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INTRODUCTION: To facilitate global implementation of lung cancer (LC) screening and early detection in a quality assured and consistent manner, common terminology is needed. Researchers and clinicians within different specialties may use the same terms but with different meanings, or different terms for the same intended meanings. METHODS: The Diagnostics Working Group of the International Association for the Study of Lung Cancer Early Detection and Screening Committee has analyzed and discussed relevant terms used on a regular basis and suggests recommendations for consensus definitions of terminology applicable in this setting. We explored how to reach consensus to define relevant and unambiguous terminology for use by health care providers, researchers, patients, screening participants and family. RESULTS: Terms and definitions for epidemiological and health-economical purposes included: Standardized incidence and mortality rates, LC specific survival, long-term survival and cure rates, and overdiagnosis, overtreatment, undertreatment. Terms and definitions for defining screening findings included: Positive, false positive, negative, false negative and indeterminate findings and additional and incidental findings. Terms and definitions for describing parameters in screening programmes included: Opportunistic vs programmatic screening, screening rounds, interval/interim diagnoses, invasive and minimally invasive procedures. Terms and definitions for shared decision making included: LC screening - possible harms and risks and LC risk and modifiers prior and posterior to a measure. CONCLUSIONS: A common set of terminology with standard definitions is recommended for describing clinical LC screening programmes, the discussion about effectiveness and outcomes, or the clinical setting. The use of the terms should be clearly defined and explained.
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Background: Measurements are not exact, so that if a measurement is repeated, one would get a different value each time. The spread of these values is the measurement uncertainty. Understanding measurement uncertainty of pulmonary nodules is important for proper interpretation of size and growth measurements. Larger amounts of measurement uncertainty may require longer follow-up intervals to be confident that any observed growth is due to actual growth rather than measurement uncertainty. We examined the influence of nodule features and software algorithm on measurement uncertainty of small, solid pulmonary nodules. Methods: Volumes of 107 nodules were measured on 4-6 repeated computed tomography (CT) scans (Siemens Somatom AS, 100 kVp, 120 mA, 1.0 mm slice thickness reconstruction) prospectively obtained during CT-guided fine needle aspiration biopsy between 2015-2021 at Department of Diagnostic, Molecular, and Interventional Radiology in Icahn School of Medicine at Mount Sinai, using two different automated volumetric algorithms. For each, the coefficient of variation (standard deviation divided by the mean) of nodule volume measurements was determined. The following features were considered: diameter, location, vessel and pleural attachments, nodule surface area, and extent of the nodule in the three acquisition dimensions of the scanner. Results: Median volume of 107 nodules was 515.23 and 535.53 mm3 for algorithm #1 and #2, respectively with excellent agreement (intraclass correlation coefficient =0.98). Median coefficient of variation of nodule volume was low for the two algorithms, but significantly different (4.6% vs. 8.7%, P<0.001). Both algorithms had a trend of decreasing coefficient of variation of nodule volume with increasing nodule diameter, though only significant for algorithm #2. Coefficient of variation of nodule volume was significantly associated with nodule volume (P=0.02), attachment to blood vessels (P=0.02), and nodule surface area (P=0.001) for algorithm #2 using a multiple linear regression model. Correlation between the coefficient of variation (CoV) of nodule volume and the CoV of the x, y, z measurements for algorithm #1 were 0.29 (P=0.0021), 0.25 (P=0.009), and 0.80 (P<0.001) respectively, and for algorithm #2, 0.46 (P<0.001), 0.52 (P<0.001), and 0.58 (P<0.001), respectively. Conclusions: Even in the best-case scenario represented in this study, using the same measurement algorithm, scanner, and scanning protocol, considerable measurement uncertainty exists in nodule volume measurement for nodules sized 20 mm or less. We found that measurement uncertainty was affected by interactions between nodule volume, algorithm, and shape complexity.
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Objectives: Time-to-treatment initiation is an important consideration for patients undergoing thoracic surgery for early-stage lung cancer because delays have the potential to adversely affect outcomes. This study seeks to quantify time-to-treatment initiation for patients with clinical stage I lung cancer, explore patient factors and predictors that lead to an increased time-to-treatment initiation, and compare surgeon perception of appropriate time-to-treatment initiation to the results. Methods: Time-to-treatment initiation was determined for patients enrolled in the Mount Sinai Initiative for Early Lung Cancer Research on Treatment study who underwent surgical resection for clinical stage I lung cancer between March 2016 and December 2021. The following dates were determined: (1) date of first suspicious radiologic imaging, (2) date of first biopsy, and (3) date of surgery. A total of 15 thoracic surgeons who participated in the Mount Sinai Initiative for Early Lung Cancer Research on Treatment were assessed on their perception on time-to-treatment initiation. Results: For 638 patients, median time from first suspicious imaging findings to biopsy was 40 days, biopsy to surgery was 37 days, and suspicious imaging to surgery was 84 days. Significant factors that resulted in longer time-to-treatment initiation in the multivariate analysis were African American or Black race (P = .005), vascular disease (P = .01), and median household income less than $75,000 (P = .04). Although the surgeon's perception was that the average time from biopsy to surgery was 28 days, it was longer for 63.5% of participants; surgeon perception of maximum time between diagnosis and surgery was 84 days and longer for 28.7% of participants. Conclusions: Patient factors such as race, income, and comorbidities were found to have differences in time-to-treatment initiation. Delays to surgery exceeded the expectations of thoracic surgeons.
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Background: Coronary artery calcium (CAC) scans contain valuable information beyond the Agatston Score which is currently reported for predicting coronary heart disease (CHD) only. We examined whether new artificial intelligence (AI) algorithms applied to CAC scans may provide significant improvement in prediction of all cardiovascular disease (CVD) events in addition to CHD, including heart failure, atrial fibrillation, stroke, resuscitated cardiac arrest, and all CVD-related deaths. Methods: We applied AI-enabled automated cardiac chambers volumetry and automated calcified plaque characterization to CAC scans (AI-CAC) of 5830 individuals (52.2% women, age 61.7±10.2 years) without known CVD that were previously obtained for CAC scoring at the baseline examination of the Multi-Ethnic Study of Atherosclerosis (MESA). We used 15-year outcomes data and assessed discrimination using the time-dependent area under the curve (AUC) for AI-CAC versus the Agatston Score. Results: During 15 years of follow-up, 1773 CVD events accrued. The AUC at 1-, 5-, 10-, and 15-year follow up for AI-CAC vs Agatston Score was (0.784 vs 0.701), (0.771 vs. 0.709), (0.789 vs.0.712) and (0.816 vs. 0.729) (p<0.0001 for all), respectively. The category-free Net Reclassification Index of AI-CAC vs. Agatston Score at 1-, 5-, 10-, and 15-year follow up was 0.31, 0.24, 0.29 and 0.29 (p<.0001 for all), respectively. AI-CAC plaque characteristics including number, location, and density of plaque plus number of vessels significantly improved NRI for CAC 1-100 cohort vs. Agatston Score (0.342). Conclusion: In this multi-ethnic longitudinal population study, AI-CAC significantly and consistently improved the prediction of all CVD events over 15 years compared with the Agatston score.
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PURPOSE: Because incidental thyroid nodules (ITNs) are common extrapulmonary findings in low-dose computed tomography (LDCT) scans for lung cancer screening, we aimed to investigate the frequency of ITNs on LDCT scans separately on baseline and annual repeat scans, the frequency of malignancy among the ITNs, and any association with demographic, clinical, CT characteristics. METHODS: Retrospective case series of all 2309 participants having baseline and annual repeat screening in an Early Lung and Cardiac Action Program (MS-ELCAP) LDCT lung screening program from January 2010 to December 2016 was performed. Frequency of ITNs in baseline and annual repeat rounds were determined. Multivariable regression analysis was performed to identify significant predictors. RESULTS: Dominant ITNs were seen in 2.5 % of 2309 participants on baseline and in 0.15 % of participants among 4792 annual repeat LDCTs. The low incidence of new ITNs suggests slow growth as it would take approximately an average of 16.8 years for a new ITN to be detected on annual rounds of screening. Newly detected ITNs on annual repeat LDCT were all smaller than 15 mm. Regression analysis showed that the increasing of age, coronary artery calcifications score and breast density grade were significant predictors for females having an ITN. No significant predictors were found for ITNs in males. CONCLUSION: ITNs are detected at LDCT however, no malignancy was found. Certain predictors for ITNs in females have been identified including breast density, which may point towards a common causal pathway.
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Neoplasias Pulmonares , Tomografía Computarizada por Rayos X , Humanos , Masculino , Femenino , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/epidemiología , Persona de Mediana Edad , Tomografía Computarizada por Rayos X/métodos , Estudios Retrospectivos , Anciano , Hallazgos Incidentales , Nódulo Tiroideo/diagnóstico por imagen , Detección Precoz del Cáncer/métodosRESUMEN
BACKGROUND: Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported. METHODS: We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CACTM) to 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). AI-CAC took on average 21 âs per CAC scan. We used the 5-year outcomes data for incident atrial fibrillation (AF) and assessed discrimination using the time-dependent area under the curve (AUC) of AI-CAC LA volume with known predictors of AF, the CHARGE-AF Risk Score and NT-proBNP. The mean follow-up time to an AF event was 2.9 â± â1.4 years. RESULTS: At 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF for Years 1, 2, and 3 (0.83 vs. 0.74, 0.84 vs. 0.80, and 0.81 vs. 0.78, respectively, all p â< â0.05), but similar for Years 4 and 5, and significantly higher than NT-proBNP at Years 1-5 (all p â< â0.01), but not for combined CHARGE-AF and NT-proBNP at any year. AI-CAC LA significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and NT-proBNP (0.68, 0.44, 0.42, 0.30, 0.37) (all p â< â0.01). CONCLUSION: AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and NT-proBNP.
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Fibrilación Atrial , Biomarcadores , Angiografía Coronaria , Enfermedad de la Arteria Coronaria , Péptido Natriurético Encefálico , Fragmentos de Péptidos , Valor Predictivo de las Pruebas , Calcificación Vascular , Humanos , Fibrilación Atrial/etnología , Fibrilación Atrial/diagnóstico por imagen , Fibrilación Atrial/sangre , Femenino , Fragmentos de Péptidos/sangre , Péptido Natriurético Encefálico/sangre , Anciano , Masculino , Enfermedad de la Arteria Coronaria/diagnóstico por imagen , Enfermedad de la Arteria Coronaria/etnología , Persona de Mediana Edad , Factores de Riesgo , Medición de Riesgo , Anciano de 80 o más Años , Calcificación Vascular/diagnóstico por imagen , Calcificación Vascular/etnología , Biomarcadores/sangre , Factores de Tiempo , Pronóstico , Estados Unidos , Inteligencia Artificial , Angiografía por Tomografía Computarizada , Atrios Cardíacos/diagnóstico por imagen , Interpretación de Imagen Radiográfica Asistida por Computador , Enfermedades Asintomáticas , Incidencia , Reproducibilidad de los ResultadosRESUMEN
INTRODUCTION: Coronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF). METHODS: We applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years. RESULTS: Over 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p â< â0.0001), and comparable to clinical risk factors (0.85, p â= â0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p â< â0.0001). CONCLUSION: AI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.
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Inteligencia Artificial , Biomarcadores , Angiografía por Tomografía Computarizada , Angiografía Coronaria , Enfermedad de la Arteria Coronaria , Insuficiencia Cardíaca , Péptido Natriurético Encefálico , Fragmentos de Péptidos , Valor Predictivo de las Pruebas , Calcificación Vascular , Humanos , Femenino , Masculino , Fragmentos de Péptidos/sangre , Péptido Natriurético Encefálico/sangre , Anciano , Insuficiencia Cardíaca/etnología , Insuficiencia Cardíaca/diagnóstico por imagen , Enfermedad de la Arteria Coronaria/diagnóstico por imagen , Enfermedad de la Arteria Coronaria/etnología , Persona de Mediana Edad , Factores de Riesgo , Biomarcadores/sangre , Calcificación Vascular/diagnóstico por imagen , Calcificación Vascular/etnología , Medición de Riesgo , Pronóstico , Estados Unidos , Factores de Tiempo , Incidencia , Anciano de 80 o más Años , Interpretación de Imagen Radiográfica Asistida por Computador , Reproducibilidad de los Resultados , Tomografía Computarizada Multidetector , Enfermedades AsintomáticasRESUMEN
OBJECTIVES: The risk factors for lung cancer screening eligibility, age as well as smoking history, are also present for osteoporosis. This study aims to develop a visual scoring system to identify osteoporosis that can be applied to low-dose CT scans obtained for lung cancer screening. MATERIALS AND METHODS: We retrospectively reviewed 1000 prospectively enrolled participants in the lung cancer screening program at the Mount Sinai Hospital. Optimal window width and level settings for the visual assessment were chosen based on a previously described approach. Visual scoring of osteoporosis and automated measurement using dedicated software were compared. Inter-reader agreement was conducted using six readers with different levels of experience who independently visually assessed 30 CT scans. RESULTS: Based on previously validated formulas for choosing window and level settings, we chose osteoporosis settings of Width = 230 and Level = 80. Of the 1000 participants, automated measurement was successfully performed on 774 (77.4 %). Among these, 138 (17.8 %) had osteoporosis. There was a significant correlation between the automated measurement and the visual score categories for osteoporosis (Kendall's Tau = -0.64, p < 0.0001; Spearman's rho = -0.77, p < 0.0001). We also found substantial to excellent inter-reader agreement on the osteoporosis classification among the 6 radiologists (Fleiss κ = 0.91). CONCLUSIONS: Our study shows that a simple approach of applying specific window width and level settings to already reconstructed sagittal images obtained in the context of low-dose CT screening for lung cancer is highly feasible and useful in identifying osteoporosis.
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Neoplasias Pulmonares , Osteoporosis , Humanos , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/epidemiología , Detección Precoz del Cáncer , Estudios Retrospectivos , Tomografía Computarizada por Rayos X/métodos , Osteoporosis/diagnóstico por imagenRESUMEN
The American College of Radiology created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.
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Neoplasias Pulmonares , Nódulos Pulmonares Múltiples , Nódulo Tiroideo , Humanos , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/terapia , Tomografía Computarizada por Rayos X , Consenso , Pulmón/diagnóstico por imagen , Estudios Retrospectivos , UltrasonografíaRESUMEN
Background: Few studies have examined the differential impact of stereotactic body radiotherapy (SBRT) and surgery for early-stage non-small cell lung cancer (NSCLC) on quality of life (QoL) during the first post-treatment year. Methods: A prospective cohort of stage IA NSCLC patients undergoing surgery or SBRT at Mount Sinai Health System had QoL measured before treatment, and 2, 6, and 12 months post-treatment using: 12-item Short Form Health Survey version 2 (SF-12v2) [physical component summary (PCS) and mental component summary (MCS)], Functional Assessment of Cancer Therapy-Lung Cancer Subscale (FACT-LCS), and the Patient Health Questionnaire-4 (PHQ-4) measuring depression and anxiety. Locally weighted scatterplot smoothing (LOWESS) was fitted to identify the best interval knot for the change in the QoL trends post-treatment, adjusted piecewise linear mixed effects model was developed to estimate differences in baseline, 2- and 12-month scores, and rates of change. Results: In total, 503 (88.6%) patients received surgery and 65 (11.4%) SBRT. LOWESS plots suggested QoL changed at 2 months post-surgery. Worsening in PCS was observed for both surgery and SBRT within 2 months after treatment but was only significant for surgical patients (-2.11, P<0.001). Two months later, improvements were observed for surgical but not SBRT patients (0.63 vs. -0.30, P<0.001). Surgical patients had significantly better PCS (P<0.001) and FACT-LCS (P<0.001) scores 1-year post-treatment compared to baseline, but not SBRT patients. Both surgical and SBRT patients reported significantly less anxiety 1-year post-treatment compared to baseline (P<0.001 and P=0.03). Decrease in depression from baseline to 1-year post-treatment was only significant for surgical patients (P<0.001). Conclusions: Post-treatment, surgical patients exhibited improvements in physical health and reductions in lung cancer symptoms following initial deterioration within the first two months; in contrast, SBRT patients showed persistent decline in these areas throughout the year. Nonetheless, improved mental health was noted across both patient categories post-treatment. Targeted interventions and continuous monitoring are recommended during the initial 2 months post-surgery and throughout the year post-SBRT to alleviate physical and mental distress in patients.
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Background: Coronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported. Methods: We have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CAC), taking on average 21 seconds per CAC scan, to 5535 asymptomatic individuals (52.2% women, ages 45-84) that were previously obtained for CAC scoring in the baseline examination (2000-2002) of the Multi-Ethnic Study of Atherosclerosis (MESA). We used the 5-year outcomes data for incident atrial fibrillation (AF) and compared the time-dependent AUC of AI-CAC LA volume with known predictors of AF, the CHARGE-AF Risk Score and NT-proBNP (BNP). The mean follow-up time to an AF event was 2.9±1.4 years. Results: At 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF or BNP at year 1 (0.836, 0.742, 0.742), year 2 (0.842, 0.807,0.772), and year 3 (0.811, 0.785, 0.745) (p<0.02), but similar for year 4 (0.785, 0.769, 0.725) and year 5 (0.781, 0.767, 0.734) respectively (p>0.05). AI-CAC LA volume significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CAC score (0.74, 0.49, 0.53, 0.39, 0.44), CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and BNP (0.68, 0.44, 0.42, 0.30, 0.37) respectively (p<0.01). Conclusion: AI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and BNP.
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Background Pulmonary noncalcified nodules (NCNs) attached to the fissural or costal pleura with smooth margins and triangular or lentiform, oval, or semicircular (LOS) shapes at low-dose CT are recommended for annual follow-up instead of immediate workup. Purpose To determine whether management of mediastinal or diaphragmatic pleura-attached NCNs (M/DP-NCNs) with the same features as fissural or costal pleura-attached NCNs at low-dose CT can follow the same recommendations. Materials and Methods This retrospective study reviewed chest CT examinations in participants from two databases. Group A included 1451 participants who had lung cancer that was first present as a solid nodule with an average diameter of 3.0-30.0 mm. Group B included 345 consecutive participants from a lung cancer screening program who had at least one solid nodule with a diameter of 3.0-30.0 mm at baseline CT and underwent at least three follow-up CT examinations. Radiologists reviewed CT images to identify solid M/DP-NCNs, defined as nodules 0 mm in distance from the mediastinal or diaphragmatic pleura, and recorded average diameter, margin, and shape. General descriptive statistics were used. Results Among the 1451 participants with lung cancer in group A, 163 participants (median age, 68 years [IQR, 61.5-75.0 years]; 92 male participants) had 164 malignant M/DP-NCNs 3.0-30.0 mm in average diameter. None of the 164 malignant M/DP-NCNs had smooth margins and triangular or LOS shapes (upper limit of 95% CI of proportion, 0.02). Among the 345 consecutive screening participants in group B, 146 participants (median age, 65 years [IQR, 59-71 years]; 81 female participants) had 240 M/DP-NCNs with average diameter 3.0-30.0 mm. None of the M/DP-NCNs with smooth margins and triangular or LOS shapes were malignant after a median follow-up of 57.8 months (IQR, 46.3-68.1 months). Conclusion For solid M/DP-NCNs with smooth margins and triangular or LOS shapes at low-dose CT, the risk of lung cancer is extremely low, which supports the recommendation of Lung Imaging Reporting and Data System version 2022 for annual follow-up instead of immediate workup. © RSNA, 2024 See also the editorial by Goodman and Baruah in this issue.
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Neoplasias Pulmonares , Nódulos Pulmonares Múltiples , Femenino , Masculino , Humanos , Anciano , Detección Precoz del Cáncer , Neoplasias Pulmonares/diagnóstico por imagen , Pleura , Estudios Retrospectivos , Tomografía Computarizada por Rayos XRESUMEN
Background CT-defined visceral pleural invasion (VPI) is an important indicator of prognosis for non-small cell lung cancer (NSCLC). However, there is a lack of studies focused on small subpleural NSCLCs (≤30 mm). Purpose To identify CT features predictive of VPI in patients with subpleural NSCLCs 30 mm or smaller. Materials and Methods This study is a retrospective review of patients enrolled in the Initiative for Early Lung Cancer Research on Treatment (IELCART) at Mount Sinai Hospital between July 2014 and February 2023. Subpleural nodules 30 mm or smaller were classified into two groups: a pleural-attached group and a pleural-tag group. Preoperative CT features suggestive of VPI were evaluated for each group separately. Multivariable logistic regression analysis adjusted for sex, age, nodule size, and smoking status was used to determine predictive factors for VPI. Model performance was analyzed with the area under the receiver operating characteristic curve (AUC), and models were compared using Akaike information criterion (AIC). Results Of 379 patients with NSCLC with subpleural nodules, 37 had subsolid nodules and 342 had solid nodules. Eighty-eight patients (22%) had documented VPI, all in solid nodules. Of the 342 solid nodules (46% in male patients, 54% in female patients; median age, 71 years; IQR: 66, 76), 226 were pleural-attached nodules and 116 were pleural-tag nodules. VPI was more frequent for pleural-attached nodules than for pleural-tag nodules (31% [69 of 226] vs 16% [19 of 116], P = .005). For pleural-attached nodules, jellyfish sign (odds ratio [OR], 21.60; P < .001), pleural thickening (OR, 6.57; P < .001), and contact surface area (OR, 1.05; P = .01) independently predicted VPI. The jellyfish sign led to a better VPI prediction (AUC, 0.84; 95% CI: 0.78, 0.90). For pleural-tag nodules, multiple tags to different pleura surfaces enabled independent prediction of VPI (OR, 9.30; P = .001). Conclusions For patients with solid NSCLC (≤30 mm), CT predictors of VPI were the jellyfish sign, pleural thickening, contact surface area (pleural-attached nodules), and multiple tags to different pleura surfaces (pleural-tag nodules). © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Nishino in this issue.
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Carcinoma de Pulmón de Células no Pequeñas , Neoplasias Pulmonares , Humanos , Femenino , Masculino , Anciano , Carcinoma de Pulmón de Células no Pequeñas/diagnóstico por imagen , Pleura/diagnóstico por imagen , Neoplasias Pulmonares/diagnóstico por imagen , Hospitales , Tomografía Computarizada por Rayos XRESUMEN
INTRODUCTION: We aimed to compare outcomes of patients with first primary clinical T1a-bN0M0 NSCLC treated with surgery or stereotactic body radiation therapy (SBRT). METHODS: We identified patients with first primary clinical T1a-bN0M0 NSCLCs on last pretreatment computed tomography treated by surgery or SBRT in the following two prospective cohorts: International Early Lung Cancer Action Program (I-ELCAP) and Initiative for Early Lung Cancer Research on Treatment (IELCART). Lung cancer-specific survival and all-cause survival after diagnosis were compared using Kaplan-Meier analysis. Propensity score matching was used to balance baseline demographics and comorbidities and analyzed using Cox proportional hazards regression. RESULTS: Of 1115 patients with NSCLC, 1003 had surgery and 112 had SBRT; 525 in I-ELCAP in 1992 to 2021 and 590 in IELCART in 2016 to 2021. Median follow-up was 57.6 months. Ten-year lung cancer-specific survival was not significantly different: 90% (95% confidence interval: 87%-92%) for surgery versus 88% (95% confidence interval: 77%-99%) for SBRT, p = 0.55. Cox regression revealed no significant difference in lung cancer-specific survival for the combined cohorts (p = 0.48) or separately for I-ELCAP (p = 1.00) and IELCART (p = 1.00). Although 10-year all-cause survival was significantly different (75% versus 45%, p < 0.0001), after propensity score matching, all-cause survival using Cox regression was no longer different for the combined cohorts (p = 0.74) or separately for I-ELCAP (p = 1.00) and IELCART (p = 0.62). CONCLUSIONS: This first prospectively collected cohort analysis of long-term survival of small, early NSCLCs revealed that lung cancer-specific survival was high for both treatments and not significantly different (p = 0.48) and that all-cause survival after propensity matching was not significantly different (p = 0.74). This supports SBRT as an alternative treatment option for small, early NSCLCs which is especially important with their increasing frequency owing to low-dose computed tomography screening. Furthermore, treatment decisions are influenced by many different factors and should be personalized on the basis of the unique circumstances of each patient.
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Carcinoma de Pulmón de Células no Pequeñas , Neoplasias Pulmonares , Radiocirugia , Humanos , Neoplasias Pulmonares/patología , Estudios Prospectivos , Radiocirugia/métodos , Resultado del Tratamiento , Carcinoma de Pulmón de Células no Pequeñas/radioterapia , Carcinoma de Pulmón de Células no Pequeñas/cirugía , Estadificación de Neoplasias , Estudios RetrospectivosRESUMEN
The ACR created the Lung CT Screening Reporting and Data System (Lung-RADS) in 2014 to standardize the reporting and management of screen-detected pulmonary nodules. Lung-RADS was updated to version 1.1 in 2019 and revised size thresholds for nonsolid nodules, added classification criteria for perifissural nodules, and allowed for short-interval follow-up of rapidly enlarging nodules that may be infectious in etiology. Lung-RADS v2022, released in November 2022, provides several updates including guidance on the classification and management of atypical pulmonary cysts, juxtapleural nodules, airway-centered nodules, and potentially infectious findings. This new release also provides clarification for determining nodule growth and introduces stepped management for nodules that are stable or decreasing in size. This article summarizes the current evidence and expert consensus supporting Lung-RADS v2022.
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Quistes , Neoplasias Pulmonares , Humanos , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/terapia , Tomografía Computarizada por Rayos X , Consenso , Pulmón/diagnóstico por imagenRESUMEN
INTRODUCTION: With global adoption of computed tomography (CT) lung cancer screening, there is increasing interest to use artificial intelligence (AI) deep learning methods to improve the clinical management process. To enable AI research using an open-source, cloud-based, globally distributed, screening CT imaging data set and computational environment that are compliant with the most stringent international privacy regulations that also protect the intellectual properties of researchers, the International Association for the Study of Lung Cancer sponsored development of the Early Lung Imaging Confederation (ELIC) resource in 2018. The objective of this report is to describe the updated capabilities of ELIC and illustrate how this resource can be used for clinically relevant AI research. METHODS: In this second phase of the initiative, metadata and screening CT scans from two time points were collected from 100 screening participants in seven countries. An automated deep learning AI lung segmentation algorithm, automated quantitative emphysema metrics, and a quantitative lung nodule volume measurement algorithm were run on these scans. RESULTS: A total of 1394 CTs were collected from 697 participants. The LAV950 quantitative emphysema metric was found to be potentially useful in distinguishing lung cancer from benign cases using a combined slice thickness more than or equal to 2.5 mm. Lung nodule volume change measurements had better sensitivity and specificity for classifying malignant from benign lung nodules when applied to solid lung nodules from high-quality CT scans. CONCLUSIONS: These initial experiments revealed that ELIC can support deep learning AI and quantitative imaging analyses on diverse and globally distributed cloud-based data sets.
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Aprendizaje Profundo , Enfisema , Neoplasias Pulmonares , Humanos , Neoplasias Pulmonares/patología , Inteligencia Artificial , Detección Precoz del Cáncer , Pulmón/patología , Enfisema/patologíaRESUMEN
Lung cancer screening involves the use of thoracic CT for both detection and measurements of suspicious lung nodules to guide the screening management. Since lung cancer screening eligibility typically requires age over 50 years along with >20 pack-year tobacco exposure, thoracic CT scans also frequently reveal evidence for pulmonary emphysema as well as coronary artery calcification. These three thoracic diseases are collectively three of the leading causes of premature death across the world. Screening for the major thoracic diseases in this heavily tobacco-exposed cohort is broadening the focus of lung cancer screening to a more comprehensive health evaluation including discussing the relevance of screen-detected findings of the heart and lung parenchyma. The status and implications of these emerging issues were reviewed in a multidisciplinary workshop focused on the process of quantitative imaging in the lung cancer screening setting to guide the evolution of this important new area of public health.
Asunto(s)
Neoplasias Pulmonares , Enfermedades Torácicas , Humanos , Persona de Mediana Edad , Neoplasias Pulmonares/epidemiología , Detección Precoz del Cáncer/métodos , Tomografía Computarizada por Rayos X/métodos , PulmónRESUMEN
Background The low-dose CT (≤3 mGy) screening report of 1000 Early Lung Cancer Action Program (ELCAP) participants in 1999 led to the International ELCAP (I-ELCAP) collaboration, which enrolled 31 567 participants in annual low-dose CT screening between 1992 and 2005. In 2006, I-ELCAP investigators reported the 10-year lung cancer-specific survival of 80% for 484 participants diagnosed with a first primary lung cancer through annual screening, with a high frequency of clinical stage I lung cancer (85%). Purpose To update the cure rate by determining the 20-year lung cancer-specific survival of participants diagnosed with first primary lung cancer through annual low-dose CT screening in the expanded I-ELCAP cohort. Materials and Methods For participants enrolled in the HIPAA-compliant prospective I-ELCAP cohort between 1992 and 2022 and observed until December 30, 2022, Kaplan-Meier survival analysis was used to determine the 10- and 20-year lung cancer-specific survival of participants diagnosed with first primary lung cancer through annual low-dose CT screening. Eligible participants were aged at least 40 years and had current or former cigarette use or had never smoked but had been exposed to secondhand tobacco smoke. Results Among 89 404 I-ELCAP participants, 1257 (1.4%) were diagnosed with a first primary lung cancer (684 male, 573 female; median age, 66 years; IQR, 61-72), with a median smoking history of 43.0 pack-years (IQR, 29.0-60.0). Median follow-up duration was 105 months (IQR, 41-182). The frequency of clinical stage I at pretreatment CT was 81% (1017 of 1257). The 10-year lung cancer-specific survival of 1257 participants was 81% (95% CI: 79, 84) and the 20-year lung cancer-specific survival was 81% (95% CI: 78, 83), and it was 95% (95% CI: 91, 98) for 181 participants with pathologic T1aN0M0 lung cancer. Conclusion The 10-year lung cancer-specific survival of 80% reported in 2006 for I-ELCAP participants enrolled in annual low-dose CT screening and diagnosed with a first primary lung cancer has persisted, as shown by the updated 20-year lung cancer-specific survival for the expanded I-ELCAP cohort. © RSNA, 2023 See also the editorials by Grenier and by Sequist and Olazagasti in this issue.
Asunto(s)
Neoplasias Pulmonares , Femenino , Masculino , Humanos , Anciano , Estudios de Seguimiento , Estudios Prospectivos , Estimación de Kaplan-Meier , InvestigadoresRESUMEN
Introduction: To assess healthcare professionals' perceptions of rural barriers and facilitators of lung cancer screening program implementation in a Veterans Health Administration (VHA) setting through a series of one-on-one interviews with healthcare team members. Methods: Based on measures developed using Reach Effectiveness Adoption Implementation Maintenance (RE-AIM), we conducted a cross-sectional qualitative study consisting of one-on-one semi-structured telephone interviews with VHA healthcare team members at 10 Veterans Affairs medical centers (VAMCs) between December 2020 and September 2021. An iterative inductive and deductive approach was used for qualitative analysis of interview data, resulting in the development of a conceptual model to depict rural barriers and facilitators of lung cancer screening program implementation. Results: A total of 30 interviews were completed among staff, providers, and lung cancer screening program directors and a conceptual model of rural barriers and facilitators of lung cancer screening program implementation was developed. Major themes were categorized within institutional and patient environments. Within the institutional environment, participants identified systems-level (patient communication, resource availability, workload), provider-level (attitudes and beliefs, knowledge, skills and capabilities), and external (regional and national networks, incentives) barriers to and facilitators of lung cancer screening program implementation. Within the patient environment, participants revealed patient-level (modifiable vulnerabilities) barriers and facilitators as well as ecological modifiers (community) that influence screening behavior. Discussion: Understanding rural barriers to and facilitators of lung cancer screening program implementation as perceived by healthcare team members points to opportunities and approaches for improving lung cancer screening reach, implementation and effectiveness in VHA rural settings.