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

Silicone deposition and adverse pulmonary events secondary to breast implant rupture

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

RATIONALE AND OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

BACKGROUND

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

RESEARCH QUESTION

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

METHODS

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

RESULTS

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

INTERPRETATION

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

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

OBJECTIVE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

Ultra-low-dose chest computed tomography with model-based iterative reconstruction in the analysis of solid pulmonary nodules: A prospective study

BACKGROUND

Incidental pulmonary nodules are an increasingly common finding on computed tomography (CT) scans of the thorax due to the exponential rise in CT examinations in everyday practice. The majority of incidental pulmonary nodules are benign and correctly identifying the small number of malignant nodules is challenging. Ultra-low-dose CT (ULDCT) has been shown to be effective in diagnosis of respiratory pathology in comparison with traditional standard dose techniques. Our hypothesis was that ULDCT chest combined with model-based iterative reconstruction (MBIR) is comparable to standard dose CT (SDCT) chest in the analysis of pulmonary nodules with significant reduction in radiation dose.

AIM

To prospectively compare ULDCT chest combined with MBIR with SDCT chest in the analysis of solid pulmonary nodules.

METHODS

A prospective cohort study was conducted on adult patients (n = 30) attending a respiratory medicine outpatient clinic in a tertiary referral university hospital for surveillance of previously detected indeterminate pulmonary nodules on SDCT chest. This study involved the acquisition of a reference SDCT chest followed immediately by an ULDCT chest. Nodule identification, nodule characterisation, nodule measurement, objective and subjective image quality and radiation dose were compared between ULDCT with MBIR and SDCT chest.

RESULTS

One hundred solid nodules were detected on ULDCT chest and 98 on SDCT chest. There was no significant difference in the characteristics of correctly identified nodules when comparing SDCT chest to ULDCT chest protocols. Signal-to-noise ratio was significantly increased in the ULDCT chest in all areas except in the paraspinal muscle at the maximum cardiac diameter level (P < 0.001). The mean subjective image quality score for overall diagnostic acceptability was 8.9/10. The mean dose length product, computed tomography volume dose index and effective dose for the ULDCT chest protocol were 5.592 mGy.cm, 0.16 mGy and 0.08 mSv respectively. These were significantly less than the SDCT chest protocol (P < 0.001) and represent a radiation dose reduction of 97.6%.

CONCLUSION

ULDCT chest combined with MBIR is non-inferior to SDCT chest in the analysis of previously identified solid pulmonary nodules and facilitates a large reduction in radiation dose.

An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study

BACKGROUND

To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED).

METHODS

In the OPTIMACT trial, 870 patients with suspected nontraumatic pulmonary disease underwent ULDCT. The ED radiologist prospectively read the examinations and reported incidental pulmonary nodules requiring follow-up. All ULDCTs were processed post hoc using an AI deep learning software marking pulmonary nodules ≥ 6 mm. Three chest radiologists independently reviewed the subset of ULDCTs with either prospectively detected incidental nodules in 35/870 patients or AI marks in 458/870 patients; findings scored as nodules by at least two chest radiologists were used as true positive reference standard. Proportions of true and false positives were compared.

RESULTS

During the OPTIMACT study, 59 incidental pulmonary nodules requiring follow-up were prospectively reported. In the current analysis, 18/59 (30.5%) nodules were scored as true positive while 104/1,862 (5.6%) AI marks in 84/870 patients (9.7%) were scored as true positive. Overall, 5.8 times more (104 versus 18) true positive pulmonary nodules were detected with the use of AI, at the expense of 42.9 times more (1,758 versus 41) false positives. There was a median number of 1 (IQR: 0-2) AI mark per ULDCT.

CONCLUSION

The use of AI on ULDCT in patients suspected of pulmonary disease in an emergency setting results in the detection of many more incidental pulmonary nodules requiring follow-up (5.8×) with a high trade-off in terms of false positives (42.9×).

RELEVANCE STATEMENT

AI aids in the detection of incidental pulmonary nodules that require follow-up at chest-CT, aiding early pulmonary cancer detection but also results in an increase of false positive results that are mainly clustered in patients with major abnormalities.

TRIAL REGISTRATION

The OPTIMACT trial was registered on 6 December 2016 in the National Trial Register (number NTR6163) (onderzoekmetmensen.nl).

KEY POINTS

An AI deep learning algorithm was tested on 870 ULDCT examinations acquired in the ED. AI detected 5.8 times more pulmonary nodules requiring follow-up (true positives). AI resulted in the detection of 42.9 times more false positive results, clustered in patients with major abnormalities. AI in the ED setting may aid in early pulmonary cancer detection with a high trade-off in terms of false positives.

Diagnostic accuracy and image quality evaluation of ultrashort echo time MRI in the lungs

This study evaluates the diagnostic accuracy of ultrashort echo time (UTE)-MRI for detecting pulmonary nodules and image quality. A total of 46 patients at our hospital underwent unenhanced computed tomography (CT) and UTE-MRI. The image quality and number of nodules detected using CT were used as the gold standards. Three diagnostic radiologists independently recorded the image quality (visibility and sharpness of normal anatomical structures) of the CT and UTE images and the number of pulmonary nodules detected. The diagnostic accuracy, subjective image quality, and consistency between observations were statistically analyzed. Among 46 patients, 36 (78.2%) had pulmonary nodules on CT images, whereas 10 patients (21.7%) had no pulmonary nodules. A total of 48 lung nodules were detected, 3 of which were ground-glass opacities. UTE-MRI revealed 46 lung nodules. Compared with CT, the sensitivity of all MRI readers for detecting lung lesions was 95.8%, and the 3-observer agreement was nearly perfect (P < .001, Kendall Wa [Kender Harmonious Coefficient] = 0.913). The overall image quality score of the observers was high, ranging from good to excellent, and the consistency of the subjective UTE-MRI image quality was good (Kendall Wa = 0.877, P < .001). For tracheal display, the subsegment of the bronchus was displayed, and the wall of the tube was clearly displayed. The difference in the Wa values between the observers was 0.804 (P < .001), indicating strong consistency. For blood vessels, subsegment blood vessels could also be displayed with clear walls and uniform signals (Kendal Wa = 0.823, P < .001), indicating strong consistency. Compared to CT, UTE-MRI can detect pulmonary nodules with a high detection rate, relatively good image quality, and strong consistency between observers. The development of UTE-MRI can provide a novel imaging method for the detection and follow-up of pulmonary nodules and diagnosis of pneumonia by reducing ionizing radiation.

Glutathione inhibits lung cancer development by reducing interleukin-6 expression and reversing the Warburg effect

Reduced glutathione (GSH) is widely used as an antioxidant in clinical practice, but whether GSH affects the development of early lung cancer remains unclear. Herein, we investigated the mechanism underlying the anticancer effect of GSH in patients with pulmonary nodules. Thirty patients with pulmonary nodules were treated with GSH intravenously for 10 days at a dose of 1.8 g/d, followed by oral administration of the drug at a dose of 0.4 g three times daily for 6 months. The results showed that GSH treatment promoted nodule absorption and reduced the IL-6 level in the peripheral blood of the patients. GSH reduced IL-6 expression in inflammatory BEAS-2B and lung cancer cells and inhibited the proliferation of lung cancer cell lines in vitro. In addition, GSH reduced IL-6 expression by decreasing ROS via down-regulating PI3K/AKT/FoxO pathways. Finally, GSH reversed the Warburg effect, restored mitochondrial function, and reduced the IL-6 expression via PI3K/AKT/FoxO pathways. The in vivo experiment confirmed that GSH inhibited lung cancer growth, improved mitochondrial function, and reduced the IL-6 expression by regulating key enzymes via the PI3K/AKT/FoxO pathway. In conclusion, we uncovered that GSH exerts an unprecedentedly potent anti-cancer effect to prevent the transformation of lung nodules to lung cancer by improving the mitochondrial function and suppressing inflammation via PI3K/AKT/FoxO pathway. This investigation innovatively positions GSH as a potentially safe and efficacious old drug with new uses, inhibiting inflammation and early lung cancer. The use of the drug offers a promising preventive strategy when administered during the early stages of lung cancer.

Recommendations for the implementation of a national lung cancer screening program in Portugal-A consensus statement

Lung cancer (LC) is a leading cause of cancer-related mortality worldwide. Lung Cancer Screening (LCS) programs that use low-dose computed tomography (LDCT) have been shown to reduce LC mortality by up to 25 % and are considered cost-effective. The European Health Union has encouraged its Member States to explore the feasibility of LCS implementation in their respective countries. The task force conducted a comprehensive literature review and engaged in extensive discussions to provide recommendations. These recommendations encompass the essential components required to initiate pilot LCS programs following the guidelines established by the World Health Organization. They were tailored to align with the specific context of the Portuguese healthcare system. The document addresses critical aspects, including the eligible population, methods for issuing invitations, radiological prerequisites, procedures for reporting results, referral processes, diagnostic strategies, program implementation, and ongoing monitoring. Furthermore, the task force emphasized that pairing LCS with evidence-based smoking cessation should be the standard of care for a high-quality screening program. This document also identifies areas for further research. These recommendations aim to guarantee that the implementation of a Portuguese LCS program ensures high-quality standards, consistency, and uniformity across centres.

Pulmonary nodules and the psychological harm they can cause: A scoping review

More than 1.6 million pulmonary nodules are diagnosed in the United States each year. Although the majority of nodules are found to be benign, nodule detection and the process of ruling out malignancy can cause patients psychological harm to varying degrees. The present study undertakes a scoping review of the literature investigating pulmonary nodule-related psychological harm as a primary or secondary outcome. Online databases were systematically searched to identify papers published through June 30, 2023, from which 19 publications were reviewed. We examined prevalence by type, measurement, associated factors, and behavioral or clinical consequences. Of the 19 studies reviewed, 11 studies investigated distress, anxiety (n = 6), and anxiety and depression (n = 4). Prevalence of distress was 24.0 %-56.7 %; anxiety 9.9 %-42.1 %, and 14.6 %-27.0 % for depression. A wide range of demographic and social characteristics and clinical factors were associated with nodule-related psychological harm. Outcomes of nodule-related harms included experiencing conflict when deciding about treatment or surveillance, decreased adherence to surveillance, adoption of more aggressive treatment, and lower health-related quality of life. Our scoping review demonstrates that nodule-related psychological harm is common. Findings provide evidence that nodule-related psychological harm can influence clinical decisions and adherence to treatment recommendations. Future research should focus on discerning between nodule-related distress and anxiety; identifying patients at risk; ascertaining the extent of psychological harm on patient behavior and clinical decisions; and developing interventions to assist patients in managing psychological harm for better health-related quality of life and treatment outcomes.

Lung cancer screening by low-dose CT scan in France: final results of the DEP KP80 study after three rounds

BACKGROUND

In prior randomised controlled trials, lung cancer screening using low-dose computed tomography (LDCT) has been shown to reduce lung cancer mortality and overall mortality. Despite these results, organised screening in France remains a challenge. This study assessed the feasibility and efficacy of lung cancer screening within a real-life context in a French administrative territory.

METHODS

DEP KP80 was a single-arm prospective study. Participants aged between 55 and 74 years, smokers or former smokers of ≥30 pack-years, were recruited. An annual LDCT scan was scheduled and three rounds were performed. Subjects were selected by general practitioners or pulmonologists, who checked the inclusion criteria and prescribed the CT scan.

FINDINGS

Between March 2016 and February 2020, 1254 participants were enrolled. Overall, 945 (75.4%) participants underwent baseline LDCT (T0), 376 (42.8%) completed the first round (T1) and 270 (31%) the second (T2) one. Forty-two lung cancers were diagnosed, 30 cancers (71.4%) were stage I or II and 34 cancers (80.9%) were treated surgically. In this study, the overall positive predictive value for a positive screening was 48% (95% CI 37-59) and the negative predictive value 100% (95% CI 100-100).

INTERPRETATION

This study demonstrated the feasibility and efficacy of lung cancer screening in a real-life context with most lung cancers diagnosed at an early stage and surgically removed. Our results also highlighted the importance of participation in each round, underlining the fact that optimising organisation is a major goal.

FUNDING

Agence Régionale de Santé de Picardie, La Ligue contre le cancer, le Conseil Départemental de la Somme, and AstraZeneca.

The Correlation Between the Natural Course, Pathologic Properties With Ki-67 Expression in Lung Adenocarcinoma Presenting as Ground-Glass Nodules

BACKGROUND

With the increasing use of lung cancer screening, the detection of ground glass nodules (GGNs) has risen. However, the natural course of GGNs and their relationship to pathologic features remains unclear. Differentiating between invasive and pre-invasive lesions based on GGN growth may improve clinical intervention timing. Ki-67, a proliferation marker, holds value in assessing tumor malignancy. This study analyzes the association between GGN growth, pathology, and Ki-67 expression to provide new insights into early-stage lung cancer management.

METHODS

We retrospectively evaluated 183 GGNs with at least two preoperative CT scans. Nodule location, type, natural course, and volume doubling time (VDT) were compared between invasive adenocarcinoma (IAC) and pre-IAC groups. We also assessed differences in Ki-67 expression and correlated VDT with Ki-67 levels.

RESULTS

A total of 183 nodules were finally included; gender, nodule location, smoking history, and duration of follow-up did not differ between the IAC group and the pre-IAC group, whereas age was statistically different between the two groups. Of the 183 nodules, 52 showed growth and the predominant pathologic type was IAC, these IACs showed more PSN in nodule type, while the IAC group showed more significant differences in nodule type, nodules growth, and VDT than the pre-IAC group. There were also differences in pathologic type and VDT between different Ki-67 expression groups, and Ki-67 expression gradually increased as VDT decreased.

CONCLUSION

Lung adenocarcinoma (LUAD) presenting as GGNs exhibit distinct natural courses among pathologic subtypes. VDT effectively distinguishes these growth characteristics, with IACs showing shorter VDT. The significant correlation between VDT and Ki-67 expression suggests that combining these parameters may provide valuable insights into the biological behavior and invasiveness of LUAD.

Volumetric Analysis: Effect on Diagnosis and Management of Indeterminate Solid Pulmonary Nodules in Routine Clinical Practice

OBJECTIVE

To evaluate the effect of volumetric analysis on the diagnosis and management of indeterminate solid pulmonary nodules in routine clinical practice.

METHODS

This was a retrospective study with 107 computed tomography (CT) cases of solid pulmonary nodules (range, 6-15 mm), 57 pathology-proven malignancies (lung cancer, n = 34; metastasis, n = 23), and 50 benign nodules. Nodules were evaluated on a total of 309 CT scans (average number of CTs/nodule, 2.9 [range, 2-7]). CT scans were from multiple institutions with variable technique. Nine radiologists (attendings, n = 3; fellows, n = 3; residents, n = 3) were asked their level of suspicion for malignancy (low/moderate or high) and management recommendation (no follow-up, CT follow-up, or care escalation) for baseline and follow-up studies first without and then with volumetric analysis data. Effect of volumetry on diagnosis and management was assessed by generalized linear and logistic regression models.

RESULTS

Volumetric analysis improved sensitivity ( P = 0.009) and allowed earlier recognition ( P < 0.05) of malignant nodules. Attending radiologists showed higher sensitivity in recognition of malignant nodules ( P = 0.03) and recommendation of care escalation ( P < 0.001) compared with trainees. Volumetric analysis altered management of high suspicion nodules only in the fellow group ( P = 0.008). κ Statistics for suspicion for malignancy and recommended management were fair to substantial (0.38-0.66) and fair to moderate (0.33-0.50). Volumetric analysis improved interobserver variability for identification of nodule malignancy from 0.52 to 0.66 ( P = 0.004) only on the second follow-up study.

CONCLUSIONS

Volumetric analysis of indeterminate solid pulmonary nodules in routine clinical practice can result in improved sensitivity and earlier identification of malignant nodules. The effect of volumetric analysis on management recommendations is variable and influenced by reader experience.

Enhancing the differential diagnosis of small pulmonary nodules: a comprehensive model integrating plasma methylation, protein biomarkers, and LDCT imaging features

BACKGROUND

Accurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5-10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules.

METHODS

Blood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules.

RESULTS

Our results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7%, specificity = 82.6%) in classifying test set. The performance of the integrated model was significantly higher than that of individual methylation (AUC = 0.799, P = 0.004), protein (AUC = 0.846, P = 0.009), and imaging models (AUC = 0.866, P = 0.01). Importantly, the integrated model achieved a higher AUC of 0.951 (sensitivity = 83.9%, specificity = 89.7%) in 5-10 mm small nodules. These results collectively confirm the accuracy and robustness of our model in detecting malignant nodules from benign ones.

CONCLUSIONS

Our study presents a promising noninvasive approach to distinguish the malignancy of pulmonary nodules using multiple molecular and imaging features, which has the potential to assist in clinical decision-making.

TRIAL REGISTRATION

This study was registered on ClinicalTrials.gov on 01/01/2020 (NCT05432128). https://classic.

CLINICALTRIALS

gov/ct2/show/NCT05432128 .

Development of a nomogram-based model incorporating radiomic features from follow-up longitudinal lung CT images to distinguish invasive adenocarcinoma from benign lesions: a retrospective study

PURPOSE

To develop and validate a radiomic model for differentiating pulmonary invasive adenocarcinomas from benign lesions based on follow-up longitudinal CT images.

METHODS

This is a retrospective study including 336 patients (161 with invasive adenocarcinomas and 175 with benign lesions) who underwent baseline (T0) and follow-up (T1) CT scans from January 2016 to June 2022. The patients were randomized in a 7:3 ratio into training and test sets. Radiomic features were extracted from lesion volumes of interest on longitudinal CT images at T0 and T1. Differences in radiomic features between T1 and T0 were defined as delta-radiomic features. Logistic regression was used to build models based on clinicoradiological (CR), T0, T1, and delta radiomic features and compute signatures. Finally, a nomogram based on the CR, T0, T1 and delta signatures was constructed. Model performance was evaluated for calibration, discrimination, and clinical utility.

RESULTS

The T1 radiomic model was superior to the other independent models. In the training set, it had an area under the curve (AUC) of 0.858), superior to the CR model (AUC 0.694), the T0 radiomic model (AUC 0.825), and the delta radiomic model (AUC 0.734). In the test set, it had an AUC of 0.817, again outperforming the CR model (AUC 0.578), the T0 radiomic model (AUC 0.789), and the delta radiomic model (AUC 0.647). The nomogram incorporating the CR, T0, T1 and delta signatures showed the best predictive performance in both the training (AUC: 0.906) and test sets (AUC: 0.856), and it exhibited excellent fit with calibration curves. Decision curve analysis provided additional validation of the clinical utility of the nomogram.

CONCLUSION

A nomogram utilizing radiomic features extracted from longitudinal CT images can enhance the discriminative capability between pulmonary invasive adenocarcinomas and benign lesions.

Software using artificial intelligence for nodule and cancer detection in CT lung cancer screening: systematic review of test accuracy studies

OBJECTIVES

To examine the accuracy and impact of artificial intelligence (AI) software assistance in lung cancer screening using CT.

METHODS

A systematic review of CE-marked, AI-based software for automated detection and analysis of nodules in CT lung cancer screening was conducted. Multiple databases including Medline, Embase and Cochrane CENTRAL were searched from 2012 to March 2023. Primary research reporting test accuracy or impact on reading time or clinical management was included. QUADAS-2 and QUADAS-C were used to assess risk of bias. We undertook narrative synthesis.

RESULTS

Eleven studies evaluating six different AI-based software and reporting on 19 770 patients were eligible. All were at high risk of bias with multiple applicability concerns. Compared with unaided reading, AI-assisted reading was faster and generally improved sensitivity (+5% to +20% for detecting/categorising actionable nodules; +3% to +15% for detecting/categorising malignant nodules), with lower specificity (-7% to -3% for correctly detecting/categorising people without actionable nodules; -8% to -6% for correctly detecting/categorising people without malignant nodules). AI assistance tended to increase the proportion of nodules allocated to higher risk categories. Assuming 0.5% cancer prevalence, these results would translate into additional 150-750 cancers detected per million people attending screening but lead to an additional 59 700 to 79 600 people attending screening without cancer receiving unnecessary CT surveillance.

CONCLUSIONS

AI assistance in lung cancer screening may improve sensitivity but increases the number of false-positive results and unnecessary surveillance. Future research needs to increase the specificity of AI-assisted reading and minimise risk of bias and applicability concerns through improved study design.

PROSPERO REGISTRATION NUMBER

CRD42021298449.

Unlocking the Potential of Computed Tomography-Guided Tracers in Pinpointing Lung Lesions during Surgery: A Collaborative Multi-Institutional Journey

Background: Multiple techniques exist for the preoperative localization of small, deeply located solid or subsolid pulmonary nodules to guide limited thoracoscopic resection. This study aims to conduct a multi-institutional comparison of three different tomography-guided tracers' methods. Methods: A retrospective multicenter cross-sectional study was conducted. All patients suitable for CT-guided tracers with microcoil (GROUP1, n = 58), hook wire (GROUP2, n = 86), or bioabsorbable hydrogel plug (GROUP3, n = 33) were scheduled for video-assisted thoracoscopic wedge resection. Outcome variables: successful nodule localization, safety, and the feasibility of the tracers' placement. A χ2 test or Fisher's test for expected numbers less than five and a Kruskal-Wallis test were used to analyze the categorical and continuous variables, respectively. For the power calculations, we used G*Power version 3.1.9.6. Results: One hundred seventy-seven patients underwent the localization and resection of 177 nodules detected with three different CT-guided tracers. A significant difference was recorded for cancer history (p = 0.030), respiratory function, Charlson comorbidity index (p = 0.018), lesion type (p < 0.0001), distance from pleura surface (p < 0.0001), and time between preoperative CT-guided tracers and surgical procedures (p < 0.0001). Four post-procedural complications were recorded and in GROUP2, four cases of tracer dislocations occurred. Finally, hook wire group was associated with the shortest surgical time (93 min, p = 0.001). Conclusions: All methods were feasible and efficient, resulting in a 100% success rate for the microcoils and the bioabsorbable hydrogel plugs and a 94.2% success rate for the hook wires. Our results highlight the need to choose a technique that is less stressful for the patient and helps the surgeon by extending the approach to deep nodules and resecting over the course of several days from deployment.

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

BACKGROUND

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

RESEARCH QUESTION

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

STUDY DESIGN AND METHODS

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

RESULTS

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

INTERPRETATION

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

Pilot study of lung cancer screening for survivors of Hodgkin lymphoma

Hodgkin lymphoma (HL) treatment increases the risk of lung cancer. Most HL survivors are not eligible for lung cancer screening (LCS) programs developed for the general population, and the utility of these programs has not been tested in HL survivors. We ran a LCS pilot in HL survivors to describe screening uptake, participant characteristics, impact of a decision aid and screen findings. HL survivors treated ≥5 years ago with mustine/procarbazine and/or thoracic radiation, were identified from a follow-up database and invited to participate. Participants underwent a low-dose computed tomography (LDCT) reported using protocols validated for the general population. Two hundred and eighteen individuals were invited, 123 were eligible, 102 were screened (58% response rate): 58% female, median age 52 years, median 22 years since HL treatment; 91.4% were deemed to have made an informed decision; participation was not influenced by age, sex, years since treatment or deprivation. Only three of 35 ever-smokers met criteria for LCS through the program aimed at the general population. Baseline LDCT results were: 90 (88.2%) negative, ten (9.8%) indeterminate, two (2.0%) positive. Two 3-month surveillance scans were positive. Of four positive scans, two patients were diagnosed with small-cell lung cancer; one underwent curative surgery. Coronary artery calcification was detected in 36.3%, and clinically significant incidental findings in 2.9%. LDCT protocols validated in ever-smokers can detect asymptomatic early-stage lung cancers in HL survivors. This finding, together with screening uptake and low false positive rates, supports further research to implement LCS for HL survivors (clinicaltrials gov. Identifier: NCT04986189.).

Comparative analysis of three-dimensional and two-dimensional models for predicting the malignancy probability of subsolid nodules

AIM

To construct three-dimensional (3D) and two-dimensional (2D) models to predict the malignancy probability of subsolid nodules (SSNs) and compare their effectiveness.

MATERIALS AND METHODS

A total of 371 SSNs from 332 patients, collected between January 2020 and January 2024, were included in the study. The SSNs were divided into a training set for constructing the models and a test set for validating the models. Models were developed using binary logistic backward regression, based on factors that showed significant differences in univariate analyses. The performance of the models was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC). The AUCs of different models were compared using the DeLong test.

RESULTS

The AUCs for the two 3D models, one 2D model, and the Brock model were 0.785 (0.733-0.836), 0.776 (0.723-0.829), 0.764 (0.710-0.818), and 0.738 (0.679-0.798) in the training set. In the test set, these AUCs were 0.817 (0.706-0.928), 0.796 (0.679-0.913), 0.771 (0.647-0.895), and 0.790 (0.678-0.903). The two 3D models demonstrated statistically significant differences from the Brock model in the training set (P=0.024 and P=0.046). None of the four models showed significant differences in the test set (all P>0.05).

CONCLUSION

The 3D models outperform both the 2D model and the Brock model in predicting the malignancy probability of SSNs, and the 3D model incorporating volume, mean CT attenuation value, and lobulation as factors performed the best.

Lung Nodule Management in Low-Dose CT Screening for Lung Cancer: Lessons from the NELSON Trial

Screening with low-dose CT (LDCT) in a high-risk population, as defined by age and smoking behavior, reduces lung cancer-related mortality. However, LDCT screening presents a major challenge. Numerous, mostly benign, nodules are seen in the lungs during screening. The question is how to distinguish the malignant from the benign nodules. Various studies use different protocols for nodule management. The Dutch-Belgian NELSON (Nederlands-Leuvens Longkanker Screenings Onderzoek) trial, the largest European lung cancer screening trial, used distinctions based on nodule volumetric assessment and growth rate. This review discusses key findings from the NELSON study regarding the characteristics of screening-detected nodules, including nodule size and its volumetric assessment, growth rate, subtype, and their associated malignancy risk. These results are compared with findings from other screening studies and current recommendations for lung nodule management. By examining differences in nodule management strategies and providing a comprehensive overview of outcomes specific to lung cancer screening, this review aims to contribute to the broader discussion on optimizing lung nodule management in screening programs.

Diagnostic Value and Safety of Addition of Transbronchial Needle Aspiration to Transbronchial Biopsy Through Endobronchial Ultrasonography Using a Guide Sheath Under Virtual Bronchoscopic Navigation for the Diagnosis of Peripheral Pulmonary Lesions

BACKGROUND

The diagnostic yield of peripheral pulmonary lesions (PPLs) through endobronchial ultrasonography with a guide sheath transbronchial biopsy (EBUS-GS TBB) under virtual bronchoscopic navigation is unsatisfactory because radial EBUS probe is not always located within the lesion. Transbronchial needle aspiration with a guide sheath (GS-TBNA) has the potential to overcome the lower diagnostic yield by improving the relationship between the probe and the lesion and enabling repeated sampling while maintaining the location of a GS near the lesion. However, there are few data regarding the diagnostic yield and safety for diagnosing PPLs in this procedure.

METHODS

We retrospectively analyzed consecutive 363 lesions (83 lesions underwent GS-TBNA/EBUS-GS TBB and 280 lesions underwent EBUS-GS TBB) at our institution between April 1, 2019 and March 31, 2022. We investigated the diagnostic efficacy and complications of GS-TBNA/EBUS-GS TBB and compared them with those of EBUS-GS TBB.

RESULTS

The lesion size, distance from the hilum, presence of bronchus leading to the lesion, and EBUS images during the examination differed significantly between the two procedures. Logistic regression analysis adjusted for these 4 covariates revealed that GS-TBNA/EBUS-GS TBB was a significant factor affecting the diagnostic success of PPLs compared with EBUS-GS TBB (odds ratio=2.43, 95% CI=1.16-5.07, P=0.018). Neither procedure differed significantly in terms of complications (6.0% vs. 5.7%, P>0.999).

CONCLUSION

GS-TBNA performed in addition to EBUS-GS TBB might be a promising sampling method for improving the diagnostic yield for PPLs without increasing the incidence of complications.

Deep Learning Models for Predicting Malignancy Risk in CT-Detected Pulmonary Nodules: A Systematic Review and Meta-analysis

BACKGROUND

There has been growing interest in using artificial intelligence/deep learning (DL) to help diagnose prevalent diseases earlier. In this study we sought to survey the landscape of externally validated DL-based computer-aided diagnostic (CADx) models, and assess their diagnostic performance for predicting the risk of malignancy in computed tomography (CT)-detected pulmonary nodules.

METHODS

An electronic search was performed in four databases (from inception to 10 August 2023). Studies were eligible if they were peer-reviewed experimental or observational articles comparing the diagnostic performance of externally validated DL-based CADx models with models widely used in clinical practice to predict the risk of malignancy. A bivariate random-effect approach for the meta-analysis on the included studies was used.

RESULTS

Seventeen studies were included, comprising 8553 participants and 9884 nodules. Pooled analyses showed DL-based CADx models were 11.6% more sensitive than physician judgement alone, and 14.5% more than clinical risk models alone. They had a similar pooled specificity to physician judgement alone [0.77 (95% CI 0.68-0.84) v 0.81 (95% CI 0.71-0.88)], and were 7.4% more specific than clinical risk models alone. They had superior pooled areas under the receiver operating curve (AUC), with relative pooled AUCs of 1.03 (95% CI 1.00-1.07) and 1.10 (95% CI 1.07-1.13) versus physician judgement and clinical risk models alone, respectively.

CONCLUSION

DL-based models are already used in clinical practice in certain settings for nodule management. Our results show their diagnostic performance potentially justifies wider, more routine deployment alongside experienced physician readers to help inform multidisciplinary team decision-making.

Comparison of electromagnetic navigation bronchoscopy localization and CT-guided percutaneous localization in resection of lung nodules: A protocol for systematic review and meta-analysis

BACKGROUND

This study aimed to evaluate the efficacy and safety between electromagnetic navigational bronchoscopy (ENB) and computed tomography (CT)-guided percutaneous localization before resection of pulmonary nodules.

METHODS

Pubmed, Embase, Web of Science, and the Cochrane Library databases were searched from January 1, 2000 to April 30, 2022, for relevant studies. Two reviewers conducted the search, selection, and extraction of data from eligible studies. The risk of bias was assessed using the Newcastle-Ottawa Scale. The primary outcome was the localization success rate, and the secondary outcomes were the pneumothorax incidence and localization time. The meta-analysis was performed by Review Manager 5.4. The protocol for the meta-analysis was registered on PROSPERO (Registration number: CRD42022345972).

RESULTS

Five cohort studies comprising 441 patients (ENB group: 185, CT group: 256) were analyzed. Compared with the CT-guided group, the ENB-guided group was associated with lower pneumothorax incidence (relative ratio = 0.16, 95% confidence interval [CI]: 0.04-0.65, P = .01). No significant differences were found in location success rates (relative ratio = 1.01, 95% CI: 0.98-1.05, P = .38) and localization time (mean difference = 0.99, 95% CI: -5.73 to 7.71, P = .77) between the ENB group and CT group.

CONCLUSION

Both ENB and CT-guided are valuable technologies in localizing lung nodules before video-assisted thoracoscopic surgery based on current investigations. ENB achieved a lower pneumothorax rate than the CT-guided group. In our opinion, there is no perfect method, and decision-making should be given the actual circumstances of each institute. Future prospective studies in the form of a randomized trial are needed to confirm their clinical value.

The study of plain CT combined with contrast-enhanced CT-based models in predicting malignancy of solitary solid pulmonary nodules

To compare the diagnostic performance between plain CT-based model and plain plus contrast CT-based modelin the classification of malignancy for solitary solid pulmonary nodules. Between January 2012 and July 2021, 527 patients with pathologically confirmed solitary solid pulmonary nodules were collected at dual centers with similar CT examinations and scanning parameters. Before surgery, all patients underwent both plain and contrast-enhanced chest CT scans. Two clinical characteristics, fifteen plain CT characteristics, and four enhanced characteristics were used to develop two logistic regression models: model 1 (plain CT only) and model 2 (plain + contrast CT). The diagnostic performance of the two models was assessed separately in the development and external validation cohorts using the AUC. 392 patients from Center A were included in the training cohort (median size, 20.0 [IQR, 15.0-24.0] mm; mean age, 55.8 [SD, 9.9] years; male, 53.3%). 135 patients from Center B were included in the external validation cohort (median size, 20.0 [IQR, 16.0-24.0] mm; mean age, 56.4 [SD, 9.6] years; male, 51.9%). Preoperative patients with 201 malignant (adenocarcinoma, 148 [73.6%]; squamous cell carcinoma, 35 [17.4%]; large cell carcinoma,18 [9.0%]) and 326 benign (pulmonary hamartoma, 118 [36.2%]; sclerosing pneumocytoma, 35 [10.7%]; tuberculosis, 104 [31.9%]; inflammatory pseudonodule, 69 [21.2%]) solitary solid pulmonary nodules were gathered from two independent centers. The mean sensitivity, specificity, accuracy, PPV, NPV, and AUC (95%CI) of model 1 (Plain CT only) were 0.79, 0.78, 0.79, 0.67, 0.87, and 0.88 (95%CI, 0.82-0.93), the model 2 (Plain + Contrast CT) were 0.88, 0.91, 0.90, 0.84, 0.93, 0.93 (95%CI, 0.88-0.98) in external validation cohort, respectively. A logistic regression model based on plain and contrast-enhanced CT characteristics showed exceptional performance in the evaluation of malignancy for solitary solid lung nodules. Utilizing this contrast-enhanced CT model would provide recommendations concerning follow-up or surgical intervention for preoperative patients presenting with solid lung nodules.

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.

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.

Radiomic-based prediction of lesion-specific systemic treatment response in metastatic disease

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

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.

Prediction and verification of benignancy and malignancy of pulmonary nodules based on inflammatory related biological markers

Objective

Inflammation plays an important role in the transformation of pulmonary nodules (PNs) from benign to malignant. Prediction of benignancy and malignancy of PNs is still lacking efficacy methods. Although Mayo or Brock model have been widely applied in clinical practices, their application conditions are limited. This study aims to construct a diagnostic model of PNs by machine learning using inflammation-related biological markers (IRBMs).

Methods

Inflammatory related genes (IRGs) were first extracted from GSE135304 chip data. Then, differentially expressed genes (DEGs) and infiltrating immune cells were screened between malignant pulmonary nodules (MN) and benign pulmonary nodule (BN). Correlation analysis was performed on DEGs and infiltrating immune cells. Molecular modules of IRGs were identified through Consistency cluster analysis. Subsequently, IRBMs in IRGs modules were filtered through Weighted gene co-expression network analysis (WGCNA). An optimal diagnostic model was established using machine learning methods. Finally, external dataset GSE108375 was used to verify this result.

Results

4 hub IRGs and 3 immune cells showed significantly difference between MN and BN, C1 and C2 module, namely PRTN3, ELANE, NFKB1 and CTLA4, T cells CD4 naïve, NK cells activated and Monocytes. IRBMs were screened from black module and yellowgreen module through WGCNA analysis. The Support vector machines (SVM) was identified as the optimal model with the Area Under Curve (AUC) was 0.753. A nomogram was established based on 5 hub IRBMs, namely HS.137078, KLC3, C13ORF15, STOM and KCTD13. Finally, external dataset GSE108375 verified this result, with the AUC was 0.718.

Conclusion

SVM model established by 5 hub IRBMs was able to effectively identify MN or BN. Accumulating inflammation and immune dysfunction were important to the transformation from BN to MN.

Estimation of pathological subtypes in subsolid lung nodules using artificial intelligence

Objective

This study aimed to investigate the value of artificial intelligence (AI) for distinguishing pathological subtypes of invasive pulmonary adenocarcinomas in patients with subsolid nodules (SSNs).

Materials and methods

This retrospective study included 110 consecutive patients with 120 SSNs. The qualitative and quantitative imaging characteristics of SSNs were extracted automatically using an artificially intelligent assessment system. Then, radiologists had to verify these characteristics again. We split all cases into two groups: non-IA including 11 Atypical adenomatous hyperplasia (AAH) and 25 adenocarcinoma in situ (AIS) or IA including 7 minimally invasive adenocarcinoma (MIA) and 77 invasive adenocarcinoma (IAC). Variables that exhibited statistically significant differences between the non-IA and IA in the univariate analysis were included in the multivariate logistic regression analysis. Receiver operating characteristic (ROC) analyses were conducted to determine the cut-off values and their diagnostic performances.

Results

Multivariate logistic regression analysis showed that the major diameter (odds ratio [OR] = 1.38; 95 % confidence interval [CI], 1.02-1.87; P = 0.036) and entropy of three-dimensional(3D) CT value (OR = 3.73, 95 % CI, 1.13-2.33, P = 0.031) were independent risk factors for adenocarcinomas. The cut-off values of the major diameter and the entropy of 3D CT value for the diagnosis of invasive adenocarcinoma were 15.5 mm and 5.17, respectively. To improve the classification performance, we fused the major diameter and the entropy of 3D CT value as a combined model, and the (AUC） of the model was 0.868 (sensitivity = 0.845, specificity = 0.806).

Conclusion

The major diameter and entropy of 3D CT value can distinguish non-IA from IA. AI can improve performance in distinguishing pathological subtypes of invasive pulmonary adenocarcinomas in patients with SSNs.

Deep Learning Radiomics Features of Mediastinal Fat and Pulmonary Nodules on Lung CT Images Distinguish Benignancy and Malignancy

This study investigated the relationship between mediastinal fat and pulmonary nodule status, aiming to develop a deep learning-based radiomics model for diagnosing benign and malignant pulmonary nodules. We proposed a combined model using CT images of both pulmonary nodules and the fat around the chest (mediastinal fat). Patients from three centers were divided into training, validation, internal testing, and external testing sets. Quantitative radiomics and deep learning features from CT images served as predictive factors. A logistic regression model was used to combine data from both pulmonary nodules and mediastinal adipose regions, and personalized nomograms were created to evaluate the predictive performance. The model incorporating mediastinal fat outperformed the nodule-only model, with C-indexes of 0.917 (training), 0.903 (internal testing), 0.942 (external testing set 1), and 0.880 (external testing set 2). The inclusion of mediastinal fat significantly improved predictive performance (NRI = 0.243, p < 0.05). A decision curve analysis indicated that incorporating mediastinal fat features provided greater patient benefits. Mediastinal fat offered complementary information for distinguishing benign from malignant nodules, enhancing the diagnostic capability of this deep learning-based radiomics model. This model demonstrated strong diagnostic ability for benign and malignant pulmonary nodules, providing a more accurate and beneficial approach for patient care.

Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

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.

Imaging features and clinical evaluation of pulmonary nodules in children

Background

With the widespread use of computed tomography (CT), the detection rate of pulmonary nodules in children has gradually increased. Due to the lack of epidemiological evidence and clinical guideline on pulmonary nodule treatment in children, we aimed to provide a reference for the clinical diagnosis and management of pediatirc pulmonary nodules.

Methods

This retrospective study collected consecutive cases from April 2012 to July 2021 in the Shanghai Children's Medical Center. The sample included children with pulmonary nodules on chest CT scans and met the inclusion criteria. All patients were categorized into tumor and non-tumor groups by pre-CT clinical diagnosis. Nodule characteristics between groups were analyzed. To establish a clinical assessment model for the benign versus malignant pulmonary nodules, patients who have been followed-up for three months were detected and a decision tree model for nodule malignancy prediction was constructed and validated.

Results

The sample comprised 1341 patients with an average age of 7.2 ± 4.6 years. More than half of them (51.7%) were diagnosed with malignancies before CT scan. 48.3% were diagnosed with non-tumor diseases or healthy. Compared to non-tumor group, children with tumor were more likely to have multiple nodules in both lungs, with larger size and often be accompanied by osteolytic or mass lesions. Based on the decision tree model, patients' history of malignancies, nodules diameter size≥5mm, and specific nodule distribution (multiple in both lungs, multiple in the right lung or solitary in the upper or middle right lobe) were important potential predictors for malignity. In the validation set, sensitivity, specificity and AUC were 0.855, 0.833 and 0.828 (95%CI: 0.712-0.909), respectively.

Conclusion

This study conducted a clinical assessment model to differentiate benignity and malignancy of pediatric pulmonary nodules. We suggested that a nodule's diameter, distribution and patient's history of malignancies are predictable factors in benign or malignant determination.

Comparison of the variability and diagnostic efficacy of respiratory-gated PET/CT based radiomics features with ungated PET/CT in lung lesions

OBJECTIVES

To investigate the variability and diagnostic efficacy of respiratory-gated (RG) PET/CT based radiomics features compared to ungated (UG) PET/CT in the differentiation of non-small cell lung cancer (NSCLC) and benign lesions.

METHODS

117 patients with suspected lung lesions from March 2020 to May 2021 and consent to undergo UG PET/CT and chest RG PET/CT (including phase-based quiescent period gating, pQPG and phase-matched 4D PET/CT, 4DRG) were prospectively included. 377 radiomics features were extracted from PET images of each scan. Paired t test was used to compare UG and RG features for inter-scan variability analysis. We developed three radiomics models with UG and RG features (i.e. UGModel, pQPGModel and 4DRGModel). ROC curves were used to compare diagnostic efficiencies, and the model-level comparison of diagnostic value was performed by five-fold cross-validation. A P value < 0.05 was considered as statistically significant.

RESULTS

A total of 111 patients (average age ± standard deviation was 59.1 ± 11.6 y, range, 29 - 88 y, and 63 were males) with 209 lung lesions were analyzed for features variability and the subgroup of 126 non-metastasis lesions in 91 patients without treatment before PET/CT were included for diagnosis analysis. 101/377 (26.8 %) 4DRG features and 82/377 (21.8 %) pQPG features showed significant difference compared to UG features (both P<0.05). 61/377 (16.2 %) and 59/377 (15.6 %) of them showed significantly better discriminant ability (ΔAUC% (i.e. (AUCRG - AUCUG) / AUCUG×100 %) > 0 and P<0.05) in malignant recognition, respectively. For the model-level comparison, 4DRGModel achieved the highest diagnostic efficacy (sen 73.2 %, spe 87.3 %) compared with UGModel (sen 57.7 %, spe 76.4 %) and pQPGModel (sen 63.4 %, spe 81.8 %).

CONCLUSION

RG PET/CT performs better in the quantitative assessment of metabolic heterogeneity for lung lesions and the subsequent diagnosis in patients with NSCLC compared with UG PET/CT.

A Retrospective External Validation of the Cleveland Clinic Malignancy Probability Prediction Model for Indeterminate Pulmonary Nodules

Objective

To perform a retrospective, multicenter, external validation of the Cleveland Clinic malignancy probability prediction model for incidental pulmonary nodules.

Patients and Methods

From July 1, 2022, to May 31, 2023, we identified 296 patients who underwent tissue acquisition at Mayo Clinic (MC) (n=198) and Loyola University Medical Center (n=98) with histopathology indicating malignant (n=195) or benign (n=101). Data was collected at initial radiographic identification (point 1) and at the time of intervention (point 2). Point 3 represented the most recent data. The areas under the receiver operating characteristics were calculated for each model per time point. Calibration was evaluated by comparing the predicted and observed rates of malignancy.

Results

The areas under the receiver operating characteristics at time points 1, 2, and 3 for the MC model were 0.67 (95% CI, 0.61-0.74), 0.67 (95% CI, 0.58-0.77), and 0.70 (95% CI, 0.63-0.76), respectively. The Cleveland Clinic model (CCM) was 0.68 (95% CI, 0.61-0.74), 0.75 (95% CI, 0.65-0.84), and 0.72 (95% CI, 0.66-0.78), respectively. The mean ± SD estimated probability for malignant pulmonary nodules (PNs) at time points 1, 2, and 3 for the CCM was 64.2±25.9, 65.8±24.0, and 64.7±24.4, which resembled the overall proportion of malignant PNs (66%). The mean estimated probability of malignancy for the MC model at each time point was 38.3±27.4, 36.2±24.4, and 42.1±27.3, substantially lower than the observed proportion of malignancies.

Conclusion

The CCM found discrimination similar to its internal validation and good calibration. The CCM can be used to augment clinical and shared decision-making when evaluating high-risk PNs.

Artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer: an evidence synthesis early value assessment

Background

Lung cancer is one of the most common types of cancer in the United Kingdom. It is often diagnosed late. The 5-year survival rate for lung cancer is below 10%. Early diagnosis may improve survival. Software that has an artificial intelligence-developed algorithm might be useful in assisting with the identification of suspected lung cancer.

Objectives

This review sought to identify evidence on adjunct artificial intelligence software for analysing chest X-rays for suspected lung cancer, and to develop a conceptual cost-effectiveness model to inform discussion of what would be required to develop a fully executable cost-effectiveness model for future economic evaluation.

Data sources

The data sources were MEDLINE All, EMBASE, Cochrane Database of Systematic Reviews, Cochrane CENTRAL, Epistemonikos, ACM Digital Library, World Health Organization International Clinical Trials Registry Platform, clinical experts, Tufts Cost-Effectiveness Analysis Registry, company submissions and clinical experts. Searches were conducted from 25 November 2022 to 18 January 2023.

Methods

Rapid evidence synthesis methods were employed. Data from companies were scrutinised. The eligibility criteria were (1) primary care populations referred for chest X-ray due to symptoms suggestive of lung cancer or reasons unrelated to lung cancer; (2) study designs that compared radiology specialist assessing chest X-ray with adjunct artificial intelligence software versus radiology specialists alone and (3) outcomes relating to test accuracy, practical implications of using artificial intelligence software and patient-related outcomes. A conceptual decision-analytic model was developed to inform a potential full cost-effectiveness evaluation of adjunct artificial intelligence software for analysing chest X-ray images to identify suspected lung cancer.

Results

None of the studies identified in the searches or submitted by the companies met the inclusion criteria of the review. Contextual information from six studies that did not meet the inclusion criteria provided some evidence that sensitivity for lung cancer detection (but not nodule detection) might be higher when chest X-rays are interpreted by radiology specialists in combination with artificial intelligence software than when they are interpreted by radiology specialists alone. No significant differences were observed for specificity, positive predictive value or number of cancers detected. None of the six studies provided evidence on the clinical effectiveness of adjunct artificial intelligence software. The conceptual model highlighted a paucity of input data along the course of the diagnostic pathway and identified key assumptions required for evidence linkage.

Limitations

This review employed rapid evidence synthesis methods. This included only one reviewer conducting all elements of the review, and targeted searches that were conducted in English only. No eligible studies were identified.

Conclusions

There is currently no evidence applicable to this review on the use of adjunct artificial intelligence software for the detection of suspected lung cancer on chest X-ray in either people referred from primary care with symptoms of lung cancer or people referred from primary care for other reasons.

Future work

Future research is required to understand the accuracy of adjunct artificial intelligence software to detect lung nodules and cancers, as well as its impact on clinical decision-making and patient outcomes. Research generating key input parameters for the conceptual model will enable refinement of the model structure, and conversion to a full working model, to analyse the cost-effectiveness of artificial intelligence software for this indication.

Study registration

This study is registered as PROSPERO CRD42023384164.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135755) and is published in full in Health Technology Assessment; Vol. 28, No. 50. See the NIHR Funding and Awards website for further award information.

Early Detection and Interception of Lung Cancer

Recent advances in lung cancer treatment have led to dramatic improvements in 5-year survival rates. And yet, lung cancer remains the leading cause of cancer-related mortality, in large part, because it is often diagnosed at an advanced stage, when cure is no longer possible. Lung cancer screening (LCS) is essential for intercepting the disease at an earlier stage. Unfortunately, LCS has been poorly adopted in the United States, with less than 5% of eligible patients being screened nationally. This article will describe the data supporting LCS, the obstacles to LCS implementation, and the promising opportunities that lie ahead.

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.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

CLINICAL RELEVANCE STATEMENT

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

KEY POINTS

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

Shared decision-making in the management of pulmonary nodules: a systematic review of quantitative and qualitative studies

OBJECTIVE

The objective of this systematic review was to explore the evidence regarding shared decision-making (SDM) in the management of pulmonary nodules.

DESIGN

Systematic review of quantitative and qualitative studies.

DATA SOURCE

Studies published in English or Chinese up to April 2022 were extracted from nine databases: PubMed, PsycINFO, EMBASE, Cochrane Library, Web of Science and CINAHL, China National Knowledge Infrastructure, Wanfang Data and SinoMed Data.

ELIGIBILITY CRITERIA

Studies were eligible if patients or healthcare providers are faced with pulmonary nodule management options or the interventions or experiences were focused on the patient-healthcare provider relationship or health education to make, increase or support shared decisions. All types of studies were included, including quantitative and qualitative studies. Grey literature and literature that had not been peer reviewed were excluded. Poster abstracts and non-empirical publications such as editorials, letters, opinion papers and review articles were excluded.

DATA EXTRACTION AND SYNTHESIS

Two reviewers independently screened abstracts and full texts, assessed quality using Joanna Briggs Institute's critical appraisal tools, and extracted data from included studies. Thematic syntheses were used to identify prominent themes emerging from the data.

RESULTS

A total of 12 studies met the inclusion criteria, 11 of which were conducted in USA. These included six qualitative studies and six quantitative studies (including both survey and quasi-experimental designs). Three major themes with specific subthemes emerged: (1) Opportunity (uncertainty in the diagnosis and treatment of pulmonary nodules, willingness to participate in decision-making); (2) Ability (patient's lack of knowledge, physician's experience); and (3) Different worldview (misconception, distress among patients, preference for diagnosis and treatment).

CONCLUSIONS

Uncertainty in the management of pulmonary nodules is the opportunity to implement SDM. Patients' lack of knowledge, distress, and misunderstandings between healthcare providers and patients are both the main obstacles and the causes of the application of SDM.

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.