The Lung Cancer Prediction Model "Stress Test": Assessment of Models' Performance in a High-Risk Prospective Pulmonary Nodule Cohort.

BACKGROUND: Pulmonary nodules represent a growing health care burden because of delayed diagnosis of malignant lesions and overtesting for benign processes. Clinical prediction models were developed to inform physician assessment of pretest probability of nodule malignancy but have not been validated in a high-risk cohort of nodules for which biopsy was ultimately performed. RESEARCH QUESTION: Do guideline-recommended prediction models sufficiently discriminate between benign and malignant nodules when applied to cases referred for biopsy by navigational bronchoscopy? STUDY DESIGN AND METHODS: We assembled a prospective cohort of 322 indeterminate pulmonary nodules in 282 patients referred to a tertiary medical center for diagnostic navigational bronchoscopy between 2017 and 2019. We calculated the probability of malignancy for each nodule using the Brock model, Mayo Clinic model, and Veterans Affairs (VA) model. On a subset of 168 patients who also had PET-CT scans before biopsy, we also calculated the probability of malignancy using the Herder model. The performance of the models was evaluated by calculating the area under the receiver operating characteristic curves (AUCs) for each model. RESULTS: The study cohort contained 185 malignant and 137 benign nodules (57% prevalence of malignancy). The malignant and benign cohorts were similar in terms of size, with a median longest diameter for benign and malignant nodules of 15 and 16 mm, respectively. The Brock model, Mayo Clinic model, and VA model showed similar performance in the entire cohort (Brock AUC, 0.70; 95% CI, 0.64-0.76; Mayo Clinic AUC, 0.70; 95% CI, 0.64-0.76; VA AUC, 0.67; 95% CI, 0.62-0.74). For 168 nodules with available PET-CT scans, the Herder model had an AUC of 0.77 (95% CI, 0.68-0.85). INTERPRETATION: Currently available clinical models provide insufficient discrimination between benign and malignant nodules in the common clinical scenario in which a patient is being referred for biopsy, especially when PET-CT scan information is not available.

Provider-ordered viral testing and antibiotic administration practices among children with acute respiratory infections across healthcare settings in Nashville, Tennessee.

Objective: Evaluate the association between provider-ordered viral testing and antibiotic treatment practices among children discharged from an ED or hospitalized with an acute respiratory infection (ARI). Design: Active, prospective ARI surveillance study from November 2017 to February 2020. Setting: Pediatric hospital and emergency department in Nashville, Tennessee. Participants: Children 30 days to 17 years old seeking medical care for fever and/or respiratory symptoms. Methods: Antibiotics prescribed during the child's ED visit or administered during hospitalization were categorized into (1) None administered; (2) Narrow-spectrum; and (3) Broad-spectrum. Setting-specific models were built using unconditional polytomous logistic regression with robust sandwich estimators to estimate the adjusted odds ratios and 95% confidence intervals between provider-ordered viral testing (ie, tested versus not tested) and viral test result (ie, positive test versus not tested and negative test versus not tested) and three-level antibiotic administration. Results: 4,107 children were enrolled and tested, of which 2,616 (64%) were seen in the ED and 1,491 (36%) were hospitalized. In the ED, children who received a provider-ordered viral test had 25% decreased odds (aOR: 0.75; 95% CI: 0.54, 0.98) of receiving a narrow-spectrum antibiotic during their visit than those without testing. In the inpatient setting, children with a negative provider-ordered viral test had 57% increased odds (aOR: 1.57; 95% CI: 1.01, 2.44) of being administered a broad-spectrum antibiotic compared to children without testing. Conclusions: In our study, the impact of provider-ordered viral testing on antibiotic practices differed by setting. Additional studies evaluating the influence of viral testing on antibiotic stewardship and antibiotic prescribing practices are needed.

Principal Component Patterns of Pediatric Respiratory Viral Testing Across Health Care Settings.

BACKGROUND AND OBJECTIVES: Factors prompting clinicians to request viral testing in children are unclear. We assessed patterns prompting clinicians to perform viral testing in children discharged from an emergency department (ED) or hospitalized with an acute respiratory infection (ARI). METHODS: Using active ARI surveillance data collected from November 2017 through February 2020, children aged between 30 days and 17 years with fever or respiratory symptoms who had a research respiratory specimen tested were included. Children's presentation patterns from their initial evaluation at each health care setting were analyzed using principal components (PCs) analysis. PC-specific models using logistic regression with robust sandwich estimators were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) between PCs and provider-ordered viral testing. PCs were assigned respiratory virus/viruses names a priori based on the patterns represented. RESULTS: In total, 4107 children were enrolled and tested, with 2616 (64%) discharged from the ED and 1491 (36%) hospitalized. In the ED, children with a coviral presentation pattern had a 1.44-fold (95% CI, 1.24-1.68) increased odds of receiving a provider-ordered viral test than children showing clinical symptoms less representative of coviral-like infection. Whereas children in the ED and hospitalized with rhinovirus-like symptoms had 71% (OR, 0.29; 95% CI, 0.24-0.34) and 39% (OR, 0.61; 95% CI, 0.49-0.76) decreased odds, respectively, of receiving a provider-ordered viral test during their medical encounter. CONCLUSIONS: Viral tests are frequently ordered by clinicians, but presentation patterns vary by setting and influence the initiation of testing. Additional assessments of factors affecting provider decisions to use viral testing in pediatric ARI management are needed to maximize patient benefits of testing.

Assessing the clinical utility of biomarkers using the intervention probability curve (IPC).

BACKGROUND: Assessing the clinical utility of biomarkers is a critical step before clinical implementation. The reclassification of patients across clinically relevant subgroups is considered one of the best methods to estimate clinical utility. However, there are important limitations with this methodology. We recently proposed the intervention probability curve (IPC) which models the likelihood that a provider will choose an intervention as a continuous function of the probability, or risk, of disease. OBJECTIVE: To assess the potential impact of a new biomarker for lung cancer using the IPC. METHODS: The IPC derived from the National Lung Screening Trial was used to assess the potential clinical utility of a biomarker for suspected lung cancer. The summary statistics of the change in likelihood of intervention over the population can be interpreted as the expected clinical impact of the added biomarker. RESULTS: The IPC analysis of the novel biomarker estimated that 8% of the benign nodules could avoid an invasive procedure while the cancer nodules would largely remain unchanged (0.1%). We showed the benefits of this approach compared to traditional reclassification methods based on thresholds. CONCLUSIONS: The IPC methodology can be a valuable tool for assessing biomarkers prior to clinical implementation.

Age-related Muscle Fat Infiltration in Lung Screening Participants: Impact of Smoking Cessation.

Rationale: Skeletal muscle fat infiltration progresses with aging and is worsened among individuals with a history of cigarette smoking. Many negative impacts of smoking on muscles are likely reversible with smoking cessation. Objectives: To determine if the progression of skeletal muscle fat infiltration with aging is altered by smoking cessation among lung cancer screening participants. Methods: This was a secondary analysis based on the National Lung Screening Trial. Skeletal muscle attenuation in Hounsfield unit (HU) was derived from the baseline and follow-up low-dose CT scans using a previously validated artificial intelligence algorithm. Lower attenuation indicates greater fatty infiltration. Linear mixed-effects models were constructed to evaluate the associations between smoking status and the muscle attenuation trajectory. Measurements and Main Results: Of 19,019 included participants (age: 61 years, 5 [SD]; 11,290 males), 8,971 (47.2%) were actively smoking cigarettes. Accounting for body mass index, pack-years, percent emphysema, and other confounding factors, actively smoking predicted a lower attenuation in both males (ß0 =-0.88 HU, P<.001) and females (ß0 =-0.69 HU, P<.001), and an accelerated muscle attenuation decline-rate in males (ß1=-0.08 HU/y, P<.05). Age-stratified analyses indicated that the accelerated muscle attenuation decline associated with smoking likely occurred at younger age, especially in females. Conclusions: Among lung cancer screening participants, active cigarette smoking was associated with greater skeletal muscle fat infiltration in both males and females, and accelerated muscle adipose accumulation rate in males. These findings support the important role of smoking cessation in preserving muscle health.

A plasma miRNA-based classifier for small cell lung cancer diagnosis.

Introduction: Small cell lung cancer (SCLC) is characterized by poor prognosis and challenging diagnosis. Screening in high-risk smokers results in a reduction in lung cancer mortality, however, screening efforts are primarily focused on non-small cell lung cancer (NSCLC). SCLC diagnosis and surveillance remain significant challenges. The aberrant expression of circulating microRNAs (miRNAs/miRs) is reported in many tumors and can provide insights into the pathogenesis of tumor development and progression. Here, we conducted a comprehensive assessment of circulating miRNAs in SCLC with a goal of developing a miRNA-based classifier to assist in SCLC diagnoses. Methods: We profiled deregulated circulating cell-free miRNAs in the plasma of SCLC patients. We tested selected miRNAs on a training cohort and created a classifier by integrating miRNA expression and patients' clinical data. Finally, we applied the classifier on a validation dataset. Results: We determined that miR-375-3p can discriminate between SCLC and NSCLC patients, and between SCLC and Squamous Cell Carcinoma patients. Moreover, we found that a model comprising miR-375-3p, miR-320b, and miR-144-3p can be integrated with race and age to distinguish metastatic SCLC from a control group. Discussion: This study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis.

The Thoracic Research Evaluation and Treatment 2.0 Model: A Lung Cancer Prediction Model for Indeterminate Nodules Referred for Specialist Evaluation.

BACKGROUND: Appropriate risk stratification of indeterminate pulmonary nodules (IPNs) is necessary to direct diagnostic evaluation. Currently available models were developed in populations with lower cancer prevalence than that seen in thoracic surgery and pulmonology clinics and usually do not allow for missing data. We updated and expanded the Thoracic Research Evaluation and Treatment (TREAT) model into a more generalized, robust approach for lung cancer prediction in patients referred for specialty evaluation. RESEARCH QUESTION: Can clinic-level differences in nodule evaluation be incorporated to improve lung cancer prediction accuracy in patients seeking immediate specialty evaluation compared with currently available models? STUDY DESIGN AND METHODS: Clinical and radiographic data on patients with IPNs from six sites (N = 1,401) were collected retrospectively and divided into groups by clinical setting: pulmonary nodule clinic (n = 374; cancer prevalence, 42%), outpatient thoracic surgery clinic (n = 553; cancer prevalence, 73%), or inpatient surgical resection (n = 474; cancer prevalence, 90%). A new prediction model was developed using a missing data-driven pattern submodel approach. Discrimination and calibration were estimated with cross-validation and were compared with the original TREAT, Mayo Clinic, Herder, and Brock models. Reclassification was assessed with bias-corrected clinical net reclassification index and reclassification plots. RESULTS: Two-thirds of patients had missing data; nodule growth and fluorodeoxyglucose-PET scan avidity were missing most frequently. The TREAT version 2.0 mean area under the receiver operating characteristic curve across missingness patterns was 0.85 compared with that of the original TREAT (0.80), Herder (0.73), Mayo Clinic (0.72), and Brock (0.68) models with improved calibration. The bias-corrected clinical net reclassification index was 0.23. INTERPRETATION: The TREAT 2.0 model is more accurate and better calibrated for predicting lung cancer in high-risk IPNs than the Mayo, Herder, or Brock models. Nodule calculators such as TREAT 2.0 that account for varied lung cancer prevalence and that consider missing data may provide more accurate risk stratification for patients seeking evaluation at specialty nodule evaluation clinics.

Short-term outcomes of robotic-assisted transthoracic diaphragmatic plication.

Background: Patients who are symptomatic from diaphragmatic dysfunction may benefit from diaphragmatic plication. We recently modified our plication approach from open thoracotomy to robotic transthoracic. We report our short-term outcomes. Methods: We conducted a single-institution retrospective review of all patients who underwent transthoracic plications from 2018, when we began using the robotic approach, to 2022. The primary outcome was short-term recurrence of diaphragm elevation with symptoms noted before or during the first planned postoperative visit. We also compared proportions of short-term recurrences in patients that underwent plication with extracorporeal knot-tying device alone versus those that used intracorporeal instrument tying (alone or supplemental). Secondary outcomes included subjective postoperative improvement of dyspnea at follow-up visit and by postoperative patient questionnaire, chest tube duration, length of stay (LOS), 30-day readmission, operative time, estimated blood loss (EBL), intraoperative complications, and perioperative complications. Results: Forty-one patients underwent robotic-assisted transthoracic plication. Four patients experienced recurrent diaphragm elevation with symptoms before or during their first routine postoperative visit, occurring on POD 6, 10, 37, and 38. All four recurrences occurred in patients whose plications were performed with the extracorporeal knot-tying device without supplemental intracorporeal instrument tying. Proportion of recurrences in the group that used extracorporeal knot-tying device alone was significantly greater than the recurrences in the group that used intracorporeal instrument tying (alone or supplemental) (P=0.016). The majority (36/41) reported clinical improvement postoperatively and 85% of questionnaire respondents also agreed they would recommend the surgery to others with similar condition. The median LOS and of chest tube duration were 3 days and 2 days, respectively. There were two patients with 30-day readmissions. Three patients developed postoperative pleural effusion necessitating thoracenteses and 8 patients (20%) had postoperative complications. No mortalities were observed. Conclusions: While our study shows the overall acceptable safety and favorable outcomes in patients undergoing robotic-assisted transthoracic diaphragmatic plications, the incidence of short-term recurrences and its association with the use of extracorporeally knot-tying device alone in diaphragm plication warrant further investigation.

Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules.

Lung cancer is the leading cause of cancer-related deaths. Early detection and diagnosis are critical, as survival decreases with advanced stages. Approximately 1.6 million nodules are incidentally detected every year on chest CT scan images in the United States. This number of nodules identified is likely much larger after accounting for screening-detected nodules. Most of these nodules, whether incidentally or screening detected, are benign. Despite this, many patients undergo unnecessary invasive procedures to rule out cancer because our current stratification approaches are suboptimal, particularly for intermediate probability nodules. Thus, noninvasive strategies are urgently needed. Biomarkers have been developed to assist through the continuum of lung cancer care and include blood protein-based biomarkers, liquid biopsies, quantitative imaging analysis (radiomics), exhaled volatile organic compounds, and bronchial or nasal epithelium genomic classifiers, among others. Although many biomarkers have been developed, few have been integrated into clinical practice as they lack clinical utility studies showing improved patient-centered outcomes. Rapid technologic advances and large network collaborative efforts will continue to drive the discovery and validation of many novel biomarkers. Ultimately, however, randomized clinical utility studies showing improved patient outcomes will be required to bring biomarkers into clinical practice.

Biomarkers in Lung Cancer Screening: a Narrative Review.

Although when used as a lung cancer screening tool low-dose computed tomography (LDCT) has demonstrated a significant reduction in lung cancer related mortality, it is not without pitfalls. The associated high false positive rate, inability to distinguish between benign and malignant nodules, cumulative radiation exposure, and resulting patient anxiety have all demonstrated the need for adjunctive testing in lung cancer screening. Current research focuses on developing liquid biomarkers to complement imaging as non-invasive lung cancer diagnostics. Biomarkers can be useful for both the early detection and diagnosis of disease, thereby decreasing the number of unnecessary radiologic tests performed. Biomarkers can stratify cancer risk to further enrich the screening population and augment existing risk prediction. Finally, biomarkers can be used to distinguish benign from malignant nodules in lung cancer screening. While many biomarkers require further validation studies, several, including autoantibodies and blood protein profiling, are available for clinical use. This paper describes the need for biomarkers as a lung cancer screening tool, both in terms of diagnosis and risk assessment. Additionally, this paper will discuss the goals of biomarker use, describe properties of a good biomarker, and review several of the most promising biomarkers currently being studied including autoantibodies, complement fragments, microRNA, blood proteins, circulating tumor DNA, and DNA methylation. Finally, we will describe future directions in the field of biomarker development.

Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary nodules.

A deep learning model (LCP CNN) for the stratification of indeterminate pulmonary nodules (IPNs) demonstrated better discrimination than commonly used clinical prediction models. However, the LCP CNN score is based on a single timepoint that ignores longitudinal information when prior imaging studies are available. Clinically, IPNs are often followed over time and temporal trends in nodule size or morphology inform management. In this study we investigated whether the change in LCP CNN scores over time was different between benign and malignant nodules. This study used a prospective-specimen collection, retrospective-blinded-evaluation (PRoBE) design. Subjects with incidentally or screening detected IPNs 6-30 mm in diameter with at least 3 consecutive CT scans prior to diagnosis (slice thickness ≤ 1.5 mm) with the same nodule present were included. Disease outcome was adjudicated by biopsy-proven malignancy, biopsy-proven benign disease and absence of growth on at least 2-year imaging follow-up. Lung nodules were analyzed using the Optellum LCP CNN model. Investigators performing image analysis were blinded to all clinical data. The LCP CNN score was determined for 48 benign and 32 malignant nodules. There was no significant difference in the initial LCP CNN score between benign and malignant nodules. Overall, the LCP CNN scores of benign nodules remained relatively stable over time while that of malignant nodules continued to increase over time. The difference in these two trends was statistically significant. We also developed a joint model that incorporates longitudinal LCP CNN scores to predict future probability of cancer. Malignant and benign nodules appear to have distinctive trends in LCP CNN score over time. This suggests that longitudinal modeling may improve radiomic prediction of lung cancer over current models. Additional studies are needed to validate these early findings.

Diagnostic models predicting paediatric viral acute respiratory infections: a systematic review.

OBJECTIVES: To systematically review and evaluate diagnostic models used to predict viral acute respiratory infections (ARIs) in children. DESIGN: Systematic review. DATA SOURCES: PubMed and Embase were searched from 1 January 1975 to 3 February 2022. ELIGIBILITY CRITERIA: We included diagnostic models predicting viral ARIs in children (<18 years) who sought medical attention from a healthcare setting and were written in English. Prediction model studies specific to SARS-CoV-2, COVID-19 or multisystem inflammatory syndrome in children were excluded. DATA EXTRACTION AND SYNTHESIS: Study screening, data extraction and quality assessment were performed by two independent reviewers. Study characteristics, including population, methods and results, were extracted and evaluated for bias and applicability using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and PROBAST (Prediction model Risk Of Bias Assessment Tool). RESULTS: Of 7049 unique studies screened, 196 underwent full text review and 18 were included. The most common outcome was viral-specific influenza (n=7; 58%). Internal validation was performed in 8 studies (44%), 10 studies (56%) reported discrimination measures, 4 studies (22%) reported calibration measures and none performed external validation. According to PROBAST, a high risk of bias was identified in the analytic aspects in all studies. However, the existing studies had minimal bias concerns related to the study populations, inclusion and modelling of predictors, and outcome ascertainment. CONCLUSIONS: Diagnostic prediction can aid clinicians in aetiological diagnoses of viral ARIs. External validation should be performed on rigorously internally validated models with populations intended for model application. PROSPERO REGISTRATION NUMBER: CRD42022308917.

Improving lung cancer diagnosis with cancer, fungal, and imaging biomarkers.

OBJECTIVE: Indeterminate pulmonary nodules (IPNs) represent a significant diagnostic burden in health care. We aimed to compare a combination clinical prediction model (Mayo Clinic model), fungal (histoplasmosis serology), imaging (computed tomography [CT] radiomics), and cancer (high-sensitivity cytokeratin fraction 21; hsCYFRA 21-1) biomarker approach to a validated prediction model in diagnosing lung cancer. METHODS: A prospective specimen collection, retrospective blinded evaluation study was performed in 3 independent cohorts with 6- to 30-mm IPNs (n = 281). Serum histoplasmosis immunoglobulin G and immunoglobulin M antibodies and hsCYFRA 21-1 levels were measured and a validated CT radiomic score was calculated. Multivariable logistic regression models were estimated with Mayo Clinic model variables, histoplasmosis antibody levels, CT radiomic score, and hsCYFRA 21-1. Diagnostic performance of the combination model was compared with that of the Mayo Clinic model. Bias-corrected clinical net reclassification index (cNRI) was used to estimate the clinical utility of a combination biomarker approach. RESULTS: A total of 281 patients were included (111 from a histoplasmosis-endemic region). The combination biomarker model including the Mayo Clinic model score, histoplasmosis antibody levels, radiomics, and hsCYFRA 21-1 level showed improved diagnostic accuracy for IPNs compared with the Mayo Clinic model alone with an area under the receiver operating characteristics curve of 0.80 (95% CI, 0.76-0.84) versus 0.72 (95% CI, 0.66-0.78). Use of this combination model correctly reclassified intermediate risk IPNs into low- or high-risk category (cNRI benign = 0.11 and cNRI malignant = 0.16). CONCLUSIONS: The addition of cancer, fungal, and imaging biomarkers improves the diagnostic accuracy for IPNs. Integrating a combination biomarker approach into the diagnostic algorithm of IPNs might decrease unnecessary invasive testing of benign nodules and reduce time to diagnosis for cancer.

Improving Lung Cancer Diagnosis with CT Radiomics and Serum Histoplasmosis Testing.

BACKGROUND: Indeterminate pulmonary nodules (IPN) are a diagnostic challenge in regions where pulmonary fungal disease and smoking prevalence are high. We aimed to determine the impact of a combined fungal and imaging biomarker approach compared with a validated prediction model (Mayo) to rule out benign disease and diagnose lung cancer. METHODS: Adults ages 40 to 90 years with 6-30 mm IPNs were included from four sites. Serum samples were tested for histoplasmosis IgG and IgM antibodies by enzyme immunoassay and a CT-based risk score was estimated from a validated radiomic model. Multivariable logistic regression models including Mayo score, radiomics score, and IgG and IgM histoplasmosis antibody levels were estimated. The areas under the ROC curves (AUC) of the models were compared among themselves and to Mayo. Bias-corrected clinical net reclassification index (cNRI) was estimated to assess clinical reclassification using a combined biomarker model. RESULTS: We included 327 patients; 157 from histoplasmosis-endemic regions. The combined biomarker model including radiomics, histoplasmosis serology, and Mayo score demonstrated improved diagnostic accuracy when endemic histoplasmosis was accounted for [AUC, 0.84; 95% confidence interval (CI), 0.79-0.88; P < 0.0001 compared with 0.73; 95% CI, 0.67-0.78 for Mayo]. The combined model demonstrated improved reclassification with cNRI of 0.18 among malignant nodules. CONCLUSIONS: Fungal and imaging biomarkers may improve diagnostic accuracy and meaningfully reclassify IPNs. The endemic prevalence of histoplasmosis and cancer impact model performance when using disease related biomarkers. IMPACT: Integrating a combined biomarker approach into the diagnostic algorithm of IPNs could decrease time to diagnosis.

Measurement of provider fidelity to immunization guidelines: a mixed-methods study on the feasibility of documenting patient refusals of the human papillomavirus vaccine.

BACKGROUND: Assessment and feedback is a common implementation strategy to improve healthcare provider fidelity to clinical guidelines. For immunization guidelines, fidelity is often measured with doses administered during eligible visits. Adding a patient refusal measure captures provider fidelity more completely (i.e., all instances of a provider recommending a vaccine, resulting in vaccination or refusal) and enables providers to track patient vaccine hesitancy patterns. However, many electronic health record (EHR) systems have no structured field to document multiple instances of refusals for specific vaccines, and existing billing codes for refusal are not vaccine specific. This study assessed the feasibility of a novel method for refusal documentation used in a study focused on human papillomavirus (HPV) vaccine. METHODS: An observational, descriptive-comparative, mixed-methods study design was used to conduct secondary data analysis from an implementation-effectiveness trial. The parent trial compared coach-based versus web-based practice facilitation, including assessment and feedback, to increase HPV vaccination in 21 community-based private pediatric practices. Providers were instructed to document initial HPV vaccine refusals in the EHR's immunization forms and subsequent refusals using dummy procedure codes, for use in assessment and feedback reports. This analysis examined adoption and maintenance of the refusal documentation method during eligible well visits, identified barriers and facilitators to documentation and described demographic patterns in patient refusals. RESULTS: Seven practices adopted the refusal documentation method. Among adopter practices, documented refusals started at 2.4% of eligible well visits at baseline, increased to 14.2% at the start of implementation, peaked at 24.0%, then declined to 18.8%. Barriers to refusal documentation included low prioritization, workflow integration and complication of the billing process. Facilitators included high motivation, documentation instructions and coach support. Among adopter practices, odds of refusing HPV vaccine were 25% higher for patients aged 15-17 years versus 11-12 years, and 18% lower for males versus females. CONCLUSIONS: We demonstrated the value of patient refusal documentation for measuring HPV vaccination guideline fidelity and ways that it can be improved in future research. Creation of vaccine-specific refusal billing codes or EHR adaptations to enable documenting multiple instances of specific vaccine refusals would facilitate consistent refusal documentation. Trial Registration NCT03399396 Registered in ClinicalTrials.gov on 1/16/2018.

Associations of Dietary Intakes of Carotenoids and Vitamin A with Lung Cancer Risk in a Low-Income Population in the Southeastern United States.

Observational studies found inverse associations of dietary carotenoids and vitamin A intakes with lung cancer risk. However, interventional trials among high-risk individuals showed that ß-carotene supplements increased lung cancer risk. Most of the previous studies were conducted among European descendants or Asians. We prospectively examined the associations of lung cancer risk with dietary intakes of carotenoids and vitamin A in the Southern Community Cohort Study, including 65,550 participants with 1204 incident lung cancer cases. Multivariate Cox regression was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Lung cancer cases had lower energy-adjusted dietary intakes of all carotenoids and vitamin A than non-cases. However, dietary intakes of carotenoids and vitamin A were not associated with overall lung cancer risk. A significant positive association of dietary vitamin A intake with lung cancer risk was observed among current smokers (HRQ4 vs. Q1 = 1.23; 95% CI: 1.02-1.49; Ptrend = 0.01). In addition, vitamin A intake was associated with an increased risk of adenocarcinoma among African Americans (HRQ4 vs. Q1 = 1.55; 95%CI: 1.08-2.21; Ptrend = 0.03). Dietary lycopene intake was associated with an increased risk of lung cancer among former smokers (HRQ4 vs. Q1 = 1.50; 95% CI: 1.04-2.17; Ptrend = 0.03). There are positive associations of dietary ß-cryptoxanthin intake with squamous carcinoma risk (HRQ4 vs. Q1 = 1.49; 95% CI: 1.03-2.15; Ptrend = 0.03). Further studies are warranted to confirm our findings.

Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model.

OBJECTIVE: Patients with indeterminate pulmonary nodules (IPN) with an intermediate to a high probability of lung cancer generally undergo invasive diagnostic procedures. Chest computed tomography image and clinical data have been in estimating the pretest probability of lung cancer. In this study, we apply a deep learning network to integrate multi-modal data from CT images and clinical data (including blood-based biomarkers) to improve lung cancer diagnosis. Our goal is to reduce uncertainty and to avoid morbidity, mortality, over- and undertreatment of patients with IPNs. METHOD: We use a retrospective study design with cross-validation and external-validation from four different sites. We introduce a deep learning framework with a two-path structure to learn from CT images and clinical data. The proposed model can learn and predict with single modality if the multi-modal data is not complete. We use 1284 patients in the learning cohort for model development. Three external sites (with 155, 136 and 96 patients, respectively) provided patient data for external validation. We compare our model to widely applied clinical prediction models (Mayo and Brock models) and image-only methods (e.g., Liao et al. model). RESULTS: Our co-learning model improves upon the performance of clinical-factor-only (Mayo and Brock models) and image-only (Liao et al.) models in both cross-validation of learning cohort (e.g. , AUC: 0.787 (ours) vs. 0.707-0.719 (baselines), results reported in validation fold and external-validation using three datasets from University of Pittsburgh Medical Center (e.g., 0.918 (ours) vs. 0.828-0.886 (baselines)), Detection of Early Cancer Among Military Personnel (e.g., 0.712 (ours) vs. 0.576-0.709 (baselines)), and University of Colorado Denver (e.g., 0.847 (ours) vs. 0.679-0.746 (baselines)). In addition, our model achieves better re-classification performance (cNRI 0.04 to 0.20) in all cross- and external-validation sets compared to the Mayo model. CONCLUSIONS: Lung cancer risk estimation in patients with IPNs can benefit from the co-learning of CT image and clinical data. Learning from more subjects, even though those only have a single modality, can improve the prediction accuracy. An integrated deep learning model can achieve reasonable discrimination and re-classification performance.

Deep Breaths: A Systematic Review of the Potential Effects of Employment in the Nuclear Industry on Mortality from Non-Malignant Respiratory Disease.

Ionizing radiation is an established carcinogen, but its effects on non-malignant respiratory disease (NMRD) are less clear. Cohorts exposed to multiple risk factors including radiation and toxic dusts conflate these relationships, and there is a need for clarity in previous findings. This systematic review was conducted to survey the body of existing evidence for radiation effects on NMRD in global nuclear worker cohorts. A PubMed search was conducted for studies with terms relating to radiation or uranium and noncancer respiratory outcomes. Papers were limited to the most recent report within a single cohort published between January 2000 and December 2020. Publication quality was assessed based upon UNSCEAR 2017 criteria. In total, 31 papers were reviewed. Studies included 29 retrospective cohorts, one prospective cohort, and one longitudinal cohort primarily comprising White men from the U.S., Canada and Western Europe. Ten studies contained subpopulations of uranium miners or millers. Papers reported standardized mortality ratio (SMR) analyses, regression analyses, or both. Neither SMR nor regression analyses consistently showed a relationship between radiation exposure and NMRD. A meta-analysis of excess relative risks (ERRs) for NMRD did not present evidence for a dose-response (overall ERR/Sv: 0.07; 95% CI: -0.07, 0.21), and results for more specific outcomes were inconsistent. Significantly elevated SMRs for NMRD overall were observed in two studies among the subpopulation of uranium miners and millers (combined n = 4229; SMR 1.42-1.43), indicating this association may be limited to mining and milling populations and may not extend to other nuclear workers. A quality review showed limited capacity of 17 out of 31 studies conducted to provide evidence for a causal relationship between radiation and NMRD; the higher-quality studies showed no consistent relationship. All elevated NMRD SMRs were among mining and milling cohorts, indicating different exposure profiles between mining and non-mining cohorts; future pooled cohorts should adjust for mining exposures or address mining cohorts separately.

Improving malignancy risk prediction of indeterminate pulmonary nodules with imaging features and biomarkers.

BACKGROUND: Non-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer. METHODS: Protein biomarkers were quantified in serum samples from patients with 6-30 mm IPNs (n = 338). A previously derived and validated radiomic score based upon nodule shape, size, and texture was calculated from features derived from CT scans. Lung cancer prediction models incorporating biomarkers, radiomics, and clinical factors were developed. Diagnostic performance was compared to the current standard of risk estimation (Mayo). IPN risk reclassification was determined using bias-corrected clinical net reclassification index. RESULTS: Age, radiomic score, CYFRA 21-1, and CEA were identified as the strongest predictors of cancer. These models provided greater diagnostic accuracy compared to Mayo with AUCs of 0.76 (95 % CI 0.70-0.81) using logistic regression and 0.73 (0.67-0.79) using random forest methods. Random forest and logistic regression models demonstrated improved risk reclassification with median cNRI of 0.21 (Q1 0.20, Q3 0.23) and 0.21 (0.19, 0.23) compared to Mayo for malignancy. CONCLUSIONS: A combined biomarker, radiomic, and clinical risk factor model provided greater diagnostic accuracy of IPNs than Mayo. This model demonstrated a strong ability to reclassify malignant IPNs. Integrating a combined approach into the current diagnostic algorithm for IPNs could improve nodule management.

The Intervention Probability Curve: Modeling the Practical Application of Threshold-Guided Decision-Making, Evaluated in Lung, Prostate, and Ovarian Cancers.

BACKGROUND: Diagnostic prediction models are useful guides when considering lesions suspicious for cancer, as they provide a quantitative estimate of the probability that a lesion is malignant. However, the decision to intervene ultimately rests on patient and physician preferences. The appropriate intervention in many clinical situations is typically defined by clinically relevant, actionable subgroups based upon the probability of malignancy. However, the "all-or-nothing" approach of threshold-based decisions is in practice incorrect. METHODS: Here, we present a novel approach to understanding clinical decision-making, the intervention probability curve (IPC). The IPC models the likelihood that an intervention will be chosen as a continuous function of the probability of disease. We propose the cumulative distribution function as a suitable model. The IPC is explored using the National Lung Screening Trial and the Prostate Lung Colorectal and Ovarian Screening Trial datasets. RESULTS: Fitting the IPC results in a continuous curve as a function of pretest probability of cancer with high correlation (R2 > 0.97 for each) with fitted parameters closely aligned with professional society guidelines. CONCLUSIONS: The IPC allows analysis of intervention decisions in a continuous, rather than threshold-based, approach to further understand the role of biomarkers and risk models in clinical practice. IMPACT: We propose that consideration of IPCs will yield significant insights into the practical relevance of threshold-based management strategies and could provide a novel method to estimate the actual clinical utility of novel biomarkers.