Management Of Indeterminate Pulmonary Nodules Research Articles

Multi-Omic Biomarkers Improve Indeterminate Pulmonary Nodule Malignancy Risk Assessment.

The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.

Diagnostic follow-up of indeterminate pulmonary nodules in the Medicare population.

Management of indeterminate pulmonary nodules (IPNs) is associated with redistribution of lung cancer to earlier stages, but most subjects with IPNs do not have lung cancer. The burden of IPN management in Medicare recipients was assessed. Surveillance, Epidemiology, and End Results-Medicare data were analyzed for IPNs, diagnostic procedures, and lung cancer status. IPNs were defined as chest computed tomography (CT) scans with accompanying International Classification of Diseases (ICD) codes of 793.11 (ICD-9) or R91.1 (ICD-10). Two cohorts were defined: persons with IPNs during 2014-2017 comprised the IPN cohort, whereas those with chest CT scans without IPNs during 2014-2017 comprised the control cohort. Excess rates of various procedures due to reported IPNs over 2 years of follow-up (chest CT, positron emission tomography [PET]/PET-CT, bronchoscopy, needle biopsy, and surgical procedures) were estimated using multivariable Poisson regression models comparing the cohorts adjusted for covariates. Prior data on stage redistribution associated with IPN management were then used to define a metric of excess procedures per late-stage case avoided. Totals of 19,009 and 60,985 subjects were included in the IPN and control cohorts, respectively; 3.6% and 0.8% had lung cancer during follow-up. Excess procedures per 100 persons with IPNs over a 2-year follow-up were 63, 8.2, 1.4, 1.9, and 0.9 for chest CT, PET/PET-CT, bronchoscopy, needle biopsy, and surgery, respectively. Corresponding excess procedures per late-stage case avoided were 48, 6.3, 1.1, 1.5, and 0.7 based on an estimated 1.3 late-stage cases avoided per 100 IPN cohort subjects. The metric of excess procedures per late-stage case avoided can be used to measure the benefits-to-harms tradeoff of IPN management.

Lung Cancer resection in the absence of pre-operative histology: The Accuracy of Multidisciplinary Team Consensus

BackgroundLung resection remains the gold-standard of treatment for non-small cell lung cancer (NSCLC). British Thoracic Society (BTS) guidelines recommends the pursuit of pre-operative histological diagnosis and staging where possible. In the absence of pre-operative histology, surgical treatment can be offered in conjunction with multidisciplinary team (MDT) and patient consensus. We undertook a single-centre analysis of the accuracy of the lung cancer MDT in recommending surgical resection for those with suspected lung malignancy in the absence of pre-operative histological diagnosis over a six-year period. MethodsA retrospective review was performed of patients undergoing any pulmonary oncological resection with therapeutic intent without pre-operative histological diagnosis at the recommendation of the lung cancer MDT in our unit between May 2016 and July 2022. Results270 consecutive patients underwent lung resection for a lung nodule of indeterminate significance. This accounted for 45% of the oncological resections performed over this period. The mean age of the cohort was 67.9 years, and 47.4% were male. Overall, 10% of resected specimens (n = 27) were benign on final histopathology. 93% of those undergoing a lobectomy received a malignant diagnosis. Across the study cohort, surgical resection was well tolerated with a low complication rate. ConclusionsLung cancer resection in the absence of pre-operative histological diagnosis is feasible in a select patient cohort. This approach requires an experienced multi-disciplinary team and careful patient counselling. Our study demonstrates this adapted approach to be a pragmatic solution to the management of indeterminate pulmonary nodules in centres where biopsy is not routinely available due to existing constraints on the health system.

Differences in VA and Non-VA Pulmonary Nodules: All Evaluations Are not Created Equal

BackgroundIndeterminate pulmonary nodules present a common challenge for clinicians who must recommend surveillance or intervention based on an assessed risk of malignancy. Patients and MethodsIn this cohort study, patients presenting for indeterminate pulmonary nodule evaluation were enrolled at sites participating in the Colorado SPORE in Lung Cancer. They were followed prospectively and included for analysis if they had a definitive malignant diagnosis, benign diagnosis, or radiographic resolution or stability of their nodule for > 2 years. ResultsPatients evaluated at the Veterans Affairs (VA) and non-VA sites were equally as likely to have a malignant diagnosis (48%). The VA cohort represented a higher-risk group than the non-VA cohort regarding smoking history and chronic obstructive pulmonary disease (COPD). There were more squamous cell carcinoma diagnoses among VA malignant nodules (25% vs. 10%) and a later stage at diagnosis among VA patients. Discrimination and calibration of risk calculators produced estimates that were wide-ranging and different when comparing between risk score calculators as well as between VA/non-VA cohorts. Application of current American College of Chest Physicians guidelines to our groups could have resulted in inappropriate resection of 12% of benign nodules. ConclusionComparison of VA with non-VA patients shows important differences in underlying risk, histology of malignant nodules, and stage at diagnosis. This study highlights the challenge in applying risk calculators to a clinical setting, as the model discrimination and calibration were variable between calculators and between our higher-risk VA and lower-risk non-VA groups.Microabstract: Risk stratification and management of indeterminate pulmonary nodules (IPNs) is a common clinical problem. In this prospective cohort study of 282 patients with IPNs from Veterans Affairs (VA) and non-VA sites, we found differences in patient and nodule characteristics, histology and diagnostic stage, and risk calculator performance. Our findings highlight challenges and shortcomings of current IPN management guidelines and tools.

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

BackgroundNon-invasive biomarkers are needed to improve management of indeterminate pulmonary nodules (IPNs) suspicious for lung cancer. MethodsProtein 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. ResultsAge, 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. ConclusionsA 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.

Evaluation and Management of Indeterminate Pulmonary Nodules on Chest Computed Tomography in Asymptomatic Subjects: The Principles of Nodule Guidelines.

With the rapidly increasing number of chest computed tomography (CT) examinations, the question of how to manage lung nodules found in asymptomatic patients has become increasingly important. Several nodule management guidelines have been developed that can be applied to incidentally found lung nodules (the Fleischner Society guideline), nodules found during lung cancer screening (International Early Lung Cancer Action Program protocol [I-ELCAP] and Lung CT Screening Reporting and Data System [Lung-RADS]), or both (American College of Chest Physicians guideline [ACCP], British Thoracic Society guideline [BTS], and National Comprehensive Cancer Network guideline [NCCN]). As the radiologic nodule type (solid, part-solid, and pure ground glass) and size are significant predictors of a nodule's nature, most guidelines categorize nodules in terms of these characteristics. Various methods exist for measuring the size of nodules, and the method recommended in each guideline should be followed. The diameter can be manually measured as a single maximal diameter or as an average of two-dimensional diameters, and software can be used to obtain volumetric measurements. It is important to properly evaluate and measure nodules and familiarize ourselves with the relevant guidelines to appropriately utilize medical resources and minimize unnecessary radiation exposure to patients.

Incidental pulmonary nodules, lung cancer screening, and lung cancer in the Medicare population.

6536 Background: Low-dose CT lung cancer screening (LDCT) saves lives, but only 5%-10% of eligible persons in the US have participated. Management of indeterminate pulmonary nodules (IPNs) identified on CT scans may also reduce lung cancer mortality. We assessed the frequency of IPNs, estimated cumulative lung cancer rates following IPNs, and compared characteristics of lung cancers diagnosed following IPNs versus LDCT screens. Methods: We defined 2 cohorts in the SEER-Medicare database: persons with 12+ months of Medicare Part A&B coverage during 2014-2019 comprised the 5% sample cohort; persons in SEER-Medicare diagnosed with lung cancer during 2015-2017 with Part A&B coverage for the prior 18-month period comprised the lung cancer cohort. We defined IPNs as chest CTs with ICD-10 codes of R91.1 (solitary pulmonary nodule) or R91.8 (other nonspecific abnormal finding of lung field) on the same date as the CT; we used corresponding ICD-9 codes through September 2015. We classified lung cancer cohort cases by whether they had an LDCT (LDCT group), an IPN without an LDCT screen (IPN-only), or neither (Referent) within 18 months before diagnosis. We compared cancer stage and survival between these groups. Results: Of 627,547 subjects in the 5% sample cohort, 58.6% were women; 85.6% were non-Hispanic White (NH-Whites) and 7.7% non-Hispanic Black (NH-Blacks). Over median 5.0 years follow-up, 26.3% had chest CTs and 12.0% had IPNs. The IPN rate was similar by sex but significantly higher in NH-Whites (12.7%) than NH-Blacks (9.7%). The cumulative lung cancer rate following initial IPNs was 2.27% at two years. Of the 44,194 lung cancer cohort cases (85.8% NH-White, 8.2% NH-Black), 26.9%, 2.9% and 70.2% were in the IPN-only, LDCT, and Referent groups, respectively. NH-Whites comprised a higher proportion of LDCT than of IPN-only cases (90.1% vs. 88.4%), while for NH-Blacks, the reverse was true (5.4% vs. 6.5%). The ratio of LDCT:IPN lung cancer cases was 1:9. Among IPN-only and LDCT group cases, 52.0% and 50.3%, respectively, were localized stage, compared to 21.5% for the Referent group. Among all localized cases, 45.4% and 4.9% were in the IPN-only and LDCT groups, respectively. Comparing 3-year survival between IPN vs LDCT vs Referent groups, respectively: aggregate overall survival rates were 53.5% v 59.2% v 29.2%; aggregate lung cancer-specific survival, 71.1% v 75.3% v 46.6%; overall survival for localized cases, 73.2% v 80.7% v 62.9%; lung cancer-specific survival rates for localized cases, 88.2%, 91.8% and 81.4%. Conclusions: Subjects with IPNs had similar stage distribution and survival as LDCT-screened subjects. Almost half of localized cases had prior IPNs, compared to a < 5% of LDCT-screened cases. IPN programs, by circumventing implementation barriers to LDCT, may expand access to early lung cancer detection, including to African American patients and in places where LDCT coverage is not available.

Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules.

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

A model based on the quantification of complement C4c, CYFRA 21-1 and CRP exhibits high specificity for the early diagnosis of lung cancer

Lung cancer screening detects early-stage cancers, but also a large number of benign nodules. Molecular markers can help in the lung cancer screening process by refining inclusion criteria or guiding the management of indeterminate pulmonary nodules. In this study, we developed a diagnostic model based on the quantification in plasma of complement-derived fragment C4c, cytokeratin fragment 21–1 (CYFRA 21–1) and C-reactive protein (CRP). The model was first validated in two independent cohorts, and showed a good diagnostic performance across a range of lung tumor types, emphasizing its high specificity and positive predictive value. We next tested its utility in two clinically relevant contexts: assessment of lung cancer risk and nodule malignancy. The scores derived from the model were associated with a significantly higher risk of having lung cancer in asymptomatic individuals enrolled in a computed tomography (CT)-screening program (OR = 1.89; 95% CI = 1.20 –2.97). Our model also served to discriminate between benign and malignant pulmonary nodules (AUC: 0.86; 95% CI = 0.80 –0.92) with very good specificity (92%). Moreover, the model performed better in combination with clinical factors, and may be used to reclassify patients with intermediate-risk indeterminate pulmonary nodules into patients who require a more aggressive work-up. In conclusion, we propose a new diagnostic biomarker panel that may dictate which incidental or screening-detected pulmonary nodules require a more active work-up.

Validation of the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a novel HRCT-based radiomic classifier for indeterminate pulmonary nodules.

Implementation of low-dose chest computed tomography (CT) lung cancer screening and the ever-increasing use of cross-sectional imaging are resulting in the identification of many screen- and incidentally detected indeterminate pulmonary nodules. While the management of nodules with low or high pre-test probability of malignancy is relatively straightforward, those with intermediate pre-test probability commonly require advanced imaging or biopsy. Noninvasive risk stratification tools are highly desirable. We previously developed the BRODERS classifier (Benign versus aggRessive nODule Evaluation using Radiomic Stratification), a conventional predictive radiomic model based on eight imaging features capturing nodule location, shape, size, texture and surface characteristics. Herein we report its external validation using a dataset of incidentally identified lung nodules (Vanderbilt University Lung Nodule Registry) in comparison to the Brock model. Area under the curve (AUC), as well as sensitivity, specificity, negative and positive predictive values were calculated. For the entire Vanderbilt validation set (n=170, 54% malignant), the AUC was 0.87 (95% CI 0.81-0.92) for the Brock model and 0.90 (95% CI 0.85-0.94) for the BRODERS model. Using the optimal cut-off determined by Youden's index, the sensitivity was 92.3%, the specificity was 62.0%, the positive (PPV) and negative predictive values (NPV) were 73.7% and 87.5%, respectively. For nodules with intermediate pre-test probability of malignancy, Brock score of 5-65% (n=97), the sensitivity and specificity were 94% and 46%, respectively, the PPV was 78.4% and the NPV was 79.2%. The BRODERS radiomic predictive model performs well on an independent dataset and may facilitate the management of indeterminate pulmonary nodules.

Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules.

Rationale: The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed.Objectives: To develop and validate a deep learning method to improve the management of IPNs.Methods: A Lung Cancer Prediction Convolutional Neural Network model was trained using computed tomography images of IPNs from the National Lung Screening Trial, internally validated, and externally tested on cohorts from two academic institutions.Measurements and Main Results: The areas under the receiver operating characteristic curve in the external validation cohorts were 83.5% (95% confidence interval [CI], 75.4–90.7%) and 91.9% (95% CI, 88.7–94.7%), compared with 78.1% (95% CI, 68.7–86.4%) and 81.9 (95% CI, 76.1–87.1%), respectively, for a commonly used clinical risk model for incidental nodules. Using 5% and 65% malignancy thresholds defining low- and high-risk categories, the overall net reclassifications in the validation cohorts for cancers and benign nodules compared with the Mayo model were 0.34 (Vanderbilt) and 0.30 (Oxford) as a rule-in test, and 0.33 (Vanderbilt) and 0.58 (Oxford) as a rule-out test. Compared with traditional risk prediction models, the Lung Cancer Prediction Convolutional Neural Network was associated with improved accuracy in predicting the likelihood of disease at each threshold of management and in our external validation cohorts.Conclusions: This study demonstrates that this deep learning algorithm can correctly reclassify IPNs into low- or high-risk categories in more than a third of cancers and benign nodules when compared with conventional risk models, potentially reducing the number of unnecessary invasive procedures and delays in diagnosis.

Integrated Biomarker for the Management of Indeterminate Pulmonary Nodules

Integrated Biomarker for the Management of Indeterminate Pulmonary Nodules

Abstract 3317: Development of a lung nodule cohort with integrated clinical, molecular and imaging biomarkers

Abstract Background: We are facing an epidemic of indeterminate pulmonary nodules (IPN). Current diagnostic strategies lack accuracy such that the management of IPNs 6-30 mm leads to an unacceptable rate of invasive biopsies and of missed opportunities for cure. Based on the critical need of evaluating candidate biomarkers across heterogeneous populations, we intended to assemble a large cohort of fully annotated IPNs from five collaborative institutions. Methods: Standard operating procedures (SOPs) were developed to capture subject demographics and assign consistent identifiers across clinical, bio-specimens, and imaging data. REDCap database was created for clinical, imaging and biospecimen data capture. Sample collection, processing, storage and shipping SOPs were created and shared among institutions to assure for consistency and quality accuracy. DICOM files, demographic and clinical data, blood and tissue samples were collected prospectively through IRB approved studies at each institution (VUMC, Nashville VAMC, Moffitt Cancer Center, and UPMC). Imaging studies and specimens were de-identified locally using custom JavaScript program in a secure web browser and assigned as specific identifier. De-identified thin slice, non-contrast chest CT studies were tested for quality control and transmitted to an imaging repository (eXtensible Neuroimaging Repository-XNAT) that can be mined by all collaborators. Results: To date, a cohort of 845 subjects, 507 (60%) males and 338 (40%) females, with lung nodules was assembled. 36 % are current smokers, 56 % former smokers and 8% never smokers, with an average of 46 pack year smoking history. Clinical data including risk prediction models such as the Mayo and PLCO m2012 are reported. Pathological confirmation of nodules is available for 322 benign and 444 malignant nodules. The cohort 283 lung adenocarcinomas, 71 squamous cell carcinomas, 53 small cell carcinoma, 17 non-small cell lung cancer, 9 carcinoid, and 79 subjects considered benign based on CT follow without growth. Serum, plasma and peripheral blood monocyte related DNA is available on all. All diagnostic chest CT are available in our thoracic imaging repository (XNAT) in a de-identified format. Conclusions: We assembled a unique cohort of incidental and screening detected lung nodules prospectively enrolled at four institutions for which full clinical data capture, chest CT DICOM files and blood specimens were collected. This repository allows the derivation and independent validation of candidate molecular and imaging biomarkers for the management of IPNs. This work is supported by UO1 152662, UO1CA186145 and UO1CA196405 Citation Format: Sanja Antic, Travis Osterman, Aneri Balar, Dhairya Lakhani, Rina Nguyen, Sara Block, Kimberly Fileds, Brandon Winston, Anel Muterspaugh, Yuankai Huo, Riqiang Gao, Joseph Leader, David Wilson, Viswam Nair, Robert Gillies, Matthew Schabath, Chirayu Shah, Bennett Landman, Pierre Massion. Development of a lung nodule cohort with integrated clinical, molecular and imaging biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3317.

Abstract 4218: Validation of a new blood-based biomarker strategy for the early detection of lung cancer

Abstract Rationale: The management of indeterminate pulmonary nodules (IPNs) remains a challenging problem. Our new assay methodology, the Free Solution Assay (FSA), measured by the Compensated Interferometric Reader (CIR), consisting of a diode laser, capillary and CCD, provides 40-fold lower limits of quantitation (LOQ) than ELISA for known candidate serum protein biomarkers, while speeding assay development, accuracy, and sensitivity. We hypothesized that lowering the LOQ of previously investigated biomarkers can increase the biomarker discriminatory power, enabling patients in an intermediate risk group (15-80%) to be reclassified into a low (&amp;lt;15) or high (&amp;gt;80) risk group. Methods: In this retrospective case-control study, FSA-CIR was used to measure the serum concentration of CYFRA 21-1 in two patient cohorts, a training cohort (N=274) of patients with IPNs collected at Vanderbilt University Medical Center and an external validation cohort (N=103) collected at the University of Pittsburgh Medical Center. Patient malignancy was determined by tissue biopsy or 2-year follow-up CT scan showing no signs of nodule growth. Baseline risk for cancer was calculated using nodule size. The added value of CYFRA 21-1 was assessed by comparing the difference in risk for lung cancer after incorporating CYFRA 21-1 into the model. Results: The limit of detection (LOD) of 6pg/mL and an average LOQ for standards was determined to be 60pg/mL. Patient samples in the training cohort were found to have CYFRA 21-1 concentrations ranging 100 pg/mL to 10 ng/mL, with a median of 0.79 (0.28-1.22, interquartile range) ng/mL in the control population and 1.90 (1.33-3.35) ng/mL in the case population, providing a ROC-AUC of 0.86 across three histological subtypes (adenocarcinoma, squamous cell carcinoma, and small cell lung cancer). The CYFRA 21-1 + nodule size risk model correctly reclassified 28 (25%) [DB1] of intermediate-risk benign nodules into the low-risk group and 28(24%) of intermediate-risk malignant nodules into the high-risk group. The independent validation cohort’s controls had a median of 0.35 (0.20-0.56) ng/mL, while cases had 0.97(0.66-1.22) ng/mL, providing a ROC-AUC of 0.84 and correct reclassification of 12(34%) of intermediate-risk benign nodules and 6(15%) of intermediate-risk malignant nodules. The CYFRA 21-1 + nodule size risk model correctly classified 90.6% in the low-risk group and 87.0% in the high-risk group. Conclusions: FSA-CIR measurements requiring only a few microliters of serum allowed for reclassification of patients in the intermediate risk group in both the training and validation cohorts. The results suggest that CYFRA 21-1 measured by FSA-CIR represents a strong candidate biomarker for risk stratification of patients with IPNs. Citation Format: Michael N. Kammer, Amanda K. Kussrow, Sanja L. Antic, Rina Nguyen, Heidi Chen, Darryl J. Bornhop, Pierre P. Massion. Validation of a new blood-based biomarker strategy for the early detection of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4218.

Management of indeterminate pulmonary nodules (

To investigate the management for the indeterminate pulmonary nodules newly detected during the follow-up for nasopharyngeal carcinoma (NPC) patients. Indeterminate pulmonary lesions were identified by searching medical records of NPC patients, who were followed-up with thoracic CT between April 2008 and February 2014. Indeterminate pulmonary nodules were defined as opacities that may be solitary or multiple, which were <1 cm in size and not calcified. Nodules which were followed-up for at least two years, were included into analysis. The nodules with growth were considered as metastatic lesions and those which remained stable or regressed were defined as benign. The relations between the risk of the nodules to develop into metastatic lesions and clinical features and characteristics of nodules were analyzed. Through a median follow-up of 50 months, the majority (81 of 102, 79.4%) had no change in the size of nodules. The nodules have regressed in 11 patients. There were 10 (9.8%) cases with progression at the follow-up. The 10 patients were considered to have lung metastases, for whom continuous increases of lesions were revealed. The volume doubling time of the increasing nodules ranged from 34 to 105 days. The nodules emerging within two years after primary therapies were more likely to develop into metastatic lesions (P = 0.043). Ten percent of the indeterminate pulmonary nodules represent metastatic diseases. A short-interval follow-up is recommended and if the nodules remain stable after six months follow-up, it may be proper to prolong the interval of follow-up.

SC18.02 Integrating Lung Cancer Biomarkers into Future Screening Programs

SC18.02 Integrating Lung Cancer Biomarkers into Future Screening Programs

Factors that influence physician decision making for indeterminate pulmonary nodules.

Pulmonologists frequently encounter indeterminate pulmonary nodules in practice, but it is unclear what clinical factors they rely on to guide the diagnostic evaluation. To assess the current approach to the management of indeterminate pulmonary nodules and to determine the extent to which the addition of a hypothetical diagnostic blood test will influence clinical decision making. Selected pulmonologists practicing in the United States were invited to participate in a conjoint exercise based on 20 randomly generated cases of varying age, smoking history, and nodule size. Some cases included the result of a hypothetical blood test. Each respondent chose from among three diagnostic options for a patient: noninvasive monitoring (i.e., serial CT or positron emission tomography scan), a minor procedure (i.e., biopsy or bronchoscopy), or a major procedure (i.e., video-assisted thorascopic surgery or thoracotomy). Multivariate logistic regression was used to assess the impact of the three risk factors and the diagnostic blood test on decision making. Four hundred nineteen physicians participated (response rate, 10%). One hundred fifty-three physician surveys met predetermined criteria and were analyzed (4% of all invitees). A diagnostic procedure was recommended for 23% of 6-mm nodules, versus 54, 66, 77, and 84% of nodules 10, 14, 18, and 22 mm, respectively (P < 0.001). Older age limited recommendations for invasive testing: 54% of 80-year-olds versus 61, 64, 63, and 61% of patients 71, 62, 53, and 44 years of age, respectively (P < 0.001). In multivariate analyses, nodule size, smoking history, age, and the blood test each influenced decision making (P < 0.001). The pulmonologists who participated in this survey were more likely to proceed with invasive testing, instead of observation or additional imaging, as the size of the nodule increased. The use of a hypothetical blood test resulted in significant alterations in the decision to pursue invasive testing.

Evaluation and management of pulmonary nodules: state-of-the-art and future perspectives

The imaging evaluation of pulmonary nodules, often incidentally detected on imaging examinations performed for other clinical reasons, is a frequently encountered clinical circumstance. With advances in imaging modalities, both the detection and characterization of pulmonary nodules continue to evolve and improve. This article will review the imaging modalities used to detect and diagnose benign and malignant pulmonary nodules, with a focus on computed tomography (CT), which continues to be the mainstay for evaluation. The authors discuss recent advances in the lung nodule management, and an algorithm for the management of indeterminate pulmonary nodules. There are set of criteria that define a benign nodule, the most important of which are the lack of temporal change for 2 years or more, and certain benign imaging criteria, including specific patterns of calcification or the presence of fat. Although some indeterminate pulmonary nodules are immediately actionable, generally those approaching 1 cm or larger in diameter, at which size the diagnostic accuracy of tools such as positron emission tomography (PET)/CT, single photon emission CT (SPECT) and biopsy techniques are sufficient to warrant their use. The majority of indeterminate pulmonary nodules are under 1 cm, for which serial CT examinations through at least 2 years for solid nodules and 3 years for ground-glass nodules, are used to demonstrate either benign biologic behavior or otherwise. The management of incidental pulmonary nodules involves a multidisciplinary approach in which radiology plays a pivotal role. Newer imaging and postprocessing techniques have made this a more accurate technique eliminating ambiguity and unnecessary follow-up.

Efficacy of F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) scans in diagnosis of pulmonary nodules

To evaluate the value of F-18 fluorodeoxyglucose positron emission tomography (FDG-PET) scans, we performed FDG-PET scans in 23 patients with indeterminate pulmonary nodules less than 3 cm in size and analyzed these scans qualitatively and semiquantitatively. Histologic specimens were obtained by thoracoscopic excisional biopsy in 16 patients, CT-guided needle aspiration cytology in three, and bronchoscopic brushing cytology in four. Pathological diagnoses were lung cancer in 16 patients, benign inflammation in six, and malignant lymphoma in one. Sensitivity, specificity and accuracy of the FDG-PET scans were 88% (15/17), 67% (4/6) and 83% (19/23), respectively. There were two false-positive cases (organizing pneumonia and cryptococcosis) and two false-negative ones (slow-growing adenocarcinoma and malignant lymphoma). Although a few false-positive cases of granulomatous disease were yielded, the FDG-PET scans were highly sensitive in the detection of lung cancer. We conclude that the FDG-PET scanning in a useful diagnostic imaging modailty in the management of indeterminate pulmonary nodules.