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.

Preoperative coronary artery calcifications in veterans predict higher all-cause mortality in early-stage lung cancer: a cohort study.

BACKGROUND: Lung cancer patients often have comorbidities that may impact survival. This observational cohort study examines whether coronary artery calcifications (CAC) impact all-cause mortality in patients with resected stage I non-small cell lung cancer (NSCLC). METHODS: Veterans with stage I NSCLC who underwent resection at a single institution between 2005 and 2018 were selected from a prospectively collected database. Radiologists blinded to patient outcomes graded CAC severity (mild, moderate, or severe) in preoperative CT scans using a visual estimation scoring system. Inter-rater reliability was calculated using the kappa statistic. All-cause mortality was the primary outcome. Kaplan-Meier survival analysis and Cox proportional hazards regression were used to compare time-to-death by varying CAC. RESULTS: The Veteran patients (n=195) were predominantly older (median age of 67) male (98%) smokers (96%). The majority (68%) were pathologic stage IA. Overall, 12% of patients had no CAC, 27% mild, 26% moderate, and 36% severe CAC. Median unadjusted survival was 8.8 years for patients with absent or mild CAC versus 6.3 years for moderate and 5.9 years for severe CAC (P=0.01). The adjusted hazard ratio for moderate CAC was 1.44 (95% CI, 0.85-2.46) and for severe CAC was 1.73 (95% CI, 1.03-2.88; P for trend <0.05). CONCLUSIONS: The presence of severe CAC on preoperative imaging significantly impacted the all-cause survival of patients undergoing resection for stage I NSCLC. This impact on mortality should be taken into consideration by multidisciplinary teams when making treatment plans for patients with early-stage disease.

Communication About the Probability of Cancer in Indeterminate Pulmonary Nodules.

Importance: Clinical guidelines recommend that clinicians estimate the probability of malignancy for patients with indeterminate pulmonary nodules (IPNs) larger than 8 mm. Adherence to these guidelines is unknown. Objectives: To determine whether clinicians document the probability of malignancy in high-risk IPNs and to compare these quantitative or qualitative predictions with the validated Mayo Clinic Model. Design, Setting, and Participants: Single-institution, retrospective cohort study of patients from a tertiary care Department of Veterans Affairs hospital from January 1, 2003, through December 31, 2015. Cohort 1 included 291 veterans undergoing surgical resection of known or suspected lung cancer from January 1, 2003, through December 31, 2015. Cohort 2 included a random sample of 239 veterans undergoing inpatient or outpatient pulmonary evaluation of IPNs at the hospital from January 1, 2003, through December 31, 2012. Exposures: Clinician documentation of the quantitative or qualitative probability of malignancy. Main Outcomes and Measures: Documentation from pulmonary and/or thoracic surgery clinicians as well as information from multidisciplinary tumor board presentations was reviewed. Any documented quantitative or qualitative predictions of malignancy were extracted and summarized using descriptive statistics. Clinicians' predictions were compared with risk estimates from the Mayo Clinic Model. Results: Of 291 patients in cohort 1, 282 (96.9%) were men; mean (SD) age was 64.6 (9.0) years. Of 239 patients in cohort 2, 233 (97.5%) were men; mean (SD) age was 65.5 (10.8) years. Cancer prevalence was 258 of 291 cases (88.7%) in cohort 1 and 110 of 225 patients with a definitive diagnosis (48.9%) in cohort 2. Only 13 patients (4.5%) in cohort 1 and 3 (1.3%) in cohort 2 had a documented quantitative prediction of malignancy prior to tissue diagnosis. Of the remaining patients, 217 of 278 (78.1%) in cohort 1 and 149 of 236 (63.1%) in cohort 2 had qualitative statements of cancer risk. In cohort 2, 23 of 79 patients (29.1%) without any documented malignancy risk statements had a final diagnosis of cancer. Qualitative risk statements were distributed among 32 broad categories. The most frequently used statements aligned well with Mayo Clinic Model predictions for cohort 1 compared with cohort 2. The median Mayo Clinic Model-predicted probability of cancer was 68.7% (range, 2.4%-100.0%). Qualitative risk statements roughly aligned with Mayo predictions. Conclusions and Relevance: Clinicians rarely provide quantitative documentation of cancer probability for high-risk IPNs, even among patients drawn from a broad range of cancer probabilities. Qualitative statements of cancer risk in current practice are imprecise and highly variable. A standard scale that correlates with predicted cancer risk for IPNs should be used to communicate with patients and other clinicians.

Assessment of Fluorodeoxyglucose F18-Labeled Positron Emission Tomography for Diagnosis of High-Risk Lung Nodules.

Importance: Clinicians rely heavily on fluorodeoxyglucose F18-labeled positron emission tomography (FDG-PET) imaging to evaluate lung nodules suspicious for cancer. We evaluated the performance of FDG-PET for the diagnosis of malignancy in differing populations with varying cancer prevalence. Objective: To determine the performance of FDG-PET/computed tomography (CT) in diagnosing lung malignancy across different populations with varying cancer prevalence. Design, Setting, and Participants: Multicenter retrospective cohort study at 6 academic medical centers and 1 Veterans Affairs facility that comprised a total of 1188 patients with known or suspected lung cancer from 7 different cohorts from 2005 to 2015. Exposures: 18F fluorodeoxyglucose PET/CT imaging. Main Outcome and Measures: Final diagnosis of cancer or benign disease was determined by pathological tissue diagnosis or at least 18 months of stable radiographic follow-up. Results: Most patients were male smokers older than 60 years. Overall cancer prevalence was 81% (range by cohort, 50%-95%). The median nodule size was 22 mm (interquartile range, 15-33 mm). Positron emission tomography/CT sensitivity and specificity were 90.1% (95% CI, 88.1%-91.9%) and 39.8% (95% CI, 33.4%-46.5%), respectively. False-positive PET scans occurred in 136 of 1188 patients. Positive predictive value and negative predictive value were 86.4% (95% CI, 84.2%-88.5%) and 48.7% (95% CI, 41.3%-56.1%), respectively. On logistic regression, larger nodule size and higher population cancer prevalence were both significantly associated with PET accuracy (odds ratio, 1.027; 95% CI, 1.015-1.040 and odds ratio, 1.030; 95% CI, 1.021-1.040, respectively). As the Mayo Clinic model-predicted probability of cancer increased, the sensitivity and positive predictive value of PET/CT imaging increased, whereas the specificity and negative predictive value dropped. Conclusions and Relevance: High false-positive rates were observed across a range of cancer prevalence. Normal PET/CT scans were not found to be reliable indicators of the absence of disease in patients with a high probability of lung cancer. In this population, aggressive tissue acquisition should be prioritized using a comprehensive lung nodule program that emphasizes advanced tissue acquisition techniques such as CT-guided fine-needle aspiration, navigational bronchoscopy, and endobronchial ultrasonography.

Timeliness of Care and Lung Cancer Tumor-Stage Progression: How Long Can We Wait?

BACKGROUND: Timely care of lung cancer is presumed critical, yet clear evidence of stage progression with delays in care is lacking. We investigated the reasons for delays in treatment and the impact these delays have on tumor-stage progression. METHODS: We queried our retrospective database of 265 veterans who underwent cancer resection from 2005 to 2015. We extracted time intervals between nodule identification, diagnosis, and surgical resection; changes in nodule radiographic size over time; final pathologic staging; and reasons for delays in care. Pearson's correlation and Fisher's exact test were used to compare cancer growth and stage by time to treatment. RESULTS: Median time from referral to surgical evaluation was 11 days (interquartile range, 8 to 17). Median time from identification to therapeutic resection was 98 days (interquartile range, 66 to 139), and from diagnosis to resection, 53 days (interquartile range, 35 to 77). Sixty-eight patients (26%) were diagnosed at resection; the remainder had preoperative tissue diagnoses. No significant correlation existed between tumor growth and time between nodule identification and resection, or between tumor growth and time between diagnosis and resection. Among 197 patients with preoperative diagnoses, 42% (83) had intervals longer than 60 days between diagnosis and resection. Most common reasons for delay were cardiac clearance, staging, and smoking cessation. Larger nodules had fewer days between identification and resection (p = 0.03). CONCLUSIONS: Evaluation, staging, and smoking cessation drive resection delays. The lack of association between tumor growth and time to treatment suggests other clinical or biological factors, not time alone, underlie growth risk. Until these factors are identified, delays to diagnosis and treatment should be minimized.

Predicting lung cancer prior to surgical resection in patients with lung nodules.

BACKGROUND: Existing predictive models for lung cancer focus on improving screening or referral for biopsy in general medical populations. A predictive model calibrated for use during preoperative evaluation of suspicious lung lesions is needed to reduce unnecessary operations for a benign disease. A clinical prediction model (Thoracic Research Evaluation And Treatment [TREAT]) is proposed for this purpose. METHODS: We developed and internally validated a clinical prediction model for lung cancer in a prospective cohort evaluated at our institution. Best statistical practices were used to construct, evaluate, and validate the logistic regression model in the presence of missing covariate data using bootstrap and optimism corrected techniques. The TREAT model was externally validated in a retrospectively collected Veteran Affairs population. The discrimination and calibration of the model was estimated and compared with the Mayo Clinic model in both the populations. RESULTS: The TREAT model was developed in 492 patients from Vanderbilt whose lung cancer prevalence was 72% and validated among 226 Veteran Affairs patients with a lung cancer prevalence of 93%. In the development cohort, the area under the receiver operating curve (AUC) and Brier score were 0.87 (95% confidence interval [CI], 0.83-0.92) and 0.12, respectively compared with the AUC 0.89 (95% CI, 0.79-0.98) and Brier score 0.13 in the validation dataset. The TREAT model had significantly higher accuracy (p < 0.001) and better calibration than the Mayo Clinic model (AUC = 0.80; 95% CI, 75-85; Brier score = 0.17). CONCLUSION: The validated TREAT model had better diagnostic accuracy than the Mayo Clinic model in preoperative assessment of suspicious lung lesions in a population being evaluated for lung resection.

Poor survival for veterans with pathologic stage I non-small-cell lung cancer.

BACKGROUND: Pathologic stage (pStage) IA and IB non-small-cell lung cancer (NSCLC) has a median survival time of 119 and 81 months, respectively. We describe the outcomes of veterans with pStage I NSCLC. METHODS: A retrospective review of 78 patients with pStage I NSCLC who underwent cancer resection was performed at the Tennessee Valley Veterans Affairs Hospital between 2005 and 2010. All-cause 30-day, 90-day, and overall mortality were determined. Survival was assessed with the Kaplan-Meier and Cox proportional hazards methods. RESULTS: There were 55 (71%) pStage IA and 23 (29%) IB patients. Thirty- and 90-day mortality was 3.8% (3 of 78) and 6.4% (5 of 78), respectively. Median survival was 59 and 28 months for pStage 1A and 1B, respectively. Postoperative events were associated with impaired survival on multivariable analysis (hazard ratio, 1.26, P = .03). CONCLUSIONS: Veterans with pStage I NSCLC at our institution have poorer survival than the general population. More research is needed to determine the etiology of this disparity.