Indeterminate Pulmonary Nodules Research Articles

Abstract LB010: Identification of malignant lung indeterminate pulmonary nodules with Plasma WGS using exon read depth of short DNA fragments

Abstract Background: Plasma WGS for non-invasive cancer detection has been an active area of research, where fragmentomics studies have been gaining attention for its promising results. Previous studies showed that the quantity of short and long fragments could be used for the prognosis of malignant tumor. In this study, the efficacy of short fragment read depth on the reference genome for malignant nodule detection was evaluated. Methods: Plasma samples from 40 patients with Indeterminate Pulmonary Nodules (IPN) (20 with Benign nodule, 20 Malignant) were collected for WGS. Low quality samples were filtered, and 31 patients (15 Benign, 16 Cancer) remained. Short fragments in each sample were extracted (30-99 bp) and exon read depth for each sample was obtained through human reference genome alignment. A proprietary data analytic machine learning pipeline using XGBoost was trained and tested using 10 fold cross validation. Results: XGBoost model testing accuracy was 96.77% in differentiating Benign and Cancer samples using read depth information from only 3 exons. GPR35’s ENSE00003591008.1, ZNF594-DT’s ENSE00002678218.2 and IGHGP’s ENSE00001819760.2 were the 3 exons used in the model. Hierarchical clustering using top features with differential read depth (p<0.001) was able to perfectly cluster the samples into the Benign group and the Malignant group. Conclusion: Read depth information of exon by short fragments from WGS plasma was able to differentiate patients with Lung benign nodules from patients with malignant nodules. This analysis demonstrates promises of developing diagnosis tools using exon read depths from plasma samples. Citation Format: Shun H. Yip, Neeti Swarup, Jason C. Hyun, Jordan C. Cheng, Raghuraman Ramamurthy, Edmund Wong, Irene Choi, Akanksha Arora, Denise Aberle, David TW Wong, Cheuk Y. Tang. Identification of malignant lung indeterminate pulmonary nodules with Plasma WGS using exon read depth of short DNA fragments [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB010.

A prediction model for lung metastases in patients with indeterminate pulmonary nodules in newly diagnosed colorectal cancer

IntroductionMultidisciplinary teams treating patients with newly diagnosed Colorectal Cancer (CRC) often encounter the appearance of Indeterminate Pulmonary Nodules (IPNs) that warrants follow–up with repetitive medical imaging and anxiety for patients. We determined the incidence of IPNs in patients with newly diagnosed CRC and developed and validated a model for individualized risk prediction of IPNs being lung metastases. Material and methodsNewly diagnosed CRC who underwent surgery between November 2011 to June 2014 were included to create the risk model, developed using both clinical experience and statistical selection. Discrimination and calibration slopes of the risk score were evaluated in an independent temporal validation sample. A nomogram is presented to assist clinicians in estimating an individual risk score. ResultsOut of 2111 CRC patients staged with chest CT, 204 (9.6%) had IPNs and 54/204 (26%) had lung metastases. We identified 4 predictors: “location of primary tumour”, “pathological nodal stage”, “size of the largest nodule” and “extrapulmonary synchronous metastases at diagnosis”. Discrimination of the final model in the validation sample was demonstrated by the difference in mean predicted risk between progressed cases en non-progressed cases (49% versus 21%, p = <0.001). ConclusionA prediction model with 4 clinical risk factors can be used to assist multidisciplinary teams in the prediction of individualized risk of lung metastases and imaging strategy in patients with IPNs and newly diagnosed colorectal cancer. The model performed well in new patients not included in the model development.

Abstract 6104: EDRN Lung Team Project 2 validation of molecular biomarkers for the early detection of lung cancer in the setting of indeterminate pulmonary nodules

Abstract Background: The goal of the Early Detection Research Network (EDRN) Lung Team Project #2 (LTP-2) is to establish a cohort (n= 300) of high-risk people with indeterminate pulmonary nodules (6mm-30mm) to clinically validate the diagnostic accuracy of existing molecular and imaging biomarkers discovered and analytically validated through the EDRN program. These biomarkers comprise signals measured in serum (glycan or cytokine), plasma (protein, miRNA, RNA, or methylated circulating tumor DNA), airway epithelial cells (RNA or DNA), nasal brush cells (RNA or DNA), and/or chest CT images. Methods: Eligible participants had an indeterminate nodule discovered incidentally or through low-dose CT screening within 14 months prior to enrollment and were aged 45-90 years, with 20 pack-years or more cigarette smoking history, free of lung cancer, and willing to provide blood, nasal brushing, and optionally bronchoscopic brush biopsy specimens. Baseline medical and demographic data were collected from each subject. Baseline and years 1 and 2 follow-up CT images were obtained. Each specimen at each respective site was labeled with a 2-D barcode provided by the DMCC and specimen information was entered into the Validation Study Information Management System (VSIMS) developed by the EDRN Data Management and Coordinating Center (DMCC). Aliquots of blinded specimens from selected cases and controls will be dispensed to each EDRN site for biomarker analysis. Analysis plan: Primary endpoint: The positive diagnostic likelihood ratio (DLR+) will be used to measure biomarker performance within values of DLR+ &amp;gt;= 2 or DLR- &amp;lt;= 0.5, a range considered clinically useful for any patients with indeterminate lung nodules. The sample size calculation targeted &amp;gt;80% power to test the null hypotheses that a) a positive biomarker does not alter risk across a range of specificities, and b) a negative biomarker does not alter risk across a range of sensitivities. The EDRN Data Management and Coordinating Center (DMCC) will unblind and complete analysis and report results. Exploratory endpoint: The diagnostic accuracy of combined biomarkers will be calculated using a logistic regression model with flexible functional forms. Results and Discussion: More than 300 subjects were enrolled into the LTP2 cohort including a sufficient number of cases and controls (cohort size n&amp;gt;300) to reach power according to calculated estimates. Chest CT images were collected and are in the process of transfer to a central database. Plasma specimens have been distributed to test sites. RNA and DNA extraction from NBC and AEC is in process and based on results thus far the specimen quality and quantity is more than sufficient from the number of subjects needed to reach power for the primary endpoint and to conduct the planned exploratory analyses. Citation Format: James C. Willey, Eric L. Grogan, Stephen A. Deppen, James G. Herman, Michael N. Kammer, Matthew B. Schabath, Lynn Sorbara, Harvey I. Pass, Jun-Chieh J. Tsay, Ziding Feng, Avrum Spira, Erin L. Crawford, Jackie Dahlgren, Steven M. Dubinett, Kimberly R. Rieger-Christ, Marc E. Lenburg. EDRN Lung Team Project 2 validation of molecular biomarkers for the early detection of lung cancer in the setting of indeterminate pulmonary nodules [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6104.

Exploring the Diagnostic Dilemma of Indeterminate Pulmonary Nodules in Patients with Primary Sarcoma of Bone.

Bone sarcomas are known to have a predilection for pulmonary metastasis. Surveillance protocols are thus focused on periodic chest imaging, typically with CT scan. Pulmonary nodules can be easily identified with this modality, but smaller nodules are not readily biopsied and may not represent metastatic disease. These are called indeterminate. The natural history of indeterminate nodules in a bone sarcoma population and factors associated with progression to true metastatic disease are not clearly defined. All bone sarcoma patients treated at a single institution from 2010 to 2020 were eligible for inclusion. We treated 327 patients over this period; 119 were excluded for age less than 16 years, 31 were excluded for evident metastatic disease at presentation, and 60 were excluded for incomplete clinical follow-up or CT chest imaging either at staging or in surveillance. We assessed chest CT images for presence of pulmonary nodules and selected variables both at the staging and on surveillance images. Nodules were considered metastatic if proven histologically with a biopsy or by clinical interpretation by the multidisciplinary sarcoma team. Clinical and imaging factors were assessed for the association of indeterminate nodule progression to true metastatic disease. Seventy three of the 117 patients had indeterminate nodules on their staging CT scan; 41.1% of those patients progressed to metastatic disease compared to 43.2% of the patients that did not have indeterminate nodules on staging CT. Fifty eight of the 117 patients developed indeterminate nodules on surveillance chest CT, and 55.2% of those patients progressed to metastatic disease. There were no clinical or imaging factors that predicted the development of metastatic disease in the group that had indeterminate nodules at presentation; however, the number and size of nodules did correlate with progression to metastasis in those that developed indeterminate nodules on surveillance. Indeterminate pulmonary nodules are common on staging CT scans in patients with a bone sarcoma. The presence or absence of these indeterminate nodules was not predictive of progression to true metastatic disease in this cohort. However, the development of indeterminate nodules on surveillance imaging was associated with progression to metastatic disease with the size and number of nodules being important factors.

Radiomics analysis to predict pulmonary nodule malignancy using machine learning approaches

BackgroundLow-dose CT screening can reduce lung cancer-related mortality. However, most screen-detected pulmonary abnormalities do not develop into cancer and it often remains challenging to identify malignant nodules, particularly among indeterminate...

The Lung Cancer Prediction Model “Stress Test”: Assessment of Models’ Performance in a High-Risk Prospective Pulmonary Nodule Cohort

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. Do guideline-recommended prediction models sufficiently discriminate between benign and malignant nodules when applied to cases referred for biopsy by navigational bronchoscopy? 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. 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). 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.

Explainable machine learning model based on clinical factors for predicting the disappearance of indeterminate pulmonary nodules

BackgroundDuring lung cancer screening, indeterminate pulmonary nodules (IPNs) are a frequent finding. We aim to predict whether IPNs are resolving or non-resolving to reduce follow-up examinations, using machine learning (ML) models. We incorporated dedicated techniques to enhance prediction explainability. MethodsIn total, 724 IPNs (size 50–500 mm3, 575 participants) from the Dutch-Belgian Randomized Lung Cancer Screening Trial were used. We implemented six ML models and 14 factors to predict nodule disappearance. Random search was applied to determine the optimal hyperparameters on the training set (579 nodules). ML models were trained using 5-fold cross-validation and tested on the test set (145 nodules). Model predictions were evaluated by utilizing the recall, precision, F1 score, and the area under the receiver operating characteristic curve (AUC). The best-performing model was used for three feature importance techniques: mean decrease in impurity (MDI), permutation feature importance (PFI), and SHAPley Additive exPlanations (SHAP). ResultsThe random forest model outperformed the other ML models with an AUC of 0.865. This model achieved a recall of 0.646, a precision of 0.816, and an F1 score of 0.721. The evaluation of feature importance achieved consistent ranking across all three methods for the most crucial factors. The MDI, PFI, and SHAP methods highlighted volume, maximum diameter, and minimum diameter as the top three factors. However, the remaining factors revealed discrepant ranking across methods. ConclusionML models effectively predict IPN disappearance using participant demographics and nodule characteristics. Explainable techniques can assist clinicians in developing understandable preliminary assessments.

A spatio-temporal image analysis for growth of indeterminate pulmonary nodules detected by CT scan.

The objective is to evaluate the performance of computational image classification for indeterminate pulmonary nodules (IPN) chronologically detected by CT scan. Total 483 patients with 670 abnormal pulmonary nodules, who were taken chest thin-section CT (TSCT) images at least twice and resected as suspicious nodules in our hospital, were enrolled in this study. Nodular regions from the initial and the latest TSCT images were cut manually for each case, and approached by Python development environment, using the open-source cv2 library, to measure the nodular change rate (NCR). These NCRs were statistically compared with clinico-pathological factors, and then, this discriminator was evaluated for clinical performance. NCR showed significant differences among the nodular consistencies. In terms of histological subtypes, NCR of invasive adenocarcinoma (ADC) were significantly distinguishable from other lesions, but not from minimally invasive ADC. Only for cancers, NCR was significantly associated with loco-regional invasivity, p53-immunoreactivity, and Ki67-immunoreactivity. Regarding Epidermal Growth Factor Receptor gene mutation of ADC-related nodules, NCR showed a significant negative correlation. On staging of lung cancer cases, NCR was significantly increased with progression from pTis-stage 0 up to pT1b-stage IA2. For clinical shared decision-making (SDM) whether urgent resection or watchful-waiting, receiver operating characteristic (ROC) analysis showed that area under the ROC curve was 0.686. For small-sized IPN detected by CT scan, this approach shows promise as a potential navigator to improve work-up for life-threatening cancer screening and assist SDM before surgery.

Indeterminate pulmonary nodules in non-rhabdomyosarcoma soft tissue sarcoma: A study of the European paediatric Soft Tissue Sarcoma Study Group.

The aim of this study was to assess the clinical impact of indeterminate pulmonary nodules (no more than four pulmonary nodules of less than 5mm or one nodule measuring between 5 and less than 10mm by computed tomography [CT]) in children and adolescents with adult-type non-rhabdomyosarcoma soft tissue sarcoma (NRSTS) at diagnosis. Patients with NRSTS treated in 11 centers as part of the European paediatric Soft Tissue Sarcoma Study Group (EpSSG) were retrospectively assessed. Local radiologists, blinded to clinical information except for patients' age and tumor histotype, reviewed the chest CT at diagnosis and filled out a case report form. Because patients with or without indeterminate nodules in the EpSSG NRSTS 2005 study received the same type of treatment, event-free survival (EFS) and overall survival (OS) between groups by log-rank test were compared. Overall, 206 patients were examined: 109 (52.9%) were without any nodules, 78 (38%) had at least one indeterminate nodule, and 19 (9.2%) had nodules meeting the definition of metastases, which were then considered to be misclassified and were excluded from further analyses. Five-year EFS was 78.5% (95% CI, 69.4%-85.1%) for patients without nodules and 69.6% (95% CI, 57.9%-78.7%) for patients with indeterminate nodules (p=.135); 5-year OS was 87.4% (95% CI, 79.3%-92.5%) and 79.0% (95% CI, 67.5%-86.8%), respectively (p=.086). This study suggests that survival does not differ in otherwise nonmetastatic patients with indeterminate pulmonary nodules compared to nonmetastatic patients without pulmonary nodules. Radiologists should be aware of the classification of indeterminate pulmonary nodules in non-rhabdomyosarcoma soft tissue sarcomas and use it in their reports. More than a third of patients with non-rhabdomyosarcoma soft tissue sarcoma can be affected by indeterminate pulmonary nodules. Indeterminate pulmonary nodules do not significantly affect the overall survival of pediatric patients with non-rhabdomyosarcoma soft tissue sarcoma.

Prediction of pulmonary metastasis in esophageal carcinoma patients with indeterminate pulmonary nodules

BackgroundIndeterminate pulmonary nodules (IPNs) are common after surgery for esophageal cancer. The paucity of data on postoperative IPNs for esophageal cancer causes a clinical dilemma.ObjectiveThe aim of this study was to identify the characteristics and clinical significance of IPNs after radical esophagectomy for metastatic esophageal cancer, determine the risk factors for pulmonary metastasis, and construct a risk score model to standardize the appropriate time to either follow up or treat the patient.MethodsAll consecutive patients with esophageal squamous cell carcinoma (ESCC) who underwent radical surgery between 2013 and 2016 were included in this retrospective study. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors and develop risk score models.ResultsA total of 816 patients were enrolled in the study. During a median follow-up period of 45 months, IPNs were detected in 221 (27.1%) patients, of whom 66 (29.9%) were diagnosed with pulmonary metastases. The following five variables maintained prognostic significance after multivariate analyses: the pathologic N category, number of IPNs, shape of IPNs, time of detection of IPNs, and size of IPNs. The Pulmonary Metastasis Prediction Model (PMPM) scale ranges from 0 to 15 points, and patients with higher scores have a higher probability of pulmonary metastases. The Hosmer–Lemeshow test showed a good calibration performance of the clinical prediction model (χ2 = 8.573, P = 0.380). After validation, the PMPM scale showed good discrimination with an AUC of 0.939.ConclusionA PMPM scale for IPNs in patients who underwent esophagectomy for ESCC may be clinically useful for diagnostic and therapeutic decision-making.

Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization.

The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.

Radiomics Model for Predicting Pulmonary Metastasis in Sarcoma Patients

The lung is the most common site of metastasis in localized extremity soft tissue sarcomas (STS). Indeterminant lung nodules leads to challenges for both patients and providers. As such, accuracy in the diagnosis of pulmonary metastasis (PM) is important for clinical decision making. The purpose of this is study is to develop and evaluate a CT-based radiomic model to predict PM in sarcoma patients with indeterminate pulmonary nodules. Fifty-seven stage I-III sarcoma patients with indeterminate lung nodules on unenhanced CT that were approved for this study by our institution's internal review board (IRB). Twenty-two developed and categorized as PM and 35 without PM. A total number of 322 indeterminate nodules were investigated, where 165 & 157 nodules were from patients with and without PM, respectively. A total of 96 radiomic features (RF) (including first order statistics, 2d shape features, gray level co-occurrence, gray level size zone, gray level run length, neighboring gray tone difference & gray level dependence matrices) were taken from unenhanced CT images of each patient. We included 2D shape features since the size and shape of indeterminate lung nodules may have some correlation with PM. Spearman correlation were used to rule out redundant features (r2>0.9). T-test statistics was used to determine feature with significant differences that can differentiate responders with and without PM. Support Vector Machine (SVM) model was fitted to combinations of groups of 2-3 radiomic features. The AUC of the ROC curve was used to evaluate the model performance. Pointwise confidence intervals (CI) for AUC were calculated using 100 bootstrap replicates. Four features were found significant after Spearman correlation and T-test statistical testing: 1. Maximum (MAX); 2. Minor Axis Length (MAL); 3. Gray Level Non-uniformity (GLNU); and 4. Interquartile Range (IR). The best performing 2-feature radiomic SVM model had an AUC of 0.69 (95% CI 0.61,0.74) for the MAX and GLNU features. The best performing 3-feature radiomic SVM model had an AUC of 0.74 (95% CI 0.70,0.80) for the MAX, MAL and IR features. CT-based radiomics model has the potential to be used to predict PM in sarcoma patients presenting with indeterminate lung nodules. Additional research will focus on investigating derived parameters for existing RF, such as the ratio of the max RF to mean RF, to see if they improve the predictive performance of the model, as well as testing the models on external patient cohort. These radiomic models could be useful tools in guiding PM screening and providing a patient specific monitoring of sarcoma patients.

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma

Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules, mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used hematoxylin and eosin histopathology images and extracted 9 biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed 3 distinct cohorts (Japan, China, and United States) covering 98 patients, 162 slides, and 669 regions of interest, including 143 normal, 129 atypical adenomatous hyperplasia, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed progressive increase of atypical epithelial cells and progressive decrease of lymphocytic cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine hematoxylin and eosin staining.

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.

Circulating proteome for pulmonary nodule malignancy.

Although lung cancer screening with low-dose computed tomography is rolling out in many areas of the world, differentiating indeterminate pulmonary nodules remains a major challenge. We conducted one of the first systematic investigations of circulating protein markers to differentiate malignant from benign screen-detected pulmonary nodules. Based on 4 international low-dose computed tomography screening studies, we assayed 1078 protein markers using prediagnostic blood samples from 1253 participants based on a nested case-control design. Protein markers were measured using proximity extension assays, and data were analyzed using multivariable logistic regression, random forest, and penalized regressions. Protein burden scores (PBSs) for overall nodule malignancy and imminent tumors were estimated. We identified 36 potentially informative circulating protein markers differentiating malignant from benign nodules, representing a tightly connected biological network. Ten markers were found to be particularly relevant for imminent lung cancer diagnoses within 1 year. Increases in PBSs for overall nodule malignancy and imminent tumors by 1 standard deviation were associated with odds ratios of 2.29 (95% confidence interval: 1.95 to 2.72) and 2.81 (95% confidence interval: 2.27 to 3.54) for nodule malignancy overall and within 1 year of diagnosis, respectively. Both PBSs for overall nodule malignancy and for imminent tumors were substantially higher for those with malignant nodules than for those with benign nodules, even when limited toLung Computed Tomography Screening Reporting and Data System (LungRADS) category 4 (P < .001). Circulating protein markers can help differentiate malignant from benign pulmonary nodules. Validation with an independent computed tomographic screening study will be required before clinical implementation.

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

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. 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? 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. 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. 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.

The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

PurposeComputed tomography is the standard method by which pulmonary nodules are detected. Greater than 40% of pulmonary biopsies are not lung cancer and therefore not necessary, suggesting that improved diagnostic tools are needed. The LungLB™ blood test was developed to aid the clinical assessment of indeterminate nodules suspicious for lung cancer. LungLB™ identifies circulating genetically abnormal cells (CGACs) that are present early in lung cancer pathogenesis.MethodsLungLB™ is a 4-color fluorescence in-situ hybridization assay for detecting CGACs from peripheral blood. A prospective correlational study was performed on 151 participants scheduled for a pulmonary nodule biopsy. Mann-Whitney, Fisher’s Exact and Chi-Square tests were used to assess participant demographics and correlation of LungLB™ with biopsy results, and sensitivity and specificity were also evaluated.ResultsParticipants from Mount Sinai Hospital (n = 83) and MD Anderson (n = 68), scheduled for a pulmonary biopsy were enrolled to have a LungLB™ test. Additional clinical variables including smoking history, previous cancer, lesion size, and nodule appearance were also collected. LungLB™ achieved 77% sensitivity and 72% specificity with an AUC of 0.78 for predicting lung cancer in the associated needle biopsy. Multivariate analysis found that clinical and radiological factors commonly used in malignancy prediction models did not impact the test performance. High test performance was observed across all participant characteristics, including clinical categories where other tests perform poorly (Mayo Clinic Model, AUC = 0.52).ConclusionEarly clinical performance of the LungLB™ test supports a role in the discrimination of benign from malignant pulmonary nodules. Extended studies are underway.

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.

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.

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.