British Thoracic Society guidelines for the investigation and management of pulmonary nodules

Risk assessment of persistent incidental pulmonary subsolid nodules to guide appropriate surveillance interval and endpoints

Guidelines for the follow-up of pulmonary subsolid nodule (SSN) vary in terms of frequency and criteria for discontinuation. We aimed to evaluate the growth risk of SSNs and define appropriate follow-up intervals and endpoints. The immediate risk (IR) and cumulative risk (CR) of SSN growth were assessed using the Kaplan-Meier method according to nodule consistency and size. Follow-up plans were proposed based on optimal growth risk threshold of 5%. 892 SSNs, comprising 833 pure ground-glass nodules (pGGNs) and 59 part-solid nodules (PSNs) were included. For pGGNs ≤ 6.6 mm, the CR exceeded 5% at every 3-year interval in the first 9 years. For pGGNs measuring 6.6-8.8 mm and >8.8 mm, the IR remained above 5% for the first 2-7 years, and the 2-year CR for pGGNs measuring 6.6-8.8 mm in the 8th and 9th years achieved 6.66%. For PSNs, the IR peaked in the 4th year (44%) and then declined. Therefore, triennial follow-up for 9 years is recommended for pGGNs ≤ 6.6 mm, annual follow-up for 7 years followed by biennial follow-up for 2 years for pGGNs measuring 6.6-8.8 mm, annual follow-up for 7 years for pGGNs > 8.8 mm, and continuous annual follow-up until nodule growth for PSNs.

Evaluation of the safety of short-term follow-up CT for the management of consolidation in lung cancer screening

OBJECTIVES

Focal consolidation on CT may be inflammatory or malignant, and PET-CT imaging is rarely discriminatory. Furthermore, consolidation may demonstrate spontaneous resolution obviating the need for PET-CT imaging. This retrospective study sought to assess the safety and cost-effectiveness of short-interval 6-week follow-up CT for consolidation in a lung cancer screening programme.

METHODS

Between January 2019 and January 2024, participants in a regional lung cancer screening programme with focal indeterminate consolidation underwent a 6-week repeat CT rather than immediate PET-CT and invasive investigation. The proportion of participants with non-resolving consolidation, the risk of malignancy in consolidation at a 6-week follow-up, and the risk of upstaging over a 6-week delay were determined. Cost savings were estimated from National Health Service reference costs.

RESULTS

In 10,247 CT studies, focal indeterminate consolidation was detected in 113 participants (1.1%) (mean age 68 years, range 55-76, 65 males). Consolidation spontaneously resolved at 6 weeks in 63/110 (57%) who attended follow-up; 14/110 (12.7%) participants had malignancy; no patients upstaged during follow-up. An estimated cost saving of £47,600/10,000 screening CTs performed might be obtained through a conservative approach of short-term interval CT, rather than immediate PET-CT and further investigation.

CONCLUSION

Early repeat CT avoids PET-CT in more than half of patients with consolidation and can be utilised to reduce over-investigation of screen-detected consolidation, which may demonstrate spontaneous resolution.

KEY POINTS

Question Is short-term interval follow-up CT in lung cancer screening a safe and cost-effective approach to managing indeterminate (inflammatory or malignant) consolidation? Findings Short-interval CT imaging demonstrates spontaneous resolution of consolidation in over 50% participants in this study, whilst persistent consolidation has a high likelihood of malignancy. Clinical relevance Short-interval CT did not result in upstaging of malignancy and therefore can be considered a safe strategy to prevent the over-investigation of screen-detected consolidation, supporting recent European and American screening recommendations.

Action plan for an international imaging framework for implementation of global low-dose CT screening for lung cancer

Reduction in lung cancer mortality is achievable through low dose computed tomography (LDCT) screening in high-risk individuals. Many countries are progressing from local LDCT screening studies to national screening programs. Implementation of effective large-scale screening programs is complex and requires a multi-disciplinary approach. A recent overview of the technical aspects of implementing high quality LDCT for screening resulted from the inaugural international expert meeting of the Alliance for Global Implementation of Lung and Cardiac Early Disease Detection and Treatment (AGILE). This covers the most important aspects of the CT imaging process: standardisation in CT image acquisition and interpretation, CT protocol management, technology developments and minimal requirements, integration of lung cancer biomarkers, and the role of AI in CT lung nodule detection, segmentation, and classification, and related data security issues.

Development of a Pulmonary Nodule Service and Clinical Pathway: A Pragmatic Approach Addressing an Unmet Need

Background/Objectives: The surveillance of patients with incidental pulmonary nodules overloads existing respiratory and lung cancer clinics, as well as multidisciplinary team meetings. In our clinical setting, until 2018, we had numerous patients with incidental pulmonary nodules inundating our outpatient clinics; therefore, the need to develop a novel service and dedicated clinical pathway arose. The aims of this study are to 1. provide (a) a model of setting up a novel pulmonary nodule service, and (b) a pragmatic clinical pathway to address the increasing need for surveillance of patients with incidental pulmonary nodules. 2. share real-world data from a dedicated pulmonary nodule service running in a tertiary setting with existing resources. Methods: A retrospective review of established processes and referral mechanisms to our tertiary pulmonary nodule service was conducted. We have also performed a retrospective collection and review of data for patients reviewed and discussed in our tertiary pulmonary nodule service between April 2018 and April 2024. Results: Our tertiary pulmonary nodule service (PNS) comprises a dedicated pulmonary nodule clinic, a nodule multidisciplinary team (MDT) meeting and a dedicated proforma referral system. Due to the current national health system legislation and relevant processes, patients are required to physically attend clinic appointments. There are various sources of referral, including other departments within the hospital, other hospitals, various specialties in primary care and self-referrals. Between 15 April 2018 and 15 April 2024, 2203 patients were reviewed in the pulmonary nodule clinic (903 females, 1300 males, mean age 64 ± 19 years). Of those patients, 65% (1432/2203) were current smokers. A total of 1365 new patients and 838 follow-up patients were reviewed in total. Emphysema was radiologically present in 72% of patients, and 75% of those (1189/1586) already had a confirmed diagnosis of chronic obstructive pulmonary disease (COPD). Coronary calcification was identified in 32% (705/2203), and 78% of those (550/705) were already known to cardiology services. Interestingly, 27% (368/1365) of the new patients were discharged following their first MDT meeting discussion, and 67% of these were discharged as the reason for their referral was an intrapulmonary lymph node which did not warrant any further action. Among all patients, 11% (246/2203) were referred to the multidisciplinary thoracic oncology service (MTOS) due to suspicious appearances/changes in their nodules that warranted further investigation, and from those, 37% were discharged (92/246) from the MTOS. The lung cancer diagnosis rate was 7% (154/2203). Conclusions: The applied pathway offers a pragmatic approach in setting up a service that addresses an increasing patient need. Its application is feasible in a tertiary care setting, and admin support is of vital importance to ensure patients are appropriately tracked and not lost to follow-up. Real-world data from pulmonary nodules services provide a clear overview and contribute to understanding patients' characteristics and improving service provision.

Kirsten Rat Sarcoma Virus Mutations Effect On Tumor Doubling Time And Prognosis Of Solid Dominant Stage I Lung Adenocarcinoma

INTRODUCTION

To analyze the impact of Kirsten-Rat-Sarcoma Virus (KRAS) mutations on tumor-growth as estimated by tumor-doubling-time (TDT) among solid-dominant clinical-stage I lung adenocarcinoma. Moreover, to evaluate the prognostic role of KRAS mutations, TDT and their combination in completely-resected pathologic-stage I adenocarcinomas.

METHODS

In this single-center retrospective analysis, completely resected clinical-stage I adenocarcinomas presenting as solid-dominant nodules (consolidation-to-tumor ratio > 0.5) in at least 2 preoperative computed-tomography scans were enrolled. Nodules' growth was scored as fast (TDT < 400 days) or slow (TDT > 400 days). KRAS-mutated adenocarcinomas were identified with next-generation sequencing. Logistic- and Cox-regressions were used to identify predictors of fast-growth and disease-free survival (DFS), respectively.

RESULTS

Among 151 patients, 83 (55%) had fast-growing nodules and 64 (42.4%) were KRAS-mutated. Fast-growing nodules outnumbered in the KRAS-mutated group (n = 45; 70.3%), median TDT 95-days (interquartile range, IQR 43.5-151.5) compared to the KRAS wild-type group (38, 43.7%), median TDT 138-days (IQR 70.3-278.5). KRAS-mutations predicted faster-growth at multivariable analysis (P = .009). In a subgroup analysis including 108 pathologic-stage I adenocarcinomas, neither KRAS-mutations (P = .081) nor fast-growing pattern (P = .146) affected DFS. Nevertheless, the association of KRAS-mutations and fast-growing pattern identified a subgroup of patients with worse DFS (P = .02). The combination of fast-growing and KRAS-mutations (hazard-ratio 2.97 [95%CI 1.22-7.25]; P = .017) and average nodule diameter at diagnosis (hazard-ratio 1.08 [95%CI 1.03-1.14]; P = .004) were independent predictors of worse DFS.

CONCLUSION

KRAS mutations are associated to faster growth, in clinical-stage I adenocarcinoma presenting at diagnosis as solid-dominant nodules undergoing complete resection. Moreover, faster-growth identifies a subgroup of pathologic-stage I KRAS-mutated adenocarcinomas with higher recurrences.

Natural history models for lung Cancer: A scoping review

INTRODUCTION

Natural history models (NHMs) of lung cancer (LC) simulate the disease's natural progression providing a baseline for assessing the impact of interventions. NHMs have been increasingly used to inform public health policies, highlighting their utility. The objective of this scoping review was to summarize existing LC NHMs, identify their limitations, and propose a framework for future NHM development.

METHODS

We searched MEDLINE, Embase, Web of Science, and IEEE Xplore from their inception to October 5, 2023, for peer-reviewed, full-length articles with an LC NHM. Model characteristics, their applications, data sources used, and limitations were extracted and narratively synthesized.

RESULTS

From 238 publications, 69 publications were included in our review, corresponding to 22 original LC NHMs and 47 model applications. The majority of the models (n = 15, 68 %) used a microsimulation approach. NHM parameters were predominately informed by cancer registries, trial and institutional data, and literature. Model quality and performance were evaluated in 8 (36 %) models. Twenty (91 %) models included at least one carcinogenesis risk factor-primarily age, sex, and smoking history. Three (14 %) LC NHMs modeled progression in never-smokers; one (5 %) addressed recurrence. Non-tobacco smoking, nodule type, and biomarker expression were not considered in existing NHMs. Based on our findings, we proposed a framework for future LC NHM development which incorporates recurrence, nodule type differentiation, biomarker expression levels, biological factors, and non-smoking-related risk factors.

CONCLUSION

Regular updating and future research are warranted to address limitations in existing NHMs thereby ensuring relevance and accuracy of modeling approaches in the evolving LC landscape.

Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation

Background

Lung cancer is one of the most common types of cancer and the leading cause of cancer death in the United Kingdom. Artificial intelligence-based software has been developed to reduce the number of missed or misdiagnosed lung nodules on computed tomography images.

Objective

 To assess the accuracy, clinical effectiveness and cost-effectiveness of using  software with artificial intelligence-derived algorithms to assist in the detection and analysis of lung nodules in computed tomography scans of the chest compared with unassisted reading.

Design

Systematic review and de novo cost-effectiveness analysis.

Methods

Searches were undertaken from 2012 to January 2022. Company submissions were accepted until 31 August 2022. Study quality was assessed using the revised tool for the quality assessment of diagnostic accuracy studies (QUADAS-2), the extension to QUADAS-2 for assessing risk of bias in comparative accuracy studies (QUADAS-C) and the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) checklist. Outcomes were synthesised narratively. Two decision trees were used for cost-effectiveness: (1) a simple decision tree for the detection of actionable nodules and (2) a decision tree reflecting the full clinical pathways for people undergoing chest computed tomography scans. Models estimated incremental cost-effectiveness ratios, cost per correct detection of an actionable nodule, and cost per cancer detected and treated. We undertook scenario and sensitivity analyses.

Results

Twenty-seven studies were included. All were rated as being at high risk of bias. Twenty-four of the included studies used retrospective data sets. Seventeen compared readers with and without artificial intelligence software. One reported prospective screening experiences before and after artificial intelligence software implementation. The remaining studies either evaluated stand-alone artificial intelligence or provided only non-comparative evidence. (1) Artificial intelligence assistance generally improved the detection of any nodules compared with unaided reading (three studies; average per-person sensitivity 0.43-0.68 for unaided and 0.79-0.99 for artificial intelligence-assisted reading), with similar or lower specificity (three studies; 0.77-1.00 for unaided and 0.81-0.97 for artificial intelligence-assisted reading). Nodule diameters were similar or significantly larger with semiautomatic measurements than with manual measurements. Intra-reader and inter-reader agreement in nodule size measurement and in risk classification generally improved with artificial intelligence assistance or were comparable to those with unaided reading. However, the effect on measurement accuracy is unclear. (2) Radiologist reading time generally decreased with artificial intelligence assistance in research settings. (3) Artificial intelligence assistance tended to increase allocated risk categories as defined by clinical guidelines. (4) No relevant clinical effectiveness and cost-effectiveness studies were identified. (5) The de novo cost-effectiveness analysis suggested that for symptomatic and incidental populations, artificial intelligence-assisted computed tomography image analysis dominated the unaided radiologist in cost per correct detection of an actionable nodule. However, when relevant costs and quality-adjusted life-years from the full clinical pathway were included, artificial intelligence-assisted computed tomography reading was dominated by the unaided reader. For screening, artificial intelligence-assisted computed tomography image analysis was cost-effective in the base case and all sensitivity and scenario analyses.

Limitations

Due to the heterogeneity, sparseness, low quality and low applicability of the clinical effectiveness evidence and the major challenges in linking test accuracy evidence to clinical and economic outcomes, the findings presented here are highly uncertain and provide indicators/frameworks for future assessment.

Conclusions

Artificial intelligence-assisted analysis of computed tomography scan images may reduce variability of and improve consistency in the measurement and clinical management of lung nodules. Artificial intelligence may increase nodule and cancer detection but may also increase the number of patients undergoing computed tomography surveillance unnecessarily. No direct comparative evidence was found, and nor was any direct evidence found on clinical outcomes and cost-effectiveness. Artificial intelligence-assisted image analysis may be cost-effective in screening for lung cancer but not for symptomatic populations. However, reliable estimates of cost-effectiveness cannot be obtained with current evidence.

Study registration

This study is registered as PROSPERO CRD42021298449.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135325) and is published in full in Health Technology Assessment; Vol. 29, No. 14. See the NIHR Funding and Awards website for further award information.

Low-dose CT for lung cancer screening in a high-risk population (SUMMIT): a prospective, longitudinal cohort study

BACKGROUND

Low-dose CT screening reduces lung cancer mortality. In advance of planned national lung cancer screening programmes, research is needed to inform policies regarding implementation. We aimed to assess the implementation of low-dose CT for lung cancer screening in a high-risk population and to validate a multicancer early detection blood test.

METHODS

In this prospective, longitudinal cohort study, individuals aged 55-77 years recorded as current smokers in their primary care records at any point within the past 20 years were identified from 329 primary care practices in London (UK) and invited for a lung health check via postal letter. Individuals meeting the 2013 United States Preventive Services Taskforce criteria (current or former smokers within the past 15 years with at least 30 pack-year smoking histories) or having a Prostate, Lung, Colorectal and Ovarian 2012 model 6-year risk of 1·3% or greater, and not currently receiving treatment for an active cancer (except adjuvant hormonal therapy), were eligible for the study. These individuals underwent lung cancer screening via non-contrast, thin collimation low-dose CT. In this analysis, we report the results of the baseline round of low-dose CT screening. Key primary endpoints were those associated with examining the performance of a lung cancer screening service. Outcome measures were analysed on a per-participant level using descriptive frequencies. The study was registered with ClinicalTrials.gov, NCT03934866.

FINDINGS

Between April 8, 2019, and May 14, 2021, 12 773 participants were recruited and analysed. 7353 (57·6%) of 12 773 participants were male and 5420 (42·4%) were female, and 10 665 (83·5%) participants were White. 261 (2·0%) of 12 773 participants were diagnosed with lung cancer (including 163 [1·3%] participants with screen-detected lung cancer and 98 [0·8%] with delayed screen-detected lung cancer [ie, after a 3-month or 6-month nodule follow-up CT]) and 276 (2·2%) participants were diagnosed with any intrathoracic malignancy after a positive baseline screen. 207 (79·3%) of 261 individuals with prevalent screen-detected lung cancer were diagnosed at stage I or II and surgical resection was the primary treatment modality in 201 (77·0%) of 261 individuals. Including cases where multiple resections were done in the same participant (eg, for synchronous primaries), 28 (11·6%) of 241 surgical resections were benign, and there was one (0·4%) death within 90 days of surgery. At 12 months, the episode sensitivity of our low-dose CT screening protocol for detecting lung cancer was 97·0% (95% CI 95·0-99·1; 261 of 269 participants). The specificity was 95·2% (94·8-95·6; 11 905 of 12 504 participants), with a false-positive rate of 4·8% (4·4-5·2).

INTERPRETATION

Large-scale lung cancer screening is effective and can be delivered efficiently to an ethnically and socioeconomically diverse population.

FUNDING

GRAIL.

Lung Cancer Risk Prediction in Patients with Persistent Pulmonary Nodules Using the Brock Model and Sybil Model

BACKGROUND/OBJECTIVES

Persistent pulmonary nodules are at higher risk of developing into lung cancers. Assessing their future cancer risk is essential for successful interception. We evaluated the performance of two risk prediction models for persistent nodules in hospital-based cohorts: the Brock model, based on clinical and radiological characteristics, and the Sybil model, a novel deep learning model for lung cancer risk prediction.

METHODS

Patients with persistent pulmonary nodules-defined as nodules detected on at least two computed tomography (CT) scans, three months apart, without evidence of shrinkage-were included in the retrospective (n = 130) and prospective (n = 301) cohorts. We analyzed the correlations between demographic factors, nodule characteristics, and Brock scores and assessed the performance of both models. We also built machine learning models to refine the risk assessment for our cohort.

RESULTS

In the retrospective cohort, Brock scores ranged from 0% to 85.82%. In the prospective cohort, 62 of 301 patients were diagnosed with lung cancer, displaying higher median Brock scores than those without lung cancer diagnosis (18.65% vs. 4.95%, p < 0.001). Family history, nodule size ≥10 mm, part-solid nodule types, and spiculation were associated with the risks of lung cancer. The Brock model had an AUC of 0.679, and Sybil's AUC was 0.678. We tested five machine learning models, and the logistic regression model achieved the highest AUC at 0.729.

CONCLUSIONS

For patients with persistent pulmonary nodules in real-world cancer hospital-based cohorts, both the Brock and Sybil models had values and limitations for lung cancer risk prediction. Optimizing predictive models in this population is crucial for improving early lung cancer detection and interception.

Intraoperative Lung Ultrasound in the Detection of Pulmonary Nodules: A Valuable Tool in Thoracic Surgery

In the last two decades, there has been an increased interest in the application of lung ultrasound (LUS), especially intraoperatively, owing to its safety and simple approach to detecting and assessing pulmonary nodules. This review focuses on recent advancements in intraoperative lung ultrasound in detecting lung nodules. A systematic search was conducted using databases such as PubMed and Google Scholar. Keywords included "Lung ultrasound", "intraoperative lung ultrasound", and "video-assisted transthoracic surgery (VATS)". Articles published between 1963 and 2024 in peer-reviewed journals were included, focusing on the ones from the 2000s. Data on methodology, key findings, and research gaps were reviewed. Results indicated a significant advantage of intraoperative lung ultrasound (ILU) in the assessment of pulmonary nodules. ILU offers a noninvasive, real-time imaging modality that demonstrates up to 100% accuracy in detecting pulmonary nodules, with shorter time needed compared to other modalities, as well as less intraoperative periods and postoperative complications. However, some disadvantages were detected, such as operator dependency and a lack of specificity and knowledge of specific signs, as well as assisted localization via percutaneous puncture and its correct interpretation. The findings suggest that ILU has a promising future in pulmonary surgeries such as LUS-VATS but needs to be engaged more in clinical applications and modified with new techniques such as artificial intelligence (AI).

A comparison of an integrated and image-only deep learning model for predicting the disappearance of indeterminate pulmonary nodules

BACKGROUND

Indeterminate pulmonary nodules (IPNs) require follow-up CT to assess potential growth; however, benign nodules may disappear. Accurately predicting whether IPNs will resolve is a challenge for radiologists. Therefore, we aim to utilize deep-learning (DL) methods to predict the disappearance of IPNs.

MATERIAL AND METHODS

This retrospective study utilized data from the Dutch-Belgian Randomized Lung Cancer Screening Trial (NELSON) and Imaging in Lifelines (ImaLife) cohort. Participants underwent follow-up CT to determine the evolution of baseline IPNs. The NELSON data was used for model training. External validation was performed in ImaLife. We developed integrated DL-based models that incorporated CT images and demographic data (age, sex, smoking status, and pack years). We compared the performance of integrated methods with those limited to CT images only and calculated sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). From a clinical perspective, ensuring high specificity is critical, as it minimizes false predictions of non-resolving nodules that should be monitored for evolution on follow-up CTs. Feature importance was calculated using SHapley Additive exPlanations (SHAP) values.

RESULTS

The training dataset included 840 IPNs (134 resolving) in 672 participants. The external validation dataset included 111 IPNs (46 resolving) in 65 participants. On the external validation set, the performance of the integrated model (sensitivity, 0.50; 95 % CI, 0.35-0.65; specificity, 0.91; 95 % CI, 0.80-0.96; AUC, 0.82; 95 % CI, 0.74-0.90) was comparable to that solely trained on CT image (sensitivity, 0.41; 95 % CI, 0.27-0.57; specificity, 0.89; 95 % CI, 0.78-0.95; AUC, 0.78; 95 % CI, 0.69-0.86; P = 0.39). The top 10 most important features were all image related.

CONCLUSION

Deep learning-based models can predict the disappearance of IPNs with high specificity. Integrated models using CT scans and clinical data had comparable performance to those using only CT images.

Multidisciplinary expert consensus on diagnosis and treatment of multiple lung cancers

The rising incidence of multiple lung cancers (MLCs), encompassing multiple primary lung cancers (MPLCs) and intrapulmonary metastasis (IPM), poses two significant clinical challenges. First, distinguishing between MPLC and IPM remains difficult due to insufficiently accurate criteria and ambiguous integration of genetic testing. Second, standardized therapeutic protocols are still lacking. To address these issues, the Lung Cancer Expert Committee of China Anti-Cancer Association (CACA) assembled a multidisciplinary expert panel spanning thoracic surgery, pulmonary medicine, oncology, radiology, and pathology. Following a comprehensive literature review ending on October 23, 2024, the panel engaged in iterative discussions and conducted two rounds of expert voting, culminating in 25 evidence-based recommendations across five key domains: epidemiology, pre-treatment evaluation, definitive diagnostics, surgical treatment, and non-surgical treatment. This consensus provides clinicians with practical guidance to enhance diagnostic precision and therapeutic decision-making in MLC management while highlighting unmet needs to inform future guideline development.

Prognostic factors of survival in patients with lung cancer after low-dose computed tomography screening: a multivariate analysis of a lung cancer screening cohort in China

OBJECTIVE

This study aimed to evaluate the prognostic factors influencing the survival of patients with lung cancer identified from a lung cancer screening cohort in the community.

METHODS

A total of 25,310 eligible participants were enrolled in this population-based prospective cohort study, derived from a community lung cancer screening program started from 2013 to 2017. Survival analyses were conducted using the Kaplan-Meier method and the log-rank test. Cox proportional hazards regression models were utilized to identify prognostic factors, including demographic characteristics, risk factors, low-dose CT (LDCT) screening, and treatment information.

RESULTS

The screening cohort identified a total of 429 patients with lung cancer (276 men, 153 women) during the study period. The 1-year, 3-year, and 5-year survival rates were 74.4%, 59.4% and 54.5%, respectively. The prognostic factors discovered by the multivariate analysis include gender (male vs. female, HR: 2.96, 95% CI: 1.88-4.64), age (HR: 1.02, 95% CI: 1.00-1.05), personal monthly income (2000-3999 CNY vs. < 2000 CNY, HR: 0.70, 95% CI: 0.52-0.95), pathological type (small cell carcinoma vs. adenocarcinoma, HR: 2.55, 95% CI: 1.39-4.66), stage (IV vs. 0-I, HR: 5.21, 95% CI: 2.78-9.75; III vs. 0-I, HR: 3.81, 95% CI: 1.88-7.74), surgery (yes vs. no, HR: 0.36, 95% CI: 0.23-0.57), and KPS (HR: 0.98, 95% CI: 0.98-0.99) among lung cancer patients identified by the basic model. Furthermore, solid nodule (non-solid nodule vs. solid nodule, HR: 0.47, 95% CI: 0.23-0.96) and larger-sized nodule (HR: 1.02, 95% CI: 1.00-1.03) were associated with a worse prognosis for lung cancer in the LDCT screening model.

CONCLUSION

Prognostic factors of patients with lung cancer detected by LDCT screening were identified, which could potentially guide clinicians in the decision-making process for lung cancer management and treatment. Further studies with larger sample sizes and more detailed follow-up data are warranted for prognostic prediction.

Molecular characterization of early-stage lung adenocarcinoma presenting as subsolid nodules in a real-life European cohort

OBJECTIVES

Subsolid nodules emerged as frequent radiological variants of lung adenocarcinoma. Radiological features including solid-component prevalence and larger tumour dimensions prompt tumoral invasiveness guiding prognosis and management. Thus, we aimed to clarify the molecular grounds that dictate these radiological appearances and clinical behaviour in a real-life European-cohort. Additionally, following the growing interest toward targeted-therapies in early-stage diseases, we aimed to present real-life epidemiological data of actionable mutations in these patients.

METHODS

In this retrospective single-centre study, targeted next-generation sequencing was performed continuatively in all the resected subsolid lung adenocarcinomas in the period between May 2016 and December 2023. Clinico-radiological data were collected. The genetic landscape of our real-life European subsolid adenocarcinoma population is defined. Common and actionable mutations (frequency > 5%) relation to key clinico-radiological features are evaluated.

RESULTS

Overall, 156 subsolid adenocarcinomas were analysed. KRAS-mutations, mostly KRAS p.G12C, were the most prevalent followed by EGFR, including 25% uncommon EGFR-mutations, TP53 and MET mutations. Amongst the clinico-radiological variables, KRAS-mutations and KRAS p.G12C-mutation were associated to smoking history (≥ 20 pack/years), aggressive histologic subtype and higher consolidation-to-tumor ratio (CTR). Moreover, KRAS-mutated nodules had faster tumour-doubling-time. Conversely, EGFR-mutations were associated to female sex and lower CTR. The latter not being confirmed in common EGFR-mutations. Additionally, in common EGFR-mutated nodules, aggressive histological components were rarer.

CONCLUSION

Our study presents the molecular profile of subsolid lung adenocarcinoma in a real-life European-cohort. KRAS-mutations were the most prevalent, and were related to smoking history, higher CTR and faster growth. Conversely, common EGFR-mutations were rarer than expected and unrelated to smoking history and radiological features.

Prospective Evaluation of Lung Cancer Screening Eligibility Criteria and Lung Cancer Detection in the Yorkshire Lung Screening Trial

INTRODUCTION

Low-dose computed tomography screening for lung cancer reduces lung cancer mortality, but there is a lack of international consensus regarding the optimal eligibility criteria for screening. The Yorkshire Lung Screening Trial was designed to evaluate lung cancer screening (LCS) implementation, and a primary objective was prospective evaluation of three predefined eligibility criteria.

METHODS

Individuals who had ever smoked, aged 55 to 80 years, who responded to written invitation, underwent telephone risk assessment and if eligible by at least one criterion (PLCOM2012 ≥ 1.51%, LLPv2 ≥ 5%, USPSTF2013) were offered biennial low-dose computed tomography screening.

RESULTS

Of 44,957 individuals invited, 22,814 responded and underwent eligibility assessment, of whom a total of 7826 were eligible according to any of the three LCS criteria. Comparing PLCOM2012 ≥ 1.51%, LLPv2 ≥ 5%, and USPSTF2013, the proportions of responders eligible for screening were 28.0%, 20.5%, and 18.9%, respectively (p < 0.0001 for each comparison), and the proportion of all cancers detected 91.1%, 77.0%, and 62.8%, respectively (p ≤ 0.0002 for each comparison). When risk thresholds were selected to result in equivalent numbers of people eligible for screening, cancer detection proportions were higher for PLCOM2012 (74.5%) and LLPv2 (71.3%) than USPSTF2013 (62.8%) (p = 0.0002 and p = 0.032, respectively), but there was no significant difference between the two risk models. Reducing the LLPv2 risk threshold from 5% to 2.5% (as currently used in the English LCS program) and reducing the pack-year requirement for the USPSTF2021 versus the USPSTF2013 criteria increased the numbers eligible for screening, but subsequent cancer yield was not measured in this study.

CONCLUSION

The PLCOM2012 ≥ 1.51% criteria identified more people eligible for screening in Yorkshire Lung Screening Trial and resulted in more screen-detected lung cancers than LLPv2 ≥ 5% or USPSTF2013. When compared in equivalent populations, there was no significant difference between risk models in terms of lung cancer detection and each appeared more efficient at screening population selection than USPSTF2013.

Impact of surgery versus follow-up on psychological distress in patients with indeterminate pulmonary nodules: A prospective observational study

PURPOSE

To investigate whether surgery is more effective than follow-up in reducing psychological distress for patients with observable indeterminate pulmonary nodules (IPNs) and to assess if psychological distress can serve as a potential surgical indication for IPNs.

METHODS

This prospective observational study included 341 patients with abnormal psychometric results, as measured by the Hospital Anxiety and Depression Scale (HADS). Of these, 262 patients opted for follow-up and 79 chose surgery. Initial psychological assessments (HADS1) were conducted at enrollment following nodule detection, with a second assessment (HADS2) one year later. A comparative analysis of dynamic psychological changes (ΔHADS: HADS2-HADS1) between the follow-up and surgical groups was performed.

RESULTS

Both groups showed reductions in HADS-A [- 3 (IQR, - 7 to - 1) for follow-up and - 3 (IQR, - 6 to - 1) for surgery] and HADS-D scores [- 2 (IQR, - 4 to 0) for follow-up and - 3 (IQR, - 7 to 0) for surgery]. Univariate analysis revealed that the surgical group had a significantly greater reduction in HADS-D scores compared to the follow-up group (Z = - 2.08, P = 0.037), but there were no significant differences in the changes in HADS-A scores between the groups (Z = - 1.04, P = 0.300). However, in multivariable analysis, surgery did not significantly improve the alleviation of depressive symptoms compared to follow-up (β = - 0.72, 95% CI: - 1.57 to 0.14, P = 0.101). Within the surgical group, female patients reported less relief from anxiety than male patients (Z = - 2.32, P = 0.021), and symptomatic patients experienced less relief from both anxiety (Z = - 2.14, P = 0.032) and depression (Z = - 3.01, P = 0.003).

CONCLUSIONS

Surgery does not provide additional psychological benefits over follow-up. This study does not support using psychological distress as a criterion for surgical intervention in IPNs from a psychological perspective. Trial registry ClinicalTrials.gov (NCT04857333).

Growth Prediction of Ground-Glass Nodules Based on Pulmonary Vascular Morphology Nomogram

RATIONALE AND OBJECTIVES

To construct a nomogram combining conventional CT features (CCTFs), morphologically abnormal tumor-related vessels (MATRVs), and clinical features to predict the two-year growth of lung ground-glass nodule (GGN).

METHODS

High-resolution CT targeted scan images of 158 patients including 167 GGNs from January 2016 to September 2019 were retrospectively analyzed. The CCTF and MATRV of each GGN were recorded. All GGNs were randomly divided into a training set (n = 118) and a validation set (n = 49). Multiple stepwise regression was used to select the features. Multivariate logistic regression was used to construct the CCTF, CCTF-CTRV (category of tumor-related vessel), and CCTF-QTRV (quantity of tumor-related vessel) nomograms. The performance and utility of the nomograms were evaluated using the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA).

RESULTS

The AUC of the CCTF-QTRV nomogram, which included the features of smoking history, nodule pattern, lobulation, and the number of distorted and dilated vessels, was higher than the AUCs of the CCTF and CCTF-CTRV nomograms in both the training set (AUC: 0.906 vs. 0.857; vs. 0.851) and the validation set (AUC: 0.909 vs. 0.796; vs. 0.871). DCA indicated that both patients and clinicians could benefit from using the nomogram.

CONCLUSION

The nomogram constructed by combining MATRV, CCTF, and clinical information can more effectively predict the two-year growth of GGNs. This integrated approach enhances the predictive accuracy, making it a valuable tool for clinicians in managing and monitoring patients with GGNs.

New insights into imaging of pulmonary metastases from extra-thoracic neoplasms

The lung is one of the most common sites of metastases from extra-thoracic neoplasms. Lung metastases can show heterogeneous imaging appearance, thus mimicking a wide range of lung diseases, from benign lesions to primary lung cancer. The proper interpretation of pulmonary findings is crucial for prognostic assessment and treatment planning, even to avoid unnecessary procedures and patient anxiety. For this purpose, computed tomography (CT) is one of the most used imaging modalities. In the last decades, cancer patients' population has steadily increased and, due to the widespread application of CT for staging and surveillance, the detection of pulmonary nodules has raised, making their characterization and management an urgent and mostly unsolved problem for both radiologists and clinicians. This review will highlight the pathways of dissemination of extra-thoracic tumours to the lungs and the heterogeneous CT imaging appearance of pulmonary metastases, providing useful clues to properly address the diagnosis. Furthermore, we will deal with the promising applications of radiomics in this field. Finally, a focus on the hot-topic of pulmonary nodule management in patients with extra-thoracic neoplasms (ETNs) will be discussed.

The Growth of Screening-Detected Pure Ground-Glass Nodules Following 10 Years of Stability

BACKGROUND

It remains uncertain for how long pure ground-glass nodules (pGGNs) detected on low-dose CT (LDCT) imaging should be followed up. Further studies with longer follow-up periods are needed to determine the optimal follow-up duration for pGGNs.

RESEARCH QUESTION

What is the percentage of enlarging nodules among pGGNs that have remained stable for 10 years?

STUDY DESIGN AND METHODS

This was a retrospective cohort study originating from participants with pGGNs detected on LDCT scans between 1997 and 2006 whose natural courses were reported in 2013. We re-analyzed all the follow-up data until July 2022. The study participants were followed up per our institutional guidelines until they were no longer a candidate for definitive treatment. The growth of the pGGNs was defined as an increase in the diameter of the entire nodule by ≥ 2 mm or the appearance of new solid portions within the nodules.

RESULTS

A total of 89 patients with 135 pGGNs were followed up for a median of 193 months. Of 135 pGGNs, 23 (17.0%) increased in size, and the median time to the first detection of a size change was 71 months. Of the 135 pGGNs, 122 were detected on the first LDCT scan and 13 were newly detected on the follow-up CT scan. An increase in size was observed within 5 years in 8 nodules (34.8%), between 5 and 10 years in 12 nodules (52.2%), and after 10 years in three nodules (13.0%). Fifteen nodules were histologically confirmed as adenocarcinoma by surgery. Among the 76 pGGNs stable for 10 years, 3 (3.9%) increased in size.

INTERPRETATION

Among pGGNs that remained stable for 10 years, 3.9% eventually grew, indicating that some pGGNs can grow even following a long period of stability. We suggest that pGGNs may need to be followed up for > 10 years to confirm growth.

Relationship between size and other radiological features with malignancy in pulmonary nodules; follow-up or pathological diagnosis?

Pulmonary lesions can be detected even at a few millimeters in size, allowing for detailed assessment of their radiological features. This study aims to determine the most appropriate approach for nodules detected by computed tomography. A total of 526 patients, who underwent surgery for pulmonary nodules or masses and had pathological diagnoses, were included in the study. Demographic features, clinical history, and surgery-related data of the patients were assessed by a thoracic surgeon, whereas radiological features were evaluated by a radiologist. Of the patients, 147 were female and 379 were male. The mean age was 63 years (min 15, max 89), and the average lesion size was 22 mm (min 4, max 116). Postoperative analysis revealed 132 benign lesions (25.1%), 380 malignant (72.2%), and 14 metastases (2.7%). Among 347 patients, the nodule size was below 30 mm. Malignant nodules showed a higher median age and larger lesion size (P < .05 for both). Lesion contour, calcification, pleural tail, changes in lesion during follow-up, presence of emphysema, enlarged lymph nodes, history of malignancy, and smoking were statistically significant in determining the nature of the detected lesion. The clinical and radiological characteristics of patients can be utilized to determine the risk of malignancy in detected nodules. Even if the nodule size is small, histopathological diagnosis may be a more suitable option for high-risk patients instead of radiological follow-up.

Development of a miRNA-Based Model for Lung Cancer Detection

BACKGROUND

Lung cancer is the leading cause of cancer-related mortality globally, with late-stage diagnoses contributing to poor survival rates. While lung cancer screening with low-dose computed tomography (LDCT) has proven effective in reducing mortality among heavy smokers, its limitations, including high false-positive rates and resource intensiveness, restrict widespread use. Liquid biopsy, particularly using microRNA (miRNA) biomarkers, offers a promising adjunct to current screening strategies. This study aimed to evaluate the predictive power of a panel of serum miRNA biomarkers for lung cancer detection.

PATIENTS AND METHODS

A case-control study was conducted at two tertiary hospitals, enrolling 82 lung cancer cases and 123 controls. We performed an extensive literature review to shortlist 25 candidate miRNAs, of which 16 showed a significant two-fold increase in expression compared to the controls. Machine learning techniques, including Random Forest, K-Nearest Neighbors, Neural Networks, and Support Vector Machines, were employed to identify the top six miRNAs. We then evaluated predictive models, incorporating these biomarkers with lung nodule characteristics on LDCT.

RESULTS

A prediction model utilising six miRNA biomarkers (mir-196a, mir-1268, mir-130b, mir-1290, mir-106b and mir-1246) alone achieved area under the curve (AUC) values ranging from 0.78 to 0.86, with sensitivities of 70-78% and specificities of 73-85%. Incorporating lung nodule size significantly improved model performance, yielding AUC values between 0.96 and 0.99, with sensitivities of 92-98% and specificities of 93-98%.

CONCLUSIONS

A prediction model combining serum miRNA biomarkers and nodule size showed high predictive power for lung cancer. Integration of the prediction model into current lung cancer screening protocols may improve patient outcomes.

Microalgae-derived astaxanthin: bioactivities, biotechnological approaches and industrial technologies for its production

Microalgae are rich sources of astaxanthin well recognized for their potent bioactivities such as antioxidant, anti-cancer, and anti-inflammatory activities. Recent interests focused on the bioactivities of microalgae-derived astaxanthin on treating or preventing cancers mediated by their antioxidant and anti-inflammatory properties. This is due to the special structural configuration of microalgae-derived astaxanthin in terms of unsaturation (conjugated double bonds), stereochemical isomerism (3S,3'S optical isomer) and esterification (monoester), which display more potent bioactivities, compared with those from the other natural sources such as yeasts and higher plants, as well as synthetic astaxanthin. This review focuses on the recent advances on the bioactivities of microalgae-derived astaxanthin in association with cancers and immune diseases, with emphasis on their potential applications as natural antioxidants. Various well-developed biotechnological approaches for inducing astaxanthin production from microalgal culture, along with the proven and emerging industrial technologies to commercialize astaxanthin products in a large-scale manner, are also critically reviewed. These would facilitate the manufacture of bioactive microalgae-derived astaxanthin products to be applied in the food and pharmaceutical industries as salutary nutraceuticals.

Diagnostic performance of shape-sensing robotic-assisted bronchoscopy for pleural-based and fissure-based pulmonary lesions

BACKGROUND

Sampling of peripheral pulmonary lesions (PPLs) abutting the pleura carries a higher risk of pneumothorax and complications. Although typically performed with image-guided transthoracic biopsy, the advent of shape-sensing robotic-assisted bronchoscopy (ssRAB) provides an alternative diagnostic procedure for this subtype of lesions.

METHODS

A retrospective study on PPL attached to the peripheral pleura (PP), comprising costal and diaphragmatic pleura, mediastinal pleura (MP), and fissural pleura (FP) sampled by ssRAB, from January 2020 to December 2023. Clinicodemographic data, PPL characteristics and procedure-related details were recorded. Primary outcome was diagnostic yield, defined as all conclusive diagnoses, malignant or benign, over the total number of procedures. Secondary outcomes were safety profile, defined as the number of procedure-related complications, and diagnostic yield with the use of mobile cone-beam CT (mCBCT) and by biopsy tool.

RESULTS

182 nodules were sampled from 178 patients. PPLs were grouped as: PP (n=95), MP (n=30) and FP (n=57). Overall diagnostic yield was 80.2% (146/182) and sensitivity for malignancy was 83.2% (104/125). Diagnostic yield was associated with upper location (OR 2.86; 95% CI 1.35 to 6.03, p=0.006), mCBCT (OR 2.27; 95% CI 1.06 to 4.86, p=0.036) and cryobiopsy (OR 2.90; 95% CI 1.31 to 6.47, p=0.009). Pneumothorax requiring chest tube was reported in five patients (2.8%), and a Nashville Scale grade 3 bleeding occurred in one patient (0.6%).

CONCLUSION

For pleural-based and fissure-based nodules, ssRAB showed a high diagnostic yield with low complications. The addition of mCBCT and cryobiopsy improved the diagnostic performance for this subtype of lesions.

Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study

BACKGROUND

Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-based radiomic features, extracted from gross tumor volume (GTV), peritumoral volume (PTV), and GTV + PTV (GPTV), for predicting the pathological invasiveness of pure ground-glass nodules present in lung adenocarcinoma.

METHODS

This was a retrospective, cross-sectional, bicentric study with data collected from January 1, 2018, to June 1, 2022. We divided the dataset into a training cohort (n = 88) from one center and an external validation cohort (n = 59) from another center. Radiomic signatures (rad-scores) were obtained after features were selected through correlation and least absolute shrinkage and selection operator analysis. Three machine learning models, a support vector machine model, a random forest model, and a generalized linear model, were then applied to build radiomic models.

RESULTS

Invasive adenocarcinoma had a higher rad-score (P<0.001) in the GTV and GPTV. The area under the curves (AUC) of GTV, PTV, and GPTV were 0.839, 0.809, and 0.855 in the training cohort and 0.755, 0.777, and 0.801 in the external validation cohort, respectively. The GPTV model had higher AUCs for predicting pathological invasiveness. The random forest model had the best validity and fit for the proposed machine learning approach, suggesting that it may be the most appropriate model.

CONCLUSIONS

GPTV had the highest diagnostic efficiency for predicting pathological invasiveness in patients with pure ground-grass nodules, and the random forest model outperformed other predictive models.

Feasibility of AI as first reader in the 4-IN-THE-LUNG-RUN lung cancer screening trial: impact on negative-misclassifications and clinical referral rate

BACKGROUND

Lung cancer screening (LCS) with low-dose CT (LDCT) reduces lung-cancer-related mortality in high-risk individuals. AI can potentially reduce radiologist workload as first-read-filter by ruling-out negative cases. The feasibility of AI as first reader was evaluated in the European 4-IN-THE-LUNG-RUN (4ITLR) trial, comparing its negative-misclassifications (NMs) to those of radiologists and the impact on referral rates.

METHODS

NMs were collected from 3678 baseline LDCTs of the 4ITLR-dataset. LDCTs were read independently by radiologists and dedicated AI software (AVIEW-LCS, v1.1.42.92, Coreline-Soft, Seoul, Korea). A case was designated as NM when nodules > 100 mm3 were present and either radiologist or AI gave a negative-classification (only nodules <100 mm3 or no nodules), with an expert-panel as reference standard. A distinction was made between an indeterminate (100-300mm3), and positive (>300 mm3) classification, warranting referral for clinical-workup. Overall, there were 102 referrals (2.8 %) at baseline.

RESULTS

Of the 3678 baseline scans, 438 NMs (11.9 %) were identified (age individuals: 68 (IQR: 64-73) years, 241 men); 31 (0.8 %) by AI and 407 (11.1 %) by radiologists. Among the 31 AI-NMs, 3 were classified positive and 28 indeterminate. Among the 407 radiologist-NMs, 4 were classified positive, and 403 were indeterminate, of which 8 were classified positive after receiving a three-month follow-up CT. Radiologists, as first reader, would have led to 12/102 (11.8 %) missed referrals, higher than the 3/102 (2.9 %) of AI.

CONCLUSION

This study showed AI outperforms radiologists with significantly less NMs and therefore shows promise as first reader in a LCS program at baseline, by independently ruling-out negative cases without substantially increasing the risk of missed clinical referrals.

Assessment of Lung Nodule Detection and Lung CT Screening Reporting and Data System Classification Using Zero Echo Time Pulmonary MRI

BACKGROUND

The detection rate of lung nodules has increased considerably with CT as the primary method of examination, and the repeated CT examinations at 3 months, 6 months or annually, based on nodule characteristics, have increased the radiation exposure of patients. So, it is urgent to explore a radiation-free MRI examination method that can effectively address the challenges posed by low proton density and magnetic field inhomogeneities.

PURPOSE

To evaluate the potential of zero echo time (ZTE) MRI in lung nodule detection and lung CT screening reporting and data system (lung-RADS) classification, and to explore the value of ZTE-MRI in the assessment of lung nodules.

STUDY TYPE

Prospective.

POPULATION

54 patients, including 21 men and 33 women.

FIELD STRENGTH/SEQUENCE

Chest CT using a 16-slice scanner and ZTE-MRI at 3.0T based on fast gradient echo.

ASSESSMENT

Nodule type (ground-glass nodules, part-solid nodules, and solid nodules), lung-RADS classification, and nodule diameter (manual measurement) on CT and ZTE-MRI images were recorded.

STATISTICAL TESTS

The percent of concordant cases, Kappa value, intraclass correlation coefficient (ICC), Wilcoxon signed-rank test, Spearman's correlation, and Bland-Altman. The p-value <0.05 is considered significant.

RESULTS

A total of 54 patients (age, 54.8 ± 11.9 years; 21 men) with 63 nodules were enrolled. Compared with CT, the total nodule detection rate of ZTE-MRI was 85.7%. The intermodality agreement of ZTE-MRI and CT lung nodules type evaluation was substantial (Kappa = 0.761), and the intermodality agreement of ZTE-MRI and CT lung-RADS classification was moderate (Kappa = 0.592). The diameter measurements between ZTE-MRI and CT showed no significant difference and demonstrated a high degree of interobserver (ICC = 0.997-0.999) and intermodality (ICC = 0.956-0.985) agreements.

DATA CONCLUSION

The measurement of nodule diameter by pulmonary ZTE-MRI is similar to that by CT, but the ability of lung-RADS to classify nodes from MRI images still requires further research.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

[18F]FDG PET/CT versus Dynamic Contrast-Enhanced CT for the diagnosis of solitary pulmonary Nodule: A Head-to-Head comparative Meta-Analysis

PURPOSE

This head-to-head comparative meta-analysis aimed to evaluate the comparative diagnostic efficacy of [18F]FDG PET/CT and dynamic contrast-enhanced CT(DCE-CT) for the differentiation between malignant and benign pulmonary nodules.

METHODS

An extensive search was conducted in the PubMed, Embase, and Web of Science to identify available publications up to March 23, 2024. Studies were included if they evaluated the diagnostic efficacy of [18F]FDG PET/CT and DCE-CT for the characterization of pulmonary nodules. Sensitivity and specificity were assessed using the inverse variance method, followed by transformation via the Freeman-Tukey double inverse sine transformation. The quality of the included studies utilizing the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.

RESULTS

Seven articles involving 1,183 patients were included in the meta-analysis. The sensitivity of [18F]FDG PET/CT was comparable to that of DCE-CT (0.88 vs. 0.87, P = 0.95). Similarly, the specificity of [18F]FDG PET/CT was not significantly different from that of DCE-CT (0.63 vs. 0.54, P = 0.47). No significant publication bias was detected for any outcome (Egger's test: all P > 0.05). For DCE-CT, meta-regression analysis identified the mean lesion size of pulmonary nodules (<20 mm vs. ≥ 20 mm, P = 0.01) as a potential source of heterogeneity. Meanwhile, the number of patients (<100 vs. ≥ 100, P < 0.01) for PET/CT may also contribute to the heterogeneity.

CONCLUSIONS

Our meta-analysis indicates that [18F]FDG PET/CT demonstrates similar sensitivity and specificity to DCE-CT for the diagnosis of pulmonary nodules. However, the number of the head-to-head studies were relatively small, further larger sample prospective research is required to confirm these findings.

Validation of a commercially available CAD-system for lung nodule detection and characterization using CT-scans

OBJECTIVES

This study aims to externally validate a commercially available Computer-Aided Detection (CAD)-system for the automatic detection and characterization of solid, part-solid, and ground-glass lung nodules (LN) on CT scans.

METHODS

This retrospective study encompasses 263 chest CT scans performed between January 2020 and December 2021 at a Dutch university hospital. All scans were read by a radiologist (R1) and compared with the initial radiology report. Conflicting scans were assessed by an adjudicating radiologist (R2). All scans were also processed by CAD. The standalone performance of CAD in terms of sensitivity and false-positive (FP)-rate for detection was calculated together with the sensitivity for characterization, including texture, calcification, speculation, and location. The R1's detection sensitivity was also assessed.

RESULTS

A total of 183 true nodules were identified in 121 nodule-containing scans (142 non-nodule-containing scans), of which R1 identified 165/183 (90.2%). CAD detected 149 nodules, of which 12 were not identified by R1, achieving a sensitivity of 149/183 (81.4%) with an FP-rate of 49/121 (0.405). CAD's detection sensitivity for solid, part-solid, and ground-glass LNs was 82/94 (87.2%), 42/47 (89.4%), and 25/42 (59.5%), respectively. The classification accuracy for solid, part-solid, and ground-glass LNs was 81/82 (98.8%), 16/42 (38.1%), and 18/25 (72.0%), respectively. Additionally, CAD demonstrated overall classification accuracies of 137/149 (91.9%), 123/149 (82.6%), and 141/149 (94.6%) for calcification, spiculation, and location, respectively.

CONCLUSIONS

Although the overall detection rate of this system slightly lags behind that of a radiologist, CAD is capable of detecting different LNs and thereby has the potential to enhance a reader's detection rate. While promising characterization performances are obtained, the tool's performance in terms of texture classification remains a subject of concern.

CLINICAL RELEVANCE STATEMENT

Numerous lung nodule computer-aided detection-systems are commercially available, with some of them solely being externally validated based on their detection performance on solid nodules. We encourage researchers to assess performances by incorporating all relevant characteristics, including part-solid and ground-glass nodules.

KEY POINTS

Few computer-aided detection (CAD) systems are externally validated for automatic detection and characterization of lung nodules. A detection sensitivity of 81.4% and an overall texture classification sensitivity of 77.2% were measured utilizing CAD. CAD has the potential to increase single reader detection rate, however, improvement in texture classification is required.

Minimally invasive biomarkers for triaging lung nodules-challenges and future perspectives

CT chest scans are commonly performed worldwide, either in routine clinical practice for a wide range of indications or as part of lung cancer screening programs. Many of these scans detect lung nodules, which are small, rounded opacities measuring 8-30 mm. While the concern about nodules is that they may represent early lung cancer, in screening programs, only 1% of such nodules turn out to be cancer. This leads to a series of complex decisions and, at times, unnecessary biopsies for nodules that are ultimately determined to be benign. Additionally, patients may be anxious about the status of detected lung nodules. The high rate of false positive lung nodule detections has driven advancements in biomarker-based research aimed at triaging lung nodules (benign versus malignant) to identify truly malignant nodules better. Biomarkers found in biofluids and breath hold promise owing to their minimally invasive sampling methods, ease of use, and cost-effectiveness. Although several biomarkers have demonstrated clinical utility, their sensitivity and specificity are still relatively low. Combining multiple biomarkers could enhance the characterisation of small pulmonary nodules by addressing the limitations of individual biomarkers. This approach may help reduce unnecessary invasive procedures and accelerate diagnosis in the future. This review offers a thorough overview of emerging minimally invasive biomarkers for triaging lung nodules, emphasising key challenges and proposing potential solutions for biomarker-based nodule differentiation. It focuses on diagnosis rather than screening, analysing research published primarily in the past five years with some exceptions. The incorporation of biomarkers into clinical practice will facilitate the early detection of malignant nodules, leading to timely interventions and improved outcomes. Further efforts are needed to increase the cost-effectiveness and practicality of many of these applications in clinical settings. However, the range of technologies is advancing rapidly, and they may soon be implemented in clinics in the near future.

Virtual bronchoscopic navigation and guided radial endobronchial ultrasound for peripheral pulmonary lesions: harmonizing modalities to optimize accuracy

Peripheral pulmonary lesions (PPLs) present a significant diagnostic challenge due to their location beyond the reach of traditional bronchoscopy. With lung cancer being the leading cause of cancer-related mortality worldwide, accurate and early diagnosis of PPLs is crucial. Virtual bronchoscopic navigation (VBN) combined with radial endobronchial ultrasound (R-EBUS) has emerged as a promising technique to enhance the diagnostic yield for these lesions. This retrospective observational study evaluated the diagnostic yield of VBN-guided R-EBUS in patients with PPLs identified on computed tomography. The study included nine patients who underwent VBN-guided R-EBUS biopsy sampling. Patient demographics, lesion characteristics, and procedural outcomes were analyzed using descriptive and inferential statistics. The mean age of the patients was 57.33 years, with a mean lesion size of 3.24 cm. The diagnostic yield of VBN-guided R-EBUS was 77.7% (95% confidence interval: 68.5-85.8%). Non-small cell carcinoma was the most frequent histopathological diagnosis (55.5%). Complications included bleeding in two patients (22.2%) and bronchospasm in one patient (11.1%), all managed conservatively. VBN-guided R-EBUS provides high diagnostic accuracy and a low risk of complications in patients with PPLs.

Adherence to guidelines for incidental pulmonary nodules: insights from a Nordic survey

BACKGROUND AND PURPOSE

There is limited data on the real-world management of incidental pulmonary nodules (IPN). In this article, we review current practices and adherence to international guidelines in the Nordic countries.

MATERIALS AND METHODS

This non-interventional, observational survey study based on an online survey consisting of 13 questions. In total, 32 hospitals responded to the survey, with 11 from Denmark, 10 from Sweden, 7 from Norway, and 4 from Finland, resulting in an overall response rate of 86% (32/37). These institutions reported following a median of 20 new lung nodules monthly (5-400 IPN cases per month).

RESULTS

In Denmark and Sweden, 100% of respondents indicated the presence of national guidelines. In Norway, this rate was 86%, and in Finland 80%. Among the primary guidelines followed, 70% of respondents reported using national guidelines, 20% used international guidelines, and only 10% reported relying on local/institutional guidelines as their first choice. Most sites used a combination of international and national guidelines (75%, 24/32). Available international guidelines were equally represented, with 35% using the Fleischner Criteria, 30% using British Thoracic Society guidelines, and 35% using others (e.g. European Society for Medical Oncology, National Comprehensive Cancer Network). There was variation in which department held primary responsibility for IPN follow-up. The article also demonstrated differences in suggested follow-up cases from the survey.

INTERPRETATION

The study reveals strong adherence to guidelines among Nordic hospitals, with a notable preference for hybrid approaches that combine different guidelines. We need continued efforts to harmonize and update guidelines.

A novel interplay between bacteria and metabolites in different early-stage lung cancer: an integrated microbiome and metabolome analysis

Background

The carcinogenesis mechanism of early-stage lung cancer (ESLC) remains unclear. Microbial dysbiosis is closely related to tumor development. This study aimed to analyze the relationship between microbiota dysbiosis in ESLC.

Methods

We investigated a total of 108 surgical specimens of lung nodules, including ground glass nodules (GGN) diagnosed as lung adenocarcinoma (n = 25), solid nodules (SN) diagnosed as lung adenocarcinoma (n = 27), lung squamous carcinoma (LUSC) presenting as solid nodules (n = 26), and benign pulmonary nodules (BPD) (n = 30) that were collected. 16S rDNA amplicon sequencing and non-targeted metabolomics analysis were performed in all of the specimens.

Results

We found a significantly lower microbiota richness in SN than in the GGN and LUSC. Ralstonia may be an important flora promoting the development of early lung adenocarcinoma, while Feacalibacterium and Blautia play a protective role in the progression of GGN to SN. Akkermansia, Escherichia-shigella, and Klebsiella exhibited high abundance in early lung squamous carcinoma. Compared with BPD, the differential metabolites of both early adenocarcinomas (SN and GGN) are mainly involved in energy metabolic pathways, while early LUSC is mainly involved in glutathione metabolism, producing and maintaining high levels of intracellular redox homeostasis. A correlation analysis revealed that different microbiota in GGN may function in energy metabolism via N-acetyl-1-aspartylglutamic acid (NAAG) when compared to BPD, while creatine and N-acetylmethionine were the main relevant molecules for the function of differential microbiota in LUSC.

Conclusion

Our study identified that early-stage lung adenocarcinoma and squamous carcinoma differ in microbial composition and metabolic status. Ralstonia may be an important flora promoting the development of early lung adenocarcinoma, while Feacalibacterium and Blautia play a protective role in the progression of GGN to SN. Conversely, Akkermansia, Escherichia-shigella, and Klebsiella exhibited high abundance in early lung squamous carcinoma. The metabolites of both early adenocarcinomas (SN and GGN) are mainly involved in energy metabolic pathways, while early LUSC is mainly involved in glutathione metabolism. Our study provides new insights into the carcinogenesis of ESLC.

Pulmonary Subsolid Nodules: Upfront Surgery or Watchful Waiting?

BACKGROUND

Patients with pulmonary subsolid nodules (SSNs) of ≤ 2 cm in diameter and a consolidation to tumor ratio (CTR) of ≤ 0.25 have good postoperative prognoses, but their management remains controversial.

RESEARCH QUESTION

Does upfront surgical intervention lead to higher survival than watchful waiting in patients with SSNs with diameter of ≤ 2 cm and CTR of ≤ 0.25?

STUDY DESIGN AND METHODS

Patients with SSNs who underwent thin-section CT scan examination between February 2005 and December 2018 were followed up retrospectively until December 2023 or until all-cause death or lung cancer recurrence or metastases. Patients were divided into observation and surgery groups and categorized further by the diameter and CTR of these SSNs. Event-free survival (EFS) was evaluated using Kaplan-Meier analysis, multivariable-adjusted Cox proportional hazards modeling, propensity score matching, and a noninferiority trial.

RESULTS

Data from 1,676 patients were included (surgery group, n = 1,122 [66.9%]; observation group, n = 554 [33.1%]), with a median EFS of 70.2 months (range, 0.3-213.6 months). Comparing the observation group with the surgery group, the 5-year EFS rates in category A (diameter ≤ 2 cm and CTR ≤ 0.25), category A1 (diameter ≤ 1 cm and CTR ≤ 0.25), category A2 (1 cm < diameter ≤ 2 cm and CTR ≤ 0.25), and the combined category (diameter ≤ 3 cm and CTR ≤ 0.5) were 100% vs 99.0%, 100% vs 99.6%, 100% vs 98.6%, and 100% vs 97.4%, respectively. In the above categories of SSNs, the EFS of the observation group was noninferior to that of the surgery group (P < .001 for noninferiority), and the results remained consistent after propensity score matching. Category A2 achieved the maximum hazard ratio of 0.0668, with corresponding 5-year EFS rates for the observation and surgery groups being 100% vs 93.3%, respectively.

INTERPRETATION

In patients with SSNs of ≤ 2 cm in diameter and CTR of ≤ 0.25, watchful waiting could be more appropriate than upfront surgical intervention.

Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer

Diagnosing lung cancer from indeterminate pulmonary nodules (IPLs) remains challenging. In this multi-institutional study involving 2032 participants with IPLs, we integrate the clinical, radiomic with circulating cell-free DNA fragmentomic features in 5-methylcytosine (5mC)-enriched regions to establish a multiomics model (clinic-RadmC) for predicting the malignancy risk of IPLs. The clinic-RadmC yields an area-under-the-curve (AUC) of 0.923 on the external test set, outperforming the single-omics models, and models that only combine clinical features with radiomic, or fragmentomic features in 5mC-enriched regions (p < 0.050 for all). The superiority of the clinic-RadmC maintains well even after adjusting for clinic-radiological variables. Furthermore, the clinic-RadmC-guided strategy could reduce the unnecessary invasive procedures for benign IPLs by 10.9% ~ 35%, and avoid the delayed treatment for lung cancer by 3.1% ~ 38.8%. In summary, our study indicates that the clinic-RadmC provides a more effective and noninvasive tool for optimizing lung cancer diagnoses, thus facilitating the precision interventions.

Malignancy risk stratification for pulmonary nodules: comparing a deep learning approach to multiparametric statistical models in different disease groups

OBJECTIVES

Incidentally detected pulmonary nodules present a challenge in clinical routine with demand for reliable support systems for risk classification. We aimed to evaluate the performance of the lung-cancer-prediction-convolutional-neural-network (LCP-CNN), a deep learning-based approach, in comparison to multiparametric statistical methods (Brock model and Lung-RADS®) for risk classification of nodules in cohorts with different risk profiles and underlying pulmonary diseases.

MATERIALS AND METHODS

Retrospective analysis was conducted on non-contrast and contrast-enhanced CT scans containing pulmonary nodules measuring 5-30 mm. Ground truth was defined by histology or follow-up stability. The final analysis was performed on 297 patients with 422 eligible nodules, of which 105 nodules were malignant. Classification performance of the LCP-CNN, Brock model, and Lung-RADS® was evaluated in terms of diagnostic accuracy measurements including ROC-analysis for different subcohorts (total, screening, emphysema, and interstitial lung disease).

RESULTS

LCP-CNN demonstrated superior performance compared to the Brock model in total and screening cohorts (AUC 0.92 (95% CI: 0.89-0.94) and 0.93 (95% CI: 0.89-0.96)). Superior sensitivity of LCP-CNN was demonstrated compared to the Brock model and Lung-RADS® in total, screening, and emphysema cohorts for a risk threshold of 5%. Superior sensitivity of LCP-CNN was also shown across all disease groups compared to the Brock model at a threshold of 65%, compared to Lung-RADS® sensitivity was better or equal. No significant differences in the performance of LCP-CNN were found between subcohorts.

CONCLUSION

This study offers further evidence of the potential to integrate deep learning-based decision support systems into pulmonary nodule classification workflows, irrespective of the individual patient risk profile and underlying pulmonary disease.

KEY POINTS

Question Is a deep-learning approach (LCP-CNN) superior to multiparametric models (Brock model, Lung-RADS®) in classifying pulmonary nodule risk across varied patient profiles? Findings LCP-CNN shows superior performance in risk classification of pulmonary nodules compared to multiparametric models with no significant impact on risk profiles and structural pulmonary diseases. Clinical relevance LCP-CNN offers efficiency and accuracy, addressing limitations of traditional models, such as variations in manual measurements or lack of patient data, while producing robust results. Such approaches may therefore impact clinical work by complementing or even replacing current approaches.

Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression

Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and compare it with established models. Materials and Methods This retrospective study included 1607 MRI scans of 1143 male patients (median age, 64 years; IQR, 59-68 years) undergoing MRI for suspicion of clinically significant PCa (csPCa) (International Society of Urological Pathology grade > 1) between January 2012 and May 2022 who were negative for csPCa at baseline MRI. A DL model was developed using baseline MRI and clinical parameters (age, prostate-specific antigen [PSA] level, PSA density, and prostate volume) to predict the time to PCa progression (defined as csPCa diagnosis at follow-up). Internal and external testing was performed. The model's ability to predict progression to csPCa was assessed by Cox regression analyses. Predictive performance of the DL model up to 5 years after baseline MRI in comparison with the European Randomized Study of Screening for Prostate Cancer (ERSPC) future-risk calculator, Prostate Cancer Prevention Trial (PCPT) risk calculator, and Prostate Imaging Reporting and Data System (PI-RADS) was assessed using the Harrell C-index. Optimized follow-up intervals were derived from Kaplan-Meier curves. Results DL scores predicted csPCa progression (internal cohort: hazard ratio [HR], 1.97 [95% CI: 1.61, 2.41; P < .001]; external cohort: HR, 1.32 [95% CI: 1.14, 1.55; P < .001]). The model identified a subgroup of patients (approximately 20%) with risks for csPCa of 3% or less, 8% or less, and 18% or less after 1-, 2-, and 4-year follow-up, respectively. DL scores had a C-index of 0.68 (95% CI: 0.63, 0.74) at internal testing and 0.56 (95% CI: 0.51, 0.61) at external testing, outperforming ERSPC and PCPT (both P < .001) at internal testing. Conclusion The DL model accurately predicted PCa progression and provided improved risk estimations, demonstrating its ability to aid in personalized follow-up for low-risk PCa. Keywords: MRI, Prostate Cancer, Deep Learning Supplemental material is available for this article. ©RSNA, 2025.

Silicone deposition and adverse pulmonary events secondary to breast implant rupture

Silicone breast implants are common but may be associated with a number of complications including implant rupture. This case reports a 38-year-old woman with bilateral breast implants who presented with breast unevenness, triggering a cascade of investigations that identified implant rupture. A computed tomography scan of the thorax showed subpleural enhancing nodules in the left lung of equal density as the implants, repeat computed tomography thorax months later showed no interval changes. In this case, extracapsular rupture causing deposits of silicone via the lymphatic system into the lungs resulted in nodules visible on imaging. Reassuring radiological findings and lack of red flag symptoms led to radiological follow-up and avoided the need for invasive procedures such as biopsy. The authors aim to remind clinicians of the importance of maintaining a high index of clinical suspicion for implant-related pathology and to add to current literature regarding this rare complication.

How I Do It: Management of Pleural-attached Pulmonary Nodules in Low-Dose CT Screening for Lung Cancer

Lung cancer is the leading cause of cancer deaths globally. In various trials, the ability of low-dose CT screening to diagnose early lung cancers leads to high cure rates. It is widely accepted that the potential benefits of low-dose CT screening for lung cancer outweigh the harms. The ability to reliably predict the benignity of nodules, especially at the baseline round, further reduces the potential for harm. Pleural-attached nodules are an important subgroup that represents nodules attached (distance from any pleural surface, 0 mm) to any pleural surfaces (fissural, costal, mediastinal, and diaphragmatic). Pleural-attached solid nodules less than 10 mm in average diameter with smooth margins and triangular, lentiform, oval, or semicircular shapes have a high likelihood of benignity. The 2019 Lung CT Screening Reporting and Data System (Lung-RADS) version 1.1 assigned pleural-attached nodules with these features to categories 3 (probably benign, recommend follow-up in 6 months) or 4 (suspicious for malignancy, recommend follow-up in 3 months or PET/CT). However, Lung-RADS version 2022 now recommends annual follow-up rather than short-term follow-up. These changes downgrade these nodules to category 2 (benign) and limits additional workup. This review article summarizes the terminology used to describe these nodules, characteristics for determining benignity, and the accuracy of the evidence used to make these recommendations.

Precise Localization of the Subsolid Lesion by Colour Marking under CT-Guided Control before Video-Assisted Surgery Resection: A Case Report

Introduction

Lung cancer is one of the leading causes of death worldwide. Lung lesions, often discovered incidentally on chest CT, pose a diagnostic challenge due to their diverse etiology, including both benign and malignant nature. A key step in the assessment of these lesions is the evaluation of their morphological features in the CT image, size, and behavior over time. Nodules are divided into solid and subsolid according to their density. When surgical resection is necessary, solid lesions are palpable peroperatively, whereas subsolid lesions may be unidentifiable by palpation, and their precise localization is difficult. To spare patients from extensive surgery such as thoracotomy, it is advantageous to use one of the methods of preoperative marking of these lesions. Best practices include marking with mixtures containing patent blue and contrast agents, applied under CT guidance. This method allows accurate visualization of the localization of the lesion, which facilitates their resection by minimally invasive video-assisted surgery (VATS).

Case Presentation

A 51-year-old female patient was found to have a subsolid lesion in the right lung during a routine follow-up CT scan of the lung for a history of malignant melanoma. The lesion was followed for 4 years and showed slow size progression and change from a pure ground glass nodule to a subsolid nodule. Due to the persistence of the nodule, change in morphology, and size progression, the patient was indicated for surgical resection. Using preoperative labeling with a mixture of blue dye and contrast agent, the nodule was successfully located and sublobary VATS resected.

Conclusion

The color marking allowed accurate identification of the subpleurally located lesion, which would otherwise have been unvisualized and intangible, thus minimizing the need for more extensive surgery. This case highlights the key role of color marking in increasing resection success and surgical safety, particularly in small and subsolid nodules.

Comparison of Lung-RADS Version 2022 and British Thoracic Society Guidelines in Classifying Solid Pulmonary Nodules Detected at Lung Cancer Screening CT

BACKGROUND AND OBJECTIVES

Lung cancer screening is critical for early detection and management, particularly through the use of computed tomography (CT). This study aims to compare the Lung Imaging Reporting and Data System (Lung-RADS) Version 2022 with the British Thoracic Society (BTS) guidelines in classifying solid pulmonary nodules detected at lung cancer screening CT examinations.

MATERIALS AND METHODS

This retrospective study included 224 patients who underwent lung cancer screening CT between 2016 and 2022 and had a reported solid pulmonary nodule. A fellowship-trained thoracic radiologist reviewed the CT images, characterizing nodules by size, location, margins, attenuation, calcification, growth at follow-up, and final pathologic diagnosis if malignant. The sensitivity and specificity of Lung-RADS Version 2022 in detecting malignant nodules were compared with those of the BTS guidelines using the McNemar test.

RESULTS

Of the 224 patients, 198 (88%) had nodules deemed benign, while 26 (12%) had malignant nodules. The Lung-RADS Version 2022 resulted in higher specificity than the BTS guidelines (85% vs. 65%, p < 0.001), without sacrificing sensitivity (92% for both). Nodules larger than 8 mm, spiculated margins, upper lobe location, and interval growth were associated with higher malignancy risk (p < 0.01).

CONCLUSIONS

Compared with the BTS guidelines, Lung-RADS Version 2022 reduces the number of false-positive screening CT examinations while maintaining high sensitivity for detecting malignant solid pulmonary nodules.

Clinical Versus Pathological Staging in Patients with Resected Ground Glass Pulmonary Lesions

BACKGROUND

A ground glass nodule (GGN) is a radiologically descriptive term for a lung parenchymal area with increased attenuation and preserved bronchial and vascular structures. GGNs are further divided into pure versus subsolid lesions. The differential diagnosis for GGNs is wide and contains a malignant possibility for a lung adenocarcinoma precursor or tumor. Clinical and pathological staging of GGNs is based on the lesions' solid component and falls into a specific classification including T0 for TIS, T1mi for minimally invasive adenocarcinoma (MIA) and T1abc for lepidic predominant adenocarcinoma (LPA) according to the eighth edition of the TNM classification of lung cancer. Correlation between solid parts seen on a CT scan and the tumor pathological invasive component is not absolute.

METHODS

This retrospective study collected the data of 68 GGNs that were operated upon in Carmel Medical Center. A comparison between preoperative clinical staging and post-surgery pathological staging was conducted.

RESULTS

Over a third of the lesions, twenty-four (35.3%), were upstaged while only four (5.9%) lesions were downstaged. Another third of the lesions, twenty-three (33.8%), kept their stage. In three (4.4%) cases, premalignant lesion atypical adenomatous hyperplasia (AAH) was diagnosed. Ten (14.7%) cases were diagnosed as non-malignant on final pathology. These findings show an overall low agreement between the clinical and pathological stages of GGNs.

CONCLUSIONS

The relatively high percentage of upstaging tumors detected in this study and the overall safe and short surgical procedure advocate for surgical resection even in the presence of a significant number of non-malignant lesions that retrospectively do not mandate intervention at all.

Interobserver Variability in Manual Versus Semi-Automatic CT Assessments of Small Lung Nodule Diameter and Volume

BACKGROUND

This study aimed to assess the interobserver variability of semi-automatic diameter and volumetric measurements versus manual diameter measurements for small lung nodules identified on computed tomography scans.

METHODS

The radiological patient database was searched for CT thorax examinations with at least one noncalcified solid nodule (∼3-10 mm). Three radiologists with four to six years of experience evaluated each nodule in accordance with the Fleischner Society guidelines using standard diameter measurements, semi-automatic lesion diameter measurements, and volumetric assessments. Spearman's correlation coefficient measured intermeasurement agreement. We used descriptive Bland-Altman plots to visualize agreement in the measured data. Potential discrepancies were analyzed.

RESULTS

We studied a total of twenty-six nodules. Spearman's test showed that there was a much stronger relationship (p < 0.05) between reviewers for the semi-automatic diameter and volume measurements (avg. r = 0.97 ± 0.017 and 0.99 ± 0.005, respectively) than for the manual method (avg. r = 0.91 ± 0.017). In the Bland-Altman test, the semi-automatic diameter measure outperformed the manual method for all comparisons, while the volumetric method had better results in two out of three comparisons. The incidence of reviewers modifying the software's automatic outline varied between 62% and 92%.

CONCLUSIONS

Semi-automatic techniques significantly reduced interobserver variability for small solid nodules, which has important implications for diagnostic assessments and screening. Both the semi-automatic diameter and semi-automatic volume measurements showed improvements over the manual measurement approach. Training could further diminish observer variability, given the considerable diversity in the number of adjustments among reviewers.

CT-Guided Transthoracic Core-Needle Biopsy of Pulmonary Nodules: Current Practices, Efficacy, and Safety Considerations

CT-guided transthoracic core-needle biopsy (CT-TTNB) is a minimally invasive procedure that plays a crucial role in diagnosing pulmonary nodules. With high diagnostic yield and low complication rates, CT-TTNB is favored over traditional surgical biopsies, providing accuracy in detecting both malignant and benign conditions. This literature review aims to present a comprehensive overview of CT-TTNB, focusing on its indications, procedural techniques, diagnostic yield, and safety considerations. Studies published between 2013 and 2024 were systematically reviewed from PubMed, Web of Science, Scopus, and Cochrane Library using the SANRA methodology. The results highlight that CT-TTNB has a diagnostic yield of 85-95% and sensitivity rates for detecting malignancies between 92 and 97%. Several factors, including nodule size, lesion depth, needle passes, and imaging techniques, influence diagnostic success. Complications such as pneumothorax and pulmonary hemorrhage were noted, with incidence rates varying from 12 to 45% for pneumothorax and 4 to 27% for hemorrhage. Preventative strategies and management algorithms are essential for minimizing and addressing these risks. In conclusion, CT-TTNB remains a reliable and effective method for diagnosing pulmonary nodules, particularly in peripheral lung lesions. Advancements such as PET/CT fusion imaging, AI-assisted biopsy planning, and robotic systems further enhance precision and safety. This review emphasizes the importance of careful patient selection and procedural planning to maximize outcomes while minimizing risks, ensuring that CT-TTNB continues to be an indispensable tool in pulmonary diagnostics.

Improving Cancer Probability Estimation in Nondiagnostic Bronchoscopies: A Meta-Analysis

BACKGROUND

In patients with peripheral pulmonary lesions (PPLs), nondiagnostic bronchoscopy results are not uncommon. The conventional approach to estimate the probability of cancer (pCA) after bronchoscopy relies on dichotomous test assumptions, using prevalence, sensitivity, and specificity to determine negative predictive value. However, bronchoscopy is a multidisease test, raising concerns about the accuracy of dichotomous methods.

RESEARCH QUESTION

By how much does calculating pCA using a dichotomous approach (pCAdichotomous) underestimate the true pCA when applied to multidisease tests like bronchoscopy for the diagnosis of PPL?

METHODS

In this meta-analysis of cohort studies involving radial endobronchial ultrasound for PPL, Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines were followed, constructing 2 × 2 contingency tables for calculating pCAdichotomous. For the multidisease test approach, 3 × 3 contingency tables for calculating probability of malignancy for a test that can have different categories of results and can diagnose multiple diseases (pCAmultidisease) using the likelihood ratio (LR) method for nondiagnostic results (LR(T0)) was used. Observed malignancy rates in patients with nondiagnostic results were compared with pCAdichotomous and pCAmultidisease.

RESULTS

In 46 studies (7,506 patients), malignancy was the underlying diagnosis in 76% of cases, another specific disease in 13% of cases, and nonspecific fibrosis or scar in 10% of cases. The percentage of patients with nondiagnostic results who had malignancy matched pCAmultidisease across all studies. In contrast, pCAdichotomous consistently underestimated cancer risk (median difference, 0.12; interquartile range, 0.06-0.23), particularly in studies with a higher prevalence of nonmalignant disease. The pooled LR(T0) was 0.46 (95% CI, 0.40-0.52; I2 = 76%; P < .001) and correlated with the prevalence of nonmalignant diseases (P = .001).

INTERPRETATION

Conventional dichotomous methods for estimating pCA after nondiagnostic bronchoscopies underestimate the likelihood of malignancy. Physicians should opt for the multidisease test approach when interpreting bronchoscopy results.

Growth and Clinical Impact of Subsolid Lung Nodules ≥6 mm During Long-Term Follow-Up After Five Years of Stability

OBJECTIVE

To investigate the incidence and timing of late growth of subsolid nodules (SSNs) ≥6 mm after initial 5-year stability, its clinical implications, and the appropriate follow-up strategy.

MATERIALS AND METHODS

This retrospective study included SSNs ≥6 mm that remained stable for the initial five years after detection. The incidence and timing of subsequent growth after five years of stability were analyzed using the Kaplan-Meier method. Descriptive analyses were conducted to evaluate the clinical stage shift in the SSNs, showing growth and the presence of metastasis during the follow-up period. Finally, an effective follow-up CT scan strategy for managing SSNs after a 5-year period of stability was investigated.

RESULTS

Two hundred thirty-five eligible SSNs (211 pure ground-glass and 24 part-solid nodules) in 235 patients (median age, 63 years; 132 female) were followed for additional <1 to 181 months (median, 87.0 months; interquartile range [IQR], 47.0-119.0 months) after 5-year stability. Fourteen SSNs (6.0%) showed growth at two to 145 months (median, 96 months; IQR: 43.0-122.25 months) from the CT scan confirming 5-year stability, with the estimated cumulative incidence of growth of 0.4%, 2.1%, and 6.5% at 1, 5, and 10 years, respectively. Nine SSNs (3.8%) exhibited clinical stage shifts. No lung cancer metastases were observed. Hypothetical follow-up CT scans performed at 5, 10, and 15 years after 5-years of stability, would have detected 5 (36%), 11 (79%), and 14 (100%) of the 14 growing SSNs, along with 4 (44%), 8 (89%), and 9 (100%) of the nine stage shifts, respectively.

CONCLUSION

During a long-term follow-up of pulmonary SSNs ≥6 mm after 5-years of stability, a low incidence of growth without occurrence of metastasis was noted. CT scans every five years after the initial 5-year stability period may be reasonable.

Outcomes of incidental pulmonary nodules detected in oral and oropharyngeal cancer patients

Computed tomography (CT) of the chest is routinely performed as part of head and neck cancer (HNC) staging. Pulmonary nodules incidentally encountered present a clinical dilemma, as they may indicate early malignancy. Clinically indeterminant nodules are those that cannot be classed as definitively malignant or benign. This study aimed to assess the outcomes of pulmonary nodules detected on initial staging chest CT in a consecutive cohort of patients with oral and oropharyngeal squamous cell carcinoma (SCC). A retrospective cohort study of newly diagnosed oral or oropharyngeal SCC patients with pulmonary nodules identified on staging chest CT at a single institution was conducted. Pulmonary nodules were categorised as benign, indeterminant, or malignant. Indeterminant nodules underwent further investigations with either repeat imaging or needle biopsy to exclude malignancy. Descriptive and bivariate statistics were used to evaluate the association between pulmonary metastasis and patient demographics, disease characteristics, and nodular features. P values of ≤ 0.05 were considered statistically significant. Of 579 patients diagnosed with HNC who had undergone staging chest CT between 2010 and 2015, 154 had pulmonary nodules. Indeterminant pulmonary nodules at staging in 26 patients (16.9%) were later confirmed to be lung metastases. Pulmonary nodules of subsolid type found in patients with N2/N3 disease were significantly more likely to be metastatic. Isolated pulmonary nodules in the right lung were more likely to be benign. A HNC-specific protocol for the management of incidental pulmonary nodules should now be developed to guide the interval and duration of required clinical and radiological surveillance, taking into account the disease characteristics and nodular features.

Prevalence of invasive lung cancer in pure ground glass nodules less than 30 mm: A systematic review

BACKGROUND

The IASLC TNM proposal suggests that pure ground glass nodules less than 30 mm should be classified as cTis corresponding to pathologic adenocarcinoma in situ implying no invasive malignancy potential. We sought to ascertain the proportion of pure ground glass nodules that harbour tissue confirmed minimally invasive or invasive adenocarcinoma.

METHODS

We analyzed data from 3874 individuals with pure ground glass nodules less than 30 mm, reported in 28 observational studies identified through a systematic search of electronic databases. The primary outcome was the prevalence of invasive malignancy by random effects meta-analysis, and we used meta-regression to determine the impact of baseline risk, size, and country of investigation on overall effect size. The study was registered with PROSPERO (CRD42021286261).

RESULTS

All published studies were retrospective (n = 28) and the majority conducted in Asia (n = 25). Baseline patient cohorts were mainly from published surgical series (n = 22) or lung cancer screening programs (n = 6). The proportion of minimally invasive and invasive cancer ranged from 0.9 % to 100 % with a pooled prevalence of 42.4 % [95 % CI: 0.28, 0.57]. Considerable heterogeneity was observed (I2 =99 %) and patient selection was the most significant contribution, accounting for 73 % of the observed heterogeneity (p < 0.0001). Meta-regression based on size selection and country of investigation revealed no significant contribution to effect size effect or heterogeneity.

CONCLUSIONS

Pure ground glass nodules less than 30 mm harbour a high proportion of invasive malignancy, contrary to the IASLC staging proposals and opinions from numerous guidelines across the world.