The impact of additional prognostic factors on survival and their relationship with the anatomical extent of disease expressed by the 6th Edition of the TNM Classification of Malignant Tumors and the proposals for the 7th Edition. J Thorac Oncol. 2008;3:457-466. [PMID: 18448996 DOI: 10.1097/jto.0b013e31816de2b8]

Clinical Significance of Preserving Pulmonary Function After Lung Resection in Early-Stage Non-Small-Cell Lung Cancer

INTRODUCTION

To determine the association between changes in pulmonary function before and after surgery, and the subsequent prognosis, of patients with early-stage non-small-cell lung cancer (NSCLC).

METHODS

A total of 485 patients who underwent lobectomy or segmentectomy for NSCLC with whole tumor size ≤2 cm and clinical stage IA at 2 institutions were retrospectively reviewed. The relationship between the postoperative reduction rate in vital capacity (VC), forced vital capacity (FVC), and forced expiratory volume in 1 second (FEV1) and overall survival (OS) was investigated. OS determined the cut-off value of the reduction rate, according to the reduction rate of every 10% in pulmonary function.

RESULTS

Multivariable Cox regression analysis showed that a reduction rate in VC at 12 months postoperatively was an independent prognostic factor for OS (hazard ratio, 1.05; 95% confidence interval [CI], 1.02-1.07; P < .001) but those in FVC and FEV1 were not. OS was classified into good and poor with 20% reduction rate in VC. OS and recurrence-free survival (RFS) in a higher than 20% reduction rate in VC were worse than those in ≤20% reduction rate in VC (5 year-OS; 82.0% vs. 93.4%; P = .0004. Five year-RFS; 80.3% vs. 89.8%; P = .0018, respectively). Multivariable logistic analysis showed that lobectomy was a risk factor for the higher than 20% reduction rate in VC (odds ratio, 1.61; 95% CI, 1.01-2.56; P = .045).

CONCLUSIONS

Postoperative decrease in VC was significantly associated with the prognosis. Preserving pulmonary function is important for survival of patients with early-stage NSCLC.

Immunotherapy combined with antiangiogenic therapy as third- or further-line therapy for stage IV non-small cell lung cancer patients with ECOG performance status 2: A retrospective study

BACKGROUND

Patients with Eastern Cooperative Oncology Group performance status (ECOG PS) 2 probably cannot tolerate chemotherapy or other antitumor therapies. Some studies have reported that immunotherapy combined with antiangiogenic therapy is well-tolerated and shows good antitumor activity. However, the efficacy of this combination as a later-line therapy in patients with ECOG PS 2 is unclear. This study evaluated the effectiveness and safety of this combination strategy as third- or further-line therapy in stage IV non-small cell lung cancer (NSCLC) patients with ECOG PS 2.

METHODS

In this retrospective study, patients treated with camrelizumab plus antiangiogenic therapy (bevacizumab, anlotinib, or recombinant human endostatin) were included. Objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), overall survival (OS), quality of life (QOL) assessed by ECOG PS, and safety were analyzed.

RESULTS

Between January 10, 2019, and February 28, 2024, a total of 59 patients were included. The ORR was 35.6% (21/59) and the DCR was 86.4%. With a median follow-up of 10.5 months (range: 0.7-23.7), the median PFS was 5.5 months (95% confidence interval [CI]: 3.8-7.3) and the median OS was 10.5 months (95% CI: 11.2-13.6). QOL was improved (≥1 reduction in ECOG PS) in 39 patients (66.1%). The most common Grade 3-4 treatment-related adverse events were hepatic dysfunction (6 [10%]), hypertension (5 [8%]), and hypothyroidism (3 [5%]). There were no treatment-related deaths.

CONCLUSIONS

Third- or further-line immunotherapy combined with antiangiogenic therapy is well-tolerated and shows good antitumor activity in stage IV NSCLC patients with ECOG PS 2. Future large-scale prospective studies are required to confirm the clinical benefits of this combination therapy.

Clinical and Therapeutic Characteristics of Hospitalized Patients with Advanced Lung Cancer in Najran, Saudi Arabia: A Retrospective Study

BACKGROUND

Lung cancer is one of the top causes of cancer deaths globally, including in Saudi Arabia. Although several prognostic markers have been established, the clinical features and outcomes of lung cancer in Saudi Arabia are not well understood. This study aimed to describe the clinical and therapeutic characteristics of advanced lung cancer in Najran, Saudi Arabia.

METHOD

A retrospective chart review of 44 patients diagnosed with advanced lung cancer between June 2018 and September 2021 and treated at the Oncology Center of King Khalid Hospital in Najran City, Saudi Arabia. The clinicopathological features, treatment used, response, and survival outcomes were collected and analyzed.

RESULT

The mean age was 69.3 ± 10.7 years, most of them (n = 35, 79.5%) were male and older than 70 years (n = 24, 54.5%). Adenocarcinoma was the most observed cancer (n = 35, 79.5%), followed by squamous cell carcinoma in six (13.6%). Most cases (n = 42, 95.5%) were in stage IV. Epidermal growth factor receptor (EGFR) mutations were positive in two (4.5%) cases and ALK mutation was positive in two (4.5%) cases. Metastasis to pleura with pleural effusion was the common presentation (n = 41, 93%). Chemotherapy was administered as the first line in 19 cases (43.2%) while 25 cases (56.8%) received chemoimmunotherapy. The commonest chemoimmunotherapy regimen used was carboplatin-pemetrexed-pembrolizumab in 16 (36.4%), followed by carboplatin-paclitaxel-pembrolizumab in 9 (20.5%) cases. The response to initial systemic therapy was as follows disease progression, stable disease, and complete remission in 10 (22.7%), 33 (75.0%), and 1 (2.3%), respectively. Median progression-free survival was 8.7 months (interquartile range (IQR): 5.7-11.4), and the median overall survival was 12.3 months (IQR: 11.1-13.4). Among the total documented 36 (81.8%) dead cases, disease progression was the main cause of death in 25 cases (56.8%). Using chemoimmunotherapy as the first-line therapy was associated with numerical survival improvement compared to using chemotherapy alone (HR: 0.75; 95% CI: 0.39-1.46) however, it was not statistically significant (p = 0.397).

CONCLUSION

In this study, the majority of lung cancer patients were male and over 70 years old. Adenocarcinoma was the most common histological type. Metastasis to pleura with pleural effusion was the common presentation. The most common treatment used was chemoimmunotherapy with a regimen of carboplatin-pemetrexed-pembrolizumab. Addressing the possible causes of delayed diagnosis of lung cancer is crucial for improved survival outcomes.

Impact of the SARS-CoV-2 pandemic on lung cancer survival in England: an analysis of the rapid cancer registration dataset

Early changes in lung cancer care can affect survival. Given the decrease in diagnosis during lockdowns, we calculated their impact on survival using National Lung Cancer Audit data. Percentage survival and HRs for death were compared between 2019 and lockdown periods of 2020. Decreased survival was observed from the first national lockdown onwards and within 90 days of diagnosis. HRs were highest for people diagnosed at the end of 2020 at 1.26 (95% CI 1.20 to 1.32) for death within 90 days and 1.51 (95% CI 1.42 to 1.60) for death between 91 and 270 days. Further work is needed on measures to mitigate this impact.

Intelligent prognosis evaluation system for stage I-III resected non-small-cell lung cancer patients on CT images: a multi-center study

Background

Prognosis is crucial for personalized treatment and surveillance suggestion of the resected non-small-cell lung cancer (NSCLC) patients in stage I-III. Although the tumor-node-metastasis (TNM) staging system is a powerful predictor, it is not perfect enough to accurately distinguish all the patients, especially within the same TNM stage. In this study, we developed an intelligent prognosis evaluation system (IPES) using pre-therapy CT images to assist the traditional TNM staging system for more accurate prognosis prediction of resected NSCLC patients.

Methods

20,333 CT images of 6371 patients from June 12, 2009 to March 24, 2022 in West China Hospital of Sichuan University, Mianzhu People's Hospital, Peking University People's Hospital, Chengdu Shangjin Nanfu Hospital and Guangan Peoples' Hospital were included in this retrospective study. We developed the IPES based on self-supervised pre-training and multi-task learning, which aimed to predict an overall survival (OS) risk for each patient. We further evaluated the prognostic accuracy of the IPES and its ability to stratify NSCLC patients with the same TNM stage and with the same EGFR genotype.

Findings

The IPES was able to predict OS risk for stage I-III resected NSCLC patients in the training set (C-index 0.806; 95% CI: 0.744-0.846), internal validation set (0.783; 95% CI: 0.744-0.825) and external validation set (0.817; 95% CI: 0.786-0.849). In addition, IPES performed well in early-stage (stage I) and EGFR genotype prediction. Furthermore, by adopting IPES-based survival score (IPES-score), resected NSCLC patients in the same stage or with the same EGFR genotype could be divided into low- and high-risk subgroups with good and poor prognosis, respectively (p < 0.05 for all).

Interpretation

The IPES provided a non-invasive way to obtain prognosis-related information from patients. The identification of IPES for resected NSCLC patients with low and high prognostic risk in the same TNM stage or with the same EGFR genotype suggests that IPES have potential to offer more personalized treatment and surveillance suggestion for NSCLC patients.

Funding

This study was funded by the National Natural Science Foundation of China (grant 62272055, 92259303, 92059203), New Cornerstone Science Foundation through the XPLORER PRIZE, Young Elite Scientists Sponsorship Program by CAST (2021QNRC001), Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities (K.C.), Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences (2021RU002), BUPT Excellent Ph.D. Students Foundation (CX2022104).

Nomograms integrating CT radiomic and deep learning signatures to predict overall survival and progression-free survival in NSCLC patients treated with chemotherapy

OBJECTIVES

This study aims to establish nomograms to accurately predict the overall survival (OS) and progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) who received chemotherapy alone as the first-line treatment.

MATERIALS AND METHODS

In a training cohort of 121 NSCLC patients, radiomic features were extracted, selected from intra- and peri-tumoral regions, and used to build signatures (S1 and S2) using a Cox regression model. Deep learning features were obtained from three convolutional neural networks and utilized to build signatures (S3, S4, and S5) that were stratified into over- and under-expression subgroups for survival risk using X-tile. After univariate and multivariate Cox regression analyses, a nomogram incorporating the tumor, node, and metastasis (TNM) stages, radiomic signature, and deep learning signature was established to predict OS and PFS, respectively. The performance was validated using an independent cohort (61 patients).

RESULTS

TNM stages, S2 and S3 were identified as the significant prognosis factors for both OS and PFS; S2 (OS: (HR (95%), 2.26 (1.40-3.67); PFS: (HR (95%), 2.23 (1.36-3.65)) demonstrated the best ability in discriminating patients with over- and under-expression. For the OS nomogram, the C-index (95% CI) was 0.74 (0.70-0.79) and 0.72 (0.67-0.78) in the training and validation cohorts, respectively; for the PFS nomogram, the C-index (95% CI) was 0.71 (0.68-0.81) and 0.72 (0.66-0.79). The calibration curves for the 3- and 5-year OS and PFS were in acceptable agreement between the predicted and observed survival. The established nomogram presented a higher overall net benefit than the TNM stage for predicting both OS and PFS.

CONCLUSION

By integrating the TNM stage, CT radiomic signature, and deep learning signatures, the established nomograms can predict the individual prognosis of NSCLC patients who received chemotherapy. The integrated nomogram has the potential to improve the individualized treatment and precise management of NSCLC patients.

Development of a well-defined tool to predict the overall survival in lung cancer patients: an African based cohort

BACKGROUND

Nomogram is a graphic representation containing the expressed factor of the mathematical formula used to define a particular phenomenon. We aim to build and internally validate a nomogram to predict overall survival (OS) in patients diagnosed with lung cancer (LC).

METHODS

We included 1200 LC patients from a single institution registry diagnosed from 2013 to 2021. The independent prognostic factors of LC patients were identified via cox proportional hazard regression analysis. Based on the results of multivariate cox analysis, we constructed the nomogram to predict the OS of LC patients.

RESULTS

We finally included a total of 1104 LC patients. Age, medical urgency at diagnosis, performance status, radiotherapy, and surgery were identified as prognostic factors, and integrated to build the nomogram. The model performance in predicting prognosis was measured by receiver operating characteristic curve. Calibration plots of 6-, 12-, and 24- months OS showed optimal agreement between observations and model predictions.

CONCLUSION

We have developed and validated a unique predictive tool that can offer patients with LC an individual OS prognosis. This useful prognostic model could aid doctors in making decisions and planning therapeutic trials.

Beyond PACIFIC: Real-World Outcomes of Adjuvant Durvalumab According to Treatment Received and PD-L1 Expression

Adjuvant durvalumab after chemoradiotherapy (CRT) is the standard of care for unresectable stage III non-small cell lung cancer (NSCLC). A post hoc exploratory analysis of PACIFIC revealed no OS benefit in the PD-L1 < 1% subgroup. This retrospective analysis assesses the real-world impact of durvalumab on OS according to PD-L1 tumor proportion score (TPS). Patients with stage III, unresectable NSCLC treated by CRT, with available PD-L1 TPS, from 1 March 2018 to 31 December 2020, at BC Cancer, British Columbia, Canada were included. Patients were divided into two groups, CRT + durvalumab and CRT alone. OS and PFS were analyzed in the PD-L1 ≥ 1% and <1% subgroups. A total of 134 patients were included in the CRT + durvalumab group and 117, in the CRT alone group. Median OS was 35.9 months in the CRT + durvalumab group and 27.4 months in the CRT alone group [HR 0.59 (95% CI 0.42-0.83), p = 0.003]. Durvalumab improved OS in the PD-L1 ≥ 1% [HR 0.53 (95% CI 0.34-0.81), p = 0.003, n = 175], but not in the <1% subgroup [HR 0.79 (95% CI 0.44-1.42), p = 0.4, n = 76]. This retrospective study demonstrates a statistically significant improvement in OS associated with durvalumab after CRT in PD-L1 ≥ 1%, but not PD-L1 < 1% NSCLC. Variables not accounted for may have biased the survival analysis. A prospective study would bring more insight.

Nomogram predicting overall survival of stage IIIB non-small-cell lung cancer patients based on the SEER database

PURPOSE

We aimed to evaluate the prognostic value of stage IIIB non-small-cell (NSCLC) lung cancer patients and to construct a nomogram to effectively predict their overall survival (OS).

METHODS

In total, 4323 patients with stage IIIB NSCLC diagnosed between 1975 and 2018 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Multiple prognostic factors were combined to construct a nomogram for predicting OS of patients with stage IIIB NSCLC. The discrimination and calibration of the nomogram were evaluated by C-indexes and calibration curves. The nomogram was evaluated for predictive ability using receiver operating characteristic (ROC) curves, decision curve analysis curve (DCA), and clinical impact curve (CIC).

RESULTS

The nomogram was built on data of 4323 patients with stage IIIB NSCLC and consisted of the following prognostic factors: age, race, sex, primary labeled, pathology, T stage, whether to receive surgery, whether to receive radiotherapy, and whether to receive chemotherapy. The C-index in the training and validation sets for the nomogram was 0.672 (95% CI: 0.661-0.683) and 0.675 (95% CI: 0.656-0.694), respectively. According to scores of the nomogram, patients in the complete set, validation set, and training set were classified into two risk groups, low risk and high risk.

CONCLUSIONS

We developed the first validated nomogram to estimate the OS of patients with stage IIIB NSCLC. The nomogram was based on nine prognostic factors and provided an individualized risk estimate of 3-year and 5-year OS survival in patients with stage IIIB NSCLC.

Best first-line therapy for people with advanced non-small cell lung cancer, performance status 2 without a targetable mutation or with an unknown mutation status

BACKGROUND

Most people who are newly diagnosed with non-small cell lung cancer (NSCLC) have advanced disease. For these people, survival is determined by various patient- and tumor-related factors, of which the performance status (PS) is the most important prognostic factor. People with PS 0 or 1 are usually treated with systemic therapies, whereas people with PS 3 or 4 most often receive supportive care. However, treatment for people with PS 2 without a targetable mutation remains unclear. Historically, people with a PS 2 cancer are frequently excluded from (important) clinical trials because of poorer outcomes and increased toxicity. We aim to address this knowledge gap, as this group of people represents a significant proportion (20% to 30%) of the total population with newly diagnosed lung cancer.

OBJECTIVES

To identify the best first-line therapy for advanced lung cancer in people with performance status 2 without a targetable mutation or with an unknown mutation status.

SEARCH METHODS

We used standard, extensive Cochrane search methods. The latest search date was 17 June 2022.

SELECTION CRITERIA

We included randomized controlled trials (RCTs) that compared different chemotherapy (with or without angiogenesis inhibitor) or immunotherapy regimens, specifically designed for people with PS 2 only or studies including a subgroup of these people.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods. Our primary outcomes were 1. overall survival (OS), 2. health-related quality of life (HRQoL), and 3. toxicity/adverse events. Our secondary outcomes were 4. tumor response rate, 5. progression-free survival, and 6. survival rates at six and 12 months' treatment. We used GRADE to assess certainty of evidence for each outcome.

MAIN RESULTS

We included 22 trials in this review and identified one ongoing trial. Twenty studies compared chemotherapy with different regimens, of which 11 compared non-platinum therapy (monotherapy or doublet) versus platinum doublet. We found no studies comparing best supportive care with chemotherapy and only two abstracts analyzing chemotherapy versus immunotherapy. We found that platinum doublet therapy showed superior OS compared to non-platinum therapy (hazard ratio [HR] 0.67, 95% confidence interval [CI] 0.57 to 0.78; 7 trials, 697 participants; moderate-certainty evidence). There were no differences in six-month survival rates (risk ratio [RR] 1.00, 95% CI 0.72 to 1.41; 6 trials, 632 participants; moderate-certainty evidence), whereas 12-month survival rates were improved for treatment with platinum doublet therapy (RR 0.92, 95% CI 0.87 to 0.97; 11 trials, 1567 participants; moderate-certainty evidence). PFS and tumor response rate were also better for people treated with platinum doublet therapy, with moderate-certainty evidence (PFS: HR 0.57, 95% CI 0.42 to 0.77; 5 trials, 487 participants; tumor response rate: RR 2.25, 95% CI 1.67 to 3.05; 9 trials, 964 participants). When analyzing toxicity rates, we found that platinum doublet therapy increased grade 3 to 5 hematologic toxicities, all with low-certainty evidence (anemia: RR 1.98, 95% CI 1.00 to 3.92; neutropenia: RR 2.75, 95% CI 1.30 to 5.82; thrombocytopenia: RR 3.96, 95% CI 1.73 to 9.06; all 8 trials, 935 participants). Only four trials reported HRQoL data; however, the methodology was different per trial and we were unable to perform a meta-analysis. Although evidence is limited, there were no differences in 12-month survival rates or tumor response rates between carboplatin and cisplatin regimens. With an indirect comparison, carboplatin seemed to have better 12-month survival rates than cisplatin compared to non-platinum therapy. The assessment of the efficacy of immunotherapy in people with PS 2 was limited. There might be a place for single-agent immunotherapy, but the data provided by the included studies did not encourage the use of double-agent immunotherapy.

AUTHORS' CONCLUSIONS

This review showed that as a first-line treatment for people with PS 2 with advanced NSCLC, platinum doublet therapy seems to be preferred over non-platinum therapy, with a higher response rate, PFS, and OS. Although the risk for grade 3 to 5 hematologic toxicity is higher, these events are often relatively mild and easy to treat. Since trials using checkpoint inhibitors in people with PS 2 are scarce, we identified an important knowledge gap regarding their role in people with advanced NSCLC and PS 2.

Prognosis of primary pulmonary adenocarcinoma after surgical resection in small-breed dogs: 52 cases (2005-2021)

BACKGROUND

Tumor size is an important prognostic factor in lung cancer in dogs, and the canine lung carcinoma stage classification (CLCSC) recently has been proposed to subdivide tumor sizes. It is unclear if the same classification scheme can be used for small-breed dogs.

OBJECTIVES

To investigate whether the tumor size classification of CLCS is prognostic for survival and progression outcomes in small-breed dogs with surgically resected pulmonary adenocarcinomas (PACs).

ANIMALS

Fifty-two client-owned small-breed dogs with PAC.

METHODS

Single-center retrospective cohort study conducted between 2005 and 2021. Medical records of dogs weighing <15 kg with surgically resected lung masses histologically diagnosed as PAC were examined.

RESULTS

The numbers of dogs with tumor size ≤3 cm, >3 cm to ≤5 cm, >5 cm to ≤7 cm, or >7 cm were 15, 18, 14, and 5, respectively. The median progression-free interval (PFI) and overall survival time (OST) were 754 and 716 days, respectively. In univariable analysis, clinical signs, lymph node metastasis, margin, and histologic grade were associated with PFI, and age, clinical signs, margin, and lymph node metastasis were associated with OST. Tumor size classification of CLCS was associated with PFI in all categories, and tumor size >7 cm was associated with OST. In multivariable analysis, tumor size >5 cm to ≤7 cm and margin were associated with PFI, and age was associated with OST.

CONCLUSIONS AND CLINICAL IMPORTANCE

The tumor size classification of CLCS would be an important prognostic factor in small-breed dogs with surgically resected PACs.

Construction and validation of a prognostic model based on ten signature cell cycle-related genes for early-stage lung squamous cell carcinoma

BACKGROUND

We performed a bioinformatics analysis to screen for cell cycle-related differentially expressed genes (DEGs) and constructed a model for the prognostic prediction of patients with early-stage lung squamous cell carcinoma (LSCC).

METHODS

From a gene expression omnibus (GEO) database, the GSE157011 dataset was randomly divided into an internal training group and an internal testing group at a 1:1 ratio, and the GSE30219, GSE37745, GSE42127, and GSE73403 datasets were merged as the external validation group. We performed single-sample gene set enrichment analysis (ssGSEA), univariate Cox analysis, and difference analysis, and identified 372 cell cycle-related genes. Additionally, we combined LASSO/Cox regression analysis to construct a prognostic model. Then, patients were divided into high-risk and low-risk groups according to risk scores. The internal testing group, discovery set, and external verification set were used to assess model reliability. We used a nomogram to predict patient prognoses based on clinical features and risk values. Clinical relevance analysis and the Human Protein Atlas (HPA) database were used to verify signature gene expression.

RESULTS

Ten cell cycle-related DEGs (EIF2B1, FSD1L, FSTL3, ORC3, HMMR, SETD6, PRELP, PIGW, HSD17B6, and GNG7) were identified and a model based on the internal training group constructed. From this, patients in the low-risk group had a higher survival rate when compared with the high-risk group. Time-dependent receiver operating characteristic (tROC) and Cox regression analyses showed the model was efficient and accurate. Clinical relevance analysis and the HPA database showed that DEGs were significantly dysregulated in LSCC tissue.

CONCLUSION

Our model predicted the prognosis of early-stage LSCC patients and demonstrated potential applications for clinical decision-making and individualized therapy.

Suicide Mortality Risk among Patients with Lung Cancer-A Systematic Review and Meta-Analysis

The risk for suicide in patients with cancer is higher compared to the general population. However, little is known about patients with lung cancer specifically. We therefore implemented a systematic review and random-effects meta-analysis of retrospective cohort studies on suicide in patients with lung cancer. We searched a high number of common databases up to 02/2021. For the systematic review, a total of 23 studies was included. To exclude bias due to patient sample overlap, the meta-analysis was performed on 12 studies. The pooled standardized mortality ratio (SMR) for suicide was 2.95 (95% Confidence Interval (CI) = 2.42-3.60) for patients with lung cancer as compared to the general population. Subgroups with a pronouncedly higher risk for suicide compared to the general population were found for patients living in the USA (SMR = 4.17, 95% CI = 3.88-4.48), with tumors of late stage (SMR = 4.68, 95% CI = 1.28-17.14), and within one year after diagnosis (SMR = 5.00, 95% CI = 4.11-6.08). An increased risk for suicide was found in patients with lung cancer, with subgroups at particular risk. Patients at increased risk should be monitored more closely for suicidality and should receive specialized psycho-oncological and psychiatric care. Further studies should clarify the role of smoking and depressive symptoms on suicidality among lung cancer patients.

Serum cytokine levels and other associated factors as possible immunotherapeutic targets and prognostic indicators for lung cancer

Lung cancer is one of the most prevalent cancer types and the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) accounts for 80-85% of all cancer incidences. Lung cancer therapy and prognosis largely depend on the disease's degree at the diagnosis time. Cytokines are soluble polypeptides that contribute to cell-to-cell communication, acting paracrine or autocrine on neighboring or distant cells. Cytokines are essential for developing neoplastic growth, but they are also known to operate as biological inducers following cancer therapy. Early indications are that inflammatory cytokines such as IL-6 and IL-8 play a predictive role in lung cancer. Nevertheless, the biological significance of cytokine levels in lung cancer has not yet been investigated. This review aimed to assess the existing literature on serum cytokine levels and additional factors as potential immunotherapeutic targets and lung cancer prognostic indicators. Changes in serum cytokine levels have been identified as immunological biomarkers for lung cancer and predict the effectiveness of targeted immunotherapy.

Clinical features and lipid metabolism genes as potential biomarkers in advanced lung cancer

BACKGROUND

Lung cancer is one of the most lethal tumors with a poor survival rate even in those patients receiving new therapies. Metabolism is considered one of the hallmarks in carcinogenesis and lipid metabolism is emerging as a significant contributor to tumor metabolic reprogramming. We previously described a profile of some lipid metabolism related genes with potential prognostic value in advanced lung cancer.

AIM

To analyze clinical and pathological characteristics related to a specific metabolic lipid genomic signature from patients with advanced lung cancer and to define differential outcome.

METHODS

Ninety samples from NSCLC (non-small cell lung cancer) and 61 from SCLC (small cell lung cancer) patients were obtained. We performed a survival analysis based on lipid metabolic genes expression and clinical characteristics. The primary end point of the study was the correlation between gene expression, clinical characteristics and survival.

RESULTS

Clinical variables associated with overall survival (OS) in NSCLC patients were clinical stage, adenocarcinoma histology, Eastern Cooperative Oncology Group (ECOG), number and site of metastasis, plasma albumin levels and first-line treatment with platinum. As for SCLC patients, clinical variables that impacted OS were ECOG, number of metastasis locations, second-line treatment administration and Diabetes Mellitus (DM). None of them was associated with gene expression, indicating that alterations in lipid metabolism are independent molecular variables providing complementary information of lung cancer patient outcome.

CONCLUSIONS

Specific clinical features as well as the expression of lipid metabolism-related genes might be potential biomarkers with differential outcomes.

The combined tumor-nodal glycolytic entropy improves survival stratification in nonsmall cell lung cancer with locoregional disease

OBJECTIVE

To investigate whether combining primary tumor and metastatic nodal glycolytic heterogeneity on 18 F-fluorodeoxyglucose PET ( 18 F-FDG PET) improves prognostic prediction in nonsmall cell lung cancer (NSCLC) with locoregional disease.

METHODS

We retrospectively analyzed 18 F-FDG PET-derived features from 94 patients who had undergone curative treatments for regional nodal metastatic NSCLC. Overall survival (OS) and progression-free survival (PFS) were analyzed using univariate and multivariate Cox regression models. We used the independent prognosticators to construct models to predict survival.

RESULTS

Combined entropy (entropy derived from the combination of the primary tumor and metastatic nodes) and age independently predicted OS (both P = 0.008) and PFS ( P = 0.007 and 0.050, respectively). At the same time, the Eastern Cooperative Oncology Group status was another independent risk factor for unfavorable OS ( P = 0.026). Our combined entropy-based models outperformed the traditional staging system (c-index = 0.725 vs. 0.540, P < 0.001 for OS; c-index = 0.638 vs. 0.511, P = 0.003 for PFS) and still showed prognostic value in subgroups according to sex, histopathology, and different initial curative treatment strategies.

CONCLUSION

Combined primary tumor-nodal glycolytic heterogeneity independently predicted survival outcomes. In combination with clinical risk factors, our models provide better survival predictions and may enable tailored treatment strategies for NSCLC with locoregional disease.

A Nomogram to Predict the Overall Survival of Patients With Resected T1-2N0-1M0 Non-Small Cell Lung Cancer and to Identify the Optimal Candidates for Adjuvant Chemotherapy in Stage IB or IIA Non-Small Cell Lung Cancer Patients

BACKGROUND

The benefit of adjuvant chemotherapy for IB/IIA non-small cell lung cancer (NSCLC) patients remains uncertain. This study aimed to develop a prognostic model to predict overall survival in resected NSCLC patients with T1-2N0-1M0 stage and identify optimal candidates for postoperative chemotherapy among those with stage IB or IIA disease.

METHODS

We conducted a retrospective study using the SEER 18 database (2000-2018, November 2020 submission) of patients who underwent radical surgery for T1-2N0-1M0 NSCLC. The patients not receiving adjuvant chemotherapy were randomly divided into training and validation cohorts. A prognostic nomogram was established and evaluated using calibration and receiver operating characteristic curves. Based on the nomogram, stage IB and IIA patients were categorized into two prognostic groups, each further divided into cohorts based on adjuvant chemotherapy status. Kaplan-Meier analysis and log-rank tests were used to compare overall survival between these groups.

RESULTS

A total of 14 789 patients were enrolled and randomly assigned to the training cohort (n = 10 352) and validation cohort (n = 4437). Ten independent prognostic factors were identified and integrated into the prognostic model. The area under the receiver operating characteristic curve was .706, .699, and .705 in the training cohort, and .700, .698, and .695 in the validation cohort at 1, 3, and 5 years, respectively. Among stage IB and IIA patients, only those in the high-risk group showed a significant benefit from adjuvant chemotherapy, with a 16.4% absolute increase in 5-year overall survival.

CONCLUSIONS

The nomogram developed in the study may help physicians choose the most appropriate management strategy for each patient.

A CT-Based Radiomics Nomogram in Predicting the Postoperative Prognosis of Colorectal Cancer: A Two-center Study

RATIONALE AND OBJECTIVES

This retrospective study aimed to develop a practical model to determine overall survival after surgery in patients with colorectal cancer according to radiomics signatures based on computed tomography (CT) images and clinical predictors.

MATERIALS AND METHODS

A total of 121 colorectal cancer (CRC) patients were selected to construct the model, and 51 patients and 114 patients were selected for internal validation and external testing. The radiomics features were extracted from each patient's CT images. Univariable Cox regression and least absolute shrinkage and selection operator regression were used to select radiomics features. The performance of the nomogram was evaluated by calibration curves and the c-index. Kaplan-Meier analysis was used to compare the overall survival between these subgroups.

RESULTS

The radiomics features of the CRC patients were significantly correlated with survival time. The c-indexes of the nomogram in the training cohort, internal validation cohort and external test cohort were 0.782, 0.721, and 0.677. Our nomogram integrated the optimal radiomics signature with clinical predictors showed a significant improvement in the prediction of CRC patients' overall survival. The calibration curves showed that the predicted survival time was close to the actual survival time. According to Kaplan-Meier analysis, the 1-, 2-, and 3-year survival rates of the low-risk group were higher than those of the high-risk group.

CONCLUSION

The nomogram combining the optimal radiomics signature and clinical predictors further improved the predicted accuracy of survival prognosis for CRC patients. These findings might affect treatment strategies and enable a step forward for precise medicine.

Consistency and prognostic value of preoperative staging and postoperative pathological staging using 18F-FDG PET/MRI in patients with non-small cell lung cancer

OBJECTIVE

In recent years, positron emission tomography/magnetic resonance imaging (PET/MRI) has been clinically used as a method to diagnose non-small cell lung cancer (NSCLC). This study aimed to evaluate the concordance of staging and prognostic ability of NSCLC patients using thin-slice computed tomography (CT) and ¹⁸F-fluorodeoxyglucose (FDG) PET/MRI.

METHODS

This retrospective study was performed on consecutive NSCLC patients who underwent both diagnostic CT and ¹⁸F-FDG PET/MRI before surgery between November 2015 and May 2019. The cTNM staging yielded from PET/MRI was compared with CT and pathological staging, and concordance was investigated, defining pathological findings as reference. To assess the prognostic value of disease-free survival (DFS) and overall survival (OS), we dichotomized the typical prognostic factors and TNM classification staging (Stage I vs. Stage II or higher). Kaplan-Meier curves derived by the log-rank test were generated, and univariate and multivariate analyses were performed to identify the factors associated with DFS and OS.

RESULTS

A total of 82 subjects were included; PET/MRI staging was more consistent (59 of 82) with pathological staging than with CT staging. There was a total of 21 cases of CT and 11 cases of PET/MRI that were judged as cStage I, but were actually pStage II or pStage III. CT tended to judge pN1 or pN2 as cN0 compared to PET/MRI. There was a significant difference between NSCLC patients with Stage I and Stage II or higher by PET/MRI staging as well as prognosis prediction of DFS by pathological staging (P < 0.001). In univariate analysis, PET/MRI, CT, and pathological staging (Stage I or lower vs. Stage II or higher) all showed significant differences as prognostic factors of recurrence or metastases. In multivariate analysis, pathological staging was the only independent factor for recurrence (P = 0.009), and preoperative PET/MRI staging was a predictor of patient survival (P = 0.013).

CONCLUSIONS

In NSCLC, pathologic staging was better at predicting recurrence, and preoperative PET/MRI staging was better at predicting survival. Preoperative staging by PET/MRI was superior to CT in diagnosing hilar and mediastinal lymph-node metastases, which contributed to the high concordance with pathologic staging.

Salvage chemotherapy in patients with nonsmall cell lung cancer after prior immunotherapy: a retrospective, real-life experience study

Patients with advanced nonsmall cell lung cancer (NSCLC) who progress with immune checkpoint inhibitors (ICIs), salvage chemotherapy remains the only viable option for tumors that do not harbor genomic alterations. Data on the efficacy of salvage chemotherapy after immunotherapy (SCAI) are scarce. Our main objective in the current study was to evaluate the efficacy of SCAI. All consecutive patients who were diagnosed as having metastatic NSCLC and received at least one dose of ICIs were retrospectively reviewed. We computed progression-free survival (PFS), overall survival (OS) and objective response rate (ORR) with SCAI. We also analyzed associations between survival and various clinicopathologic factors. We identified 35 patients with advanced NSCLC who received at least one dose of SCAI. The median age was 66 years. Most of patients were male ( n  = 26, 74.3%) and former or current smokers ( n  = 33, 94.3%). The majority of the patients were Eastern Cooperative Oncology Group Performance Status (ECOG PS) 2 ( n  = 22; 62.9%), and there were no patients with driver mutations. SCAI was administered as second-line therapy in 21 (60.0%) patients and third-line in 14 (40.0%) patients. The ORR to SCAI was 20.0% with median PFS and OS were 2.43 (95% CI, 1.69-3.16) months and 4.40 (95% CI, 2.17-6.62) months, respectively. In multivariate analysis, ECOG PS 2 was confirmed as being independently associated with inferior OS. We demonstrated that patients with NSCLC who progressed to ICIs had limited clinical benefit with salvage chemotherapy, particularly for patients who were ECOG PS 2.

Prognostic Factors and Survival in Gastrointestinal Extrapulmonary Small Cell Carcinoma: A Retrospective Cohort Study

BACKGROUND

Gastrointestinal extrapulmonary small cell carcinoma (GI EPSCCa) is a rare, aggressive neuroendocrine tumor. Factors affecting survival, including the prognostic significance of primary tumor site, remain under investigation.

METHODS

Data from the surveillance, epidemiology, and end results (SEER) program were extracted to identify patients diagnosed with GI EPSCCa between 2000 and 2018. Cox proportional hazard models were used to assess prognostic factors based on primary tumor site.

RESULTS

A total of 1687 patients were included in the survival analysis. The distribution of the primary tumor location was as follows: 31.5% colorectum (CRC), 22.1% esophageal, 20.6% pancreatic, 13.3% hepatobiliary (HB), 10.6% stomach, and 1.8% small intestine (SI). Esophagogastric and SI EPSCCa were more common among Black individuals, whereas CRC, HB, and pancreatic EPSCCa were more common among White patients (p = 0.012). There were no racial differences in OS for GI EPSCCa. HB EPSCCa was associated with inferior OS compared with esophageal tumors (adjusted hazard ratio [aHR] 1.21, 95% confidence interval [CI] 1.00-1.46; p = 0.048), and SI EPSCCa was associated with prolonged survival compared with esophageal EPSCCa (aHR 0.76, 95% CI 0.48-1.20; p = 0.237) but did not reach statistical significance. Surgical intervention and a treatment period after 2006 were associated with superior OS.

CONCLUSIONS

The prognosis for GI ESPCCa varies based on site. Chemotherapy, radiation, and surgical resection are associated with improved outcomes; however, the prognosis for patients with EPSCCa remains dismal. Prospective studies are needed to guide therapy for this aggressive tumor.

Wait Times and Survival in Lung Cancer Patients across the Province of Quebec, Canada

Lung cancer is the leading cause of cancer death worldwide, with a five-year survival of 22% in Canada. Guidelines recommend rapid evaluation of patients with suspected lung cancer, but the impact on survival remains unclear. We reviewed medical records of all patients with newly diagnosed lung cancer in four hospital networks across the province of Quebec, Canada, between 1 February and 30 April 2017. Patients were followed for 3 years. Wait times for diagnosis and treatment were collected, and survival analysis using a Cox regression model was conducted. We included 1309 patients, of whom 39% had stage IV non-small cell lung cancer (NSCLC). Median wait times were, in general, significantly shorter in patients with stage III–IV NSCLC or SCLC. Surgery was associated with delays compared to other types of treatments. Median survival was 12.9 (11.1–15.7) months. The multivariate survival model included age, female sex, performance status, histology and stage, treatment, and the time interval between diagnosis and treatment. Longer wait times had a slightly protective to neutral effect on survival, but this was not significant in the stage I–II NSCLC subgroup. Wait times for the diagnosis and treatment of lung cancer were generally within targets. The shorter wait times observed for advanced NSCLC and SCLC might indicate a tendency for clinicians to act quicker on sicker patients. This study did not demonstrate the detrimental effect of longer wait times on survival.

A Review of the Correlation Between Epidermal Growth Factor Receptor Mutation Status and 18F-FDG Metabolic Activity in Non-Small Cell Lung Cancer

PET/CT with 18F-2-fluoro-2-deoxyglucose (18F-FDG) has been proposed as a promising modality for diagnosing and monitoring treatment response and evaluating prognosis for patients with non-small cell lung cancer (NSCLC). The status of epidermal growth factor receptor (EGFR) mutation is a critical signal for the treatment strategies of patients with NSCLC. Higher response rates and prolonged progression-free survival could be obtained in patients with NSCLC harboring EGFR mutations treated with tyrosine kinase inhibitors (TKIs) when compared with traditional cytotoxic chemotherapy. However, patients with EGFR mutation treated with TKIs inevitably develop drug resistance, so predicting the duration of resistance is of great importance for selecting individual treatment strategies. Several semiquantitative metabolic parameters, e.g., maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), measured by PET/CT to reflect 18F-FDG metabolic activity, have been demonstrated to be powerful in predicting the status of EGFR mutation, monitoring treatment response of TKIs, and assessing the outcome of patients with NSCLC. In this review, we summarize the biological and clinical correlations between EGFR mutation status and 18F-FDG metabolic activity in NSCLC. The metabolic activity of 18F-FDG, as an extrinsic manifestation of NSCLC, could reflect the mutation status of intrinsic factor EGFR. Both of them play a critical role in guiding the implementation of treatment modalities and evaluating therapy efficacy and outcome for patients with NSCLC.

Updated Prognostic Factors in Localized NSCLC

Lung cancer is the most common cause of cancer mortality worldwide, and non-small cell lung cancer (NSCLC) represents 80% of lung cancer subtypes. Patients with localized non-small cell lung cancer may be considered for upfront surgical treatment. However, the overall 5-year survival rate is 59%. To improve survival, adjuvant chemotherapy (ACT) was largely explored and showed an overall benefit of survival at 5 years < 7%. The evaluation of recurrence risk and subsequent need for ACT is only based on tumor stage (TNM classification); however, more than 25% of patients with stage IA/B tumors will relapse. Recently, adjuvant targeted therapy has been approved for EGFR-mutated resected NSCLC and trials are evaluating other targeted therapies and immunotherapies in adjuvant settings. Costs, treatment duration, emergence of resistant clones and side effects stress the need for a better selection of patients. The identification and validation of prognostic and theranostic markers to better stratify patients who could benefit from adjuvant therapies are needed. In this review, we report current validated clinical, pathological and molecular prognosis biomarkers that influence outcome in resected NSCLC, and we also describe molecular biomarkers under evaluation that could be available in daily practice to drive ACT in resected NSCLC.

Real-world data prognostic model of overall survival in patients with advanced NSCLC receiving anti-PD-1/PD-L1 immune checkpoint inhibitors as second-line monotherapy

Background and aim

The objective of this retrospective, observational, noninterventional cohort study was to investigate prognostic factors of overall survival (OS) in patients with advanced non‐small cell lung cancer (aNSCLC) and to develop a novel prognostic model.

Methods

A total of 4049 patients with aNSCLC diagnosed between January 2011 and February 2020 who received atezolizumab, nivolumab, or pembrolizumab as second‐line monotherapy were selected from a real‐world deidentified database to build the cohort. Patients could not have received first‐line treatment with clinical study drug(s) nor immune checkpoint inhibitors including anti‐programmed cell death 1 (PD‐1)/programmed death‐ligand 1 (PD‐L1), and anti‐cytotoxic T‐lymphocyte‐associated protein 4 therapies.

Results

Patients had a median age of 69 years; 45% were female, 75% White, 70% had stage IV at initial diagnosis, and 70% had nonsquamous histology. A Cox proportional hazards model with lasso regularization was used to build a prognostic model for OS using 18 baseline demographic and clinical factors based on the real‐world data cohort. The risk‐increasing prognostic factors were abnormally low albumin and chloride levels, Eastern Cooperative Oncology Group performance status score ≥ 2, and abnormally high levels of alkaline phosphatase and white blood cells. The risk‐decreasing prognostic factors were PD‐L1 positivity, longer time from advanced diagnosis to start of first‐line therapy, and higher systolic blood pressure. The performance of the model was validated using data from the OAK trial, and the c‐index for the OAK trial validation cohort was 0.65 and 0.67 for the real‐world data cohort.

Conclusions

Based on baseline demographic and clinical factors from a real‐world setting, this prognostic model was developed to discriminate the risk of death in patients with aNSCLC treated with checkpoint inhibitors as second‐line monotherapy, and it performed well in the real‐world data and clinical trial cohorts.

Examining the Landscape of Prognostic Factors and Clinical Outcomes for Cancer Control

Prognostic factors have important utility in various aspects of cancer surveillance, including research, patient care, and cancer control programmes. Nevertheless, there is heterogeneity in the collection of prognostic factors and outcomes data globally. This study aimed to investigate perspectives on the utility and application of prognostic factors and clinical outcomes in cancer control programmes. A qualitative phenomenology approach using expert interviews was taken to derive a rich description of the current state and future outlook of cancer prognostic factors and clinical outcomes. Individuals with expertise in this work and from various regions and institutions were invited to take part in one-on-one semi-structured interviews. Four areas related to infrastructure and funding challenges were identified by participants, including (1) data collection and access; (2) variability in data reporting, coding, and definitions; (3) limited coordination among databases; and (4) conceptualization and prioritization of meaningful prognostic factors and outcomes. Two areas were identified regarding important future priorities for cancer control: (1) global investment and intention in cancer surveillance and (2) data governance and exchange globally. Participants emphasized the need for better global collection of prognostic factors and clinical outcomes data and support for standardized data collection and data exchange practices by cancer registries.

Multi-level multi-modality (PET and CT) fusion radiomics: prognostic modeling for non-small cell lung carcinoma

We developed multi-modality radiomic models by integrating information extracted from¹⁸F-FDG PET and CT images using feature- and image-level fusions, toward improved prognosis for non-small cell lung carcinoma (NSCLC) patients. Two independent cohorts of NSCLC patients from two institutions (87 and 95 patients) were cycled as training and testing datasets. Fusion approaches were applied at two levels, namely feature- and image-levels. For feature-level fusion, radiomic features were extracted individually from CT and PET images and concatenated. Alternatively, radiomic features extracted separately from CT and PET images were averaged. For image-level fusion, wavelet fusion was utilized and tuned with two parameters, namely CT weight and Wavelet Band Pass Filtering Ratio. Clinical and combined clinical + radiomic models were developed. Gray level discretization was performed at 3 different levels (16, 32 and 64) and 225 radiomics features were extracted. Overall survival (OS) was considered as the endpoint. For feature reduction, correlated (redundant) features were excluded using Spearman's correlation, and best combination of top ten features with highest concordance-indices (via univariate Cox model) were selected in each model for further multivariate Cox model. Moreover, prognostic score's median, obtained from the training cohort, was used intact in the testing cohort as a threshold to classify patients into low- versus high-risk groups, and log-rank test was applied to assess differences between the Kaplan-Meier curves. Overall, while models based on feature-level fusion strategy showed limited superiority over single-modalities, image-level fusion strategy significantly outperformed both single-modality and feature-level fusion strategies. As such, the clinical model (C-index = 0.656) outperformed all models from single-modality and feature-level strategies, but was outperformed by certain models from image-level fusion strategy. Our findings indicated that image-level fusion multi-modality radiomics models outperformed single-modality, feature-level fusion, and clinical models for OS prediction of NSCLC patients.

Current state of knowledge on immunotherapy in ECOG PS 2 patients. A systematic review

BACKGROUND

Patients with Eastern Cooperative Oncology Group Performance Status 2 (ECOG PS 2) are not included in most randomized clinical trials and registry studies. Nevertheless, immune checkpoint inhibitors are registered in the USA and Europe regardless of the performance status. Evidence regarding the effectiveness and safety of such treatment in this cohort is sparse.

METHODS

Using PubMed (to July 2020), the relevant literature on the effect of ECOG PS 2 on the efficacy and safety of immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) with ECOG PS 2 was searched.

RESULTS

A database search conducted using an international repository (PubMed) identified 191 records. Additional 3 records were identified through other sources. After pre-selection, 92 records were excluded, and 102 full-text articles were assessed for eligibility. With further exclusion of articles not meeting the inclusion criteria, 44 studies were entered into the qualitative synthesis.

CONCLUSIONS

Immunotherapy seems to be justified in PS 2 patients with NSCLC. This method of treatment has been proven to be safe and tolerable. However, outcomes in this population remain suboptimal and the impact of immunotherapy in this cohort is less dramatic. Multiple scales evaluating many factors beyond PS scores have been suggested to help stratify the PS 2 to reinforce the chance of achieving better treatment outcomes. Randomized trials are needed to determine the benefits of immune checkpoint inhibitors (ICIs) for patients with poor ECOG PS.

Impact of coexistent preserved ratio impaired spirometry on the survival of patients with lung cancer: Analysis of data from the Korean Association for Lung Cancer Registry

BACKGROUND

Preserved ratio impaired spirometry (PRISm) is a common spirometric pattern that is associated with respiratory symptoms and higher mortality rates. However, the relationship between lung cancer and PRISm remains unclear. This study investigated the clinical characteristics of lung cancer patients with PRISm and the potential role of PRISm as a prognostic factor.

METHODS

We retrospectively reviewed data collected from 2014 to 2015 in the Korean Association for Lung Cancer Registry. We classified all patients into three subgroups according to lung function as follows: normal lung function; PRISm (forced expiratory volume in 1 s [FEV₁ ] < 80% predicted and FEV₁ /forced vital capacity [FVC] ≥ 0.7); and chronic obstructive pulmonary disease (COPD; FEV1/FVC < 0.7). In non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), the overall survival period was compared among the three subgroups. The prognostic factors were investigated using Cox regression analysis.

RESULTS

Of the 3763 patients, 38.6%, 40.1%, and 21.3% had normal lung function, COPD, and PRISm, respectively. Patients with PRISm had poorer overall survival than those with COPD or normal lung function in NSCLC and SCLC (Mantel-Cox log-rank test, p < 0.05). In the risk-adjusted analysis, overall survival was independently associated with COPD (hazard ratio [HR] 1.209, p = 0.027) and PRISm (HR 1.628, p < 0.001) in NSCLC, but was only associated with PRISm (HR 1.629, p = 0.004) in SCLC.

CONCLUSIONS

PRISm is a significant pattern of lung function in patients with lung cancer. At the time of lung cancer diagnosis, pre-existing PRISm should be considered a predictive factor of poor prognosis.

Impact of Physical Inactivity on the Risk of Disability and Hospitalization in Older Patients with Advanced Lung Cancer

Purpose

This prospective observational study aimed to explore the influence of physical inactivity during initial chemotherapy on the risk of disability and hospitalization in later life among older patients with advanced non-small-cell lung cancer (NSCLC).

Patients and Methods

Patients aged 70 or above who were scheduled to receive first-line chemotherapy for newly diagnosed advanced NSCLC were recruited for the study. An electronic pedometer was used to measure daily steps; based on the change rate (cutoff: −12.5%) from pretreatment to 12 ± 4 weeks after enrolment, patients were classified as active or inactive. The Barthel Index estimated activities of daily living. We compared disability-free survival time, mean cumulative functions of hospital stays, and medical costs, between the active and inactive groups.

Results

Among the 29 patients enrolled, 21 were evaluable. Compared with active patients (n = 11), inactive patients (n = 10) showed shorter disability-free survival (6.4 vs 19.9 months, p < 0.05) and tended to have longer hospital stays (23.7 vs 6.3 days/person) and higher inpatient care cost (¥1.6 vs ¥0.3 million/person [US$16,000 vs US$3000/person]) during the first year.

Conclusion

Physical inactivity during initial chemotherapy may be a risk factor for developing disability and requiring hospitalization in later life for older patients with advanced NSCLC. Our findings may indicate the need for lifestyle interventions with multidisciplinary teams, which include physicians, nurses, and physiotherapists, for older patients with advanced lung cancer during an active cancer treatment. A large-sample-sized study is needed to validate our findings.

EPAC-lung: European pooled analysis of the prognostic value of circulating tumour cells in small cell lung cancer

BACKGROUND

Circulating tumour cell (CTC) number is an independent prognostic factor in patients with small cell lung cancer (SCLC) but there is no consensus on the CTC threshold for prognostic significance. We undertook a pooled analysis of individual patient data to clinically validate CTC enumeration and threshold for prognostication.

METHODS

Four European cancer centres, experienced in CellSearch CTC enumeration for SCLC provided pseudo anonymised data for patients who had undergone pre-treatment CTC count. Data was collated, and Cox regression models, stratified by centre, explored the relationship between CTC count and survival. The added value of incorporating CTCs into clinico-pathological models was investigated using likelihood ratio tests.

RESULTS

A total of 367 patient records were evaluated. A one-unit increase in log-transformed CTC counts corresponded to an estimated hazard ratio (HR) of 1.24 (95% CI: 1.19-1.29, P<0.0001) for progression free survival (PFS) and 1.23 (95% CI: 1.18-1.28, P<0.0001) for overall survival (OS). CTC count of ≥15 or ≥50 was significantly associated with an increased risk of progression (CTC ≥15: HR 3.20, 95% CI: 2.50-4.09, P<0.001; CTC ≥50: HR 2.56, 95% CI: 2.01-3.25, P<0.001) and an increased risk of death (CTC ≥15: HR 2.90, 95% CI: 2.28-3.70, P<0.001; CTC ≥50: HR 2.47, 95% CI: 1.95-3.13, P<0.001). There was no significant inter-centre heterogeneity observed. Addition of CTC count to clinico-pathological models as a continuous log-transformed variable, offers further prognostic value (both likelihood ratio P<0.001 for OS and PFS).

CONCLUSIONS

Higher pre-treatment CTC counts are a negative independent prognostic factor in SCLC when considered as a continuous variable or dichotomised counts of ≥15 or ≥50. Incorporating CTC counts, as a continuous variable, improves clinic-pathological prognostic models.

Expanding TNM for lung cancer through machine learning

Background

Expanding the tumor, lymph node, metastasis (TNM) staging system by accommodating new prognostic and predictive factors for cancer will improve patient stratification and survival prediction. Here, we introduce machine learning for incorporating additional prognostic factors into the conventional TNM for stratifying patients with lung cancer and evaluating survival.

Methods

Data were extracted from SEER. A total of 77 953 patients were analyzed using factors including primary tumor (T), regional lymph node (N), distant metastasis (M), age, and histology type. Ensemble algorithm for clustering cancer data (EACCD) and C‐index were applied to generate prognostic groups and expand the current staging system.

Results

With T, N, and M, EACCD stratified patients into 11 groups, resulting in a significantly higher accuracy in survival prediction than the 10 AJCC stages (C‐index = 0.7346 vs. 0.7247, increase in C‐index = 0.0099, 95% CI: 0.0091–0.0106, p‐value = 9.2 × 10⁻¹⁴⁷). There nevertheless remained a strong association between the EACCD grouping and AJCC staging (rank correlation = 0.9289; p‐value = 6.7 × 10⁻²²). A further analysis demonstrated that age and histological tumor could be integrated with the TNM. Data were stratified into 12 prognostic groups with an even higher prediction accuracy (C‐index = 0.7468 vs. 0.7247, increase in C‐index = 0.0221, 95% CI: 0.0212–0.0231, p‐value <5 × 10⁻³²⁴).

Conclusions

EACCD can be successfully applied to integrate additional factors with T, N, M for lung cancer patients.

Does Very Poor Performance Status Systematically Preclude Single Agent Anti-PD-1 Immunotherapy? A Multicenter Study of 35 Consecutive Patients

Simple Summary

Immunotherapies prolong survival of metastatic non-small-cell lung cancer patients. However, their efficacy in patients with very poor general condition is unknown. Best supportive care is the standard of care for these patients because chemotherapy is more toxic and less effective than for patients with good general condition. Most patients die within 1 to 4 months of diagnosis. Consecutive metastatic non-small-cell lung cancer patients with very poor general condition receiving compassionate immunotherapy were accrued by 12 French thoracic oncology departments, over 24 months. Tolerance was acceptable. Overall, 20% of patients were alive at 1 year, and 14% at 2 years. We feel that our study results might suggest that some patients with a very poor general condition (namely those without brain metastases or heavy smokers) could derive long-term benefit from immunotherapy as salvage therapy. We initiated such a prospective phase 2 trial based on these results, which is a cause for hope.

Abstract

Anti-PD-1 antibodies prolong survival of performance status (PS) 0–1 advanced non-small-cell lung cancer (aNSCLC) patients. Their efficacy in PS 3–4 patients is unknown. Conse- cutive PS 3–4 aNSCLC patients receiving compassionate nivolumab were accrued by 12 French thoracic oncology departments, over 24 months. Overall survival (OS) was calculated using the Kaplan-Meier method. Prognostic variables were assessed using Cox proportional hazards models. Overall, 35 PS 3–4 aNSCLC patients (median age 65 years) received a median of 4 nivolumab infusions (interquartile range [IQR], 1–7) as first- (n = 6) or second-line (n = 29) therapy. At a median of 52-month follow-up (95%CI, 41–63), 32 (91%) patients had died. Median progression-free survival was 2.1 months (95%CI, 1.1–3.2). Median OS was 4.4 months (95%CI, 0.5–8.2). Overall, 20% of patients were alive at 1 year, and 14% at 2 years. Treatment-related adverse events occurred in 8/35 patients (23%), mostly of low-grade. After adjustment, brain metastases (HR = 5.2; 95%CI, 9–14.3, p = 0.001) and <20 pack-years (HR = 4.8; 95%CI, 1.7–13.8, p = 0.003) predicted worse survival. PS improvement from 3–4 to 0–1 (n = 9) led to a median 43-month (95%CI, 0–102) OS. Certain patients with very poor general condition could derive long-term benefit from nivolumab salvage therapy.

Overall Survival With Second-Line Pembrolizumab in Patients With Non-Small-Cell Lung Cancer: Randomized Phase III Clinical Trial Versus Propensity-Adjusted Real-World Data

PURPOSE

To compare and characterize overall survival (OS) differences between clinical trial data from the KEYNOTE-010 trial and real-world data (RWD) from the Flatiron Health database in patients with programmed death ligand 1 (PD-L1)-expressing advanced non-small-cell lung cancer (NSCLC) who received second-line pembrolizumab monotherapy.

METHODS

Clinical trial data were from the randomized phase II/III KEYNOTE-010 trial that enrolled patients from August 28, 2013, to February 27, 2015. At data cutoff for KEYNOTE-010, the median survival follow-up time for pembrolizumab patients was 11.2 months. RWD were from Flatiron Health advanced NSCLC database and included patients who initiated second-line pembrolizumab from January 26, 2015, to February 28, 2019. At data cutoff for Flatiron, the median survival follow-up time for pembrolizumab-treated patients was 6.1 months. Clinical trial data from KEYNOTE-010 and RWD from Flatiron were analyzed without adjustment, with propensity adjustment, and filtered per the main KEYNOTE-010 eligibility criteria (EC) of histologically/cytologically confirmed PD-L1-positive NSCLC, Eastern Cooperative Oncology Group performance status of 0/1, no prior therapy with docetaxel for NSCLC, and laboratory values indicative of adequate organ function in addition to prior line of therapy requirements.

RESULTS

Among 243 patients from KEYNOTE-010 and 782 from Flatiron, median age was 63 v 68 years, and 64% v 54% were male, respectively. OS data from KEYNOTE-010 and Flatiron were similar without any adjustment (n = 782; hazard ratio [HR], 0.96; 95% CI, 0.80 to 1.15) and after both filtering and propensity adjustment (n = 221; HR, 0.99; 95% CI, 0.73 to 1.34).

CONCLUSION

Without any adjustment, as well as after applying similar EC and appropriate statistical methods, RWD demonstrated similar effectiveness for pembrolizumab in second-line NSCLC as observed in randomized clinical trials.

Value of 18F-FDG PET/CT-Based Radiomics Nomogram to Predict Survival Outcomes and Guide Personalized Targeted Therapy in Lung Adenocarcinoma With EGFR Mutations

Objectives

To investigate the development and validation of a radiomics nomogram based on PET/CT for guiding personalized targeted therapy in patients with lung adenocarcinoma mutation(s) in the EGFR gene.

Methods

A cohort of 109 (77/32 in training/validation cohort) consecutive lung adenocarcinoma patients with an EGFR mutation was enrolled in this study. A total of 1672 radiomic features were extracted from PET and CT images, respectively. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to select the radiomic features and construct the radiomics nomogram for the estimation of overall survival (OS), which was then assessed with respect to calibration and clinical usefulness. Patients with an EGFR mutation were divided into high- and low- risk groups according to their nomogram score. The treatment strategy for high- and low-risk groups was analyzed using Kaplan–Meier analysis and a log-rank test.

Results

The C-index of the radiomics nomogram for the prediction of OS in lung adenocarcinoma in patients with an EGFR mutation was 0.840 and 0.803 in the training and validation cohorts, respectively. Distant metastasis [(Hazard ratio, HR),1.80], metabolic tumor volume (MTV, HR, 1.62), and rad score (HR, 17.23) were the independent risk factors for patients with an EGFR mutation. The calibration curve showed that the predicted survival time was remarkably close to the actual time. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful. Targeted therapy for patients with high-risk EGFR mutations attained a greater benefit than other therapies (p < 0.0001), whereas the prognoses of the two therapies were similar in the low-risk group (p = 0.85).

Conclusions

Development and validation of a radiomics nomogram based on PET/CT radiomic features combined with clinicopathological factors may guide targeted therapy for patients with lung adenocarcinoma with EGFR mutations. This is conducive to the advancement of precision medicine.

Impact of tumor disappearance ratio on the prognosis of lung adenocarcinoma ≤2 cm in size: A retrospective cohort study

BACKGROUND/PURPOSE

Lung cancer patients can have advanced-stages at diagnosis, even the tumor size is ≤2 cm. We aimed to study the relationship between image characteristics, clinical, and patholoigcal results.

METHODS

We retrospectively enrolled patients with lung adenocarcinoma at Taichung Veterans General Hospital and Chang Gung Memorial Hospital from 2007 to 2015, who were diagnosed with treatment naïve primary tumor lesions at sizes less than 2 cm, as measured by computed tomography (CT) scans. The patient was analyzed for lymph node (LN) and distant metastasis evaluation, with clinicopathological characteristics, including tumor-disappearance ratio (TDR) (tumor diameter at the mediastinal/lung window) over chest CT scans, pathological diagnosis, disease-free survival (DFS), and overall survival (OS).

RESULTS

Totally 280 patients were surveyed initially and showed significantly increase of clinical LN involvement and distant metastasis when TDR ≤75% compared with >75% (21.6% vs 0% for LN involvement; 27.1% vs 0% for distant metastasis; both p < 0.001). We included 199 patients having surgical treatment and follow-up for the survival analysis. With a TDR ≤75%, significantly worse DFS (HR, 19.23; 95% CI, 2.60-142.01; p = 0.004) and a trend of worse OS (HR, 4.97; 95% CI, 0.61-40.61; p = 0.134) were noted by Kaplan-Meier method. TDR ≤75% revealed more advanced pathological stage, and more tumors containing micropapillary or solid subtypes when diagnosed adenocarcinoma.

CONCLUSION

For lung cancer patients with primary tumor ≤2 cm, TDR ≤75% was related to more advanced stages, the presence of micropapillary or solid components of adenocarcinoma subtypes, worse DFS, and a trend of worse OS.

Mining prognostic factors of extensive-stage small-cell lung cancer patients using nomogram model

This study is to establish the nomogram model and provide clinical therapy decision-making for extensive-stage small-cell lung cancer (ES-SCLC) patients with different metastatic sites using the Surveillance, Epidemiology, and End Results (SEER) Program.A total of 10,025 patients of ES-SCLC with metastasis from January 2010 to December 2016 were enrolled from the SEER database. All samples were randomly divided into a derivation cohort and a validation cohort, and the derivation cohort was divided into 6 groups by different metastatic sites: bone, liver, lung, brain, multiple organs, and other organs. Using Cox proportional hazards models to analyze candidate prognostic factors, screening out the independent prognostic factors to establish the nomogram. Compare the different models by Net reclassification improvement and integrated discrimination improvement. Concordance index (C-index) and the calibration curve were used to verify the prediction efficiency of the nomogram in the derivation cohort and validation cohort.In the derivation cohort, the median overall survival was 7 months. The overall survival rates at 6-month, 1-year, and 2-year were 55.07%, 24.61%, and 7.56%, respectively. The median survival time was 10, 8, 7, 9, 7, and 6 months for the 6 groups of different metastatic sites: other, bone, liver, lung, brain, and multiple organs, respectively. Age, sex, race, T, N, distant metastatic site, and chemotherapy were contained in the final nomogram prognostic model. The C-index was 0.6569777 in the derivation cohort and 0.8386301 in the validation cohort.The survival time of ES-SCLC patients with different metastatic sites was significantly different. The nomogram can effectively predict the prognosis of individuals and provide a basis for clinical decision-making.

Prognostic value of the TGFβ1 rs4803455 single nucleotide polymorphism in small cell lung cancer

BACKGROUND

Small cell lung cancer (SCLC) is one of the greatest therapeutic challenges of oncology. Potential associations between single nucleotide polymorphisms in heat shock protein β1 (HSPB1) and transforming growth factor β1 (TGFβ1) and survival have been investigated.

METHODS

A prospective multicenter study of 94 patients with SCLC treated between 2013 and 2016 was conducted. Clinical, tumour-related, therapeutic, and genetic (9 SNPs of TGFβ1 gene and 5 of HSPB1 gene) variables were analyzed.

RESULTS

The cohort included 77 men and 17 women with a median age of 61 years. Eighty percent presented with limited stage at diagnosis and received thoracic radiation with a median dose of 45 Gy (twice-daily radiation in 42%). Forty-seven percent received concurrent platinum-based chemotherapy and 57% received prophylactic cranial irradiation (PCI). Overall survival (OS) was 34% at 2 years and 16% at 3 years. In multivariate analysis, the rs4803455:CA genotype of the TGFβ1 gene showed a statistically significant association with lower disease-free survival (DFS; hazard ratio [HR] 3.13; confidence interval [CI] 1.19-8.17; p = 0.020) and higher local recurrence (HR 3.80; CI 1.37-10.5; p = 0.048), and a marginal association with lower OS (HR 1.94; CI 0.98-3.83; p = 0.057). A combined analysis showed that patients receiving PCI and carrying the rs4803455:CA genotype had statistically significant lower OS (p < 0.001) and DFS (p < 0.001) than patients receiving PCI and carrying the rs4803455:AA genotype.

CONCLUSIONS

Genetic analysis showed the CA genotype of TGFβ1 SNP rs4803455 was associated with worse prognosis in patients with SCLC and could be considered as a potential biomarker.

Survival prediction model for patients with mycosis fungoides/Sezary syndrome

Aim: A nomogram was constructed to forecast the overall survival (OS) of patients with mycosis fungoides/Sezary syndrome. Patients & methods: The clinicopathological information of patients was obtained from the Surveillance, Epidemiology and End Results (SEER) database. A model was established based on the independent prognostic factors. Predictive ability of the model was evaluated with the concordance index and calibration curves. Risk stratification was conducted for patients with similar tumor node metastasis (TNM) stages. Results: The model included 1997 eligible patients and seven prognostic factors for OS. The concordance index of the nomogram was 0.84 in the training and external validation cohorts, which indicated good predictive ability of the model and reliability of the results. The high agreement between the model predictions and actual observations was identified by calibration curves, which demonstrated the prediction accuracy of the model. Risk stratification displayed significant differences for patients with similar TNM stages, which suggested that the OS of patients with similar TNM stages could be further distinguished. Conclusion: We established a reliable nomogram to predict the OS of patients with mycosis fungoides/Sezary syndrome, which highlighted the advantages of nomograms over the conventional TNM staging system and promoted the application of individualized therapeutic strategies.

Genomic characteristics in Chinese non-small cell lung cancer patients and its value in prediction of postoperative prognosis

Background

The genomic profile of non-small cell lung cancer (NSCLC) in Asians is distinct from that of Caucasians, but comprehensive genetic profiling reports have been limited for Asian patients. We aimed to elucidate genomic characteristics of Chinese NSCLC patients and develop potential model including genomic characteristics to predict postoperative prognosis.

Methods

Resected tumor samples from 511 patients with stage I–IV lung cancer were subjected to targeted sequencing using a panel of 295 cancer-related genes. Based on the molecular profiles and clinical features, we established nomogram models with predictors consisting of integrated clinical and genomic characteristics to provide post-operative risk stratification.

Results

Compared to the TCGA population (mainly Caucasians), there was a significantly higher frequency of EGFR (53.7% vs. 14.4%) and NOTCH3 (8.4% vs. 1.3%) mutations and less mutated KRAS (11.0% vs. 32.6%), KEAP1 (4.4% vs. 17.4%) and LRP1B (16.3% vs. 29.6%) in Chinese lung adenocarcinomas (LUAD). Distinct patterns of mutually exclusive and co-occurring mutations were identified between LUAD and lung squamous cell carcinoma (LUSC), indicating the unique histology-specific tumorigenesis mechanism of each subtype. We observed alterations in pathways correlated with clinical characteristics. Additionally, we constructed nomogram model with predictors consisting of clinical and genomic characteristics, which were more accurate than models with clinical characteristics or TNM staging only both in stage I–IIIA patients and T1-2N0M0 sub-cohort.

Conclusions

This study revealed Chinese NSCLC patients have unique genomic profile. Furthermore, the nomogram model combining clinical features with genomic characteristics could improve risk stratification in early-stage NSCLC.

Construction of a nomogram to predict the prognosis of non-small-cell lung cancer with brain metastases

Patients with non-small-cell lung cancer (NSCLC) often have a poor prognosis when brain metastases (BM) occur. This study aimed to evaluate the prognostic factors of BM in newly diagnosed NSCLC patients and construct a nomogram to predict the overall survival (OS).We included NSCLC patients with BM newly diagnosed from 2010 to 2015 in Surveillance, Epidemiology, and End Results database. The independent prognostic factors for NSCLC with BM were determined by Cox proportional hazards regression analysis. We then constructed and validated a nomogram to predict the OS of NSCLC with BM.We finally included 4129 NSCLC patients with BM for analysis. Age, race, sex, liver metastasis, primary site, histologic type, grade, bone metastasis, T stage, N stage, surgery, chemotherapy, and lung metastasis were identified as the prognostic factors for NSCLC with BM and integrated to establish the nomogram. The calibration, receiver operating characteristic curve, and decision curve analyses also showed that the clinical prediction model performed satisfactorily in predicting prognosis.A clinical prediction model was constructed and validated to predict individual OS for NSCLC with BM. The establishment of this clinical prediction model has great significance for clinicians and individuals.

An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm

Non-small-cell lung cancer (NSCLC) patients often develop bone metastases (BM), and the overall survival for these patients is usually perishing. However, a model with high accuracy for predicting the survival of NSCLC with BM is still lacking. Here, we aimed to establish a model based on artificial intelligence for predicting the 1-year survival rate of NSCLC with BM by using extreme gradient boosting (XGBoost), a large-scale machine learning algorithm. We selected NSCLC patients with BM between 2010 and 2015 from the Surveillance, Epidemiology, and End Results database. In total, 5973 cases were enrolled and divided into the training (n = 4183) and validation (n = 1790) sets. XGBoost, random forest, support vector machine, and logistic algorithms were used to generate predictive models. Receiver operating characteristic curves were used to evaluate and compare the predictive performance of each model. The parameters including tumor size, age, race, sex, primary site, histological subtype, grade, laterality, T stage, N stage, surgery, radiotherapy, chemotherapy, distant metastases to other sites (lung, brain, and liver), and marital status were selected to construct all predictive models. The XGBoost model had a better performance in both training and validation sets as compared with other models in terms of accuracy. Our data suggested that the XGBoost model is the most precise and personalized tool for predicting the 1-year survival rate for NSCLC patients with BM. This model can help the clinicians to design more rational and effective therapeutic strategies.

Development and Validation of a Deep Learning Model for Non-Small Cell Lung Cancer Survival

IMPORTANCE

There is a lack of studies exploring the performance of a deep learning survival neural network in non-small cell lung cancer (NSCLC).

OBJECTIVES

To compare the performances of DeepSurv, a deep learning survival neural network with a tumor, node, and metastasis staging system in the prediction of survival and test the reliability of individual treatment recommendations provided by the deep learning survival neural network.

DESIGN, SETTING, AND PARTICIPANTS

In this population-based cohort study, a deep learning-based algorithm was developed and validated using consecutive cases of newly diagnosed stages I to IV NSCLC between January 2010 and December 2015 in a Surveillance, Epidemiology, and End Results database. A total of 127 features, including patient characteristics, tumor stage, and treatment strategies, were assessed for analysis. The algorithm was externally validated on an independent test cohort, comprising 1182 patients with stage I to III NSCLC diagnosed between January 2009 and December 2013 in Shanghai Pulmonary Hospital. Analysis began January 2018 and ended June 2019.

MAIN OUTCOMES AND MEASURES

The deep learning survival neural network model was compared with the tumor, node, and metastasis staging system for lung cancer-specific survival. The C statistic was used to assess the performance of models. A user-friendly interface was provided to facilitate the survival predictions and treatment recommendations of the deep learning survival neural network model.

RESULTS

Of 17 322 patients with NSCLC included in the study, 13 361 (77.1%) were white and the median (interquartile range) age was 68 (61-74) years. The majority of tumors were stage I disease (10 273 [59.3%]) and adenocarcinoma (11 985 [69.2%]). The median (interquartile range) follow-up time was 24 (10-43) months. There were 3119 patients who had lung cancer-related death during the follow-up period. The deep learning survival neural network model showed more promising results in the prediction of lung cancer-specific survival than the tumor, node, and metastasis stage on the test data set (C statistic = 0.739 vs 0.706). The population who received the recommended treatments had superior survival rates than those who received treatments not recommended (hazard ratio, 2.99; 95% CI, 2.49-3.59; P < .001), which was verified by propensity score-matched groups. The deep learning survival neural network model visualization was realized by a user-friendly graphic interface.

CONCLUSIONS AND RELEVANCE

The deep learning survival neural network model shows potential benefits in prognostic evaluation and treatment recommendation with respect to lung cancer-specific survival. This novel analytical approach may provide reliable individual survival information and treatment recommendations.

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.

Efficacy and Safety of Anlotinib in Advanced Non-Small Cell Lung Cancer: A Real-World Study

Purpose

The ALTER0303 trial showed that anlotinib, a novel antiangiogenic tyrosine kinase inhibitor, administered as third-line or further treatment prolonged progression-free survival (PFS) and overall survival (OS) in patients with advanced non-small cell lung cancer (NSCLC). This retrospective study investigated the efficacy and safety of anlotinib in real-world settings.

Patients and Methods

Medical records of patients with advanced NSCLC receiving anlotinib as third-line or further treatment were collected, and survival curves were derived using the Kaplan–Meier method. Univariate analysis was performed by log-rank testing. Cox regression analysis was used to evaluate the significance of factors obtained from the univariate analysis.

Results

Fifty-two patients with advanced NSCLC were included. The objective response rate was 16%, and the disease control rate was 80%. The median PFS was 4.5 months (95% confidence interval [CI]: 3.6–5.4), and the median OS was 9 months (95% CI: 6.5–11.5). Univariate analysis revealed that the group of patients with longer PFS and OS included Eastern Cooperative Oncology Group performance status (ECOG PS) ≤1, ≤2 distant metastases, no liver metastases, ≤3 previous treatment lines, and ≤2 previous chemotherapy lines. Cox regression analysis demonstrated that only patients with ECOG PS ≤1 or no liver metastases had longer PFS and OS. Grade 3 treatment-related adverse events were reported in 14% of the patients, but no life-threatening adverse events were reported.

Conclusion

Anlotinib was well tolerated and effective in patients with advanced NSCLC in real-world conditions. Patients with ECOG PS ≤1 or no liver metastases have longer PFS and OS.

Prognostic Value of Pretreatment Albumin-to-Alkaline Phosphatase Ratio in Extensive-Disease Small-Cell Lung Cancer: A Retrospective Cohort Study

PURPOSE

Evidence regarding the relationship between albumin-to-alkaline phosphatase ratio (AAPR) and overall survival (OS) in extensive-disease small-cell lung cancer (ED-SCLC) patients is limited. This study aimed to investigate whether AAPR was independently related to OS in ED-SCLC patients after adjusting for potential covariates.

PATIENTS AND METHODS

This was a retrospective cohort study of 224 patients with ED-SCLC. The target independent and dependent variables were pretreatment AAPR and OS, respectively. Covariates included age; sex; Eastern Cooperative Oncology performance status score; smoking history; existence of metastasis to organs such as the bone, lung, liver, brain, malignant plural effusion and others; sum of organ metastasis (≤3, >3), evaluation of first-line treatment; and sum of treatment lines (<2, ≥2). Student's t test or chi-squared test was used to analyze the associations between AAPR and clinical characteristics. Kaplan-Meier survival analysis and Cox's proportional hazards regression model were used to assess the prognostic value of AAPR for OS.

RESULTS

The average patient age was 60.51±8.73 years, and 87.95% were men. A non-linear relationship between AAPR and OS was detected, with an inflection point of 0.35. The hazard ratios (HRs) of the left (AAPR <0.35) and right sides (AAPR ≥0.35) of inflection point were 0.04 (95% CI=0.00-0.70, p=0.0268) and 0.52 (95% CI=0.16-1.64, p=0.2659), respectively. Kaplan-Meier analysis showed a median OS of 9.73 months (95% CI=8.6-12.33) for AAPR <0.35 and 13.7 months (95% CI=11.43-16.37) for AAPR ≥0.35 (log-rank p<0.0001). The Cox proportional hazards model showed that AAPR <0.35 increased the risk of death after adjusting for potential confounders (HR=1.65, 95% CI=1.11-2.46). In subgroup analysis, the trends of HRs were increased across all subgroups with AAPR <0.35 after stratification.

CONCLUSION

Pretreatment AAPR might be served as an independent prognostic indicator in ED-SCLC patients. Our findings should be further validated in large-scale and prospective clinical trials.

Survival prediction models for patients with anal carcinoma receiving definitive chemoradiation: A population-based study

The present study aimed to develop two nomograms in order to predict cancer-specific survival (CSS) and overall survival (OS) of patients with anal carcinoma receiving definitive chemoradiotherapy. Data from studies including patients with anal carcinoma, who were determined to be positive histologically and diagnosed between 2004 and 2010, were obtained from the Surveillance, Epidemiology, and End Results database. Significant prognostic factors for CSS and OS of patients were screened to develop nomograms through univariate and multivariate analyses. Nomograms were validated using internal and external data. The predictive abilities of the generated models were evaluated by concordance index (C-index) and calibration curves. Risk stratification was performed for patients with the same TNM stage. A total of 1,473 patients and six independent prognostic factors for CSS and OS, namely age, sex, ethnicity, marital status at diagnosis, T stage and N stage, were included in the nomogram calculations. Calibration curves demonstrated that nomogram prediction was in high accordance with actual observation. The C-indices of nomograms were greater than those of models based on the sixth edition of the American Joint Committee on Cancer TNM staging system for CSS prediction (training cohort, 0.72 vs. 0.70; validation cohort, 0.68 vs. 0.62) and OS (training cohort, 0.70 vs. 0.66; validation cohort, 0.68 vs. 0.62). Survival curves demonstrated significant survival differences among the different risk groups. Nomograms were more accurate than the conventional TNM staging system in prognosis prediction. In addition, survival performances of patients with the same TNM stage could be further distinguished by risk stratification, which provided individualized prediction for patients. These survival prediction methods may aid clinicians in patient counseling and in selecting more individualized therapeutic strategies.