Surgery for pre- and minimally invasive lung adenocarcinoma

Predicting invasion in early-stage ground-glass opacity pulmonary adenocarcinoma: a radiomics-based machine learning approach

BACKGROUND

To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma.

METHODS

This retrospective study included pulmonary GGN patients who were histologically confirmed to have adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma cancer (IAC) from 2020 to 2023. CT images of all patients were automatically segmented and 107 radiomic features were obtained for each patient. Classification models were developed using random forest (RF) and cross-validation, including three one-versus-others models and one three-class model. For each model, features were ranked by normalized Gini importance, and a minimal subset was selected with a cumulative importance exceeding 0.9. These selected features were then used to train the final models. The models' performance metrics, including area under the curve (AUC), accuracy, sensitivity, and specificity, were computed. AUC and accuracy were compared to determine the final optimal method.

RESULTS

The study comprised 193 patients (mean age 54 ± 11 years, 65 men), including 65 AIS, 54 MIA, and 74 IAC, divided into one training cohort (N = 154) and one test cohort (N = 39). The final three-class RF model outperformed three individual one-versus-others models in distinguishing each class from the other two. For the multiclass classification model, the AUC, accuracy, sensitivity, and specificity were 0.87, 0.79, 0.62, and 0.88 for AIS; 0.90, 0.79, 0.54, and 0.89 for MIA; and 0.87, 0.69, 0.73, and 0.67 for IAC, respectively.

CONCLUSIONS

A radiomics-based multiclass RF model could effectively differentiate three types of pulmonary GGN, which enabled early diagnosis of GGO pulmonary adenocarcinoma.

Lung adenocarcinoma manifested as ground-glass nodules in teenagers: characteristics, surgical outcomes and management strategies

OBJECTIVES

Ground-glass nodules-featured lung cancer have been identified in some teenagers in recent years. This study aims to investigate the characteristics and surgical outcomes of these patients and explore proper management strategy.

METHODS

Patients aged ≤20 with incidentally diagnosed lung cancer were retrospectively reviewed from February 2016 to March 2023. Based on lymph node evaluation status, these patients were divided into non-lymph node evaluation and lymph node evaluation groups. The clinical and pathological characteristics were analysed.

RESULTS

A total of 139 teenage patients were included, with an obviously increased cases observed from 2019, corresponding to the COVID-19 pandemic. The median age of the 139 patients was 18 years (range 12-20). Eighty-five patients had pure ground-glass nodules, while others had mixed ground-glass nodules. The mean diameter of nodules was 8.87 ± 2.20 mm. Most of the patients underwent wedge resection (64%) or segmentectomy (31.7%). Fifty-two patients underwent lymph node sampling or dissection. None of these patients had lymph node metastasis. The majority of lesions were adenocarcinoma in situ (63 cases) and minimally invasive adenocarcinoma (72 cases), while four lesions were invasive adenocarcinoma. The median follow-up time was 2.46 years, and none of these patients experienced recurrence or death during follow-up. The lymph node evaluation group had longer hospital stays (P < 0.001), longer surgery time (P < 0.001), and greater blood loss (P = 0.047) than the non-lymph node evaluation group.

CONCLUSIONS

The COVID-19 pandemic significantly increased the number of teenage patients incidentally diagnosed with lung cancer, presenting as ground-glass nodules on CT scans. These patients have favourable surgical outcomes. We propose a management strategy for teenage patients, and suggest that sub-lobar resection without lymph node dissection may be an acceptable surgical procedure for these patients.

The Overlooked Cornerstone in Precise Medicine: Personalized Postoperative Surveillance Plan for NSCLC

Non-small cell lung cancer recurrence after curative-intent surgery remains a challenge despite advancements in treatment. We review postoperative surveillance strategies and their impact on overall survival, highlighting recommendations from clinical guidelines and controversies. Studies suggest no clear benefit from more intensive imaging, whereas computed tomography scans reveal promise in detecting recurrence. For early-stage disease, including ground-glass opacities and adenocarcinoma in situ or minimally invasive adenocarcinoma, less frequent surveillance may suffice owing to favorable prognosis. Liquid biopsy, especially circulating tumor deoxyribonucleic acid, holds potential for detecting minimal residual disease. Clinicopathologic factors and genomic profiles can also provide information about site-specific metastases. Machine learning may enable personalized surveillance plans on the basis of multi-omics data. Although precision medicine transforms non-small cell lung cancer treatment, optimizing surveillance strategies remains essential. Tailored surveillance strategies and emerging technologies may enhance early detection and improve patients' survival, necessitating further research for evidence-based protocols.

Differentiating the Invasiveness of Lung Adenocarcinoma Manifesting as Ground Glass Nodules: Combination of Dual-energy CT Parameters and Quantitative-semantic Features

RATIONALE AND OBJECTIVES

To evaluate the diagnostic performance of dual-energy CT (DECT) parameters and quantitative-semantic features for differentiating the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs).

MATERIALS AND METHODS

Between June 2022 and September 2023, 69 patients with 74 surgically resected GGNs who underwent DECT examinations were included. CT numbers on virtual monochromatic images were calculated at 40-130 keV generated from DECT. Quantitative morphological measurements and semantic features were evaluated on unenhanced CT images and compared between pathologically confirmed adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive lung adenocarcinoma (IAC). Multivariable logistic regression analysis was used to identify independent predictors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.

RESULTS

Monochromatic CT numbers at 40-130 keV were significantly higher in IAC than in AIS-MIA (all P < 0.05). Multivariate logistic analysis revealed that CT number of 130 keV (odds ratio [OR] = 1.02, P = 0.013), maximum cross-sectional long diameter (OR =1.40, P = 0.014), deep or moderate lobulation sign (OR =19.88, P = 0.005), and abnormal intranodular vessel morphology (OR = 25.57, P = 0.017) were independent predictors of IAC. The combined prediction model showed a favorable differentiation performance with an AUC of 0.966 (95.2% sensitivity, 94.3% specificity, 94.8% accuracy), which was significantly higher than that for each risk factor (AUC = 0.791-0.822, all P < 0.05).

CONCLUSION

A multi-parameter combined prediction model integrating monochromatic CT numbers from DECT and quantitative-semantic features is promising for the preoperative discrimination of IAC and AIS-MIA in GGN-predominant lung adenocarcinoma.

Operation room nursing based on humanized nursing mode combined with nitric oxide on rehabilitation effect after lung surgery

BACKGROUND

With advancements in the diagnosis and treatment of lung diseases, lung segment surgery has become increasingly common. Postoperative rehabilitation is critical for patient recovery, yet challenges such as complications and adverse outcomes persist. Incorporating humanized nursing modes and novel treatments like nitric oxide inhalation may enhance recovery and reduce postoperative complications.

AIM

To evaluate the effects of a humanized nursing mode combined with nitric oxide inhalation on the rehabilitation outcomes of patients undergoing lung surgery, focusing on pulmonary function, recovery speed, and overall treatment costs.

METHODS

A total of 79 patients who underwent lung surgery at a tertiary hospital from March 2021 to December 2021 were divided into a control group (n = 39) receiving a routine nursing program and an experimental group (n = 40) receiving additional humanized nursing interventions and atomized inhalation of nitric oxide. Key indicators were compared between the two groups alongside an analysis of treatment costs.

RESULTS

The experimental group demonstrated significant improvements in pulmonary function, reduced average recovery time, and lower total treatment costs compared to the control group. Moreover, the quality of life in the experimental group was significantly better in the 3 months post-surgery, indicating a more effective rehabilitation process.

CONCLUSION

The combination of humanized nursing mode and nitric oxide inhalation in postoperative care for lung surgery patients significantly enhances pulmonary rehabilitation outcomes, accelerates recovery, and reduces economic burden. This approach offers a promising reference for improving patient care and rehabilitation efficiency following lung surgery.

NGEF is a potential prognostic biomarker and could serve as an indicator for immunotherapy and chemotherapy in lung adenocarcinoma

BACKGROUND

Neuronal guanine nucleotide exchange factor (NGEF) plays a key role in several cancers; however, its role in lung adenocarcinoma (LUAD) remains unclear. The aim of this study was to evaluate the efficacy of NGEF as a prognostic biomarker and potential therapeutic target for LUAD.

METHODS

NGEF expression data for multiple cancers and LUAD were downloaded from multiple databases. The high- and low-NGEF expression groups were constructed based on median NGEF expression in LUAD samples, and then performed Kaplan-Meier survival analysis. Differentially expressed genes (DEGs) from the two NGEF expression groups were screened and applied to construct a protein-protein interaction network. The primary pathways were obtained using gene set enrichment analysis. The associations between NGEF expression and clinical characteristics, immune infiltration, immune checkpoint inhibitors (ICIs), sensitivity to chemotherapy, and tumor mutation burden (TMB) were investigated using R. Levels of NGEF expression in the lung tissue was validated using single-cell RNA sequencing, quantitative polymerase chain reaction (qPCR), immunohistochemical staining, and western blot analysis.

RESULTS

The expression of NGEF mRNA was upregulated in multiple cancers. mRNA and protein expression levels of NGEF were higher in patients with LUAD than in controls, as validated using qPCR and western blot. High NGEF expression was an independent prognostic factor for LUAD and was associated with advanced tumor stage, large tumor size, more lymph node metastasis, and worse overall survival (OS). A total of 182 overlapping DEGs were screened between The Cancer Genome Atlas and GSE31210, among which the top 20 hub genes were identified. NGEF expression was mainly enriched in the pathways of apoptosis, cell cycle, and DNA replication. Moreover, elevated NGEF expression were associated with a high fraction of activated memory CD4+ T cells and M0 macrophages; elevated expression levels of the ICIs: programmed cell death 1 and programmed cell death 1 ligand 1 expression; higher TMB; and better sensitivity to bortezomib, docetaxel, paclitaxel, and parthenolide, but less sensitivity to axitinib and metformin.

CONCLUSION

NGEF expression is upregulated in LUAD and is significantly associated with tumor stages, OS probability, immune infiltration, immunotherapy response, and chemotherapy response. NGEF may be a potential diagnostic and prognostic biomarker and therapeutic target in LUAD.

Extent of surgical resection for radiologically subsolid T1N0 invasive lung adenocarcinoma: When is a wedge resection acceptable?

OBJECTIVE

To evaluate whether wedge resection (WR) was appropriate for the patients with peripheral T1 N0 solitary subsolid invasive lung adenocarcinoma.

METHODS

Patients with peripheral T1N0 solitary subsolid invasive lung adenocarcinoma who received sublobar resection were retrospectively reviewed. Clinicopathologic characteristics, 5-year recurrence-free survival, and 5-year lung cancer-specific overall survival were analyzed. Cox regression model was used to elucidate risk factors for recurrence.

RESULTS

Two hundred fifty-eight patients receiving WR and 1245 patients receiving segmentectomy were included. The mean follow-up time was 36.87 ± 16.21 months. Five-year recurrence-free survival following WR was 96.89% for patients with ground-glass nodule (GGN) ≤2 cm and 0.25< consolidation-to-tumor ratio (CTR) ≤0.5, not statistically different from 100% for those with GGN≤2 cm and CTR ≤0.25 (P = .231). The 5-year recurrence-free survival was 90.12% for patients with GGN between 2 and 3 cm and CTR ≤0.5, significantly lower than that of patients with GGN ≤2 cm and CTR ≤0.25 (P = .046). For patients with GGN≤2 cm and 0.25

CONCLUSIONS

WR might be appropriate for patients with invasive lung adenocarcinoma appearing as peripheral GGN ≤2 cm and CTR ≤0.5, but inappropriate for those with invasive lung adenocarcinoma appearing as peripheral GGN between 2 and 3 cm and CTR ≤0.5.

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Key Words

• lung invasive adenocarcinoma
• segmentectomy
• sublobar resection
• subsolid nodule
• wedge resection

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MESH Headings

• Humans
• Retrospective Studies
• Neoplasm Staging
• Pneumonectomy/adverse effects
• Adenocarcinoma of Lung/diagnostic imaging
• Adenocarcinoma of Lung/surgery
• Lung Neoplasms/diagnostic imaging
• Lung Neoplasms/surgery

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Affiliation(s)

Chao Zhang Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Yunjian Pan Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Hang Li Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Yang Zhang Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Bin Li Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Yiliang Zhang Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Xiaoyang Luo Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Longsheng Miao Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Longfei Ma Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Sufeng Chen Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Hong Hu Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Yihua Sun Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Yawei Zhang Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Jiaqing Xiang Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China
Shengping Wang Department of Oncology, Fudan University, Shanghai, China; Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
Yajia Gu Department of Oncology, Fudan University, Shanghai, China; Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
Yuan Li Department of Oncology, Fudan University, Shanghai, China; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
Xuxia Shen Department of Oncology, Fudan University, Shanghai, China; Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
Zezhou Wang Department of Oncology, Fudan University, Shanghai, China; Department of Cancer Prevention, Fudan University Shanghai Cancer Center, Shanghai, China
Ting Ye Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China.
Haiquan Chen Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudan University Shanghai Cancer Center, Shanghai, China; Institute of Thoracic Oncology, Fudan University, Shanghai, China; Department of Oncology, Fudan University, Shanghai, China.

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The Clinical Relevance of the Expression of SGLT2 in Lung Adenocarcinoma

PURPOSE

We aimed to elucidate the functions and clinical relevance of sodium-glucose cotransporter 2 (SGLT2) in resected lung adenocarcinoma.

METHODS

The protein expression of SGLT2 in tumor samples from 199 patients with lung adenocarcinoma was analyzed by immunohistochemistry, and the protein expression, clinical variables, and survival outcomes were compared.

RESULTS

The median SGLT2 expression was significantly higher in advanced-stage and more aggressive adenocarcinomas. Age ≥70 (p < 0.01), BI ≥600 (p < 0.01), PRDX4 <25 (p < 0.01), and SGLT2 ≥12% (p = 0.03) were significant factors for RFS in multivariate analysis. Significant differences were observed in the RFS rates of the groups divided using the cutoff value of SGLT2 ≥12% (5-year RFS: 72.6% vs. 90%) (p < 0.01).

CONCLUSION

The expression of SGLT2 was more frequently detected in advanced-stage and more aggressive adenocarcinomas with aggressive biological behavior than in their counterparts. The survival analysis revealed that the strong expression of SGLT2 was associated with poorer RFS. The SGLT2 expression predicts postoperative recurrence in lung adenocarcinoma patients.

Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT

BACKGROUND. Pure ground-glass nodules (pGGNs) on chest CT representing invasive adenocarcinoma (IAC) warrant lobectomy with lymph node resection. For pGGNs representing other entities, close follow-up or sublobar resection without node dissection may be appropriate. OBJECTIVE. The purpose of this study was to develop and validate an automated deep learning model for differentiation of pGGNs on chest CT representing IAC from those representing atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and minimally invasive adenocarcinoma (MIA). METHODS. This retrospective study included 402 patients (283 women, 119 men; mean age, 53.2 years) with a total of 448 pGGNs on noncontrast chest CT that were resected from January 2019 to June 2022 and were histologically diagnosed as AAH (n = 29), AIS (n = 83), MIA (n = 235), or IAC (n = 101). Lung-PNet, a 3D deep learning model, was developed for automatic segmentation and classification (probability of IAC vs other entities) of pGGNs on CT. Nodules resected from January 2019 to December 2021 were randomly allocated to training (n = 327) and internal test (n = 82) sets. Nodules resected from January 2022 to June 2022 formed a holdout test set (n = 39). Segmentation performance was assessed with Dice coefficients with radiologists' manual segmentations as reference. Classification performance was assessed by ROC AUC and precision-recall AUC (PR AUC) and compared with that of four readers (three radiologists, one surgeon). The code used is publicly available (https://github.com/XiaodongZhang-PKUFH/Lung-PNet.git). RESULTS. In the holdout test set, Dice coefficients for segmentation of IACs and of other lesions were 0.860 and 0.838, and ROC AUC and PR AUC for classification as IAC were 0.911 and 0.842. At threshold probability of 50.0% or greater for prediction of IAC, Lung-PNet had sensitivity, specificity, accuracy, and F1 score of 50.0%, 92.0%, 76.9%, and 60.9% in the holdout test set. In the holdout test set, accuracy and F1 score (p values vs Lung-PNet) for individual readers were as follows: reader 1, 51.3% (p = .02) and 48.6% (p = .008); reader 2, 79.5% (p = .75) and 75.0% (p = .10); reader 3, 66.7% (p = .35) and 68.3% (p < .001); reader 4, 71.8% (p = .48) and 42.1% (p = .18). CONCLUSION. Lung-PNet had robust performance for segmenting and classifying (IAC vs other entities) pGGNs on chest CT. CLINICAL IMPACT. This automated deep learning tool may help guide selection of surgical strategies for pGGN management.

Interpretable CT radiomics model for invasiveness prediction in patients with ground-glass nodules

AIM

To evaluate the performance of an interpretable computed tomography (CT) radiomic model in predicting the invasiveness of ground-glass nodules (GGNs).

MATERIALS AND METHODS

The study was conducted retrospectively from 1 August 2017 to 1 August 2022, at three different centres. Two hundred and thirty patients with GGNs were enrolled at centre I as a training cohort. Centres II (n=157) and III (n=156) formed two external validation cohorts. Radiomics features extracted based on CT were reduced by a coarse-fine feature screening strategy. A radiomic model was developed through the use of the LASSO (least absolute shrinkage and selection operator) and XGBoost algorithms. Then, a radiological model was established through multivariate logistic regression analysis. Finally, the interpretability of the model was explored using SHapley Additive exPlanations (SHAP).

RESULTS

The radiomic XGBoost model outperformed the radiomic logistic model and radiological model in assessing the invasiveness of GGNs. The area under the curve (AUC) values for the radiomic XGBoost model were 0.885 (95% confidence interval [CI] 0.836-0.923), 0.853 (95% CI 0.790-0.906), and 0.838 (95% CI 0.773-0.902) in the training and the two external validation cohorts, respectively. The SHAP method allowed for both a quantitative and visual representation of how decisions were made using a given model for each individual patient. This can provide a deeper understanding of the decision-making mechanisms within the model and the factors that contribute to its prediction effectiveness.

CONCLUSIONS

The present interpretable CT radiomics model has the potential to preoperatively evaluate the invasiveness of GGNs. Furthermore, it can provide personalised, image-based clinical-decision support.

A Shift in Paradigm: Selective Lymph Node Dissection for Minimizing Oversurgery in Early Stage Lung Cancer

Systematic lymph node dissection has been widely accepted and turned into a standard procedure for lung cancer surgery. In recent years, the concept of "minimal invasive surgery (MIS)" has greatly changed the surgical paradigm of lung cancer. Previous studies revealed that excessive dissection of lymph nodes without metastases had uncertain clinical benefit. Meanwhile, it leads to the elevated risk of postoperative complications including chylothorax and laryngeal nerve injury. In addition, dissection of nonmetastatic lymph nodes may disturb systematic immunity, resulting in the secondary effect on primary tumor or latent metastases. The past decades have witnessed the innovative strategies such as lobe-specific lymph node dissection and selective lymph node dissection. On the basis of evolution of lymph node dissection strategy, we discuss the negative effects of excessive nonmetastatic lymph node dissection and summarize the recent advances in the optimized dissection strategies, hoping to provide unique perspectives on the future directions.

Rapid On-Site AI-Assisted Grading for Lung Surgery Based on Optical Coherence Tomography

The determination of resection extent traditionally relies on the microscopic invasiveness of frozen sections (FSs) and is crucial for surgery of early lung cancer with preoperatively unknown histology. While previous research has shown the value of optical coherence tomography (OCT) for instant lung cancer diagnosis, tumor grading through OCT remains challenging. Therefore, this study proposes an interactive human-machine interface (HMI) that integrates a mobile OCT system, deep learning algorithms, and attention mechanisms. The system is designed to mark the lesion's location on the image smartly and perform tumor grading in real time, potentially facilitating clinical decision making. Twelve patients with a preoperatively unknown tumor but a final diagnosis of adenocarcinoma underwent thoracoscopic resection, and the artificial intelligence (AI)-designed system mentioned above was used to measure fresh specimens. Results were compared to FSs benchmarked on permanent pathologic reports. Current results show better differentiating power among minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IA), and normal tissue, with an overall accuracy of 84.9%, compared to 20% for FSs. Additionally, the sensitivity and specificity, the sensitivity and specificity were 89% and 82.7% for MIA and 94% and 80.6% for IA, respectively. The results suggest that this AI system can potentially produce rapid and efficient diagnoses and ultimately improve patient outcomes.

A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence

PURPOSE

To construct a nomogram based on subjective CT signs and artificial intelligence (AI) histogram parameters to identify invasiveness of lung adenocarcinoma presenting as pure ground-glass nodules (pGGNs) and to evaluate its diagnostic performance.

METHODS

187 patients with 228 pGGNs confirmed by postoperative pathology were collected retrospectively and divided into pre-invasive group [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS)] and invasive group [minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC)]. All pGGNs were randomly assigned to training cohort (n = 160) and validation cohort (n = 68). Nomogram was developed using subjective CT signs and AI-based histogram parameters by logistic regression analysis. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) curve.

RESULTS

The nomogram was constructed with nodule shape, 3D mean diameter, maximum CT value, and skewness. It showed better discriminative power in differentiating invasive lesions from pre-invasive lesions with area under curve (AUC) of 0.849 (95% CI 0.790-0.909) in the training cohort and 0.831 (95% CI 0.729-0.934) in the validation cohort, which performed better than nodule shape (AUC 0.675, 95% CI 0.609-0.741), 3D mean diameter (AUC 0.762, 95% CI 0.688-0.835), maximum CT value (AUC 0.794, 95% CI 0.727-0.862), or skewness (AUC 0.594, 95% CI 0.506-0.682) alone in training cohort (for all, P < 0.05).

CONCLUSION

For pulmonary pGGNs, the nomogram based on subjective CT signs and AI histogram parameters had a good predictive ability to discriminate invasive lung adenocarcinoma from pre-invasive lung adenocarcinoma, and it has the potential to improve diagnostic efficiency and to help the patient management.

Short- and long-term outcomes of robotic-assisted versus video-assisted thoracoscopic lobectomy in non-small cell lung cancer patients aged 35 years or younger: a real-world study with propensity score-matched analysis

PURPOSE

This study compared short- and long-term outcomes of robotic-assisted thoracoscopic surgery (RATS) versus video-assisted thoracoscopic surgery (VATS) for lobectomy in young adults aged ≤ 35 years with non-small cell lung cancer (NSCLC), aiming to assess the superiority of RATS over VATS for this special group of patients.

METHODS

A total of 1355 consecutive NSCLC cases aged 18-35 years undergoing RATS (n = 105) or VATS (n = 1250) between 2014 and 2021 were retrospectively identified from a prospectively maintained database. Propensity score matching (PSM) was applied to establish a 1:3 RATS versus VATS ratio. Baseline clinicopathological characteristics, perioperative outcomes, lymph node (LN) assessment, and long-term survival were investigated.

RESULTS

Following PSM, 105 and 315 cases were in the RATS and VATS groups, respectively. RATS led to a shorter postoperative hospital stay than VATS (4.0 ± 1.5 vs 4.3 ± 1.7 days, p = 0.02). The two groups were comparable in other perioperative outcomes and postoperative complications (all p > 0.05). Moreover, RATS assessed more LNs (9.4 ± 4.4 vs 8.3 ± 3.6, p = 0.03), especially N1 LNs (4.2 ± 3.1 vs 3.5 ± 2.2, p = 0.02), than VATS. By comparison, no difference in 5-year recurrence-free survival (RFS), overall survival (OS), or recurrence or mortality patterns was found between the two groups (all p > 0.05). Further subgroup analyses also observed similar long-term outcomes between the two groups regarding age, gender, and smoking history. Finally, Cox's analyses found that the surgical approach was not independently correlated with RFS or OS.

CONCLUSION

RATS shortened postoperative hospital stay, assessed more N1 and total LNs, and achieved comparable long-term outcomes to VATS for very young NSCLC patients.

Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules

Objectives

To explore the value of radiomics based on Dual-energy CT (DECT) for discriminating preinvasive or MIA from IA appearing as GGNs before surgery.

Methods

The retrospective study included 92 patients with lung adenocarcinoma comprising 30 IA and 62 preinvasive-MIA, which were further divided into a training (n=64) and a test set (n=28). Clinical and radiographic features along with quantitative parameters were recorded. Radiomics features were derived from virtual monoenergetic images (VMI), including 50kev and 150kev images. Intraclass correlation coefficients (ICCs), Pearson's correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were conducted to eliminate unstable and redundant features. The performance of the models was evaluated by area under the curve (AUC) and the clinical utility was assessed using decision curve analysis (DCA).

Results

The DECT-based radiomics model performed well with an AUC of 0.957 and 0.865 in the training and test set. The clinical-DECT model, comprising sex, age, tumor size, density, smoking, alcohol, effective atomic number, and normalized iodine concentration, had an AUC of 0.929 in the training and 0.719 in the test set. In addition, the radiomics model revealed a higher AUC value and a greater net benefit to patients than the clinical-DECT model.

Conclusion

DECT-based radiomics features were valuable in predicting the invasiveness of GGNs, yielding a better predictive performance than the clinical-DECT model.

Ten-Year Follow-up Results of Pure Ground-Glass Opacity-Featured Lung Adenocarcinomas After Surgery

BACKGROUND

Previously, we have demonstrated that the 5-year recurrence-free survival after surgery of pure ground-glass opacity (GGO)-featured lung adenocarcinoma is 100%. This study aimed to reveal the long-term outcomes of these patients 10 years after surgery.

METHODS

Lung adenocarcinoma patients who underwent surgery between December 2007 and December 2013 were reviewed. Patients with pure GGO-featured lung adenocarcinoma were enrolled. Postoperative survival and the risk of developing second primary lung cancer were analyzed.

RESULTS

Overall, 308 cases of pure GGO-featured lung adenocarcinomas were included. Of these patients, 226 (73.4%) were female, 268 (87.0%) were nonsmokers, and 187 (60.7%) underwent sublobar resection. The median follow-up period after surgery was 112 months. The 10-year recurrence-free survival rate of these patients was 100%, and 10-year overall survival rate was 96.9%. Both 5-year and 10-year lung cancer-specific survival were 100%. There was no difference in 10-year recurrence-free survival rates between patients who underwent lobectomy or sublobar resection (P = .697). EGFR mutations were detected in 55.6% (84 of 151) of patients who underwent mutational analysis. The risk of developing secondary primary lung cancer for pure GGO-featured lung adenocarcinoma patients at 10 years after resection was 2.4%, and was not correlated with EGFR mutation status (P = .452).

CONCLUSIONS

No recurrence was observed in patients with pure GGO-featured lung adenocarcinomas 10 years after surgery, even when pathologically evaluated as invasive adenocarcinoma. Pure GGO can be cured by surgery. Surgery is recommended for the appropriate time window with the view to cure. Our study emphasizes that radiologic pure GGO-featured lung adenocarcinomas should be distinguished from other lung adenocarcinomas.

Impact of Prior Cancer History on Outcomes of Resected Lung Cancer

BACKGROUND

Previous studies concerning the impact of prior cancer on newly diagnosed lung cancer are mainly based on databases and obtained mixed results. Utilizing a large study population, we aimed to reveal this impact.

PATIENTS AND METHODS

Lung cancer patients from January 2008 to April 2021 were enrolled. The Kaplan-Meier method was used to perform survival analyses. To investigate the impact of prior cancer, a Cox proportional hazards model was conducted. To minimize the influence of the heterogeneity of prior cancer, stratified analyses were carried out.

RESULTS

In total, 17,423 lung cancer patients were reviewed, among which we identified 1469 (8.4%) patients with a history of prior cancer. Cox regression analysis revealed that prior cancer was an independent poor prognostic factor on overall survival (HR = 1.430, 95% CI: 1.147-1.784, p = 0.001) but did not affect lung cancer-specific survival (HR = 1.120, 95% CI: 0.876-1.434, p = 0.366). Interestingly, in further stratified analyses, we found that prior cancer history affected overall survival only in pTNM stage 0/I patients (HR = 1.670, 95% CI: 1.247-2.237, p = 0.001), but not in pTNM stage II/III/IV patients (HR = 1.237, 95% CI: 0.877-1.743, p = 0.226). Similarly, prior cancer was an independent poor prognostic factor on overall survival only for pN0 patients. Subsequently, subgroup analyses indicated that the impact of prior cancer varied in pTNM stage 0/I patients according to the type of prior cancer and the interval time.

CONCLUSIONS

Considering that prior cancer affects overall survival in patients with clinically curable lung cancer, clinicians should pay attention to this effect and improve the management of these patients to achieve a better prognosis.

Predicting the pathological invasiveness of early lung adenocarcinoma prior to surgery using Deauville criteria: reliability and validity

PURPOSE

This retrospective study aimed to investigate the validity and reliability of FDG-PET/CT visual assessment using Deauville criteria to predict pathological invasiveness of early lung adenocarcinoma prior to surgery.

MATERIALS AND METHODS

Between April 2020 and January 2022, 51 patients who underwent surgery for pathological stage 0/I lung adenocarcinoma were enrolled. The pulmonary lesions were divided into two groups according to pathological invasiveness: less invasive (including adenocarcinoma in situ and minimally invasive adenocarcinoma and invasive adenocarcinoma. We compared CT size (total and solid size), SUVmax, and Deauville score between the two groups. Furthermore, we investigated inter-rater and intra-rater agreements regarding the Deauville score. Receiver operating characteristic (ROC) curve analysis was performed to identify the diagnostic performance of each method.

RESULTS

Based on pathologic diagnoses, 51 lesions in the 51 patients were divided into 6 less invasive and 45 invasive adenocarcinoma lesions. According to quadratic-weighted Kappa statistics, inter-rater (k = 0.93) and intra-rater (k = 0.97) agreements among all five components of the Deauville score indicated high agreement. There was a statistically significant difference in CT solid size, SUVmax, and Deauville score between the two groups. There were no significant differences between CT solid size and FDG-PET/CT assessments (AUC = 0.93 for Deauville score and SUVmax, AUC = 0.84 for CT solid size).

CONCLUSION

FDG-PET/CT visual assessment using the Deauville score could assist in deciding upon minimally invasive surgery for early lung adenocarcinoma.

Molecular characteristics of multiple primary pulmonary nodules under a three-dimensional reconstruction model and relevant multi-omics analyses: a case report

Background

In addition to CT images and pathological features, many other molecular characteristics remain unknown about multiple primary lung cancer (MPLC) from intrapulmonary metastatic lung cancer.

Case presentation

In this study, we reported a patient with an early-stage MPLC with both adenocarcinoma in situ (AIS) subtype and minimally invasive adenocarcinoma (MIA) subtype. The patient was diagnosed with more than 10 nodules and underwent precise surgery assisted by three-dimensional (3D) reconstruction at the left upper lung lobe. Whole-exome sequencing (WES) and multiple immunohistochemistry (mIHC) were performed to reveal the genomic profiling and tumor microenvironments of multiple nodules in this patient with MPLC. Based on 3D reconstruction location information, we found that the genomic and pathological results of adjacent lymph nodes were quite different. On the other hand, PD-L1 expression and the proportion of infiltrating lymphocytes in tumor microenvironments were all at a low status and did not vary in adjacent lymph nodes. Additionally, maximum diameter and tumor mutational burden levels were found to be significantly associated with CD8+ T cell proportion (p<0.05). Besides, CD163+ macrophages and CD4+ T cell proportion were higher in MIA nodules than in AIS nodules (p<0.05). This patient reached a recurrence-free survival of 39 months.

Conclusion

Generally, in addition to CT imaging and pathological results, genomic profiling and tumor microenvironments may facilitate identifying the potential molecular mechanisms and clinical outcomes in patients with early-stage MPLC.

CT-Based Radiomic Analysis for Preoperative Prediction of Tumor Invasiveness in Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodule

It remains a challenge to preoperatively forecast whether lung pure ground-glass nodules (pGGNs) have invasive components. We aimed to construct a radiomic model using tumor characteristics to predict the histologic subtype associated with pGGNs. We retrospectively reviewed clinicopathologic features of pGGNs resected in 338 patients with lung adenocarcinoma between 2011-2016 at a single institution. A radiomic prediction model based on forward sequential selection and logistic regression was constructed to differentiate adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma. The study cohort included 133 (39.4%), 128 (37.9%), and 77 (22.8%) patients with AIS, MIA, and invasive adenocarcinoma (acinar 55.8%, lepidic 33.8%, papillary 10.4%), respectively. The majority (83.7%) underwent sublobar resection. There were no nodal metastases or tumor recurrence during a mean follow-up period of 78 months. Three radiomic features-cluster shade, homogeneity, and run-length variance-were identified as predictors of histologic subtype and were selected to construct a prediction model to classify the AIS/MIA and invasive adenocarcinoma groups. The model achieved accuracy, sensitivity, specificity, and AUC of 70.6%, 75.0%, 70.0%, and 0.7676, respectively. Applying the developed radiomic feature model to predict the histologic subtypes of pGGNs observed on CT scans can help clinically in the treatment selection process.

Cost and effectiveness of microwave ablation versus video-assisted thoracoscopic surgical resection for ground-glass nodule lung adenocarcinoma

Purpose

To retrospectively evaluate the cost and effectiveness in consecutive patients with ground-glass nodules (GGNs) treated with video-assisted thoracoscopic surgery (VATS; i.e., wedge resection or segmentectomy) or microwave ablation (MWA).

Materials and methods

From May 2017 to April 2019, 204 patients who met our study inclusion criteria were treated with VATS (n = 103) and MWA (n = 101). We calculated the rate of 3-year overall survival (OS), local progression-free survival (LPFS), and cancer−specific survival (CSS), as well as the cost during hospitalization and the length of hospital stay.

Results

The rates of 3-year OS, LPFS, and CSS were 100%, 98.9%, and 100%, respectively, in the VATS group and 100%, 100% (p = 0.423), and 100%, respectively, in the MWA group. The median cost of VATS vs. MWA was RMB 54,314.36 vs. RMB 21,464.98 (p < 0.001). The length of hospital stay in the VATS vs. MWA group was 10.0 vs. 6.0 d (p < 0.001).

Conclusions

MWA had similar rates of 3-year OS, LPFS, and CSS for patients with GGNs and a dramatically lower cost and shorter hospital stay compared with VATS. Based on efficacy and cost, MWA provides an alternative treatment option for patients with GGNs.

A Ferroptosis-Related Gene Signature for Overall Survival Prediction and Immune Infiltration in Lung Squamous Cell Carcinoma

Background: Ferroptosis is associated with cancer initiation and progression. However, the molecular mechanism and prognostic value of ferroptosis-related genes in lung squamous cell carcinoma (LUSC) are poorly understood.

Methods: The mRNA expression profiles, methylation data, and clinical information of patients with LUSC were downloaded from TCGA and GEO database. Ferroptosis-related differentially expressed genes (DEGs) were identified between cancerous and non-cancerous tissues, and their prognostic value was systemically investigated by bioinformatic analyses.

Results: A ferroptosis-related gene signature (ALOX5, TFRC, PHKG2, FADS2, NOX1) was constructed using multivariate Cox regression analysis and represented as a risk score. Overall survival (OS) probability was significantly lower in the high-risk group than in the low-risk group (P<0.001), and receiver operating characteristic curve showed a good predictive capacity (AUC = 0.739). The risk score was an independent prognostic factor for LUSC. A nomogram was constructed to predict the OS probabilities at 1, 3, and 5 years. High-risk score was associated with increased immune infiltration, lower methylation levels, higher immune checkpoint genes expression levels, and better chemotherapy response. Cell adhesion molecules, focal adhesion, and extracellular matrix receptor interaction were the main pathways in the high-risk group. The signature was validated using the TCGA test cohort, entire TCGA cohort, GSE30219, GSE157010, GSE73403, and GSE4573 datasets. The gene disorders in patients with LUSC were validated using real-time PCR and single-cell RNA sequencing analysis.

Conclusions: A ferroptosis-related gene signature was constructed to predict OS probability in LUSC. This could facilitate novel therapeutic methods and guide individualized therapy.

A random forest algorithm predicting model combining intraoperative frozen section analysis and clinical features guides surgical strategy for peripheral solitary pulmonary nodules

Background

Intraoperative frozen section (FS) analysis has been used to guide the extent of resection in patients with solitary pulmonary nodules (SPNs), but its accuracy varies greatly among different hospitals. Artificial intelligence (AI) and multidimensional data technology are developing rapidly these years, meanwhile, surgeons need better methods to guide the surgical strategy of SPNs. We established predicting models combining FS results with multidimensional perioperative clinical features using logistic regression analysis and the random forest (RF) algorithm to get more accurate extent of SPN resection.

Methods

Patients with peripheral SPNs who underwent FS-guided surgical resection at the Shanghai Chest Hospital (January 2017-December 2018) were retrospectively examined (N=3,089). The accuracy of intraoperative FS-guided resection extent was analyzed and used as Model 1. The clinical features (sex, age, CT features, tumor markers, smoking history, lesion size and nodule location) of patients were collected, and Models 2 and 3 were established using logistic regression and RF algorithms to combine the FS with clinical features. We confirmed the performance of these models in an external validation cohort of 117 patients from Hwa Mei Hospital, University of Chinese Academy of Science (Ningbo No. 2 Hospital). We compared the effectiveness in classifying low/high-risk groups of SPN among them.

Results

The accuracy of FS analysis was 61.3%. Model 3 exhibited the best diagnostic accuracy and had an area under the curve of 0.903 in n the internal validation cohort and 0.919 in the external validation cohort. The calibration plots and net reclassification index (NRI) of Model 3 also exhibited significantly better performance than the other models. Improved diagnostic accuracy was observed in in both internal and external validation cohort.

Conclusions

Using an RF algorithm, clinical characteristics can be combined with intraoperative FS analysis to significantly improve intraoperative judgment accuracy for low- and high-risk tumors, and may serve as a reliable complementary method when FS evaluation is equivocal, improving the accuracy of the extent of surgical resection.

Detection and treatment of lung adenocarcinoma at pre-/minimally invasive stage: is it lead-time bias?

OBJECTIVES

This study investigates whether lead-time bias contributes to the excellent survival of AIS and MIA.

METHODS

We enrolled patients with resected adenocarcinoma from 2008 to 2012. Age, sex, smoke history, surgical approach, radiological features, invasive stage and postoperative follow-up data were documented. 1:1 PSM was performed to balance the influence of sex and smoking status on survival. After matching, the average age of the two groups was compared to calculate the lead time of diagnosis. The gain in life years for adenocarcinoma diagnosed at pre-/minimally invasive stage was estimated by subtracting the "lead time" and "median survival year of IAC" from "the life expectancy of AIS/MIA patients" referring to the Centre for Health and Information.

RESULTS

There were 124 AIS/MIA patients and 1148 IAC patients. The frequency of female and never-smoking patients in AIS/MIA group was much higher than that in IAC group. PSM analysis identified 124 patient pairs. No cancer-related death and recurrence were observed among AIS/MIA patients 5 years after surgery. For IAC patients, the 5-year disease-specific survival rate was 73.5% and the median survival is 13.5 years. The average age of AIS/MIA group and IAC group are 53.6 years and 58.2 years, respectively. The lead time between diagnosis of AIS/MIA and IAC is 4.6 years. Referring to the Centre for Health and Information, the life expectancy of patients with AIS/MIA diagnosed at 53.6 years old is 28.9 years. With adjustment for the lead time, the gain in life years for adenocarcinoma diagnosed at pre-/minimally invasive stage is 10.8 years.

CONCLUSIONS

With adjustment for the lead time between diagnosis of AIS/MIA and IAC, resecting lung adenocarcinoma at pre-/minimally invasive stage can improve life expectancy. The excellent survival of AIS/MIA is not lead-time bias.

Prognosis and Survival Analysis of 922,317 Lung Cancer Patients from the US Based on the Most Recent Data from the SEER Database (April 15, 2021)

Background

On April 15, 2021, the Surveillance, Epidemiology, and End Results (SEER) database released the latest lung cancer follow-up data. We selected 922,317 lung cancer patients diagnosed from 2000 to 2017 for survival analysis to provide updated data for lung cancer researchers.

Research Question

This study explored the latest trends of survival time in terms of gender, race, nationality, age, income, address, histological type and primary site.

Study Design and Methods

The SEER database covers 27.8% of the US population. We used life table, Kaplan-Meier, log-rank, Breslow and Tarone-Ware tests to calculate survival rate, time, and curve and to compare differences in survival distribution. We performed univariate and multivariate Cox proportional hazards analyses.

Results

The median survival time of all lung cancer patients diagnosed in 2017 increased by 41.72% compared to 2000. Median survival time of female patients diagnosed in 2017 increased by 70.94% compared to 2000. Median survival time of those diagnosed in 2017 for different primary sites was as follows: right middle lobe was the longest, then left lower lobe, right upper lobe, right lower lobe, and left upper lobe. Lung cancer patients older than 75 years had a significantly shorter median survival time. Patients living in metropolitan areas of 250,000 to 1 million had a longer median survival time. Median survival time in the adenocarcinoma group was significantly greater than other patients. Median survival of Asian and other races diagnosed in 2017 was 97.87% higher than those diagnosed in 2000. Survival rate of lung cancer increased gradually with the year of diagnosis.

Interpretation

The rapid improvement of the prognosis of female and young lung cancer patients contributes to the improvement of the overall prognosis. Primary lung cancer in the right middle lobe has the best prognosis.

Prognostic significance of postoperative longitudinal change of serum carcinoembryonic antigen level in patients with stage I lung adenocarcinoma completely resected by single-port video-assisted thoracic surgery: a retrospective study

Background

Patients with stage I lung adenocarcinoma (LUAD) have varying postoperative prognosis. This study aimed to investigate the prognostic significance of postoperative longitudinal change of serum carcinoembryonic antigen (CEA) level in patients with stage I LUAD.

Methods

The study cohort comprised 241 patients with stage I LUAD completely resected with single-port video-assisted thoracic surgery (VATS). The patients were categorized into 4 groups according to the postoperative longitudinal change of serum CEA levels measured in the third and sixth months after surgery: the NN group (continuously normal), HN group (increase first and then decrease), NH group (decrease first and then increase), and HH group (continuously high). Recurrence-free survival (RFS) was analyzed by the Kaplan-Meier method and compared by log-rank test. A nomogram was developed to predict recurrence in the stage I LUAD patients.

Results

In univariate analysis, differentiation (P<0.001), visceral pleural invasion (VPI) (P=0.025), tumor diameter (P<0.001), tumor-node-metastasis (TNM) stage (P=0.008), preoperative CEA levels (≥10.0 vs. <10.0 ng/mL, P<0.001), and postoperative CEA grouping (NH/HH vs. NN/HN, P<0.001) were significant prognostic factors for stage I LUAD patients. Multivariate analysis showed that tumor diameter (P=0.009) and postoperative CEA grouping (P<0.001) were considered to be independent prognostic factors of postoperative recurrence of stage I LUAD. Tumor diameter (≥20 mm) and postoperative CEA (NH/HH vs. NN/HN) were associated with worse RFS. Receiver operating characteristic (ROC) curve analysis showed that postoperative CEA (NH/HH vs. NN/HN) have high sensitivity (64.7%) and specificity (83.2%) for early prediction of postoperative recurrence of stage I LUAD. The area under curve (AUC) value was 0.745. The nomogram based on multivariate Cox regression had a concordance index (value of 0.789). The calibration plot showed that the predicted probabilities closely matched the observed probabilities.

Conclusions

Longitudinal change in serum CEA level after surgery was found to be an independent unfavorable prognostic factor in completely resected stage I LUAD patients. The NH group and HH group were significantly associated with worse RFS. A nomogram was established to predict the postoperative recurrence of patients with stage I LUAD.

Potential Immune Biomarker Candidates and Immune Subtypes of Lung Adenocarcinoma for Developing mRNA Vaccines

mRNA vaccines against cancer have advantages in safety, improved therapeutic efficacy, and large-scale production. Therefore, our purpose is to identify immune biomarkers and to analyze immune status for developing mRNA vaccines and selecting appropriate patients for vaccination. We downloaded clinical information and RNA-seq data of 494 LUAD patients from TCGA. LUAD mutational information was hierarchically clustered by NMF package (Version 0.23.0). DeconstructSigs package (Version 1.8.0) and NMF consistency clustering were used to identify mutation signatures. Maftools package (Version 2.6.05) was used to select LUAD-related immune biomarkers. TIMER was used to discuss the correlation between genetic mutations and cellular components. Unsupervised clustering Pam method was used to identify LUAD immune subtypes. Log-rank test and univariate/multivariate cox regression were used to predict the prognosis of immune subtypes. Dimensionality reduction analysis was dedicated to the description of LUAD immune landscape. LUAD patients are classified into four signatures: T >C, APOBEC mutation, age, and tobacco. Then, GPRIN1, MYRF, PLXNB2, SLC9A4, TRIM29, UBA6, and XDH are potential LUAD-related immune biomarker candidates to activate the immune response. Next, we clustered five LUAD-related immune subtypes (IS1–IS5) by prognostic prediction. IS3 showed prolonged survival. The reliability of our five immune subtypes was validated by Thorsson’s results. IS2 and IS4 patients had high tumor mutation burden and large number of somatic mutations. Besides, we identified that immune subtypes of cold immunity (patients with IS2 and IS4) are ideal mRNA vaccination recipients. Finally, LUAD immune landscape revealed immune cells and prognostic conditions, which provides important information to select patients for vaccination. GPRIN1, MYRF, PLXNB2, SLC9A4, TRIM29, UBA6, and XDH are potential LUAD-related immune biomarker candidates to activate the immune response. Patients with IS2 and IS4 might potentially be immunization-sensitive patients for vaccination.