Patient Identification of Lung Cancer Screening Follow-Up Recommendations and the Association with Adherence

Triplette, Matthew; Wenger, David; Kross, Erin K.; Kava, Christine M.; Phipps, Amanda I.; Hawes, Stephen E.; Cole, Allison; Snidarich, Madison; Crothers, Kristina

doi:10.1513/annalsats.202107-887oc

Cited by 14 publications

(15 citation statements)

References 29 publications

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“…40,41 Patient education is critical to a successful lung cancer screening journey, including what the eligibility criteria are, the benefits and potential harms of screening, what to expect from their screening test result, and the likely outcomes and follow-up testing that may follow from an interim follow-up nodule CT or more intensive management, incidental findings, and the importance of returning for annual screening next year if their test was negative. 42 Important to communication is the language, we use and the readability of written and online materials you may be using. 43,44 A successful lung cancer screening program has multiple components.…”

Section: Implementation Frameworkmentioning

confidence: 99%

Lung Cancer Screening

Lee¹,

Kazerooni²

2022

Semin Respir Crit Care Med

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Lung cancer is a leading cause of cancer death in the United States and globally with the majority of lung cancer cases attributable to cigarette smoking. Given the high societal and personal cost of a diagnosis of lung cancer including that most cases of lung cancer when diagnosed are found at a late stage, work over the past 40 years has aimed to detect lung cancer earlier when curative treatment is possible. Screening trials using chest radiography and sputum failed to show a reduction in lung cancer mortality however multiple studies using low dose CT have shown the ability to detect lung cancer early and a survival benefit to those screened. This review will discuss the history of lung cancer screening, current recommendations and screening guidelines, and implementation and components of a lung cancer screening program.

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Section: Implementation Frameworkmentioning

confidence: 99%

Lung Cancer Screening

Lee¹,

Kazerooni²

2022

Semin Respir Crit Care Med

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“…While prior studies examined how frequently nodule guidelines are followed [11,13,26], there is limited data on whether invasive procedures for benign lesions or diagnostic delays are minimized by guideline adherence (or, conversely, whether these adverse outcomes are more common in the setting of non-adherent evaluation). This study provides indirect support for the existing ACCP guidelines in that patients whose evaluations deviated from the pathway (based upon ACCP guidelines) were more likely to experience a composite outcome that included an invasive procedure for benign lesions or delays in cancer diagnosis.…”

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Prior studies have demonstrated that non-adherence to published guidelines in the management of pulmonary nodules is common (up to 40% depending on the setting) [11][12][13]. Patient preferences, individual risk factors, and system factors all may impact the management of incidentally discovered pulmonary nodules [13][14][15][16][17]. There is, however, a dearth of evidence that examines whether or how management of nodules that mirrors the approach outlined in evidence-based guidelines affects clinically relevant patient-centered outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Prevalence and consequences of non-adherence to an evidence-based approach for incidental pulmonary nodules

Wayne

Prescott

2022

PLoS ONE

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Importance Distinguishing benign from malignant pulmonary nodules is challenging. Evidence-based guidelines exist, but their impact on patient-centered outcomes is unknown. Objective To understand if the evaluation of incidental pulmonary nodules that follows an evidence-based management strategy is associated with fewer invasive procedures for benign lesions and/or fewer delays in cancer diagnosis. Design Retrospective cohort study. Setting Large academic medical center. Participants Adults (≥18 years age) with an incidental pulmonary nodule discovered between January 2012 and December 2014. Patients with calcified nodules, prior nodules, prior diagnosis of cancer, high suspicion for pulmonary metastasis, or limited life expectancy were excluded. Exposure Nodule management strategy (pre-specified based on evidence-based practices). Outcome Composite of any invasive procedure for a benign nodule or delay in diagnosis in patients with cancer (>3 month delay once probability of cancer was >15%). Results Of 314 patients that met inclusion criteria, median age was 61, 46.5% were men, and 66.5% had current or former tobacco use. The mean nodule size was 10.3 mm, mean probability of cancer was 11.8%, and 14.3% of nodules were malignant. Evaluation followed an evidence-based strategy in 245 patients (78.0%), and deviated in 69 patients (22%). The composite outcome occurred in 26 (8.3%) patients. Among patients whose nodule evaluation was concordant with an evidence-based evaluation, 6.1% (15/245) experienced the composite outcome versus 15.9% (11/69) of patients with an evaluation that deviated from evidence-based recommendations (P<0.01). Conclusions and relevance At a large academic medical center, more than 1 in 5 patients with an incidental pulmonary nodule underwent evaluation that deviated from evidence-based practice recommendations. Nodule evaluation that deviated from an evidence-based strategy was associated with biopsy of benign lesions and delays in cancer diagnosis, suggesting a need to improve guideline uptake.

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“…[3][4][5] Evidence also suggests those being screened are not being optimally routed to follow-up or kept engaged in long-term screening. [6][7][8] In parallel, lung cancer diagnoses among never-and lighter-smokers are rapidly rising, 9,10 suggesting that if we continue to focus research about LCS only on heavier smokers, a gap will persist between the screen population and the disease population.…”

Section: Introductionmentioning

confidence: 99%

Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography

Mikhael

Wohlwend

Yala

et al. 2023

JCO

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PURPOSE Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data. METHODS We developed a model called Sybil using LDCTs from the National Lung Screening Trial (NLST). Sybil requires only one LDCT and does not require clinical data or radiologist annotations; it can run in real time in the background on a radiology reading station. Sybil was validated on three independent data sets: a heldout set of 6,282 LDCTs from NLST participants, 8,821 LDCTs from Massachusetts General Hospital (MGH), and 12,280 LDCTs from Chang Gung Memorial Hospital (CGMH, which included people with a range of smoking history including nonsmokers). RESULTS Sybil achieved area under the receiver-operator curves for lung cancer prediction at 1 year of 0.92 (95% CI, 0.88 to 0.95) on NLST, 0.86 (95% CI, 0.82 to 0.90) on MGH, and 0.94 (95% CI, 0.91 to 1.00) on CGMH external validation sets. Concordance indices over 6 years were 0.75 (95% CI, 0.72 to 0.78), 0.81 (95% CI, 0.77 to 0.85), and 0.80 (95% CI, 0.75 to 0.86) for NLST, MGH, and CGMH, respectively. CONCLUSION Sybil can accurately predict an individual's future lung cancer risk from a single LDCT scan to further enable personalized screening. Future study is required to understand Sybil's clinical applications. Our model and annotations are publicly available.

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Patient Identification of Lung Cancer Screening Follow-Up Recommendations and the Association with Adherence

Cited by 14 publications

References 29 publications

Lung Cancer Screening

Lung Cancer Screening

Prevalence and consequences of non-adherence to an evidence-based approach for incidental pulmonary nodules

Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography

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