Application of the new 8th TNM staging system for non-small cell lung cancer: treated with curative concurrent chemoradiotherapy

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

As a disease with high morbidity and high mortality, lung cancer has seriously harmed people's health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

Genomic profiling for non-small cell lung cancer: Clinical relevance in staging and prognosis

Lung cancer is one of the most common cancers prevalent and around 80% of all cases are non-small cell lung cancer (NSCLC). Due to high recurrence rates, the mortality of NSCLC is high. Conventional staging systems allowed risk classification of patients in order to simplify the patient selection for adjuvant chemotherapy. Gene expression analysis has been shown to possess advantage over conventional staging systems in NSCLC in terms of patients risk classification. This article reviews the evidences on the genomic profiling of NSCLC patients into high and low-risk groups based on the expression of genes involved in various proliferative pathways.

Treatment of unresectable stage III NSCLC: Real world cohort study and literature review

INTRODUCTION

Until recently, the treatment for patients with locally advanced unresectable stage III non-small cell lung cancer (NSCLC) was combined chemoradiotherapy (CRT), delivered either concurrently (cCRT) or sequentially (sCRT). There is limited data on the outcomes and safety of CRT in a real-world setting. We conducted a real-world cohort analysis of our Leuven Lung Cancer Group (LLCG) experience with CRT for unresectable stage III NSCLC, prior to the era of consolidation treatment with immunotherapy.

PATIENTS AND METHODS

In this observational, real-world monocentric cohort study, a total of 163 consecutive patients were included. They were diagnosed with unresectable stage III primary NSCLC and treated with CRT between January 1st, 2011, and December 31st, 2018. Patient and tumor characteristics, treatment patterns, toxicity, and primary outcome parameters such as PFS, OS and pattern of relapse were captured.

RESULTS

CRT was concurrent in 108 patients, sequential in 55. Overall tolerability was good, with two thirds of patients without severe adverse events such as severe febrile neutropenia, ≥ grade 2 pneumonitis, or ≥ grade 3 esophagitis. All registered adverse events were more frequent in the cCRT group compared to the sCRT group. Median PFS was 13.2 months (95% CI 10.3-16.2), median OS was 23.3 months (95% CI 18.3-28.0), with a 47.5% survival rate at 2 years, and 29.4% at five years.

CONCLUSIONS

This study provides a clinically relevant benchmark on the outcomes and toxicity of concurrent and sequential chemoradiotherapy in unresectable stage III NSCLC in a real-world setting in the pre-PACIFIC era.

Early recurrence factors in patients with stage III non-small cell lung cancer treated with concurrent chemoradiotherapy

BACKGROUND

The clinical characteristics and risk factors for cancer recurrence have not been well evaluated regarding early recurrence in patients with unresectable locally advanced non-small cell lung cancer (LA-NSCLC) who receive concurrent chemoradiotherapy (CRT). The aim of this study was to determine the clinical characteristics and risk factors of patients with stage III unresectable LA-NSCLC treated with CRT who developed early recurrence.

METHODS

We retrospectively reviewed the clinical records of 46 patients diagnosed with stage III unresectable LA-NSCLC treated with CRT at our center between July 2012 and July 2021. A tumor proportion score (TPS) < 50% was defined as "low expression" and a TPS > 50% was defined as "high expression."

RESULTS

A total of 17 (37.0%) patients had a confirmed recurrence within 1 year of treatment. More patients had a lower body mass index in the early recurrence group than in the later recurrence group (p = 0.038). A higher number of patients in the late recurrence group underwent surgery after CRT (p = 0.036). Patients with a higher TPS were more likely to experience late recurrence than early recurrence (p = 0.001), whereas more patients with stage N3 disease were in the early recurrence group (p = 0.011). Multivariate analysis identified lower TPS expression as an independent risk factor for early recurrence after CRT. Overall survival was prolonged in the late recurrence group (p < 0.001).

CONCLUSIONS

A lower TPS may be a predictor of early recurrence after CRT in patients with LA-NSCLC. These patients should be closely monitored for post-treatment recurrence.

Diagnosis of lung cancer by flexible fiberoptic bronchoscopy: a descriptive study

Flexible fiberoptic bronchoscopy (FFB) remains the most important minimally invasive method for the diagnosis of lung cancer (LC). We performed a retrospective study to assess the main endoscopic findings of malignant lung tumors in the large airways in a cohort of Romanian patients. The group consisted of 32 (84.21%) men and six (15.78%) women, with an average age of 64.63±6.07 years. The bronchoscopic examination allowed the detection and biopsy of 36 malignant lung tumors, and in two other cases, due to malignant atelectasis, the patients were sent to a Department of Thoracic Surgery, to perform the biopsy following the surgery. Histopathological (HP) examination revealed the presence of squamous cell carcinoma (SCC) in 19 (50%) patients, adenocarcinoma (ADC) in 11 (28.94%) patients and small cell lung cancer (SCLC) in eight (21.05%) patients. The macroscopic and microscopic analysis of the lung tumors showed that infiltrative forms were found in most cases (58.33%), followed by exophytic (mass) endobronchial lesions (22.22%) and mixed forms (19.44%). If most infiltrative forms were SCC (66.66%), the exophytic and mixed lesions were most frequently ADC (50% and 57.14%). The tumor lesions caused both malignant bronchial stenosis (57.89%) and malignant atelectasis (42.1%). The main mechanisms involved in bronchial malignant obstruction were endoluminal (50%), mixed (31.57%) and extraluminal (18.42%) mechanisms. In conclusion, FFB remains the main method of diagnosing LC in the large airways. The most common macroscopic appearance of lung tumors revealed by bronchoscopy was the infiltrative appearance. In half of our patients, the malignant bronchial obstruction was achieved by endoluminal mechanism. The most common pathological form found in our patients was the SCC, as described in half of the investigated patients.

End-to-End Non-Small-Cell Lung Cancer Prognostication Using Deep Learning Applied to Pretreatment Computed Tomography

PURPOSE

Clinical TNM staging is a key prognostic factor for patients with lung cancer and is used to inform treatment and monitoring. Computed tomography (CT) plays a central role in defining the stage of disease. Deep learning applied to pretreatment CTs may offer additional, individualized prognostic information to facilitate more precise mortality risk prediction and stratification.

METHODS

We developed a fully automated imaging-based prognostication technique (IPRO) using deep learning to predict 1-year, 2-year, and 5-year mortality from pretreatment CTs of patients with stage I-IV lung cancer. Using six publicly available data sets from The Cancer Imaging Archive, we performed a retrospective five-fold cross-validation using pretreatment CTs of 1,689 patients, of whom 1,110 were diagnosed with non-small-cell lung cancer and had available TNM staging information. We compared the association of IPRO and TNM staging with patients' survival status and assessed an Ensemble risk score that combines IPRO and TNM staging. Finally, we evaluated IPRO's ability to stratify patients within TNM stages using hazard ratios (HRs) and Kaplan-Meier curves.

RESULTS

IPRO showed similar prognostic power (concordance index [C-index] 1-year: 0.72, 2-year: 0.70, 5-year: 0.68) compared with that of TNM staging (C-index 1-year: 0.71, 2-year: 0.71, 5-year: 0.70) in predicting 1-year, 2-year, and 5-year mortality. The Ensemble risk score yielded superior performance across all time points (C-index 1-year: 0.77, 2-year: 0.77, 5-year: 0.76). IPRO stratified patients within TNM stages, discriminating between highest- and lowest-risk quintiles in stages I (HR: 8.60), II (HR: 5.03), III (HR: 3.18), and IV (HR: 1.91).

CONCLUSION

Deep learning applied to pretreatment CT combined with TNM staging enhances prognostication and risk stratification in patients with lung cancer.

CHMP4C regulates lung squamous carcinogenesis and progression through cell cycle pathway

Background

Lung cancer is a common kind of human malignancies. Lung squamous cell carcinoma (LUSC) is a key subtype of lung cancer. Cell cycle plays an important role in the development and occurrence of LUSC, however, there is still a lack of cell cycle-related genes in LUSC diagnosis and prediction of prognosis.

Methods

We identified differentially expressed genes (DEGs) with "limma" package in R software, and determined the biomarkers of LUSC in diagnosing by performing receiver operating characteristic (ROC) curve analysis, the biomarker effectiveness in diagnosing LUSC was assessed by performing five-fold cross-validation with logistic regression. Kaplan-Meier plot and the nomogram assessed the relationship between the biomarker and patient survival, and WB and qRT-PCR detected the biomarker expression in cells and tissues. Flow cytometry detects the role of the biomarker in the cell cycle.

Results

Integration analysis with The Cancer Genome Atlas (TCGA) database obtained a unique gene related to cell cycle in LUSC (Charged multivesicular body protein 4C, CHMP4C), and the protein of CHMP4C was highly expressed in LUSC tissues. ROC analysis indicated that CHMP4C was a biomarker for the diagnosis of LUSC. Bioinformatic analysis indicated that CHMP4C might be associated with cell cycle in LUSC. CHMP4C knockdown resulted in S-phase arrest of cells with LUSC. According to the survival rate analysis, CHMP4C overexpression indicated poor prognosis in patients with LUSC.

Conclusions

CHMP4C regulates the proliferation process of tumor cells through the cell cycle. It can be used as a potential diagnostic and prognostic biomarker for LUSC.

Comprehensive genomic profile of Chinese lung cancer patients and mutation characteristics of individuals resistant to icotinib/gefitinib

Lung cancer is the leading causes of cancer-related death worldwide. Precise treatment based on next-generation sequencing technology has shown advantages in the diagnosis and treatment of lung cancer. This cohort study included 371 lung cancer patients. The lung cancer subtype was related to the smoking status and sex of the patients. The most common mutated genes were TP53 (62%), EGFR (55%), and KRAS (11%). The mutation frequencies of EGFR, TP53, PIK3CA, NFE2L2, KMT2D, FGFR1, CCND1, and CDKN2A were significantly different between lung adenocarcinoma and lung squamous cell carcinoma. We identified the age-associated mutations in ALK, ERBB2, KMT2D, RBM10, NRAS, NF1, PIK3CA, MET, PBRM1, LRP2, and CDKN2B; smoking-associated mutations in CDKN2A, FAT1, FGFR1, NFE2L2, CCNE1, CCND1, SMARCA4, KEAP1, KMT2C, and STK11; tumor stage-associated mutations in ARFRP1, AURKA, and CBFB; and sex-associated mutations in EGFR. Tumor mutational burden (TMB) is associated with tumor subtype, age, sex, and smoking status. TMB-associated mutations included CDKN2A, LRP1B, LRP2, TP53, and EGFR. EGFR amplification was commonly detected in patients with acquired lcotinib/gefitinib resistance. DNMT3A and NOTCH4 mutations may be associated with the benefit of icotinib/gefitinib treatment.

Predictive and prognostic significance of M descriptors of the 8th TNM classification for advanced NSCLC patients treated with immune checkpoint inhibitors

Background

A strong association between M descriptors and prognosis of non-small cell lung cancer (NSCLC) has been demonstrated recently. However, its predictive and prognostic significance for advanced NSCLC patients treated with immune checkpoint inhibitors (ICIs) remain unclear. In this study, we aimed at investigating the impact of M descriptors on clinical outcomes in those patients.

Methods

A retrospective analysis was conducted. Patients treated with more than two cycles of ICIs were included. Detailed characteristics and clinical response after immunotherapy were recorded. M descriptors were classified into M1a, M1b, and M1c according to the 8th TNM classification.

Results

A total of 103 patients were enrolled, including 42 with M1a disease, 16 with M1b disease and 45 with M1c disease. Patients with M1a disease demonstrated significant longer median progress-free survival (PFS) (11.9 vs. 4.1 and 3.2 months, respectively, P=0.0002) and overall survival (OS) (35 vs. 22.1 and 12 months, P=0.02) than those with M1b and M1c disease. Patients with M1a disease showed higher objective response rate (ORR) (28.6% vs. 14.8%, P=0.08) and disease control rate (DCR) (81% vs. 59%, P=0.02) compared with those with M1b and M1c disease. Multivariate analysis identified M1a stage as being independently associated with prolonged PFS and had better OS than those with M1c disease (P=0.05) but not M1b disease (P=0.06).

Conclusions

The current study demonstrated a clear association between M descriptors and the therapeutic response to ICIs and confirmed its prognostic role in advanced patients treated with ICIs monotherapy. M descriptors may need to be stratified in future study design.

Chemoradioimmunotherapy of inoperable stage III non-small cell lung cancer: immunological rationale and current clinical trials establishing a novel multimodal strategy

Immune-checkpoint inhibitors (ICI) have dramatically changed the landscape of lung cancer treatment. Preclinical studies investigating combination of ICI with radiation show a synergistic improvement of tumor control probability and have resulted in the development of novel therapeutic strategies. For advanced non-small cell lung cancer (NSCLC), targeting immune checkpoint pathways has proven to be less toxic with more durable treatment response than conventional chemotherapy. In inoperable Stage III NSCLC, consolidation immune checkpoint inhibition with the PD-L1 inhibitor durvalumab after completion of concurrent platinum-based chemoradiotherapy resulted in remarkable improvement of progression-free and overall survival. This new tri-modal therapy has become a new treatment standard. Development of predictive biomarkers and improvement of patient selection and monitoring is the next step in order to identify patients most likely to derive maximal benefit from this new multimodal approach. In this review, we discuss the immunological rationale and current trials investigating chemoradioimmunotherapy for inoperable stage III NSCLC.

An individualised radiomics composite model predicting prognosis of stage 1 solid lung adenocarcinoma

AIM

To develop and evaluate a radiomics composite model for predicting disease-free survival (DFS) in stage I solid lung adenocarcinoma, and compare it to a simple radiomics model.

MATERIALS AND METHODS

Patients of pathological stage I solid lung adenocarcinoma treated with lobectomy (n = 119) were enrolled retrospectively. Three hundred and ninety-seven radiomics features per lesion were extracted from enhanced chest computed tomography (CT) imaging. Spearman's correlation coefficient and the LASSO (least absolute shrinkage and selection operator) regression model were used to reduce the dimension and select radiomics features. Univariate or multivariate logistic regression was used to build prediction models. A survival curve based on the radiomics composite model was plotted with Kaplan-Meier survival analysis to stratify the risk of recurrence. The confusion matrix, receiver operating characteristic (ROC) curve, and decision curve analysis were used to evaluate the performance of the prediction models.

RESULTS

Recurrence occurred in 22.6% of patients. The survival curve of the radiomics composite model could accurately differentiate high-risk from low-risk patients. In the validation sets, the areas under the ROC curves (AUCs) of the pathological TNM stage (8th IASLC), clinicopathological model, radiomics model, and radiomics composite model were 0.587 (95% confidence interval [CI] 0.502-0.650), 0.629 (95% CI 0.558-0.682), 0.726 (95% CI 0.681-0.770), and 0.849 (95% CI 0.783-0.898), respectively.

CONCLUSION

The prognosis of stage I solid lung adenocarcinoma predicted by an individualised radiomics composite model was more accurate than that of the simple radiomics model.

Development of a Survival Prognostic Model for Non-small Cell Lung Cancer

Lung cancer is a leading cause of cancer-related death, and >80% of lung cancer diagnoses are non-small-cell lung cancer (NSCLC). However, when using current staging and prognostic indices, the prognosis can vary significantly. In the present study, we calculated a prognostic index for predicting overall survival (OS) in NSCLC patients. The data of 545 NSCLC patients were retrospectively reviewed. Univariate and multivariate Cox proportional hazards regression analyses were performed to evaluate the prognostic value of clinicopathological factors. Age (hazard ratio [HR] = 1.25, 95% confidence interval [CI] = 1.02–1.54), TNM stage (III, HR = 1.64, 95% CI = 1.08–2.48; IV, HR = 2.33, 95% CI = 1.48–3.69), lung lobectomy (HR = 1.96, 95% CI = 1.45–2.66), chemotherapy (HR = 1.42, 95% CI = 1.15–1.74), and pretreatment hemoglobin level (HR = 1.61, 95% CI = 1.28–2.02) were independent prognosticators. A prognostic index for NSCLC (PInscl, 0–6 points) was calculated based on age (≥65 years, 1 point), tumor-node-metastasis (TNM) stage (III, 1 point; IV, 2 points), lung lobectomy (no, 1 point), chemotherapy (no, 1 point), and pretreatment hemoglobin level (low, 1 point). In comparison with the “PInscl = 0” subgroup (survival time = 2.71 ± 1.86 years), the “PInscl = 2” subgroup (survival time = 1.86 ± 1.24 years), “PInscl = 3” subgroup (survival time = 1.45 ± 1.07 years), “PInscl = 4” subgroup (survival time = 1.17 ± 1.06 years), “PInscl = 5” subgroup (survival time = 0.81 ± 0.78 years), and “PInscl = 6” subgroup (survival time = 0.65 ± 0.56 years) exhibited significantly shorter survival times. Kaplan-Meier survival analysis showed that patients with higher PInscl scores had poorer OS than those with lower scores (log-rank test: χ² = 155.82, P < 0.0001). The area under the curve of PInscl for predicting the 1-year OS was 0.73 (95 % CI = 0.69–0.77, P < 0.001), and the PInscl had a better diagnostic performance than the Karnofsky performance status or TNM stage (P < 0.01). In conclusion, the PInscl, which is calculated from age, TNM stage, lung lobectomy, chemotherapy, and pretreatment hemoglobin level, significantly predicted OS in NSCLC patients.

Treatment of cT3N1M0/IIIA non-small cell lung cancer and the risk of underuse of surgery

OBJECTIVE

Surgery may be underused for stage IIIA non-small cell lung cancer. Although an argument can be made for definitive chemoradiation for N2/3 mediastinal nodal disease, the role of a nonsurgical strategy is less clear in patients with cT3N1M0 stage IIIA given a lack of randomized data. We sought to determine the outcomes of patients with cT3N1M0 by treatment type from the National Cancer Database.

METHODS

The National Cancer Database (2004-2014) was queried for patients with cT3N1M0 non-small cell lung cancer, known treatment modalities, and sequence. Comparisons between groups were performed using Mann-Whitney and chi-square tests. Cox regression was performed to identify predictors of overall survival. Propensity score matching analysis was performed to compare overall survival in surgery versus definitive chemoradiation.

RESULTS

We identified 1937 patients undergoing surgery (1518 up-front and 419 after neoadjuvant treatment) and 1844 patients undergoing definitive chemoradiation. Among patients undergoing surgery without prior treatment, 19% were overstaged and were found to have pN0, whereas 9.6% had pN2/3. Median overall survival was 33.1 months in the surgery group (± adjuvant/neoadjuvant) versus 18 months in definitive chemoradiation. To compare outcomes in balanced groups, we propensity matched 1081 pairs of patients. Median overall survival was 31.1 months in the surgery group compared with 19.1 months in the definitive chemoradiation group (P < .001). By multivariable analysis, surgery (hazard ratio, 0.65; confidence interval, 0.59-0.73), female sex (hazard ratio, 0.88; confidence interval, 0.79-0.98), age (hazard ratio, 1.02; confidence interval, 1.01-1.03), squamous histology (hazard ratio, 1.22; confidence interval, 1.07-1.38), and Charlson score of 2 (hazard ratio, 1.31; confidence interval, 1.11-1.54) were predictors of survival.

CONCLUSIONS

In the National Cancer Database, approximately half of patients with clinical T3N1M0 were treated with definitive chemoradiation rather than surgery. This practice should be avoided in operable patients, because surgical resection is associated with better survival.

Prognostic value of PD-L1 expression on tumor cells combined with CD8+ TIL density in patients with locally advanced non-small cell lung cancer treated with concurrent chemoradiotherapy

Background/aim

mmune checkpoint inhibition (CPI) has an increasing impact in the multimodal treatment of locally advanced non-small cell lung cancer (LA-NSCLC). Increasing evidence suggests treatment outcome depending on tumor cell PD-L1 expression. The purpose of this retrospective study was to investigate the prognostic value of PD-L1 expression on tumor cells in combination with CD8+ tumor stroma-infiltrating lymphocyte (TIL) density in inoperable LA-NSCLC treated with concurrent chemoradiotherapy (CRT).

Patients and method

We retrospectively assessed clinical characteristics and initial tumor biopsy samples of 31 inoperable LA-NSCLC patients treated with concurrent CRT. Prognostic impact of tumor cell PD-L1 expression (0% versus ≥1%) and CD8+ TIL density (0–40% vs. 41–100%) for local control, progression-free (PFS) and overall survival (OS) as well as correlations with clinicopathological features were evaluated.

Results

Median OS was 14 months (range: 3–167 months). The OS rates at 1- and 2 years were 68 and 20%. Local control of the entire cohort at 1 and 2 years were 74 and 61%. Median PFS, 1-year and 2-year PFS were 13 ± 1.4 months, 58 and 19%. PD-L1 expression < 1% on tumor cells was associated with improved OS, PFS and local control in patients treated with concurrent CRT. Univariate analysis showed a trend towards improved OS and local control in patients with low CD8+ TIL density. Evaluation of Tumor Immunity in the MicroEnvironment (TIME) appears to be an independent prognostic factor for local control, PFS and OS. The longest and shortest OS were achieved in patients with type I (PD-L1^neg/CD8^low) and type IV (PD-L1^pos/CD8^low) tumors (median OS: 57 ± 37 vs. 10 ± 5 months, p = 0.05), respectively.

Conclusion

Assessment of PD-L1 expression on tumor cells in combination with CD8+ TIL density can be a predictive biomarker in patients with inoperable LA-NSCLC treated with concurrent CRT.

Identification of seven-gene signature for prediction of lung squamous cell carcinoma

Background and aim: Lung squamous cell carcinoma (LUSC), is a pathological subtype of lung cancer, accounting for 30% of the lung cancers. A reliable model was constructed, based on the whole gene expression profiles, to predict the prognosis of patients with LUSC. Methods: The RNA-Seq data of LUSC was downloaded from the TCGA database, and differentially expressed genes (p<0.05, |log2fold change| >1) were screened out. By univariate and multivariate Cox regression analysis, we identified seven prognosis-related genes. Then, we established a risk score staging system to predict the prognosis of patients with LUSC. Compared with other clinical parameters, the risk score was an independent prognostic factor and had a better performance in predicting prognosis. Finally, GSEA analysis was carried out to determine the enrichment pathway significantly. The risk score models were established by Cox proportional hazard regression analysis; the ROC curve was applied to test the performance of risk score model. All the statistical analysis was accomplished by R packages. Results: In this study, a model was constructed to predict prognosis, which contains seven genes: CSRNP1, CLEC18B, MIR27A, AC130456.4, DEFA6, ARL14EPL, and ZFP42. Based on the model, the risk score of each patient was calculated with LUSC (hazard ratio [HR]=2.673, 95% CI=1.871-3.525). It was found that the risk score can distinguish high-risk and low-risk groups in prognosis of LUSC patients, independently. Furthermore, the model was validated by ROC curves in the testing dataset and the whole dataset. Lastly, by gene set enrichment analysis (GSEA), we showed the main enrichment pathways were DNA damage stimulus, DNA repair, and DNA replication. It was suggested that the risk score may provide a new and reliable method for prognosis prediction. Conclusion: The results of this study suggested that the risk score based on seven-genes could indicate a promising and independent prognostic biomarker for LUSC patients.

Association of serum follistatin levels with histological types and progression of tumor in human lung cancer

Background

Follistatin (FST), an activin-binding protein, inhibits activin action by interfering with activin binding to its receptor. The prognostic value of FST has been studied in various cancers. However, these studies rarely focus on lung cancer. In our study, we investigated the relationship between serum FST levels and lung cancer with histologic types, TNM staging, and recurrence.

Methods

A total of 150 serum samples were collected, including 91 from patients with SCLC or NSCLC, 22 from patients with benign lung diseases, and 37 from healthy subjects. Enzyme-linked immunosorbent assay was used to determine serum FST levels in healthy subjects, patients with benign lung diseases and patients with lung cancers.

Results

Serum FST levels in patients with LADC, SCC, LASC, LCLC, and SCLC were much higher than those in healthy subjects and in patients with lung benign disease. A ROC curve was constructed for differentiating the lung cancer from the healthy subjects and benign lung diseases. The results indicated that the area under the ROC curve (AUC) was 0.971 and 0.728 respectively. According to TNM staging, serum FST level increased significantly in patients with stage III and IV of LADC. Moreover, serum FST expression were increased in LADC patients with different TNM category. Furthermore, we found that a higher expression of serum FST was correlated with recurrence in LADC patients.

Conclusions

The serum FST levels gradually increased with the rise of TNM staging and category in lung cancer patients. These data suggest that serum FST levels not only can be used in auxiliary diagnosis for lung cancer but also might be associated with the disease progression and metastasis of lung cancers.

The 8th lung cancer TNM classification and clinical staging system: review of the changes and clinical implications

Lung cancer is the leading cause of cancer death in both men and women. Clinical staging plays a crucial role in predicting survivor as well as influencing management option in lung cancer patients. Guidelines are constantly being reviewed as more data becomes available to provide the most accurate prognostic markers, hence aiding in the clinical detection and staging of lung cancer. Since its introduction in the 1970s, the TNM staging has undergone significant revisions with the latest, 8^th edition, being effective internationally from 2018. This edition re-categorizes the tumour size and other non-quantitative tumour descriptors (T), and further subclassifies extra-thoracic metastases (M). The clinical nodal (N) classifier is unchanged as the earlier version correlates well with prognosis. The downstream effects on staging to accommodate for the new T and M classifications are highlighted. The survival is inversely proportional to every centimeter increase in tumour size up till 7 cm, where the same prognosis as a T4 disease is reached. Hence, some of the T-classifiers based on size of the tumour is upstaged to reflect that. Invasion of the diaphragm is considered T4 instead of T3. On the other hand, involvement of the main bronchus regardless of tumour distance to carina as well as atelectasis is down-staged from a T3 to a T2 disease. Since the 7^th edition, new entities of lung tumour known as adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) have been introduced. The T-defining features are also described in this manuscript. Extrathoracic metastases that were classified as M1b in the 7^th edition is further subcategorized into M1b and M1c in the 8^th edition, to better define oligometastasis which has a better prognosis, and may benefit from more aggressive local therapy. This overview aims to provide radiologists with a description of the changes in the latest edition including staging of subsolid and multiple nodules, outline potential limitations of this 8^th edition, as well as discussion on the implications on treatment.

Adjuvant Chemotherapy Following Surgical Resection Improves Survival in Patients With Early Stage Small Cell Lung Cancer

The purpose of this study was to determine the effects of resection coupled with standard chemotherapy on the survival prognosis of patients with early stage small cell lung carcinoma (SCLC). Patients (n = 110) with mediastinal lymph node-negative SCLC were enrolled in this study. The baseline clinical data of patients with surgery were retrospectively reviewed. Overall survival (OS) and progression-free survival (PFS) were measured by Kaplan-Meier and log-rank test analyses. Ninety-eight patients received mediastinoscopy biopsy, and pulmonary lobectomy or sublobar resection, and 67 patients underwent adjuvant chemotherapy after pulmonary lobectomy. Adjuvant chemotherapy after surgical intervention was associated with longer OS (median OS: 42.14 vs. 33.53 months, p = 0.01) and PFS (median PFS: 25.20 vs. 13.48 months, p = 0.000) compared to resection alone for all patients. Adjuvant chemotherapy was associated with improvement of survival for N1 patients with stage II (median OS: 36.42 vs. 26.68 months, p = 0.021). The median PFS was 19.02 m (16.08, 21.96) and 13.25 m (10.19, 16.30) (p = 0.031), respectively, for patients of N1 stage who received chemotherapy and those who did not. Cox regression analysis demonstrated that age, TNM stage (N stage, not T stage), and chemotherapy were independent risk factors that might affect overall survival in patients with mediastinal lymph node-negative SCLC. These findings suggest that the application of adjuvant chemotherapy following pulmonary lobectomy is associated with improvements of survival prognoses for patients with SCLC. The combination of surgical intervention with conventional therapy should be taken into consideration as a prospective multidisciplinary regimen for early stage SCLC.

Bordetella bronchiseptica pneumonia in a patient with lung cancer; a case report of a rare infection

BACKGROUND

Bordetella bronchiseptica (B.bronchiseptica) is a frequent cause of respiratory infections in animals but rarely causes serious infection in humans. We present a rare case of B. bronchiseptica pneumonia in a patient with lung cancer.

CASE PRESENTATION

A 52-year-old white male with non small cell lung cancer developed fever during treatment with nivolumab. A persistent productive cough and a deterioration in his clinical condition led to his hospitalization for evaluation. Bronchoscopy was performed and a diagnosis of B. bronchiseptica pneumonia was made. The infection was successfully managed by antiobiotic therapy.

CONCLUSIONS

B. bronchiseptica is a pathogen that can cause serious infection in humans, especially in immunocompromised or immunoincompetent individuals. In our patient it showed unusual resistance to cephalosporins and poor sensitivity to amikacin. To our knowledge this is the first case of such an infection in a lung cancer patient undergoing treatment with nivolumab. When B. bronchiseptica is identified, the possibility of a nosocomial transmission must be considered.