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

Role of textural and radiomic analysis parameters in predicting histopathological parameters of the tumor in breast cancer patients

INTRODUCTION

Texture and radiomic analysis characterizes the tumor's phenotype and evaluates its microenvironment in quantitative terms. This study aims to investigate the role of textural and radiomic analysis parameters in predicting histopathological factors in breast cancer patients.

MATERIALS AND METHODS

Two hundred and twelve primary breast cancer patients underwent 18 F-FDG PET/computed tomography for staging. The images were processed in a commercially available textural analysis software. ROI was drawn over the primary tumor with a 40% threshold and was processed further to derive textural and radiomic parameters. These parameters were then compared with histopathological factors of tumor. Receiver-operating characteristic analysis was performed with a P -value <0.05 for statistical significance. The significant parameters were subsequently utilized in various machine learning models to assess their predictive accuracy.

RESULTS

A retrospective study of 212 primary breast cancer patients was done. Among all the significant parameters, SUVmin, SUVmean, SUVstd, SUVmax, discretized HISTO_Entropy, and gray level co-occurrence matrix_Contrast were found to be significantly associated with ductal carcinoma type. Four parameters (SUVmin, SUVmean, SUVstd, and SUVmax) were significant in differentiating the luminal subtypes of the tumor. Five parameters (SUVmin, SUVmean, SUVstd, SUVmax, and SUV kurtosis) were significant in predicting the grade of the tumor. These parameters showcased robust capabilities in predicting multiple histopathological parameters when tested using machine learning algorithms.

CONCLUSION

Though textural analysis could not predict hormonal receptor status, lymphovascular invasion status, perineural invasion status, microcalcification status of tumor, and all the molecular subtypes of the tumor, it could predict the tumor's histologic type, triple-negative subtype, and score of the tumor noninvasively.

Socio-Economic Factors and Clinical Context Can Predict Adherence to Incidental Pulmonary Nodule Follow-up via Machine Learning Models

OBJECTIVE

To quantify the relative importance of demographic, contextual, socio-economic, and nodule-related factors that influence patient adherence to incidental pulmonary nodule (IPN) follow-up visits and evaluate the predictive performance of machine learning models utilizing these features.

METHODS

We curated a 1,610-subject patient data set from electronic medical records consisting of 13 clinical and socio-economic predictors and IPN follow-up adherence status (timely, delayed, or never) as the outcome. Univariate analysis and multivariate logistic regression were performed to quantify the predictors' contributions to follow-up adherence. Three additional machine learning models (random forests, neural network, and support vector machine) were fitted and cross-validated to examine prediction performance across different model architectures and evaluate intermodel concordance.

RESULTS

On univariate basis, all 13 predictors except comorbidity were found to have a significant association with follow-up. In multiple logistic regression, inpatient or emergency clinical context (odds ratio favoring never following up: 7.28 and 8.56 versus outpatient, respectively) and high nodule risk (odds ratio: 0.25 versus low risk) are the most significant predictors of follow-up, and sex, race, and marital status become additionally significant if clinical context is removed from the model. Clinical context itself is associated with sex, race, insurance, employment, marriage, income, nodule risk, and smoking status, suggesting its role in mediating socio-economic inequities. On cross-validation, all four machine learning models demonstrated comparable and good predictive performances, with mean area under the curve ranging from 0.759 to 0.802, with sensitivity 0.641 to 0.660 and specificity 0.768 to 0.840.

CONCLUSION

Socio-economic factors and clinical context are predictive of IPN follow-up adherence, with clinical context being the most significant contributor and likely representing uncaptured socio-economic determinants.

Ovarian cancer risk among older patients with stable adnexal masses

BACKGROUND

Few studies have evaluated the risk of cancer among older patients with stable adnexal masses in community-based settings to determine the duration of observation time needed.

OBJECTIVE

This study aimed to assess the ovarian cancer risk among older patients with stable adnexal masses on ultrasound.

STUDY DESIGN

This was a retrospective cohort study of patients in a large community-based health system aged ≥50 years with an adnexal mass <10 cm on ultrasound between 2016 and 2020 who had at least 1 follow-up ultrasound performed ≥6 weeks after initial ultrasound. Masses were considered stable on follow-up examination if they did not exhibit an increase of >1 cm in the greatest dimension or a change in standardized reported ultrasound characteristics. Ovarian cancer risk was determined at increasing time intervals of stability after initial ultrasound.

RESULTS

Among 4061 patients with stable masses, the average age was 61 years (range, 50-99), with an initial mass size of 3.8 cm (range, 0.2-9.9). With a median follow-up of 3.7 years, 11 cancers were detected, with an absolute risk of 0.27%. Ovarian cancer risk declined with longer duration of stability, from 0.73 (95% confidence interval, 0.30-1.17) per 1000 person-years at 6 to 12 weeks, 0.63 (95% confidence interval, 0.19-1.07) at 13 to 24 weeks, 0.44 (95% confidence interval, 0.01-0.87) at 25 to 52 weeks, and 0.00 (95% confidence interval, 0.00-0.00) at >52 weeks. Expressed as number needed to reimage, ongoing ultrasound imaging would be needed for 369 patients whose masses show stability at 6 to 12 weeks, 410 patients at 13 to 24 weeks, 583 patients at 25 to 52 weeks, and >1142 patients with stable masses at 53 to 104 weeks to detect 1 case of ovarian cancer.

CONCLUSION

In a diverse community-based setting, among patients aged ≥50 years with an adnexal mass that was stable for at least 6 weeks after initial ultrasound, the risk of ovarian cancer was very low at 0.27%. Longer demonstrated duration of stability was associated with progressively lower risk, with no cancer cases observed after 52 weeks of stability. These findings suggest that the benefit of ultrasound monitoring of stable masses beyond 12 months is minimal and may be outweighed by potential risks of repeated imaging.

[The pulmonary nodule: from incidental finding to pathological confirmation]

As the number of CT examinations of the lungs increases, so does the prevalence of incidentally discovered pulmonary nodules. While most lung nodules are benign, the risk of malignancy significantly rises with the presence of risk factors and specific imaging features. Upon encountering an incidental nodule, efforts should focus on achieving an accurate pathological diagnosis, particularly to ascertain malignancy while minimizing the risks associated with unnecessary diagnostic procedures. A comprehensive understanding of the typical characteristics and behavior of malignant lung nodules, along with a detailed patient history and standardized clinical and imaging risk assessment, is crucial for determining the optimal diagnostic approach. Additionally, the decision regarding histologic confirmation should consider the patient's comorbidities, preferences, and the examiner's expertise. Emerging sampling technologies provide methods for addressing peripheral lung nodules with minimal risk of complications.

Preoperative localization for pulmonary nodules: a meta-analysis of coil and liquid materials

PURPOSE

This study was designed to conduct pooled comparisons of the relative clinical efficacy and safety of computed tomography (CT)-guided localization for pulmonary nodules (PNs) using either coil- or liquid material-based approaches.

MATERIAL AND METHODS

Relevant articles published as of July 2023 were identified in the Web of Science, PubMed, and Wanfang databases, and pooled analyses of relevant endpoints were then conducted.

RESULTS

Six articles that enrolled 287 patients (341 PNs) and 247 patients (301 PNs) that had respectively undergone CT-guided localization procedures using coil- and liquid material-based approaches prior to video-assisted thoracic surgery (VATS) were included in this meta-analysis. The liquid material group exhibited a significantly higher pooled successful localization rate as compared to the coil group (p = 0.01), together with significantly lower pooled total complication rates (p = 0.0008) and pneumothorax rates (p = 0.01). Both groups exhibited similar rates of pulmonary hemorrhage (p = 0.44) and successful wedge resection (p = 0.26). Liquid-based localization was also associated with significant reductions in pooled localization and VATS procedure durations (p = 0.004 and 0.007).

CONCLUSIONS

These data are consistent with CT-guided localization procedures performed using liquid materials being safer and more efficacious than coil-based localization in patients with PNs prior to VATS resection.

MRI-Based Clinical-Imaging-Radiomics Nomogram Model for Discriminating Between Benign and Malignant Solid Pulmonary Nodules or Masses

RATIONALE AND OBJECTIVES

Pulmonary nodules or masses are highly prevalent worldwide, and differential diagnosis of benign and malignant lesions remains difficult. Magnetic resonance imaging (MRI) can provide functional and metabolic information of pulmonary lesions. This study aimed to establish a nomogram model based on clinical features, imaging features, and multi-sequence MRI radiomics to identify benign and malignant solid pulmonary nodules or masses.

MATERIALS AND METHODS

A total of 145 eligible patients (76 male; mean age, 58.4 years ± 13.7 [SD]) with solid pulmonary nodules or masses were retrospectively analyzed. The patients were randomized into two groups (training cohort, n = 102; validation cohort, n = 43). The nomogram was used for predicting malignant pulmonary lesions. The diagnostic performance of different models was evaluated by receiver operating characteristic (ROC) curve analysis.

RESULTS

Of these patients, 95 patients were diagnosed with benign lesions and 50 with malignant lesions. Multivariate analysis showed that age, DWI value, LSR value, and ADC value were independent predictors of malignant lesions. Among the radiomics models, the multi-sequence MRI-based model (T1WI+T2WI+ADC) achieved the best diagnosis performance with AUCs of 0.858 (95%CI: 0.775, 0.919) and 0.774 (95%CI: 0.621, 0.887) for the training and validation cohorts, respectively. Combining multi-sequence radiomics, clinical and imaging features, the predictive efficacy of the clinical-imaging-radiomics model was significantly better than the clinical model, imaging model and radiomics model (all P < 0.05).

CONCLUSION

The MRI-based clinical-imaging-radiomics model is helpful to differentiate benign and malignant solid pulmonary nodules or masses, and may be useful for precision medicine of pulmonary diseases.

Precision patient selection for improved detection of circulating genetically abnormal cells in pulmonary nodules

Circulating genetically abnormal cells (CACs) have emerged as a promising biomarker for the early diagnosis of lung cancer, particularly in patients with pulmonary nodules. However, their performance may be suboptimal in certain patient populations. This study aimed to refine patient selection to improve the detection of CACs in pulmonary nodules. A retrospective analysis was conducted on 241 patients with pulmonary nodules who had undergone pathological diagnosis through surgical tissue specimens. Utilizing consensus clustering analysis, the patients were categorized into three distinct clusters. Cluster 1 was characterized by older age, larger nodule size, and a higher prevalence of hypertension and diabetes. Notably, the diagnostic efficacy of CACs in Cluster 1 surpassed that of the overall patient population (AUC: 0.855 vs. 0.689, P = 0.044). Moreover, for Cluster 1, an integrated diagnostic model was developed, incorporating CACs, sex, maximum nodule type, and maximum nodule size, resulting in a further improved AUC of 0.925 (95% CI 0.846-1.000). In conclusion, our study demonstrates that CACs detection shows better diagnostic performance in aiding the differentiation between benign and malignant nodules in older patients with larger pulmonary nodules and comorbidities such as diabetes and hypertension. Further research and validation are needed to explore how to better integrate CACs detection into clinical practice.

Predicting malignant potential of solitary pulmonary nodules in patients with COVID-19 infection: a comprehensive analysis of CT imaging and tumor markers

OBJECTIVE

To analyze the value of combining computed tomography (CT) with serum tumor markers in the differential diagnosis of benign and malignant solitary pulmonary nodules (SPNs).

METHODS

The case data of 267 patients diagnosed with SPNs in the First Affiliated Hospital of Zhengzhou University from March 2020 to January 2023 were retrospectively analyzed. All individuals diagnosed with coronavirus disease 2019 (COVID-19) were confirmed via respiratory specimen viral nucleic acid testing. The included cases underwent CT, serum tumor marker testing and pathological examination. The diagnostic efficacy and clinical significance of CT, serum tumor marker testing and a combined test in identifying benign and malignant SPNs were analyzed using pathological histological findings as the gold standard. Finally, a nomogram mathematical model was established to predict the malignant probability of SPNs.

RESULTS

Of the 267 patients with SPNs, 91 patients were not afflicted with COVID-19, 36 exhibited malignant characteristics, whereas 55 demonstrated benign features. Conversely, within the cohort of 176 COVID-19 patients presenting with SPNs, 62 were identified as having malignant SPNs, and the remaining 114 were diagnosed with benign SPNs. CT scans revealed statistically significant differences between the benign and malignant SPNs groups in terms of CT values (P<0.001), maximum nodule diameter (P<0.001), vascular convergence sign (P<0.001), vacuole sign (P = 0.0007), air bronchogram sign (P = 0.0005), and lobulation sign (P = 0.0005). Malignant SPNs were associated with significantly higher levels of carcinoembryonic antigen (CEA) and neuron-specific enolase (NSE) compared to benign SPNs (P < 0.05), while no significant difference was found in carbohydrate antigen 125 (CA125) levels (P = 0.054 for non-COVID-19; P = 0.072 for COVID-19). The sensitivity (95.83%), specificity (95.32%), and accuracy (95.51%) of the comprehensive diagnosis combining serum tumor markers and CT were significantly higher than those of CT alone (70.45%, 79.89%, 76.78%) or serum tumor marker testing alone (56.52%, 73.71%, 67.79%) (P < 0.05). A visual nomogram predictive model for malignant pulmonary nodules was constructed.

CONCLUSION

Combining CT with testing for CEA, CA125, and NSE levels offers high diagnostic accuracy and sensitivity, enables precise differentiation between benign and malignant nodules, particularly in the context of COVID-19, thereby reducing the risk of unnecessary surgical interventions.

Knowledge mapping analysis of ground glass nodules: a bibliometric analysis from 2013 to 2023

Background

In recent years, the widespread use of computed tomography (CT) in early lung cancer screening has led to an increase in the detection rate of lung ground glass nodules (GGNs). The persistence of GGNs, which may indicate early lung adenocarcinoma, has been a focus of attention for scholars in the field of lung cancer prevention and treatment in recent years. Despite the rapid development of research into GGNs, there is a lack of intuitive content and trend analyses in this field, as well as a lack of detailed elaboration on possible research hotspots. The objective of this study was to conduct a comprehensive analysis of the knowledge structure and research hotspots of lung ground glass nodules over the past decade, employing bibliometric methods.

Method

The Web of Science Core Collection (WoSCC) database was searched for relevant ground-glass lung nodule literature published from 2013-2023. Bibliometric analyses were performed using VOSviewer, CiteSpace, and the R package "bibliometrix".

Results

A total of 2,218 articles from 75 countries and 2,274 institutions were included in this study. The number of publications related to GGNs has been high in recent years. The United States has led in GGNs-related research. Radiology has one of the highest visibilities as a selected journal and co-cited journal. Jin Mo Goo has published the most articles. Travis WD has been cited the most frequently. The main topics of research in this field are Lung Cancer, CT, and Deep Learning, which have been identified as long-term research hotspots. The GGNs-related marker is a major research trend in this field.

Conclusion

This study represents the inaugural bibliometric analysis of applied research on ground-glass lung nodules utilizing three established bibliometric software. The bibliometric analysis of this study elucidates the prevailing research themes and trends in the field of GGNs over the past decade. It also furnishes pertinent recommendations for researchers to provide objective descriptions and comprehensive guidance for future related research.

Exploring experiences and coping strategies of the surveillance of indeterminate pulmonary nodules: a qualitative content analysis among participants in the SCAPIS trial

OBJECTIVE

To elucidate experiences and coping strategies among adults in the surveillance of indeterminate pulmonary nodules detected with CT in the population-based Swedish CardioPulmonary bioImage Study (SCAPIS).

DESIGN

A qualitative study of conventional content analysis.

SETTINGS

The study was conducted at a university hospital in a southern region of Sweden. The SCAPIS setting is similar to the first round of a population-based lung cancer screening programme.

PARTICIPANTS

Participants in SCAPIS who had experienced psychosocial consequences of the surveillance were eligible. Participants of both genders, current, former and non-smokers and of different follow-ups in the surveillance were included. Face-to-face semi-structured interviews with 19 participants were performed using an interview guide with open-ended questions. The participants were aged 56-68 years. Nine were women, 6 and 13 were non-smokers and smokers or former smokers, respectively, and all participants had undergone at least one follow-up of the lungs in the surveillance programme.

RESULTS

The results depicted an emotional and mental journey for the participants from being distressed when informed about the need of surveillance, and realising their risks of getting sick if they did not take care of their own health, to eventually gathering the strength to cope with the situation, so the surveillance was finally valued with trust and satisfaction. The experiences and coping strategies in the surveillance programme developed a revelation of the value of health consciousness among the participants.

CONCLUSION

The study results demonstrated that a surveillance programme of pulmonary nodules might develop health consciousness among people. Still, some individuals might experience psychosocial consequences of the surveillance of indeterminate nodules. Therefore, healthcare professionals should be facilitated to perform person-centred communication to support individuals under surveillance. Preventive care to engage individuals as partners in the management of their own health should receive more attention and needs to be explored.

The scoring system combined with radiomics and imaging features in predicting the malignant potential of incidental indeterminate small (<20 mm) solid pulmonary nodules

OBJECTIVE

Develop a practical scoring system based on radiomics and imaging features, for predicting the malignant potential of incidental indeterminate small solid pulmonary nodules (IISSPNs) smaller than 20 mm.

METHODS

A total of 360 patients with malignant IISSPNs (n = 213) and benign IISSPNs (n = 147) confirmed after surgery were retrospectively analyzed. The whole cohort was randomly divided into training and validation groups at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) algorithm was used to debase the dimensions of radiomics features. Multivariate logistic analysis was performed to establish models. The receiver operating characteristic (ROC) curve, area under the curve (AUC), 95% confidence interval (CI), sensitivity and specificity of each model were recorded. Scoring system based on odds ratio was developed.

RESULTS

Three radiomics features were selected for further model establishment. After multivariate logistic analysis, the combined model including Mean, age, emphysema, lobulated and size, reached highest AUC of 0.877 (95%CI: 0.830-0.915), accuracy rate of 83.3%, sensitivity of 85.3% and specificity of 80.2% in the training group, followed by radiomics model (AUC: 0.804) and imaging model (AUC: 0.773). A scoring system with a cutoff value greater than 4 points was developed. If the score was larger than 8 points, the possibility of diagnosing malignant IISSPNs could reach at least 92.7%.

CONCLUSION

The combined model demonstrated good diagnostic performance in predicting the malignant potential of IISSPNs. A perfect accuracy rate of 100% can be achieved with a score exceeding 12 points in the user-friendly scoring system.

Advances in artificial intelligence applications in the field of lung cancer

Lung cancer remains a leading cause of cancer-related deaths globally, with its incidence steadily rising each year, representing a significant threat to human health. Early detection, diagnosis, and timely treatment play a crucial role in improving survival rates and reducing mortality. In recent years, significant and rapid advancements in artificial intelligence (AI) technology have found successful applications in various clinical areas, especially in the diagnosis and treatment of lung cancer. AI not only improves the efficiency and accuracy of physician diagnosis but also aids in patient treatment and management. This comprehensive review presents an overview of fundamental AI-related algorithms and highlights their clinical applications in lung nodule detection, lung cancer pathology classification, gene mutation prediction, treatment strategies, and prognosis. Additionally, the rapidly advancing field of AI-based three-dimensional (3D) reconstruction in lung cancer surgical resection is discussed. Lastly, the limitations of AI and future prospects are addressed.

Clinical application of convolutional neural network lung nodule detection software: An Australian quaternary hospital experience

INTRODUCTION

Early-stage lung cancer diagnosis through detection of nodules on computed tomography (CT) remains integral to patient survivorship, promoting national screening programmes and diagnostic tools using artificial intelligence (AI) convolutional neural networks (CNN); the software of AI-Rad Companion™ (AIRC), capable of self-optimising feature recognition. This study aims to demonstrate the practical value of AI-based lung nodule detection in a clinical setting; a limited body of research.

METHODS

One hundred and eighty-three non-contrast CT chest studies from a single centre were assessed for AIRC software analysis. Prospectively collected data from AIRC detection and characterisation of lung nodules (size: ≥3 mm) were assessed against the reference standard; reported findings of a blinded consultant radiologist.

RESULTS

One hundred and sixty-seven CT chest studies were included; 52% indicated for nodule or lung cancer surveillance. Of 289 lung nodules, 219 (75.8%) nodules (mean size: 10.1 mm) were detected by both modalities, 28 (9.7%) were detected by AIRC alone and 42 (14.5%) by radiologist alone. Solid nodules missed by AIRC were larger than those missed by radiologist (11.5 mm vs 4.7 mm, P < 0.001). AIRC software sensitivity was 87.3%, with significant false positive and negative rates demonstrating 12.5% specificity (PPV 0.6, NPV 0.4).

CONCLUSION

In a population of high nodule prevalence, AIRC lung nodule detection software demonstrates sensitivity comparable to that of consultant radiologist. The clinical significance of larger sized nodules missed by AIRC software presents a barrier to current integration in practice. We consider this research highly relevant in providing focus for ongoing software development, potentiating the future success of AI-based tools within diagnostic radiology.

Significance of lung nodules detected on chest CT among adult Aboriginal Australians - a retrospective descriptive study

INTRODUCTION

There are limited data on chest computed tomography (CT) findings in the assessment of lung nodules among adult Aboriginal Australians. In this retrospective study, we assessed lung nodules among a group of adult Aboriginal Australians in the Northern Territory of Australia.

METHODS

Patients who underwent at least two chest CT scans between 2012 and 2020 among those referred to undergo lung function testing (spirometry) were included. Chest CT scans were assessed for the number, location, size and morphological characteristics of lung nodules.

RESULTS

Of the 402 chest CTs assessed, 75 patients (18.7%) had lung nodules, and 57 patients were included in the final analysis with at least two CT scans available for assessment over a median follow-up of 87 weeks. Most patients (68%) were women, with a median age of 58 years and smoking history in 83%. The majority recorded only a single nodule 43 (74%). Six patients (10%) were diagnosed with malignancy, five with primary lung cancer and one with metastatic thyroid cancer. Of the 51 (90%) patients assessed to be benign, 64 nodules were identified, of which 25 (39%) resolved, 38 (59%) remained stable and one (1.8%) enlarged on follow-up. Nodules among patients with malignancy were typically initially larger and enlarged over time, had spiculated margins and were solid, showing no specific lobar predilection.

CONCLUSIONS

Most lung nodules in Aboriginal Australians are likely to be benign. However, a proportion could be malignant. Further prospective studies are required for prognostication and monitoring of lung nodules in this population.

Risk of Malignancy in Incidentally Detected Lung Nodules in Patients Aged Younger Than 35 Years

BACKGROUND

The risk of malignancy in pulmonary nodules incidentally detected on computed tomography (CT) in patients who are aged younger than 35 years is unclear.

OBJECTIVE

The aim of this study was to evaluate the incidence of lung cancer in incidental pulmonary nodules in patients who are 15-34 years old.

METHODS

This retrospective study included patients aged 15-34 years who had an incidental pulmonary nodule on chest CT from 2010 to 2018 at our hospital. Patients with prior, current, or suspected malignancy were excluded. A chart review identified patients with diagnosis of malignancy. Incidental pulmonary nodule was deemed benign if stable or resolved on a follow-up CT at least 2 years after initial or if there was a medical visit in our health care network at least 2 years after initial CT without diagnosis of malignancy.Receiver operating characteristic curve analysis was performed with nodule size. Association of categorical variables with lung cancer diagnosis was performed with Fisher exact test, and association of continuous variables was performed with logistic regression.

RESULTS

Five thousand three hundred fifty-five chest CTs performed on patients aged 15-34 years between January 2010 and December 2018. After excluding patients without a reported pulmonary nodule and prior or current malignancy, there were a total of 779 patients. Of these, 690 (89%) had clinical or imaging follow-up after initial imaging. Of these, 545 (70% of total patients) patients had imaging or clinical follow-up greater than 2 years after their initial imaging.A malignant diagnosis was established in 2/779 patients (0.3%; 95% confidence interval, 0.1%-0.9%). Nodule size was strongly associated with malignancy ( P = 0.007), with area under the receiver operating characteristic curve of 0.97. There were no malignant nodules that were less than 10 mm in size. Smoking history, number of nodules, and nodule density were not associated with malignancy.

CONCLUSIONS

Risk of malignancy for incidentally detected pulmonary nodules in patients aged 15-34 years is extremely small (0.3%). There were no malignant nodules that were less than 10 mm in size. Routine follow-up of subcentimeter pulmonary nodules should be carefully weighed against the risks.

Prevalence of pulmonary nodules detected incidentally on noncancer-related imaging: a review

Pulmonary nodules are common incidental findings requiring surveillance. Follow-up recommendations vary depending on risk factors, size and solid or subsolid characteristics. This review aimed to evaluate the prevalence of clinically significant nodules detected on noncancer-dedicated imaging and the prevalence of part-solid and ground-glass nodules. We conducted a systematic search of literature and screened texts for eligibility. Clinically significant nodules were noncalcified nodules >4-6 mm. Prevalence estimates were calculated for all studies and risk of bias was assessed by one reviewer. Twenty-four studies were included, with a total of 30 887 participants, and 21 studies were cross-sectional in design. Twenty-two studies used computed tomography (CT) imaging with cardiac-related CT being the most frequent. Prevalence of significant nodules was highest in studies with large field of view of the chest and low size thresholds for reporting nodules. The prevalence of part-solid and ground-glass nodules was only described in two cardiac-related CT studies. The overall risk of bias was low in seven studies and moderate in 17 studies. While current literature frequently reports incidental nodules on cardiovascular-related CT, there is minimal reporting of subsolid characteristics. Unclear quantification of smoking history and heterogeneity of imaging protocol also limits reliable evaluation of nodule prevalence in nonscreening cohorts.

Altruistic seagull optimization algorithm enables selection of radiomic features for predicting benign and malignant pulmonary nodules

Accurately differentiating indeterminate pulmonary nodules remains a significant challenge in clinical practice. This challenge becomes increasingly formidable when dealing with the vast radiomic features obtained from low-dose computed tomography, a lung cancer screening technique being rolling out in many areas of the world. Consequently, this study proposed the Altruistic Seagull Optimization Algorithm (AltSOA) for the selection of radiomic features in predicting the malignancy risk of pulmonary nodules. This innovative approach incorporated altruism into the traditional seagull optimization algorithm to seek a global optimal solution. A multi-objective fitness function was designed for training the pulmonary nodule prediction model, aiming to use fewer radiomic features while ensuring prediction performance. Among global radiomic features, the AltSOA identified 11 interested features, including the gray level co-occurrence matrix. This automatically selected panel of radiomic features enabled precise prediction (area under the curve = 0.8383 (95 % confidence interval 0.7862-0.8863)) of the malignancy risk of pulmonary nodules, surpassing the proficiency of radiologists. Furthermore, the interpretability, clinical utility, and generalizability of the pulmonary nodule prediction model were thoroughly discussed. All results consistently underscore the superiority of the AltSOA in predicting the malignancy risk of pulmonary nodules. And the proposed malignant risk prediction model for pulmonary nodules holds promise for enhancing existing lung cancer screening methods. The supporting source codes of this work can be found at: https://github.com/zzl2022/PBMPN.

Risk and Time to Diagnosis of Lung Cancer in Incidental Pulmonary Nodules

PURPOSE

To determine the risk of lung cancer in incidental pulmonary nodules, as well as the time until cancer growth is detected.

PATIENTS AND METHODS

This retrospective study examined patients with incidental nodules detected on chest computed tomography (CT) in 2017. Characteristics of the dominant nodule were automatically extracted from CT reports, and cancer diagnoses were manually verified by a thoracic radiologist. Nodules were categorized per Fleischner Society guideline categories: solid <6 mm, solid 6 to 8 mm, solid >8 mm, subsolid <6 mm, ground glass nodules ≥6 mm, and part-solid nodules ≥6 mm. The time to nodule growth was determined by CT reports.

RESULTS

A total of 3180 patients (nodules) were included, of which 155 (5%) were diagnosed with lung cancer. By category, 7/1601 (0.4%) solid nodules <6 mm, 11/713 (1.5%) solid nodules 6 to 8 mm, 71/446 (15.9%) solid nodules >8 mm, 1/124 (0.8%) subsolid nodules <6 mm, 29/202 (14.4%) ground glass nodules ≥6 mm, and 36/94 (37.9%) part-solid nodules ≥6 mm were malignant. Of solid lung cancers <6 mm, growth was observed in 1/4 imaged by 1 year and 2/5 by 2 years; of solid lung cancers 6 to 8 mm, growth was observed in 3/10 imaged by 1 year and 6/10 by 2 years.

CONCLUSION

Solid nodules <6 mm have a very low risk of malignancy and may not require routine follow-up. However, when malignant, growth is often not observed until 2 or more years later; therefore, stability at 1 to 2 years does not imply benignity.

The 2023 American Association for Thoracic Surgery (AATS) Expert Consensus Document: Management of subsolid lung nodules

OBJECTIVE

Lung cancers that present as radiographic subsolid nodules represent a subtype with distinct biological behavior and outcomes. The objective of this document is to review the existing literature and report consensus among a group of multidisciplinary experts, providing specific recommendations for the clinical management of subsolid nodules.

METHODS

The American Association for Thoracic Surgery Clinical Practice Standards Committee assembled an international, multidisciplinary expert panel composed of radiologists, pulmonologists, and thoracic surgeons with established expertise in the management of subsolid nodules. A focused literature review was performed with the assistance of a medical librarian. Expert consensus statements were developed with class of recommendation and level of evidence for each of 4 main topics: (1) definitions of subsolid nodules (radiology and pathology), (2) surveillance and diagnosis, (3) surgical interventions, and (4) management of multiple subsolid nodules. Using a modified Delphi method, the statements were evaluated and refined by the entire panel.

RESULTS

Consensus was reached on 17 recommendations. These consensus statements reflect updated insights on subsolid nodule management based on the latest literature and current clinical experience, focusing on the correlation between radiologic findings and pathological classifications, individualized subsolid nodule surveillance and surgical strategies, and multimodality therapies for multiple subsolid lung nodules.

CONCLUSIONS

Despite the complex nature of the decision-making process in the management of subsolid nodules, consensus on several key recommendations was achieved by this American Association for Thoracic Surgery expert panel. These recommendations, based on evidence and a modified Delphi method, provide guidance for thoracic surgeons and other medical professionals who care for patients with subsolid nodules.

[18F]FDG PET/CT is useful in discriminating invasive adenocarcinomas among pure ground-glass nodules: comparison with CT findings-a bicenter retrospective study

PURPOSE

Predicting the malignancy of pure ground-glass nodules (GGNs) using CT is challenging. The optimal role of [18F]FDG PET/CT in this context has not been clarified. We compared the performance of [18F]FDG PET/CT in evaluating GGNs for predicting invasive adenocarcinomas (IACs) with CT.

METHODS

From June 2012 to December 2020, we retrospectively enrolled patients with pure GGNs on CT who underwent [18F]FDG PET/CT within 90 days. Overall, 38 patients with 40 ≥ 1-cm GGNs were pathologically confirmed. CT images were analyzed for size, attenuation, uniformity, shape, margin, tumor-lung interface, and internal/surrounding characteristics. Visual [18F]FDG positivity, maximum standardized uptake value (SUVmax), and tissue fraction-corrected SUVmax (SUVmaxTF) were evaluated on PET/CT.

RESULTS

The histopathology of the 40 GGNs were: 25 IACs (62.5%), 9 minimally invasive adenocarcinomas (MIA, 22.5%), and 6 adenocarcinomas in situ (AIS, 15.0%). No significant differences were found in CT findings according to histopathology, whereas visual [18F]FDG positivity, SUVmax, and SUVmaxTF were significantly different (P=0.001, 0.033, and 0.018, respectively). The size, visual [18F]FDG positivity, SUVmax, and SUVmaxTF showed significant diagnostic performance to predict IACs (area under the curve=0.693, 0.773, 0.717, and 0.723, respectively; P=0.029, 0.001, 0.018, and 0.013, respectively). In the multivariate logistic regression analysis, visual [18F]FDG positivity discriminated IACs among GGNs among various CT and PET findings (P=0.008).

CONCLUSIONS

[18F]FDG PET/CT demonstrated superior diagnostic performance compared to CT in differentiating IAC from AIS/MIA among pure GGNs, thus it has the potential to guide the proper management of patients with pure GGNs.

Prevalence of major embolic findings and incidental findings on early cardiac CT in patients with suspected ischemic stroke

PURPOSE

The purpose of this study was to assess the type and prevalence of stroke and non-stroke-related findings diagnosed on early cardiac computed tomography (CT) in patients with suspected stroke. The secondary objective was to assess the clinical consequences on the management of patients with non-stroke-related conditions identified by early cardiac CT.

MATERIALS AND METHODS

This single-center, retrospective, observational study included 1111 consecutive patients with suspected ischemic stroke between November 2018 and March 2020 who underwent cardiac CT examination in addition to the usual brain CT protocol (i.e., non-enhanced brain CT, perfusion brain CT when needed, aortic arch and supra-aortic CT angiography, and post contrast brain CT). There were 562 women and 549 men with a median age of 74 years (range: 60-85 years). Of these, 415 (415/1111; 37.4%) patients had ischemic stroke and 692 (692/1111; 62.3%) had no stroke. Cardiac CT examinations were retrospectively reviewed for cardiac CT findings at high embolic risk and clinically significant extracardiac incidental findings.

RESULTS

Among 1111 included patients, 89 (89/1111; 8.0%) had a stroke-related condition identified on early cardiac CT. This was significantly more frequent in patients with ischemic stroke (66/415; 15.9%) by comparison with those without ischemic stroke (23/696; 3.3%) (P < 0.001), with 41 patients (41/415; 9.9%) diagnosed with left atrial thrombus. Cardiac CT revealed a clinically significant non-stroke-related finding in 173 patients (173/1111; 15.6%), including 17 pulmonary embolisms (1.5%), seven suspicious pulmonary lesions (0.6%), and three breast lesions suspected to be malignant (0.3%). Twenty out of 173 patients (20/173; 11.5%) with incidental findings on early cardiac CT had a change in their management.

CONCLUSION

This study shows that adding early cardiac CT to brain CT during the acute phase of an ischemic stroke leads to a higher rate of etiological diagnoses and highlights the major interest of looking at the bigger picture.

Deep Learning-Based Reconstruction Algorithm With Lung Enhancement Filter for Chest CT: Effect on Image Quality and Ground Glass Nodule Sharpness

OBJECTIVE

To assess the effect of a new lung enhancement filter combined with deep learning image reconstruction (DLIR) algorithm on image quality and ground-glass nodule (GGN) sharpness compared to hybrid iterative reconstruction or DLIR alone.

MATERIALS AND METHODS

Five artificial spherical GGNs with various densities (-250, -350, -450, -550, and -630 Hounsfield units) and 10 mm in diameter were placed in a thorax anthropomorphic phantom. Four scans at four different radiation dose levels were performed using a 256-slice CT (Revolution Apex CT, GE Healthcare). Each scan was reconstructed using three different reconstruction algorithms: adaptive statistical iterative reconstruction-V at a level of 50% (AR50), Truefidelity (TF), which is a DLIR method, and TF with a lung enhancement filter (TF + Lu). Thus, 12 sets of reconstructed images were obtained and analyzed. Image noise, signal-to-noise ratio, and contrast-to-noise ratio were compared among the three reconstruction algorithms. Nodule sharpness was compared among the three reconstruction algorithms using the full-width at half-maximum value. Furthermore, subjective image quality analysis was performed.

RESULTS

AR50 demonstrated the highest level of noise, which was decreased by using TF + Lu and TF alone (P = 0.001). TF + Lu significantly improved nodule sharpness at all radiation doses compared to TF alone (P = 0.001). The nodule sharpness of TF + Lu was similar to that of AR50. Using TF alone resulted in the lowest nodule sharpness.

CONCLUSION

Adding a lung enhancement filter to DLIR (TF + Lu) significantly improved the nodule sharpness compared to DLIR alone (TF). TF + Lu can be an effective reconstruction technique to enhance image quality and GGN evaluation in ultralow-dose chest CT scans.

Analysis of Longitudinal Assessment: Role of Radiology Online Longitudinal Assessment-Type Questions

OBJECTIVE

The purpose of this investigation was to assess gaps in radiologists' medical knowledge using abdominal subspecialty online longitudinal assessment (OLA)-type questions. Secondarily, we evaluated what question-centric factors influenced radiologists to pursue self-directed additional reading on topics presented.

METHODS

A prospective OLA-type test was distributed nationally to radiologists over a 4-month period. Questions were divided into multiple groupings, including arising from three different time periods of literature (≤5 years, 6-15 years, and >20 years), relating to common versus uncommon modalities, and guideline-based versus knowledge-based characterization. After each question, participants rated their confidence in diagnosis and perceived question relevance. Answers were provided, and links to answer explanations and references were provided and tracked. A series of regression models were used to test potential predictors of correct response, participant confidence, and perceived question relevance.

RESULTS

In all, 119 participants initiated the survey, with 100 answering at least one of the questions. Participants had significantly lower perceived relevance (mean: 51.3, 59.2, and 62.1 for topics ≤5 years old, 6-15 years old, and >20 years old, respectively; P < .001) and confidence (mean: 48.4, 57.8, and 63.4, respectively; P < .001) with questions on newer literature compared with older literature. Participants were significantly more likely to read question explanations for questions on common modalities compared with uncommon (46% versus 40%; P = .005) and on guideline-based questions compared with knowledge-based questions (49% versus 43%; P = .01).

DISCUSSION

OLA-type questions function by identifying areas in which radiologists lack knowledge or confidence and highlight areas in which participants have interest in further education.

AI-enhanced diagnostic model for pulmonary nodule classification

Background

The identification of benign and malignant pulmonary nodules (BPN and MPN) can significantly reduce mortality. However, a reliable and validated diagnostic model for clinical decision-making is still lacking.

Methods

Enzyme-linked immunosorbent assay and electro chemiluminescent immunoassay were utilized to determine the serum concentrations of 7AABs (p53, GAGE7, PGP9.5, CAGE, MAGEA1, SOX2, GBU4-5), and 4TTMs (CYFR21, CEA, NSE and SCC) in 260 participants (72 BPNs and 188 early-stage MPNs), respectively. The malignancy probability was calculated using Artificial intelligence pulmonary nodule auxiliary diagnosis system, or Mayo model. Along with age, sex, smoking history and nodule size, 18 variables were enrolled for model development. Baseline comparison, univariate ROC analysis, variable correlation analysis, lasso regression, univariate and stepwise logistic regression, and decision curve analysis (DCA) was used to reduce and screen variables. A nomogram and DCA were built for model construction and clinical use. Training (60%) and validation (40%) cohorts were used to for model validation.

Results

Age, CYFRA21_1, AI, PGP9.5, GAGE7, and GBU4_5 was screened out from 18 variables and utilized to establish the regression model for identifying BPN and early-stage MPN, as well as nomogram and DCA for clinical practical use. The AUC of the nomogram in the training and validation cohorts were 0.884 and 0.820, respectively. Moreover, the calibration curve showed high coherence between the predicted and actual probability.

Conclusion

This diagnostic model and DCA could provide evidence for upgrading or maintaining the current clinical decision based on malignancy probability stratification. It enables low and moderate risk or ambiguous patients to benefit from more precise clinical decision stratification, more timely detection of malignant nodules, and early treatment.

Growth dynamics of lung nodules: implications for classification in lung cancer screening

BACKGROUND

Lung nodules observed in cancer screening are believed to grow exponentially, and their associated volume doubling time (VDT) has been proposed for nodule classification. This retrospective study aimed to elucidate the growth dynamics of lung nodules and determine the best classification as either benign or malignant.

METHODS

Data were analyzed from 180 participants (73.7% male) enrolled in the I-ELCAP screening program (140 primary lung cancer and 40 benign) with three or more annual CT examinations before resection. Attenuation, volume, mass and growth patterns (decelerated, linear, subexponential, exponential and accelerated) were assessed and compared as classification methods.

RESULTS

Most lung cancers (83/140) and few benign nodules (11/40) exhibited an accelerated, faster than exponential, growth pattern. Half (50%) of the benign nodules versus 26.4% of the malignant ones displayed decelerated growth. Differences in growth patterns allowed nodule malignancy to be classified, the most effective individual variable being the increase in volume between two-year-interval scans (ROC-AUC = 0.871). The same metric on the first two follow-ups yielded an AUC value of 0.769. Further classification into solid, part-solid or non-solid, improved results (ROC-AUC of 0.813 in the first year and 0.897 in the second year).

CONCLUSIONS

In our dataset, most lung cancers exhibited accelerated growth in contrast to their benign counterparts. A measure of volumetric growth allowed discrimination between benign and malignant nodules. Its classification power increased when adding information on nodule compactness. The combination of these two meaningful and easily obtained variables could be used to assess malignancy of lung cancer nodules.

Comparative long-term prognosis of early surgery and surgery after surveillance for patients with ground-glass nodule adenocarcinomas

To compare the pathological results and long-term survival results of early surgery and surgery after at least one year follow-up for ground-glass component predominant lung adenocarcinoma patients. From January 1, 2013 to August 31, 2017, a total of 279 patients with ground-glass nodules (GGNs) undergoing surgical resection and pathologically proved to be pulmonary adenocarcinoma were included in this study. All patients were divided into early surgery group (ES Group) (210 cases) and surgery after follow-up group (FS Group) (69 cases). Patients in FS group experienced at least one year surveillance. Clinical and imaging features were analyzed by using univariate analysis. After analysis, there was no statistical difference in pathological results and long-term prognosis between the two groups. In the follow-up group, grown GGNs have proved to have more aggressive pathological results. The one-year follow-up may be a feasible management method for patients with ground-glass component predominant GGN.

Increased Scan Speed and Pitch on Ultra-Low-Dose Chest CT: Effect on Nodule Volumetry and Image Quality

Background and Objectives: This study's objective was to investigate the influence of increased scan speed and pitch on image quality and nodule volumetry in patients who underwent ultra-low-dose chest computed tomography (CT). Material and Methods: One hundred and two patients who had lung nodules were included in this study. Standard-speed, standard-pitch (SSSP) ultra-low-dose CT and high-speed, high-pitch (HSHP) ultra-low-dose CT were obtained for all patients. Image noise was measured as the standard deviation of attenuation. One hundred and sixty-three nodules were identified and classified according to location, volume, and nodule type. Volume measurement of detected pulmonary nodules was compared according to nodule location, volume, and nodule type. Motion artifacts at the right middle lobe, the lingular segment, and both lower lobes near the lung bases were evaluated. Subjective image quality analysis was also performed. Results: The HSHP CT scan demonstrated decreased motion artifacts at the left upper lobe lingular segment and left lower lobe compared to the SSSP CT scan (p < 0.001). The image noise was higher and the radiation dose was lower in the HSHP scan (p < 0.001). According to the nodule type, the absolute relative volume difference was significantly higher in ground glass opacity nodules compared with those of part-solid and solid nodules (p < 0.001). Conclusion: Our study results suggest that HSHP ultra-low-dose chest CT scans provide decreased motion artifacts and lower radiation doses compared to SSSP ultra-low-dose chest CT. However, lung nodule volumetry should be performed with caution for ground glass opacity nodules.

Lung adenocarcinoma manifested as ground-glass nodules in teenagers: characteristics, surgical outcomes and management strategies

OBJECTIVES

Ground-glass nodules-featured lung cancer have been identified in some teenagers in recent years. This study aims to investigate the characteristics and surgical outcomes of these patients and explore proper management strategy.

METHODS

Patients aged ≤20 with incidentally diagnosed lung cancer were retrospectively reviewed from February 2016 to March 2023. Based on lymph node evaluation status, these patients were divided into non-lymph node evaluation and lymph node evaluation groups. The clinical and pathological characteristics were analysed.

RESULTS

A total of 139 teenage patients were included, with an obviously increased cases observed from 2019, corresponding to the COVID-19 pandemic. The median age of the 139 patients was 18 years (range 12-20). Eighty-five patients had pure ground-glass nodules, while others had mixed ground-glass nodules. The mean diameter of nodules was 8.87 ± 2.20 mm. Most of the patients underwent wedge resection (64%) or segmentectomy (31.7%). Fifty-two patients underwent lymph node sampling or dissection. None of these patients had lymph node metastasis. The majority of lesions were adenocarcinoma in situ (63 cases) and minimally invasive adenocarcinoma (72 cases), while four lesions were invasive adenocarcinoma. The median follow-up time was 2.46 years, and none of these patients experienced recurrence or death during follow-up. The lymph node evaluation group had longer hospital stays (P < 0.001), longer surgery time (P < 0.001), and greater blood loss (P = 0.047) than the non-lymph node evaluation group.

CONCLUSIONS

The COVID-19 pandemic significantly increased the number of teenage patients incidentally diagnosed with lung cancer, presenting as ground-glass nodules on CT scans. These patients have favourable surgical outcomes. We propose a management strategy for teenage patients, and suggest that sub-lobar resection without lymph node dissection may be an acceptable surgical procedure for these patients.

Outcomes of Resected Lung Cancer Diagnosed Through Screening and Incidental Pulmonary Nodule Programs in a Mississippi Delta Cohort

Introduction

Early lung cancer detection programs improve surgical resection rates and survival but may skew toward more indolent cancers.

Methods

Hypothesizing that differences in stage-stratified survival indicate differences in biological aggressiveness and possible length-time and overdiagnosis bias, we assessed a cohort who had curative-intent resection, categorized by diagnostic pathways: screening, incidental pulmonary nodule program, and non-program based. Survival was analyzed using Kaplan-Meier plots, log-rank tests, and Cox regression, comparing aggregate and stage-stratified survival across cohorts with Tukey's method for multiple testing.

Results

Of 1588 patients, 111 patients (7%), 357 patients (22.5%), and 1120 patients (70.5%) were diagnosed through screening, pulmonary nodule, and non-program-based pathways; 0% versus 9% versus 6% were older than 80 years (p = 0.0048); 17%, 23%, and 24% had a Charlson Comorbidity score greater than or equal to 2 (p = 0.0143); 7%, 6%, and 9% had lepidic adenocarcinoma; 26%, 31%, and 34% had poorly or undifferentiated tumors (p = 0.1544); and 93%, 87%, and 77% had clinical stage I (p < 0.0001).Aggregate 5-year survival was 87%, 72%, and 65% (p = 0.0009), including 95%, 74%, and 74% for pathologic stage I. Adjusted pairwise comparisons showed similar survival in screening and nodule program cohorts (p = 0.9905). Nevertheless, differences were significant between screening and non-program-based cohorts (p = 0.0007, adjusted hazard ratio 0.33 [95% confidence interval: 0.18-0.6]) and between nodule and nonprogram cohorts (adjusted hazard ratio 0.77 [95% confidence interval: 0.61-0.99]). Stage I comparisons yielded p = 0.2256, 0.1131, and 0.911. In respective pathways, 0%, 2%, and 2% of patients with stage I disease who were older than 80 years had a Charlson score greater than or equal to 2 (p = 0.3849).

Conclusions

Neither length-time nor overdiagnosis bias was evident in NSCLC diagnosed through screening or incidental pulmonary nodule programs.

Solitary pulmonary capillary hemangioma - An underrecognized rare tumor. Report of 32 new cases with literature review

OBJECTIVE

To explore the clinical, imaging, pathologic characteristics and differential diagnosis of solitary pulmonary capillary hemangioma (SPCH).

METHODS

Thirty two cases of SPCH were collected and studied, with literature review.

RESULTS

This study included 13 males and 19 females, with a male-to-female ratio of 1:1.5. The age ranged from 26 to 70 years (median age of 43 years). All patients were asymptomatic at presentation. Lung nodules were incidentally discovered during chest computed tomography (CT). Imaging features included 21 cases with partial solid nodules (PSN), 7 cases with ground-glass nodules (GGN), and 4 cases with solid nodules (SN). Eleven cases were in the left lung lower basal segment, 11 cases in the right lung lower basal segment, 6 cases in the right lung upper anterior segment, and 4 cases in the right lung middle lateral segment. The lower basal segments of the lungs were involved in 22 (11 in each lung) cases (22/32, 68 %). The tumors ranged from 6 to 18 mm (average 10 mm). Macroscopically, 16 cases had clear boundaries, while 16 cases had unclear boundaries, and gray-red or dark brown on cut surfaces. Intraoperative frozen section was performed in 27 cases, with diagnosis of SPCH in 12 and pneumonia or inflammatory lesion in 15. Microscopically, the nodules were composed of densely proliferated and dilated capillaries. The capillary walls were lined with a single layer of flat endothelial cells, without atypical features. Collapsed alveolar septa were replaced by a large number of capillaries. All cases showed proliferating capillaries spreading into the walls of small veins/arteries and bronchi, with 3 cases showing dilated capillaries protruding into the bronchiolar lumens as polyp-like structures. Twenty-six cases (26/32, 81 %) showed proliferating capillaries passed over the interlobular septa. Twenty-six cases (26/32, 81 %) showed irregular intimal thickening of small muscular arteries in the peripheral areas of the lesions, with the thickened intima being cellular or fibrous. In twenty-seven cases (27/32, 84 %) the lesions were located in the subpleura, with 6 cases involving the pleura.

CONCLUSION

SPCH is a rare benign lung tumor that mostly occurs in the lung lower basal segments with predominance in females. It usually appears as a ground-glass nodule on CT and is very similar to early-stage lung cancer. Accurate diagnosis requires collaboration of radiologists, surgeons, and pathologists. SPCH should be regarded as an important differential diagnosis of small incidental lung nodules.

Expert consensus on the multidisciplinary diagnosis and treatment of multiple ground glass nodule-like lung cancer (2024 Edition)

ABSTRACT

This expert consensus reviews current literature and provides clinical practice guidelines for the diagnosis and treatment of multiple ground glass nodule-like lung cancer. The main contents of this review include the following: ① follow-up strategies, ② differential diagnosis, ③ diagnosis and staging, ④ treatment methods, and ⑤ post-treatment follow-up.

Differentiating lung neuroendocrine neoplasms from tumor-like infection using CT in patients with ectopic ACTH syndrome

OBJECTIVES

Pulmonary neuroendocrine neoplasms (NENs) are the most frequent cause of ectopic adrenocorticotropic hormone syndrome (EAS); lung infection is common in EAS. An imaging finding of infection in EAS patients can mimic NENs. This retrospective study investigated EAS-associated pulmonary imaging indicators.

METHODS

Forty-five pulmonary NENs and 27 tumor-like infections from 59 EAS patients (45 NEN and 14 infection patients) were included. Clinical manifestations, CT features, 18F-FDG, or 68Ga-DOTATATE-PET/CT images and pathological results were collected.

RESULTS

High-sensitivity C-reactive protein (p < 0.001) and expectoration occurrence (p = 0.04) were higher, and finger oxygen saturation (p = 0.01) was lower in the infection group than the NENs group. Higher-grade NENs were underrepresented in our cohort. Pulmonary NENs were solitary primary tumors, 80% of which were peripheral tumors. Overlying vessel sign and airway involvement were more frequent in the NENs group (p < 0.001). Multifocal (p = 0.001) and peripheral (p = 0.02) lesions, cavity (p < 0.001), spiculation (p = 0.01), pleural retraction (p < 0.001), connection to pulmonary veins (p = 0.02), and distal atelectasis or inflammatory exudation (p = 0.001) were more frequent in the infection group. The median CT value increment between the non-contrast and arterial phases was significantly higher in NENs lesions (p < 0.001). Receiver operating characteristic curve analysis indicated a moderate predictive ability at 48.3 HU of delta CT value (sensitivity, 95.0%; specificity, 54.1%).

CONCLUSION

Chest CT scans are valuable for localizing and characterizing pulmonary lesions in rare EAS, thereby enabling prompt differential diagnosis and treatment. CRITICAL RELEVANCE STATEMENT: Thin-slice CT images are valuable for the localization and identification of pulmonary ectopic adrenocorticotropic hormone syndrome lesions, leading to prompt differential diagnosis and effective treatment.

KEY POINTS

Lung tumor-like infections can mimic neuroendocrine neoplasms (NENs) in ectopic adrenocorticotropic hormone syndrome (EAS) patients. NENs are solitary lesions, whereas infections are multiple peripheral pseudotumors each with identifying imaging findings. Typical CT signs aid in localization and creating an appropriate differential diagnosis.

Distribution of Solid Lung Nodules Presence and Size by Age and Sex in a Northern European Nonsmoking Population

Background Most of the data regarding prevalence and size distribution of solid lung nodules originates from lung cancer screening studies that target high-risk populations or from Asian general cohorts. In recent years, the identification of lung nodules in non-high-risk populations, scanned for clinical indications, has increased. However, little is known about the presence of solid lung nodules in the Northern European nonsmoking population. Purpose To study the prevalence and size distribution of solid lung nodules by age and sex in a nonsmoking population. Materials and Methods Participants included nonsmokers (never or former smokers) from the population-based Imaging in Lifelines study conducted in the Northern Netherlands. Participants (age ≥ 45 years) with completed lung function tests underwent chest low-dose CT scans. Seven trained readers registered the presence and size of solid lung nodules measuring 30 mm3 or greater using semiautomated software. The prevalence and size of lung nodules (≥30 mm3), clinically relevant lung nodules (≥100 mm3), and actionable nodules (≥300 mm3) are presented by 5-year categories and by sex. Results A total of 10 431 participants (median age, 60.4 years [IQR, 53.8-70.8 years]; 56.6% [n = 5908] female participants; 46.1% [n = 4812] never smokers and 53.9% [n = 5619] former smokers) were included. Of these, 42.0% (n = 4377) had at least one lung nodule (male participants, 47.5% [2149 of 4523]; female participants, 37.7% [2228 of 5908]). The prevalence of lung nodules increased from age 45-49.9 years (male participants, 39.4% [219 of 556]; female participants, 27.7% [236 of 851]) to age 80 years or older (male participants, 60.7% [246 of 405]; female participants, 50.9% [163 of 320]). Clinically relevant lung nodules were present in 11.1% (1155 of 10 431) of participants, with prevalence increasing with age (male participants, 8.5%-24.4%; female participants, 3.7%-15.6%), whereas actionable nodules were present in 1.1%-6.4% of male participants and 0.6%-4.9% of female participants. Conclusion Lung nodules were present in a substantial proportion of all age groups in the Northern European nonsmoking population, with slightly higher prevalence for male participants than female participants. © RSNA, 2024 Supplemental material is available for this article.

CT-guided percutaneous marking of small pulmonary nodules with [99mTc]Tc-Macrosalb is very accurate and allows minimally invasive lung-sparing resection: a single-centre quality control

PURPOSE

The detection of small lung nodules in thoracoscopic procedure is difficult when the lesions are not located within the outer border of the lung. In the case of ground-glass opacities, it is often impossible to palpate the lesion. Marking lung nodules using a radiotracer is a known technique. We analysed the accuracy and safety of the technique and the potential benefits of operating in a hybrid operating room.

METHODS

57 patients, including 33 (58%) females with a median age of 67 years (range 21-82) were included. In 27 patients, we marked and resected the lesion in a hybrid room. In 30 patients, the lesion was marked at the department of radiology the day before resection. [99mTc]Tc-Macrosalb (Pulmocis®) was used at an activity of 1 MBq in the hybrid room and at an activity of 3 MBq the day before to get technical feasible results. Radioactivity was detected using the Neoprobe® detection system.

RESULTS

Precise detection and resection of the nodules was possible in 95% of the lesions and in 93% of the patients. Complete thoracoscopic resection was possible in 90% of the patients. Total conversion rate was 10%, but conversion due to failure of the marking of the nodule was observed in only 5% of the patients. Histology revealed 28 (37%) primary lung cancers, 24 (32%) metastases and 21 (28%) benign lesions. In 13 (23%) patients, minor complications were observed. None of them required additional interventions.

CONCLUSION

The radio-guided detection of small pulmonary nodules is very accurate and safe after CT-guided injection of [99mTc]Tc-Macrosalb. Performing the operation in a hybrid room has several logistic advantages and allows using lower technetium-99m activities. The technique allows minimally invasive lung sparing resection and prevents overtreatment of benign and metastatic lesions.

Clinical implications of non-breast cancer related findings on FDG-PET/CT scan prior to neoadjuvant chemotherapy in patients with breast cancer

PURPOSE

Breast cancer (BC) patients undergoing FDG-PET/CT scans for neoadjuvant chemotherapy (NAC) may have additional non-BC related findings. The aim of this study is to describe the clinical implications of these findings.

METHODS

We included BC patients who underwent an FDG-PET/CT scan in our institute between 2011-2020 prior to NAC. We focused on patients with an additional non-BC related finding (i.e. BC metastases were excluded) for which diagnostic work-up was performed. Information about the diagnostic work-up and the clinical consequences was retrospectively gathered. A revision of all FDG-PET/CT scans was conducted by an independent physician to assess the suspicion level of the additional findings.

RESULTS

Of the 1337 patients who underwent FDG-PET/CT, 202 patients (15%) had an non-BC related additional finding for which diagnostic work-up was conducted, resulting in 318 examinations during the first year. The non-BC related findings were mostly detected in the endocrine region (26%), gastro-intestinal region (16%), or the lungs (15%). Seventeen patients (17/202: 8%, 17/1337: 1.3%) had a second primary malignancy. Only 8 patients (8/202: 4%, 8/1337: 0.6%) had a finding that was considered more prognosis-determining than their BC disease. When revising all FDG-PET/CT scans, 57 (202/57: 28%) of the patients had an additional finding categorized as low suspicious, suggesting no indication for diagnostic work-up.

CONCLUSION

FDG-PET/CT scans used for dissemination imaging in BC patients detect a high number of non-BC related additional findings, often clinically irrelevant and causing a large amount of unnecessary work-up. However, in 8% of the patients undergoing diagnostic work-up for an additional finding, a second primary malignancy was detected, warranting diagnostic attention in selected patients.

Detecting pulmonary malignancy against benign nodules using noninvasive cell-free DNA fragmentomics assay

BACKGROUND

Early screening using low-dose computed tomography (LDCT) can reduce mortality caused by non-small-cell lung cancer. However, ∼25% of the 'suspicious' pulmonary nodules identified by LDCT are later confirmed benign through resection surgery, adding to patients' discomfort and the burden on the healthcare system. In this study, we aim to develop a noninvasive liquid biopsy assay for distinguishing pulmonary malignancy from benign yet 'suspicious' lung nodules using cell-free DNA (cfDNA) fragmentomics profiling.

METHODS

An independent training cohort consisting of 193 patients with malignant nodules and 44 patients with benign nodules was used to construct a machine learning model. Base models using four different fragmentomics profiles were optimized using an automated machine learning approach before being stacked into the final predictive model. An independent validation cohort, including 96 malignant nodules and 22 benign nodules, and an external test cohort, including 58 malignant nodules and 41 benign nodules, were used to assess the performance of the stacked ensemble model.

RESULTS

Our machine learning models demonstrated excellent performance in detecting patients with malignant nodules. The area under the curves reached 0.857 and 0.860 in the independent validation cohort and the external test cohort, respectively. The validation cohort achieved an excellent specificity (68.2%) at the targeted 90% sensitivity (89.6%). An equivalently good performance was observed while applying the cut-off to the external cohort, which reached a specificity of 63.4% at 89.7% sensitivity. A subgroup analysis for the independent validation cohort showed that the sensitivities for detecting various subgroups of nodule size (<1 cm: 91.7%; 1-3 cm: 88.1%; >3 cm: 100%; unknown: 100%) and smoking history (yes: 88.2%; no: 89.9%) all remained high among the lung cancer group.

CONCLUSIONS

Our cfDNA fragmentomics assay can provide a noninvasive approach to distinguishing malignant nodules from radiographically suspicious but pathologically benign ones, amending LDCT false positives.

Super Resolution of Pulmonary Nodules Target Reconstruction Using a Two-Channel GAN Models

RATIONALE AND OBJECTIVES

To develop a Dual generative-adversarial-network (GAN) Cascaded Network (DGCN) for generating super-resolution computed tomography (SRCT) images from normal-resolution CT (NRCT) images and evaluate the performance of DGCN in multi-center datasets.

MATERIALS AND METHODS

This retrospective study included 278 patients with chest CT from two hospitals between January 2020 and June 2023, and each patient had all three NRCT (512×512 matrix CT images with a resolution of 0.70 mm, 0.70 mm,1.0 mm), high-resolution CT (HRCT, 1024×1024 matrix CT images with a resolution of 0.35 mm, 0.35 mm,1.0 mm), and ultra-high-resolution CT (UHRCT, 1024×1024 matrix CT images with a resolution of 0.17 mm, 0.17 mm, 0.5 mm) examinations. Initially, a deep chest CT super-resolution residual network (DCRN) was built to generate HRCT from NRCT. Subsequently, we employed the DCRN as a pre-trained model for the training of DGCN to further enhance resolution along all three axes, ultimately yielding SRCT. PSNR, SSIM, FID, subjective evaluation scores, and objective evaluation parameters related to pulmonary nodule segmentation in the testing set were recorded and analyzed.

RESULTS

DCRN obtained a PSNR of 52.16, SSIM of 0.9941, FID of 137.713, and an average diameter difference of 0.0981 mm. DGCN obtained a PSNR of 46.50, SSIM of 0.9990, FID of 166.421, and an average diameter difference of 0.0981 mm on 39 testing cases. There were no significant differences between the SRCT and UHRCT images in subjective evaluation.

CONCLUSION

Our model exhibited a significant enhancement in generating HRCT and SRCT images and outperformed established methods regarding image quality and clinical segmentation accuracy across both internal and external testing datasets.

Shifting the Cancer Screening Paradigm: Developing a Multi-Biomarker Class Approach to Multi-Cancer Early Detection Testing

Guideline-recommended screening programs exist for only a few cancer types. Although all these programs are understood to lead to reductions in cancer-related mortality, standard-of-care screening tests vary in accuracy, adherence and effectiveness. Recent advances in high-throughput technologies and machine learning have facilitated the development of blood-based multi-cancer cancer early detection (MCED) tests. MCED tests are positioned to be complementary to standard-of-care screening and they may broaden screening availability, especially for individuals who are not adherent with current screening programs and for individuals who may harbor cancers with no available screening options. In this article, we outline some key features that should be considered for study design and MCED test development, provide an example of the developmental pathway undertaken for an emerging multi-biomarker class MCED test and propose a clinical algorithm for an imaging-based diagnostic resolution strategy following MCED testing.

Artificial intelligence-based graded training of pulmonary nodules for junior radiology residents and medical imaging students

BACKGROUND

To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and medical imaging students.

METHODS

The participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the 'AI intelligent assisted diagnosis system' teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5-10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared.

RESULTS

There was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 (P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups (P<0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1-4 and 5-7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively.

CONCLUSION

The AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.

Case report: Pulmonary sarcomatoid carcinoma demonstrating rapid growth on follow-up CT

Background

The tumor growth rate and tumor volume doubling time are crucial parameters in diagnosing and managing lung lesions. Pulmonary sarcomatoid carcinoma (PSC) is a unique and highly malignant subtype of lung cancer, with limited documentation on its growth feature. This article aims to address the gap in knowledge regarding a PSC's growth patterns by describing the characteristics of a confirmed case using computed tomography, thereby enhancing the understanding of this rare disease.

Case presentation

A 79-year-old man was transferred to our center presenting with a mild cough, blood-tinged sputum, and a malignant nodule in the left upper lobe. Chest CT revealed a solid nodule in the left upper lobe. A follow-up CT ten days later showed a significant increase in the size of the nodule, accompanied by ground-glass opacity in the surrounding lung. The rapid preoperative growth of the nodule suggested a non-neoplastic lesion, and intraoperative frozen pathology also considered the possibility of tuberculosis. Subsequently, a left upper apical-posterior segment (S1 + 2) resection was performed. Postoperative tumor pathology confirmed the diagnosis of pulmonary sarcomatoid carcinoma with extensive giant cell carcinoma and necrosis. Immunohistochemistry indicated approximately 60% PD-L1 positive and genetic testing revealed a MET mutation. The patient was discharged with oral crizotinib targeted therapy, and his condition remained stable postoperatively. The patient is currently undergoing regular follow-up at our hospital, with no evidence of distant metastasis or recurrence.

Conclusion

Pulmonary sarcomatoid carcinoma can exhibit rapid tumor growth on imaging, and PSC should be considered in the differential diagnosis for lesions that present with a fast growth rate. Timely and appropriate treatment for PSC may lead to a good prognosis.

Prediction of the Benign or Malignant Nature of Pulmonary Pure Ground-Glass Nodules Based on Radiomics Analysis of High-Resolution Computed Tomography Images

To evaluate the efficacy of radiomics features extracted from preoperative high-resolution computed tomography (HRCT) scans in distinguishing benign and malignant pulmonary pure ground-glass nodules (pGGNs), a retrospective study of 395 patients from 2016 to 2020 was conducted. All nodules were randomly divided into the training and validation sets in the ratio of 7:3. Radiomics features were extracted using MaZda software (version 4.6), and the least absolute shrinkage and selection operator (LASSO) was employed for feature selection. Significant differences were observed in the training set between benign and malignant pGGNs in sex, mean CT value, margin, pleural retraction, tumor-lung interface, and internal vascular change, and then the mean CT value and the morphological features model were constructed. Fourteen radiomics features were selected by LASSO for the radiomics model. The combined model was developed by integrating all selected radiographic and radiomics features using logistic regression. The AUCs in the training set were 0.606 for the mean CT value, 0.718 for morphological features, 0.756 for radiomics features, and 0.808 for the combined model. In the validation set, AUCs were 0.601, 0.692, 0.696, and 0.738, respectively. The decision curves showed that the combined model demonstrated the highest net benefit.

Lung-Reporting and Data System 2.0: Impact of the Updated Approach to Juxtapleural Nodules During Lung Cancer Screening Using the National Lung Cancer Screening Trial Data Set

PURPOSE

To determine the frequency of malignancy of nonperifissural juxtapleural nodules (JPNs) measuring 6 to < 10 mm in a subset of low-dose chest computed tomographies from the National Lung Cancer Screening Trial and the rate of down-classification of such nodules in Lung-Reporting and Data System (RADS) 2.0 compared with Lung-RADS 1.1.

MATERIALS AND METHODS

A secondary analysis of a subset of the National Lung Screening Trial was performed. An exemption was granted by the Institutional Review Board. The dominant noncalcified nodule measuring 6 to <10 mm was identified on all available prevalence computed tomographies. Nodules were categorized as pleural or nonpleural. Benign or malignant morphology was recorded. Initial and updated categories based on Lung-RADS 1.1 and Lung-RADS 2.0 were assigned, respectively. The impact of the down-classification of JPN was assessed. Both classification schemes were compared using the McNemar test ( P < 0.01).

RESULTS

A total of 2813 patients (62 ± 5 y, 1717 men) with 4408 noncalcified nodules were studied. One thousand seventy-three dominant nodules measuring 6 to <10 mm were identified. Three hundred forty-eight (32.4%) were JPN. The updated scheme allowed down-classification of 310 JPN from categories 3 (n = 198) and 4A (n = 112) to category 2. We, therefore, estimate a 4.8% rate of down-classification to category 2 in the entire National Lung Screening Trial screening group. Two/348 (0.57%) JPN were malignant, both nonbenign in morphology. The false-positive rate decreased in the updated classification ( P < 0.01).

CONCLUSION

This study demonstrates the low malignant potential of benign morphology JPN measuring 6 mm to <10 mm. The Lung-RADS 2.0 approach to JPN is estimated to reduce short-term follow-ups and false-positive results.

Diagnosis and treatment of high-risk bilateral lung ground-glass opacity nodules

In recent years, there has been a significant increase in the detection rate of Ground Glass Opacity (GGO) nodules through high-resolution computed tomography (HRCT). GGO is an imaging finding that encompasses various pathological types, some of which exhibit indolent growth, while others may represent early lung cancer or remain relatively stable, not significantly impacting the surgical treatment outcome. In clinical practice, patients often experience psychological anxiety when multiple pulmonary GGO nodules are present, and they may request simultaneous resection. However, there is currently no standardized criterion for determining when multiple GGO nodules should be resected. As personalized medicine continues to advance, the treatment approach for multiple pulmonary GGO nodules needs to prioritize accuracy. High-risk factors associated with multiple pulmonary GGO nodules may necessitate surgical intervention along with mediastinal lymph node dissection or sampling. This article provides a review of the characteristics, treatment methods, and clinical experiences related to multiple pulmonary GGO nodules, offering practical insights and guidance for healthcare professionals.

Nomogram for predicting invasive lung adenocarcinoma in small solitary pulmonary nodules

Background

This study aimed to construct a clinical prediction model and nomogram to differentiate invasive from non-invasive pulmonary adenocarcinoma in solitary pulmonary nodules (SPNs).

Method

We analyzed computed tomography and clinical features as well as preoperative biomarkers in 1,106 patients with SPN who underwent pulmonary resection with definite pathology at Qilu Hospital of Shandong University between January 2020 and December 2021. Clinical parameters and imaging characteristics were analyzed using univariate and multivariate logistic regression analyses. Predictive models and nomograms were developed and their recognition abilities were evaluated using receiver operating characteristic (ROC) curves. The clinical utility of the nomogram was evaluated using decision curve analysis (DCA).

Result

The final regression analysis selected age, carcinoembryonic antigen, bronchus sign, lobulation, pleural adhesion, maximum diameter, and the consolidation-to-tumor ratio as associated factors. The areas under the ROC curves were 0.844 (95% confidence interval [CI], 0.817-0.871) and 0.812 (95% CI, 0.766-0.857) for patients in the training and validation cohorts, respectively. The predictive model calibration curve revealed good calibration for both cohorts. The DCA results confirmed that the clinical prediction model was useful in clinical practice. Bias-corrected C-indices for the training and validation cohorts were 0.844 and 0.814, respectively.

Conclusion

Our predictive model and nomogram might be useful for guiding clinical decisions regarding personalized surgical intervention and treatment options.

Differentiating the Invasiveness of Lung Adenocarcinoma Manifesting as Ground Glass Nodules: Combination of Dual-energy CT Parameters and Quantitative-semantic Features

RATIONALE AND OBJECTIVES

To evaluate the diagnostic performance of dual-energy CT (DECT) parameters and quantitative-semantic features for differentiating the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs).

MATERIALS AND METHODS

Between June 2022 and September 2023, 69 patients with 74 surgically resected GGNs who underwent DECT examinations were included. CT numbers on virtual monochromatic images were calculated at 40-130 keV generated from DECT. Quantitative morphological measurements and semantic features were evaluated on unenhanced CT images and compared between pathologically confirmed adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive lung adenocarcinoma (IAC). Multivariable logistic regression analysis was used to identify independent predictors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.

RESULTS

Monochromatic CT numbers at 40-130 keV were significantly higher in IAC than in AIS-MIA (all P < 0.05). Multivariate logistic analysis revealed that CT number of 130 keV (odds ratio [OR] = 1.02, P = 0.013), maximum cross-sectional long diameter (OR =1.40, P = 0.014), deep or moderate lobulation sign (OR =19.88, P = 0.005), and abnormal intranodular vessel morphology (OR = 25.57, P = 0.017) were independent predictors of IAC. The combined prediction model showed a favorable differentiation performance with an AUC of 0.966 (95.2% sensitivity, 94.3% specificity, 94.8% accuracy), which was significantly higher than that for each risk factor (AUC = 0.791-0.822, all P < 0.05).

CONCLUSION

A multi-parameter combined prediction model integrating monochromatic CT numbers from DECT and quantitative-semantic features is promising for the preoperative discrimination of IAC and AIS-MIA in GGN-predominant lung adenocarcinoma.

The role of radiological and clinical findings in determining lobectomy decision in patients with undiagnosed resectable lung lesions

Background

The aim of this study was to evaluate the role of radiological and clinical findings in determining lobectomy decision in undiagnosed resectable lung lesions.

Methods

Between January 2014 and April 2023, a total of 135 patients (114 males, 21 females; mean age: 60.8±11.5 years; range, 17 to 84 years) who underwent lobectomy or wedge resection based on clinical and radiological data were retrospectively analyzed. Patients with undiagnosed lung lesions, whose diagnosis could not be confirmed through transthoracic fine needle aspiration biopsy or bronchoscopic endobronchial ultrasound, were included in the study. Clinical data including age, sex, smoking status, history of extrapulmonary cancer, family history of lung cancer, and presence of chronic obstructive pulmonary disease/idiopathic pulmonary fibrosis were noted. Radiological data including lesion size, margin characteristics, internal structure of the lesion, relationship of the lesion with surrounding tissues, and nuclear imaging results were also recorded.

Results

Malignant lesions were detected in 74 patients, while benign lesions were detected in 61 patients. Comparing benign and malignant lesions, age, lesion size, lesion localization, presence of pleural retraction, and moderate-to-high maximum standardized uptake value (SUVmax) on positron emission tomography-computed tomography were found to be correlated with malignancy.

Conclusion

The accurate assessment of lung lesions and prompt identification of possible malignancy are of paramount importance for implementing appropriate treatment strategies.

Impact of number of dissected lymph nodes on recurrence and survival following thoracoscopic segmentectomy for clinical stage I non-small cell lung cancer

OBJECTIVE

This study aimed to identify the impact of number of dissected lymph nodes during thoracoscopic segmentectomy on recurrence and survival of clinical stage I non-small cell lung cancer (NSCLC).

PATIENTS AND METHODS

We retrospectively analysed data from prospectively collected consecutive thoracoscopic segmentectomies conducted between June 2008 and September 2023 at a single institution. Kaplan-Meier analysis with log-rank test assessed OS. Fine-Gray's test assessed specific death in a competing risk model. The logistic regression model was utilized to predict recurrence, while the Cox regression model was employed to analyse overall survival (OS). Subgroup and sensitivity analyses were performed.

RESULTS

A total of 227 patients were included in the final analyses. The mean follow-up was 38.4 months (standard deviation 35.8). Among all patients, 37 patients (16.3 %) experienced recurrence and 51 (22.5 %) deceased during the follow-up period. The median number of dissected lymph nodes was 9 (interquartile range (IQR) 6-12). No statistical difference in recurrence rate and 5-year OS was observed between cases with dissected lymph nodes > 9 and ≤ 9 (14.6 % vs. 17.6 %, p = 0.549; 75.5 % vs. 69.5 %, p = 0.760). On multivariable analysis, body mass index (odds ratio [OR] 1.15, p = 0.002), Charlson Comorbidity index (OR 1.28, p = 0.002), synchronous pulmonary cancer (OR 3.05, p = 0.019), and tumour size (OR 1.04, p = 0.044) increased of the recurrence rate, while percentage of predicted forced expiratory volume in 1 s (hazard ratio (HR) 1.09, p = 0.048), history of smoking (HR 1.02, p = 0.009), and solid nodule (HR 1.56, p = 0.010) was related to poorer survival.

CONCLUSIONS

In this study, number of dissected lymph nodes did not impact recurrence rate or overall survival after thoracoscopic segmentectomy for clinical stage I NSCLC.

Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images

OBJECTIVES

To establish deep learning models for malignancy risk estimation of sub-centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical settings.

MATERIALS AND METHODS

Four deep learning models were trained using CT images of sub-centimeter pulmonary nodules from West China Hospital, internally tested, and externally validated on three cohorts. The four models respectively learned 3D deep features from the baseline whole lung region, baseline image patch where the nodule located, baseline nodule box, and baseline plus follow-up nodule boxes. All regions of interest were automatically segmented except that the nodule boxes were additionally manually checked. The performance of models was compared with each other and that of three respiratory clinicians.

RESULTS

There were 1822 nodules (981 malignant) in the training set, 806 (416 malignant) in the testing set, and 357 (253 malignant) totally in the external sets. The area under the curve (AUC) in the testing set was 0.754, 0.855, 0.928, and 0.942, respectively, for models derived from baseline whole lung, image patch, nodule box, and the baseline plus follow-up nodule boxes. When baseline models externally validated (follow-up images not available), the nodule-box model outperformed the other two with AUC being 0.808, 0.848, and 0.939 respectively in the three external datasets. The resident, junior, and senior clinicians achieved an accuracy of 67.0%, 82.5%, and 90.0%, respectively, in the testing set. The follow-up model performed comparably to the senior clinician.

CONCLUSION

The deep learning algorithms solely mining nodule information can efficiently predict malignancy of incidental sub-centimeter pulmonary nodules.

CLINICAL RELEVANCE STATEMENT

The established models may be valuable for supporting clinicians in routine clinical practice, potentially reducing the number of unnecessary examinations and also delays in diagnosis.

KEY POINTS

• According to different regions of interest, four deep learning models were developed and compared to evaluate the malignancy of sub-centimeter pulmonary nodules by CT images. • The models derived from baseline nodule box or baseline plus follow-up nodule boxes demonstrated sufficient diagnostic accuracy (86.4% and 90.4% in the testing set), outperforming the respiratory resident (67.0%) and junior clinician (82.5%). • The proposed deep learning methods may aid clinicians in optimizing follow-up recommendations for sub-centimeter pulmonary nodules and may lead to fewer unnecessary diagnostic interventions.