Development and Validation of a Nomogram Prognostic Model for Patients With Advanced Non-Small-Cell Lung Cancer

Development and validation of a survival prediction model for patients with advanced non-small cell lung cancer based on LASSO regression

Introduction: Lung cancer remains a significant global health burden, with non-small cell lung cancer (NSCLC) being the predominant subtype. Despite advancements in treatment, the prognosis for patients with advanced NSCLC remains unsatisfactory, underscoring the imperative for precise prognostic assessment models. This study aimed to develop and validate a survival prediction model specifically tailored for patients diagnosed with NSCLC.

METHODS

A total of 523 patients were randomly divided into a training dataset (n=313) and a validation dataset (n=210). We conducted initial variable selection using three analytical methods: univariate Cox regression, LASSO regression, and random survival forest (RSF) analysis. Multivariate Cox regression was then performed on the variables selected by each method to construct the final predictive models. The optimal model was selected based on the highest bootstrap C-index observed in the validation dataset. Additionally, the predictive performance of the model was evaluated using time-dependent receiver operating characteristic (Time-ROC) curves, calibration plots, and decision curve analysis (DCA).

RESULTS

The LASSO regression model, which included N stage, neutrophil-lymphocyte ratio (NLR), D-dimer, neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC), driver alterations, and first-line treatment, achieved a bootstrap C-index of 0.668 (95% CI: 0.626-0.722) in the validation dataset, the highest among the three models tested. The model demonstrated good discrimination in the validation dataset, with area under the ROC curve (AUC) values of 0.707 (95% CI: 0.633-0.781) for 1-year survival, 0.691 (95% CI: 0.616-0.765) for 2-year survival, and 0.696 (95% CI: 0.611-0.781) for 3-year survival predictions, respectively. Calibration plots indicated good agreement between predicted and observed survival probabilities. Decision curve analysis demonstrated that the model provides clinical benefit at a range of decision thresholds.

CONCLUSION

The LASSO regression model exhibited robust performance in the validation dataset, predicting survival outcomes for patients with advanced NSCLC effectively. This model can assist clinicians in making more informed treatment decisions and provide a valuable tool for patient risk stratification and personalized management.

Construction, validation, and visualization of a web-based nomogram to predict overall survival in small-cell lung cancer patients with brain metastasis

INTRODUCTION

Brain metastasis (BM) is an aggressive complication with an extremely poor prognosis in patients with small-cell lung cancer (SCLC). A well-constructed prognostic model could help in providing timely survival consultation or optimizing treatments.

METHODS

We analyzed clinical data from SCLC patients between 2000 and 2018 based on the Surveillance, Epidemiology, and End Results (SEER) database. We identified significant prognostic factors and integrated them using a multivariable Cox regression approach. Internal validation of the model was performed through a bootstrap resampling procedure. Model performance was evaluated based on the area under the curve (AUC) and calibration curve.

RESULTS

A total of 2,454 SCLC patients' clinical data was collected from the database. It was determined that seven clinical parameters were associated with prognosis in SCLC patients with BM. A satisfactory level of discrimination was achieved by the predictive model, with 6-, 12-, and 18-month AUC values of 0.726, 0.707, and 0.737 in the training cohort; and 0.759, 0.742, and 0.744 in the validation cohort. As measured by survival rate probabilities, the calibration curve agreed well with actual observations. Furthermore, prognostic scores were found to significantly alter the survival curves of different risk groups. We then deployed the prognostic model onto a website server so that users can access it easily.

CONCLUSIONS

In this study, a nomogram and a web-based predictor were developed to predict overall survival in SCLC patients with BM. It may assist physicians in making informed clinical decisions and determining the best treatment plan for each patient.

The Prognostic Significance of Pretreatment White Blood Cell and Platelet Counts for Survival Outcome in Primary Lung Cancer

Background

In Vietnam, lung cancer ranks second among common types of cancer. Although there have been many advances in the diagnosis and treatment of lung cancer, it is still one of the deadliest types of cancer.

Objective

We investigated the prognostic value of pretreatment white blood cell (WBC) and platelet counts of patients with lung cancer.

Methods

This was a prospective, descriptive study with longitudinal follow-up. Data from 203 patients with stage IIIA-IV lung cancer presenting at Can Tho City Oncology Hospital between June 2020 and June 2022 were analyzed. Complete blood cell counts were obtained using standard methods. Lung cancer diagnoses and histological classifications were obtained from cancer registries. The optimal overall survival cutoff point for pretreatment WBC and platelet counts was determined using maximally selected rank statistics.

Results

The median follow-up was 6 (interquartile range 4-8) months and the median age was 61.3 years. The number of male patients was higher than the number of female patients. Most (71.4%) patients had adenocarcinoma; 62.1% of the patients had a WBC count of > 10 × 109/L and 38.4% had a platelet count of > 400 × 109/L. The median overall survival (OS) of all patients was 8 months. The 3-month, 6-month, and 1-year OS was 88.7%, 62.4%, and 28.3%, respectively. Patients with a WBC count of <9.18 × 109/L had a higher OS than those with a count of ≥ 9.18 × 109/L (17 months versus 8 months; p < 0.001) Patients with a platelet count of < 453 × 109/L had a higher OS than those with a count of ≥ 453 × 109/L (8 months versus 7 months; p < 0.001).

Conclusion

White blood cell and platelet count tests are routine investigations that are valuable, in combination with other factors, for predicting OS of lung cancer patients. They can help clinicians to monitor treatment response and survival.

Identification of a visualized web-based nomogram for overall survival prediction in patients with limited stage small cell lung cancer

Small-cell lung cancer (SCLC) is an aggressive lung cancer subtype with an extremely poor prognosis. The 5-year survival rate for limited-stage (LS)-SCLC cancer is 10-13%, while the rate for extensive-stage SCLC cancer is only 1-2%. Given the crucial role of the tumor stage in the disease course, a well-constructed prognostic model is warranted for patients with LS-SCLC. The LS-SCLC patients' clinical data extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2018 were reviewed. A multivariable Cox regression approach was utilized to identify and integrate significant prognostic factors. Bootstrap resampling was used to validate the model internally. The Area Under Curve (AUC) and calibration curve evaluated the model's performance. A total of 5463 LS-SCLC patients' clinical data was collected from the database. Eight clinical parameters were identified as significant prognostic factors for LS-SCLC patients' OS. The predictive model achieved satisfactory discrimination capacity, with 1-, 2-, and 3-year AUC values of 0.91, 0.88, and 0.87 in the training cohort; and 0.87, 0.87, and 0.85 in the validation cohort. The calibration curve showed a good agreement with actual observations in survival rate probability. Further, substantial differences between survival curves of the different risk groups stratified by prognostic scores were observed. The nomogram was then deployed into a website server for ease of access. This study developed a nomogram and a web-based predictor for predicting the overall survival of patients with LS-SCLC, which may help physicians make personalized clinical decisions and treatment strategies.

Prognostic value of plasma circulating tumor DNA fraction across four common cancer types: a real-world outcomes study

BACKGROUND

Genomic analysis of circulating tumor DNA (ctDNA) is increasingly incorporated into the clinical management of patients with advanced cancer. Beyond tumor profiling, ctDNA analysis also can enable calculation of circulating tumor fraction (TF), which has previously been found to be prognostic. While most prognostic models in metastatic cancer are tumor type specific and require significant patient-level data, quantification of TF in ctDNA has the potential to serve as a pragmatic, tumor-agnostic prognostic tool.

PATIENTS AND METHODS

This study utilized a cohort of patients in a nationwide de-identified clinico-genomic database with metastatic castration-resistant prostate cancer (mCRPC), metastatic breast cancer (mBC), advanced non-small-cell lung cancer (aNSCLC), or metastatic colorectal cancer (mCRC) undergoing liquid biopsy testing as part of routine care. TF was calculated based on single-nucleotide polymorphism aneuploidy across the genome. Clinical, disease, laboratory, and treatment data were captured from the electronic health record. Overall survival (OS) was evaluated by TF level while controlling for relevant covariables.

RESULTS

A total of 1725 patients were included: 198 mCRPC, 402 mBC, 902 aNSCLC, and 223 mCRC. TF ≥10% was highly correlated with OS in univariable analyses for all cancer types: mCRPC [hazard ratio (HR) 3.3, 95% confidence interval (CI) 2.04-5.34, P < 0.001], mBC (HR 2.4, 95% CI 1.71-3.37, P < 0.001), aNSCLC (HR 1.68, 95% CI 1.34-2.1, P < 0.001), and mCRC (HR 2.11, 95% CI 1.39-3.2, P < 0.001). Multivariable assessments of TF had similar point estimates and CIs, suggesting a consistent and independent association with survival. Exploratory analysis showed that TF remained consistently prognostic across a wide range of cutpoints.

CONCLUSIONS

Plasma ctDNA TF is a pragmatic, independent prognostic biomarker across four advanced cancers with potential to guide clinical conversations around expected treatment outcomes. With further prospective validation, ctDNA TF could be incorporated into care paradigms to enable precision escalation and de-escalation of cancer therapy based on patient-level tumor biology.

Lung Cancer Computational Biology and Resources

Comprehensive clinical, pathological, and molecular data, when appropriately integrated with advanced computational approaches, are transforming the way we characterize and study lung cancer. Clinically, cancer registry and publicly available historical clinical trial data enable retrospective analyses to examine how socioeconomic factors, patient demographics, and cancer characteristics affect treatment and outcome. Pathologically, digital pathology and artificial intelligence are revolutionizing histopathological image analyses, not only with improved efficiency and accuracy, but also by extracting additional information for prognostication and tumor microenvironment characterization. Genetically and molecularly, individual patient tumors and preclinical models of lung cancer are profiled by various high-throughput platforms to characterize the molecular properties and functional liabilities. The resulting multi-omics data sets and their interrogation facilitate both basic research mechanistic studies and translation of the findings into the clinic. In this review, we provide a list of resources and tools potentially valuable for lung cancer basic and translational research. Importantly, we point out pitfalls and caveats when performing computational analyses of these data sets and provide a vision of future computational biology developments that will aid lung cancer translational research.

Prognostic Nomogram for Overall Survival in Small Cell Lung Cancer Patients Treated with Chemotherapy: A SEER-Based Retrospective Cohort Study

INTRODUCTION

Small cell lung cancer (SCLC) is known for its rapid clinical progression and poor prognosis. In this study, we sought to establish a prognostic nomogram among SCLC patients who received chemotherapy.

METHODS

We obtained 4971 SCLC patients' clinical information from the Surveillance, Epidemiology, and End Results (SEER) database for the period between 2004 and 2015. Patients were divided into training and validation sets. Two nomograms were established based on limited stage (LS) and extensive stage (ES) SCLC patients to predict 1-, 2-, and 3-year overall survival (OS) incorporating superior parameters from multivariate Cox regression. Receiver-operating characteristic curves (ROCs) were applied to assess the discrimination ability of the nomogram while the calibration plots were applied to verify the model. Kaplan-Meier method was applied to find survival curves. Decision curve analysis (DCA) was applied to compare OS between the nomograms and 7th American Joint Committee on Cancer (AJCC) tumor node metastasis (TNM) staging system.

RESULTS

Four and six clinical parameters were identified as significant prognostic factors for LS-SCLC and ES-SCLC patient's OS, respectively. The ROC curves indicated satisfactory discrimination capacity of the nomogram, with 1-, 2-, and 3-year area under curve (AUC) values of 0.89, 0.81, and 0.79 in LS-SCLC patients and 0.71, 0.66, and 0.66 in ES-SCLC patients, respectively. Calibration curves indicated that the nomogram showed good agreement with actual observations in survival rate probability. The survival curves among the LS-SCLC and ES-SCLC cohorts were consistent with the high-risk group having a worse prognosis than the low-risk group. Moreover, ROC and DCA curves showed our nomograms had more benefits than the 7th AJCC-TNM staging system.

CONCLUSIONS

We established two nomograms that can present individual predictions of OS among LS-SCLC and ES-SCLC patients who received chemotherapy. These proposed nomograms may aid clinicians in treatment strategy and design of clinical trials.

Characteristics and Outcomes of Locally Recurrent Retroperitoneal Sarcoma After First Relapse in a Single Tertiary Asian Centre and Applicability of the Sarculator

Objective

Retroperitoneal sarcomas (RPS) comprise of 15% of soft tissue sarcomas where five-year overall survival rate is about 50%. Locoregional recurrences are observed in up to 50% of patients within the first five years following resection. Various factors have been shown to influence survival outcomes, such as histological subtype and tumour size. A nomogram for first relapse locally recurrent RPS was developed using 602 patients from 22 centres. The recurrent RPS Sarculator is available in an electronic interface and includes variables of age, size, margins of re-resection, radiotherapy, chemotherapy and histology to predict for 6-year disease-free survival (DFS) and overall survival (OS). It has not been validated externally. This study aims to validate the Sarculator recurrence nomogram in predicting the survival outcomes of recurrent RPS in an Asian population as well as examine relapse patterns.

Methods

Patients diagnosed with first recurrent RPS from 1 January 2000 to 31 December 2017 with first local relapse and eligible for curative re-resection were retrospectively analysed. The type of surgery was unique for individual patients and suggestions of adjuvant therapy were based on globally recognised standards. Patients were followed up every 3 to 4 months post-operatively for the first 2 to 3 years and 6-monthly to a year thereafter. A R0 or R1 margin is deemed as complete resection, including a microscopically negative margin (R0) and microscopically positive but macroscopically clear margin (R1). R2 is classified as an incomplete resection with tumour rupture or remaining disease. Harrell’s C concordance index was used to determine the nomogram’s discriminative ability and calibration plots were used to assess accuracy. For the calibration, the patients were divided into 3 groups. Death data was retrieved from the National Birth and Death registry for accuracy.

Results

There were 53 patients included in this study. Patient and tumour characteristics have been summarised in Table 1. All patients had their second resection at a single centre. 66.0% had their first resection at the same centre. The median age was 53 (range 21- 79) at diagnosis, median tumour size was 17cm (12cm to 28cm) and median follow-up duration was 44.1 months. The most commonly encountered subtypes were de-differentiated liposarcoma (DDLPS) (56.6%), well-differentiated liposarcoma (WDLPS) (20.8%) and leiomyosarcoma (LMS) (11.3%) with a majority being high-grade (75.5%). The median disease-free interval was 2.9 years (2- 5.3 years) from the first surgery. The median age at second surgery was 56 (21- 79) and all patients had a complete resection (R0/R1). Recurrence patterns differed with subtypes where 90.9% and 9.1% of WDLS, 76.7% and 16.7% of DDLPS and 83.3% and 16.7% of LMS had local and distant relapses respectively from the second surgery. 62.5% of distant relapses was in the lung followed by nodes (18.8%) and liver (12.5%). The 5-year OS from the second surgery was 66.2% (95% CI: 54.3%- 80.8%). The 1-year, 3 years and 6 years DFS were 50.2% (95% CI: 38.2% - 65.9%), 10.4% (4.26% - 25.5%) and 3.91% (0.684% - 22.4%) respectively. Overall, 32 patients (60.4%) had passed away from sarcoma. The concordance indices for 6-year OS and DFS were 0.7 and 0.65 (Figure 1) respectively which represents a fairly accurate prediction by Sarculator.

Conclusion

Our study has shown the Sarculator nomogram for primary recurrent was applicable in our cohort and its potential application in an Asian setting. The Sarculator nomogram will be a useful tool in clinical practice to improve risk stratification and facilitate prognosis-based decision-making. Moving forward, novel therapeutic strategies are required to enhance the prognosis of patients with recurrent RPS.

Overall survival nomogram for patients with spinal bone metastases (SBM)

•Demographic features are essential for a more personalize survival prediction of spinal bone metastasis (SBM).•Women have a relatively better survival chance than men before 75 years, while men have better survival after this age.•SBM survival is not dependent on the number of spinal metastases.

The development and validation of a nomogram for predicting brain metastases in lung squamous cell carcinoma patients: an analysis of the Surveillance, Epidemiology, and End Results (SEER) database

Background

The incidence of brain metastasis (BM) in patients suffering from lung squamous cell carcinoma (LUSC) is lower than that in patients suffering from non-squamous cell carcinoma (NSCC) and there are few studies on BM of LUSC. The purpose of this investigation was to ascertain the risk factors of LUSC, as well as to establish a nomogram prognostic model to predict the incidence of BM in patients with LUSC.

Methods

Patients diagnosed with LUSC between 2010 and 2015 were identified from the Surveillance, Epidemiology, and End Results (SEER) database and the patient data were collated. All patients diagnosed from 2010–2012 were allocated into the training cohort, and the remaining patients diagnosed from 2013–2015 formed the test cohort. Using factors that were screened out through logistic regression analyses, the nomogram in the training cohort was established. It was then evaluated for discrimination and calibration using the test cohort. The performance of the nomogram was assessed by quantifying the area under the receiver operating characteristic (ROC) curve and evaluating the calibration curve.

Results

A total of 26,154 LUSC patients were included in the study. The training cohort consisted of 16,543 patients and there were 8611 patients in the test cohort. Age, marital status, insurance status, histological grade, tumor location, laterality, stage of the cancer, number of metastatic organs, chemotherapy, surgery, and radiotherapy were highly correlated with the incidence of BM. The area under the ROC curve (AUC) of the nomogram for the training cohort and the test cohort were 0.810 [95% confidence interval (CI): 0.796 to 0.823] and 0.805 (95% CI: 0.784 to 0.825), respectively. The slope of the calibration curve was close to 1.

Conclusions

The nomogram was able to accurately predict the incidence of BM. This may be beneficial for the early identification of high-risk LUSC patients and the establishment of individualized treatments.

A Novel Nomogram including AJCC Stages Could Better Predict Survival for NSCLC Patients Who Underwent Surgery: A Large Population-Based Study

Objective

In this study, we aimed to establish a novel nomogram model which was better than the current American Joint Committee on Cancer (AJCC) stage to predict survival for non-small-cell lung cancer (NSCLC) patients who underwent surgery. Patients and Methods. 19617 patients with initially diagnosed NSCLC were screened from Surveillance Epidemiology and End Results (SEER) database between 2010 and 2015. These patients were randomly divided into two groups including the training cohort and the validation cohort. The Cox proportional hazard model was used to analyze the influence of different variables on overall survival (OS). Then, using R software version 3.4.3, we constructed a nomogram and a risk classification system combined with some clinical parameters. We visualized the regression equation by nomogram after obtaining the regression coefficient in multivariate analysis. The concordance index (C-index) and calibration curve were used to perform the validation of nomogram. Receiver operating characteristic (ROC) curves were used to evaluate the clinical utility of the nomogram.

Results

Univariate and multivariate analyses demonstrated that seven factors including age, sex, stage, histology, surgery, and positive lymph nodes (all, P < 0.001) were independent predictors of OS. Among them, stage (C-index = 0.615), positive lymph nodes (C-index = 0.574), histology (C-index = 0.566), age (C-index = 0.563), and sex (C-index = 0.562) had a relatively strong ability to predict the OS. Based on these factors, we established and validated the predictive model by nomogram. The calibration curves showed good consistency between the actual OS and predicted OS. And the decision curves showed great clinical usefulness of the nomogram. Then, we built a risk classification system and divided NSCLC patients into two groups including high-risk group and low-risk group. The Kaplan-Meier curves revealed that OS in the two groups was accurately differentiated in the training cohort (P < 0.001). And then, we validated this result in the validation cohort which also showed that patients in the high-risk group had worse survival than those in the low-risk group.

Conclusion

The results proved that the nomogram model had better performance to predict survival for NSCLC patients who underwent surgery than AJCC stage. These tools may be helpful for clinicians to evaluate prognostic indicators of patients undergoing operation.

Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer

The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). To facilitate the study of cellular spatial organization and interactions, we developed Histology-based Digital-Staining, a deep learning-based computation model, to segment the nuclei of tumor, stroma, lymphocyte, macrophage, karyorrhexis, and red blood cells from standard hematoxylin and eosin-stained pathology images in lung adenocarcinoma. Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas lung adenocarcinoma dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (P = 0.001), with a HR of 2.23 (1.37-3.65) after adjusting for clinical variables. Furthermore, the image-derived TME features significantly correlated with the gene expression of biological pathways. For example, transcriptional activation of both the T-cell receptor and programmed cell death protein 1 pathways positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway positively correlated with the density of stromal cells. In summary, we demonstrate that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways. SIGNIFICANCE: These findings present a deep learning-based analysis tool to study the TME in pathology images and demonstrate that the cell spatial organization is predictive of patient survival and is associated with gene expression.See related commentary by Rodriguez-Antolin, p. 1912.