Validation of the 8th TNM classification for small-cell lung cancer in a retrospective material from Sweden

Exploring the discrepancies between clinical trials and real-world data: A small-cell lung cancer study

The potential of real-world data to inform clinical trial design and supplement control arms has gained much interest in recent years. The most common approach relies on reproducing control arm outcomes by matching real-world patient cohorts to clinical trial baseline populations. However, recent studies pointed out that there is a lack of replicability, generalisability, and consensus. In this article, we propose a novel approach that aims to explore and examine these discrepancies by concomitantly investigating the impact of selection criteria and operations on the measurements of outcomes from the patient data. We tested the approach on a dataset consisting of small-cell lung cancer patients receiving platinum-based chemotherapy regimens from a real-world data cohort (n = 223) and six clinical trial control arms (n = 1224). The results showed that the discrepancy between real-world and clinical trial data potentially depends on differences in both patient populations and operational conditions (e.g., frequency of assessments, and censoring), for which further investigation is required. Discovering and accounting for confounders, including hidden effects of differences in operations related to the treatment process and clinical trial study protocol, would potentially allow for improved translation between clinical trials and real-world data. Continued development of the method presented here to systematically explore and account for these differences could pave the way for transferring learning across clinical studies and developing mutual translation between the real-world and clinical trials to inform clinical study design.

Comparation between novel online models and the AJCC 8th TNM staging system in predicting cancer-specific and overall survival of small cell lung cancer

Background

Most of previous studies on predictive models for patients with small cell lung cancer (SCLC) were single institutional studies or showed relatively low Harrell concordance index (C-index) values. To build an optimal nomogram, we collected clinicopathological characteristics of SCLC patients from Surveillance, Epidemiology, and End Results (SEER) database.

Methods

24,055 samples with SCLC from 2010 to 2016 in the SEER database were analyzed. The samples were grouped into derivation cohort (n=20,075) and external validation cohort (n=3,980) based on America's different geographic regions. Cox regression analyses were used to construct nomograms predicting cancer-specific survival (CSS) and overall survival (OS) using derivation cohort. The nomograms were internally validated by bootstrapping technique and externally validated by calibration plots. C-index was computed to compare the accuracy and discrimination power of our nomograms with the 8th of version AJCC TNM staging system and nomograms built in previous studies. Decision curve analysis (DCA) was applied to explore whether the nomograms had better clinical efficiency than the 8th version of AJCC TNM staging system.

Results

Age, sex, race, marital status, primary site, differentiation, T classification, N classification, M classification, surgical type, lymph node ratio, radiotherapy, and chemotherapy were chosen as predictors of CSS and OS for SCLC by stepwise multivariable regression and were put into the nomograms. Internal and external validations confirmed the nomograms were accurate in prediction. C-indexes of the nomograms were relatively satisfactory in derivation cohort (CSS: 0.761, OS: 0.761) and external validation cohort (CSS: 0.764, OS: 0.764). The accuracy of the nomograms was superior to that of nomograms built in previous studies. DCA showed the nomograms conferred better clinical efficiency than 8th version of TNM staging system.

Conclusions

We developed practical nomograms for CSS (https://guowei2020.shinyapps.io/DynNom-CSS-SCLC/) and OS (https://drboidedwater.shinyapps.io/DynNom-OS-SCLC/) prediction of SCLC patients which may facilitate clinicians in individualized therapeutics.

Retrospective Clinical Study on Integrated Chinese and Western Medicine in Treatment of Limited-Stage Small Cell Lung Cancer

OBJECTIVE

To investigate the efficacy of integrated Chinese and Western medicine extending the progression-free survival (PFS) and overall survival (OS) of limited-stage small cell lung cancer (LS-SCLC) patients after the first-line chemoradiotherapy.

METHODS

The data of 67 LS-SCLC patients who received combined treatment of CM and Western medicine (WM) between January 2013 and May 2020 at the outpatient clinic of Guang'anmen Hospital were retrospectively analyzed. Thirty-six LS-SCLC patients who received only WM treatment was used as the WM control group. The medical data of the two groups were statistically analyzed. Survival analysis was performed using the product-limit method (Kaplan-Meier analysis). The median OS and PFS were calculated, and survival curves were compared by the Log rank test. The cumulative survival rates at 1, 2, and 5 years were estimated by the life table analysis. Stratified survival analysis was performed between patients with different CM administration time.

RESULTS

The median PFS in the CM and WM combination treatment group and the WM group were 19 months (95% CI: 12.357-25.643) vs. 9 months (95% CI: 5.957-12.043), HR=0.43 (95% CI: 0.27-0.69, P<0.001), respectively. The median OS in the CM and WM combination group and the WM group were 34 months (95% CI could not be calculated) vs. 18.63 months (95% CI: 16.425-20.835), HR=0.40 (95% CI: 0.24-0.66, P<0.001), respectively. Similar results were obtained in the further stratified analysis of whether the duration of CM administration exceeded 18 and 24 months (P<0.001).

CONCLUSION

The combination treatment of CM and WM with continuing oral administration of CM treatment after the first-line chemoradiotherapy for LS-SCLC patients produced better prognosis, lower risks of progression, and longer survival than the WM treatment alone. (Registration No. ChiCTR2200056616).

Interpretable deep learning survival predictive tool for small cell lung cancer

Background

Small cell lung cancer (SCLC) is an aggressive and almost universally lethal neoplasm. There is no accurate predictive method for its prognosis. Artificial intelligence deep learning may bring new hope.

Methods

By searching the Surveillance, Epidemiology, and End Results database (SEER), 21,093 patients' clinical data were eventually included. Data were then divided into two groups (train dataset/test dataset). The train dataset (diagnosed in 2010-2014, N = 17,296) was utilized to conduct a deep learning survival model, validated by itself and the test dataset (diagnosed in 2015, N = 3,797) in parallel. According to clinical experience, age, sex, tumor site, T, N, M stage (7th American Joint Committee on Cancer TNM stage), tumor size, surgery, chemotherapy, radiotherapy, and history of malignancy were chosen as predictive clinical features. The C-index was the main indicator to evaluate model performance.

Results

The predictive model had a 0.7181 C-index (95% confidence intervals, CIs, 0.7174-0.7187) in the train dataset and a 0.7208 C-index (95% CIs, 0.7202-0.7215) in the test dataset. These indicated that it had a reliable predictive value on OS for SCLC, so it was then packaged as a Windows software which is free for doctors, researchers, and patients to use.

Conclusion

The interpretable deep learning survival predictive tool for small cell lung cancer developed by this study had a reliable predictive value on their overall survival. More biomarkers may help improve the prognostic predictive performance of small cell lung cancer.

A novel analytical framework for risk stratification of real-world data using machine learning: A small cell lung cancer study

In recent studies, small cell lung cancer (SCLC) treatment guidelines based on Veterans' Administration Lung Study Group limited/extensive disease staging and resulted in broad and inseparable prognostic subgroups. Evidence suggests that the eight versions of tumor, node, and metastasis (TNM) staging can play an important role to address this issue. The aim of the present study was to improve the detection of prognostic subgroups from a real-word data (RWD) cohort of patients and analyze their patterns using a development pipeline with thoracic oncologists and machine learning methods. The method detected subgroups of patients informing unsupervised learning (partition around medoids) including the impact of covariates on prognosis (Cox regression and random survival forest). An analysis was carried out using patients with SCLC (n = 636) with stage IIIA-IVB according to TNM classification. The analysis yielded k = 7 compacted and well-separated clusters of patients. Performance status (Eastern Cooperative Oncology Group-Performance Status), lactate dehydrogenase, spreading of metastasis, cancer stage, and CRP were the baselines that characterized the subgroups. The selected clustering method outperformed standard clustering techniques, which were not capable of detecting meaningful subgroups. From the analysis of cluster treatment decisions, we showed the potential of future RWD applications to understand disease, develop individualized therapies, and improve healthcare decision making.

Treatment Outcomes of 9,994 Patients With Extensive-Disease Small-Cell Lung Cancer From a Retrospective Nationwide Population-Based Cohort in the Korean HIRA Database

To investigate the efficacy of irinotecan-based (IP) and etoposide-based (EP) platinum combinations, and of single-agent chemotherapy, for treatment of extensive-disease small cell lung cancer (ED-SCLC), we performed a large-scale, retrospective, nationwide, cohort study. The population data were extracted from the Health Insurance Review and Assessment Service of Korea database from January 1, 2008, to November 30, 2016. A total of 9,994 patients were allocated to ED-SCLC and analyzed in this study. The primary objectives were to evaluate the survival outcomes of systemic first-line treatments for ED-SCLC. For first-line treatment, patients who received IP showed a better time to first subsequent therapy (TFST) of 8.9 months (95% confidence interval [CI], 8.50–9.40) than those who received EP, who had a TFST of 6.8 months (95% CI, 6.77–6.97, P < 0.0001). In terms of overall survival (OS), IP was superior to EP (median OS, 10.8 months; 95% CI, 10.13–11.33 vs. 9.5 months; 95% CI, 9.33–9.73; P < 0.0001). Taken together, in the Korean population, first-line IP combination chemotherapy had significantly favorable effects on OS and TFST.

Evaluation of Eighth AJCC TNM Sage for Lung Cancer NSCLC: A Meta-analysis

INTRODUCTION

The AJCC 8th edition TNM classification for lung cancer was released in 2017. This edition has made major changes in many tumor descriptors including sites of metastasis. The new staging system has been a subject of multiple validation studies, of which many have had mixed results. The present study is designed to critically evaluate the results of these external validation studies.

METHODS

A metaanalysis of these external validation studies was performed to critically evaluate the new staging system. Out of 12 studies, 8 were found to fulfill the eligibility criteria, with 654,185 patients being included in the analysis. Hazard ratios (HRs) and associated 95% confidence intervals (CI) extracted from these studies were utilized for analysis. The primary outcomes were survival discrimination and prognostic ability of the 8th edition compared with the 7th edition.

RESULTS

The HRs for the 8th edition staging system were 1.41 in IB, 1.64 in IIA, 1.24 in IIB, 1.95 in IIIA, 3.96 in IIIB, and 4.82 in IIIC compared with IA. The new edition fared better than the 7th edition in survival discrimination in all but stage IIA and IIB. The C-index of the 8th and 7th editions was 0.690 and 0.688, respectively, suggesting almost similar prognostic values.

CONCLUSIONS

This study shows that the survival discrimination of the 8th edition fared better than the 7th edition in all but stage IIA and IIB. The prognostic value of the two staging systems is similar, with no added advantage of the new edition.

Bronchovascular bundle thickening on CT as a predictor of survival and brain metastasis in patients with stage IA peripheral small cell lung cancer

AIM

To determine if bronchovascular bundle (BVB) thickening on pretreatment computed tomography (CT) images helps predict survival in patients with peripheral small cell lung cancer (pSCLC) ≤3 cm.

MATERIALS AND METHODS

The pretreatment CT examinations of 79 histopathologically proven pSCLC ≤3 cm (TNM stage I, 21; II, 13; III, 22; IV, 23) were reviewed retrospectively. The CT characteristics of the nodule and associated findings, including BVB thickening, were evaluated. Progression-free survival (PFS), overall survival (OS), and brain metastasis-free survival were compared with the presence of BVB thickening using Kaplan-Meier and Cox regression analysis.

RESULTS

Among the 79 patients, 34 (43%) had BVB thickening. BVB thickening was prevalent in patients with mediastinal lymph node metastasis (50.9% versus 22.7%; p=0.024) and distant metastasis (60.9% versus 35.7%; p=0.049). Out of the 21 patients with TNM stage IA disease, the 16 patients (76.2%) without BVB thickening showed better PFS, OS, and brain metastasis-free survival (mean, 1,762 versus 483 days; p=0.019: 2,243 versus 1,328 days; p=0.038: 2,274 versus 1,287 days; p=0.038, respectively). Multivariate Cox regression analysis showed that the absence of BVB thickening (hazard ratio [HR], 7.806; 95% CI, 1.241-49.091; p=0.029) and surgery (HR, 0.075; 95% CI, 0.008-0.746; p=0.027) were independent and useful prognostic factors for PFS.

CONCLUSIONS

BVB thickening was found more frequently in patients with advanced-stage pSCLC ≤3 cm, and the PFS was more favourable in patients without BVB thickening, with a similar tendency to that of OS and brain metastasis-free survival, in stage IA pSCLC.

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

Background

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

Methods

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

Results

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

Conclusions

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

Lung immune prognostic index as a prognostic factor in patients with small cell lung cancer

Background

The lung immune prognostic index (LIPI) is a marker that combines the derived neutrophil‐to‐lymphocyte ratio (dNLR) and serum lactate dehydrogenase (LDH) level and is a recently reported prognostic factor of immune checkpoint inhibitor therapy for non‐small cell lung cancer (NSCLC). However, there are no reports regarding the prognostic value of LIPI in small cell lung cancer (SCLC).

Methods

We retrospectively enrolled 171 patients diagnosed with SCLC and treated at Shinshu University School of Medicine between January 2003 and November 2019. Progression‐free survival (PFS) and overall survival (OS) were compared according to LIPI, and we investigated whether LIPI could be a prognostic factor in SCLC using the Kaplan‐Meier method and univariate and multivariate Cox models.

Results

The median OS of the LIPI 0 group was significantly longer than that of the LIPI 1 plus 2 group (21.0 vs. 11.6 months, P < 0.001). The multivariate analysis associated with OS indicated that LIPI 1 plus 2 was an independent unfavorable prognostic factor in addition to poor performance status (2–3), old age (≥ 75 years) and stage (extensive disease [ED]). However, PFS of the LIPI 0 group was not significantly different from that of the LIPI 1 plus 2 group. In ED‐SCLC patients, the median PFS and OS of the LIPI 0 group were significantly longer than those of the LIPI 2 group (6.6 vs. 4.0 months, P = 0.006 and 17.1 vs. 5.9 months, P < 0.001, respectively).

Conclusions

We confirmed the prognostic value of LIPI in SCLC, especially ED‐SCLC.

Key points

Significant findings of the study: The present study is the first to demonstrate that pretreatment lung immune prognostic index is an independent prognostic factor associated with overall survival for small cell lung cancer.

What this study adds: The utility of the lung immune prognostic index as a prognostic factor for small cell lung cancer.

Treatment patterns and survival outcomes for small-cell lung cancer patients - a Swedish single center cohort study

Objectives: This real-world study on small-cell lung cancer (SCLC) patients aimed to investigate treatment patterns, outcome of re-challenge with platinum doublet chemotherapy (PDCT), and associations between clinical characteristics and survival outcomes.Material and methods: This retrospective single center cohort study was based on patients diagnosed with SCLC between 2008 and 2016 at the Karolinska University Hospital, Stockholm, Sweden. Patients were divided into two subgroups; limited disease (LD), receiving concomitant chemo- and radiotherapy and extensive disease (ED), receiving palliative PDCT. The progression-free survival (PFS) was defined as the interval between the start of CT and the earliest date of documented progression. 'Refractory relapse' (Rr) and 'Sensitive relapse' (Sr) were defined as relapse occurring < or ≥180 days after start of PDCT, respectively. The results for treatment patterns were reported as numbers and percentages of patients, and descriptive analyses including medians and 95% confidence intervals (CIs). The Cox proportional hazards regression model was applied to assess the relationship between clinical characteristics and overall survival (OS).Results: The study included 544 patients; 408 with ED and 136 patients had LD. The median PFS and OS for ED patients were 5.1 and 7.0, respectively. In the ED subgroup, Sr occurred in 169 patients (41%), with a longer median OS when compared to Rr patients (10.8 vs. 3.6 months). Patients with LD had a median PFS and OS of 12 and 24 months, respectively. Some LD patients did not show a sign of relapse (22%). The majority of LD patients who relapsed had Sr (66%), with a longer median OS when compared to patients with Rr (20.9 vs. 7.8 mo).Conclusions: The survival outcomes for ED and LD SCLC patients correspond to historical data. Patients with Sr after 1st line therapy might benefit from re-challenge with PDCT in the 2nd line setting.

Imaging phenotype using radiomics to predict dry pleural dissemination in non-small cell lung cancer

Background

Dry pleural dissemination (DPD) in non-small cell lung cancer (NSCLC) is defined as having solid pleural metastases without malignant pleural effusion. We aim to identify DPD by applying radiomics, a novel approach to decode the tumor phenotype.

Methods

Preoperative chest computed tomographic images and basic clinical feature were retrospectively evaluated in patients with surgically resected NSCLC between January 1, 2015 and December 31, 2016. Propensity score was applied to match the DPD and non-DPD groups. One thousand and eighty radiomics features were quantitatively extracted by the 3D slicer software and "pyradiomics" package. Least absolute shrinkage and selection operator (LASSO) binary model was applied for feature selection and developing the radiomics signature. The discrimination was evaluated using area under the curve (AUC) and Youden index.

Results

Sixty-four DPD patients and paired 192 non-DPD patients were enrolled. Using the LASSO model, this study developed a radiomics signature including 10 radiomic features. The mean ± standard deviation values of the radiomics signature with DPD status (-2.129±1.444) was significantly higher compared to those with non-DPD disease (0.071±0.829, P<0.001). The ten-feature based signature showed good discrimination between DPD and non-DPD, with an AUC of 0.93 (95% confidence-interval, 0.891-0.958) respectively. The sensitivity and specificity of the radiomics signature was 85.94% and 85.94%, with the optimal cut-off value of -0.696 and Youden index of 0.71.

Conclusions

The signature based on radiomics features can provide potential predictive value to identify DPD in patients with NSCLC.

Multigene Mutation Profiling and Clinical Characteristics of Small-Cell Lung Cancer in Never-Smokers vs. Heavy Smokers (Geno1.3-CLICaP)

Objectives: Lung cancer is a heterogeneous disease. Presentation and prognosis are known to vary according to several factors, such as genetic and demographic characteristics. Small-cell lung cancer incidence is increasing in never-smokers. However, the disease phenotype in this population is different compared with patients who have a smoking history.

Material and Methods: To further investigate the clinical and genetic characteristics of this patient subgroup, a cohort of small cell lung cancer patients was divided into smokers (n = 10) and never/ever-smokers (n = 10). A somatic mutation profile was obtained using a comprehensive NGS assay. Clinical outcomes were compared using the Kaplan-Meier method and Cox proportional models.

Results: Median age was 63 years (46–81), 40% were men, and 90% had extended disease. Smoker patients had significantly more cerebral metastases (p = 0.04) and were older (p = 0.03) compared to their non-smoker counterparts. For never/ever smokers, the main genetic mutations were TP53 (80%), RB1 (40%), CYLD (30%), and EGFR (30%). Smoker patients had more RB1 (80%, p = 0.04), CDKN2A (30%, p = 0.05), and CEBPA (30%, p = 0.05) mutations. Response rates to first-line therapy with etoposide plus cisplatin/carboplatin were 50% in smokers and 90% in never/ever smokers (p = 0.141). Median overall survival was significantly longer in never smokers compared with smokers (29.1 months [23.5–34.6] vs. 17.3 months [4.8–29.7]; p = 0.0054). Never/ever smoking history (HR 0.543, 95% CI 0.41–0.80), limited-stage disease (HR 0.56, 95% CI 0.40–0.91) and response to first-line platinum-based chemotherapy (HR 0.63, 95% CI 0.60–0.92) were independently associated with good prognosis.

Conclusion: Our data supports that never/ever smoker patients with small-cell lung cancer have better prognosis compared to their smoker counterparts. Further, patients with never/ever smoking history who present with small-cell lung cancer have a different mutation profile compared with smokers, including a high frequency of EGFR, MET, and SMAD4 mutations. Further studies are required to assess whether the differential mutation profile is a consequence of a diverse pathological mechanism for disease onset.