Lung Cancer Nodules Research Articles

Immunogenomic features of radiologically distinctive nodules in multiple primary lung cancer

ObjectivesTo provide molecular and immunological attributes mechanistic insights for the management of radiologically distinctive multiple primary lung cancer (MPLC).MethodsThe Bulk RNA-seq data of MPLC were obtained from our center. The Bulk RNA-seq data and CT images of patients with single primary lung cancer (SPLC) were obtained from GSE103584. Immune infiltration algorithms were performed to investigate the disparities in the immunological microenvironment between the two groups. Single-cell gene analysis was used to explore immune cells composition and communication relationships between cells in MPLC.ResultsIn MPLC, 11 pure ground-glass opacity nodules (pGGN) and 10 mixed GGN (mGGN) were identified, while in SPLC, the numbers were 18 pGGN and 22 mGGN, respectively. In MPLC, compared to pGGN, mGGN demonstrated a significantly elevated infiltration of CD8+ T cells. Single-cell gene analysis demonstrated that CD8+ T cells play a central role in the signaling among immune cells in MPLC. The transcription factors including MAFG, RUNX3, and TBX21 may play pivotal roles in regulation of CD8+ T cells. Notably, compared to SPLC nodules for both mGGN and pGGN, MPLC nodules demonstrated a significantly elevated degree of tumor-infiltrating immune cells, with this difference being particularly pronounced in mGGN. There was a positive correlation between the proportion of immune cells and consolidation/tumor ratio (CTR).ConclusionsOur findings provided a comprehensive description about the difference in the immune microenvironment between pGGN and mGGN in early-stage MPLC, as well as between MPLC and SPLC for both mGGN and pGGN. The findings may provide evidence for the design of immunotherapeutic strategies for MPLC.

Diagnostic accuracy of CT and PET/CT radiomics in predicting lymph node metastasis in non-small cell lung cancer.

This study evaluates the accuracy of radiomics in predicting lymph node metastasis in non-small cell lung cancer, which is crucial for patient management and prognosis. Adhering to PRISMA and AMSTAR guidelines, we systematically reviewed literature from March 2012 to December 2023 using databases including PubMed, Web of Science, and Embase. Radiomics studies utilizing computed tomography (CT) and positron emission tomography (PET)/CT imaging were included. The quality of studies was appraised with QUADAS-2 and RQS tools, and the TRIPOD checklist assessed model transparency. Sensitivity, specificity, and AUC values were synthesized to determine diagnostic performance, with subgroup and sensitivity analyses probing heterogeneity and a Fagan plot evaluating clinical applicability. Our analysis incorporated 42 cohorts from 22 studies. CT-based radiomics demonstrated a sensitivity of 0.84 (95% CI: 0.79-0.88, p < 0.01) and specificity of 0.82 (95% CI: 0.75-0.87, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.92), indicating no publication bias (p-value = 0.54 > 0.05). PET/CT radiomics showed a sensitivity of 0.82 (95% CI: 0.76-0.86, p < 0.01) and specificity of 0.86 (95% CI: 0.81-0.90, p < 0.01), with an AUC of 0.90 (95% CI: 0.87-0.93), with a slight publication bias (p-value = 0.03 < 0.05). Despite high clinical utility, subgroup analysis did not clarify heterogeneity sources, suggesting influences from possible factors like lymph node location and small subgroup sizes. Radiomics models show accuracy in predicting lung cancer lymph node metastasis, yet further validation with larger, multi-center studies is necessary. Radiomics models using CT and PET/CT imaging may improve the prediction of lung cancer lymph node metastasis, aiding personalized treatment strategies. International Prospective Register of Systematic Reviews (PROSPERO), CRD42023494701. This study has been registered on the PROSPERO platform with a registration date of 18 December 2023. https://www.crd.york.ac.uk/prospero/ KEY POINTS: The study explores radiomics for lung cancer lymph node metastasis detection, impacting surgery and prognosis. Radiomics improves the accuracy of lymph node metastasis prediction in lung cancer. Radiomics can aid in the prediction of lymph node metastasis in lung cancer and personalized treatment.

Predicting non-small cell lung cancer lymph node metastasis: integrating ctDNA mutation/methylation profiling with positron emission tomography-computed tomography (PET-CT) scan: protocol for a prospective clinical trial (LUNon-invasive Study).

Non-small cell lung cancer (NSCLC) accounts for approximately 85% of lung cancer cases and remains a leading cause of cancer-related death. Lymph node metastasis (LNM) significantly affects recurrence, survival rates, and treatment options. While lymph node sampling is standard for surgically removing operable NSCLC, it can lead to complications. Positron emission tomography-computed tomography (PET-CT) helps assess preoperative LNM despite false positive or negative rates. Additionally, circulating tumor DNA (ctDNA) detects minimal residual disease with high sensitivity and specificity. Whether ctDNA can predict LNM in operable NSCLC remains uncertain. Our goal is to develop a precise model for predicting NSCLC LNM using non-invasive ctDNA/methylation profiling combined with PET-CT imaging. This is a prospective study conducted in three stages. We will enroll patients with clinical stage I-IIIB [8th tumor, node, metastasis (TNM) staging] NSCLC requiring lobectomy plus lymph node sampling/dissection. The distribution of clinical stages in the enrolled population is as follows: clinical stage cN0 (n=100) and cN1/cN2 (n=100). During Stage 1, we will establish LNMs-specific ctDNA methylation signatures and compare negative predictive value (NPV) rates of LNMs using preoperative blood ctDNA somatic mutation/methylation alone or combined with PET-CT across different groups. For Stage 2, we will compare detection rates between ctDNA somatic mutation/methylation profiles alone or combined with PET-CT and traditional mediastinoscopy/endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA). As for Stage 3, ctDNA-free interval (CFI) and disease-free survival between systematic lymph node presence and absence in patients will be compared with preoperative negative ctDNA profiling and/or PET-CT. In Stage 3, patients will be followed up for 5 years to collect recurrence and survival data. Post-surgery follow-up ctDNA tests will be conducted every 3 months for the first 2 years, every 6 months for years 3-4, and annually in year five. Demographics and baseline data will be summarized with mean, standard deviation, median, max, and min values. Tests will include t-tests, Welch/Behren-Fisher test, and Wilcoxon rank-sum test for continuous variables. Categorical data will be presented as counts/percentages and compared using χ2 test or Fisher's exact test. By utilizing preoperative ctDNA/methylation profiling in conjunction with PET-CT, this study is expected to yield substantial evidence for accurately predicting LNM before surgery. This will help inform surgeons in selecting the appropriate intraoperative lymph node dissection strategy for operable NSCLC patients. This study is registered on www.clinicaltrials.gov (NCT06358222).

Deep learning-based prediction of nodal metastasis in lung cancer using endobronchial ultrasound

ObjectiveEndobronchial ultrasound-guided transbronchial needle aspiration is a vital tool for mediastinal and hilar lymph node staging in patients with lung cancer. Despite its high diagnostic performance and safety, it has a limited negative predictive value. Our objective was to evaluate the diagnostic performance of deep learning-based prediction of lung cancer lymph node metastases using convolutional neural networks developed from automatically extracted images of endobronchial ultrasound videos without supervision of the lymph node location. MethodsPatient and lymph node data were collected from a single-center database. The diagnosis of metastasis was confirmed with endobronchial ultrasound-guided transbronchial needle aspiration and/or surgically resected specimens; the diagnosis of normal lymph node was confirmed with surgically resected specimens only. An annotation system facilitated automated image extraction from endobronchial ultrasound videos. Image frames were randomly selected and split into training and validation datasets on a per-patient basis. A deep learning model with convolutional neural networks, SqueezeNet, was used for image classification via transfer learning based on pretraining from ImageNet. Adaptive moment estimation and stochastic gradient descent were applied as optimizers. ResultsSqueezeNet, with adaptive moment estimation, achieved a sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 96.7% each after 300 epochs, whereas SqueezeNet with stochastic gradient descent achieved 91.1% each. However, SqueezeNet with stochastic gradient descent demonstrated more stable performance than with adaptive moment estimation. ConclusionsDeep learning-based image classification using convolutional neural networks showed promising diagnostic accuracy for lung cancer nodal metastasis. Future clinical trials are warranted to validate the algorithm's efficacy in a prospective, large-cohort study.

Application of tissue pneumoperitoneum technique around lymph nodes in thoracoscopic lung cancer resection.

Thoracoscopic surgery is a primary treatment for lung cancer, with lobectomy and mediastinal lymph node dissection being the predominant surgical approaches for invasive lung cancer. While many thoracic surgeons can proficiently perform lobectomy, thorough and standardized lymph node dissection remains challenging. This study aimed to explore a safer and more efficient surgical method for mediastinal lymph node dissection in lung cancer. A prospective randomized controlled study was conducted, involving 100 patients with right lung cancer who were admitted to our hospital from January 2021 to April 2024 and met the inclusion criteria. These patients were randomly divided into an observation group (tissue pneumoperitoneum technique around lymph nodes group) and a control group (conventional surgery group). Thoracoscopic lobectomy and mediastinal lymph node dissection were performed. Intraoperative and postoperative related indicators were observed to validate the effectiveness and safety of the tissue pneumoperitoneum technique around lymph nodes. The observation group showed a significantly shorter lymph node dissection surgery time compared to the control group, with a statistically significant difference (p < 0.05). The number of lymph nodes dissected in the observation group was significantly higher than that in the control group, with a statistically significant difference (p < 0.05). Although the observation group had slightly more mediastinal lymph node stations dissected than the control group, the difference was not statistically significant (p > 0.05). The total drainage volume within three days postoperatively was comparable between the two groups, with no statistically significant difference (p > 0.05). The observation group had shorter chest tube indwelling time and postoperative hospital stay than the control group, with statistically significant differences (p < 0.05). The incidence of surgical complications was similar between the two groups, and there were no perioperative deaths. The tissue pneumoperitoneum technique around lymph nodes is a more efficient method for mediastinal lymph node dissection in lung cancer, demonstrating safety and feasibility, and is worthy of promotion.

Growth dynamics of lung nodules: implications for classification in lung cancer screening

BackgroundLung nodules observed in cancer screening are believed to grow exponentially, and their associated volume doubling time (VDT) has been proposed for nodule classification. This retrospective study aimed to elucidate the growth dynamics of lung nodules and determine the best classification as either benign or malignant.MethodsData were analyzed from 180 participants (73.7% male) enrolled in the I-ELCAP screening program (140 primary lung cancer and 40 benign) with three or more annual CT examinations before resection. Attenuation, volume, mass and growth patterns (decelerated, linear, subexponential, exponential and accelerated) were assessed and compared as classification methods.ResultsMost lung cancers (83/140) and few benign nodules (11/40) exhibited an accelerated, faster than exponential, growth pattern. Half (50%) of the benign nodules versus 26.4% of the malignant ones displayed decelerated growth. Differences in growth patterns allowed nodule malignancy to be classified, the most effective individual variable being the increase in volume between two-year-interval scans (ROC-AUC = 0.871). The same metric on the first two follow-ups yielded an AUC value of 0.769. Further classification into solid, part-solid or non-solid, improved results (ROC-AUC of 0.813 in the first year and 0.897 in the second year).ConclusionsIn our dataset, most lung cancers exhibited accelerated growth in contrast to their benign counterparts. A measure of volumetric growth allowed discrimination between benign and malignant nodules. Its classification power increased when adding information on nodule compactness. The combination of these two meaningful and easily obtained variables could be used to assess malignancy of lung cancer nodules.

The value of net influx constant based on FDG PET/CT dynamic imaging in the differential diagnosis of metastatic from non-metastatic lymph nodes in lung cancer.

This study aims to evaluate the value of the dynamic and static quantitative metabolic parameters derived from 18F-fluorodeoxyglucose (FDG)-positron emission tomography/CT (PET/CT) in the differential diagnosis of metastatic from non-metastatic lymph nodes (LNs) in lung cancer and to validate them based on the results of a previous study. One hundred and twenty-one patients with lung nodules or masses detected on chest CT scan underwent 18F-FDG PET/CT dynamic + static imaging with informed consent. A retrospective collection of 126 LNs in 37 patients with lung cancer was pathologically confirmed. Static image analysis parameters include LN-SUVmax and LN-SUVmax/primary tumor SUVmax (LN-SUVmax/PT-SUVmax). Dynamic metabolic parameters including the net influx rate (Ki) and the surrogate of perfusion (K1) and of each LN were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. Ki/K1 was then calculated as a separate marker. Based on the pathological findings, we divided into a metastatic group and a non-metastatic group. The χ2 test was used to evaluate the agreement of the individual and combined diagnosis of each metabolic parameter with the gold standard. The receiver-operating characteristic (ROC) analysis was performed for each parameter to determine the diagnostic efficacy in differentiating non-metastatic from metastatic LNs with high FDG-avid. P < 0.05 was considered statistically significant. Among the 126 FDG-avid LNs confirmed by pathology, 70 LNs were metastatic, and 56 LNs were non-metastatic. For ROC analysis, in separate assays, the dynamic metabolic parameter Ki [sensitivity (SEN) of 84.30%, specificity (SPE) of 94.60%, accuracy of 88.89%, and AUC of 0.895] had a better diagnostic value than the static metabolic parameter SUVmax (SEN of 82.90%, SPE of 62.50%, accuracy of 74.60%, and AUC of 0.727) in differentiating between metastatic from non-metastatic LNs groups, respectively. In the combined diagnosis group, the combined SUVmax + Ki diagnosis had a better diagnostic value in the differential diagnosis of metastatic from non-metastatic LNs, with SEN, SPE, accuracy, and AUC of 84.3%, 94.6%, 88.89%, and 0.907, respectively. When the cutoff value of Ki was 0.022ml/g/min, it had a high diagnostic value in the differential diagnosis between metastasis and non-metastasis in FDG-avid LNs of lung cancer, especially in improving the specificity. The combination of SUVmax and Ki is expected to be a reliable metabolic parameter for N-staging of lung cancer.

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.

Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data. Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features. For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer. This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

Preoperative computed tomography semantic features in predicting lymph node metastasis of part-solid nodules in non-small cell lung cancer: a multicenter retrospective study.

Lymph node metastasis (LNM) is the most common route of metastasis for lung cancer, and it is an independent risk factor for long-term survival and recurrence in patients with non-small cell lung cancer (NSCLC). The purpose of this study was to explore the value of preoperative computed tomography (CT) semantic features in the differential diagnosis of LNM in part-solid nodules (PSNs) of NSCLC. A total of 955 patients with NSCLC confirmed by postoperative pathology were retrospectively enrolled from January 2019 to March 2023. The clinical, pathological data and preoperative CT images of these patients were investigated and statistically analyzed in order to identify the risk factors for LNM. Multivariate logistic regression was used to select independent risk factors and establish different prediction models. Ten-fold cross-validation was used for model training and validation. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated, and the Delong test was used to compare the predictive performance between the models. LNM occurred in 68 of 955 patients. After univariate analysis and adjustment for confounding factors, smoking history, pulmonary disease, solid component proportion, pleural contact type, and mean diameter were identified as the independent risk factors for LNM. The image predictors model established by the four independent factors of CT semantic features, except smoking history, showed a good diagnostic efficacy for LNM. The AUC in the validation group was 0.857, and the sensitivity, specificity, and accuracy of the model were all 77.6%. Preoperative CT semantic features have good diagnostic value for the LNM of NSCLC. The image predictors model based on pulmonary disease, solid component proportion, pleural contact type, and mean diameter demonstrated excellent diagnostic efficacy and can provide non-invasive evaluation in clinical practice.

Advances and Applications of Three-Dimensional-Printed Patient-Specific Chest Phantoms in Radiology: A Systematic Review

Lung cancer screening would benefit from low-dose CT protocols optimized by means of a highly accurate three-dimensional radiation-equivalent thoracic phantom. However, whether three-dimensional (3D)-printed chest phantoms have been used for this purpose is unclear, as is their current scope of application. This systematic review aims to explore the range of applications of 3D-printed thoracic phantoms, along with the techniques, materials, and anatomical structures they replicate. Relevant articles were identified using a systematic search strategy across PubMed and Scopus databases, based on pre-determined selection criteria. In total, 20 articles were eligible and critically analysed, all consisting of phantom experiments. Findings reveal that a diverse range of thoracic organs have been 3D-printed, predominantly via fused-deposition modelling incorporating polylactic acid, however, often representing discreet or limited structures. A comprehensive radiation-equivalent chest phantom that mimics the full gamut of thoracic structures is warranted. Most studies are still in their preliminary testing stages, primarily assessing the feasibility of creating morphologically accurate thoracic structures with radiation equivalence. Few studies have progressed to explore their applications. Notably, most investigations into applications have concentrated on dose reduction and CT protocol optimisation for cardiac purposes, rather than pulmonary applications, despite the inclusion of lung cancer nodules in some phantoms.

Characterizing the accuracy of robotic bronchoscopy in localization & targeting of small pulmonary lesions.

Considering the recent implementation of lung cancer screening guidelines, it is crucial that small pulmonary nodules are accurately diagnosed. There is a significant need for quick, precise, and minimally invasive biopsy methods, especially for patients with small lung lesions in the outer periphery. Robotic bronchoscopy (RB) has recently emerged as a novel solution. The purpose of this study was to evaluate the accuracy of RB compared to the existing standard, electromagnetic navigational bronchoscopy (EM-NB). A prospective, single-blinded, and randomized-controlled study was performed to compare the accuracy of RB to EM-NB in localizing and targeting pulmonary lesions in a porcine lung model. Four operators were tasked with navigating to four pulmonary targets in the outer periphery of a porcine lung, to which they were blinded, using both the RB and EM-NB systems. The dependent variable was accuracy. Accuracy was measured as a rate of success in lesion localization and targeting, the distance from the center of the pulmonary target, and by anatomic location. The independent variable was the navigation system, RB was compared to EM-NB using 1:1 randomization. Of 75 attempts, 72 were successful in lesion localization and 60 were successful in lesion targeting. The success rate for lesion localization was 100% with RB and 91% with EM- NB. The success rate for lesion targeting was 93% with RB and 80% for EM-NB. RB demonstrated superior accuracy in reaching the distance from the center of the lesion, at 0.62mm compared to EM-NB at 1.28mm (p = 0.001). Accuracy was improved using RB compared to EM- NB for lesions in the LLL (p = 0.025), LUL (p < 0.001), and RUL (p < 0.001). Our findings support RB as a more accurate method of navigating and localizing small peripheral pulmonary targets when compared to standard EM-NB in a porcine lung model. This may be attributed to the ability of RB to reduce substantial tissue displacement seen with standard EM-NB navigation. As the development and application of RB advances, so will the ability to accurately diagnose small peripheral lung cancer nodules, providing patients with early-stage lung cancer the best possible outcomes.

Comprehensive genomic and transcriptomic profiling of pulmonary nodules in synchronous multiple primary lung cancer.

e20022 Background: Synchronous Multiple Primary Lung Cancer (sMPLC) exhibits distinct histopathological characteristics among pulmonary nodules. However, a comprehensive understanding of the somatic mutation landscape and transcriptome heterogeneity is lacking. Therefore, our study aims to meticulously investigate genomic distinctions among multiple pulmonary nodules within individual patients. Methods: We performed targeted sequencing on tumor specimens and conducted bulk RNA transcriptome analysis on 53 multiple nodules originating from 26 lung cancer patients. The multiple nodules from the same patient was determined as major nodule and minor nodule. Additionally, the tumor tissues underwent histopathological evaluation through H&amp;E staining, complemented by a comprehensive series of immunohistochemistry (IHC) analyses to detect protein expression. The detected protein markers encompassed PD-L1, Ki67, and others. Results: For the 53 nodule samples from 26 MPLCs patients, EGFR was the mostly mutated genes, and the TP53 mutation frequency demonstrated notably different between major and minor nodules. Furthermore, pathway enrichment analysis based on the differentially expressed genes (DEGs) between major and minor nodules revealed the significantly active cell cycle and p53 pathways in the major nodules. Additionally, both major and minor nodules demonstrated mostly similar immune microenvironment and PD-L1 protein expression, and a significantly higher expression of Ki67. A noteworthy suppression was observed in the immune microenvironment in nodules, revealed by the expression of macrophage, neutrophils, and NK cells. Furthermore, minor nodules exhibited a modestly elevated expression of macrophages compared to major nodules. Additionally, among the significantly up-regulated cell cycle related genes in the major nodules when compared with minor nodules, CCNE1 mRNA expression demonstrated significant correlation with poor prognosis in the lung cancer. Conclusions: This study validated molecular distinctions between samples from major and minor nodules in patients with sMPLC at both genomic and transcriptomic levels. The major nodules exhibited heightened activity in tumor cell proliferation pathways and demonstrated malignancy-related biological characteristics, which correlated with pathological assessment results.

Non-invasive diagnosis of nature of pulmonary nodules using the whole genome methylation sequencing of circulating cell-free DNA.

e20038 Background: The frequent false-positive results of lung cancer screening of low-dose computed tomography (LDCT) remains a challenge. In this study, we aimed to establish a novel panel of cancer-specific methylated regions detected from circulating cell-free DNA (cfDNA) for differentiating malignant pulmonary nodules (PNs) from benign lesions in high-risk patients. Methods: We performed DNA methylation profiling by high throughput DNA bisulfite sequencing in tissue samples (nodule size &lt; 3 cm in diameter) to learn methylation patterns that differentiate cancerous tumors from benign lesions. Then we filtered out differential methylation patterns in circulating cfDNA that are consistent with tissue expression patterns and built an assay for plasma sample classification. Area under the curve was computed for each receiving operation curve, and 95% confidence intervals were also estimated by bootstrapping with 1,000 iterations. Results: We first performed the whole genome methylation sequencing of 100 tissue samples from PNs to learn cancer-specific methylation patterns, and obtained 2,406 differential hypermethylated regions and 16,028 differential hypomethylated regions. These tissue-derived DNA methylation markers were further filtered using a training set of 42 plasma samples and 11 markers were ultimately identified to build a diagnostic prediction model. This methylation panel provided promising results in an independent validation set of additional 17 plasma samples, with a sensitivity of 66.67% (95% CI: 0.25–1.00) and a specificity of 79.17% for differentiating patients with malignant tumor (N = 12) from patients with benign lesions (n = 5). A further validation study of this methylation panel in the cancer genome atlas data set demonstrated significant discriminatory performance in distinguishing patients with lung cancer from subjects without malignancy (area under the curve = 0.88). Conclusions: Our model based on the measurement of circulating cfDNA methylation provide a minimally invasive procedure for differentiation of early-stage lung cancer and nonmalignant pulmonary nodules, and may guide management of positive LDCT screening results. Additional clinical studies with larger sample size are needed to establish the robustness of this model.

“Percentage” and “size” of residual viable tumor in lymph node, the performance in estimating pathologic response of lymph node in non-small cell lung cancer treated with neoadjuvant chemoimmunotherapy

There is no universally accepted method for evaluating lymph node metastasis (LNM) in non-small cell lung cancer (NSCLC) after neoadjuvant chemoimmunotherapy. Different protocols recommend evaluating the percentage of residual viable tumor (RVT%) and metastatic tumor size (MTS). Our aim was to determine the prognostic significance of RVT% and MTS, and identify the more effective parameter for pathological evaluating LNM. Two independent cohorts were collected (derivation, n = 84; external validation, n = 42). All patients exhibited metastatic cancer or treatment response in lymph nodes post-surgery. In the derivation cohort, we assessed the mean and largest values of MTS and RVT% in LNM, estimating their optimal cutoffs for event-free survival (EFS) using maximally selected rank statistics. Validation was subsequently conducted in the external validation cohort. The quality of prognostic factors was evaluated using the Area Under Curve (AUC). A positive association was identified between RVT% and MTS, but an absolute association could not be conclusively established. In the derivation cohort, neither the largest MTS (cutoff = 6 mm, p = 0.28), largest RVT% (cutoff = 75%, p = 0.23), nor mean RVT% (cutoff = 55%, p = 0.06) were associated with EFS. However, mean MTS (cutoff = 4.5 mm) in lymph nodes was statistically associated with EFS (p = 0.018), validated by the external cohort (p = 0.017). The prognostic value of MTS exceeded that of ypN staging in both cohorts, as evidenced by higher AUC values. The mean value of MTS can effectively serve as a parameter for the pathological evaluation of lymph nodes, with a threshold of 4.5 mm, closely linked to EFS. Its prognostic value outperforms that of ypN staging.

Enhancing cancer prediction in challenging screen-detected incident lung nodules using time-series deep learning

Lung cancer screening (LCS) using annual computed tomography (CT) scanning significantly reduces mortality by detecting cancerous lung nodules at an earlier stage. Deep learning algorithms can improve nodule malignancy risk stratification. However, they have typically been used to analyse single time point CT data when detecting malignant nodules on either baseline or incident CT LCS rounds. Deep learning algorithms have the greatest value in two aspects. These approaches have great potential in assessing nodule change across time-series CT scans where subtle changes may be challenging to identify using the human eye alone. Moreover, they could be targeted to detect nodules developing on incident screening rounds, where cancers are generally smaller and more challenging to detect confidently.Here, we show the performance of our Deep learning-based Computer-Aided Diagnosis model integrating Nodule and Lung imaging data with clinical Metadata Longitudinally (DeepCAD-NLM-L) for malignancy prediction. DeepCAD-NLM-L showed improved performance (AUC = 88%) against models utilizing single time-point data alone. DeepCAD-NLM-L also demonstrated comparable and complementary performance to radiologists when interpreting the most challenging nodules typically found in LCS programs. It also demonstrated similar performance to radiologists when assessed on out-of-distribution imaging dataset. The results emphasize the advantages of using time-series and multimodal analyses when interpreting malignancy risk in LCS.

Clinical application of serum seven tumour-associated autoantibodies in patients with pulmonary nodules

BackgroundThe incidence of pulmonary nodules is increasing because of the promotion and popularisation of low-dose computed tomography (LDCT) screening for populations with suspected lung cancer. However, a high rate of false positives and concerns regarding the radiation-related cancer risk of repeated CT scanning remain major obstacles to its wide application. This study aimed to investigate the clinical value of seven tumour-associated autoantibodies (7-TAAbs) in the differentiation of malignant pulmonary tumours from benign ones and the early detection of lung cancer in routine clinical practice. MethodsWe included 377 patients who underwent both the 7-TAAbs panel test and LDCT screening, and were diagnosed with pulmonary nodules using LDCT. An enzyme-linked immunosorbent assay (ELISA) was used to measure the serum levels antibodies for P53, PGP9.5, SOX2, GAGE7, GBU4-5, CAGE, and MAGE-A1. The relationships between the positive rates of the 7-TAAbs and the patient sex, and age, and the number, size, and composition of pulmonary nodules were analysed. We then statistically evaluated the clinical application value. ResultsThe positive rates of the 7-TAAbs did not correlate with sex, age, number, size, or composition of pulmonary nodules. The serum antibody level of GBU4-5 in patients with pulmonary nodules tended to increase with age; the serum antibody level of SOX2 tended to increase with nodule size and was the highest among patients with mixed ground-glass opacity (mGGO) nodules. The antibody positive rate for CAGE in female patients with pulmonary nodules was significantly higher than that in male patients (P < 0.05). The positive rate of GBU4-5 antibody in patients aged 60 years and above was higher than that in younger patients (P < 0.05). The positive rate of GAGE7 antibody in patients with pulmonary nodules sized 8–20 mm was also significantly higher than that in patients with pulmonary nodules sized less than 8 mm (P < 0.01). Significant differences were observed in the GAGE7 antibody levels of patients with pulmonary nodules of different compositions (P < 0.01). The positive rate of the 7-TAAbs panel test in patients with lung cancer was significantly higher than in patients with pulmonary nodules (P < 0.01). Serum levels of P53, SOX2, GBU4-5, and MAGE-A1 antibodies were significantly higher in patients with lung cancer than in those with pulmonary nodules (P < 0.05). ConclusionThe low positive rates of serum 7-TAAbs in patients with lung cancer and pulmonary nodules may be related to different case selection, population differences, geographical differences, different degrees of progression, and detection methods. The combined detection of 7-TAAbs has some clinical value for screening and early detection of lung cancer.

Clonal expansion of shared T cell receptors reveals the existence of immune commonality among different lesions of synchronous multiple primary lung cancer

The increase in the detection rate of synchronous multiple primary lung cancer (MPLC) has posed remarkable clinical challenges due to the limited understanding of its pathogenesis and molecular features. Here, comprehensive comparisons of genomic and immunologic features between MPLC and solitary lung cancer nodule (SN), as well as different lesions of the same patient, were performed. Compared with SN, MPLC displayed a lower rate of EGFR mutation but higher rates of BRAF, MAP2K1, and MTOR mutation, which function exactly in the upstream and downstream of the same signaling pathway. Considerable heterogeneity in T cell receptor (TCR) repertoire exists among not only different patients but also among different lesions of the same patient. Invasive lesions of MPLC exhibited significantly higher TCR diversity and lower TCR expansion than those of SN. Intriguingly, different lesions of the same patient always shared a certain proportion of TCR clonotypes. Significant clonal expansion could be observed in shared TCR clonotypes, particularly in those existing in all lesions of the same patient. In conclusion, this study provided evidences of the distinctive mutational landscape, activation of oncogenic signaling pathways, and TCR repertoire in MPLC as compared with SN. The significant clonal expansion of shared TCR clonotypes demonstrated the existence of immune commonality among different lesions of the same patient and shed new light on the individually tailored precision therapy for MPLC.

Cell-free DNA assay for malignancy classification of high-risk lung nodules

ObjectiveAlthough low-dose computed tomography has been proven effective to reduce lung cancer–specific mortality, a considerable proportion of surgically resected high-risk lung nodules were still confirmed pathologically benign. There is an unmet need of a novel method for malignancy classification in lung nodules. MethodsWe recruited 307 patients with high-risk lung nodules who underwent curative surgery, and 247 and 60 cases were pathologically confirmed malignant and benign lung lesions, respectively. Plasma samples from each patient were collected before surgery and performed low-depth (5×) whole-genome sequencing. We extracted cell-free DNA characteristics and determined radiomic features. We built models to classify the malignancy using our data and further validated models with 2 independent lung nodule cohorts. ResultsOur models using one type of profile were able to distinguish lung cancer and benign lung nodules at an area under the curve metrics of 0.69 to 0.91 in the study cohort. Integrating all the 5 base models using cell-free DNA profiles, the cell-free DNA–based ensemble model achieved an area under the curve of 0.95 (95% CI, 0.92-0.97) in the study cohort and 0.98 (95% CI, 0.96-1.00) in the validation cohort. At a specificity of 95.0%, the sensitivity reached 80.0% in the study cohort. With the same threshold, the specificity and sensitivity had similar performances in both validation cohorts. Furthermore, the performance of area under the curve reached 0.97 in both the study and validation cohorts when considering the radiomic profile. ConclusionsThe cell-free DNA profiles-based method is an efficient noninvasive tool to distinguish malignancies and high-risk but pathologically benign lung nodules.

Curating retrospective multimodal and longitudinal data for community cohorts at risk for lung cancer.

Large community cohorts are useful for lung cancer research, allowing for the analysis of risk factors and development of predictive models. A robust methodology for (1) identifying lung cancer and pulmonary nodules diagnoses as well as (2) associating multimodal longitudinal data with these events from electronic health record (EHRs) is needed to optimally curate cohorts at scale. In this study, we leveraged (1) SNOMED concepts to develop ICD-based decision rules for building a cohort that captured lung cancer and pulmonary nodules and (2) clinical knowledge to define time windows for collecting longitudinal imaging and clinical concepts. We curated three cohorts with clinical data and repeated imaging for subjects with pulmonary nodules from our Vanderbilt University Medical Center. Our approach achieved an estimated sensitivity 0.930 (95% CI: [0.879, 0.969]), specificity of 0.996 (95% CI: [0.989, 1.00]), positive predictive value of 0.979 (95% CI: [0.959, 1.000]), and negative predictive value of 0.987 (95% CI: [0.976, 0.994]) for distinguishing lung cancer from subjects with SPNs. This work represents a general strategy for high-throughput curation of multi-modal longitudinal cohorts at risk for lung cancer from routinely collected EHRs.

Preoperative evaluation of mediastinal lymph nodes in non-small cell lung cancer using [68Ga]FAPI-46 PET/CT: a prospective pilot study

PurposeMediastinal nodal staging is crucial for surgical candidate selection in non-small cell lung cancer (NSCLC), but conventional imaging has limitations often necessitating invasive staging. We investigated the additive clinical value of fibroblast activation protein inhibitor (FAPI) PET/CT, an imaging technique targeting fibroblast activation protein, for mediastinal nodal staging of NSCLC.MethodsIn this prospective pilot study, we enrolled patients scheduled for surgical resection of NSCLC based on specific criteria designed to align with indications for invasive staging procedures. Patients were included when meeting at least one of the following: (1) presence of FDG-positive N2 lymph nodes, (2) clinical N1 stage, (3) central tumor location or tumor diameter of ≥ 3 cm, and (4) adenocarcinoma exhibiting high FDG uptake. [68Ga]FAPI-46 PET/CT was performed before surgery after a staging workup including [18F]FDG PET/CT. The diagnostic accuracy of [68Ga]FAPI-46 PET/CT for “N2” nodes was assessed through per-patient visual assessment and per-station quantitative analysis using histopathologic results as reference standards.ResultsTwenty-three patients with 75 nodal stations were analyzed. Histopathologic examination confirmed that nine patients (39.1%) were N2-positive. In per-patient assessment, [68Ga]FAPI-46 PET/CT successfully identified metastasis in eight patients (sensitivity 0.89 (0.52–1.00)), upstaging three patients compared to [18F]FDG PET/CT. [18F]FDG PET/CT detected FDG-avid nodes in six (42.8%) of 14 N2-negative patients. Among them, five were considered non-metastatic based on calcification and distribution pattern, and one was considered metastatic. In contrast, [68Ga]FAPI-46 PET/CT correctly identified all non-metastatic patients solely based on tracer avidity. In per-station analysis, [68Ga]FAPI-46 PET/CT discriminated metastasis more effectively compared to [18F]FDG PET/CT-based (AUC of ROC curve 0.96 (0.88–0.99) vs. 0.68 (0.56–0.78), P < 0.001).Conclusion[68Ga]FAPI-46 PET/CT holds promise as an imaging tool for preoperative mediastinal nodal staging in NSCLC, with improved sensitivity and the potential to reduce false-positive results, optimizing the need for invasive staging procedures.