AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CACTM) predicts heart failure and outperforms NT-proBNP: The multi-ethnic study of Atherosclerosis.

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€‹< â€‹0.0001), and comparable to clinical risk factors (0.85, p â€‹= â€‹0.4141). 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€‹< â€‹0.0001). 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.

Visual scoring of osteoporosis on low-dose CT in lung cancer screening population.

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.

Change in quality of life of stage IA non-small cell lung cancer after surgery or radiation therapy.

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.

CT Predictors of Visceral Pleural Invasion in Patients with Non-Small Cell Lung Cancers 30 mm or Smaller.

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.

Radiologic Features of Nodules Attached to the Mediastinal or Diaphragmatic Pleura at Low-Dose CT for Lung Cancer Screening.

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.

Prospective Cohort Study to Compare Long-Term Lung Cancer-Specific and All-Cause Survival of Clinical Early Stage (T1a-b; ≤20 mm) NSCLC Treated by Stereotactic Body Radiation Therapy and Surgery.

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.

The International Association for the Study of Lung Cancer Early Lung Imaging Confederation Open-Source Deep Learning and Quantitative Measurement Initiative.

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.

Quantitative imaging workshop XIX: Utilizing quantitative thoracic imaging to optimize population health final summary.

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.

A 20-year Follow-up of the International Early Lung Cancer Action Program (I-ELCAP).

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.

Rural barriers and facilitators of lung cancer screening program implementation in the veterans health administration: a qualitative study.

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.

The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules.

PURPOSE: Computed tomography is the standard method by which pulmonary nodules are detected. Greater than 40% of pulmonary biopsies are not lung cancer and therefore not necessary, suggesting that improved diagnostic tools are needed. The LungLB™ blood test was developed to aid the clinical assessment of indeterminate nodules suspicious for lung cancer. LungLB™ identifies circulating genetically abnormal cells (CGACs) that are present early in lung cancer pathogenesis. METHODS: LungLB™ is a 4-color fluorescence in-situ hybridization assay for detecting CGACs from peripheral blood. A prospective correlational study was performed on 151 participants scheduled for a pulmonary nodule biopsy. Mann-Whitney, Fisher's Exact and Chi-Square tests were used to assess participant demographics and correlation of LungLB™ with biopsy results, and sensitivity and specificity were also evaluated. RESULTS: Participants from Mount Sinai Hospital (n = 83) and MD Anderson (n = 68), scheduled for a pulmonary biopsy were enrolled to have a LungLB™ test. Additional clinical variables including smoking history, previous cancer, lesion size, and nodule appearance were also collected. LungLB™ achieved 77% sensitivity and 72% specificity with an AUC of 0.78 for predicting lung cancer in the associated needle biopsy. Multivariate analysis found that clinical and radiological factors commonly used in malignancy prediction models did not impact the test performance. High test performance was observed across all participant characteristics, including clinical categories where other tests perform poorly (Mayo Clinic Model, AUC = 0.52). CONCLUSION: Early clinical performance of the LungLB™ test supports a role in the discrimination of benign from malignant pulmonary nodules. Extended studies are underway.

The Association of Organizational Readiness With Lung Cancer Screening Utilization.

INTRODUCTION: Lung cancer screening is widely underutilized. Organizational factors, such as readiness for change and belief in the value of change (change valence), may contribute to underutilization. The aim of this study was to evaluate the association between healthcare organizations' preparedness and lung cancer screening utilization. METHODS: Investigators cross-sectionally surveyed clinicians, staff, and leaders at10 Veterans Affairs from November 2018 to February 2021 to assess organizational readiness to implement change. In 2022, investigators used simple and multivariable linear regression to evaluate the associations between facility-level organizational readiness to implement change and change valence with lung cancer screening utilization. Organizational readiness to implement change and change valence were calculated from individual surveys. The primary outcome was the proportion of eligible Veterans screened using low-dose computed tomography. Secondary analyses assessed scores by healthcare role. RESULTS: The overall response rate was 27.4% (n=1,049), with 956 complete surveys analyzed: median age of 49 years, 70.3% female, 67.6% White, 34.6% clinicians, 61.1% staff, and 4.3% leaders. For each 1-point increase in median organizational readiness to implement change and change valence, there was an associated 8.4-percentage point (95% CI=0.2, 16.6) and a 6.3-percentage point increase in utilization (95% CI= -3.9, 16.5), respectively. Higher clinician and staff median scores were associated with increased utilization, whereas leader scores were associated with decreased utilization after adjusting for other roles. CONCLUSIONS: Healthcare organizations with higher readiness and change valence utilized more lung cancer screening. These results are hypothesis generating. Future interventions to increase organizations' preparedness, especially among clinicians and staff, may increase lung cancer screening utilization.

Using the Framework for Reporting Adaptations and Modifications-Expanded (FRAME) to study adaptations in lung cancer screening delivery in the Veterans Health Administration: a cohort study.

BACKGROUND: Lung cancer screening is a complex clinical process that includes identification of eligible individuals, shared decision-making, tobacco cessation, and management of screening results. Adaptations to the delivery process for lung cancer screening in situ are understudied and underreported, with the potential loss of important considerations for improved implementation. The Framework for Reporting Adaptations and Modifications-Expanded (FRAME) allows for a systematic enumeration of adaptations to implementation of evidence-based practices. We applied FRAME to study adaptations in lung cancer screening delivery processes implemented by lung cancer screening programs in a Veterans Health Administration (VHA) Enterprise-Wide Initiative. METHODS: We prospectively conducted semi-structured interviews at baseline and 1-year intervals with lung cancer screening program navigators at 10 Veterans Affairs Medical Centers (VAMCs) between 2019 and 2021. Using this data, we developed baseline (1st) process maps for each program. In subsequent years (year 1 and year 2), each program navigator reviewed the process maps. Adaptations in screening processes were identified, documented, and mapped to FRAME categories. RESULTS: We conducted a total of 16 interviews across 10 VHA lung cancer screening programs (n=6 in year 1, n=10 in year 2) to collect adaptations. In year 1 (2020), six programs were operational and eligible. Of these, three reported adaptations to their screening process that were planned or in response to COVID-19. In year 2 (2021), all 10 programs were operational and eligible. Programs reported 14 adaptations in year 2. These adaptations were planned and unplanned and often triggered by increased workload; 57% of year 2 adaptations were related to the identification and eligibility of Veterans and 43% were related to follow-up with Veterans for screening results. Throughout the 2 years, adaptations related to data management and patient tracking occurred in 60% of programs to improve the data collection and tracking of Veterans in the screening process. CONCLUSIONS: Using FRAME, we found that adaptations occurred primarily in the areas of patient identification and communication of results due to increased workload. These findings highlight navigator time and resource considerations for sustainability and scalability of existing and future lung cancer screening programs as well as potential areas for future intervention.

Evaluation of emphysema on thoracic low-dose CTs through attention-based multiple instance deep learning.

In addition to lung cancer, other thoracic abnormalities, such as emphysema, can be visualized within low-dose CT scans that were initially obtained in cancer screening programs, and thus, opportunistic evaluation of these diseases may be highly valuable. However, manual assessment for each scan is tedious and often subjective, thus we have developed an automatic, rapid computer-aided diagnosis system for emphysema using attention-based multiple instance deep learning and 865 LDCTs. In the task of determining if a CT scan presented with emphysema or not, our novel Transfer AMIL approach yielded an area under the ROC curve of 0.94 ± 0.04, which was a statistically significant improvement compared to other methods evaluated in our study following the Delong Test with correction for multiple comparisons. Further, from our novel attention weight curves, we found that the upper lung demonstrated a stronger influence in all scan classes, indicating that the model prioritized upper lobe information. Overall, our novel Transfer AMIL method yielded high performance and provided interpretable information by identifying slices that were most influential to the classification decision, thus demonstrating strong potential for clinical implementation.

Impact of Duration of Diagnostic Workup on Prognosis for Early Lung Cancer.

INTRODUCTION: Growth assessment for pulmonary nodules is an important diagnostic tool; however, the impact on prognosis due to time delay for follow-up diagnostic scans needs to be considered. METHODS: Using the data between 2003 and 2019 from the International Early Lung Cancer Action Program, a prospective cohort study, we determined the size-specific, 10-year Kaplan-Meier lung cancer (LC) survival rates as surrogates for cure rates. We estimated the change in LC diameter after delays of 90, 180, and 365 days using three representative LC volume doubling times (VDTs) of 60 (fast), 120 (moderate), and 240 (slow). We then estimated the decrease in the LC cure rate resulting from time between computed tomography scans to assess for growth during the diagnostic workup. RESULTS: Using a regression model of the 10-year LC survival rates on LC diameter, the estimated LC cure rate of a 4.0 mm LC with fast (60-d) VDT is 96.0% (95% confidence interval [CI]: 95.2%-96.7%) initially, but it would decrease to 94.3% (95% CI: 93.2%-95.0%), 92.0% (95% CI: 90.5%-93.4%), and 83.6%(95% CI: 80.6%-86.6%) after delays of 90, 180, and 365 days, respectively. A 20.0-mm LC with the same VDTs has a lower LC cure rate of 79.9% (95% CI: 76.2%-83.5%) initially and decreases more rapidly to 71.5% (95% CI: 66.4%-76.7%), 59.8% (95% CI: 52.4%-67.1%), and 17.9% (95% CI: 3.0%-32.8%) after the same delays of 90, 180, and 365 days, respectively. CONCLUSIONS: Time between scans required to measure growth of lung nodules affects prognosis with the effect being greater for fast growing and larger cancers. Quantifying the extent of change in prognosis is required to understand efficiencies of different management protocols.

Measuring the margin distance in pulmonary wedge resection.

BACKGROUND: Margin distance contributes to survival and recurrence during wedge resections for early-stage non-small cell lung cancer. The Initiative for Early Lung Cancer Research on Treatment sought to standardize a surgeon-measured margin intraoperatively. METHODS: Lung cancer patients who underwent wedge resection were reviewed. Margins were measured by the surgeon twice as per a standardized protocol. Intraobserver variability as well as surgeon-pathologist variability were compared. RESULTS: Forty-five patients underwent wedge resection. Same-surgeon measurement analysis indicated good reliability with a small mean difference and narrow limit of agreement for the two measures. The median surgeon-measured margin was 18.0 mm, median pathologist-measured margin was 16.0 mm and the median difference between the surgeon-pathologist margin was -1.0 mm, ranging from -18.0 to 12.0 mm. Bland-Altman analysis for margin measurements demonstrated a mean difference of 0.65 mm. The limit of agreement for the two approaches were wide, with the difference lying between -16.25 and 14.96 mm. CONCLUSIONS: A novel protocol of surgeon-measured margin was evaluated and compared with pathologist-measured margin. High intraobserver agreement for repeat surgeon measurements yet low-to-moderate correlation or directionality between surgeon and pathologic measurements were found. DISCUSSION: A standardized protocol may reduce variability in pathologic assessment. These findings have critical implications considering the impact of margin distance on outcomes.

Comparison of lung cancer aggressiveness in patients who never smoked compared to those who smoked.

OBJECTIVES: To determine whether radiographic measures of tumor aggressiveness differ by smoking status. MATERIALS AND METHODS: All patients diagnosed with non-small-cell lung cancer(NSCLC) ≤ 30 mm in maximum diameter, without clinical evidence of metastasis who had both pre-treatment PET scans and two CT scans at least 90 days apart in a prospective cohort, the Initiative for Early Lung Cancer Research on Treatment(IELCART) at Mount Sinai between 2016 and 2020 were identified. Comparison of two measures of tumor aggressiveness, positron emission tomography(PET) SUVmax and tumor volume doubling time(VDT) by smoking status was performed. RESULTS: Of 417 patients identified, 158 patients had pre-treatment PET scans and at least two CT scans available. The two measures of tumor aggressiveness, SUVmax and VDT values were significantly different between patients who had never smoked and those who smoked: patients who never smoked had lower median SUVmax[2.5(IQR: 1.1-4.8) vs. 4.2(IQR:2.1-9.2),p = 0.002] and longer median VDT[(372.6 days vs. 225.6 days,p = 0.001)] compared to those who smoked. Using multivariable analyses, when adjusting for age and sex alone, SUVmax(p = 0.004) and VDT(p = 0.0001) remained significantly different by smoking status. The final multivariable analysis, adjusted for all three co-variates(sex, age and tumor histology) showed no significant difference in SUVmax and VDT by smoking status [SUVmax(p = 0.25) and VDT(p = 0.06)]. CONCLUSION: Smoking history does not influence VDT or PET SUVmax measures of lung cancer aggressiveness.

Using the Framework for Reporting Adaptations and Modifications-Expanded (FRAME) to study lung cancer screening adaptations in the Veterans Health Administration.

Background: Lung cancer screening includes identification of eligible individuals, shared decision-making inclusive of tobacco cessation, and management of screening results. Adaptations to the implemented processes for lung cancer screening in situ are understudied and underreported, with potential loss of important considerations for improved implementation. The Framework for Reporting Adaptations and Modifications-Expanded (FRAME) allows for systematic enumeration of adaptations to implementations of evidence-based practices. We used FRAME to study adaptations in lung cancer screening processes that were implemented as part of a Veterans Health Administration (VHA) Enterprise-Wide Initiative. Methods: We conducted semi-structured interviews at baseline and 1-year intervals with lung cancer screening program navigators at 10 Veterans Affairs Medical Centers (VAMC) between 2019-2021. Using this data, we developed baseline (1st) process maps for each program. In subsequent years (year 1 and year 2), each program navigator reviewed the process maps. Adaptations in screening processes were identified, recorded and mapped to FRAME categories. Results: A total of 14 program navigators across 10 VHA lung cancer screening programs participated in 20 interviews. In year 1 (2019-2020), seven programs were operational and of these, three reported adaptations to their screening process that were either planned and in response to COVID-19. In year 2 (2020-2021), all 10 programs were operational. Programs reported 14 adaptations in year 2. These adaptations were both planned and unplanned and often triggered by increased workload; 57% of year 2 adaptations were related to identification and eligibility of Veterans and 43% were related to follow-up with Veterans for screening results. Throughout the 2 years, adaptations related to data management and patient tracking occurred in 6 of 10 programs to improve the data collection and tracking of Veterans in the screening process. Conclusions: Using FRAME, we found that adaptations occurred throughout the lung cancer screening process but primarily in the areas of patient identification and communication of results. These findings highlight considerations for lung cancer screening implementation and potential areas for future intervention.