Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017

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.

Understanding East-West differences in subsolid nodules: prevalence and overdiagnosis implications in lung cancer screening

BACKGROUND

Owing to the widespread opportunistic LDCT screening leading to increased overdiagnosis in Asian countries, such as South Korea, mainland China, and Taiwan, this study seeks to analyze the divergence in SSN prevalence between Eastern and Western nations, focusing on the influence of SSN on the growing overdiagnosis trend, notably among females.

METHODS

This retrospective study collected data from 4166 participants who underwent baseline LDCT in a hospital-based cohort between January 2014 and August 2021. Clinical parameters, including age, sex, lung imaging reporting and data system (Lung-RADS) categories, smoking history, pack-year dose, and SSN characteristics, were extracted from electronic medical records. Additionally, a narrative review and pooled analysis integrated relevant published studies on the prevalence of subsolid nodules and sex disparities.

RESULTS

The study encompassed 4166 participants, with females accounting for 49.3% and males for 50.7%, with a mean age of 53.38 ± 10.89. The prevalence of SSNs was significantly higher in females (20.1%) than in males (12.6%). Pooled analysis across seven studies revealed a significantly higher prevalence of SSN in Eastern countries (12.6%) compared to the prevalence in Western countries (3.6%) (test for subgroup differences: p < 0.01; I2 = 100%). Additionally, a notable sex difference was observed in the prevalence of SSNs (risk ratio = 0.489, 95% CI: 0.301-0.796, p < 0.01; reference group: male group).

CONCLUSIONS

Apart from differences in clinical management and health literacy regarding SSNs between Eastern and Western countries, the high prevalence of SSNs in Asian nations, particularly among females, significantly contributes to the issue of overdiagnosis in opportunistic lung cancer screening in Asian countries. Tailored sex-specific strategies and risk prediction models are essential for effective screening optimization.

Preoperative CT-guided lung nodule localization: Comparison of Chiba needle and Trocar needle

OBJECTIVE

To compare the relative safety and efficacy of the Chiba needle and the Trocar needle in CT-guided microcoil localization of pulmonary nodules.

METHODS

A retrospective study was conducted on 118 patients who underwent CT-guided microcoil localization and subsequent video-assisted thoracoscopic surgery (VATS) resection from September to November 2023. Patients were divided into the Chiba needle group (n = 75) and the Trocar needle group (n = 43). Characteristics of patients, lesions, procedures, and surgeries were statistically analyzed. Univariate and multivariate logistic regression analyses were used to determine potential risk factors for technical failure and complications.

RESULTS

The success rate of localization was 97.3 % for the Chiba needle group and 100 % for the Trocar needle group, with no significant difference (p = 0.533). Complications included pneumothorax in 16 % of the Chiba group and 18.6 % of the Trocar group (p = 0.914), and parenchymal hemorrhage in 25.3 % and 41.9 % respectively (p = 0.098). There were no significant differences in puncture depth, procedure duration, or interval between procedure and surgery. Multivariate logistic regression analyses identified longer puncture depth as a risk factor both for pneumothorax (p = 0.027) and parenchymal hemorrhage (p = 0.006), while the Trocar needle was identified as a risk factor for parenchymal hemorrhage (p = 0.035).

CONCLUSION

This study found no significant difference in the effectiveness of Chiba and Trocar needles for preoperative CT-guided lung nodule localization. Both needles showed high success rates and comparable pneumothorax profiles. However, the Trocar needle was found associated with a higher incidence of parenchymal hemorrhage.

Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm

PURPOSE

To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR).

MATERIAL AND METHODS

This prospective cross-sectional study included 60 patients referred to our institution for assessment or follow-up of solid pulmonary nodules. All patients underwent low-dose (LD) and ULD chest CT within the same examination session. LD CT data were reconstructed using Adaptive Statistical Iterative Reconstruction-V (ASIR-V), whereas ULD CT data were reconstructed using DLIR and ASIR-V. ULD CT images were reviewed by 2 readers and LD CT images were reviewed by an experienced thoracic radiologist as the reference standard. Quantitative image quality analysis was performed, and the detectability of pulmonary nodules was assessed according to their size and location.

RESULTS

The effective radiation dose for ULD CT and LD CT were 0.13±0.01 and 1.16±0.6 mSv, respectively. Over the whole population, LD CT revealed 733 nodules. At ULD, DLIR images significantly exhibited better image quality than ASIR-V images. The overall sensitivity of DLIR reconstruction for the detection of solid pulmonary nodules from the ULD CT series was 93% and 82% for the 2 readers, with a good to excellent agreement with LD CT (ICC=0.82 and 0.66, respectively). The best sensitivities were observed in the middle lobe (97% and 85%, respectively).

CONCLUSIONS

At ULD, DLIR reconstructions, with minimal radiation exposure that could facilitate large-scale screening, allow the detection of pulmonary nodules with high sensitivity in an unrestricted BMI population.

Lung lesion detectability on images obtained from decimated and CNN-based denoised [18F]-FDG PET/CT scan: an observer-based study for lung-cancer screening

PURPOSE

To assess feasibility of lung cancer screening, we analysed lung lesion detectability simulating low-dose and convolutional neural network (CNN) denoised [18F]-FDG PET/CT reconstructions.

METHODS

Retrospectively, we analysed lung lesions on full statistics and decimated [18F]-FDG PET/CT. Reduced count PET data were emulated according to various percentage levels of total. Full and reduced statistics datasets were denoised using a CNN algorithm trained to recreate full statistics PET. Two readers assessed a detectability score from 3 to 0 for each lesion. The resulting detectability score and quantitative measurements were compared between full statistics and the different decimation levels (100%, 30%, 5%, 2%, 1%) with and without denoising.

RESULTS

We analysed 141 lung lesions from 49 patients across 588 reconstructions. The dichotomised lung lesion malignancy score was significantly different from 10% decimation without denoising (p < 0.029) and from 5% decimation with denoising (p < 0.001). Compared to full statistics, detectability score distribution differed significantly from 2% decimation without denoising (p < 0.001) and from 5% decimation with denoising (p < 0.001). Detectability scores at same decimation levels with or without denoising differed significantly at 10%, 2%, and 1% decimation (p < 0.019); dichotomised scores did not differ significantly. Denoising significantly increased the proportion of lung lesion scores with a high diagnostic confidence (3 and 0) (p < 0.038).

CONCLUSION

Lung lesion detectability was preserved down to 30% of injected activity without denoising and to 10% with denoising. These results support the feasibility of reduced-activity [18F]-FDG PET/CT as a potential tool for lung lesion detection. Further studies are warranted to compare this approach with low-dose CT in screening settings.

Effect of lung inflation states on chest CT image quality and pulmonary nodule detection with visualized respiratory Indicator

BACKGROUND

Parts of lung cancer screening guidelines describe the specific scanning protocol of low dose CT (LDCT), among which the requirement for respiratory state is full inspiration end-breath hold. The main focus of lung cancer screening is to evaluate and follow-up pulmonary nodule (PN), so the display and detection of PNs are important. To achieve full inspiration, strict breathing training is required for patients. In clinical scans, the lung inflation state of patient is not visualized and the possibility of incomplete inspiration exists. Thus, the image quality and nodule detection of chest CT in different lung inflation states need to be explored.

METHODS

Fifty-six participants (32 females, 24 males) were included in this prospective study. Each participant underwent non-contrast chest CT scanned three times continually with different lung inflation state, including deep inspiration end-breath hold, calm breath hold, and deep expiration end-breath hold. A respiratory indicator was used to monitor the state of lung inflation visually. Subjective and objective image quality and nodule detection among these lung inflation states were analyzed in this study.

RESULTS

The images of deep inspiration end-breath hold yielded the best, with superior subjective ratings and objective image quality, including the lowest image noise and the best signal-to-noise ratio. PN detection was most accurate in the inflation state of deep inspiration end-breath hold, particularly for nodules ≤ 5 mm, while fewer nodules detected in the inflation state of calm breath hold and deep expiration end-breath hold.

CONCLUSIONS

Lung inflation states significantly impact both image quality and PN detection in chest CT. Deep inspiration end-breath hold provided optimal image quality and nodule detection, while non-fully inflated states reduced diagnostic accuracy, especially for PNs≤5 mm. In clinical application, deep inspiration end-breath hold is recommended as the best inflation state of chest CT.

Longitudinal MRI in comparison to low-dose CT for follow-up of incidental pulmonary nodules in patients with COPD-a nationwide multicenter trial

PURPOSE

This multicenter trial was conducted to evaluate MRI for the longitudinal management of incidental pulmonary nodules in heavy smokers.

MATERIALS AND METHODS

239 participants (63.9 ± 8.4 years, 43-82 years) at risk of or with COPD GOLDI-IV from 16 centers prospectively underwent two rounds of same-day low-dose computed tomography (LDCT1&2) and MRI1&2 at an interval of three years in the nationwide COSYCONET trial. All exams were independently assessed for incidental pulmonary nodules in a standardized fashion by two blinded readers, incl. axis measurements and Lung-RADS categorization, with consensual LDCT results serving as the standard of reference. A change in diameter ≥ 2 mm was rated as progress. 11 patients underwent surgery for suspicious nodules after the first round.

RESULTS

Two hundred twenty-four of two hundred forty nodules (93.3%) persisted from LDCT1 to LDCT2, with a sensitivity of MRI2 of 82.8% and 81.5% for readers 1 and 2, respectively. Agreement in Lung-RADS categories between LDCT2 and MRI2 was substantial in per-nodule (κ = 0.62-0.70) and excellent in a per-patient (κ = 0.86-0.88) approach for both readers, respectively. Concordance between LDCT2 and MRI2 for growth was excellent to almost perfect (κ = 0.88-1.0). The accuracy of LDCT1 and MRI1 for lung cancer was 87.5%. Lung-RADS ≥ 3 category on MRI1 had higher accuracy for predicting progress (23.1% and 21.4%, respectively) than LDCT1 (15.8%).

CONCLUSION

Compared to LDCT, MRI shows similar capabilities for the longitudinal evaluation of incidental nodules in heavy smokers. Decision-making for nodule management guided by Lung-RADS seems feasible based on longitudinal MRI.

KEY POINTS

Question Can MRI serve as an alternative to low-dose CT (LDCT) for the longitudinal management of pulmonary nodules in heavy smokers, addressing concerns over radiation exposure? Findings MRI demonstrated substantial agreement with LDCT in detecting nodule growth, accurately categorizing Lung-RADS, and comparable accuracy in identifying malignancy over a three-year follow-up. Clinical relevance Longitudinal MRI demonstrates high consistency with LDCT in assessing the growth of incidental pulmonary nodules and categorizing per-patient Lung-RADS, offering a reliable, radiation-free alternative for monitoring and early malignancy detection in high-risk populations.

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.

Efficacy and safety of transcutaneous electrical acupoint stimulation for preoperative anxiety in thoracoscopic surgery: a randomized controlled trial

Background

Patients undergoing video-assisted thoracoscopic surgery (VATS) often experience preoperative anxiety, which can significantly impact the surgical process and postoperative recovery. However, the efficacy of Transcutaneous Electrical Acupoint Stimulation (TEAS) in managing preoperative anxiety in VATS patients is unknown.

Methods

A total of 82 patients scheduled for thoracoscopic surgery were randomly divided into TEAS group (n = 41) and sham TEAS (STEAS) group (n = 41). The TEAS/STEAS intervention began 3 days before the thoracoscopic surgery, with one session lasting 30 min per day for three consecutive days. The primary outcome measure will be the change in Generalized Anxiety Disorder Scale scores between the day before surgery and the baseline. Secondary outcome include intraoperative anesthetic consumption, time to postoperative chest tube removal, postoperative analgesic consumption and pain scores, length of postoperative hospital stay, serum concentrations of 5-hydroxytryptamine (5-HT), norepinephrine (NE), and gamma-aminobutyric acid (GABA).

Results

On the third intervention day, anxiety levels in the TEAS group were significantly lower than in the STEAS group (p < 0.01). TEAS patients required less intraoperative sufentanil, remifentanil, and dexamethasone (p < 0.01). Chest tube removal time and hospital stay were shorter in the TEAS group (p < 0.01). Postoperative meperidine consumption and VAS pain scores were lower in the TEAS group (p < 0.01). Serum 5-HT levels were lower in the TEAS group on day three (p < 0.01), while NE levels remained lower from day three of intervention to postoperative day three (p < 0.05). GABA levels were higher in the TEAS group (p < 0.01).

Conclusion

TEAS effectively reduces preoperative anxiety, decreases intraoperative anesthetic and anti-inflammatory drug use, shortens postoperative chest tube removal time and hospitalization, and alleviates postoperative pain. These results indicate that TEAS, as an adjunctive therapy, has valuable potential in improving surgical outcomes and postoperative experience for patients with pulmonary nodules.

Clinical trial registration

https://clinicaltrials.gov, NCT04887090.

MRI based volumetric lung nodule assessment - a comparison to computed tomography

Purpose

Previous studies have demonstrated that nodule volumetry allows for the deduction of imaging-based biomarkers such as volume doubling time, enabling superior discrimination between benign and malignant lesions compared to 2D-based morphological characteristics. The study aimed to assess the feasibility and accuracy of in-vivo magnetic resonance imaging (MRI)-based volumetric assessment of lung nodules larger than 6 mm, in comparison to the current gold standard, CT.

Materials and methods

This study involved a subgroup analysis of 233 participants from a prospective, single-center lung cancer screening program using CT and MRI. Patients were included if foci ≥6 mm were detected in CT during the initial screening round, resulting in 23 participants with 47 pulmonary nodules. MRI was performed using a 1.5 Tesla unit with a transverse T2-weighted MultiVane XD imaging technique, while low-dose CT (LDCT) was performed on a 128-slice spiral CT scanner. Volumetric nodule assessment was conducted using a computer-aided diagnosis system, with images reviewed by two experienced radiologists. Statistical analysis included regression analysis, Bland-Altman analysis, and calculation of the interclass correlation coefficient (ICC) to assess correlation and reproducibility.

Results

Comparison of MRI-based volumetric assessment with LDCT as the reference standard revealed a mean nodule volume of 1.1343 ± 3.1204 cm3 for MRI versus 1.2197 ± 3.496 cm3 for LDCT (p = 0.203). Regression analysis demonstrated a strong linear relationship between the modalities (r 2 = 0.981, p < 0.001), consistently observed even for nodules <5 cm3 (r 2 = 0.755, p < 0.001). Bland-Altman analysis indicated no significant systematic bias in nodule volume measurements between MRI and CT, with a mean difference of 0.12 cm3 and narrow 95% confidence intervals (-6.852 to 6.854 cm3). Intra-reader reproducibility for CT-based volumetry was excellent (ICC = 0.9984), while MRI-based measurements showed good reproducibility (ICC = 0.7737). Inter-reader reproducibility was high for CT (ICC = 0.995) and moderate for MRI (ICC = 0.7135).

Conclusion

This study demonstrates that MRI-based volumetry of lung nodules ≥6 mm is feasible and accurate, showing comparable precision to CT with minimal bias in volume measurements, and highlights the potential of MRI as a radiation-free alternative for lung nodule follow-up and screening.

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.

Natural History of subcentimeter pulmonary nodules in clinical stage I seminoma patients

INTRODUCTION AND OBJECTIVE

Subcentimeter pulmonary nodules (SPN) found in clinical stage I (CS I) seminoma may be early pulmonary metastases or incidental, benign entities that may lead to patient anxiety and overtreatment. This study aims to demonstrate the incidence and natural history of SPN in CS I seminoma patients.

METHODS

A retrospective study reviewing the medical records of CS I seminoma patients treated at UC San Diego Health between 2003 and 2023. Data collection included demographics, serum tumor markers (STM), imaging reports, pathologic findings, treatment records, and records of disease relapse. We described SPN as a finding either from a chest X-ray (CXR) or a computed tomography (CT) scan of the chest at the time of seminoma diagnosis, with a size <1cm. The incidence of SPN and relationship with disease relapse was explored.

RESULTS

79 patients with CS I seminoma were included in the study, and mean follow-up time was 40 months. Our general practice is to observe all patients with stage I seminoma except under extenuating circumstances. Among them, 21 patients were found to have SPN, all which were diagnosed on CT scan of chest, resulting in an incidence rate of 26.6%. Notably, there was no statistically significant difference in the occurrence of SPN between patients with CS IA and CS IB (27.9% and 22.2%, respectively, P = 0.227). Four patients (5%) experienced disease relapse. None of the patients that had a relapse had an incidental subcentimeter nodules. Six patients received adjuvant chemotherapy (CMT); 1 patient had a pulmonary nodule and did not relapse; 1 patient experienced disease relapse without nodule. 10 patients underwent adjuvant radiation (RT), with no recurrence observed despite 4 of them having nodules. Additionally, 5 patients with nodules received adjuvant CMT or RT; none recurred. 16 patients with nodules were under surveillance, none recurred.

CONCLUSIONS

The incidence of SPN in CS I seminoma patient is high. Subcentimeter nodules do not appear to be related to risk of disease relapse. Our findings suggest that patients with CS I seminoma and incidental SPN can be counseled that this is a common, clinically insignificant finding. Further validation in a larger population is necessary.

Investigation of the integration of computed tomography (CT) value doubling time for lung cancer with subsolid nodules using a three-dimensional image analysis system

AIM

In the natural history of lung cancer with pure ground-glass nodule (GGN) or part solid GGN, not only the size but also the density of the tumour should be evaluated with computed tomography (CT). However, quantitative evaluation methods for tumour density are scarce. We hypothesised that the density could be quantitatively evaluated using the tumour CT values.

MATERIALS AND METHODS

Patients undergoing surgery at our department between 2016 and 2021 and meeting the following conditions were considered eligible: two or more CT scans at least 6 months apart before surgery, primary lung adenocarcinoma, and pure or part solid GGN. Integration of CT values (ICV) of tumours at two time points was carried out using three-dimensional image analysis. ICV doubling time (IDT) calculated from changes in ICV was compared with volume doubling time (VDT). IDT and VDT were calculated based on the CT imaging interval.

RESULTS

A total of 107 cases (54 men and 53 women; median age, 71) were analysed, consisting of 61 cases of pure GGN and 46 cases of part solid GGN. For all patients, IDT (median, 789 days) was significantly shorter than VDT (median, 1000 days) (p=0.026). IDT for part solid GGN (median, 588 days) was significantly shorter than that for pure GGN (median, 961 days) (p=0.005).

CONCLUSIONS

The integration of CT values consistently reflected changes in both size and density of tumours containing ground-glass components. IDT could be a reliable indicator in the natural history of lung cancer with a ground-glass component.

Feasibility of Sub-milliSievert Low-dose Computed Tomography with Deep Learning Image Reconstruction in Evaluating Pulmonary Subsolid Nodules: A Prospective Intra-individual Comparison Study

RATIONALE AND OBJECTIVES

To comprehensively assess the feasibility of low-dose computed tomography (LDCT) using deep learning image reconstruction (DLIR) for evaluating pulmonary subsolid nodules, which are challenging due to their susceptibility to noise.

MATERIALS AND METHODS

Patients undergoing both standard-dose CT (SDCT) and LDCT between March and June 2023 were prospectively enrolled. LDCT images were reconstructed with high-strength DLIR (DLIR-H), medium-strength DLIR (DLIR-M), adaptive statistical iterative reconstruction-V level 50% (ASIR-V-50%), and filtered back projection (FBP); SDCT with FBP as the reference standard. Objective assessment, including image noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR), and subjective assessment using five-point scales by five radiologists were performed. Detection and false-positive rate of subsolid nodules, and morphologic features of nodules were recorded.

RESULTS

102 patients (mean age, 57.0 ± 12.3 years) with 358 subsolid nodules in SDCT were enrolled. The mean effective dose of SDCT and LDCT were 5.37 ± 0.80mSv and 0.86 ± 0.14mSv, respectively (P < 0.001). DLIR-H showed the lowest noise, highest CNRs, SNRs, and subjective scores among LDCT groups (all P < 0.001), almost approaching comparability with SDCT. The detection rates for DLIR-H, DLIR-M, ASIR-V-50%, and FBP were 76.5%, 76.3%, 83.8%, and 72.1%, respectively (P < 0.001), with false-positive rate of 2.5%, 2.2%, 8.3%, and 1.1%, respectively (P < 0.001). DLIR-H showed the highest detection rates for morphologic features (79.4%-95.2%) compared to DLIR-M (74.6%-88.9%), ASIR-V-50% (72.0%-88.4%), and FBP (66.1%-84.1%) (all P ≤ 0.001).

CONCLUSION

Sub-milliSievert LDCT with DLIR-H offers substantial dose reduction without compromising image quality. It is promising for evaluating subsolid nodules with a high detection rate and better identification of morphologic features.

Enhancing identification of early-stage lung adenocarcinomas through solid component analysis of three-dimensional computed tomography images

OBJECTIVES

As the role of segmentectomy expands in managing early-stage lung adenocarcinoma, precise preoperative assessments of tumor invasiveness via computed tomography become crucial. This study aimed to evaluate the effectiveness of solid component analysis of three-dimensional (3D) computed tomography images and establish segmentectomy criteria for early-stage lung adenocarcinomas.

METHODS

This retrospective study included 101 cases with adenocarcinoma diagnoses, with patients undergoing segmentectomy for clinical stage 0 or IA between 2012 and 2017. The solid component volume (3D-volume) and solid component ratio (3D-ratio) of tumors were calculated using 3D computed tomography. Additionally, based on two-dimensional (2D) computed tomography, the solid component diameter (2D-diameter) and solid component ratio (2D-ratio) were calculated. The area under the receiver-operating characteristic curve (AUC) was calculated for each method, facilitating predictions of mortality and recurrence within 5 years. The AUC of each measurement was compared with those of invasive component diameter (path-diameter) and invasive component ratio (path-ratio) obtained through pathology analysis.

RESULTS

The predictive performance of 3D-volume did not differ significantly from that of path-diameter, whereas 2D-diameter exhibited less predictive accuracy (AUC: 3D-volume, 2D-diameter, and path-diameter: 0.772, 0.624, and 0.747, respectively; 3D-volume vs. path-diameter: p = 0.697; 2D-diameter vs. path-diameter: p = 0.048). Results were similar for the solid component ratio (AUC: 3D-ratio, 2D-ratio, path-ratio: 0.707, 0.534, and 0.698, respectively; 3D-ratio vs. path-ratio: p = 0.882; 2D-ratio vs. path-ratio: p = 0.038).

CONCLUSION

Solid component analysis using 3D computed tomography offers advantages in prognostic prediction for early-stage lung adenocarcinomas.

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.

Early Lung Adenocarcinoma Manifesting as Irregular Subsolid Nodules: Clinical and CT Characteristics

RATIONALE AND OBJECTIVES

To explore the clinical and computed tomography (CT) characteristics of early-stage lung adenocarcinoma (LADC) that presents with an irregular shape.

MATERIALS AND METHODS

The CT data of 575 patients with stage IA LADC and 295 with persistent inflammatory lesion (PIL) manifesting as subsolid nodules (SSNs) were analyzed retrospectively. Among these patients, we selected 233 patients with LADC and 140 patients with PIL, who showed irregular SSNs, hereinafter referred to as irregular LADC (I-LADC) and irregular PIL (I-PIL), respectively. The incidence rates, clinical characteristics, and CT features of I-LADC and I-PIL were compared. Additionally, binary logistic regression analysis was performed to determine the independent factors for diagnosing I-LADC.

RESULTS

The incidence rates of I-LADC and I-PIL were 40.5% (233/575) and 47.5% (140/295), respectively, with no statistically significant difference observed between the two groups (P > 0.05). Univariate analysis revealed significant differences in three clinical characteristics and 13 radiological features between I-LADC and I-PIL (all P < 0.05). Binary logistic regression indicated that the alignment of the long axis of SSN with the bronchial vascular bundle, a well-defined boundary of ground-glass opacity, lobulation, arc concave sign, and absence of knife-like change were the independent predictors of I-LADC, yielding an area under the curve and accuracy of 0.979% and 93.5%, respectively.

CONCLUSION

Early LADC presenting as SSNs is associated with a high incidence of irregular shape. I-LADC and I-PIL exhibited different clinical and imaging characteristics. A good understanding of these differences may be helpful for the accurate diagnosis of I-LADC.

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.

Deep Learning-Based Computer-Aided Diagnosis in Coronary Artery Calcium-Scoring CT for Pulmonary Nodule Detection: A Preliminary Study

PURPOSE

To evaluate the feasibility and utility of deep learning-based computer-aided diagnosis (DL-CAD) for the detection of pulmonary nodules on coronary artery calcium (CAC)-scoring computed tomography (CT).

MATERIALS AND METHODS

This retrospective study included 273 patients (aged 63.9±13.2 years; 129 men) who underwent CAC-scoring CT. A DL-CAD system based on thin-section images was used for pulmonary nodule detection, and two independent junior readers reviewed the standard CAC-scoring CT scans with and without referencing DL-CAD results. A reference standard was established through the consensus of two experienced radiologists. Sensitivity, positive predictive value, and F1-score were assessed on a per-nodule and per-patient basis. The patients' medical records were monitored until November 2023.

RESULTS

A total of 269 nodules were identified in 129 patients. With DL-CAD assistance, the readers' sensitivity significantly improved (65% vs. 80% for reader 1; 82% vs. 86% for reader 2; all p<0.001), without a notable increase in the number of false-positives per case (0.11 vs. 0.13, p=0.078 for reader 1; 0.11 vs. 0.11, p>0.999 for reader 2). Per-patient analysis also enhanced sensitivity with DL-CAD assistance (73% vs. 84%, p<0.001 for reader 1; 89% vs. 91%, p=0.250 for reader 2). During follow-up, lung cancer was diagnosed in four patients (1.5%). Among them, two had lesions detected on CAC-scoring CT, both of which were successfully identified by DL-CAD.

CONCLUSION

DL-CAD based on thin-section images can assist less experienced readers in detecting pulmonary nodules on CAC-scoring CT scans, improving detection sensitivity without significantly increasing false-positives.

Relevance of indeterminate pulmonary nodules in predicting distant metastasis in colorectal cancer

BACKGROUND

The detection of indeterminate pulmonary nodules (IPN) at diagnosis of colorectal cancer (CRC) has increased. However, there is limited information on predictive factors for its progression (pPF) to pulmonary metastases (PM). This study aims to identify these pPF to select appropriate management strategies.

METHODS

Single-center observational retrospective study including patients who underwent elective surgery for first non-metastatic CRC episode (January 2016- June 2019) with IPN at diagnosis. Patients were divided into those who developed PM in the same location as previous IPN (LM group) and those who did not (FM group).

RESULTS

One hundred twenty-one patients were included; 4.9% developed PM in the same location as previous IPN. Univariate analysis revealed a significant difference in IPN size between groups with 8 (5, 10) mm in LM versus 3 (1, 5) mm in FM (P=0.006). ROC curve showed a size of ≥5 mm as the best cutoff point to predict IPN progression. Multivariate analysis identified size ≥5mm as the only independent pPF (OR 11.9, 95%CI 1.3-105.8, P=0.026). The median time to diagnose PM in LM group was 13.8(SD 5.2) months.

CONCLUSIONS

We recommend a closer follow-up for patients with CRC and IPN ≥5 mm at diagnosis so they will have a higher risk of developing PM.

Speed and efficiency: evaluating pulmonary nodule detection with AI-enhanced 3D gradient echo imaging

OBJECTIVES

Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.

MATERIALS AND METHODS

In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15). Two readers evaluated image quality (5-point Likert scale), nodule detection and characterisation (size and morphology) of all sequences compared to CT in a blinded setting. Reader agreement was determined using the intraclass correlation coefficient (ICC).

RESULTS

Thirty-seven patients with 64 pulmonary nodules (solid n = 57 [3-107 mm] part-solid n = 6 [ground glass/solid 8 mm/4-28 mm/16 mm] ground glass nodule n = 1 [20 mm]) were analysed. Nominal scan times were CS-AI-7 3:53 min; CS-AI-10 2:34 min; CS-AI-15 1:50 min. CS-AI-7 showed higher image quality, while quality remained diagnostic even for CS-AI-15. Detection rates of pulmonary nodules were 100%, 98.4%, and 96.8% for CS-AI factors 7, 10, and 15, respectively. Nodule morphology was best at the lowest acceleration and was inferior to CT in only 5% of cases, compared to 10% for CS-AI-10 and 23% for CS-AI-15. The nodule size was comparable for all sequences and deviated on average < 1 mm from the CT size.

CONCLUSION

The combination of compressed sensing and AI enables a substantial reduction in the scan time of lung MRI while maintaining a high detection rate of pulmonary nodules.

CLINICAL RELEVANCE STATEMENT

Incorporating compressed sensing and AI in pulmonary MRI achieves significant time savings without compromising nodule detection or characteristics. This advancement holds clinical promise, enhancing efficiency in lung cancer screening without sacrificing diagnostic quality.

KEY POINTS

Lung cancer screening by MRI may be possible but would benefit from scan time optimisation. Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors. Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.

Radiofrequency Ablation Versus Thoracoscopic Sublobar Resection for the Treatment of Pulmonary Ground Glass Nodules: A Retrospective Observational Study

PURPOSE

A variety of treatments options for pulmonary ground glass nodules (GGNs) are currently under investigation. This study aimed to retrospectively compare the safety and efficacy of two therapeutic concepts, radiofrequency ablation (RFA) and thoracoscopic sublobar resection, in the treatment of patients with resectable pulmonary GGNs.

MATERIALS AND METHODS

This bi-center retrospective study included patients with resectable pulmonary GGNs who received either thoracoscopic sublobar resection or percutaneous computed tomography (CT)-guided RFA. Patients' clinical outcomes were compared between the two groups.

RESULTS

Between November 2019 and June 2023, a total of 71 patients were included, with 34 patients undergoing CT-guided RFA after refusing surgery, and 37 patients receiving thoracoscopic sublobar resection. No local recurrence or distant metastasis was observed in either group during the follow-up period (24 (interquartile range, 17.5-34.0) months for sublobectomy group and 30 (interquartile range, 17.5-38.75) months for RFA group). Compared to the thoracoscopic sublobar resection group, the RFA group had significantly fewer postoperative complications according to the Clavien-Dindo classification, particularly a lower incidence of pleural effusion (P < 0.001). The overall hospital stay length was also significantly shorter in the RFA patients (1 day versus 8 days, P < 0.001).

CONCLUSIONS

This limited series suggests that percutaneous CT-guided RFA may be a safe and effective treatment option for patients with resectable pulmonary GGNs who refuse surgical intervention.

Computed Tomography-Guided Radiofrequency Ablation Combined With Video-Assisted Thoracoscopic Surgery for Multiple Pulmonary Nodules: A Retrospective Study From the National Cancer Center in China

BACKGROUND

Recently, the incidence of multiple pulmonary nodules (MPNs) is gradually rising. Therefore, this study aims to evaluate the safety and efficacy of computed tomography (CT)-guided radiofrequency ablation (RFA) combined with video-assisted thoracoscopic surgery (VATS) for patients with MPNs.

MATERIAL AND METHODS

The clinicopathological data and perioperative results of the patients with MPNs who underwent RFA combined with VATS at our center from October 2022 to September 2024 were reviewed. The primary endpoints were the safety and feasibility of this combined technique.

RESULTS

A total of 105 patients were enrolled in this study, including 30 males and 75 females with a mean age of 55.1 years. In total, 293 lesions were treated, 113 of which were ablated and 180 were surgically resected. The mean nodule size was 6.58 mm for ablated nodules and 10.3 mm for resected nodules. Of the 113 nodules treated using RFA, 112 were ground-glass nodules. The median ablation time and power of RFA were 5 min and 60 W, respectively. Of the 180 surgically resected nodules, 169 had ground-glass opacity. Total postoperative complication morbidity was 9.5% (10/105), with major complications (Clavien-Dindo classification ≥ 3) in 1.0% (1/105). No perioperative deaths occurred, and the median hospital stay was 5 days (range, 5-7 days). Notably, no recurrence has been observed in any patients during the short-term follow-up period.

CONCLUSIONS

Our study demonstrated that CT-guided RFA combined with VATS is a safe and feasible therapeutic technique for the patients with MPNs. Given the increasing incidence of MPNs, this combination strategy holds significant potential for clinical application.

Leveraging Artificial Intelligence as a Safety Net for Incidentally Identified Lung Nodules at a Tertiary Center

BACKGROUND

Artificial intelligence (AI)-powered platforms may be used to ensure that clinically significant lung nodules receive appropriate management. We studied the impact of a commercially available AI natural language processing tool on the detection of clinically significant indeterminate pulmonary nodules (IPNs) based on radiology reports and provision of guideline-consistent care.

STUDY DESIGN

All CT scans performed at a single tertiary care center in the outpatient or emergency room setting between February 20, 2024, and March 20, 2024, were processed by the AI natural language processing algorithm. CT radiology reports mentioning a lung nodule or focal indeterminate lesion were flagged. All flagged reports were reviewed by a lung nodule expert 2 weeks after nodule identification. IPNs were classified as "appropriately followed" if follow-up imaging, referral to a nodule clinic, or other guideline-consistent care was ordered. IPNs were classified as "not appropriately followed" if no acknowledgment of the reported nodule was documented in the electronic health record within 2 weeks of being flagged.

RESULTS

The AI software processed 76,507 unique radiology reports, identified 2,585 CT scans with chest imaging, and found 389 IPNs. Review determined that 272 (70%) nodules were appropriately followed, whereas 117 (30%) were not appropriately followed. Of the 117 nodules without documented follow-up, 67 (57%) were more than 8 mm and 24 (20.5%) were more than 15 mm. IPNs that would not have received follow-up in the absence of the AI software generated 43 additional clinical appointments and 3 procedures.

CONCLUSIONS

At a large tertiary care center, 30% of clinically significant incidental pulmonary nodules that would have otherwise been missed were brought to the attention of lung nodule clinicians by an AI software, allowing for initiation of appropriate follow-up.

Lung cancer detection by electronic nose analysis of exhaled breath: a multi-center prospective external validation study

BACKGROUND

Electronic nose (eNose) analysis of exhaled breath shows potential for accurate and timely lung cancer diagnosis, yet prospective external validation studies are lacking. Our study primarily aimed to prospectively and externally validate a published eNose model for lung cancer detection in COPD patients and assess its diagnostic performance alongside a new eNose model, specifically tailored to the target population, in a more general outpatient population.

PATIENTS AND METHODS

This multi-center prospective external validation study included adults with clinical and/or radiological suspicion of lung cancer who were recruited from thoracic oncology outpatient clinics of two sites in The Netherlands. Breath profiles were collected using a cloud-connected eNose (SpiroNose®). The diagnostic performance of the original and new eNose model was assessed in various population subsets based on ROC-AUC, specificity, positive predictive value (PPV), and negative predictive value (NPV), targeting 95% sensitivity. For the new eNose model, a training and validation cohort were used.

RESULTS

Between March 2019 and November 2023, 364 participants were included. The original eNose model detected lung cancer with a ROC-AUC of 0.92 (95% CI: 0.85-0.99) in COPD patients (n=98/116; 84%) and 0.80 (95% CI: 0.75-0.85) in all participants (n=216/364; 59%). At 95% sensitivity, the specificity, PPV, and NPV, were 72% and 51%, 95% and 74%, and 72% and 88%, respectively. In the validation cohort, the new eNose model identified lung cancer across all participants (n=72/121; 60%) with a ROC-AUC of 0.83 (95% CI: 0.75-0.91), 94% sensitivity, 63% specificity, PPV of 79%, and NPV of 89%. Notably, accurate detection was consistent across tumour characteristics, disease stage, diagnostic centers, and clinical characteristics.

CONCLUSION

This multi-center prospective external validation study confirms that eNose analysis of exhaled breath enables accurate lung cancer detection at thoracic oncology outpatient clinics, irrespective of tumour characteristics, disease stage, diagnostic center, and clinical characteristics.

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.

Limited effect of antibiotic use on the management of pulmonary ground-glass nodules

Although pulmonary ground-glass nodules (GGNs) are encountered as common incidental findings, limited evidence exists regarding antibiotic prescriptions in managing GGNs. This study aimed to examine the clinical impact of antibiotics in treating patients presenting with GGNs. This retrospective study was conducted at West China Hospital of Sichuan University, involving 2,609 participants with incidentally detected GGNs between August 10, 2018 and July 22, 2022. Treatments were classified into antibiotic prescription versus no antibiotic prescription. Baseline characteristics and incidences of clinical outcomes (surgical resection, lung cancer diagnosis, beneficial response, and GGN growth) were evaluated. Of the 867 participants finally analyzed (184 antibiotic users; 683 antibiotic non-users), 85.2% were never smokers, and 34.7% presented with respiratory symptoms. The decision to prescribe antibiotics was correlated with the presence of symptoms and larger nodules. After propensity score matching, a higher incidence of surgical resection was observed in antibiotic users versus matched controls (40.8% vs. 29.9%, p = 0.049), whereas there was a trend toward an increased rate of lung cancer diagnosis, which was not statistically significant (32.6% vs. 22.8%, p = 0.054). Significant differences in radiographic response were not found, even among patients with suspected infection. In conclusion, limited beneficial effects of antibiotic use in the management of GGNs were observed, even among patients with suspected infection. These findings do not support empiric antibiotic administration in GGNs and call for efforts to develop outpatient antibiotic stewardship programs.

Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images

Large language models have shown efficacy across multiple medical tasks. However, their value in the assessment of longitudinal follow-up computed tomography (CT) images of patients with lung nodules is unclear. In this study, we evaluate the ability of the latest generative pre-trained transformer (GPT)-4o model to assess changes in malignancy probability, size, and features of lung nodules on longitudinal CT scans from 647 patients (547 from two local centers and 100 from a public dataset). GPT-4o achieves an average accuracy of 0.88 in predicting lung nodule malignancy compared to pathological results and an average intraclass correlation coefficient of 0.91 in measuring nodule size compared with manual measurements by radiologists. Six radiologists' evaluations demonstrate GPT-4o's ability to capture changes in nodule features with a median Likert score of 4.17 (out of 5.00). In summary, GPT-4o could capture dynamic changes in lung nodules across longitudinal follow-up CT images, thus providing high-quality radiological evidence to assist in clinical management.

A review of silicosis and other silica-related diseases in the engineered stone countertop processing industry

BACKGROUND

Engineered stone (ES), a material that has become widespread for its use in kitchen and bathroom countertops since the 1980s, is composed of over 90% crystalline silica by weight, significantly exceeding the silica content of natural stones such as granite (40-50%) and marble (< 10%). Workers fabricating ES are exposed to dangerously high levels of respirable crystalline silica (RCS) and other toxic chemicals, which increases the risk of developing silicosis and other lung and systemic diseases. The purpose of this review is to explore the epidemiology, occupational risks, regulatory gaps, diagnostic evaluation, and clinical challenges associated with ES dust exposure.

MAIN BODY

ES silicosis was first described in the early 2010s among ES countertop workers in Spain, Italy, and Israel. Since then, hundreds of cases have emerged worldwide, namely in China, Australia, the United States, the United Kingdom, and Belgium. Silicosis from ES dust is accelerated and diagnosed after 7-19 years of exposure, often affecting young individuals (median age 33-55 years) from marginalized or immigrant communities. Morbidity and mortality are poor, with high rates of lung transplantation and death. Industrial hygiene air sample monitoring data shows that despite engineering controls such as wet saws and exhaust ventilation, exposure to respirable crystalline silica when cutting ES frequently exceeds safe exposure levels. Diagnostic evaluation and treatment are clinically challenging due to delayed medical screening, misdiagnosis, and lack of treatment options.

CONCLUSIONS

This review underscores the urgent need for enhanced occupational safety regulations, active screening, and healthcare support to address the rising burden of ES silicosis among vulnerable worker populations globally.

Application and evaluation of NCCN guidelines in health education for lung nodule screening: A perspective

This study investigates the application and evaluation of National Comprehensive Cancer Network (NCCN) guidelines within health education frameworks aimed at lung nodule screening. Through the integration of NCCN directives, tailored educational strategies catering to diverse demographics, and robust interdisciplinary collaboration, the research underscores the pivotal role of health education in optimizing screening efficacy and patient outcomes. Moreover, it critically analyzes the challenges encountered, offering insightful recommendations for future research and practice while avoiding replication of existing literature. This study contributes to the field with scholarly rigor, emphasizing the imperative of continuous education in improving patient care standards and mitigating the burden of lung cancer.

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.

Computed Tomography Combined With Real-time Ultrasound-guided Percutaneous Needle Biopsy of Peripleural Lung Nodules

BACKGROUND

Ultrasound is rarely used for lung biopsy because the ultrasound window is too narrow to capture puncture points and perform whole imaging of lesions. The present study examined the usefulness of computed tomography (CT) combined with real-time ultrasound-guided percutaneous needle biopsy for peripleural lesions in clinical practice.

METHODS

In total, 59 patients with peripleural lesions who had undergone CT combined with ultrasound-guided percutaneous biopsy and 70 patients who had undergone conventional CT-guided biopsy at the Radiology Department of Wuxi NO.2 People's Hospital between January 2017 and June 2023 were enrolled. The operation duration, machine room occupation duration, number of CT-guided scans, radiation dose absorbed from the CT scans, and puncture-related complications were compared between the 2 groups of patients.

RESULTS

The operation duration (CT: 31.21 ± 7.99 min vs. CT + ultrasound: 22.20 ± 5.14 min, P < 0.001) and room occupation duration (43.17 ± 7.94 vs. 32.78 ± 5.15 min, P < 0.001) were significantly shorter and the number of CT-guided scans (3.31 ± 0.84 vs. 2.22 ± 0.42 times, P < 0.01) and the radiation dose absorbed from the CT scans were significantly lower (3.89 ± 1.07 vs. 2.56 ± 0.64 mSv, P < 0.001) in the CT combined with ultrasound group than in the conventional CT-guided puncture group. The results were significant after adjusting for age, sex, lesion thickness, and puncture depth.

CONCLUSIONS

CT combined with real-time ultrasound-guided biopsy may be a useful biopsy technique for peripleural lesions in general hospitals.

The Effect of X-ray Dose Photon-Counting Detector Computed Tomography on Nodule Properties in a Lung Cancer Screening Cohort: A Prospective Study

OBJECTIVES

The aim of the study was to evaluate the effect of photon-counting detector (PCD-)CT dose reduction to x-ray equivalent levels on nodule detection, diameter, volume, and density compared to a low-dose reference standard using semiautomated and manual methods.

MATERIALS AND METHODS

Between February and July 2023, 101 prospectively enrolled participants underwent noncontrast same-study low- and chest x-ray-dose CT scans using PCD-CT. Patients who were not referred for lung cancer screening or nodule follow-up, as well as those with nodules smaller than 5 mm in diameter, were excluded. Nodule detection and measurement of nodule diameters and volumes was semiautomatically performed for low- and x-ray-dose scans using computer-aided diagnosis software. Additionally, 2 blinded readers manually measured largest nodule diameters and examined nodule density. Nodules were classified using Lung-RADS v2022. Image quality was assessed with subjective and objective measures.

RESULTS

Mean CTDIvol for x-ray dose scans was 0.11 ± 0.03 mGy, compared to 0.65 ± 0.15 mGy for low-dose images (P < 0.001). One hundred seventy-two nodules larger than 5 mm were detected in 53 of the 101 participants (32 male, 61.6 ± 12.5 years; 21 female, 60.3 ± 12.5 years). The semiautomated method had high overall sensitivity for nodule detection (0.94) on x-ray dose scans, with a higher sensitivity for solid nodules (>0.95) and lower for subsolid nodules (>0.86). Nodules not detected on x-ray dose scans were significantly smaller. Semiautomated measurements underestimated nodule diameter for solid nodules on x-ray dose scans (P = 0.01), but no significant effect for nodule volume was found (P = 0.775). Readers rated nodule density less dense on x-ray dose scans (R1: P < 0.001, R2: P = 0.006). There was no significant difference in nodule diameter for both readers between scan doses (R1: P = 0.141; R2: P = 0.554). There were good to excellent correlations between semiautomated and reader nodule diameters. Agreement and accuracy between low-dose and x-ray dose Lung-RADS classifications across methods were good (Cohens' к = 0.73, 0.62, 0.76 for semiautomated method, R1 and R2; resp. Accuracy: 0.82, 0.78, 0.85). No Lung-RADS classification changes were observed with semiautomated volumetric measurements of nodules.

CONCLUSIONS

Semiautomated nodule detection is highly sensitive in PCD-CT x-ray dose scans. Semiautomated nodule volume measurement is more robust to image quality changes than nodule diameter. Accurate semiautomated and manual nodule measurements are feasible on x-ray dose scans, but nodule density was in tendency underestimated. Nodule classification using Lung-RADS was shown to be accurate on x-ray dose scans.

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.

PMILACG Model: A Predictive Model for Identifying Invasiveness of Lung Adenocarcinoma Based on High-Resolution CT-Determined Ground Glass Nodule Features

The morphology of ground-glass nodule (GGN) under high-resolution computed tomography (HRCT) has been suggested to indicate different histological subtypes of lung adenocarcinoma (LUAD); however, existing studies only include the limited number of GGN characteristics, which lacks a systematic model for predicting invasive LUAD. This study aimed to construct a predictive model based on GGN features under HRCT for LUAD. A total of 1,189 surgical LUAD patients were enrolled, and their GGN-related features were assessed by 2 individual radiologists. The pathological diagnosis of the invasive LUAD was established by pathologic examination following surgery (including 1,073 invasive and 526 non-invasive LUAD). After adjustment by multivariate logistic regression, GGN diameter (OR = 1.382, 95% CI: 1.300-1.469), mean CT attenuation (OR = 1.007, 95% CI: 1.006-1.009), heterogeneous uniformity of density (OR = 2.151, 95% CI: 1.587-2.915), not defined nodule-lung interface (OR = 1.915, 95% CI: 1.384-2.651), GGN with spiculation (OR = 2.097, 95% CI: 1.519-2.896), type I (OR = 1.678, 95% CI: 1.216-2.371), and type II (OR = 3.577, 95% CI: 1.153-11.097) vessel changes were independent risk factors for invasive LUAD. In addition, a predictive model integrating these six independent GGN features was established (named as invasion of lung adenocarcinoma by GGN features (ILAG)), and receiver-operating characteristic curve illustrated that the ILAG model presented good predictive value for invasive LUAD (AUC: 0.905, 95% CI: 0.890-0.919). In conclusion, The ILAG predictive model, which integrates imaging features of GGN via HRCT, including diameter, mean CT attenuation, heterogeneous uniformity of density, not defined nodule-lung interface, GGN with spiculation, type I, and type II vessel changes, shows great potential for early estimation of LUAD invasiveness.

Recommendation for Clinical T Staging in Patients with Non-Small Cell Lung Cancer: Volumetric Measurement: A Retrospective Study from Turkey

Background

Currently, clinical T staging in non-small cell lung cancer (NSCLC) is based on the largest radiological diameter observed on computed tomography (CT). Under this system, tumors with varying shapes-such as spherical, amorphous, or spiculated tumors- can be assigned the same T stage even with different volumes. We aimed to propose a 3-dimensional (3D) volumetric staging system for NSCLC as an alternative to diameter- based T staging and to conduct comparative survival analyses between these methods.

Methods

We retrospectively analyzed data from patients who underwent surgery for pT1-4N0M0 primary NSCLC between January 2018 and May 2022. Digital Imaging and Communications in Medicine data from patient CT scans were uploaded to 3D Slicer software for volumetric tumor measurement. Using the paired samples t-test or the Wilcoxon test, we compared the expected tumor volumes, calculated by tumor diameter, with the actual volumes measured by 3D Slicer. Receiver operating characteristic analysis was employed to determine the cut-off value for tumor volume. Kaplan-Meier analysis was utilized to assess overall survival, while the log-rank method was applied to compare survival differences between groups. The significance of changes in T stage was evaluated using the marginal homogeneity test.

Results

The study included 136 patients. Significant differences were observed between expected and actual tumor volumes (p=0.01), and associated changes in T stage were also significant (p=0.04). The survival analysis performed using tumor volume (p=0.009) yielded superior results compared to that based on diameter (p=0.04) in paients with early tumor stage.

Conclusion

T-factor staging based on tumor volume could represent an alternative staging method for NSCLC.

Tumor localization strategies of multicancer early detection tests: a quantitative assessment

BACKGROUND

Multicancer early detection tests may expand cancer screening. Characterizing diagnostic resolution approaches following positive multicancer early detection tests is critical. Two trials employed distinct resolution approaches: a molecular signal to predict tissue of origin and an imaging-based diagnostic strategy. This modeling study characterizes diagnostic journeys and impact in a hypothetical population of average-risk multicancer early detection-eligible patients.

METHODS

A mathematical expression for diagnostic burden was derived using positive predictive value (PPV), molecular tissue of origin localization accuracy, and numbers of procedures associated with each diagnostic outcome. Imaging-based and molecular tissue of origin-informed strategies were compared. Excess lifetime cancer risk due to futile radiation exposure was estimated using organ-specific diagnostic imaging radiation doses.

RESULTS

Across all PPVs and localization performances, a molecular tissue of origin strategy resulted in a higher diagnostic burden (mean = 3.6 [0.445] procedures vs mean = 2.6 [0.100] procedures) for the imaging strategy. Estimated diagnostic burden was higher for molecular tissue of origin in 95.5% of all PPV and tissue of origin accuracy combinations; at least 79% PPV and 90% accuracy would be required for a molecular tissue of origin-informed strategy to be less burdensome than imaging. The maximum rate of excess cancer incidence from radiation exposure for multicancer early detection false-positive results (individuals aged 50-84 years) was 64.6 of 100 000 (annual testing, 99% specificity), 48.5 of 100 000 (biennial testing, 98.5% specificity), and 64.6 of 100 000 (biennial testing, 98% specificity).

CONCLUSIONS

An imaging-based diagnostic strategy is more efficient than a molecular tissue of origin-informed approach across almost all PPV and tissue of origin accuracy combinations. The use of an imaging-based approach for cancer localization can be efficient and low-risk compared with a molecular-informed approach.

Calcified Lung Nodules: A Diagnostic Challenge in Clinical Daily Practice

Calcified lung nodules are frequently encountered on chest imaging, often as incidental findings. While calcifications are typically associated with benign conditions, they do not inherently exclude malignancy, making accurate differentiation essential. The primary diagnostic challenge lies in distinguishing benign from malignant nodules based solely on imaging features. Various calcification patterns, including diffuse, popcorn, lamellated and eccentric, provide important diagnostic clues, though overlap among different conditions may persist. A comprehensive diagnostic approach integrates clinical history with multimodal imaging, including magnetic resonance and nuclear medicine, when necessary, to improve accuracy. When imaging findings remain inconclusive, tissue sampling through biopsy may be required for definitive characterization. This review provides an overview of the imaging features of calcified lung nodules, emphasizing key diagnostic challenges and their clinical implications.

ACPA is a main risk factor for CT-proven pulmonary nodule progression in patients with rheumatoid arthritis

OBJECTIVES

To determine the features of rheumatoid pulmonary nodules and the factors associated with nodule progression in patients with rheumatoid arthritis.

METHODS

Between January 2010 and September 2018, RA patients with at least one chest computed tomography (CT) were included. Two experienced radiologists examined chest CTs. Nodules with changing dimensions on follow-up or at least two nodules with different sizes or cavitary nodules were considered rheumatoid pulmonary nodules. To identify follow-up changes in the nodules, progression was defined as the appearance of any new nodules or increase in the size of the nodules, regression was no new nodules and no increase in the size of any nodules and decrease in the size of at least one nodule, and stability was no appearance of new nodules and no change in the size of nodules and no disappearance of the nodule. We compared the demographics, comorbidities, RA-specific treatments, and nodule characteristics according to seropositivity. Factors that may be associated with RPN progression were studied.

RESULTS

A total of 204 (136 (66.7%) female) patients were included in the study. The median disease duration at baseline CT was 7.29 years (0.05-57.5). Pulmonary nodules were detected in the first CT of 21 (10.2%) patients before RA diagnosis, with a median time of 10.38 (0.46-254) months. The median number of nodules and median diameter of the dominant nodule were higher, and cavitation was more prevalent in seropositive patients. ACPA positivity was independently associated with progression (OR 3.69 (1.33-12.4), p = 0.03). Cs-DMARDs and b/ts-DMARDs, especially anti-TNF agents, did not affect nodule progression.

CONCLUSION

Rheumatoid pulmonary nodules may precede RA, and seropositivity, especially ACPA, is an important independent risk factor for RPN occurrence and progression. Key Points • Rheumatoid pulmonary nodules were mainly located peripherally, in the right lobe, and had a high cavitation rate. • ACPA positivity was found as a main effective factor in RPN progression. • Cs/b-DMARD treatments were not associated with RPN progression.

Performance of Lung Cancer Prediction Models for Screening-detected, Incidental, and Biopsied Pulmonary Nodules

Purpose To evaluate the performance of eight lung cancer prediction models on patient cohorts with screening-detected, incidentally detected, and bronchoscopically biopsied pulmonary nodules. Materials and Methods This study retrospectively evaluated promising predictive models for lung cancer prediction in three clinical settings: lung cancer screening with low-dose CT, incidentally detected pulmonary nodules, and nodules deemed suspicious enough to warrant a biopsy. The area under the receiver operating characteristic curve of eight validated models, including logistic regressions on clinical variables and radiologist nodule characterizations, artificial intelligence (AI) on chest CT scans, longitudinal imaging AI, and multimodal approaches for prediction of lung cancer risk was assessed in nine cohorts (n = 898, 896, 882, 219, 364, 117, 131, 115, 373) from multiple institutions. Each model was implemented from their published literature, and each cohort was curated from primary data sources collected over periods from 2002 to 2021. Results No single predictive model emerged as the highest-performing model across all cohorts, but certain models performed better in specific clinical contexts. Single-time-point chest CT AI performed well for screening-detected nodules but did not generalize well to other clinical settings. Longitudinal imaging and multimodal models demonstrated comparatively good performance on incidentally detected nodules. When applied to biopsied nodules, all models showed low performance. Conclusion Eight lung cancer prediction models failed to generalize well across clinical settings and sites outside of their training distributions. Keywords: Diagnosis, Classification, Application Domain, Lung Supplemental material is available for this article. © RSNA, 2025 See also commentary by Shao and Niu in this issue.

Towards ultra-low-dose CT for detecting pulmonary nodules using DenseNet

Low-radiation techniques should be used to detect and follow lung nodules on CT images, but reducing radiation dose to ultra-low-dose CT with submilliSievert dose level would drastically impede image quality and sensitivity for nodule detection. This study investigated the feasibility of using DenseNet to suppress image noise in ultra-low-dose CT for lung cancer screening. DenseNet was trained using input-label pairs from 1, 2, 4, and 6 patients. After training, the model was tested with chest CT from 14 patients that were not used in training process. Seven patients have solid nodules and 7 patients have subsolid nodules. Root mean square error (RMSE) and peak signal-to-noise ratio (PSNR) were calculated to quantify the difference between reference and test images. The contrast-to-noise ratio (CNR) between lung nodule and lung parenchyma was calculated to evaluate the target detectability of chest CT. Subjective image quality assessment was performed using 4-point ranking scale to evaluate the visual quality of CT images perceived by end user. Substantial improvements in RMSE and PSNR were observed after denoising. The lung nodules in denoised images could be distinguished more easily in comparison with those in the original ultra-low-dose CT, which is supported by the CNRs and subjective image quality scores. The comparison of intensity profiles for lung nodules demonstrated that the image noise in ultra-low-dose CT could be suppressed effectively after denoising without causing edge blurring or variation in Hounsfield unit (HU) values. A two-sample t-test revealed no statistically significant differences between full-dose CT and denoised ultra-low-dose CT in the evaluation of lung nodules, lung parenchyma, paraspinal muscle, or vertebral body. Since the linear no-threshold model suggests that no amount of ionizing radiation is entirely risk-free, the quest for further dose reduction remains a consistently important focus in radiology. Overall, our findings suggest that DenseNet could be a viable approach for reducing image noise in ultra-low-dose CT scans used for lung cancer screening.

Diagnostic yield and complications of CT-guided biopsy of lung lesions as a radiological outpatient clinic procedure

Background

Computerized tomography-guided transthoracic needle biopsy (CT-TTNB) plays an important role in the diagnostic work-up of lung lesions. The literature reports varying results on complication rates, severity of complications, and diagnostic yield.

Purpose

To evaluate CT-TTNB as a radiological outpatient clinic procedure and explore diagnostic yield and complication rates.

Material and methods

Between January 2017 and October 2019, a total of 559 patients underwent CT-TTNB. Patient records and CT scans were retrospectively reviewed and patient characteristics, lesion characteristics, biopsy procedure, and per- and post-procedural complications, as well as pathological diagnosis, were registered.

Results

Of 559 patients included, 511 had biopsies performed. Thereby, 48 biopsies (8.6%) were discontinued because of patient compliance issues and/or the occurrence of pneumothorax before the biopsy was performed. The overall pneumothorax rate was 49.2% (n = 275 of 559 patients). Insertion of a drainage catheter was needed in 85 of the 275 patients with pneumothorax. Parenchymal bleeding was seen in 26.5% of the patients and haemoptysis in 5.5%. No cases of bleeding or haemoptysis required intervention or admission. Small mean lesion size and increased distance from pleura to the lesion were associated with a higher occurrence of complications. A conclusive pathological diagnosis was obtained in 278 of 511 (54.4%) biopsies. No patients were re-admitted after the two-hour observational period in the radiological department.

Conclusion

CT-TTNB as an outpatient clinic procedure is feasible but has a moderate diagnostic yield and relatively high complication rates for minor complications.

A Predictive Model Integrating AI Recognition Technology and Biomarkers for Lung Nodule Assessment

BACKGROUND

 Lung cancer is the most prevalent and lethal cancer globally, necessitating accurate differentiation between benign and malignant pulmonary nodules to guide treatment decisions. This study aims to develop a predictive model that integrates artificial intelligence (AI) analysis with biomarkers to enhance early detection and stratification of lung nodule malignancy.

METHODS

 The study retrospectively analyzed the patients with pathologically confirmed pulmonary nodules. AI technology was employed to assess CT features, such as nodule size, solidity, and malignancy probability. Additionally, lung cancer blood biomarkers were measured. Statistical analysis involved univariate analysis to identify significant differences among factors, followed by multivariate logistic regression to establish independent risk factors. The model performance was validated using receiver operating characteristic curves and decision curve analysis (DCA) for internal validation. Furthermore, an external dataset comprising 51 cases of lung nodules was utilized for independent validation to assess robustness and generalizability.

RESULTS

 A total of 176 patients were included, divided into benign/preinvasive (n = 76) and invasive cancer groups (n = 100). Multivariate analysis identified eight independent predictors of malignancy: lobulation sign, bronchial inflation sign, AI-predicted malignancy probability, nodule nature, diameter, solidity proportion, vascular endothelial growth factor, and lung cancer autoantibodies. The combined predictive model demonstrated high accuracy (area under the curve [AUC] = 0.946). DCA showed that the combined model significantly outperformed the traditional model, and also proved superior to models using AI-predicted malignancy probability or the seven lung cancer autoantibodies plus traditional model. External validation confirmed its robustness (AUC = 0.856), achieving a sensitivity of 0.80 and specificity of 0.86, effectively distinguishing between invasive and noninvasive nodules.

CONCLUSION

 This combined approach of AI-based CT features analysis with lung cancer biomarkers provides a more accurate and clinically useful tool for guiding treatment decisions in pulmonary nodule patients. Further studies with larger cohorts are warranted to validate these findings across diverse patient populations.

Head-to-head comparison of 68 Ga-FAPI-46 PET/CT, 18F-FDG PET/CT, and contrast-enhanced CT for the detection of various tumors

OBJECTIVE

FAPI-PET/CT exhibits high tumor uptake and low background accumulation, enabling high-sensitivity tumor detection. We compared the diagnostic performance of 68 Ga-FAPI-46 PET/CT plus contrast-enhanced CT (CE-CT), 18F-FDG PET/CT plus CE-CT, and standalone CE-CT in patients with various malignancies.

METHODS

232 patients underwent 68 Ga-FAPI-46 PET/CT,18F-FDG PET/CT, and CE-CT each within 4 weeks. Detection rates were assessed by a blinded reader, with ≥ 2 weeks between scans of the same patient to avoid recall bias. A sub-analysis of diagnostic performance was performed for 490 histopathologically validated lesions. Detection rates were compared using McNemar's test.

RESULTS

Lesion-based detection rates in 68 Ga-FAPI-46 PET/CT plus CE-CT, 18F-FDG PET/CT plus CE-CT, and CE-CT alone were 91.2% (1540/1688), 82.5% (1393/1688) and 60.2% (1016/1688). The detection rates were significantly higher for 68 Ga-FAPI-46 PET/CT plus CE-CT than for 18F-FDG PET/CT plus CE-CT (p < 0.02 for primary lesions and p < 0.001 for total, abdominopelvic nodal, liver and other visceral lesions) and CE-CT (p < 0.0001 for total, primary, cervicothoracic nodal, abdominopelvic nodal, liver, other visceral, and bone lesions). In the sub-analysis, sensitivity, specificity, positive and negative predictive value, and accuracy were 61.3%, 96.7%, 81.4%, 91.4% and 90.0% for 68 Ga-FAPI-46 PET/CT plus CE-CT, 57.0%, 95.7%, 75.7%, 90.5% and 88.4% for 18F-FDG PET/CT plus CE-CT, and 51.6%, 97.2%, 81.4%, 89.6% and 88.6% for CECT, respectively.

CONCLUSIONS

68 Ga-FAPI-46 PET/CT plus CE-CT demonstrates a higher tumor detection rate than 18F-FDG PET/CT plus CE-CT and CE-CT in a diverse spectrum of malignancies, especially for primary, abdominopelvic nodal, liver, and other visceral lesions. Further studies on which entities draw particular benefit from 68 Ga-FAPI-46 PET/CT are warranted to aid appropriate diagnostic workup.

TRIAL REGISTRATION

A total of N = 232 patients were analyzed. Of these, N = 50 patients were included in a prospective interventional trial (NCT05160051), and N = 175 in a prospective observational trial (NCT04571086) for correlation and clinical follow-up of PET findings; N = 7 patients were analyzed retrospectively.

Metastasis-directed therapy in oligometastatic and oligoprogressive renal cell carcinoma

PURPOSE OF REVIEW

This review addresses the evolving role of metastasis-directed therapy (MDT) in the management of oligometastatic and oligoprogressive renal cell carcinoma (RCC). With advances in both surgical techniques and stereotactic ablative radiotherapy (SABR), it is timely to explore how MDT can improve patient outcomes in these distinct disease states. The review highlights the potential of MDT to delay systemic therapy and improve quality of life while noting the lack of randomized clinical trial data guiding its use.

RECENT FINDINGS

Recent literature emphasizes the outcomes of MDT, including metastasectomy and SABR, in managing oligometastatic and oligoprogressive RCC. Key studies suggest that MDT may prolong progression-free survival and delay systemic therapy. SABR has demonstrated high local control rates and manageable toxicity, offering a less invasive alternative to surgery. Despite these findings, there remains uncertainty about MDT's long-term impact on overall survival due to the absence of prospective randomized trials.

SUMMARY

MDT holds promise in treating RCC by offering symptom relief, improving quality of life, and potentially delaying systemic therapy. However, the long-term benefits, particularly regarding survival outcomes, remain unclear. Further research, including prospective trials, is needed to better define the role of MDT in clinical practice, particularly in the absence of clear guidelines for patient selection.