines matched the inclusion criteria and were assessed. A multinational collaboration produced three out of five guidelines. The multivariable analysis shows that improved scores of stakeholders' involvement were related to internationally developed guidelines. Improved methodological quality was related to the involvement of scientific societies due to the better rigor of development and editorial independence. Countries with higher expenditure on healthcare produced significantly better guidelines. CONCLUSIONS: Assessed by the AGREE II criteria, the methodological quality of previous guidelines was relatively low. Nevertheless, the National Comprehensive Cancer Network Guidelines should be recommended as a model for the development of best methodological quality guidelines.

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer.

Background: No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed. Methods: Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded. Cox proportional hazards (PH) with least absolute shrinkage and selection operator (LASSO) regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low- and high-volume lesions. Results: Radiomic models significantly outperformed models built on only clinical variables and volume. However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models. Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low- and high-volume models performed significantly better with the inclusion of radiomic features (P<0.0001), but the improvement was largest in the high-volume group (+10.2% against +8.7% improvement for CatBoost models and +10.0% against +5.4% in Cox PH models). Conclusions: Radiomic features complement well-known prognostic factors such as volume, but their volume-dependency is high and should be managed with vigilance. The informative content of radiomic features may be diminished in small lesion volumes, which could limit the applicability of radiomics in early-stage NSCLC, where tumors tend to be small. Our results also suggest an advantage of CatBoost models over the Cox PH models.

A segmentation tool for pulmonary nodules in lung cancer screening: Testing and clinical usage.

PURPOSE: With the future goal of defining a large dataset based on low-dose CT with labelled pulmonary lesions for lung cancer screening (LCS) research, the aim of this work is to propose and evaluate into a clinical context a tool for semi-automatic segmentation able to facilitate the process of labels collection from a LCS study (COSMOS, Continuous Observation of SMOking Subjects). METHODS: Considering a preliminary set of manual annotations, a segmentation model based on a 2D-Unet was trained from scratch. Contour quality of the final 2D-Unet was assessed on an internal test set of manual annotations and on a subset of the public available LIDC dataset used as external test set. The tool for semi-automatic segmentation was then designed integrating the tested model into a Graphical User Interface. According to the opinion of two clinical users, the percentage of lesions properly contoured through the tool was quantified (Acceptance Rate, AR). The variability between segmentations derived by the two readers was estimated as mean percentage of difference (MPD) between the two sets of volumes and comparing the likelihood of malignancy derived from Volume Doubling Time (VDT). RESULTS: Performance in test sets were found similar (DICE ~ 0.75(0.15)). Accordingly, a good mean AR (80.1%) resulted from the two readers. Variability in terms of MPD was equal to 23.6% while 2.7% was the VDTs percentage of disagreement. CONCLUSIONS: A semi-automatic segmentation tool was developed and its applicability evaluated into a clinical context demonstrating the efficacy of the tool in facilitating the collection of labelled data.

The Challenge of Choosing the Best Classification Method in Radiomic Analyses: Recommendations and Applications to Lung Cancer CT Images.

Radiomics uses high-dimensional sets of imaging features to predict biological characteristics of tumors and clinical outcomes. The choice of the algorithm used to analyze radiomic features and perform predictions has a high impact on the results, thus the identification of adequate machine learning methods for radiomic applications is crucial. In this study we aim to identify suitable approaches of analysis for radiomic-based binary predictions, according to sample size, outcome balancing and the features-outcome association strength. Simulated data were obtained reproducing the correlation structure among 168 radiomic features extracted from Computed Tomography images of 270 Non-Small-Cell Lung Cancer (NSCLC) patients and the associated to lymph node status. Performances of six classifiers combined with six feature selection (FS) methods were assessed on the simulated data using AUC (Area Under the Receiver Operating Characteristics Curves), sensitivity, and specificity. For all the FS methods and regardless of the association strength, the tree-based classifiers Random Forest and Extreme Gradient Boosting obtained good performances (AUC ≥ 0.73), showing the best trade-off between sensitivity and specificity. On small samples, performances were generally lower than in large-medium samples and with larger variations. FS methods generally did not improve performances. Thus, in radiomic studies, we suggest evaluating the choice of FS and classifiers, considering specific sample size, balancing, and association strength.

Impact of the COVID-19 pandemic on the selection of chest imaging modalities and reporting systems: a survey of Italian radiologists.

PURPOSE: Chest imaging modalities play a key role for the management of patient with coronavirus disease (COVID-19). Unfortunately, there is no consensus on the optimal chest imaging approach in the evaluation of patients with COVID-19 pneumonia, and radiology departments tend to use different approaches. Thus, the main objective of this survey was to assess how chest imaging modalities have been used during the different phases of the first COVID-19 wave in Italy, and which diagnostic technique and reporting system would have been preferred based on the experience gained during the pandemic. MATERIAL AND METHODS: The questionnaire of the survey consisted of 26 questions. The link to participate in the survey was sent to all members of the Italian Society of Medical and Interventional Radiology (SIRM). RESULTS: The survey gathered responses from 716 SIRM members. The most notable result was that the most used and preferred chest imaging modality to assess/exclude/monitor COVID-19 pneumonia during the different phases of the first COVID-19 wave was computed tomography (51.8% to 77.1% of participants). Additionally, while the narrative report was the most used reporting system (55.6% of respondents), one-third of participants would have preferred to utilize structured reporting systems. CONCLUSION: This survey shows that the participants' responses did not properly align with the imaging guidelines for managing COVID-19 that have been made by several scientific, including SIRM. Therefore, there is a need for continuing education to keep radiologists up to date and aware of the advantages and limitations of the chest imaging modalities and reporting systems.

HHV8-Negative Effusion-Based Large B Cell Lymphoma Arising in Chronic Myeloid Leukemia Patients under Dasatinib Treatment: A Report of Two Cases.

Tyrosine kinase inhibitors (TKIs) are the treatment of choice for BCR-ABL1-positive chronic myeloid leukemia (CML). Although TKIs have substantially improved prognosis of CML patients, their use is not free of adverse effects. Dasatinib is a second generation TKI frequently associated with pleural effusion in up to 33% of patients. This results in symptoms as dyspnea, cough and chest pain that may require therapy discontinuation. In the present report, we describe two exceptional cases of HHV8-negative large B-cell effusion-based lymphoma (EBL) confined to the pleura, incidentally, diagnosed in patients presenting with dasatinib-related pleural effusion. One patient (case 1) is alive and is in remission at 17 months from large B-cell EBL diagnosis while unfortunately the other patient (case 2) died of progressive disease and COVID-19 pneumonia 16 months from large B-cell EBL diagnosis. These cases raise concern about a possible association between large B-cell EBL and dasatinib, and the different clinical outcome of the two cases poses a challenge in treatment decision. For this reason, we strongly recommend cytological investigation in patients with persistent/relapsing pleural effusion under dasatinib, primarily to validate its possible association with lymphoma development and to improve the knowledge about this entity.

The Impact of Multidisciplinary Team Meetings on Patient Management in Oncologic Thoracic Surgery: A Single-Center Experience.

BACKGROUND: the aim of this paper is to quantify multidisciplinary team meeting (MDT) impact on the decisional clinical pathway of thoracic cancer patients, assessing the modification rate of the initial out-patient evaluation. METHODS: the impact of MDT was classified as follows: confirmation: same conclusions as out-patient hypothesis; modification: change of out-patient hypothesis; implementation: definition of a clear clinical track/conclusion for patients that did not receive any clinical hypothesis; further exams required: the findings that emerged in the MDT meeting require further exams. RESULTS: one thousand consecutive patients evaluated at MDT meetings were enrolled. Clinical settings of patients were: early stage lung cancer (17.4%); locally advanced lung cancer (27.4%); stage IV lung cancer (9.8%); mesothelioma (1%); metastases to the lung from other primary tumors (4%); histologically proven or suspected recurrence from previous lung cancer (15%); solitary pulmonary nodule (19.2%); mediastinal tumors (3.4%); other settings (2.8%). CONCLUSIONS: MDT meetings impact patient management in oncologic thoracic surgery by modifying the out-patient clinical hypothesis in 10.6% of cases; the clinical settings with the highest decisional modification rates are "solitary pulmonary nodule" and "proven or suspected recurrence" with modification rates of 14.6% and 13.3%, respectively.

An innovative approach to identify past exposure to asbestos integrating questionnaire information and administrative data.

Reconstruction of work history of subjects exposed to occupational carcinogens might be extremely challenging and provide unreliable results. This study, carried out in Italy from February to November 2014, aimed to explore the validity of an innovative approach to reconstruct the occupational history of workers who have previously been exposed to asbestos combining the administration of structured questionnaire with the use of administrative data. Subjects recruited in this study were enrolled in the cohorts of COSMOS 1 and 2 studies. Participants indicating an exposure to asbestos were contacted and a structured questionnaire was administered to them to verify the validity of the self-reported asbestos exposure. Subsequently, work histories of respondents were investigated using administrative information. The record linkage with social security archives allowed the reassembling of the complete work history of 487 participants. In detail, administrative files allow the retrieval of 98 % of workers declaring not to be exposed, versus 77 % using the questionnaire. Furthermore, the percentage of retrieved cases is not relevant for high risk sectors but it is almost double for industries with probable presence of asbestos. The combined and integrated use of structured questionnaire with administrative data proved effective in accurately identifying subjects who actually had an asbestos exposure. This innovative strategy, being cost-effective and easily adaptable to other carcinogens, could be particularly useful in selecting subjects to recruit in specific screening and control programs for the early diagnosis of occupational cancers.

Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe.

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached.

External validation of radiomics-based predictive models in low-dose CT screening for early lung cancer diagnosis.

PURPOSE: Low-dose CT screening allows early lung cancer detection, but is affected by frequent false positive results, inter/intra observer variation and uncertain diagnoses of lung nodules. Radiomics-based models have recently been introduced to overcome these issues, but limitations in demonstrating their generalizability on independent datasets are slowing their introduction to clinic. The aim of this study is to evaluate two radiomics-based models to classify malignant pulmonary nodules in low-dose CT screening, and to externally validate them on an independent cohort. The effect of a radiomics features harmonization technique is also investigated to evaluate its impact on the classification of lung nodules from a multicenter data. METHODS: Pulmonary nodules from two independent cohorts were considered in this study; the first cohort (110 subjects, 113 nodules) was used to train prediction models, and the second cohort (72 nodules) to externally validate them. Literature-based radiomics features were extracted and, after feature selection, used as predictive variables in models for malignancy identification. An in-house prediction model based on artificial neural network (ANN) was implemented and evaluated, along with an alternative model from the literature, based on a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO). External validation was performed on the second cohort to evaluate models' generalization ability. Additionally, the impact of the Combat harmonization method was investigated to compensate for multicenter datasets variabilities. A new training of the models based on harmonized features was performed on the first cohort, then tested separately on the harmonized and non-harmonized features of the second cohort. RESULTS: Preliminary results showed a good accuracy of the investigated models in distinguishing benign from malignant pulmonary nodules with both sets of radiomics features (i.e., non-harmonized and harmonized). The performance of the models, quantified in terms of Area Under the Curve (AUC), was > 0.89 in the training set and > 0.82 in the external validation set for all the investigated scenarios, outperforming the clinical standard (AUC of 0.76). Slightly higher performance was observed for the SVM-LASSO model than the ANN in the external dataset, although they did not result significantly different. For both harmonized and non-harmonized features, no statistical difference was found between Receiver operating characteristic (ROC) curves related to training and test set for both models. CONCLUSIONS: Although no significant improvements were observed when applying the Combat harmonization method, both in-house and literature-based models were able to classify lung nodules with good generalization to an independent dataset, thus showing their potential as tools for clinical decision-making in lung cancer screening.

Results of Multilevel Containment Measures to Better Protect Lung Cancer Patients From COVID-19: The IEO Model.

A novel coronavirus causing severe acute respiratory syndrome (SARS), named SARS-CoV-2, was identified at the end of 2019. The spread of coronavirus disease 2019 (COVID-19) has progressively expanded from China, involving several countries throughout the world, leading to the classification of the disease as a pandemic by the World Health Organization (WHO). According to published reports, COVID-19 severity and mortality are higher in elderly patients and those with active comorbidities. In particular, lung cancer patients were reported to be at high risk of pulmonary complications related to SARS-CoV2 infection. Therefore, the management of cancer care during the COVID-19 pandemic is a crucial issue, to which national and international oncology organizations have replied with recommendations concerning patients receiving anticancer treatments, delaying follow-up visits and limiting caregiver admission to the hospitals. In this historical moment, medical oncologists are required to consider the possibility to delay active treatment administration based on a case-by-case risk/benefit evaluation. Potential risks associated with COVID-19 infection should be considered, considering tumor histology and natural course, disease setting, clinical conditions, and disease burden, together with the expected benefit, toxicities (e.g., myelosuppression or interstitial lung disease), and response obtained from the planned or ongoing treatment. In this study, we report the results of proactive measures including social media, telemedicine, and telephone triage for screening patients with lung cancer during the COVID-19 outbreak in the European Institute of Oncology (Milan, Italy). Proactive management and containment measures, applied in a structured and daily way, has significantly aided the identification of advance patients with suspected symptoms related to COVID-19, limiting their admission to our cancer center; we have thus been more able to protect other patients from possible contamination and at the same time guarantee to the suspected patients the immediate treatment and evaluation in referral hospitals for COVID-19.

SARS-CoV-2 detection in formalin-fixed paraffin-embedded tissue specimens from surgical resection of tongue squamous cell carcinoma.

In the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, pathologists can be exposed to infection handling surgical specimens. Guidelines related to safety procedures in the laboratory have been released. However, there is a lack of studies performed on biopsy and surgical resection specimens. Here we report the detection of SARS-CoV-2 in formalin-fixed paraffin-embedded samples from surgical resection of tongue squamous cell carcinoma of a patient who developed COVID-19 postsurgery. RNA of SARS-CoV-2 strain was detected in the tumour and the normal submandibular gland samples using real-time PCR-based assay. No viral RNA was found in metastatic and reactive lymph nodes. We demonstrated that SARS-CoV-2 RNA can be detected in routine histopathological samples even before COVID-19 disease development. These findings may give important information on the possible sites of infection or virus reservoir, and highlight the necessity of proper handling and fixation before sample processing.

Low-Dose Aspirin in High-Risk Individuals With Screen-Detected Subsolid Lung Nodules: A Randomized Phase II Trial.

Lung cancer screening by helical low-dose computed tomography detects nonsolid nodules that may be lung adenocarcinoma precursors. Aspirin's anti-inflammatory properties make it an attractive target for prevention of multiple cancers, including lung cancer. Therefore, we conducted a phase IIb trial (NCT02169271) to study the efficacy of low-dose aspirin to reduce the size of subsolid lung nodules (SSNs). A total of 98 current or former smokers (67.3% current) undergoing annual low-dose computed tomography screening with persistent SSNs were randomly assigned to receive aspirin 100 mg/day or placebo for 1 year. There was no difference in change in the sum of the longest diameters of target nodules in the placebo and aspirin arm after 12 months of treatment (-0.12 mm [SD = 1.55 mm] and +0.30 mm [SD= 2.54 mm], respectively; 2-sided P = .33 primary endpoint). There were no changes observed in subgroup analyses by individual characteristics or nodule type. One year of low-dose aspirin did not show any effect on lung SSNs. SSNs regression may not be the proper target for aspirin, and/or longer duration may be needed to see SSNs modifications.