Low-dose Screening Research Articles

Assessing Bronchiectasis Progression in Low-dose Screening for Lung Cancer: Frequency and Predictors.

Bronchiectasis is associated with loss of lung function, substantial use of health care resources, and increased morbidity and mortality in people with cardiopulmonary diseases. We assessed the frequency of progression or new development of bronchiectasis and predictors of progression in participants in low-dose computed tomography (CT) screening programs. We reviewed our prospectively enrolled screening cohort in the Early Lung and Cardiac Action Program cohort of smokers, aged 40 to 90, between 2010 and 2019, and medical records to assess the progression of bronchiectasis after five or more years of follow-up after baseline low-dose CT. Logistic and multivariate-analysis-of-covariance regression analyses were used to examine factors associated with bronchiectasis progression. Among 2182 baseline screening participants, we identified 534 (mean age: 65±9y; 53.6% women) with follow-up screening of 5+ years (median follow-up: 103.2mo). Of the 534 participants, 34 (6.4%) participants had progressed (25/126, 19.8%) or newly developed (9/408, 2.2%) bronchiectasis. Significant predictors of progression (progressed+newly developed) were: age (P=0.03), pack-years of smoking (P=0.004), baseline components of the ELCAP Bronchiectasis Score, including the severity of bronchial dilatation (P=0.01), its extent (P=0.01), bronchial wall thickening (P=0.04), and mucoid impaction (P<0.001). Assuming similar progression rates, ~136 out of 2182 participants are expected to progress on follow-up screening. This study sheds light on bronchiectasis progression and its significant predictors in a low-dose CT screening program. We recommend reporting bronchiectasis as participants who have smoked are at increased risk, and continued assessment over the entire period of participation in the low-dose CT screening program would allow for the identification of possible causes, early warning, and even early treatment.

MA19.11 Predicting Future Lung Cancer Risk with Low-Dose Screening CT Using an Artificial Intelligence Model

MA19.11 Predicting Future Lung Cancer Risk with Low-Dose Screening CT Using an Artificial Intelligence Model

Label-free drug interaction screening via Raman microscopy

Development of a simple, label-free screening technique capable of precisely and directly sensing interaction-in-solution over a size range from small molecules to large proteins such as antibodies could offer an important tool for researchers and pharmaceutical companies in the field of drug development. In this work, we present a thermostable Raman interaction profiling (TRIP) technique that facilitates low-concentration and low-dose screening of binding between protein and ligand in physiologically relevant conditions. TRIP was applied to eight protein-ligand systems, and produced reproducible high-resolution Raman measurements, which were analyzed by principal component analysis. TRIP was able to resolve time-depending binding between 2,4-dinitrophenol and transthyretin, and analyze biologically relevant SARS-CoV-2 spike-antibody interactions. Mixtures of the spike receptor-binding domain with neutralizing, nonbinding, or binding but nonneutralizing antibodies revealed distinct and reproducible Raman signals. TRIP holds promise for the future developments of high-throughput drug screening and real-time binding measurements between protein and drug.

Abstract A111: Smoking cessation and relapse among Black and White patients referred for lung cancer screening

Abstract Background: Tobacco quit rates in the adult U.S. population range from 4% to 8% annually. Lung cancer screening (LCS) provides an opportunity for discussion of smoking cessation. Outside of LCS, Blacks have been shown to make more quit attempts and have higher readiness to quit, but have lower quit rates among current smokers and higher relapse rates among former smokers. In this study, we determined quit and relapse rates after a provider referral for LCS in Black and White patients. Methods: We conducted an observational study within an integrated health system that has both a LCS program and an independent smoking cessation program. Patients, aged 55-77 years who received at least one provider referral (order) for LCS between September 2016 and April 2022 and a second order within 9-36 months, were included in this study.Smoking status was captured in LCS orders. Demographic variables, completion of a low-dose screening CT (LDSCT), smoking cessation counseling referral and medication prescription were captured from the electronic medical record. Pearson Chi-squared test and multivariable logistic regression were used to determine group differences and important predictors of tobacco quit and relapse. Results: During the study period, 6,096 patients (21% Black) received a referral to LCS and 75% of these patients completed an LDSCT between orders. Among current smokers (N=3715) the quit rate was 14% and relapse occurred in 10% of former smokers (N=2381), overall. Rates of quit (12.0% LDSCT no vs. 14.6% LDSCT yes, p=0.048) and relapse (13.7% LDSCT no vs. 9.4% LDSCT yes, p=0.006) were worse for those who did not complete the LDSCT between orders. Black patients had lower quit (11% vs. 14%, p=0.003) and higher relapse (13.6% vs. 9.7%, p=0.03) rates than Whites. Among Black former smokers who did not complete the LDSCT, the relapse rate was nearly 20% and these patients received referrals to counseling and prescriptions for cessation medications at higher rates than White former smokers. In multivariable models, older age, receiving a prescription for a cessation medication, and completing the LDSCT were associated with smoking cessation, while Black race and receiving a referral for counseling were associated with lower odds of quitting. Black race and longer duration between orders were associated with greater likelihood of relapse, while older age and completing the LDSCT were associated with lower likelihood of relapse. Discussion: Rates of smoking cessation appear higher in patients referred for LCS than in the general population. In the context of LCS, Black patients still have lower quit and higher relapse rates than White patients. Relapse is particularly high in Black former smokers who do not complete LDSCT after a referral for screening. Lung screening and smoking cessation programs will need to work together to find culturally appropriate resources and ways to reach out to patients referred for screening, especially those who do not complete the LDSCT, to encourage quitting and provide equitable cessation support. Citation Format: Christine Neslund-Dudas, Amy Tang, Katie Zarins, Elizabeth Alleman, Amanda Holm, Vritti Gupta, Michael Simoff. Smoking cessation and relapse among Black and White patients referred for lung cancer screening [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr A111.

Pulmonary nodule volumetric accuracy of a deep learning-based reconstruction algorithm in low-dose computed tomography: A phantom study

PurposeTo compare the image properties and pulmonary nodule volumetric accuracies among deep learning-based reconstruction (DLR), filtered back projection (FBP), and hybrid iterative reconstruction (hybrid IR) in low-dose computed tomography (LDCT). MethodsA multipurpose chest phantom containing artificial spherical pulmonary nodules with 5-, 8-, 10-, and 12-mm diameters and Hounsfield units (HUs) of −630 and +100 HU was scanned 20 times at a standard dose, based on a low-dose screening CT trial, and at 1/2, 1/6, and 1/12 of the standard dose. To assess noise reduction performance and volumetric accuracy, the standard deviations (SDs) of the pixel values and volumetric percentage errors (PEs) were compared among FBP, hybrid IR, and DLR. The noise non-stationarity index (NNSI) was calculated from 20 image replicates and compared among FBP, hybrid IR, and DLR to evaluate noise stationarity. ResultsThe SD reduction rates for FBP in hybrid IR and DLR were 62 %–85 % and 79 %–90 %, respectively. For the four nodules with +100 HU, the PE of all reconstruction methods was <±25 % (not clinically relevant). For the four nodules with −630 HU, the PEs were equivalent or lower for hybrid IR and DLR than for FBP, and the PE difference between hybrid IR and DLR ranged from 0 % to 7%. The NNSI was significantly higher for DLR than for FBP and hybrid IR (p < 0.01). ConclusionsGreater noise suppression was achieved with DLR than with hybrid IR without compromising nodule volumetric accuracy in LDCT despite the representative noise non-stationarity.

CORRELATING MALIGNANCY RISK FROM AN ARTIFICIAL INTELLIGENCE (AI) ALGORITHM AND LUNG-RADS-BASED CLASSIFICATION FROM SCREENING LOW-DOSE CT IMAGING

CORRELATING MALIGNANCY RISK FROM AN ARTIFICIAL INTELLIGENCE (AI) ALGORITHM AND LUNG-RADS-BASED CLASSIFICATION FROM SCREENING LOW-DOSE CT IMAGING

EP01.02-007 It’s Not How You Start, It’s How You Finish: Patient Motivators to Follow Low-Dose Screening Recommendations

Low-Dose CT (LDCT) for lung cancer screening (LCS) is recommended for ages 50-80 with 20 pack-years smoking history that currently smoke or have quit within 15 years. Although LCS decreases lung cancer mortality 20-25%, recent US screening rates are only 6.5%. Furthermore, adherence to follow-up is suboptimal outside of studies due to provider, patient, and system barriers. Our goal was to explore patients’ motivating factors and deterrents to LDCT follow-up in an underserved population.

Outcomes of Shared Decision-Making for Low-Dose Screening for Lung Cancer in an Academic Medical Center.

Shared decision-making (SDM) helps patients weigh risks and benefits of screening approaches. Little is known about SDM visits between patients and healthcare providers in the context of lung cancer screening. This study explored the extent that patients were informed by their provider of the benefits and harms of lung cancer screening and expressed certainty about their screening choice. We conducted a survey with 75 patients from an academic medical center in the Southeastern U.S. Survey items included knowledge of benefits and harms of screening, patients' value elicitation during SDM visits, and decisional certainty. Patient and provider characteristics were collected through electronic medical records or self-report. Descriptive statistics, Kruskal-Wallis tests, and Pearson correlations between screening knowledge, value elicitation, and decisional conflict were calculated. The sample was predominately non-Hispanic White (73.3%) with no more than high school education (53.4%) and referred by their primary care provider for screening (78.7%). Patients reported that providers almost always discussed benefits of screening (81.3%), but infrequently discussed potential harms (44.0%). On average, patients had low knowledge about screening (score = 3.71 out of 8) and benefits/harms. Decisional conflict was low (score = - 3.12) and weakly related to knowledge (R= - 0.25) or value elicitation (R= - 0.27). Black patients experienced higher decisional conflict than White patients (score = - 2.21 vs - 3.44). Despite knowledge scores being generally low, study patients experienced low decisional conflict regarding their decision to undergo lung cancer screening. Additional work is needed to optimize the quality and consistency of information presented to patients considering screening.

Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT

Background Accurate estimation of the malignancy risk of pulmonary nodules at chest CT is crucial for optimizing management in lung cancer screening. Purpose To develop and validate a deep learning (DL) algorithm for malignancy risk estimation of pulmonary nodules detected at screening CT. Materials and Methods In this retrospective study, the DL algorithm was developed with 16 077 nodules (1249 malignant) collected -between 2002 and 2004 from the National Lung Screening Trial. External validation was performed in the following three -cohorts -collected between 2004 and 2010 from the Danish Lung Cancer Screening Trial: a full cohort containing all 883 nodules (65 -malignant) and two cancer-enriched cohorts with size matching (175 nodules, 59 malignant) and without size matching (177 -nodules, 59 malignant) of benign nodules selected at random. Algorithm performance was measured by using the area under the receiver operating characteristic curve (AUC) and compared with that of the Pan-Canadian Early Detection of Lung Cancer (PanCan) model in the full cohort and a group of 11 clinicians composed of four thoracic radiologists, five radiology residents, and two pulmonologists in the cancer-enriched cohorts. Results The DL algorithm significantly outperformed the PanCan model in the full cohort (AUC, 0.93 [95% CI: 0.89, 0.96] vs 0.90 [95% CI: 0.86, 0.93]; P = .046). The algorithm performed comparably to thoracic radiologists in cancer-enriched cohorts with both random benign nodules (AUC, 0.96 [95% CI: 0.93, 0.99] vs 0.90 [95% CI: 0.81, 0.98]; P = .11) and size-matched benign nodules (AUC, 0.86 [95% CI: 0.80, 0.91] vs 0.82 [95% CI: 0.74, 0.89]; P = .26). Conclusion The deep learning algorithm showed excellent performance, comparable to thoracic radiologists, for malignancy risk estimation of pulmonary nodules detected at screening CT. This algorithm has the potential to provide reliable and reproducible malignancy risk scores for clinicians, which may help optimize management in lung cancer screening. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Tammemägi in this issue.

Interobserver variability in Lung CT Screening Reporting and Data System categorisation in subsolid nodule-enriched lung cancer screening CTs.

To assess interobserver agreement in Lung CT Screening Reporting and Data System (Lung-RADS) categorisation in subsolid nodule-enriched low-dose screening CTs. A retrospective review of low-dose screening CT reports from 2013 to 2017 using keyword searches for subsolid nodules identified 54 baseline CT scans. With an additional 108 negative screening CT scans, a total of 162 CT scans were categorised according to the Lung-RADS by two fellowship-trained thoracic radiologists in consensus. We randomly selected 20, 20, 10, and 10 scans from categories 1/2, 3, 4A, and 4B CT scans, respectively, to ensure balanced category representation. Five radiologists classified the 60 CT scans into Lung-RADS categories. The frequencies of concordance and minor and major discordance were calculated, with major discordance defined as at least 6 months of management discrepancy. We used Cohen's κ statistics to analyse reader agreement. An average of 60.3% (181 of 300) of all cases and 45.0% (90 of 200) of positive screens were correctly categorised. The minor and major discordance rates were 12.3% and 27.3% overall and 18.5% and 36.5% in positive screens, respectively. The concordance rate was significantly higher among experienced thoracic radiologists. Overall, the interobserver agreement was moderate (mean κ, 0.45; 95% confidence interval: 0.40-0.51). The proportion of part-solid risk-dominant nodules was significantly higher in cases with low rates of accurate categorisation. This retrospective study observed variable accuracy and moderate interobserver agreement in radiologist categorisation of subsolid nodules in screening CTs. This inconsistency may affect management recommendations for lung cancer screening. • Diagnostic performance for Lung-RADS categorisation is variable among radiologists with fair to moderate interobserver agreement in subsolid nodule-enriched CT scans. • Experienced thoracic radiologists showed more accurate and consistent Lung-RADS categorisation than radiology residents. • The relative abundance of part-solid nodules was a potential factor related to increased disagreement in Lung-RADS categorisation.

Risk Perception Among a Lung Cancer Screening Population

A successful lung cancer screening program requires a patient cohort at sufficient risk of developing cancer who are willing to participate. Among other factors, a patient's lung cancer risk perception may inform their attitudes toward screening and smoking cessation programs. This study analyzed data from the Pan-Canadian Early Detection of Lung Cancer (PanCan) Study to address the following questions: Which factors are associated with the perception of lung cancer risk? Is there an association between risk perception for lung cancer and actual calculated risk? Is there an association between risk perception for lung cancer and the intent to quit smoking? Are there potential targets for lung cancer screening awareness? The PanCan study recruited current or former smokers aged 50 to 75 years who had at least a 2%risk of developing lung cancer over 6 years to undergo low-dose screening CT. Risk perception and worry about lung cancer were captured on a baseline questionnaire. Cumulative logistic regression analysis was used to assess the relationship between baseline risk variables and both lung cancer risk perception and worry. Among the 2,514 individuals analyzed, a higher perceived risk of lung cancer was positively associated with calculated risk (P= .032). Younger age, being a former smoker, respiratory symptoms, lower FEV1, COPD, and a family history of lung cancer were associated with higher perceived risk. Conversely, a consistent relationship between calculated risk and worry was not identified. There was a positive association between risk perception and lung cancer worry and reported intent to quit smoking. Individuals' lung cancer risk perception correlated positively with calculated risk in a screening population. Promotion of screening programs may benefit from focusing on factors associated with higher risk perception; conversely, harnessing worry seemingly holds less value.

Deferral and Resumption of Lung Cancer Screening After COVID-19 Surge: Patient Perspectives From Two Institutions.

Deferral and Resumption of Lung Cancer Screening After COVID-19 Surge: Patient Perspectives From Two Institutions.

Repeatability of small lung nodule measurement in low-dose lung screening: a phantom study

BackgroundLung cancer screening revealed that people with small pulmonary nodules are mostly asymptomatic and that some of these people are at risk of developing lung cancer, so we intended to explore the repeatability of small lung nodule measurement in low-dose lung screening.MethodsWe scanned eight ground-glass nodules (GGNs) and solid nodules, with diameters of 3, 5, 8, and 10 mm. They were divided according to the different combination schemes of tube voltage (KV) and tube current (mA) as 70, 80, 100, and 120 KV, and currents of nine tubes were divided as 20, 30, 40, 50, 60, 70, 80, 90, and 100 mAs.ResultsCompared with the conventional dose group (120 kVp, 100 mAs), the nodule diameter and solid nodule volume measured by all scanning combinations were more consistent (P > 0.05), the volumes of 10 mm GGNs combinations were consistent (P > 0.05), the volumes of 8 mm GGNs were consistent (P > 0.05), the volumes of 5 mm GGNs combinations were consistent (P > 0.05), and the volumes of 3 mm were consistent (P > 0.05).ConclusionIn lung cancer screening, CT parameters should be as follows: tube voltage is more than 80 kVp, and tube current is 80 mAs in order to meet the requirements for the accurate measurement of the diameter and volume of pulmonary nodules.

Long-term cancer risk associated with lung nodules observed on low-dose screening CT scans

ObjectiveNon-calcified nodules (NCNs) associated with false positive low-dose CT (LDCT) lung cancer screens have been attributed to various causes. Some, however, may represent lung cancer precursors. An association of NCNs with long-term lung cancer risk would provide indirect evidence of some NCNs being cancer precursors. MethodsLDCT arm participants in the National Lung Screening Trial (NLST) received LDCT screens at baseline and years 1-2. The relationship between NCNs found on LDCT screens and subsequent lung cancer diagnosis over different time periods was examined at the person and lobe level. For the latter, a lobe had a cancer outcome only if the cancer was located in the lobe. Separate analyses were performed on baseline and post-baseline LDCT findings; for the latter, those with baseline NCNs were excluded and only new (non-pre-existing) NCNs examined. Raw and adjusted rate-ratios (RRs) were computed for presence of NCNs and subsequent lung cancer risk; adjusted RRs controlled for demographic and smoking factors. Results26,309 participants received the baseline LDCT screen. Over median 11.3 years follow-up, 1675 lung cancers were diagnosed. Adjusted RRs for time periods 0–4, 4–8 and 8−12 years following the baseline screen were 5.1 (95 % CI:4.4–5.9), 1.5 (95 % CI:1.3–1.9) and 1.5 (95 % CI:1.2-1.8) at the person-level and 14.7 (95 % CI:12.6–17.2), 2.6 (95 % CI: 2.0–3.4) and 2.2 (95 % CI:1.6–2.9) at the lobe-level. 18,585 participants were included in the post-baseline analysis. Adjusted RRs for periods 0–4, 4–8 and 8−11 years were 5.6 (95 % CI: 4.5–7.0), 1.9 (95 % CI: 1.3–2.7) and 1.6 (95 % CI: 0.9–2.9) at the person-level and 19.6 (95 % CI:14.9–25.3), 2.5 (95 % CI:1.3–4.7) and 3.3 (95 % CI:1.4–7.6) at the lobe-level. Raw RRs were similar. ConclusionNCNs are associated with excess long-term lung cancer risk, suggesting that some may be lung cancer precursors.

Investigation of Low-Dose CT Lung Cancer Screening Scan "Over-Range" Issue Using Machine Learning Methods.

Low-dose computed tomography (CT) lung cancer screening is recommended by the US Preventive Services Task Force for high lung cancer-risk populations. In this study, we investigated an important factor affecting the CT dose-the scan length, for this CT exam. A neural network model based on the "UNET" framework was established to segment the lung region in the CT scout images. It was trained initially with 247 chest X-ray images and then with 40 CT scout images. The mean Intersection over Union (IOU) and Dice coefficient were reported to be 0.954 and 0.976, respectively. Lung scan boundaries were determined from this segmentation and compared with the boundaries marked by an expert for 150 validation images, resulting an average 4.7% difference. Seven hundred seventy CT low-dose lung screening exams were retrospectively analyzed with the validated model. The average "desired" scan length was 252mm with a standard deviation of 28mm. The average "over-range" was 58.5mm or 24%. The upper boundary (superior) on average had an "over-range" of 17mm, and the lower boundary (inferior) on average had an "over-range" of 41mm. Further analysis of this data showed that the extent of "over-range" was independent of acquisition date, acquisition time, acquisition station, and patient age, but dependent on technologist and patient weight. We concluded that this machine learning method could effectively support quality control on the scan length for CT low-dose screening scans, enabling the eliminations of unnecessary patient dose.

P2.11-23 Risk Perception Among a Lung Cancer Screening Population

To make lung cancer screening feasible, populations with the highest risk of developing cancer need to be targeted. Furthermore, factors which motivate individuals to participate in lung cancer screening programs should be integrated into recruitment strategies. Among these motivators, an individual’s perception of their lung cancer risk is an important consideration. This paper analyzes factors associated with risk perception in subjects enrolled in the Pan-Canadian Early Detection of Lung Cancer Study (PanCan), and assesses the relationship between subjects’ risk perception and actual calculated risk. The PanCan low-dose screening CT study recruited individuals from the general population who were current or former smokers age 50-75 having at least a 2% risk of developing lung cancer over 6 years as calculated by the PanCan model. Risk perception was captured at baseline with a 5-point Likert scale question asking the subject to assess their personal chances of being diagnosed with lung cancer compared with other smokers of the same age. Multivariate linear regression analysis was used to assess the relationship between risk factors and risk perception. Baseline risk variables in the model include demographics, smoking history, symptoms, medications, occupation, previous chest imaging, history of COPD, medical comorbidities, and family history of cancer. 2514 patients were included in the analysis. Median age was 62.3, 55.3% were male, median pack-year smoking history was 50 years (range 2.2-230), and median calculated lung cancer risk was 3.4% over 6 years (range 2-38.2). Calculated lung cancer risk increased by 0.08% (SE 0.02, p-value=0.001) for each increase in Likert risk perception category. On multivariable analysis, the following variables were associated with risk perception category: cigarettes smoked per day (+0.003 increase in category / cigarette, p=0.083), presence of dyspnea (+0.192), presence of wheeze (+0.272), known COPD (+0.110), no family history of cancer (-0.476) and no family history of lung cancer (-0.385) (all p<0.001). Increased perception of risk was associated with intent to quit smoking within 6 months (p<0.001). In this lung cancer screening study, risk perception was positively associated with calculated risk for lung cancer, despite a minimum 2% risk in the cohort. Individual factors and family history of cancer predicted risk perception. Risk perception was also associated with a willingness to quit smoking. Self-risk perception and associated factors could be used to tailor recruitment strategies to screening programs. The link between risk perception and willingness to quit smoking could aid integrated tobacco cessation programs.

X-ray-induced acoustic computed tomography for 3D breast imaging: A simulation study.

The objective of this study is to demonstrate the feasibility of x-ray-induced acoustic computed tomography (XACT) for early breast un-palpable microcalcification (μCa) detection in three dimensions (3D). The proposed technique provides the true 3D imaging for breast volume which overcomes the disadvantage of the tissue superposition in mammography. A 3D breast digital phantom was rendered from two-dimensional (2D) breast CT slices. Three different tissue types, including the skin, adipose tissue, and glandular tissue, were labeled in the 3D breast phantom. μCas were manually embedded in different locations inside the breast phantom. For each tissue type, the initial pressure rise caused by the x-ray-induced acoustic (XA) effect was calculated according to its themoacoustic properties. The XA wave's propagation from the point of generation and its detection by ultrasound detector array were simulated by Matlab K-Wave toolbox. The 3D breast XACT volume with μCa was acquired without tissue superposition, and the system was characterized by μCas placed at different locations. The simulation results illustrated that the proposed breast XACT system has the ability to show the μCa cluster in 3D without any tissue superposition. Meanwhile, μCa as small as 100 μm in size can be detected with high imaging contrast, high signal to noise ratio (SNR), and high contrast to noise ratio (CNR). The dose required by the proposed XACT configuration was calculated to be 0.4 mGy for a 4.5 cm-thick compressed breast. This is one-tenth of the dose level of a typical two-view mammography for a breast with the same compression thickness. The initial exploration for the feasibility of 3D breast XACT has been conducted in this study. The system feasibility and characterization were illustrated through a 3D breast phantom and simulation works. The 3D breast XACT with the proposed system configuration has great potential to be applied as a low-dose screening and diagnostic technique for early un-palpable lesion in the breast.

Comparing Treatment Strategies for Stage I Small-cell lung Cancer

IntroductionThe diagnosis of stage I small-cell lung cancer (SCLC) is increasing in incidence with the advent of low-dose screening computed tomography. Surgery is considered the standard of care but there are very few data to guide clinical decision-making. The purpose of this study was to compare outcomes for patients receiving definitive surgery, stereotactic body radiation therapy (SBRT), or external beam radiation therapy (EBRT) for stage I SCLC. Patients and MethodsPatients with a primary diagnosis of stage I SCLC were identified in the National Cancer Database. Patients were defined as having a first course of treatment of either surgery, EBRT, or SBRT. Overall survival (OS) was determined using the Kaplan–Meier method and Cox proportional hazards regression methods were used to estimate risk of overall mortality. ResultsA total of 2678 patients were included in the analysis. The 2- and 3-year OS for the whole cohort was 62% and 50%. Comparing treatment strategies in a multivariate model, surgical resection showed improved OS over EBRT (P < .001) and SBRT (P < .001), however, the OS benefit over SBRT did not persist for patients who underwent limited resection. When excluding patients who underwent surgery, SBRT showed improved OS compared with EBRT (P = .04). Additional use of chemotherapy with any treatment modality resulted in improved OS (P < .001). ConclusionIn this hospital-based registry study, definitive surgical resection and use of chemotherapy resulted in improved survival for patients with early stage SCLC. For patients who are not candidates for surgery, SBRT may offer a survival benefit compared with standard EBRT.

Prophylactic Cranial Irradiation Following Surgical Resection of Early-Stage Small-Cell Lung Cancer: A Review of the Literature.

With increasing use of low-dose screening CT scans, the diagnosis of early-stage small-cell lung cancer (SCLC) without evidence of mediastinal nodal or distant metastasis is likely to become more common, but the role of adjuvant therapies such as prophylactic cranial irradiation (PCI) are not well understood in this population. We performed a review of the literature pertaining to the impact of PCI in patients who underwent surgical resection of early-stage SCLC. Four studies were identified that were pertinent including three single-institution retrospective analyses and a National Cancer Database analysis. Based upon these studies, we estimate the rate of brain metastases to be 10–15% for Stage I and 15–25% for Stage II disease without PCI. However, the impact of PCI on the development of brain metastases and its ultimate impact on overall survival were not consistent across these studies. In summary, there is sparse evidence to guide recommendations for PCI following resection of early-stage SCLC. While it may be reasonable to offer PCI to maximize likelihood of cure, alternative strategies such as observation with close imaging follow-up can also be considered for the appropriate patient given the known neurocognitive side effects of PCI.

FDG PET/CT and N0 mediastinal nodal status in early stage non-small cell lung cancer: A single institution retrospective experience.

11570 Background: The increasing use of CT and now low-dose screening CT scans for at-risk patients have led to increasing detection of lung cancer at early stages. FDG PET/CT is used as an adjunct to conventional imaging to assess loco-regional lymph node spread. However, there is a potential for false-negative results, especially in smaller lesions or early nodal involvement. The main objective of this study was to study the value of PET/CT scan to evaluate for true negative mediastinal nodes in patients with early stage NSCLC. Accurate determination of N0 status can have a significant impact on the cost-effectiveness and timely management of early stage NSCLC. Methods: Of a total of 404 patients with NSCLC managed at our facility between 2008 to 2015, 29 adult patients whose PET scan showed no or equivocal mediastinal nodal involvement and subsequently underwent surgical exploration of mediastinal lymph nodes were included in the study. Data variables that were collected included the cancer site, date of PET, node status on PET, type and date of surgery, cancer histology, and the tissue diagnosis of the sampled nodes. SAS software was used for the analysis of the data. Results: Of the 29 patients with N0 or equivocal nodes on FDG PET/CT, 7 (24.13%) had evidence of malignancy on biopsy of the surgically resected lymph nodes. No statistically significant differences were noted between the site of the neoplastic lesion, cancer histology, duration between the date of PET and the date of surgery in the true negative (TN) and false negative (FN) groups. The recurrence rate was higher in the FN group 60 % (3/5) compared to 21 % (4/19) in the TN group who had follow up for atleast 2 years. Conclusions: Our findings suggest incidence of false negative results of FDG PET/CT for evaluation of N0 nodes in early stage NSCLC is 24.13%. This is comparatively higher than false negatives rates with mediastinoscopy (5-10%) reported in other studies. Although our sample size is small, if confirmed, such a relatively high incidence of false negative results on FDG PET/CT for N0 disease supports the current recommendations for exploratory mediastinoscopy and/or surgery for definitive staging in early stage NSCLC.