Improving lung cancer diagnosis at a large urban minority-based medical center: Where can we do better?

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e18017-e18017
Author(s):  
Christopher Su ◽  
Vincent Chau ◽  
Amit Bhargava ◽  
Chirag D Shah ◽  
Nitin Ohri ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Cancer Diagnosis ◽  
Capita Income ◽  
Medical Center ◽  
Lung Cancer Screening ◽  
Advanced Stage ◽  
Lung Cancer Diagnosis ◽  
Significant Difference ◽  
Per Capita

e18017 Background: Lung cancer diagnosis is a complex process with barriers to care which are more apparent in underserved communities. We examined factors affecting lung cancer diagnosis in an underserved urban community, including demographics, lung cancer screening, and survival outcomes. Methods: All new lung cancer diagnoses with confirmed pathology at an urban academic medical center in 2015 were identified. Retrospective chart review was conducted and time from initial abnormal imaging to tissue sampling was calculated. Analyses were performed with χ2, ANOVA, linear regression, and log-rank tests. Results: In 2015, 229 patients were diagnosed with lung cancer. 36 patients (16%) expired or were referred to hospice due to clinical deterioration without treatment. 162 patients (71%) were ultimately started on therapy. Patients were predominantly Black (38%), Hispanic (30%), underserved (mean per capita income $21729), and enrolled in Medicare or Medicaid (83%). Only 62% of the patients had a PCP at time of diagnosis. Most presented at an advanced stage (63% III or IV) and 88% were former/current smokers. 78 patients (48%) were eligible for low-dose CT screening but only 9 (12%) completed screening. Screening completion was correlated with established PCP (p = 0.012). Time from abnormal imaging to biopsy was 31±40 days without significant difference across age, gender, race, ethnicity, income, insurance, and primary language. Cancers diagnosed in the inpatient vs. outpatient setting were found at a more advanced stage (p = 0.002) and had lower survival (p < 0.001). Hispanics had better survival (p = 0.008) despite lower per capita income and higher incidence of smoking. Conclusions: There was no significant difference in imaging to biopsy time across major demographic factors and they are unlikely to be a source of poor outcomes. However, the advanced stage and poor prognosis of cancers detected in the inpatient setting, proportion of patients who expired or were referred to hospice immediately after presentation, and disparity between screening eligible and completed patients underscores the critical importance of increasing lung cancer screening and establishment of primary care.

scholarly journals Optimizing the timing of diagnostic testing after positive findings in lung cancer screening: a proof of concept radiomics study

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Zixing Wang ◽  
Ning Li ◽  
Fuling Zheng ◽  
Xin Sui ◽  
Wei Han ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Cancer Diagnosis ◽  
Diagnostic Testing ◽  
Delayed Diagnosis ◽  
Pulmonary Nodules ◽  
Lung Cancer Screening ◽  
Lung Cancer Diagnosis ◽  
Risk Patients

Abstract Background The timeliness of diagnostic testing after positive screening remains suboptimal because of limited evidence and methodology, leading to delayed diagnosis of lung cancer and over-examination. We propose a radiomics approach to assist with planning of the diagnostic testing interval in lung cancer screening. Methods From an institute-based lung cancer screening cohort, we retrospectively selected 92 patients with pulmonary nodules with diameters ≥ 3 mm at baseline (61 confirmed as lung cancer by histopathology; 31 confirmed cancer-free). Four groups of region-of-interest-based radiomic features (n = 310) were extracted for quantitative characterization of the nodules, and eight features were proven to be predictive of cancer diagnosis, noise-robust, phenotype-related, and non-redundant. A radiomics biomarker was then built with the random survival forest method. The patients with nodules were divided into low-, middle- and high-risk subgroups by two biomarker cutoffs that optimized time-dependent sensitivity and specificity for decisions about diagnostic workup within 3 months and about repeat screening after 12 months, respectively. A radiomics-based follow-up schedule was then proposed. Its performance was visually assessed with a time-to-diagnosis plot and benchmarked against lung RADS and four other guideline protocols. Results The radiomics biomarker had a high time-dependent area under the curve value (95% CI) for predicting lung cancer diagnosis within 12 months; training: 0.928 (0.844, 0.972), test: 0.888 (0.766, 0.975); the performance was robust in extensive cross-validations. The time-to-diagnosis distributions differed significantly between the three patient subgroups, p < 0.001: 96.2% of high-risk patients (n = 26) were diagnosed within 10 months after baseline screen, whereas 95.8% of low-risk patients (n = 24) remained cancer-free by the end of the study. Compared with the five existing protocols, the proposed follow-up schedule performed best at securing timely lung cancer diagnosis (delayed diagnosis rate: < 5%) and at sparing patients with cancer-free nodules from unnecessary repeat screenings and examinations (false recommendation rate: 0%). Conclusions Timely management of screening-detected pulmonary nodules can be substantially improved with a radiomics approach. This proof-of-concept study’s results should be further validated in large programs.

Radiomic Phenotyping of the Lung Parenchyma: Feasibility Study in a Screening Cohort

2021 ◽  
Author(s):  
Babak Haghighi ◽  
Hannah Horng ◽  
Peter B Noël ◽  
Eric Cohen ◽  
Lauren Pantalone ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Low Dose ◽  
Lung Cancer Screening ◽  
Lung Parenchyma ◽  
Ct Scans ◽  
Lung Field ◽  
Reconstruction Algorithms ◽  
Low Dose Ct ◽  
Significant Difference

Abstract Rationale: High-throughput extraction of radiomic features from low-dose CT scans can characterize the heterogeneity of the lung parenchyma and potentially aid in identifying subpopulations that may have higher risk of lung diseases, such as COPD, and lung cancer due to inflammation or obstruction of the airways. We aim to determine the feasibility a lung radiomics phenotyping approach in a lung cancer screening cohort, while quantifying the effect of different CT reconstruction algorithms on phenotype robustness. Methods: We identified low-dose CT scans (n = 308) acquired with Siemens Healthineers scanners from patients who completed low-dose CT within our lung cancer screening program between 2015-2018 and had two different sets of image reconstructions kernel available (i.e., medium (I30f), sharp (I50f)) for the same acquisition. Following segmentation of the lung field, a total of 26 radiomic features were extracted from the entire 3D lung-field using a previously validated fully-automated lattice-based software pipeline, adapted for low-dose CT scans. The features extracted included gray-level histogram, co-occurrence, and run-length descriptors. Each feature was averaged for each scan within a range of lattice window sizes (W) ranging from 4-20mm. The extracted imaging features from both datasets were harmonized to correct for differences in image acquisition parameters. Subsequently, unsupervised hierarchal clustering was applied on the extracted features to identify distinct phenotypic patterns of the lung parenchyma, where consensus clustering was used to identify the optimal number of clusters (K = 2). Differences between? phenotypes for demographic and clinical covariates including sex, age, BMI, pack-years of smoking, Lung-RADS and cancer diagnosis were assessed for each phenotype cluster, and then compared across clusters for the two different CT reconstruction algorithms using the cluster entanglement metric, where a lower entanglement coefficient corresponds to good cluster alignment. Furthermore, an independent set of low-dose CT scans (n = 88) from patients with available pulmonary function data on lung obstruction were analyzed using the identified optimal clusters to assess associations to lung obstruction and validate the lung phenotyping paradigm. Results: Heatmaps generated by radiomic features identified two distinct lung parenchymal phenotype patterns across different feature extraction window sizes, for both reconstruction algorithms (P < 0.05 with K = 2). Associations of radiomic-based clusters with clinical covariates showed significant difference for BMI and pack-years of smoking (P < 0.05) for both reconstruction kernels. Radiomic phenotype patterns where similar across the two reconstructed kernels, specifically when smaller window sizes (W=4 and 8mm) were used for radiomic feature extraction, as deemed by their entanglement coefficient. Validation of clustering approaches using cluster mapping for the independent sample with lung obstruction also showed two statistically significant phenotypes (P < 0.05) with significant difference for BMI and smoking pack-years.ConclusionsRadiomic analysis can be used to characterize lung parenchymal phenotypes from low-dose CT scans, which appear reproducible for different reconstruction kernels. Further work should seek to evaluate the effect of additional CT acquisition parameters and validate these phenotypes in characterizing lung cancer screening populations, to potentially better stratify disease patterns and cancer risk.

Development and Patient Usability Analysis of a Veteran Centric Web-based Decision Aid for Lung Cancer Screening (Preprint)

2021 ◽  
Author(s):  
Marilyn Schapira ◽  
Sumedha Chhatre ◽  
Jason Prigge ◽  
Jessica Meline ◽  
Dana Kaminstein ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Clinical Trial ◽  
Cancer Screening ◽  
Medical Center ◽  
Clinical Care ◽  
Lung Cancer Screening ◽  
Potential Range ◽  
Medical Terminology ◽  
Web Based ◽  
System Usability Scale

BACKGROUND Web based tools developed to facilitate a Shared Decision Making (SDM) process may facilitate implementation of lung cancer screening (LCS), an evidence based intervention to improve cancer outcomes. Veterans have specific risk factors and shared experiences that impact the benefit and potential harms of LCS, so may value a Veteran centric LCS SDM too OBJECTIVE To develop and conduct usability testing of a LCS Decision Tool (LCSDecTool) designed for Veterans receiving care at a Veteran Affairs Medical Center (VAMC). METHODS A user-centered design approach was undertaken to develop the LCSDecTool. Usability of a prototype was assessed among 18 Veterans from two VA sites. Usability of a high fidelity version was assessed among 43 Veterans as part of a clinical trial. Outcomes included the System Usability Scale (SUS), the End User Computer Satisfaction (EUCS), and the Patient Engagement (PE) scale. Qualitative data from observations and short interviews with users were analyzed and themes pertaining to usability identified. RESULTS The mean (SD) in the pilot clinical trial (n=43) for the SUS (potential range 0 [low] to 100 [high] was 65.76 [15.23]); EUCS (potential range 1 [low] to 5 [high] was 3.91 [0.95]); and PE (potential range 1[low) to 5 [high] was 4.62 [0.67]). Time to completion of the LCSDecTool in minutes (median, intra-quartile range) was (13, 10-16). Emerging themes included: 1) a baseline gap in awareness of LCS with knowledge gained from using the LCSDecTool, 2) an interest in details about the LCS process, 3) the LCSDecTool was easy to use overall but specific navigation challenges identified, and 4) difficulty in understanding medical terminology. CONCLUSIONS The LCSDecTool demonstrates a good level of usability among Veterans when testing in the context of clinical care. Study findings will inform further modifications of the tool, including shortening the length and simplifying language. CLINICALTRIAL ClinicalTrials.gov Identifier: NCT02899754

scholarly journals Symptoms at lung cancer diagnosis are associated with major differences in prognosis

Thorax ◽  
2018 ◽  
Vol 73 (12) ◽  
pp. 1177-1181 ◽  
Author(s):  
Victoria L Athey ◽  
Stephen J Walters ◽  
Trevor K Rogers
Keyword(s):  
Public Health ◽  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Health Campaigns ◽  
Prognostic Variables ◽  
Lung Cancer Diagnosis ◽  
Significant Difference ◽  
Systemic Symptoms ◽  
Public Health Campaigns ◽  
Symptom Group

We report a cohort study of survival of patients with lung cancer presenting to a single multidisciplinary team between 1997 and 2011, according to symptoms at presentation. The overall median survival of the 3800 lung cases was 183 days (95% CI 171 to 195). There was a statistically significant difference in survival between the 12 symptom groups identified both without and with adjustment for the prognostic variables of age, gender and histology (P<0.001). Compared with the cough-alone symptom group, the risks of dying or HRs were significantly higher for the groups presenting with breathlessness (HR 1.86, 95% CI 1.54 to 2.24, n=359), systemic symptoms (HR 1.91, 95% CI 1.48 to 2.45, n=95), weight loss (HR 2.46, 95% CI 1.90 to 3.18, n=106), chest pain (HR 1.96, 95% CI 1.56 to 2.45, n=159), cough with breathlessness (HR 1.59 95% CI 1.28 to 1.98, n=177), neurological symptoms (HR 3.07, 95% CI 2.45 to 3.84, n=155) and other symptom combinations (HR 2.05, 95% CI 1.75 to 2.40, n=1963). Cough may deserve particular prominence in public health campaigns.

scholarly journals High-resolution metabolomic biomarkers for lung cancer diagnosis and prognosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shi-ang Qi ◽  
Qian Wu ◽  
Zhenpu Chen ◽  
Wei Zhang ◽  
Yongchun Zhou ◽  
...  
Keyword(s):  
Lung Cancer ◽  
High Resolution ◽  
Cancer Diagnosis ◽  
Human Cancer ◽  
Point Of Care ◽  
Area Under The Curve ◽  
Lung Cancer Screening ◽  
Diagnosis And Prognosis ◽  
Lung Cancer Diagnosis ◽  
Cancer Diagnosis And Prognosis

AbstractLung cancer is the leading cause of human cancer mortality due to the lack of early diagnosis technology. The low-dose computed tomography scan (LDCT) is one of the main techniques to screen cancers. However, LDCT still has a risk of radiation exposure and it is not suitable for the general public. In this study, plasma metabolic profiles of lung cancer were performed using a comprehensive metabolomic method with different liquid chromatography methods coupled with a Q-Exactive high-resolution mass spectrometer. Metabolites with different polarities (amino acids, fatty acids, and acylcarnitines) can be detected and identified as differential metabolites of lung cancer in small volumes of plasma. Logistic regression models were further developed to identify cancer stages and types using those significant biomarkers. Using the Variable Importance in Projection (VIP) and the area under the curve (AUC) scores, we have successfully identified the top 5, 10, and 20 metabolites that can be used to differentiate lung cancer stages and types. The discrimination accuracy and AUC score can be as high as 0.829 and 0.869 using the five most significant metabolites. This study demonstrated that using 5 + metabolites (Palmitic acid, Heptadecanoic acid, 4-Oxoproline, Tridecanoic acid, Ornithine, and etc.) has the potential for early lung cancer screening. This finding is useful for transferring the diagnostic technology onto a point-of-care device for lung cancer diagnosis and prognosis.

When Is a Harm a Harm? Discordance between Patient and Medical Experts’ Evaluation of Lung Cancer Screening Attributes

2021 ◽  
Vol 41 (3) ◽  
pp. 317-328
Author(s):  
Marilyn M. Schapira ◽  
Keri L. Rodriguez ◽  
Sumedha Chhatre ◽  
Liana Fraenkel ◽  
Lori A. Bastian ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Radiation Exposure ◽  
Incidental Findings ◽  
Medical Center ◽  
Indirect Costs ◽  
Lung Cancer Screening ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
Semistructured Interviews

Background A shared decision-making (SDM) process for lung cancer screening (LCS) includes a discussion between clinicians and patients about benefits and potential harms. Expert-driven taxonomies consider mortality reduction a benefit and consider false-positives, incidental findings, overdiagnosis, overtreatment, radiation exposure, and direct and indirect costs of LCS as potential harms. Objective To explore whether patients conceptualize the attributes of LCS differently from expert-driven taxonomies. Design Cross-sectional study with semistructured interviews and a card-sort activity. Participants Twenty-three Veterans receiving primary care at a Veterans Affairs Medical Center, 55 to 73 y of age with 30 or more pack-years of smoking. Sixty-one percent were non-Hispanic African American or Black, 35% were non-Hispanic White, 4% were Hispanic, and 9% were female. Approach Semistructured interviews with thematic coding. Main Measures The proportion of participants categorizing each attribute as a benefit or harm and emergent themes that informed this categorization. Key Results In addition to categorizing reduced lung cancer deaths as a benefit (22/23), most also categorized the following as benefits: routine annual screening (8/9), significant incidental findings (20/23), follow-up in a nodule clinic (20/23), and invasive procedures (16/23). Four attributes were classified by most participants as a harm: false-positive (13/22), overdiagnosis (13/23), overtreatment (6/9), and radiation exposure (20/22). Themes regarding the evaluation of LCS outcomes were 1) the value of knowledge about body and health, 2) anticipated positive and negative emotions, 3) lack of clarity in terminology, 4) underlying beliefs about cancer, and 5) risk assessment and tolerance for uncertainty. Conclusions Anticipating discordance between patient- and expert-driven taxonomies of the benefits and harms of LCS can inform the development and interpretation of value elicitation and SDM discussions.

Sign in / Sign up

Export Citation Format

Share Document