Prognostic factors in non-small cell lung cancer: a decade of progress

Incorporating Lymph Node Size at CT as an N1 Descriptor in Clinical N Staging for Lung Cancer

Background The ninth edition of the TNM classification for lung cancer revised the N2 categorization, improving patient stratification, but prognostic heterogeneity remains for the N1 category. Purpose To define the optimal size cutoff for a bulky lymph node (LN) on CT scans and to evaluate the prognostic value of bulky LN in the clinical N staging of lung cancer. Materials and Methods This retrospective study analyzed patients who underwent lobectomy or pneumonectomy for lung cancer between January 2013 and December 2021, divided into development (2016-2021) and validation (2013-2015) cohorts. The optimal threshold for a bulky LN was defined based on the short-axis diameter of the largest clinically positive LN at CT. Prognostic differences according to presence of bulky LN in cN1 category for overall survival (OS) were evaluated using multivariable Cox analysis. Survival discrimination was assessed using the Harrell concordance index (C-index). Results A total of 3426 patients (mean age, 64.0 years ± 9.3 [SD]; 1837 male) and 1327 patients (mean age, 63.0 years ± 9.7; 813 male) were included in the development and validation cohorts, respectively. The cutoff size for a bulky LN was established at 15 mm, and the presence of bulky LN was an independent risk factor for OS (hazard ratio [HR], 1.54; 95% CI: 1.10, 2.16; P = .01). In the development and validation cohorts, the cN1-bulky group had higher mortality risk than the cN1-nonbulky group (HR, 2.82 [95% CI: 1.73, 4.58; P < .001]; 2.29 [95% CI: 1.34, 3.92; P = .002], respectively). The bulky LN descriptor improved prognostic discrimination within the cN1 category compared with the current staging (C-index from 0.50 to 0.60 and to 0.58 in the development and validation cohorts [P < .001, P = .006], respectively]). Conclusion Defining bulky LN with a size cutoff of 15 mm was an effective descriptor in the clinical staging of N1 lung cancer. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Horst in this issue.

Prognostic performance of the N category in the 9th edition of lung cancer staging

OBJECTIVES

To compare the prognostic performance of the N category of lung cancer in the 9th edition with previous editions (7th edition and 8th edition's proposal).

METHODS

Patients who underwent lobectomy or pneumonectomy for lung cancer from January 2015 to December 2021 were retrospectively analyzed. Clinical and pathologic N categories were reclassified according to the 9th edition (N0, N1, N2a, and N2b), the 8th edition's proposal (N0, N1a, N1b, N2a1, N2a2, and N2b), and the 7th edition (N0, N1, and N2). Concordance index (C-index) and calibration were assessed for each edition.

RESULTS

A total of 3864 patients were included (962 pN positive and 513 cN positive). The 9th edition demonstrated clear hazard stratification between neighboring pN categories after multivariable adjustment, whereas multiple overlaps were observed in the 8th edition's proposal. It had superior discrimination performance compared with the 7th edition in pathologic staging (all p < 0.05). Compared with the 8th edition's proposal, the 9th edition showed comparable performance in pN2 and overall patients (C-index, 0.560 vs 0.569 [p = 0.163]; 0.666 vs 0.668 [p = 0.396]), In clinical staging, there was no difference in discrimination across 7th to 9th editions (all p > 0.05). N1 dichotomization in the 8th edition's proposal showed discrimination ability (C-index, 0.539 [95% confidence interval: 0.502-0.576]) only in pathologic staging. The calibration was acceptable across the clinical 7th to 9th editions for 5-year survival.

CONCLUSION

The revision of the N category in the 9th edition appears reasonable, offering enhanced prognostic discrimination compared with the 7th edition and comparability to the 8th edition's proposal.

KEY POINTS

Question Does the revised N category in the 9th edition offer added value in discrimination over previous editions? Findings The discrimination performance of the 9th edition is comparable to that of the 8th edition's proposal, demonstrating a distinct hazard stratification between neighboring pN categories. Clinical relevance The revision of the N category in the 9th edition appears reasonable; however, survival heterogeneity within the pathologic N1 category needs to be considered in future updates.

Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer

PURPOSE

Radical surgery is the primary treatment for early-stage resectable lung cancer, yet recurrence after curative surgery is not uncommon. Identifying patients at high risk of recurrence using preoperative computed tomography (CT) images could enable more aggressive surgical approaches, shorter surveillance intervals, and intensified adjuvant treatments. This study aims to analyze lung cancer sites in CT images to predict potential recurrences in high-risk individuals.

METHODS

We retrieved anonymized imaging and clinical data from an institutional database, focusing on patients who underwent curative pulmonary resections for non-small cell lung cancers. Our study used a deep learning model, the Mask Region-based Convolutional Neural Network (MRCNN), to predict cancer locations and assign recurrence classification scores. To find optimized trained weighted values in the model, we developed preprocessing python codes, adjusted dynamic learning rate, and modifying hyper parameter in the model.

RESULTS

The model training completed; we performed classifications using the validation dataset. The results, including the confusion matrix, demonstrated performance metrics: bounding box (0.390), classification (0.034), mask (0.266), Region Proposal Network (RPN) bounding box (0.341), and RPN classification (0.054). The model successfully identified lung cancer recurrence sites, which were then accurately mapped onto chest CT images to highlight areas of primary concern.

CONCLUSION

The trained model allows clinicians to focus on lung regions where cancer recurrence is more likely, acting as a significant aid in the detection and diagnosis of lung cancer. Serving as a clinical decision support system, it offers substantial support in managing lung cancer patients.

Advancing non-small cell lung cancer treatment: the power of combination immunotherapies

Non-small cell lung cancer (NSCLC) remains an unsolved challenge in oncology, signifying a substantial global health burden. While considerable progress has been made in recent years through the emergence of immunotherapy modalities, such as immune checkpoint inhibitors (ICIs), monotherapies often yield limited clinical outcomes. The rationale behind combining various immunotherapeutic or other anticancer agents, the mechanistic underpinnings, and the clinical evidence supporting their utilization is crucial in NSCLC therapy. Regarding the synergistic potential of combination immunotherapies, this study aims to provide insights to help the landscape of NSCLC treatment and improve clinical outcomes. In addition, this review article discusses the challenges and considerations of combination regimens, including toxicity management and patient selection.

Significance of metastatic lymph nodes ratio in overall survival for patients with resected nonsmall cell lung cancer: a retrospective cohort study

OBJECTIVE

The tumor, node and metastasis stage is widely applied to classify lung cancer and is the foundation of clinical decisions. However, increasing studies have pointed out that this staging system is not precise enough for the N status. In this study, we aim to build a convenient survival prediction model that incorporates the current items of lymph node status.

METHODS

We performed a retrospective cohort study and collected the data from resectable nonsmall cell lung cancer (NSCLC) (IA-IIIB) patients from the Surveillance, Epidemiology, and End Results database (2006-2015). The x-tile program was applied to calculate the optimal threshold of metastatic lymph node ratio (MLNR). Then, independent prognostic factors were determined by multivariable Cox regression analysis and enrolled to build a nomogram model. The calibration curve as well as the Concordance Index (C-index) were selected to evaluate the nomogram. Finally, patients were grouped based on their specified risk points and divided into three risk levels. The prognostic value of MLNR and examined lymph node numbers (ELNs) were presented in subgroups.

RESULTS TOTALLY,

40853 NSCLC patients after surgery were finally enrolled and analyzed. Age, metastatic lymph node ratio, histology type, adjuvant treatment and American Joint Committee on Cancer 8th T stage were deemed as independent prognostic parameters after multivariable Cox regression analysis. A nomogram was built using those variables, and its efficiency in predicting patients' survival was better than the conventional American Joint Committee on Cancer stage system after evaluation. Our new model has a significantly higher concordance Index (C-index) (training set, 0.683 v 0.641, respectively; P < 0.01; testing set, 0.676 v 0.638, respectively; P < 0.05). Similarly, the calibration curve shows the nomogram was in better accordance with the actual observations in both cohorts. Then, after risk stratification, we found that MLNR is more reliable than ELNs in predicting overall survival.

CONCLUSION

We developed a nomogram model for NSCLC patients after surgery. This novel and useful tool outperforms the widely used tumor, node and metastasis staging system and could benefit clinicians in treatment options and cancer control.

The Prognostic Value of Positron Emission Tomography/Computed Tomography in Clinical Stage I Lung Cancer Patients: A Propensity-Match Analysis

Background: The application of positron emission tomography/computed tomography (PET/CT) helps provide accurate clinical staging for lung cancer patients. However, the effects and trends in early-stage lung cancer remain unclear. The aim of this study was to compare differences between clinical stage I lung cancer patients who received PET/CT for staging and those who did not. Methods: Data were obtained from the Taiwan Society of Cancer Registry. There were 6587 clinical stage I lung cancer patients between 2009 and 2014 analyzed in this study. We compared the characteristics of the PET/CT and no PET/CT groups. After propensity score matching, it resulted in both groups having 2649 patients. We measured the overall survival rates of all clinical stage I lung cancer patients and the overall survival rates of patients with PET/CT and without PET/CT. Results: The 1-, 3-, and 5-year survival rates of all clinical stage I lung cancer patients were 97.2%, 88.2%, and 79.0%, respectively. Patients with a larger tumor size tended to receive PET/CT for staging (stage Ib: 38.25% vs. 27.82%, p < 0.0001) and a larger resection (lobectomy: 74.62% vs. 66.61%, p < 0.0001). The 5-year survival rates were 79.8% in the PET/CT group and 78.2% in the no PET/CT group after propensity score matching (p = 0.6528). Conclusions: For clinical stage I lung cancer in Taiwan, patients with larger tumor sizes tend to have PET/CT for staging. Although PET/CT provided more precise clinical staging, these patients still received larger resections and had more pathological migration. However, there was no overall survival rate benefit after PET/CT.

Evaluating clinical and radiomic features for predicting lung cancer recurrence pre- and post-tumor resection

Among patients with early-stage non-small cell lung cancer (NSCLC) undergoing surgical resection, identifying who is at high-risk of recurrence can inform clinical guidelines with respect to more aggressive follow-up and/or adjuvant therapy. While predicting recurrence based on pre-surgical resection data is ideal, clinically important pathological features are only evaluated postoperatively. Therefore, we developed two supervised classification models to assess the importance of pre- and post-surgical features for predicting 5-year recurrence. An integrated dataset was generated by combining clinical covariates and radiomic features calculated from pre-surgical computed tomography images. After removing correlated radiomic features, the SHapley Additive exPlanations (SHAP) method was used to measure feature importance and select relevant features. Binary classification was performed using a Support Vector Machine, followed by a feature ablation study assessing the impact of radiomic and clinical features. We demonstrate that the post-surgical model significantly outperforms the pre-surgical model in predicting lung cancer recurrence, with tumor pathological features and peritumoral radiomic features contributing significantly to the model's performance.

Lung cancer detection based on computed tomography image using convolutional neural networks

BACKGROUND

Lung cancer is the most common type of cancer, accounting for 12.8% of cancer cases worldwide. As initially non-specific symptoms occur, it is difficult to diagnose in the early stages.

OBJECTIVE

Image processing techniques developed using machine learning methods have played a crucial role in the development of decision support systems. This study aimed to classify benign and malignant lung lesions with a deep learning approach and convolutional neural networks (CNNs).

METHODS

The image dataset includes 4459 Computed tomography (CT) scans (benign, 2242; malignant, 2217). The research type was retrospective; the case-control analysis. A method based on GoogLeNet architecture, which is one of the deep learning approaches, was used to make maximum inference on images and minimize manual control.

RESULTS

The dataset used to develop the CNNs model is included in the training (3567) and testing (892) datasets. The model's highest accuracy rate in the training phase was estimated as 0.98. According to accuracy, sensitivity, specificity, positive predictive value, and negative predictive values of testing data, the highest classification performance ratio was positive predictive value with 0.984.

CONCLUSION

The deep learning methods are beneficial in the diagnosis and classification of lung cancer through computed tomography images.

Malignant pleural mesothelioma mimics thoracic empyema: A case report

BACKGROUND

Thoracic empyema and malignant pleural mesothelioma (MPM) are distinct medical conditions with similar symptoms, including cough, chest pain, and breathing difficulty. We present a rare MPM case mimicking thoracic empyema. Physicians must consider MPM risks for patients exposed to building material who exhibit lobulated pleural effusions, indicating thoracic empyema.

CASE SUMMARY

A 68-year-old retired male construction worker suffered from shortness of breath and chest tightness over 10 d, particularly during physical activity. A poor appetite and 4 kg weight loss over the past 3 wk were also reported. Chest images and laboratory data concluded a tentative impression of empyema thoracis (right). Video-assisted thoracic surgery with decortication and delobulation (right) was conducted. The pathological report yielded an MPM diagnosis. Refractory pleural bilateral effusions and respiratory failure developed postoperatively, and the patient died three weeks after the operation.

CONCLUSION

Thoracic empyema and MPM are distinct medical conditions that can present similar symptoms, and video-assisted thoracic surgery facilitates an accurate diagnosis. Empyema-mimicking presentations and postoperative refractory pleural effusion may indicate a poor MPM outcome.

Nuclear factor kappa B expression in non-small cell lung cancer

In this mini-review, we discuss the role of NF-κB, a proinflammatory transcription factor, in the expression of genes involved in inflammation, proliferation, and apoptosis pathways, and link it with prognosis of various human cancers, particularly non-small cell lung cancer (NSCLC). We and others have shown that NF-κB activity can be impacted by post-translational S-glutathionylation through reversible formation of a mixed disulfide bond between its cysteine residues and glutathione (GSH). Clinical data analysis showed that high expression of NF-κB correlated with shorter overall survival (OS) in NSCLC patients, suggesting a tumor promotion function for NF-κB. Moreover, NF-κB expression was associated with tumor stage, lymph node metastasis, and 5-year OS in these patients. NF-κB was over-expressed in the cytoplasm of tumor tissue compared to adjacent normal tissues. S-glutathionylation of NF-κB caused negative regulation by interfering with DNA binding activities of NF-κB subunits. In response to oxidants, S-glutathionylation of NF-κB also correlated with enhanced lung inflammation. Thus, S-glutathionylation is an important contributor to NF-κB regulation and clinical results highlight the importance of NF-κB in NSCLC, where NF-κB levels are associated with unfavorable prognosis.

Issues with the targeted therapy of non‑small cell lung cancer with thyroid metastases: A case report

Lung cancer is a common malignancy that has usually already metastasized at the time of diagnosis; however, thyroid metastases are extremely rare. Echinoderm microtubule-associated protein-like 4-anaplastic lymphoma kinase (ALK) fusion has been observed in 3-7% of cases of lung adenocarcinoma. ALK inhibitor therapy has been shown to exert a positive effect on disease progression. The present study describes the case of a patient with ALK-positive non-small cell lung carcinoma and thyroid metastases who exhibited a minimal response to ALK inhibitor therapy in the primary lesion, but had a complete pathological response in the thyroid, as confirmed by a thyroid biopsy. The present case report undermines the need for further evidence from genomic testing following this different tumor course in thyroid tissue.

Anchoring Filament Protein Ladinin-1 is an Immunosuppressive Microenvironment and Cold Tumor Correlated Prognosticator in Lung Adenocarcinoma

Anchoring filament protein ladinin-1 (LAD1) codes for an anchor filament protein in the basement membrane. Here, we have aimed to determine its potential role in LUAD. According to the comprehensive analyses conducted in this study, we studied the expression, prognostic significance, function, methylation, copy number variations, and the immune cell infiltration of LAD1 in LUAD. A higher level of LAD1 gene expression was observed in the LUAD tumor tissues compared to the normal lung tissues (p < 0.001). Furthermore, the multivariate analysis indicated that a higher LAD1 gene expression level was the independent prognostic factor. Additionally, the DNA methylation level of the LAD1 was inversely linked to its expression (p < 0.001). We noted that the patients affected due to LAD1 hypomethylation showed a very low overall survival rate compared to the patients with a higher LAD1 methylation score (p < 0.05). Moreover, the results of the immunity analysis indicated that the LAD1 expression might be inversely linked to the immune cell infiltration degree, expression of the infiltrated immune cells, and the PD-L1 levels. Lastly, we supplemented some verification to increase the rigor of the study. The results suggested that high expression of LAD1 may be related to cold tumors. Hence, this indirectly reflects that the immunotherapy effect of LUAD patients with high LAD1 expression might be worse. Based on the role played by the LAD1 in the tumor immune microenvironment, it can be considered a potential biomarker for predicting the immunotherapy response to LUAD.

A multi-kernel and multi-scale learning based deep ensemble model for predicting recurrence of non-small cell lung cancer

Predicting recurrence in patients with non-small cell lung cancer (NSCLC) before treatment is vital for guiding personalized medicine. Deep learning techniques have revolutionized the application of cancer informatics, including lung cancer time-to-event prediction. Most existing convolutional neural network (CNN) models are based on a single two-dimensional (2D) computational tomography (CT) image or three-dimensional (3D) CT volume. However, studies have shown that using multi-scale input and fusing multiple networks provide promising performance. This study proposes a deep learning-based ensemble network for recurrence prediction using a dataset of 530 patients with NSCLC. This network assembles 2D CNN models of various input slices, scales, and convolutional kernels, using a deep learning-based feature fusion model as an ensemble strategy. The proposed framework is uniquely designed to benefit from (i) multiple 2D in-plane slices to provide more information than a single central slice, (ii) multi-scale networks and multi-kernel networks to capture the local and peritumoral features, (iii) ensemble design to integrate features from various inputs and model architectures for final prediction. The ensemble of five 2D-CNN models, three slices, and two multi-kernel networks, using 5 × 5 and 6 × 6 convolutional kernels, achieved the best performance with an accuracy of 69.62%, area under the curve (AUC) of 72.5%, F1 score of 70.12%, and recall of 70.81%. Furthermore, the proposed method achieved competitive results compared with the 2D and 3D-CNN models for cancer outcome prediction in the benchmark studies. Our model is also a potential adjuvant treatment tool for identifying NSCLC patients with a high risk of recurrence.

An eleven-gene risk model associated with lymph node metastasis predicts overall survival in lung adenocarcinoma

Lung adenocarcinoma (LUAD) occupies major causes of tumor death. Identifying potential prognostic risk genes is crucial to predict the overall survival of patients with LUAD. In this study, we constructed and proved an 11-gene risk signature. This prognostic signature divided LUAD patients into low- and high-risk groups. The model outperformed in prognostic accuracy at varying follow-up times (AUC for 3 years: 0.699, 5 years: 0.713, and 7 years: 0.716). Two GEO datasets also indicate the great accuracy of the risk signature (AUC = 782 and 771, respectively). Multivariate analysis identified 4 independent risk factors including stage N (HR 1.320, 95% CI 1.102-1.581, P = 0.003), stage T (HR 3.159, 95% CI 1.920-3.959, P < 0.001), tumor status (HR 5.688, 95% CI 3.883-8.334, P < 0.001), and the 11-gene risk model (HR 2.823, 95% CI 1.928-4.133, P < 0.001). The performance of the nomogram was good in the TCGA database (AUC = 0.806, 0.798, and 0.818 for 3-, 5- and 7-year survival). The subgroup analysis in different age, gender, tumor status, clinical stage, and recurrence stratifications indicated that the accuracy was high in different subgroups (all P < 0.05). Briefly, our work established an 11-gene risk model and a nomogram merging the model with clinicopathological characteristics to facilitate individual prediction of LUAD patients for clinicians.

Prognostic significance of location index in resected T1-sized early-stage non-small cell lung cancer

BACKGROUND

While the central location is a known adverse prognostic factor in lung cancer, a precise definition of central lung cancer has not yet emerged.

PURPOSE

To determine the prognostic significance of central lung cancer (defined by location index) in resected T1-sized early-stage non-small cell lung cancer (NSCLC).

MATERIAL AND METHODS

Patients with resected T1-sized early-stage NSCLC between 2010 and 2015 at a single tertiary cancer center were retrospectively reviewed. Central lung cancer was defined by a location index of the second tertile or less. Kaplan-Meier analysis with log-rank test and multivariable Cox regression analysis were performed to analyze the relationship between central lung cancer and the prognosis of relapse-free survival (RFS) and overall survival (OS). Inter-observer agreement was assessed using Cohen's kappa value and intraclass correlation coefficient (ICC).

RESULTS

Overall, 289 patients (169 men; median age 65 years; interquartile range 58-70 years) were evaluated. Central lung cancer (defined by location index) was adversely associated with RFS (P = 0.005) and OS (P = 0.01). Multivariable Cox regression analysis showed that central lung cancer was independently associated with poor RFS (adjusted hazard ratio 1.91; 95% confidence interval [CI] 1.12-3.24; P = 0.017) and OS (adjusted hazard ratio 1.69; 95% CI 1.04-2.74; P = 0.033). Location index demonstrated excellent inter-observer agreement (Cohen's kappa value 0.88; 95% CI 0.82-0.93) with a high ICC (0.98; 95% CI 0.97-0.98).

CONCLUSION

Central lung cancer defined by a location index of the second tertile or lower is an independent adverse prognostic factor in resected T1-sized early-stage NSCLC.

Lung stereotactic radiation therapy: Intercomparison of irradiation devices in terms of outcome and predictive factors

PURPOSE

To compare three different radiotherapy devices able to perform pulmonary stereotactic radiotherapy: CyberKnife® (CK), Helical Tomotherapy® (HT), and volumetric modulated arc therapy (VMAT). This study aims to define the patients' outcome in terms of SBRT efficacy and toxicities depending of the device choice.

MATERIALS AND METHODS

We retrospectively analyzed the clinical, radiological, and dosimetric data of patients treated with lung SBRT between 2016 and 2020 at Lausanne University Hospital, using the Chi² test for proportions, the t-test for means comparisons, the Kaplan-Meier method for survival, and the Log-rank test and Cox-regression for intergroups comparisons.

RESULTS

We identified 111 patients treated by either CK (59.9%), VMAT (38.0%), or HT (2.1%). Compared to other techniques, CK treated comparable gross tumor volume (GTV; 2.1 vs. 1.4cm³, P=0.84) with smaller planning treatment volume (PTV; 12.3 vs. 21.9cm³, P=0.013) and lower V5 (13.5 vs. 19.9cm³, P=0.002). Local control rates at 2years were not different whatever the irradiation device, respectively of 96.2% (range, 90.8-100) and 98.1% (range, 94.4-100), P=0.68. Toxicity incidence significantly increased with V5 value>17.2% (56.0 vs. 77.4%, P=0.021).

CONCLUSION

Compared to other SBRT techniques, CK treatments permitted to treat comparable GTV with reduced PTV and V5. Toxicity incidence was less frequent when reducing the V5. CK is particularly attractive in case of multiple courses of lung SBRT or lung reirradiation.

Prognostic value of pretherapeutic FDG PET/CT in non-small cell lung cancer with pulmonary lymphangitic carcinomatosis

Pulmonary lymphangitic carcinomatosis (PLC) is associated with a poor prognosis in patients with non-small cell lung cancer (NSCLC). We sought to determine prognostic value of pretherapeutic fluorine-18-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) in NSCLC with radiologically diagnosed PLC. We retrospectively reviewed 50 NSCLC patients with radiologically diagnosed PLC. Among eight clinical variables and five imaging parameters, metabolic PLC burden, which represents the overall tumor burden of PLC, and cPLC, which represents the location and extent of PLC in a three-grade system, were used. In multivariate analyses for progression-free survival, metabolic PLC burden (P = 0.0181), cPLC (P = 0.0401), and clinical stage (P = 0.0284) were identified as independent prognostic factors. High metabolic PLC burden had a worse prognosis, and the prognosis of cPLC3 was significantly worse than that of cPLC1 or cPLC2. In univariate analyses for overall survival, only age (P = 0.0073) was identified a prognostic factor. In conclusion, FDG PET/CT parameters were identified as independent prognostic factors in NSCLC with radiologically diagnosed PLC. Furthermore, a combination of anatomical and metabolic information about PLC obtained using FDG PET/CT provides insight into the overall tumor burden of PLC and is useful in predicting prognosis.

Multivariate analysis of prognostic factors in patients with lung cancer

Objective

Lung cancer is the leading cause of cancer-related mortality in China. The purpose of this study was to determine the effect of non-therapeutic and therapeutic factors of patients with lung cancer on survival rate.

Methods

In this retrospective study, a total of 458 patients diagnosed as lung cancer at the Department of Thoracic Surgery, the Fourth Affiliated Hospital of Hebei Medical University from September 2008 to October 2013 were enrolled. The COX proportional hazards model was used to analyze the possible factors affecting the survival of patients. Model variables included age, sex, family history, smoking, tumor location, pathological type, stage, chemotherapy, radiotherapy, operation, and targeted therapy.

Results

The median survival time (MST) was 32.0 months (95% CI: 29.0-34.0 months), while the 1-, 3-, and 5-year survival rates were 70.74%, 36.90%, and 30.13%, respectively. The univariate analysis showed that stage, chemotherapy, radiotherapy, and operation significantly affected the median survival time of patients. Multivariate cox regression analysis suggested that sex (female vs male, 2.096, 95% CI: 1.606-2.736), stage (stage I vs IV, 0.111, 95% CI: 0.039-0.314; stage II vs IV, 0.218, 95%CI: 0.089-0.535), chemotherapy (no vs yes, 0.469, 95% CI: 0.297-0.742), and operation (no vs yes, 2.667, 95% CI: 1.174-6.055) were independently associated with the survival of patients with lung cancer.

Conclusion

Our study showed that male, early stage, operation were protective factors for the survival of patients, while female, advanced stage, chemotherapy were risk factors for the survival of patients. Larger studies are required to address the usefulness of these prognostic factors in defining the management of patients with lung cancer.

Five-Year Overall Survival and Prognostic Factors in Patients with Lung Cancer: Results from the Korean Association of Lung Cancer Registry (KALC-R) 2015

PURPOSE

This study aimed to provide the clinical characteristics, prognostic factors, and 5-year relative survival rates of lung cancer diagnosed in 2015.

MATERIALS AND METHODS

The demographic risk factors of lung cancer were calculated using the KALC-R (Korean Association of Lung Cancer Registry) cohort in 2015, with survival follow-up until December 31, 2020. The 5-year relative survival rates were estimated using Ederer II methods, and the general population data used the death rate adjusted for sex and age published by the Korea Statistical Information Service from 2015 to 2020.

RESULTS

We enrolled 2,657 patients with lung cancer who were diagnosed in South Korea in 2015. Of all patients, 2,098 (79.0%) were diagnosed with non-small cell lung cancer (NSCLC) and 345 (13.0%) were diagnosed with small cell lung cancer (SCLC), respectively. Old age, poor performance status, and advanced clinical stage were independent risk factors for both NSCLC and SCLC. In addition, the 5-year relative survival rate declined with advanced stage in both NSCLC (82%, 59%, 16%, 10% as the stage progressed) and SCLC (16%, 4% as the stage progressed). In patients with stage IV adenocarcinoma, the 5-year relative survival rate was higher in the presence of epidermal growth factor receptor (EGFR) mutation (19% vs. 11%) or anaplastic lymphoma kinase (ALK) translocation (38% vs. 11%).

CONCLUSION

In this Korean nationwide survey, the 5-year relative survival rates of NSCLC were 82% at stage I, 59% at stage II, 16% at stage III, and 10% at stage IV, and the 5-year relative survival rates of SCLC were 16% in cases with limited disease, and 4% in cases with extensive disease.

Cannabinoid receptor 2 expression in early-stage non-small cell lung cancers identifies patients with good prognosis and longer survival

BACKGROUND

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related death with a 5-year survival of only 21%. Reliable prognostic and/or predictive biomarkers are needed to improve NSCLC patient stratification, particularly in curative disease stages. Since the endogenous cannabinoid system is involved in both carcinogenesis and anticancer immune defense, we hypothesized that tumor tissue expression of cannabinoid 1 and 2 receptors (CB1 and CB2) may affect survival.

METHODS

Tumor tissue samples collected from 100 NSCLC patients undergoing radical surgery were analyzed for CB1 and CB2 gene and protein expression using the quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). The gene and protein expression data were correlated with disease stage, histology, tumor grading, application of chemotherapy, and survival. Additional paired tumor and normal tissue samples of 10 NSCLC patients were analyzed independently for comparative analysis of CB1 and CB2 gene expression.

RESULTS

Patients with tumors expressing the CB2 gene had significantly longer overall survival (OS) (P<0.001), cancer specific survival (CSS) (P=0.002), and disease-free survival (DFS) (P<0.001). They also presented with fewer lymph node metastases at the time of surgery (P=0.011). A multivariate analysis identified CB2 tumor tissue gene expression as a positive prognostic factor for CSS [hazard ratio (HR) =0.274; P=0.013] and DFS (HR =0.322; P=0.009), and increased CSS. High CB2 gene and protein expression were detected in 79.6% and 31.5% of the tested tumor tissue samples, respectively. Neither CB1 gene nor CB1 or CB2 protein expression affected survival. When comparing paired tumor and tumor-free lung tissue samples, we observed reduced CB1 (P=0.008) and CB1 (P=0.056) gene expression in tumor tissues.

CONCLUSIONS

In NSCLC patients undergoing radical surgery, expression of the CB1 and CB2 receptor genes is significantly decreased in neoplastic versus tumor-free lung tissue. CB2 tumor tissue gene expression is strongly associated with longer survival (OS, CSS, DFS) and fewer lymph node metastases at the time of surgery. More studies are needed to evaluate its role as a biomarker in NSCLC and to investigate the potential use of CB2 modulators to treat or prevent lung cancers.

Prognostic Impact of Using Combined Plasma Fibrinogen Level and Neutrophil-to-Lymphocyte Ratio in Resectable Non-small Cell Lung Cancer

BACKGROUND

Both plasma fibrinogen level and neutrophil-to-lymphocyte ratio (NLR) are associated with malignant potential in different cancer types. The current study evaluated the use of combined plasma fibrinogen level and NLR (F-NLR) as a prognostic predictor in patients with non-small cell lung cancer (NSCLC).

METHODS

Data collected from 279 patients with resectable NSCLC were retrospectively reviewed. Patients were divided into three groups based on the F-NLR score: score 2, high fibrinogen level (≥350 mg/dL) and high NLR (≥2.5); score 1, either high fibrinogen level or high NLR; and score 0, neither abnormal. Overall survival (OS) and relapse-free survival (RFS) were evaluated using the Kaplan-Meier method and log-rank test. Cox proportional hazard model was used to assess prognostic factors.

RESULTS

Numbers of patients with F-NLR score of 0, 1, and 2 were 122 (43.7%), 105 (37.6%), and 52 (18.6%), respectively. The F-NLR was found to be significantly associated with age, male sex, heavy smoking history, high pT status and pathological stage, and nonadenocarcinoma. Moreover, the OS and RFS significantly differed according to the F-NLR score (P < 0.001, P = 0.003). A multivariate analysis revealed that a high F-NLR score (≥1) was an independent poor prognostic factor for OS (P = 0.027). In subgroup analyses, an adverse prognostic impact of the F-NLR score on OS was identified regardless of nodal involvement or pathological stage.

CONCLUSIONS

The F-NLR score, which is based on histological inflammation and coagulability, could be a potential prognostic indicator in patients with resectable NSCLC.

Prognostic factors for long-term survival following complete resection by lobectomy in stage I non-small cell lung cancer

BACKGROUND

The aim of this study was to evaluate predictors for long-term overall survival (OS) in patients with stage I non-small cell lung cancer (NSCLC).

METHODS

All patients undergoing complete resection by lobectomy for stage I NSCLC between October 2012 and December 2015 at a single center were included. Univariable and multivariable Cox regression analyses were performed to identify prognostic factors.

RESULTS

A total of 92 patients were included. Univariable and multivariable Cox regression analyses revealed preoperative neutrophil to lymphocyte ratio (NLR, p = 0.005), preoperative diffusion capacity of the lungs for carbon monoxide (DLCO, p = 0.010) and forced expiratory volume in 1 second (FEV1, p = 0.041) as well as male gender (p = 0.026) as independent prognostic factors for OS. Combining the calculated cutoff values for FEV1 (<73.0%) and NLR (>3.49) into one parameter resulted in a highly significant difference in survival times when stratified by this variable.

CONCLUSIONS

Recently, much emphasis has been put on the prognostic importance of blood biomarkers in NSCLC. In our study, NLR was an independent factor for OS, as were baseline characteristics such as DLCO, FEV1, and gender. Further studies on the association of biomarkers for systemic inflammation and lung function parameters with respect to patient survival are warranted.

Unraveling the Rewired Metabolism in Lung Cancer Using Quantitative NMR Metabolomics

Lung cancer cells are well documented to rewire their metabolism and energy production networks to enable proliferation and survival in a nutrient-poor and hypoxic environment. Although metabolite profiling of blood plasma and tissue is still emerging in omics approaches, several techniques have shown potential in cancer diagnosis. In this paper, the authors describe the alterations in the metabolic phenotype of lung cancer patients. In addition, we focus on the metabolic cooperation between tumor cells and healthy tissue. Furthermore, the authors discuss how metabolomics could improve the management of lung cancer patients.

RS1 gene is a novel prognostic biomarker for lung adenocarcinoma

Background

Although it has a poor prognosis, patients with lung adenocarcinoma (LUAD) have a relatively higher 5‐year survival period. Thus, it is necessary to identify effective prognostic markers to evaluate the effect of early treatment. RS1 gene encodes retinoschisin, a key protein in congenital retinoschisis, while few studies have been reported on the association between RS1 and cancer prognosis.

Methods

We performed bioinformatic analyses based on the data obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases to demonstrate the expression level of RS1 was related to the LUAD prognosis and our findings were verified in‐vitro and clinical samples. Then, we explored the potential mechanism of how RS1 expression influenced the prognosis of LUAD.

Results

Compared with normal tissues, the RS1 expression was significantly lower in tumor tissues. The Multivariate Cox regression model showed that RS1 could be used as an independent prognostic indicator. Furthermore, we found significant differences in immune cell infiltration between RS1 high and low expression groups, and the proteasome pathway was found enriched in RS1 low expression samples.

Conclusion

In conclusion, our study suggests that RS1 is a novel prognostic biomarker for LUAD. Differences in immune cell infiltration and signaling pathways may contribute to the poor prognosis of LUAD caused by low RS1 expression.

Construction and evaluation of a nomogram for predicting survival in patients with lung cancer

BACKGROUND

Lung cancer is a heterogeneous disease with a severe disease burden. Because the prognosis of patients with lung cancer varies, it is critical to identify effective biomarkers for prognosis prediction.

METHODS

A total of 2325 lung cancer patients were integrated into four independent sets (training set, validation set I, II and III) after removing batch effects in our study. We applied the microarray data algorithm to screen the differentially expressed genes in the training set. The most robust markers for prognosis were identified using the LASSO-Cox regression model, which was then used to create a Cox model and nomogram.

RESULTS

Through LASSO and multivariate Cox regression analysis, eight genes were identified as prognosis-associated hub genes, followed by the creation of prognosis-associated risk scores (PRS). The results of the Kaplan-Meier analysis in the three validation sets demonstrate the good predictive performance of PRS, with hazard ratios of 2.38 (95% confidence interval (CI), 1.61-3.53) in the validation set I, 1.35 (95% CI, 1.06-1.71) in the validation set II, and 2.71 (95% CI, 1.77-4.18) in the validation set III. Additionally, the PRS demonstrated superior survival prediction in subgroups by age, gender, p-stage, and histologic type (p < 0.0001). The complex model integrating PRS and clinical risk factors also have a good predictive performance for 3-year overall survival.

CONCLUSIONS

In this study, we developed a PRS signature to help predict the survival of lung cancer. By combining it with clinical risk factors, a nomogram was established to quantify the individual risk assessments.

[Pattern of Recurrence and Metastasis after Radical Resection of Non-small Cell Lung Cancer]

The rate of recurrence and metastasis of non-small cell lung cancer after radical resection is still very high. The risk factors for recurrence and metastasis have been extensively studied, but the dynamic pattern of postoperative recurrence hazard over time is relatively lacking. The dynamic recurrence hazard rate curve is applied to describe the rate of recurrence at any point time among the "at-risk" patients. In this article, by reviewing the previous literature, the characteristics of the dynamic recurrence and metastasis pattern after radical resection of non-small cell lung cancer and the clinical factors affecting the recurrence and metastasis pattern are summarized, in order to screen out specific populations with high recurrence risk and give them personalized follow-up strategy and diagnosis and treatment.
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Improved outcomes and staging in non-small-cell lung cancer guided by a molecular assay

There remains a critical need for improved staging of non-small-cell lung cancer, as recurrence and mortality due to undetectable metastases at the time of surgery remain high even after complete resection of tumors currently categorized as 'early stage.' A 14-gene quantitative PCR-based expression profile has been extensively validated to better identify patients at high-risk of 5-year mortality after surgical resection than conventional staging - mortality that almost always results from previously undetectable metastases. Furthermore, prospective studies now suggest a predictive benefit in disease-free survival when the assay is used to guide adjuvant chemotherapy decisions in early-stage non-small-cell lung cancer patients.

The Value of the Illness-Death Model for Predicting Outcomes in Patients with Non‒Small Cell Lung Cancer

Purpose

The illness-death model (IDM) is a comprehensive approach to evaluate the relationship between relapse and death. This study aimed to illustrate the value of the IDM for identifying risk factors and evaluating predictive probabilities for relapse and death in patients with non–small cell lung cancer (NSCLC) in comparison with the disease-free survival (DFS) model.

Materials and Methods

We retrospectively analyzed 612 NSCLC patients who underwent a curative operation. Using the IDM, the risk factors and predictive probabilities for relapse, death without relapse, and death after relapse were simultaneously evaluated and compared to those obtained from a DFS model.

Results

The IDM provided more detailed risk factors according to the patient’s disease course, including relapse, death without relapse, and death after relapse, in patients with resected lung cancer. In the IDM, history of malignancy (other than lung cancer) was related to relapse and smoking history was associated with death without relapse; both were indistinguishable in the DFS model. In addition, the IDM was able to evaluate the predictive probability and risk factors for death after relapse; this information could not be obtained from the DFS model.

Conclusion

Compared to the DFS model, we found that the IDM provides more comprehensive information on transitions between states and disease stages and provides deeper insights with respect to understanding the disease process among lung cancer patients.

A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?

Objective

To discover potential predictors and explore how to build better models by summarizing the existing prognostic prediction models of non-small cell lung cancer (NSCLC).

Background

Research on clinical prediction models of NSCLC has experienced explosive growth in recent years. As more predictors of prognosis are discovered, the choice of predictors to build models is particularly important, and in the background of more applications of next-generation sequencing technology, gene-related predictors are widely used. As it is more convenient to obtain samples and follow-up data, the prognostic model is preferred by researchers.

Methods

PubMed and the Cochrane Library were searched using the items “NSCLC”, “prognostic model”, “prognosis prediction”, and “survival prediction” from 1 January 1980 to 5 May 2021. Reference lists from articles were reviewed and relevant articles were identified.

Conclusions

The performance of gene-related models has not obviously improved. Relative to the innovation and diversity of predictors, it is more important to establish a highly stable model that is convenient for clinical application. Most of the prevalent models are highly biased and referring to PROBAST at the beginning of the study may be able to significantly control the bias. Existing models should be validated in a large external dataset to make a meaningful comparison.

Metabolic active tumour volume quantified on [18F]FDG PET/CT further stratifies TNM stage IV non-small cell lung cancer patients

PURPOSE

This study aimed to assess whether the whole body metabolic active tumour volume (MTV_WB), quantified on staging [¹⁸F]FDG PET/CT, could further stratify stage IV non-small cell lung cancer (NSCLC) patients.

METHODS

A group of 160 stage IV NSCLC patients, submitted to staging [¹⁸F]FDG PET/CT between July 2010 and May 2020, were retrospectively evaluated. MTV_WB was quantified. Univariate and multivariate Cox regressions were carried out to assess correlation with overall survival (OS). C-statistic was used to test predictive power. Kaplan-Meier survival curves with Log-Rank tests were performed to compute statistical differences between strata from dichotomized variables and to calculate the estimated mean survival times (EMST). Survival rates at 1 and 5 years were calculated.

RESULTS

MTV_WB was a statistically significant predictor of OS on univariate (p < 0.0001) and multivariate analyses (p < 0.0001). The multivariate model with MTV_WB (Cindex ± SE = 0.657 ± 0.024) worked significantly better as an OS predictor than the cTNM model (Cindex ± SE = 0.544 ± 0.028) (p = 0.003). An EMST of 29.207 ± 3.627(95% CI 22.099-36.316) months and an EMST of 10.904 ± 1.171(95% CI 8.609-13.199) months (Log-Rank p < 0.0001) were determined for patients with MTV_WB < 104.3 and MTV_WB ≥ 104.3, respectively. In subsamples of stage IVA (cut-off point = 114.5) and IVB patients (cut-off point = 191.1), statistically significant differences between EMST were also reported, with p-values of 0.0001 and 0.0002, respectively. In both substages and in the entire cohort, patients with MTV_WB ≥ cut-off points had lower EMST and survival rates.

CONCLUSION

Baseline MTV_WB, measured on staging [¹⁸F]FDG PET/CT, further stratifies stage IV NSCLC patients. This parameter is an independent predictor of OS and provides valuable prognostic information over the 8th edition of cTNM staging.

Prognostic impact of the 18F-fluorodeoxyglucose positron-emission tomography/computed tomography metabolic parameters and correlation with hematological inflammatory markers in lung cancer

Introduction

Hematological inflammatory markers and metabolic parameters in positron-emission tomography/computed tomography (PET/CT) are important indicators predicting the prognosis of the disease in lung cancer as in many cancers. This study aimed to evaluate the correlation between pretreatment hematological inflammatory markers and PET/CT metabolic parameters in nonsmall cell lung cancer (NSCLC) patients and to predict the prognostic value of these parameters.

Materials and Methods

A total of 132 patients with diagnosed NSCLC who underwent PET/CT at staging were retrospectively evaluated. Hematological parameters were obtained from the hemogram taken no more than 2 weeks prior to PET/CT. Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and mean platelet volume (MPV) were recorded. Maximum standard uptake value, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were calculated. Clinical stage, tumor pathology, and overall survival were analyzed with these parameters.

Results

NLR and PLR were significantly positively correlated with MTV and TLG (all P < 0.001), MPV was negatively correlated with TLG (P = 0.021). While TLG, MTV, NLR, and PLR were increased in advanced stage disease, MPV was decreased. Univariate Cox-regression analysis demonstrated that greater age (P = 0.015), advanced stage (P < 0.001), low MPV (P = 0.017), high NLR (P < 0.001), PLR (P < 0.001), MTV (P = 0.004), TLG (P = 0.001) values, multivariate Cox-regression analysis revealed that NLR (P < 0.001) and advanced stage (P < 0.001) were significant predictors of poor prognosis in patients with NSCLC.

Conclusions

There were significant associations between hematological inflammatory markers and PET/CT metabolic parameters in the patients with NSCLC at the time of diagnosis. These indicators can contribute to predicting prognosis in patients with NSCLC.

A fourteen-lncRNA risk score system for prognostic prediction of patients with non-small cell lung cancer

Growing evidence has underscored long non-coding RNAs (lncRNAs) serving as potential biomarkers for cancer prognosis. However, systematic tracking of a lncRNA signature for prognosis prediction in non-small cell lung cancer (NSCLC) has not been accomplished yet. Here, comprehensive analysis with differential gene expression analysis, univariate and multivariate Cox regression analysis based on The Cancer Genome Atlas (TCGA) database was performed to identify the lncRNA signature for prediction of the overall survival of NSCLC patients. A risk-score model based on a 14-lncRNA signature was identified, which could classify patients into high-risk and low-risk groups and show poor and improved outcomes, respectively. The receiver operating characteristic (ROC) curve revealed that the risk-score model has good performance with high AUC value. Multivariate Cox's regression model and stratified analysis indicated that the risk-score was independent of other clinicopathological prognostic factors. Furthermore, the risk-score model was competent for the prediction of metastasis-free survival in NSCLC patients. Moreover, the risk-score model was applicable for prediction of the overall survival in the other 30 caner types of TCGA. Our study highlighted the significant implications of lncRNAs as prognostic predictors in NSCLC. We hope the lncRNA signature could contribute to personalized therapy decisions in the future.

Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Identify and Estimate Survival in a Longitudinal Cohort of Patients With Lung Cancer

IMPORTANCE

Electronic health records (EHRs) provide a low-cost means of accessing detailed longitudinal clinical data for large populations. A lung cancer cohort assembled from EHR data would be a powerful platform for clinical outcome studies.

OBJECTIVE

To investigate whether a clinical cohort assembled from EHRs could be used in a lung cancer prognosis study.

DESIGN, SETTING, AND PARTICIPANTS

In this cohort study, patients with lung cancer were identified among 76 643 patients with at least 1 lung cancer diagnostic code deposited in an EHR in Mass General Brigham health care system from July 1988 to October 2018. Patients were identified via a semisupervised machine learning algorithm, for which clinical information was extracted from structured and unstructured data via natural language processing tools. Data completeness and accuracy were assessed by comparing with the Boston Lung Cancer Study and against criterion standard EHR review results. A prognostic model for non-small cell lung cancer (NSCLC) overall survival was further developed for clinical application. Data were analyzed from March 2019 through July 2020.

EXPOSURES

Clinical data deposited in EHRs for cohort construction and variables of interest for the prognostic model were collected.

MAIN OUTCOMES AND MEASURES

The primary outcomes were the performance of the lung cancer classification model and the quality of the extracted variables; the secondary outcome was the performance of the prognostic model.

RESULTS

Among 76 643 patients with at least 1 lung cancer diagnostic code, 42 069 patients were identified as having lung cancer, with a positive predictive value of 94.4%. The study cohort consisted of 35 375 patients (16 613 men [47.0%] and 18 756 women [53.0%]; 30 140 White individuals [85.2%], 1040 Black individuals [2.9%], and 857 Asian individuals [2.4%]) after excluding patients with lung cancer history and less than 14 days of follow-up after initial diagnosis. The median (interquartile range) age at diagnosis was 66.7 (58.4-74.1) years. The area under the receiver operating characteristic curves of the prognostic model for overall survival with NSCLC were 0.828 (95% CI, 0.815-0.842) for 1-year prediction, 0.825 (95% CI, 0.812-0.836) for 2-year prediction, 0.814 (95% CI, 0.800-0.826) for 3-year prediction, 0.814 (95% CI, 0.799-0.828) for 4-year prediction, and 0.812 (95% CI, 0.798-0.825) for 5-year prediction.

CONCLUSIONS AND RELEVANCE

These findings suggest the feasibility of assembling a large-scale EHR-based lung cancer cohort with detailed longitudinal clinical measurements and that EHR data may be applied in cancer progression with a set of generalizable approaches.

Identification of Prognostic Signature and Gliclazide as Candidate Drugs in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer, with high incidence and mortality. To improve the curative effect and prolong the survival of patients, it is necessary to find new biomarkers to accurately predict the prognosis of patients and explore new strategy to treat high-risk LUAD.

Methods

A comprehensive genome-wide profiling analysis was conducted using a retrospective pool of LUAD patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE18842, GSE19188, GSE40791 and GSE50081 and The Cancer Genome Atlas (TCGA). Differential gene analysis and Cox proportional hazard model were used to identify differentially expressed genes with survival significance as candidate prognostic genes. The Kaplan–Meier with log-rank test was used to assess survival difference. A risk score model was developed and validated using TCGA-LUAD and GSE50081. Additionally, The Connectivity Map (CMAP) was used to predict drugs for the treatment of LUAD. The anti-cancer effect and mechanism of its candidate drugs were studied in LUAD cell lines.

Results

We identified a 5-gene signature (KIF20A, KLF4, KRT6A, LIFR and RGS13). Risk Score (RS) based on 5-gene signature was significantly associated with overall survival (OS). Nomogram combining RS with clinical pathology parameters could potently predict the prognosis of patients with LUAD. Moreover, gliclazide was identified as a candidate drug for the treatment of high-RS LUAD. Finally, gliclazide was shown to induce cell cycle arrest and apoptosis in LUAD cells possibly by targeting CCNB1, CCNB2, CDK1 and AURKA.

Conclusion

This study identified a 5-gene signature that can predict the prognosis of patients with LUAD, and Gliclazide as a potential therapeutic drug for LUAD. It provides a new direction for the prognosis and treatment of patients with LUAD.

Durvalumab after Chemoradiotherapy for PD-L1 Expressing Inoperable Stage III NSCLC Leads to Significant Improvement of Local-Regional Control and Overall Survival in the Real-World Setting

Concurrent chemoradiotherapy (CRT) followed by maintenance treatment with the PD-L1 inhibitor durvalumab is a new standard of care for inoperable stage III NSCLC. The present study compares the oncological outcome of patients treated with CRT to those treated with CRT and durvalumab (CRT-IO) in the real-world setting. The analysis was performed based on the retro- and prospectively collected data of 144 consecutive inoperable stage III NSCLC patients treated between 2011-2020. Local-regional-progression-free-survival (LRPFS-defined as progression in the mediastinum, hilum and/or supraclavicular region at both sites and the involved lung), progression-free survival (PFS), and overall survival (OS) were evaluated from the last day of thoracic radiotherapy (TRT). Median follow-up for the entire cohort was 33.1 months (range: 6.3-111.8) and median overall survival was 27.2 (95% CI: 19.5-34.9) months. In the CRT-IO cohort after a median follow-up of 20.9 (range: 6.3-27.4) months, median PFS was not reached, LRPFS (p = 0.002), PFS (p = 0.018), and OS (p = 0.005) were significantly improved vs. the historical cohort of conventional CRT patients. After propensity-score matching (PSM) analysis with age, gender, histology, tumor volume, and treatment mode, and exact matching for T-and N-stage, 22 CRT-IO patients were matched 1:2 to 44 CRT patients. Twelve-month LRPFS, PFS, and OS rates in the CRT-IO vs. CRT cohort were 78.9 vs. 45.5% (p = 0.002), 60.0 vs. 31.8% (p = 0.007), and 100 vs. 70.5% (p = 0.003), respectively. This real-world analysis demonstrated that durvalumab after CRT led to significant improvement of local-regional control, PFS, and OS in PD-L1 expressing inoperable stage III NSCLC patients compared to a historical cohort.

Prognostic Factors Determining Survival of Patients Operated for Non-Small Cell Lung Cancer with Consideration Given to Morphological Parameters of Blood

Introduction

Prognostic biomarkers are the area of high interest in non-small cell lung cancer (NSCLC). Inflammatory blood markers can be routinely determined from complete blood counts which are inexpensive and reliable. The aim of the study was to determine prognostic parameters which, in early diagnostics, best determine survival of patients, operated on due to NSCLC.

Materials

The study was conducted on 532 (174 females and 358 males) patients, operated on due to NSCLC, in stages IA – III, aged 36–84 years (the mean age: 63.6 years). The following parameters were subjected to a statistical analysis, conducted in order to determine prognostic values of the number of leukocytes, neutrophils, monocytes, platelets, haemoglobin, RDW-CV and MCV, calculated values of PLR, NLR, and LMR ratios, age, sex, smoking, histopathological diagnosis, T stage, N stage, the Charlson Comorbidity Index (CCI), type of surgery, and potential complications.

Results

The univariate analysis revealed an impact of NLR, PLR, and LMR values, RDW-CW and CCI ranges, and also the number of monocytes on patients’ overall survival (OS). The multivariate analysis identified six independent negative prognostic factors: male sex (0.001), CCI > 4 (p=0.000007), RDW-CV > 14.5% and PLR > 144 (p=0.000001, p= 0.001, respectively), the number of metastatic N2 lymphatic nodes (p=0.0003), and existence of post-operative complications (p=0.008).

Conclusion

Patients’ sex, RDW and PLR values, Charlson index, the number of involved N2 nodes by cancer and postoperative complications are independent and significant prognostic factors in patients operated on due to NSCLC.

Synergy between early-incorporation immunotherapy and extracranial radiotherapy in metastatic non-small cell lung cancer

Background

Combining radiotherapy (RT) and immunotherapy (IT) may enhance outcomes for metastatic non-small cell lung cancer (mNSCLC). However, data on the immunomodulatory effects of extracranial RT remains limited. This retrospective database analysis examined real-world practice patterns, predictors of survival, and comparative effectiveness of extracranial radioimmunotherapy (RT + IT) versus early-incorporation immunotherapy (eIT) in patients with mNSCLC.

Methods

Patients diagnosed with mNSCLC between 2004-2016 treated with eIT or RT + IT were identified in the National Cancer Database. Practice patterns were assessed using Cochrane-Armitrage trend test. Cox proportional hazards and Kaplan-Meier method were used to analyze overall survival (OS). Propensity score matching was performed to account for baseline imbalances. Biologically effective doses (BED) were stratified based on the median (39 Gy₁₀). Stereotactic body radiotherapy (SBRT) was defined as above median BED in ≤5 fractions.

Results

eIT utilization increased from 0.3% in 2010 to 13.2% in 2016 (P<0.0001). Rates of RT + eIT increased from 38.8% in 2010 to 49.1% in 2016 among those who received eIT (P<0.0001). Compared to eIT alone, RT + eIT demonstrated worse median OS (11.2 vs. 13.2 months) while SBRT + eIT demonstrated improved median OS (25 vs. 13.2 months) (P<0.0001). There were no significant differences in OS based on sequencing of eIT relative to RT (log-rank P=0.4415) or irradiated site (log-rank P=0.1606). On multivariate analysis, factors associated with improved OS included chemotherapy (HR 0.86, P=0.0058), treatment at academic facilities (HR 0.83, P<0.0001), and SBRT (HR 0.60, P=0.0009); after propensity-score multivariate analysis, SBRT alone showed improved OS (HR 0.28, P<0.0001).

Conclusions

Utilization of RT + eIT in mNSCLC is increasing. SBRT + eIT was associated with improved OS on propensity-score matched analysis. There were no significant differences in OS based on RT + eIT sequencing or site irradiated. Whether these observations reflect patient selection or possible immunomodulatory benefits of RT is unclear and warrants further study.

Dose individualisation in oncology using chemotherapy-induced neutropenia: Example of docetaxel in non-small cell lung cancer patients

AIMS

Chemotherapy-induced neutropenia has been associated with an increase in overall survival in non-small cell lung cancer patients. Therefore, neutrophil counts could be an interesting biomarker for drug efficacy as well as linked directly to toxicity. For drugs where neutropenia is dose limiting, neutrophil counts might be used for monitoring drug effect and for dosing optimisation.

METHODS

The relationship between drug effect on the first cycle neutrophil counts and patient survival was explored in a Phase III clinical trial where patients with non-small cell lung cancer were treated with docetaxel. Once the association has been established, dosing optimisation was performed for patients with severe toxicities (neutropenia) without compromising drug efficacy (overall survival).

RESULTS

After taking into account baseline prognostic factors, such as Eastern Cooperative Oncology Group performance status, smoking status, liver metastasis, tumour burden, neutrophil counts and albumin levels, a model-predicted drug effect on the first cycle neutrophil counts was strongly associated with patient survival (P = .005). Utilising this relationship in a dose optimisation algorithm, 194 out of 366 patients would have benefited from a dose reduction after the first cycle of docetaxel. The simulated 1-year survival probabilities associated with the original dose and the individualised dose were not different.

CONCLUSION

The strong relationship between drug effect on the first cycle neutrophil counts and patient survival suggests that this variable could be used to individualise dosing, possibly without needing pharmacokinetic samples. The algorithm highlights that doses could be reduced in case of severe haematological toxicities without compromising drug efficacy.

Detection of circulating tumor DNA by digital droplet PCR in resectable lung cancer as a predictive tool for recurrence

Lung cancer is the leading cause of cancer-related mortality worldwide due to difficulties in early detection and high postsurgical recurrence rate. Current European Guidelines recommend follow-up via computerized tomography (CT) scans on regular basis within the first 2 years after radical surgical resection. Despite these efforts, recurrence rates remain high with 30-70 %. Therefore, it is imperative to develop predictive markers for metastases and postsurgical recurrence using minimally invasive methods. This prospective study aims at defining the feasibility of detecting circulating tumor DNA (ctDNA) in presurgical plasma samples of patients with lung cancer by digital droplet PCR. Resected tumor tissue and simultaneous blood samples were collected from 24 patients with lung cancer in stage I-IIIA (12 stage I, 8 stage II, 4 stage III). Genomic DNA from the tumor tissue samples were analyzed for hotspot mutations using a 17 gene panel next-generation sequencing (NGS) assay. CtDNA from corresponding plasma samples were analyzed using digital droplet PCR (ddPCR). Additionally, DNA sequencing results were correlated with patients' outcome. At least one somatic mutation was detected by NGS (96 %) in 23 of the tested tissue samples. DdPCR detected mutations in circulating cell-free DNA (ccfDNA) of nine patients' samples (9/23, 39 %). Postsurgical outcome analysis was performed for those patients who had received complete tumor resection (n = 21). Four of them suffered from an early relapse within the first two years after surgery, including two with detectable somatic mutations in ccfDNA during primary staging. Taken together, we showed that the 17 gene panel assay revealed in 23 of 24 patients at least one somatic mutation in the primary tumor by NGS. Tumor-specific mutation was detectable in 39 % from the blood of early stage lung cancer patients by ddPCR.

[Semiquantitative parameters of 18F-FDG PET/CT, gene mutation states of epidermal growth factor receptor and anaplastic lymphoma kinase in prognosis evaluation of patients with lung adenocarcinoma]

OBJECTIVE

To explore the valuable predictors for evaluating progression-free survival (PFS) in patients with lung adenocarcinoma, we analyzed the potential roles of standardized uptake value (SUV)-derived parameters from ¹⁸F-FDG PET/CT, combining with the gene mutation states of epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), and other clinical characteristics.

METHODS

Data of 84 lung adenocarcinoma patients pre-treated, who underwent ¹⁸F-FDG PET/CT scans, EGFR gene mutations test, ALK rearrangement assay and other relative tests, were retrospectively collected. Then a series of clinical parameters including EGFR/ALK mutation status and SUV-derived features [maximum standardized uptake value (SUVmax), average of standardized uptake value (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)] were evaluated. Best possible cutoff points for all measuring parameters were calculated using receiver operating characteristic curve (ROC) analysis. Survival analysis was performed using Cox proportional hazards model to determine the prognostic markers for progression-free survival (PFS). Survival curves were obtained through Log-rank test and Kaplan-Meier curve.

RESULTS

The median follow-up period was 31 months (24 to 58 months). It was found that SUVmax (≥3.01), SUVmean (≥2.25), MTV (≥25.41 cm³), and TLG (≥55.02) of the primary tumors were significantly associated with PFS in univariate Cox proportional hazards regression. Then regardless of age, gender, co-morbidity, EGFR/ALK mutation status, and treatment program, TLG (≥ 55.02, HR=4.965, 95%CI: 1.360-18.133), TNM stage (Ⅲ/Ⅳ, HR=7.811, 95%CI: 2.977-20.489), pro-gastrin releasing peptide (proGRP) (≥45.65 ng/L, HR=4.070, 95%CI: 1.442-11.487), tissue polypeptide antigen (TPA) (≥68.20 U/L, HR=6.996, 95%CI: 1.458-33.574), alkaline phosphatase (ALP) (≥82.50 IU/L, HR=4.160, 95%CI: 1.416-12.219) and ratio of activated partial thromboplastin time (aPTTR) (≥1.16: HR=4.58, 95%CI: 1.913-10.946) showed the independently relevant to PFS through multivariate Cox proportional hazards analysis. The EGFR mutant (P=0.343) and ALK rearrangement (P=0.608) were not significant either in survival analysis.

CONCLUSION

High SUV-derived parameters (SUVmax, SUVmean, MTV and TLG) might provide prognostic value to some extent. Especially, TLG, and other clinical features [TNM stage, proGRP, TPA, ALP, and aPTTR] could be independently and significantly associated with PFS of lung adenocarcinoma patients. However, EGFR/ALK gene status could not be effectively relevant to PFS in lung adenocarcinoma patients.

Risk Stratification of Locally Advanced Non-Small Cell Lung Cancer (NSCLC) Patients Treated with Chemo-Radiotherapy: An Institutional Analysis

Background

The purpose of this study was to determine which factors predicted survival and to derive a risk prediction model for patients with locally advanced non-small cell lung cancer (NSCLC) receiving concurrent chemo-radiotherapy (cCRT).

Methods

This investigation included 149 patients with locally advanced NSCLC who were treated with cCRT at Stony Brook University Hospital between 2007 and 2015. A finite set of demographic, clinical, and treatment variables were evaluated as independent prognostic factors. Kaplan–Meier survival curves were generated, and log rank tests were used to evaluate difference in survival between groups. To derive a risk score for mortality, a machine learning approach was utilized. To maximize statistical power while examining replicability, the sample was split into discovery (n=99) and replication (n=50) subsamples. Elastic-net regression was used to identify a linear prediction model. Youden’s index was used to identify appropriate cutoffs. Cox proportional hazards regression was used to examine mortality risk; model concordance and hazards ratios were reported.

Results

One-quarter of the patients survived for three years after initiation of cCRT. Prognostic factors for survival in the discovery group included age, sex, smoking status, albumin, histology, largest tumor size, number of nodal stations, stage, induction therapy, and radiation dose. The derived model had good risk predictive accuracy (C=0.70). Median survival time was shorter in the high-risk group (0.93 years) vs the low-risk group (2.40 years). Similar findings were noted in the replication sample with strong model accuracy (C=0.69) and median survival time of 0.93 years and 2.03 years for the high- and low-risk groups, respectively.

Conclusion

This novel risk prediction model for overall survival in patients with stage III NSCLC highlights the importance of integrating patient, clinical, and treatment variables for accurately predicting outcomes. Clinicians can use this tool to make personalized treatment decisions for patients with locally advanced NSCLC treated with concurrent chemo-radiation.

Image Based Data Mining Using Per-voxel Cox Regression

Image Based Data Mining (IBDM) is a novel analysis technique allowing the interrogation of large amounts of routine radiotherapy data. Using this technique, unexpected correlations have been identified between dose close to the prostate and biochemical relapse, and between dose to the base of the heart and survival in lung cancer. However, most analyses to date have considered only dose when identifying a region of interest, with confounding variables accounted for post-hoc, most often using a multivariate Cox regression. In this work, we introduce a novel method to account for confounding variables directly in the analysis, by performing a Cox regression in every voxel of the dose distribution, and apply it in the analysis of a large cohort of lung cancer patients. Our method produces three-dimensional maps of hazard for clinical variables, accounting for dose at each spatial location in the patient. Results confirm that a region of interest exists in the base of the heart where those patients with poor performance status (PS), PS > 1, have a stronger adverse reaction to incidental dose, but that the effect changes when considering other clinical variables, with patient age becoming dominant. Analyses such as this will help shape future clinical trials in which hypotheses generated by the analysis will be tested.

The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non-Small Cell Lung Cancer: A Variable Importance Approach

BACKGROUND

Prognostic modeling in health care has been predominantly statistical, despite a rapid growth of literature on machine-learning approaches in biological data analysis. We aim to assess the relative importance of variables in predicting overall survival among patients with non-small cell lung cancer using a Variable Importance (VIMP) approach in a machine-learning Random Survival Forest (RSF) model for posttreatment planning and follow-up.

METHODS

A total of 935 non-small cell lung cancer patients were randomly and equally divided into 2 training and testing cohorts in an RFS model. The prognostic variables included age, sex, race, the TNM Classification of Malignant Tumors (TNM) stage, smoking history, Eastern Cooperative Oncology Group performance status, histologic type, treatment category, maximum standard uptake value of whole-body tumor (SUVmaxWB), whole-body metabolic tumor volume (MTVwb), and Charlson Comorbidity Index. The VIMP was calculated using a permutation method in the RSF model. We further compared the VIMP of the RSF model to that of the standard Cox survival model. We examined the order of VIMP with the differential functional forms of the variables.

RESULTS

In both the RSF and the standard Cox models, the most important variables are treatment category, TNM stage, and MTVwb. The order of VIMP is more robust in RSF model than in Cox model regarding the differential functional forms of the variables.

CONCLUSIONS

The RSF VIMP approach can be applied alongside with the Cox model to further advance the understanding of the roles of prognostic factors, and improve prognostic precision and care efficiency.

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.

Prognostic factors in stage IB non-small cell lung cancer according to the 8th edition of the TNM staging system after curative resection

Background

Patients with stage IB non-small cell lung cancer (NSCLC) with poor prognostic factors can be treated selectively with postoperative adjuvant chemotherapy. The aim of this study was to identify the prognostic factors of stage IB NSCLC according to the new 8^th edition of the tumor, node, and metastasis (TNM) staging system.

Methods

From 2005 to 2016, 211 patients who were diagnosed with stage IB NSCLC according to the 8^th edition of the TNM staging system underwent anatomical pulmonary resection (lobectomy or bilobectomy). We analyzed the outcomes of patients receiving adjuvant chemotherapy. The risk factors for prognosis after surgery were also analyzed for NSCLC stage IB.

Results

Differences between the 5-year recurrence-free-survival (RFS) rates (71.4% vs. 60.2%, P=0.173) and the 5-year disease-specific-survival (DSS) rates (88.0% vs. 81.4%, P=0.437) obtained by patients receiving surgical treatment only versus patients receiving both surgery and adjuvant chemotherapy, retrospectively, were not significant. Multivariate analysis was conducted to identify the risk factors for recurrence and cancer-related death. Lymphovascular invasion was an independent risk factor for both recurrence and cancer-related death [hazard ratio (HR) =2.045, P=0.020; HR =3.150, P=0.048, respectively).

Conclusions

Lymphovascular invasion was the only prognostic factor identified in patients with 8^th edition stage IB NSCLC. Adjuvant chemotherapy was not an effective treatment for patients with stage IB NSCLC. The efficacy of adjuvant chemotherapy for stage IB patients with lymphovascular invasion should be evaluated in a future study.

Validation of Independent Prognostic Value of Asphericity of 18F-Fluorodeoxyglucose Uptake in Non-Small-Cell Lung Cancer Patients Undergoing Treatment With Curative Intent

BACKGROUND

In patients with non-small-cell lung cancer (NSCLC), asphericity (ASP) of the primary tumor's metabolic tumor volume (MTV) has shown prognostic significance. This study aimed at validation in an independent and sufficiently large cohort.

PATIENTS AND METHODS

A retrospective study was performed of 311 NSCLC patients undergoing ¹⁸F-fluorodeoxyglucose positron emission tomography / computed tomography (¹⁸F-FDG PET/CT) before curatively intended treatment (always including surgery). A total of 140 patients had International Union Against Cancer (UICC) stage I disease, 78 had stage II disease, and 93 had stage III disease (adenocarcinoma, n = 153; squamous-cell carcinoma, n = 141). Primary tumor MTV was delineated with semiautomated background-adapted threshold relative to the standardized maximum uptake value (SUV_max). Cox regression (progression-free survival [PFS] and overall survival [OS]) analysis for positron emission tomography (MTV, ASP, SUV_max) as well as for clinical (T/N descriptor, UICC stages), histologic, and treatment variables (Rx/1 vs. R0 resection, chemotherapy/radiotherapy yes/no) were performed.

RESULTS

Events (progression and relapse) occurred in 167 of 311 patients; 137 died (median survivor follow-up, 37 months). In multivariable Cox regression for OS, ASP > 33.3% (hazard ratio, 1.58 [1.04-2.39]), male sex (1.84), age (1.04 per year), Eastern Cooperative Oncology Group performance status ≥ 2 versus 0/1 (2.68), stage II versus I (1.96), and Rx/1 versus R0 resection (2.1) were significant. Among separate UICC stages, ASP only predicted OS in stage II (optimal, > 19.5%; median OS, 33 vs. 59 months). Regarding PFS, ASP > 21.2%, male sex, Eastern Cooperative Oncology Group performance status ≥ 2, stage II versus I disease, and Rx/1 resection were prognostic. ASP remained prognostic for stage II disease (optimal, > 19.5%; PFS, 12 vs. 47 months). Log-rank test for ASP was significant at any cutoff ≥ 18% (OS) or from 9% to 59% (PFS).

CONCLUSION

ASP was validated as prognostic factor for PFS and OS in patients with NSCLC and curative treatment intent, especially stage II. High ASP in stage II could imply intensified treatment or intensified follow-up.

Survival score to characterize prognosis in inoperable stage III NSCLC after chemoradiotherapy

Background

Stage III non-small cell lung cancer (NSCLC) represents a heterogeneous disease regarding principal patient- and tumor characteristics. A simple score may aid in personalizing multimodal therapy.

Methods

The data of 99 consecutive patients with performance status ECOG 0-1 treated until the end of 2016 with multimodal approach for inoperable NSCLC (UICC 7^th edition stage IIIA/B) were evaluated. Patient- and tumor-related factors were examined for their impact on overall survival. Factors showing a negative association with prognosis were then included in the score. Three subgroups with low, intermediate and high-risk score were defined. The results were then validated in the prospective cohort, which includes 45 patients.

Results

Most Patients were treated with concurrent (78%) or sequential (11%) chemoradiotherapy. 53% received induction chemotherapy. Median survival for the entire cohort was 20.8 (range: 15.3-26.3) months. Age (P=0.020), gender (P=0.007), pack years (P=0.015), tumor-associated atelectasis (P=0.004) and histology (P=0.004) had a significant impact on overall survival and were scored with one point each. Twelve, 59 and 28 patients were defined to have a low (0-1 points), intermediate (2-3 points) and high-risk (4-5 points) score. Median survival, 1-, 2- and 3-year survival rates were not reached, 100%, 83% and 67% in the low, 22.9 months, 80%, 47% and 24% intermediate and 13.7 months, 57%, 25% and 18% high-risk patients, respectively (P<0.001). Median survival was not reached in prospective cohort; analysis has revealed a trend for the 1-year survival rates with 100% for the low, 93% intermediate and 69% high-risk patients (P=0.100).

Conclusions

The score demonstrated remarkable survival differences in inoperable stage III NSCLC patients with good performance status receiving multimodal therapy.

Predicting survival in cancer patients with and without 30-day readmission of an unplanned hospitalization using a deficit accumulation approach

BACKGROUND

For cancer patients with an unplanned hospitalization, estimating survival has been limited. We examined factors predicting survival and investigated the concept of using a deficit-accumulation survival index (DASI) in this population.

METHODS

Data were abstracted from medical records of 145 patients who had an unplanned 30-day readmission between 01/01/16 and 09/30/16. Comparison data were obtained for patients who were admitted as close in time to the date of index admission of a study patient, but who did not experience a readmission within 30 days of their discharge date. Our survival analysis compared those readmitted within 30 days versus those who were not. Scores from 23 medical record elements used in our DASI system categorized patients into low-, moderate-, and high-score groups.

RESULTS

Thirty-day readmission was strongly associated with the survival (adjusted hazard ratio [HR] 2.39; 95% confidence interval [CI], 1.46-3.92). Patients readmitted within 30 days of discharge from index admission had a median survival of 147 days (95% CI, 85-207) versus patients not readmitted who had not reached median survival by the end of the study (P < .0001). DASI was useful in predicting the survival; median survival time was 78 days (95% CI, 61-131) for the high score, 318 days (95% CI, 207-426) for the moderate score, and not reached as of 426 days (95% CI, 251 to undetermined) for the low-score DASI group (P < .0001).

CONCLUSIONS

Patients readmitted within 30 days of an unplanned hospitalization are at higher risk of mortality than those not readmitted. A novel DASI developed from clinical documentation may help to predict survival in this population.

Predictive and Prognostic Value of 18F-fluorodeoxyglucose Uptake Combined with Thymidylate Synthase Expression in Patients with Advanced Non-Small Cell Lung Cancer

We investigated the relationship between tumor ¹⁸F-fluorodeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT) scans and thymidylate synthase (TS) expression. In addition, we evaluated the value of FDG uptake in predicting treatment response and prognosis when combined with TS expression in patients with advanced non-small cell lung cancer (NSCLC). We measured the maximum standard uptake value, metabolic tumor volume, and total lesion glycolysis (TLG) of tumor lesions on pretreatment scan in 234 patients (age: 60.1 ± 9.4 years; males: 56.4%) with stage IV non-squamous NSCLC who were enrolled in the prospective phase II clinical trial. We investigated the correlation of the parameters with TS expression and the predictive values of the parameters compared with other clinical factors. Among these parameters, TLG was the most relevant parameter that had a significant correlation with TS expression (ρ = 0.192, P = 0.008). A multivariable Cox proportional-hazards model revealed that high TLG was a significant independent predictor for treatment response (hazard ratio [HR]: 2.05; P = 0.027), progression-free survival (HR: 1.39; P = 0.043), and overall survival (HR: 1.65; P = 0.035) with other factors. In patients with advanced non-squamous NSCLC, tumor TLG on pretreatment PET/CT scan has predictive and prognostic value.