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

Guidelines for the management of pulmonary nodules detected by low-dose CT lung cancer screening 6th edition: compiled by the Japanese Society of CT Screening

OBJECTIVE

The aim of this special report is to describe the 6th edition of "The Guidelines for the Management of Pulmonary Nodules Detected by Low-Dose CT Lung Cancer Screening ".

METHODS

Since the 5th edition six years ago, a review of the literature and consideration of consistency with new evidence led to the revision of the 6th edition.

RESULTS

The main revisions in the 6th edition can be summarized as follows: 1) addition of the section "Recommendations for Low-Dose CT Lung Cancer Screening in Japan"; 2) change in the recommended solid component diameter, and follow-up interval for nodules with a total mean diameter of less than 15 mm and a solid component diameter of less than 8 mm; 3) replacement of the recommended case images; and 4) introduction of the criteria of the Accreditation Council for Lung Cancer CT Screening.

CONCLUSION

This guideline is gradually gaining acceptance in Japan. This guideline should be applied carefully in clinical practice, considering various factors such as the patient's condition.

A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity

Chest computed tomography (CT) scans are essential for accurately assessing the severity of the novel Coronavirus (COVID-19), facilitating appropriate therapeutic interventions and monitoring disease progression. However, determining COVID-19 severity requires a radiologist with significant expertise. This study introduces a pioneering utilization of deep learning (DL) for evaluate COVID-19 severity using lung CT images, presenting a novel and effective method for assessing the severity of pulmonary manifestations in COVID-19 patients. Inception-Residual networks (Inception-ResNet), advanced hybrid models known for their compactness and effectiveness, were used to extract relevant features from CT scans. Inception-ResNet incorporates the dilated mechanism into its ResNet component, enhancing its ability to accurately classify lung involvement stages. This study demonstrates that dilated residual networks (dResNet) outperform their non-dilated counterparts in image classification tasks, as their architectural designs allow the systems to acquire comprehensive global data by expanding their receptive fields. Our study utilized an initial dataset of 1548 human thoracic CT scans, meticulously annotated by two experienced specialists. Lung involvement was determined by calculating a percentage based on observations made at each scan. The hybrid methodology successfully distinguished the ten distinct severity levels associated with COVID-19, achieving a maximum accuracy of 96.40%. This system demonstrates its effectiveness as a diagnostic framework for assessing lung involvement in COVID-19-affected individuals, facilitating disease progression tracking.

Hybrid iterative reconstruction in ultra-low-dose CT for accurate pulmonary nodule assessment: A Phantom study

This study evaluated hybrid iterative reconstruction in ultra-low-dose computed tomography (ULDCT) for solid pulmonary nodule detection. A 256-slice CT machine operating at 120 kVp imaged a chest phantom with 5 mm nodules. The imaging process involved adjusting low-dose computed tomography (LDCT) settings and conducting 3 ULDCT scans (A-C) with varied minimum and maximum mA settings (10/40 mA). Images were processed using iDose4 iterative reconstruction at levels 5 to 7. Measurements were taken for noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), noise power spectrum (NPS), and detectability index (D') to assess image quality, noise texture, and detectability. Analysis of variance (ANOVA) was used to compare the protocols. Noise levels varied significantly across iDose4 iterative reconstruction levels, with the highest noise at 178 HU in iDose4 L5 (protocol C) and the lowest at 54.85 HU in level 7 (protocol A). ULDCT scans showed noise increases of 38.5%, 104.2%, and 118.7% for protocols A, B, and C, respectively, compared to LDCT. Protocol A (iDose4 level 7) significantly improved SNR and CNR (P < .001). The mean volume CT dose index was 2.4 mGy for LDCT and 2.0 mGy, 1.2 mGy, and 0.7 mGy for ULDCT protocols A, B, and C, respectively. Increasing iDose4 levels reduced noise magnitude in the NPS and improved the D'. ULDCT with iDose4 level 7 provides diagnostically acceptable image quality for solid pulmonary nodule assessment at significantly reduced radiation doses. This approach, supported by advanced metrics like NPS and D', demonstrates a potential pathway for safer, effective lung cancer screening in high-risk populations. Further clinical studies are needed to validate these findings in diverse patient populations.

Spectral Shaping Computed Tomography Applications

Spectral shaping (also known as spectral filtration) has been utilized in some of the latest computed tomography (CT) systems. This technique involves using tin (Sn) or silver (Ag) filters, which selectively absorb low-energy photons. This review aims to demonstrate the utility of spectral shaping across a wide range of protocols and clinical situations. Spectral-shaped CT protocols using tin filters allow for the acquisition of diagnostic images and greatly reduce the radiation dose, metal artifacts, and photon starvation. These features make spectral shaping suitable for various clinical situations in diagnostic and interventional CT imaging.

Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point 18F-FDG PET/CT

BACKGROUND

Accurately predicting the malignant risk of ground-glass nodules (GGOs) is crucial for precise treatment planning. This study aims to utilize convolutional neural networks based on dual-time-point 18F-FDG PET/CT to predict the malignant risk of GGOs.

METHODS

Retrospectively analyzing 311 patients with 397 GGOs, this study identified 118 low-risk GGOs and 279 high-risk GGOs through pathology and follow-up according to the new WHO classification. The dataset was randomly divided into a training set comprising 239 patients (318 lesions) and a testing set comprising 72 patients (79 lesions), we employed a self-configuring 3D nnU-net convolutional neural network with majority voting method to segment GGOs and predict malignant risk of GGOs. Three independent segmentation prediction models were developed based on thin-section lung CT, early-phase 18F-FDG PET/CT, and dual-time-point 18F-FDG PET/CT, respectively. Simultaneously, the results of the dual-time-point 18F-FDG PET/CT model on the testing set were compared with the diagnostic of nuclear medicine physicians.

RESULTS

The dual-time-point 18F-FDG PET/CT model achieving a Dice coefficient of 0.84 ± 0.02 for GGOs segmentation and demonstrating high accuracy (84.81%), specificity (84.62%), sensitivity (84.91%), and AUC (0.85) in predicting malignant risk. The accuracy of the thin-section CT model is 73.42%, and the accuracy of the early-phase 18F-FDG PET/CT model is 78.48%, both of which are lower than the accuracy of the dual-time-point 18F-FDG PET/CT model. The diagnostic accuracy for resident, junior and expert physicians were 67.09%, 74.68%, and 78.48%, respectively. The accuracy (84.81%) of the dual-time-point 18F-FDG PET/CT model was significantly higher than that of nuclear medicine physicians.

CONCLUSIONS

Based on dual-time-point 18F-FDG PET/CT images, the 3D nnU-net with a majority voting method, demonstrates excellent performance in predicting the malignant risk of GGOs. This methodology serves as a valuable adjunct for physicians in the risk prediction and assessment of GGOs.

Non-Pharmacological Management of Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a relatively common progressive fibrotic interstitial lung disease associated with significant morbidity and mortality. The available medications for IPF only slow down the disease process, with lung transplantation the only option for a cure. Non-pharmacological therapies are significant adjuncts that can improve symptom burden and quality of life with minimal or no side effects. Supplemental oxygen can improve exercise capacity and the sensation of dyspnea in a significant portion of patients with resting or exertional hypoxemia and has been supported by several professional societies. Pulmonary rehabilitation is a comprehensive program that includes education and therapeutic exercises to improve patient stamina and strength. It is one of the few interventions that have been shown to produce a meaningful increase in a patient's exercise capacity, but its wide adoption is limited by availability, especially in rural areas. Sleep optimization with supplemental oxygen and positive airway pressure therapy should actively be investigated for all patients diagnosed with IPF. Although gastroesophageal reflux control with non-pharmacological means is still controversial as an intervention to reduce the rate of lung function decline, it can help control reflux symptoms and improve cough intensity. IPF patients should be educated on the importance of balanced nutrition and the potential benefits of screening for lung transplantation. Palliative medicine can help with symptom control and should be considered for all patients regardless severity, but especially in those in the later stages of disease.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules

Background

Currently, the computed tomography (CT) radiomics-based models, which can evaluate small (≤ 20 mm) solid pulmonary nodules (SPNs) are lacking. This study aimed to develop a CT radiomics-based model that can differentiate between benign and malignant small SPNs.

Methods

This study included patients with small SPNs between January 2019 and November 2021. The participants were then randomly categorized into training and testing cohorts with an 8:2 ratio. CT images of all the patients were analyzed to extract radiomics features. Furthermore, a radiomics scoring model was developed based on the features selected in the training group via univariate and multivariate logistic regression analyses. The testing cohort was then used to validate the developed predictive model.

Results

This study included 210 patients, 168 in the training and 42 in the testing cohorts. Radiomics scores were ultimately calculated based on 9 selected CT radiomics features. Furthermore, traditional CT and clinical risk factors associated with SPNs included lobulation (P < 0.001), spiculation (P < 0.001), and a larger diameter (P < 0.001). The developed CT radiomics scoring model comprised of the following formula: X = -6.773 + 12.0705×radiomics score+2.5313×lobulation (present: 1; no present: 0)+3.1761×spiculation (present: 1; no present: 0)+0.3253×diameter. The area under the curve (AUC) values of the CT radiomics-based model, CT radiomics score, and clinicoradiological score were 0.957, 0.945, and 0.853, respectively, in the training cohort, while that of the testing cohort were 0.943, 0.916, and 0.816, respectively.

Conclusions

The CT radiomics-based model designed in the present study offers valuable diagnostic accuracy in distinguishing benign and malignant SPNs.

MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images

Introduction

Pulmonary granulomatous nodules (PGN) often exhibit similar CT morphological features to solid lung adenocarcinomas (SLA), making preoperative differentiation challenging. This study aims to address this diagnostic challenge by developing a novel deep learning model.

Methods

This study proposes MAEMC-NET, a model integrating generative (Masked AutoEncoder) and contrastive (Momentum Contrast) self-supervised learning to learn CT image representations of intra- and inter-solitary nodules. A generative self-supervised task of reconstructing masked axial CT patches containing lesions was designed to learn intra- and inter-slice image representations. Contrastive momentum is used to link the encoder in axial-CT-patch path with the momentum encoder in coronal-CT-patch path. A total of 494 patients from two centers were included.

Results

MAEMC-NET achieved an area under curve (95% Confidence Interval) of 0.962 (0.934-0.973). These results not only significantly surpass the joint diagnosis by two experienced chest radiologists (77.3% accuracy) but also outperform the current state-of-the-art methods. The model performs best on medical images with a 50% mask ratio, showing a 1.4% increase in accuracy compared to the optimal 75% mask ratio on natural images.

Discussion

The proposed MAEMC-NET effectively distinguishes between benign and malignant solitary pulmonary nodules and holds significant potential to assist radiologists in improving the diagnostic accuracy of PGN and SLA.

Exploring prognostic precision: a nomogram approach for malignant pleural effusion in lung cancer

BACKGROUND

Patients with lung cancer and malignant pleural effusion (MPE) often have poor prognoses. Accurate prognostic tools are needed to guide interventions and improve outcomes.

METHODS

We retrospectively analyzed clinical and imaging data from MPE patients at two medical centers. A nomogram was developed and externally validated. Clinical and imaging features were refined using least absolute shrinkage and selection operator (LASSO), and independent predictors were identified via multivariate logistic regression. Predictors were integrated into the nomogram, whose predictive performance, calibration, and clinical utility were evaluated using statistical analyses, including receiver operating characteristic (ROC) curves, calibration curves, Hosmer-Lemeshow tests, and decision curve analysis (DCA). Survival curves illustrated prognostic differences among risk groups.

RESULTS

The final nomogram included five variables: Lactate Dehydrogenase (LDH) levels in pleural fluid, clarity of pleural effusion, treatment regimen, presence of pericardial effusion, and total volume of pleural effusion. In both cohorts, the nomogram demonstrated strong predictive accuracy (Area Under the Curve (AUC): 0.929 and 0.941, respectively) and excellent calibration (Hosmer-Lemeshow test p-values: 0.944 and 0.425, respectively). DCA confirmed the nomogram's clinical utility. Risk stratification revealed significant survival disparities among patients.

CONCLUSION

Our nomogram accurately predicts the prognosis of lung cancer patients with MPE at initial diagnosis, incorporating key variables such as LDH levels in pleural fluid, clarity of pleural effusion, treatment regimen, pericardial effusion, and total volume of pleural effusion. Its robust predictive performance, calibration, and clinical utility support its use in guiding clinical decision-making for this patient population.

The Winner and still champion: Nodule volume doubling times

There have been enormous advances in the approach to assessing malignancy status of indeterminate pulmonary nodules including risk models, image based biomarkers and numerous types of biologic and molecular markers. All of these have the advantage of guiding further workup once the nodule is identified. The traditional method, especially for smaller nodules relies primarily on assessing whether a nodule changes in size over time and is a feature in virtually every management protocol for both screen detected as well as incidentally detected nodules. Here, the potential downside is that during the waiting period for obtaining a second scan to assess for growth prognosis changes. However, there must be enough of a time delay to overcome potential measurement error. These two features must be balanced for optimal use of this approach. The alternative approaches do not have this inherent delay, however, their usefulness is a balance between the improvement in prognosis by not having any delays versus their potential to produce false positive and false negative results. Currently nodule volumetric approaches, especially for small nodules remains the method of choice for evaluation.

The Efficacy of a Named Entity Recognition AI Model for Identifying Incidental Pulmonary Nodules in CT Reports

Purpose: This study evaluates the efficacy of a commercial medical Named Entity Recognition (NER) model combined with a post-processing protocol in identifying incidental pulmonary nodules from CT reports. Methods: We analyzed 9165 anonymized CT reports and classified them into 3 categories: no nodules, nodules present, and nodules >6 mm. For each report, a generic medical NER model annotated entities and their relations, which were then filtered through inclusion/exclusion criteria selected to identify pulmonary nodules. Ground truth was established by manual review. To better understand the relationship between model performance and nodule prevalence, a subset of the data was programmatically balanced to equalize the number of reports in each class category. Results: In the unbalanced subset of the data, the model achieved a sensitivity of 97%, specificity of 99%, and accuracy of 99% in detecting pulmonary nodules mentioned in the reports. For nodules >6 mm, sensitivity was 95%, specificity was 100%, and accuracy was 100%. In the balanced subset of the data, sensitivity was 99%, specificity 96%, and accuracy 97% for nodule detection; for larger nodules, sensitivity was 94%, specificity 99%, and accuracy 98%. Conclusions: The NER model demonstrated high sensitivity and specificity in detecting pulmonary nodules reported in CT scans, including those >6 mm which are potentially clinically significant. The results were consistent across both unbalanced and balanced datasets indicating that the model performance is independent of nodule prevalence. Implementing this technology in hospital systems could automate the identification of at-risk patients, ensuring timely follow-up and potentially reducing missed or late-stage cancer diagnoses.

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

BACKGROUND

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

PURPOSE

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

STUDY TYPE

Prospective.

POPULATION

54 patients, including 21 men and 33 women.

FIELD STRENGTH/SEQUENCE

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

ASSESSMENT

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

STATISTICAL TESTS

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

RESULTS

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

DATA CONCLUSION

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

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 2.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The psychological disorder and personality traits of individuals with pulmonary nodules

INTRODUCTION

With the widespread use of Low-dose computed tomography (LDCT) in the chest, more and more people will be detected with pulmonary nodules. The presence of uncertainty following the detection of these nodules can impose significant psychological distress. This study aimed to investigate personality traits, psychological distress, and their impact on pulmonary nodule patients in China.

METHODS

We conducted a cross-sectional survey of adults with pulmonary nodules accidently discovered by LDCT in the chest from the respiratory outpatient department.

RESULTS

A total of 224 patients with pulmonary nodules were included in this study. The prevalence of anxiety among patients with pulmonary nodules was found to be 47.8 %, while the prevalence of depression was reported to be 44.2 %. The present study also demonstrated a higher prevalence of anxiety among female patients with pulmonary nodules compared to their male counterparts, with mild anxiety being the predominant manifestation. The multivariate logistic regression analysis revealed that age (OR = 0.926, P < 0.01), gender (OR = 3.24, P < 0.01), number of pulmonary nodules (OR = 0.586, P < 0.05), lung cancer-related characteristics (OR = 5.423, P < 0.01), PTSD (OR = 5.715, P < 0.01), and Extroversion personality traits (OR = 1.087, P < 0.05) were significant factors contributing to anxiety in patients with pulmonary nodules. Similarly, (OR = 0.891, P < 0.01), gender (OR = 2.981, P < 0.05), duration (OR = 0.663, P < 0.05), lung cancer-related characteristics (OR = 5.707, P < 0.01), PTSD (OR = 4.420, P < 0.01)emerged as key factors associated with depression in this patient population.

CONCLUSION

Approximately 50 % of patients with pulmonary nodules exhibit negative affective states. Furthermore, as time progresses, the negative emotional burden of anxiety and depression in individuals with pulmonary nodules tends to alleviate.

Clinical and Computed Tomography Characteristics of Inflammatory Solid Pulmonary Nodules with Morphology Suggesting Malignancy

RATIONALE AND OBJECTIVES

To investigate the clinical and computed tomography characteristics of inflammatory solid pulmonary nodules (SPNs) with morphology suggesting malignancy, hereinafter referred to as atypical inflammatory SPNs (AI-SPNs).

MATERIALS AND METHODS

The CT data of 515 patients with SPNs who underwent surgical resection were retrospectively analyzed. These patients were divided into inflammatory and malignant groups and their clinical and imaging features were compared. Binary logistic regression analysis was performed to identify the independent factors for diagnosing AI-SPNs. An external validation cohort included 133 consecutive patients to test the model's predictive efficiency.

RESULTS

Univariate analysis showed that age < 62 years, male sex, maximum spiculation length > 9 mm, polygonal shapes, three-planar ratio > 1.48, Lung window/mediastinal window (L/M) ratio > 1.13, pleural tag type I, satellite lesions, and halo sign were more frequent in AI-SPNs, whereas pleural tag type III, bronchial truncation, and perifocal fibrosis were more common in malignant SPNs (M-SPNs) (all P < 0.05). Binary logistic regression showed age < 62 years, male sex, polygonal shape, three-planar ratio > 1.48, L/M ratio > 1.13, pleural tag type I, satellite lesions, halo sign, and absence of bronchial truncation were independent factors for diagnosing AI-SPNs (AUC, sensitivity, specificity, and accuracy of 0.951, 83.30%, 92.30%, and 87.20%, respectively). In the external validation cohort, the AUC, sensitivity, specificity, and accuracy were 0.969, 90.47%, 90.00%, and 90.23%, respectively.

CONCLUSION

AI-SPNs and M-SPNs exhibited different clinical and imaging characteristics. A good understanding of these differences may help reduce diagnostic errors in AI-SPNs and enable to choose an optimal treatment strategy.

Incidental Appendiceal Neuroendocrine Tumor Post Appendectomy: Surgery Is Here to Stay

Neuroendocrine tumors (NETs) arising from the appendix are rare neoplasms but carry significant consequences if missed. These malignancies are typically diagnosed after an appendectomy via histopathological evaluation of the appendix. This aspect further solidifies surgery's place in the treatment of appendicitis. A 25-year-old female patient presented to the emergency department with a three-day history of right-sided abdominal pain associated with nausea and two episodes of non-bilious vomiting. Physical examination was initially benign but later showed tenderness to the right of the umbilicus. A CT scan revealed an inflamed appendix. Based on clinical and radiological findings, the diagnosis of acute appendicitis was made. The patient underwent a laparoscopic appendectomy. Histopathological analysis of the appendix was performed, identifying an appendiceal neuroendocrine tumor (aNET). Following the initial diagnosis, an appropriate workup was conducted, which included a colonoscopy, computed tomography (CT) scan, and further biopsies. Histopathological analysis of the appendix revealed a well-differentiated grade 1 NET, measuring 3.5 cm, with invasion into peri-appendiceal tissues. Further evaluation through a repeat CT scan and colonoscopy revealed inflammation in the rectum, cecum, and right colon. Furthermore, a subsequent laparoscopic right hemicolectomy was performed. Pathology of the hemicolectomy specimen revealed no residual NET, though lymph node involvement was present, with three out of 18 nodes testing positive for lymphatic spread. This case report highlights the diagnostic and management challenges associated with aNETs, emphasizing the importance of surgical intervention in the context of acute appendicitis. The discovery of aNETs can significantly alter the clinical management course, as it did for this patient, who required further surgical intervention and ongoing surveillance. The timely identification and removal of the tumor likely improved the patient's prognosis.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

CLINICAL RELEVANCE STATEMENT

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

KEY POINTS

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

Peripheral blood immune cell dynamics associate with growth of incidental indeterminate pulmonary nodules

OBJECTIVES

Previous studies suggest peripheral blood immune cells associate with the progression and prognosis of lung cancer. The main purpose of this study was to explore the association of peripheral immune cell and its dynamics with the growth of pulmonary nodules.

MATERIALS AND METHOD

Of 9280 subjects whom had blood cell counts and chest CT scan in health check-up, 1068 participants were enrolled with the incidental pulmonary nodules of above 5 mm in diameter and subsequently followed up for 2 years. The pulmonary nodules were identified as growth based on the increase of at least 2 mm in the diameters within the two years. The relationships of pulmonary nodules growth with peripheral immune cell dynamics and clinical variables were analyzed using univariable inter-group comparison and multivariable logistic regression analyses.

RESULTS

During the two years, 116 (10.9 %) of 1068 participants had the growth pulmonary nodules. Overall, emphysema, nodule diameter and non-solid characteristics associated with the growth of nodules. In the subgroup of pure solid nodules, high baseline eosinophil percentage (OR 1.220; 95 % CI 1.009-1.474; P = 0.040) and the increase of neutrophil count (OR 3.805; 95 % CI 1.027-14.093; P = 0.045) were significant risk factors for the nodule growth. In the subgroup of solid-predominant nodules, the increase of lymphocyte was associated with a lower risk of growth (OR 0.039; 95 % CI 0.002-0.839; P = 0.038).

CONCLUSIONS

High baseline eosinophil and increase of neutrophil were associated with the growth of pure solid pulmonary nodules. The decrease of lymphocyte related to the growth of solid-predominant nodules.

Barriers to Lung Cancer Screening in a Multi-Disciplinary Thoracic Oncology Program Cohort: Effects of an Incidental Pulmonary Nodule Program

INTRODUCTION

Lung cancer screening (LCS) reduces mortality; nevertheless, its adoption has been slow, and some people who develop lung cancer are ineligible. Incidental pulmonary nodule (IPN) programs can also detect lung cancer early. We quantified the barriers to LCS and the impact of the IPN program.

METHODS

We categorized patients with lung cancer enrolled in a Multidisciplinary Thoracic Oncology Program from 2015 to 2023 as screened, unscreened, eligible, or ineligible for LCS. We further categorized the unscreened cohorts according to their exposure to IPN programs. We compared the lung cancer outcomes between the groups.

RESULTS

Of the 1904 patients, 6.4%, 41.4%, and 52.2% were screened, eligible unscreened, and ineligible, respectively; 42% of the eligible unscreened (17% of the whole cohort) and 46% of the ineligible cohort (24% of the whole cohort) were diagnosed through the IPN program. Thirty-three percent of the eligible unscreened non-IPN cohort had clinical encounters 12 to 36 months before diagnosis. Among the ineligible participants, 28% were age-ineligible, 20% had never smoked, 20.5% had a less than 20-pack-year history, and 32.5% had an excessive quitting duration. The five-year overall survival rates were 77% (95% confidence interval: 73-89), 45% (41-49), and 50% (46-54), respectively (p < 0.0001). With the eligible unscreened as a reference, the adjusted hazard ratios were 0.36 (0.23-0.54) and 0.87 (0.75-1.01) for the screened and ineligible cohorts. Five-year overall survival was 61% (55-68) versus 35% (30-39) and 60% (55-67) versus 42% (37-47) among IPN versus non-IPN cohorts of eligible unscreened and ineligible cohorts, respectively.

CONCLUSIONS

Screening improved the survival in this community-based cohort. The eligibility criteria excluded more patients than the failure to screen eligible patients. The IPN program alleviated both barriers.

Enhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis

BACKGROUND

Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.

OBJECTIVE

This study aimed to evaluate the impact of an AI-assisted diagnostic system on the diagnostic efficiency of radiologists. It specifically examined the report modification rates and missed and misdiagnosed rates of junior radiologists with and without AI assistance.

METHODS

We obtained effective data from 12,889 patients in 2 tertiary hospitals in Beijing before and after the implementation of the AI system, covering the period from April 2018 to March 2022. Diagnostic reports written by both junior and senior radiologists were included in each case. Using reports by senior radiologists as a reference, we compared the modification rates of reports written by junior radiologists with and without AI assistance. We further evaluated alterations in lung nodule detection capability over 3 years after the integration of the AI system. Evaluation metrics of this study include lung nodule detection rate, accuracy, false negative rate, false positive rate, and positive predictive value. The statistical analyses included descriptive statistics and chi-square, Cochran-Armitage, and Mann-Kendall tests.

RESULTS

The AI system was implemented in Beijing Anzhen Hospital (Hospital A) in January 2019 and Tsinghua Changgung Hospital (Hospital C) in June 2021. The modification rate of diagnostic reports in the detection of lung nodules increased from 4.73% to 7.23% (χ21=12.15; P<.001) at Hospital A. In terms of lung nodule detection rates postimplementation, Hospital C increased from 46.19% to 53.45% (χ21=25.48; P<.001) and Hospital A increased from 39.29% to 55.22% (χ21=122.55; P<.001). At Hospital A, the false negative rate decreased from 8.4% to 5.16% (χ21=9.85; P=.002), while the false positive rate increased from 2.36% to 9.77% (χ21=53.48; P<.001). The detection accuracy demonstrated a decrease from 93.33% to 92.23% for Hospital A and from 95.27% to 92.77% for Hospital C. Regarding the changes in lung nodule detection capability over a 3-year period following the integration of the AI system, the detection rates for lung nodules exhibited a modest increase from 54.6% to 55.84%, while the overall accuracy demonstrated a slight improvement from 92.79% to 93.92%.

CONCLUSIONS

The AI system enhanced lung nodule detection, offering the possibility of earlier disease identification and timely intervention. Nevertheless, the initial reduction in accuracy underscores the need for standardized diagnostic criteria and comprehensive training for radiologists to maximize the effectiveness of AI-enabled diagnostic systems.

Ex Vivo Fluorescence Confocal Microscopy for Intraoperative Evaluations of Staple Lines and Surgical Margins in Specimens of the Lung-A Proof-of-Concept Study

Intraoperative consultation is frequently used during the surgical treatment of lung tumors for the diagnosis of malignancy and the assessment of surgical margins. The latter is often problematic given the nature of applied staple lines, which cannot be readily examined in frozen sections. Seventy-nine samples of surgical margins (71 staple lines and 8 open margins) from 52 lung specimens were examined using an ex vivo fluorescence confocal microscope (FCM). The diagnoses of the FCM scans were compared with the corresponding paraffin section images of the same material. The procedure provided intraoperative FCM imaging of the surgical margins and staple lines without having to remove the metal clips. Tumor-involved open margins (5/5) and tumor-involved staple lines (3/4) were correctly identified in the FCM images. The results also provided additional information to the conventional frozen sections (FS). To our knowledge, this is the first time staple lines of lung specimens have been visualized as preserved tissue using FCM. The method potentially provides an additional approach for intraoperative decisions when the margins in conventional frozen sections are unclear. Our promising results, however, need to be validated on a larger number of cases.

Diameter thresholds for pure ground-glass pulmonary nodules at low-dose CT screening: Chinese experience

BACKGROUND

Limited research exists on screening thresholds for low-dose CT in detecting malignant pure ground-glass lung nodules (pGGNs) in the Chinese population.

MATERIALS AND METHODS

A retrospective analysis of the Guangzhou Lung-Care programme was conducted, retrieving average transverse diameter, location, histopathology, frequency and follow-up intervals. Diagnostic performances for 'lung cancers' were evaluated using areas under the curve (AUCs), decision curve analysis (DCA), sensitivities and specificities, with thresholds ranging from 5 mm to 10 mm. We divide malignant pGGNs into three groups: (1) minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA), (2) atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS) and MIA and IA and (3) IA-only.

RESULTS

In 'MIA+IA', increasing the threshold from 5 mm to 8 mm improved specificity (60.97% to 88.85%, p<0.001) and positive predictive values (PPVs; 5.87% to 14.88%, p<0.001), but decreased sensitivity (94.44% to 75.56%, p<0.001). Further raising threshold from 8 mm reduced sensitivity (75.56% to 60.00%, p<0.001), while slightly increasing specificity (88.85% to 93.47%, p<0.001) and PPVs (14.88% to 19.15%, p<0.001). Increasing threshold from 5 mm to 7 mm enhanced the AUC for 'MIA+IA' (from 0.711 to 0.829), 'AAH+AIS+MIA+IA' (from 0.748 to 0.804) and 'IA-only' (from 0.783 to 0.833). At 8 mm, the AUCs for these categories were similar. However, increasing the threshold from 7 mm to 10 mm resulted in reduced AUCs for 'MIA+IA' (0.829 to 0.767), 'AAH+AIS+MIA+IA' (0.804 to 0.744) and 'IA-only' (0.833 to 0.800). DCA reveals that the 8 mm predictive model demonstrates greater clinical utility compared with models with other thresholds.

CONCLUSIONS

Increasing the diameter threshold for positive results for pGGNs, up to 8 mm could enhance diagnostic performance.

TRIAL REGISTRATION NUMBER

NCT04938804.

Automated Incidental Findings Notification Through the Electronic Health Record Utilizing Dictation Macros

The objective of this study is to implement an actionable incidental findings (AIFs) communication workflow integrated into the electronic health record (EHR) using dictation macros to improve the quality of radiology reports and facilitate delivery of findings to clinicians. The workflow was implemented across an academic multi-hospital health system and used by over 100 radiologists from 12 divisions. Standardized macros were created for different organ systems including the thyroid, lungs, liver, pancreas, spleen, kidney, female reproductive, and others, designed based on the ACR Novel Quality Measure Set. All macros contained special codes enabling automated notification of clinicians in Epic EHR and unique codes to allow for tracking. When notified, clinicians can fast track ordering of follow-up imaging exams. All alerts were monitored by radiology operations who ensured messages were acknowledged within 73 h. From September 2023 to March 2024, 12,919 AIFs alerts were filed for 10,766 patients. Median age was 65 years, and 63.6% were female and 36.4% were male. Most alerts were submitted for outpatients (73.5%), and a majority originated from CT exams (57.3%) followed by radiographs (12.2%) and ultrasound (11.5%). Number of submissions per radiologist ranged from 0 to 930 with a median of 62. Median time to alert acknowledgment was 8.1 h, and 93.9% were acknowledged within 73 h. Follow-up orders were placed for 62.3% of patients. A standardized AIFs communication workflow utilizing dictation macros can help facilitate delivery of findings and follow-up recommendations to clinicians.

Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V

OBJECTIVES

To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain compared to low-dose CT (LDCT) and ULDCT without DLIR.

MATERIALS AND METHODS

A total of 210 patients undergoing lung cancer screening underwent LDCT (mean ± SD, 0.81 ± 0.28 mSv) and ULDCT (0.17 ± 0.03 mSv) scans. ULDCT images were reconstructed with ASiR-V (ULDCT-ASiR-V) and post-processed using DLIR (ULDCT-DLIR). The quality of the three CT images was analyzed. Three radiologists detected and measured pulmonary nodules on all CT images, with LDCT results serving as references. Nodule conspicuity was assessed using a five-point Likert scale, followed by further statistical analyses.

RESULTS

A total of 463 nodules were detected using LDCT. The image noise of ULDCT-DLIR decreased by 60% compared to that of ULDCT-ASiR-V and was lower than that of LDCT (p < 0.001). The subjective image quality scores for ULDCT-DLIR (4.4 [4.1, 4.6]) were also higher than those for ULDCT-ASiR-V (3.6 [3.1, 3.9]) (p < 0.001). The overall nodule detection rates for ULDCT-ASiR-V and ULDCT-DLIR were 82.1% (380/463) and 87.0% (403/463), respectively (p < 0.001). The percentage difference between diameters > 1 mm was 2.9% (ULDCT-ASiR-V vs. LDCT) and 0.5% (ULDCT-DLIR vs. LDCT) (p = 0.009). Scores of nodule imaging sharpness on ULDCT-DLIR (4.0 ± 0.68) were significantly higher than those on ULDCT-ASiR-V (3.2 ± 0.50) (p < 0.001).

CONCLUSION

DLIR-based image domain improves image quality, nodule detection rate, nodule imaging sharpness, and nodule measurement accuracy of ASiR-V on ULDCT.

KEY POINTS

Question Deep learning post-processing is simple and cheap compared with raw data processing, but its performance is not clear on ultralow-dose CT. Findings Deep learning post-processing enhanced image quality and improved the nodule detection rate and accuracy of nodule measurement of ultralow-dose CT. Clinical relevance Deep learning post-processing improves the practicability of ultralow-dose CT and makes it possible for patients with less radiation exposure during lung cancer screening.

Ultra-low dose dual-layer detector spectral CT for pulmonary nodule screening: image quality and diagnostic performance

OBJECTIVES

To investigate the image quality and diagnostic performance with ultra-low dose dual-layer detector spectral CT (DLSCT) by various reconstruction techniques for evaluation of pulmonary nodules.

MATERIALS AND METHODS

Between April 2023 and December 2023, patients with suspected pulmonary nodules were prospectively enrolled and underwent regular-dose chest CT (RDCT; 120 kVp/automatic tube current) and ultra-low dose CT (ULDCT; 100 kVp/10 mAs) on a DLSCT scanner. ULDCT was reconstructed with hybrid iterative reconstruction (HIR), electron density map (EDM), and virtual monoenergetic images at 40 keV and 70 keV. Quantitative and qualitative image analysis, nodule detectability, and Lung-RADS evaluation were compared using repeated one-way analysis of variance, Friedman test, and weighted kappa coefficient.

RESULTS

A total of 249 participants (mean age ± standard deviation, 50.0 years ± 12.9; 126 male) with 637 lung nodules were included. ULDCT resulted in a significantly lower mean radiation dose than RDCT (0.3 mSv ± 0.0 vs. 3.6 mSv ± 0.8; p < 0.001). Compared with RDCT, ULDCT EDM showed significantly higher signal-noise-ratio (44.0 ± 77.2 vs. 4.6 ± 6.6; p < 0.001) and contrast-noise-ratio (26.7 ± 17.7 vs. 5.0 ± 4.4; p < 0.001) with qualitative scores ranked higher or equal to the average. Using the regular-dose images as a reference, ULDCT EDM images had a satisfactory nodule detection rate (84.6%) and good inter-observer agreements compared with RDCT (κw > 0.60).

CONCLUSION

Ultra-low dose dual-layer detector CT with 91.2% radiation dose reduction achieves sufficient image quality and diagnostic performance of pulmonary nodules.

CRITICAL RELEVANCE STATEMENT

Dual-layer detector spectral CT enables substantial radiation dose reduction without impairing image quality for the follow-up of pulmonary nodules or lung cancer screening.

KEY POINTS

Radiation dose is a major concern for patients requiring pulmonary nodules CT screening. Ultra-low dose dual-layer detector spectral CT with 91.2% dose reduction demonstrated satisfactory performance. Dual-layer detector spectral CT has the potential for lung cancer screening and management.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

Five year mortality in an RCT of a lung cancer biomarker to select people for low dose CT screening

The role of biomarkers in risk-based early detection of lung cancer may enable screening to become cost effective and widely accessible. EarlyCDT-Lung is an example of such a blood-based autoantibody biomarker which may improve accessibility to Low dose Computed Tomography (LDCT) screening for those at highest risk. We randomized 12 208 individuals aged 50-75 at high risk of developing lung cancer to either the test or to standard clinical care. Outcomes were ascertained from Register of Deaths and Cancer Registry. Cox proportional hazards models were used to estimate the hazard ratio of the rate of deaths from all causes and lung cancer. Additional analyses were performed for cases of lung cancer diagnosed within two years of the initial test. After 5 years 326 lung cancers were detected (2.7% of those enrolled). The total number of deaths reported from all causes in the intervention group was 344 compared to 388 in the control group. There were 73 lung cancer deaths in the intervention arm and 90 in the controls (Adjusted HR 0.789 (0.636, 0.978). An analysis of cases of lung cancer detected within 2 years of randomization in the intervention group showed that there were 34 deaths from all causes and 29 from lung cancer. In the control group there were 56 deaths with 49 from lung cancer. In those diagnosed with lung cancer within 2 years of randomization the hazard ratio for all cause mortality was 0.615 (0.401,0.942) and for lung cancer 0.598 (0.378, 0.946). Further large-scale studies of the role of biomarkers to target lung cancer screening, in addition to LDCT, are likely to provide additional value.

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

BACKGROUND AND PURPOSE

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

MATERIALS AND METHODS

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

RESULTS

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

INTERPRETATION

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

Gender Disparities and Lung Cancer Screening Outcomes Among Individuals Who Have Never Smoked

Importance

Lung cancer in individuals who have never smoked (INS) is a growing global concern, with a rapidly increasing incidence and proportion among all lung cancer cases. Particularly in East Asia, opportunistic lung cancer screening (LCS) programs targeting INS have gained popularity. However, the sex-specific outcomes and drawbacks of screening INS remain unexplored, with data predominantly focused on women.

Objective

To compare LCS outcomes between Asian women and men with no smoking history.

Design, Setting, and Participants

This multicenter cohort study was conducted at health checkup centers in South Korea from 2009 to 2021. Participants included individuals aged 50 to 80 years with no smoking history who underwent low-dose computed tomography (LDCT) screening. Data were retrospectively analyzed from November 2023 to June 2024.

Exposures

Opportunistic LDCT screening for lung cancer.

Main Outcomes and Measures

Participants were followed up until December 2022 for the outcome of death. Lung cancer diagnosis, diagnostic characteristics, clinical course, and lung cancer-specific deaths (LCSD) were compared between women and men.

Results

A total of 21 062 participants (16 133 [76.6%] women and 4929 [23.4%] men) with a mean (SD) age of 59.8 (7.2) years were included. From baseline screening, 176 participants (139 women [0.9%] and 37 men [0.8%]) were diagnosed with lung cancer (screen-detected); 131 of 139 women (94.3%) and 33 of 37 men (89.2%) were diagnosed with stage 0 to I disease, with 133 of 139 women (95.7%) and 36 of 37 men (97.3%) having adenocarcinoma. There were no significant sex-based differences in stage or histologic type distribution. Among the 21 062 screened individuals, LCSD was reported in 8 women and 3 men during a mean (SD) follow-up of 83.8 (41.7) months. Multivariable analyses found no significant association between sex and cumulative hazards of lung cancer diagnosis (adjusted hazard ratio [aHR], 0.90 [95% CI, 0.64-1.26] for men vs women) or LCSD (aHR, 1.06 [95% CI, 0.28-4.00] for men vs women). The estimated 5-year lung cancer-specific survival rate was 97.7% for women and 100% for men with screen-detected lung cancer, showing no significant sex differences.

Conclusions and Relevance

In this cohort study of Asian individuals with no smoking history who underwent LDCT screening, no significant sex-based differences were detected in lung cancer diagnosis, stage distribution, or LCSD. These findings suggest that men and women who have never smoked would experience similar risks of overdiagnosis with little to no benefit when exposed to indiscriminate screening.

Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer

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

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

OBJECTIVES

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

KEY POINTS

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

Silicone deposition and adverse pulmonary events secondary to breast implant rupture

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

Radiomics Analysis for the Identification of Invasive Pulmonary Subsolid Nodules From Longitudinal Presurgical CT Scans

PURPOSE

Effective identification of malignant part-solid lung nodules is crucial to eliminate risks due to therapeutic intervention or lack thereof. We aimed to develop delta radiomics and volumetric signatures, characterize changes in nodule properties over three presurgical time points, and assess the accuracy of nodule invasiveness identification when combined with immediate presurgical time point radiomics signature and clinical biomarkers.

MATERIALS AND METHODS

Cohort included 156 part-solid lung nodules with immediate presurgical CT scans and a subset of 122 nodules with scans at 3 presurgical time points. Region of interest segmentation was performed using ITK-SNAP, and feature extraction using CaPTk. Image parameter heterogeneity was mitigated at each time point using nested ComBat harmonization. For 122 nodules, delta radiomics features (ΔR AB = (R B -R A )/R A ) and delta volumes (ΔV AB = (V B -V A )/V A ) were computed between the time points. Principal Component Analysis was performed to construct immediate presurgical radiomics (Rs 1 ) and delta radiomics signatures (ΔRs 31 + ΔRs 21 + ΔRs 32 ). Identification of nodule pathology was performed using logistic regression on delta radiomics and immediate presurgical time point signatures, delta volumes (ΔV 31 + ΔV 21 + ΔV 32 ), and clinical variable (smoking status, BMI) models (train test split (2:1)).

RESULTS

In delta radiomics analysis (n= 122 nodules), the best-performing model combined immediate pre-surgical time point and delta radiomics signatures, delta volumes, and clinical factors (classification accuracy [AUC]): (77.5% [0.73]) (train); (71.6% [0.69]) (test).

CONCLUSIONS

Delta radiomics and volumes can detect changes in nodule properties over time, which are predictive of nodule invasiveness. These tools could improve conventional radiologic assessment, allow for earlier intervention for aggressive nodules, and decrease unnecessary intervention-related morbidity.

Diagnostic Performance of European and American College of Radiology Thyroid Imaging Reporting and Data System Classification Systems in Thyroid Nodules Over 20 mm in Diameter

OBJECTIVE

The challenge of selecting thyroid nodules for fine needle aspiration (FNA) cytology has led to the development of the Thyroid Imaging Reporting and Data System, primarily in 2 formats: European Thyroid Imaging Reporting and Data System (EU-TIRADS) and American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS). Clinical observations suggest imperfect risk assessment for TIRADS 3 nodules ≥20 mm. This study aimed to evaluate the efficacy of TIRADS systems in distinguishing benign from malignant nodules in this subgroup.

METHODS

From May 2023 to March 2024, 1094 patients with thyroid nodules were referred for ultrasound at a University Hospital. Data on clinical, ultrasound, cytological, and histopathological parameters were collected. Nodules ≥20 mm were categorized by EU-TIRADS and ACR-TIRADS, and their predictive performance for malignancy was assessed through postthyroidectomy histopathology or FNA cytology (Bethesda classification).

RESULTS

Two hundred sixty-seven patients (mean age 60.3 ± 14.3 years; 46 men, 221 women) with 308 nodules were analyzed. Twenty-two malignancies and 286 benign nodules were recorded. Recalculating European Thyroid Imaging Reporting and Data System 3 performance using 25-mm and 30-mm thresholds (ACR-modified EU-TIRADS) avoided 24% and 41% of FNAs, respectively, while ACR-TIRADS would prevent 26.6% (P > .05). Two malignancies were missed.

CONCLUSION

EU-TIRADS and ACR-TIRADS show similar efficacy when using a 25 mm FNA threshold. Raising the cutoff for FNA in European Thyroid Imaging Reporting and Data System 3 nodules could reduce unnecessary procedures but may increase the risk of missed malignancies, impacting patient outcomes.

Differentiating Pulmonary Nodule Malignancy Using Exhaled Volatile Organic Compounds: A Prospective Observational Study

BACKGROUND

Advances in imaging technology have enhanced the detection of pulmonary nodules. However, determining malignancy often requires invasive procedures or repeated radiation exposure, underscoring the need for safer, noninvasive diagnostic alternatives. Analyzing exhaled volatile organic compounds (VOCs) shows promise, yet its effectiveness in assessing the malignancy of pulmonary nodules remains underexplored.

METHODS

Employing a prospective study design from June 2023 to January 2024 at the Affiliated Hospital of Yangzhou University, we assessed the malignancy of pulmonary nodules using the Mayo Clinic model and collected exhaled breath samples alongside lifestyle and health examination data. We applied five machine learning (ML) algorithms to develop predictive models which were evaluated using area under the curve (AUC), sensitivity, specificity, and other relevant metrics.

RESULTS

A total of 267 participants were enrolled, including 210 with low-risk and 57 with moderate-risk pulmonary nodules. Univariate analysis identified 11 exhaled VOCs associated with nodule malignancy, alongside two lifestyle factors (smoke index and sites of tobacco smoke inhalation) and one clinical metric (nodule diameter) as independent predictors for moderate-risk nodules. The logistic regression model integrating lifestyle and health data achieved an AUC of 0.91 (95% CI: 0.8611-0.9658), while the random forest model incorporating exhaled VOCs achieved an AUC of 0.99 (95% CI: 0.974-1.00). Calibration curves indicated strong concordance between predicted and observed risks. Decision curve analysis confirmed the net benefit of these models over traditional methods. A nomogram was developed to aid clinicians in assessing nodule malignancy based on VOCs, lifestyle, and health data.

CONCLUSIONS

The integration of ML algorithms with exhaled biomarkers and clinical data provides a robust framework for noninvasive assessment of pulmonary nodules. These models offer a safer alternative to traditional methods and may enhance early detection and management of pulmonary nodules. Further validation through larger, multicenter studies is necessary to establish their generalizability.

TRIAL REGISTRATION

Number ChiCTR2400081283.

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

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

Comparison of hook wire and microcoil preoperative localisation in subsolid pulmonary nodules: a retrospective analysis

AIM

Subsolid nodules are increasingly detected during physical examinations with computed tomography (CT) scan and video-assisted thoracoscopic surgery (VATS) is the standard treatment. This study compared the effectiveness of preoperative localisation of subsolid pulmonary nodules using a hook-wire and a microcoil under CT guidance prior to VATS.

MATERIALS AND METHODS

Patients with solitary subsolid pulmonary nodules (n = 342) underwent percutaneous puncture localisation guided by CT before VATS. Overall, 107 were localised using a hook wire (hook-wire group), and 235 patients were localised using a microcoil (microcoil group). Localisation-related indicators, complications associated with localisation surgery, and VATS-related indicators were compared between the two groups.

RESULTS

The success rate of localisation was not different between the two groups [hook-wire group: 92.52% (99/107) vs microcoil group: 96.17% (226/235), P = 0.150). The localisation time and time window between localisation and surgery were shorter in the hook-wire group than in the microcoil group (P < 0.001). However, the overall incidence of complications related to localisation surgery was greater in the hook-wire group than in the microcoil group [48.60% (52/107) vs. 31.49% (74/235), P = 0.002]. Both groups mainly underwent pulmonary lobectomy as the primary surgical procedure, with no statistically significant difference in the surgical approach between the groups (P = 0.084). Surgical time for patients who underwent pulmonary lobectomy was shorter in the microcoil group than in the hook-wire group (P = 0.023).

CONCLUSION

The effectiveness of preoperative localisation of subsolid pulmonary nodules using hook-wire and microcoils under CT guidance prior to VATS is comparable. The microcoil technique has a longer localisation time but a lower overall complication rate and shorter surgical time for pulmonary lobectomy.

Pulmonary nodule malignancy probability: a meta-analysis of the Brock model

AIM

This study aims to quantify the performance of the Brock model through a systematic review and meta-analysis and to clarify its overall accuracy in predicting malignant pulmonary nodules.

MATERIALS AND METHODS

A systematic search was conducted in databases including the Cochrane Library, Excerpta Medica database (EMBASE), MEDLINE, Web of Science, Chinese Biological Medicine Database (CBM), China National Knowledge Infrastructure (CNKI), VIP, and Wanfang from their inception until May 1, 2024, to collect observational cohort studies involving the Brock model. The primary outcome was the pooled area under the receiver operating characteristic curve (ROC) the area under curve (AUC) for the Brock model. Secondary outcomes included sensitivity and specificity. The metaprotocol was registered in the International Prospective Register of Systematic Reviews (CRD42024538163).

RESULTS

A total of 52 studies involving 85,558 patients were included. The pooled AUC was 0.796 (95% confidence interval [CI]: 0.771-0.820), with a pooled sensitivity of 0.82 (95% CI: 0.76-0.87) and specificity of 0.80 (95% CI: 0.72-0.86). Subgroup analysis showed that the performance of the full model was significantly better than that of the simplified model (0.822, 95% CI: 0.794-0.849 versus 0.687, 95% CI: 0.611-0.763). The model performed excellently for pulmonary nodules with diameters of 1- to 8 mm (AUC: 0.927, 95% CI: 0.900-0.954). However, its performance was lower in Asian populations (AUC = 0.741, 95% CI: 0.703-0.780), solitary pulmonary nodules (AUC = 0.767, 95% CI: 0.693-0.842), and subsolid pulmonary nodules (AUC = 0.747, 95% CI: 0.661-0.832).

CONCLUSION

This meta-analysis confirms the Brock model's overall strong performance. However, the results indicate certain application limitations of the Brock model, with reduced accuracy for larger nodules (>15 mm), solitary pulmonary nodules, subsolid nodules, and in Asian populations.

Improved Consistency of Lung Nodule Categorization in CT Scans with Heterogeneous Slice Thickness by Deep Learning-Based 3D Super-Resolution

Background/Objectives: Accurate volumetric assessment of lung nodules is an essential element of low-dose lung cancer screening programs. Current guidance recommends applying specific thresholds to measured nodule volume to make the following clinical decisions. In reality, however, CT scans often have heterogeneous slice thickness which is known to adversely impact the accuracy of nodule volume assessment. Methods: In this study, a deep learning (DL)-based 3D super-resolution method is proposed for generating thin-slice CT images from heterogeneous thick-slice CT images in lung cancer screening. We evaluated the performance in a qualitative way by radiologist's perceptual assessment as well as in a quantitative way by accuracy of nodule volume measurements and agreement of volume-based Lung-RADS nodule category. Results: A 5-point Likert scale tabulated by two radiologists showed that the quality of DL-generated thin-slice images from thick-slice CT images were on a par with the image quality of ground truth thin-slice CT images. Furthermore, thick- and thin-slice CT images had a nodule volume difference of 52.2 percent on average which was reduced to a 15.7 percent difference with DL-generated thin-slice CT. In addition, the proposed method increased the agreement of lung nodule categorization using Lung-RADS by 74 percent. Conclusions: The proposed DL approach for slice thickness normalization has a potential for improving the accuracy of lung nodule volumetry and facilitating more reliable early lung nodule detection.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

The American Cancer Society National Lung Cancer Roundtable strategic plan: Optimizing strategies for lung nodule evaluation and management

Lung nodules are frequently detected on low-dose computed tomography scans performed for lung cancer screening and incidentally detected on imaging performed for other reasons. There is wide variability in how lung nodules are managed by general practitioners and subspecialists, with high rates of guideline-discordant care. This may be due in part to the level of evidence underlying current practice guideline recommendations (primarily based on findings from uncontrolled studies of diagnostic accuracy). The primary aims of lung nodule management are to minimize harms of diagnostic evaluations while expediting the evaluation, diagnosis, and treatment of lung cancer. Potentially useful tools such as lung cancer probability calculators, automated methods to identify patients with nodules in the electronic health record, and multidisciplinary team evaluation are often underused due to limited availability, accessibility, and/or provider knowledge. Finally, relatively little attention has been paid to identifying and reducing disparities among individuals with screening-detected or incidentally detected lung nodules. This contribution to the American Cancer Society National Lung Cancer Roundtable Strategic Plan aims to identify and describe these knowledge gaps in lung nodule management and propose recommendations to advance clinical practice and research. Major themes that are addressed include improving the quality of evidence supporting lung nodule evaluation guidelines, strategically leveraging information technology, and placing emphasis on equitable approaches to nodule management. The recommendations outlined in this strategic plan, when carried out through interdisciplinary efforts with a focus on health equity, ultimately aim to improve early detection and reduce the morbidity and mortality of lung cancer. PLAIN LANGUAGE SUMMARY: Lung nodules may be identified on chest scans of individuals who undergo lung cancer screening (screening-detected nodules) or among patients for whom a scan was performed for another reason (incidental nodules). Although the vast majority of lung nodules are not lung cancer, it is important to have evidence-based, standardized approaches to the evaluation and management of a lung nodule. The primary aims of lung nodule management are to diagnose lung cancer while it is still in an early stage and to avoid unnecessary procedures and other harms.

Incidental Pulmonary Nodules: Differential Diagnosis and Clinical Management

BACKGROUND

According to data from the USA, the incidence of incidentally discovered pulmonary nodules is 5.8 per 100 000 person- years for women and 5.2 per 100 000 person-years for men. Their management as recommended in the pertinent guidelines can substantially improve clinical outcomes. More than 95% of all pulmonary nodules revealed by computerized tomography (CT) are benign, but many cases are not managed in conformity with the guidelines. In this article, we summarize the appropriate clinical approach and provide an overview of the pertinent diagnostic studies and when they should be performed.

METHODS

This review is based on relevant publications retrieved by a selective search in PubMed. The authors examined Englishlanguage recommendations issued since 2010 for the management of pulmonary nodules, supplemented by comments from the German lung cancer guideline.

RESULTS

In general, the risk that an incidentally discovered pulmonary nodule is malignant is low but rises markedly with increasing size and the presence of risk factors. When such a nodule is detected, the further recommendation, depending on size, is either for follow-up examinations with chest CT or else for an extended evaluation with positron emission tomography-CT and biopsy for histology. The diagnostic evaluation should include consideration of any earlier imaging studies that may be available as an indication of possible growth over time. Single nodules measuring less than 6 mm, in patients with few or no risk factors, do not require any follow-up. Lung cancer is diagnosed in just under 10% of patients with a nodule measuring more than 8 mm.

CONCLUSION

The recommendations of the guidelines for the management of incidentally discovered pulmonary nodules are intended to prevent both overand undertreatment. If a tumor is suspected, further care should be provided by an interdisciplinary team.

Screening for Lung Cancer, Overdiagnosis, and Healthcare Utilization: A Nationwide Population-Based Study

INTRODUCTION

Guideline-discordant low-dose computed tomography (LDCT) screening may cause lung cancer (LC) overdiagnosis, but its extent and consequences are unclear. This study aimed to investigate the prevalence of self-initiated, non-reimbursed LDCT screening in a predominantly non-smoking population and its impact on LC epidemiology and healthcare utilization.

METHODS

This nationwide cohort study analyzed data from Korea's National Health Information Database and 11 academic hospital screening centers (1999-2022). The overall analysis encompassed the entire Korean population. For non-reimbursed LDCT screening prevalence, which the National Health Information Database does not capture, a separate analysis was conducted on a cohort of 1.7 million adults to extrapolate nationwide rates. Outcomes included trends in self-initiated, non-reimbursed LDCT screening, LC incidence, mortality, stage and age at diagnosis, 5-year survival, and LC-related healthcare utilization, including surgeries and biopsies. Joinpoint regression assessed trend changes.

RESULTS

Self-initiated, non-reimbursed LDCT screening during health check-ups increased from 29% to 60% in men and 7% to 46% in women, despite only 2.4% of men and 0.04% of women qualifying for risk-based screening. In women, localized-stage LC incidence nearly doubled (age-standardized incidence rate: from 7.6 to 13.7 per 100,000), whereas distant-stage incidence decreased (age-standardized incidence rate: from 16.1 to 15.0 per 100,000). LC mortality declined (age-standardized mortality rate: from 23.3 to 19.8 per 100,000), whereas 5-year survival rates improved substantially. LC diagnoses in women shifted towards earlier stages and younger ages. Lung surgeries for both malignant and benign lesions, frequently lacking nonsurgical biopsies, increased sharply in women.

CONCLUSIONS

Widespread guideline-discordant LDCT screening correlates with LC overdiagnosis and increased healthcare utilization, particularly in women. Randomized controlled trials are needed to assess the risks and benefits of screening in low-risk populations to determine its efficacy and consequences.

Advancements in lung cancer: a comprehensive perspective on diagnosis, staging, therapy and follow-up from the SAKK Working Group on Imaging in Diagnosis and Therapy Monitoring

In 2015, around 4400 individuals received a diagnosis of lung cancer, and Switzerland recorded approximately 3200 deaths related to lung cancer. Advances in detection, such as lung cancer screening and improved treatments, have led to increased identification of early-stage lung cancer and higher chances of long-term survival. This progress has introduced new considerations in imaging, emphasising non-invasive diagnosis and characterisation techniques like radiomics. Treatment aspects, such as preoperative assessment and the implementation of immune response evaluation criteria in solid tumours (iRECIST), have also seen advancements. For those undergoing curative treatment for lung cancer, guidelines propose follow-up with computed tomography (CT) scans within a specific timeframe. However, discrepancies exist in published guidelines, and there is a lack of universally accepted recommendations for follow-up procedures. This white paper aims to provide a certain standard regarding the use of imaging on the diagnosis, staging, treatment and follow-up of patients with lung cancer.

Role of Textural Analysis Parameters Derived from FDG PET/CT in Diagnosing Cardiac Sarcoidosis

Introduction  Texture and radiomic analysis characterize the lesion's phenotype and evaluate its microenvironment in quantitative terms. The aim of this study was to investigate the role of textural features of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography-computed tomography (PET/CT) images in differentiating patients with cardiac sarcoidosis (CS) from patients with physiologic myocardial uptake. Methods  This is a retrospective, single-center study of 67 patients, 17 diagnosed CS patients, and 50 non-CS patients. These patients underwent FDG PET/CT for the diagnosis of CS. The non-CS group underwent 18F-FDG PET/CT for other oncological indications. The PET/CT images were then processed in a commercially available textural analysis software. Region of interest was drawn over primary tumor with a 40% threshold and was processed further to derive 92 textural and radiomic parameters. These parameters were then compared between the CS group and the non-CS group. Receiver operating characteristics (ROC) curves were used to identify cutoff values for textural features with a p -value < 0.05 for statistical significance. These parameters were then passed through a principle component analysis algorithm. Five different machine learning classifiers were then tested on the derived parameters. Results  A retrospective study of 67 patients, 17 diagnosed CS patients, and 50 non-CS patients, was done. Twelve textural analysis parameters were significant in differentiating between the CS group and the non-CS group. Cutoff values were calculated for these parameters according to the ROC curves. The parameters were Discretized_HISTO_Entropy, GLCM_Homogeneity, GLCM_Energy, GLRLM_LRE, GLRLM_LGRE, GLRLM_SRLGE, GLRLM_LRLGE, NGLDM_Coarseness, GLZLM_LZE, GLZLM_LGZE, GLZLM_SZLGE, and GLZLM_LZLGE. The gradient boosting classifier gave best results on these parameters with 85.71% accuracy and an F1 score of 0.86 (max 1.0) on both classes, indicating the classifier is performing well on both classes. Conclusion  Textural analysis parameters could successfully differentiate between the CS and non-CS groups noninvasively. Larger multicenter studies are needed for better clinical prognostication of these parameters.