Effects of different metastasis patterns, surgery and other factors on the prognosis of patients with stage IV non-small cell lung cancer: A Surveillance, Epidemiology, and End Results (SEER) linked database analysis

Development and Validation of a Prognostic Nomogram for Lung Adenocarcinoma: A Population-Based Study

PURPOSE

To establish an effective and accurate prognostic nomogram for lung adenocarcinoma (LUAD). Patients and Methods. 62,355 LUAD patients from 1975 to 2016 enrolled in the Surveillance, Epidemiology, and End Results (SEER) database were randomly and equally divided into the training cohort (n = 31,179) and the validation cohort (n = 31,176). Univariate and multivariate Cox regression analyses screened the predictive effects of each variable on survival. The concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) were used to examine and validate the predictive accuracy of the nomogram. Kaplan-Meier curves were used to estimate overall survival (OS).

RESULTS

10 prognostic factors associated with OS were identified, including age, sex, race, marital status, American Joint Committee on Cancer (AJCC) TNM stage, tumor size, grade, and primary site. A nomogram was established based on these results. C-indexes of the nomogram model reached 0.777 (95% confidence interval (CI), 0.773 to 0.781) and 0.779 (95% CI, 0.775 to 0.783) in the training and validation cohorts, respectively. The calibration curves were well-fitted for both cohorts. The AUC for the 3- and 5-year OS presented great prognostic accuracy in the training cohort (AUC = 0.832 and 0.827, respectively) and validation cohort (AUC = 0.835 and 0.828, respectively). The Kaplan-Meier curves presented significant differences in OS among the groups.

CONCLUSION

The nomogram allows accurate and comprehensive prognostic prediction for patients with LUAD.

Prognostic factors and survival prediction for patients with metastatic lung adenocarcinoma: A population-based study

The prognosis of metastatic lung adenocarcinoma (MLUAD) varies greatly. At present, no studies have constructed a satisfactory prognostic model for MLUAD. We identified 44,878 patients with MLUAD. The patients were randomized into the training and validation cohorts. Cox regression models were performed to identify independent prognostic factors. Then, R software was employed to construct a new nomogram for predicting overall survival (OS) of patients with MLUAD. Accuracy was assessed by the concordance index (C-index), receiver operating characteristic curves and calibration plots. Finally, clinical practicability was examined via decision curve analysis. The OS time range for the included populations was 0 to 107 months, and the median OS was 7.00 months. Nineteen variables were significantly associated with the prognosis, and the top 5 prognostic factors were chemotherapy, grade, age, race and surgery. The nomogram has excellent predictive accuracy and clinical applicability compared to the TNM system (C-index: 0.723 vs 0.534). The C-index values were 0.723 (95% confidence interval: 0.719-0.726) and 0.723 (95% confidence interval: 0.718-0.729) in the training and validation cohorts, respectively. The area under the curve for 6-, 12-, and 18-month OS was 0.799, 0.764, and 0.750, respectively, in the training cohort and 0.799, 0.762, and 0.746, respectively, in the validation cohort. The calibration plots show good accuracy, and the decision curve analysis values indicate good clinical applicability and effectiveness. The nomogram model constructed with the above 19 prognostic factors is suitable for predicting the OS of MLUAD and has good predictive accuracy and clinical applicability.

An Interpretable Two-Phase Modeling Approach for Lung Cancer Survivability Prediction

Although lung cancer survival status and survival length predictions have primarily been studied individually, a scheme that leverages both fields in an interpretable way for physicians remains elusive. We propose a two-phase data analytic framework that is capable of classifying survival status for 0.5-, 1-, 1.5-, 2-, 2.5-, and 3-year time-points (phase I) and predicting the number of survival months within 3 years (phase II) using recent Surveillance, Epidemiology, and End Results data from 2010 to 2017. In this study, we employ three analytical models (general linear model, extreme gradient boosting, and artificial neural networks), five data balancing techniques (synthetic minority oversampling technique (SMOTE), relocating safe level SMOTE, borderline SMOTE, adaptive synthetic sampling, and majority weighted minority oversampling technique), two feature selection methods (least absolute shrinkage and selection operator (LASSO) and random forest), and the one-hot encoding approach. By implementing a comprehensive data preparation phase, we demonstrate that a computationally efficient and interpretable method such as GLM performs comparably to more complex models. Moreover, we quantify the effects of individual features in phase I and II by exploiting GLM coefficients. To the best of our knowledge, this study is the first to (a) implement a comprehensive data processing approach to develop performant, computationally efficient, and interpretable methods in comparison to black-box models, (b) visualize top factors impacting survival odds by utilizing the change in odds ratio, and (c) comprehensively explore short-term lung cancer survival using a two-phase approach.

Oral metastasis as the first indication of undiscovered malignancy at a distant site: A systematic review of 413 cases

This systematic review is the first to provide evidence regarding demographic, clinical, and imaging characteristics, as well as information related to survival, of patients with oral and maxillofacial metastases of occult primary tumors. Case reports, case series, and cross-sectional studies were included. Ten databases were searched. The risk of bias was assessed using the Joanna Briggs Institute appraisal tools. Overall, 353 articles (413 patients) were included. Statistically significant associations between survival and multiplicity of metastatic foci, and between each of the main primary sites and some features of the oral lesions were observed. Some clinical and imaging characteristics can help dentists in raising diagnostic suspicions and also in relating to plausible primary sites. Early diagnosis of oral and maxillofacial metastases can positively affect the survival rate when they are the only focus of dissemination, conferring an important role on the dentist.

Percutaneous Radiofrequency Ablation Is an Effective Method for Local Control of Liver Metastases From Lung Cancer

OBJECTIVE

To investigate the clinical value of percutaneous radiofrequency ablation (RFA) for liver metastasis from lung cancer (LCLM).

MATERIALS AND METHODS

We retrospectively enrolled 58 patients who underwent RFA for LCLM between January 2014 and December 2019. Primary lung cancer histology included 38 adenocarcinomas, 15 squamous carcinomas, and 5 small cell carcinomas. For 83 metastatic lesions (mean tumor diameter 3.3 ± 1.1 cm, range 0.9-5.0 cm), 65 RFA sessions were performed. Before RFA, 17 and 41 patients presented no and stable extrahepatic metastasis, respectively, whereas 18 and 40 patients had synchronous and metachronous liver metastasis, respectively. Survival was analyzed using the Kaplan-Meier method. Cox proportional hazards model was used for multivariable analysis.

RESULTS

The technical success rate was 96.3% (80/83 lesions). Local tumor progression was observed in 8 (9.8%, 8/82) lesions of 57 (14.0%, 8/57) patients at 4-12 months after RFA. New liver metastases occurred in 27 (46.6%) patients. The overall survival (OS) rates at 1, 2, 3, and 5 years after RFA were 55.2%, 26.0%, 22.0%, and 14.4%, respectively. The median OS after RFA and after liver metastasis were 14.0 ± 1.6 and 20.0 ± 1.5 months, respectively. Based on the univariable analysis, tumor size (p=0.017), histological type (p=0.015), and timing of liver metastasis (p=0.046) were related to OS. In further multivariable analyses, squamous carcinoma (hazard ratio= 2.269, 95% confidence interval: 1.186-4.339, p=0.013) was an independent unfavorable prognostic factor for OS. Based on the univariable analysis, histological type (p=0.010) was identified as parameters significantly related to local tumor progression (LTP)-free survival. Further multivariable analyses revealed that squamous carcinoma (hazard ratio=2.394, 95% confidence interval: 1.260-4.550, p=0.008) was an independent unfavorable prognostic factor for LTP-free survival.

CONCLUSION

RFA is a safe therapeutic option for LCLM with acceptable local tumor control, especially in patients with a tumor size ≤3 cm, adenocarcinoma/small cell carcinoma, and metachronous liver metastases.

Surgical Survival Benefits With Different Metastatic Patterns for Stage IV Extrathoracic Metastatic Non-Small Cell Lung Cancer: A SEER-Based Study

Background:

With the knowledge of oligometastases, primary surgery plays an increasingly vital role in metastatic non-small cell lung cancer. We aimed to evaluate the survival benefit of primary surgery based on metastatic patterns.

Materials and Methods:

The selected patients with stage IV extrathoracic metastatic (m1b) non-small cell lung cancer between 2010 and 2015 were included in a retrospective cohort study from the Surveillance, Epidemiology, and End Results (SEER) database. Multiple imputation was used for the missing data. Patients were divided into 2 groups depending on whether surgery was performed. After covariate balancing propensity score (CBPS) weighting, multivariate Cox regression models and Kaplan-Meier survival curve were built to identify the survival benefit of different metastatic patterns.

Results:

Surgery can potentially increase the overall survival (OS) (adjusted HR: 0.68, P < 0.001) of non-small cell lung cancer. The weighted 3-year OS in the surgical group was 16.9%, compared with 7.8% in the nonsurgical group. For single organ metastasis, surgery could improve the survival of metastatic non-small cell lung cancer. Meanwhile, no significant survival improvements in surgical group were observed in patients with multiple organ metastases.

Conclusion:

The surgical survival benefits for extrathoracic metastatic non-small cell lung cancer could be divided by metastatic pattern.

Predictors for survival in patients with bone metastasis of small cell lung cancer: A population-based study

The objective of the current study is to analyze the clinical and demographic characteristics of patients with bone metastasis of small cell lung cancer (SCLC) and explore their survival predictors.We retrospectively extracted patients with bone metastasis of SCLC from the Surveillance, Epidemiology, and End Results database. We applied Cox regression analyses to identify independent survival predictor of overall survival (OS) and cancer-specific survival (CSS). Only significant predictors from univariable analysis were included for multivariable Cox analysis. Kaplan-Meier method was used to evaluate survival differences between groups by the log-rank test.A total of 5120 patients with bone metastasis of SCLC were identified and included for survival analysis. The 1-year OS and CSS rates of bone metastasis of SCLC were 19.8% and 21.4%, respectively. On multivariable analysis, gender, age, radiotherapy, chemotherapy, liver metastasis, brain metastasis, insurance status, and marital status independently predicted OS and CSS. There was no significant difference of OS and CSS in terms of race and tumor size.Independent predictors of survival were identified among patients with bone metastasis of SCLC, which could be valuable to clinicians in treatment decision. Patients with bone metastasis of SCLC may benefit from radiotherapy and chemotherapy.

The Cancers-Specific Survival of Metastatic Pulmonary Carcinoids and Sites of Distant Metastasis: A Population-Based Study

Background:

Lung cancer is the leading cause of cancer-related deaths and pulmonary carcinoids (PCs) account for almost 2% of all pulmonary malignancies. However, few published articles have reported prognosis and related factors of pulmonary carcinoid patients.

Material and Method:

The Surveillance, Epidemiology, and End Results (SEER) database was used to collect data of patients diagnosed with metastatic PCs from 2010 to 2016. The prognosis and survival of these patients were compared by employing Cox proportional hazards and the Kaplan-Meier survival analysis.

Results:

A total of 1763 patients were analyzed. The liver (668, 25.6%) was shown to be the most common metastatic site in the isolated organ metastasis cohort, followed by the lung (636, 24.4%), bone (562, 21.6%), and brain (460, 17.6%). Among the patients, the tumor metastasized to a single distant site included the liver, bone, lung, and brain. Cancer-specific survival (CSS) in metastatic PCs is determined by the site of metastasis and the total number of such sites. Pulmonary carcinoid patients with isolated liver metastasis manifested more favorable survival rates in comparison to patients having isolated metastasis in the lung, brain, or bone. The median CSS was 45, 7, 6, 5 months (P = 0.011). The number of distant metastatic sites and the location of distant metastasis were found to be independent risk factors for CSS. For patients with distant isolated metastasis, liver metastasis (P < 0.0001) had better CSS in comparison to those with bone metastasis. When compared to patients whose carcinoids had metastasized to the bones, patients with a brain (P = 0.273) or lung (P = 0.483) metastasis had the same CSS.

Conclusion:

Cancer-specific survival in metastatic PCs depends on the site of metastasis and the total number of such locations. PC patients with isolated liver metastasis manifested more favorable survival in comparison to patients with isolated metastasis in the lung, brain, or bone.

Association between PD-L1 expression and initial brain metastasis in patients with non-small cell lung cancer and its clinical implications

Background

Brain metastases frequently occur in patients with non‐small cell lung cancer (NSCLC) resulting in a poor prognosis. Here, we investigated the association between PD‐L1 expression and brain metastasis in patients with NSCLC and its clinical significance.

Methods

A total of 270 patients diagnosed with metastatic NSCLC who underwent PD‐L1 testing on their tumor tissue between January 2017 and March 2019 were retrospectively reviewed. The VENTANA PD‐L1 (SP263) assay was used, and positive PD‐L1 expression was defined as staining in ≥1% of tumor cells.

Results

Positive PD‐L1 expression was observed in 181 (67.0%) patients, and 74 (27.4%) patients had brain metastasis at diagnosis. Synchronous brain metastases were more frequently observed in PD‐L1‐positive compared with PD‐L1‐negative patients (31.5% vs. 19.1%, p = 0.045). Multiple logistic regression analysis identified positive PD‐L1 expression (odds ratio [OR]: 2.24, p = 0.012) as an independent factor associated with synchronous brain metastasis, along with the histological subtype of nonsquamous cell carcinoma (OR: 2.84, p = 0.003). However, the incidence of central nervous system (CNS) progression was not associated with PD‐L1 positivity, with a two‐year cumulative CNS progression rate of 26.3% and 28.4% in PD‐L1‐positive and PD‐L1‐negative patients, respectively (log rank p = 0.944). Furthermore, positive PD‐L1 expression did not affect CNS progression or overall survival in patients with synchronous brain metastasis (long rank p = 0.513 and 0.592, respectively).

Conclusions

Initial brain metastases are common in NSCLC patients with positive PD‐L1 expression. Further studies are necessary to understand the relationship between early brain metastasis and cancer immunity.

EZH2 inhibitors reverse resistance to gefitinib in primary EGFR wild-type lung cancer cells

Background

Lung cancer is the leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer. In traditional anti-cancer therapy, epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKI) have been proven to be beneficial for patients with EGFR mutations. However, patients with EGFR wild-type NSCLC were usually not respond to EGFR-TKIs. Enhancer of zeste homolog 2 (EZH2) is a key molecular in the PRC2 complex and plays an important role in epigenetic regulation and is overexpressed in variant tumors. EZH2 inhibitors have been reported to sensitize variant tumor cells to anticancer drugs. This study aimed to investigate whether the EZH2 inhibitors, GSK343 and DZNep when combined with gefitinib can reverse EGFR-TKIs resistance in EGFR wild-type NSCLC cells.

Methods

The RNA-sequencing data of patients with NSCLC [502 patients with lung squamous cell carcinoma, including 49 paracancerous lung tissues and 513 patients with lung adenocarcinoma (LUAD), including 59 paracancerous lung tissues] from the Cancer Genome Atlas (TCGA), were analyzed for EZH2 expression. EZH2 expression was verified in 40 NSCLC tissue cancer samples and their corresponding paracancerous tissues from our institute (TJMUGH) via RT-PCR. A549 and H1299 cells treated with siRNA or EZH2 inhibitors were subjected to cell viability and apoptosis analyses as well to EGFR pathway proteins expression analyses via western blotting.

Results

EZH2 was upregulated in human NSCLC tissues and correlated with poor prognosis in patients with LUAD based on data from both TCGA and TJMUGH. Both GSK343 and DZNep sensitized EGFR wild-type LUAD cells (A549 and H1299) to gefitinib and suppressed cell viability and proliferation in vitro by downregulating the phosphorylation of EGFR and AKT and by inducing cell apoptosis. Co-administration of EZH2 inhibitors (GSK343 or DZNep) with gefitinib exerted a stronger inhibitory effect on tumor activity, cell proliferation and cell migration than single drug administration in vitro and in vivo.

Conclusions

These data suggest that the combination of EZH2 inhibitors with EGFR-TKIs may be an effective method for treating NSCLC-patients with EGFR-wild type, who do not want to undergo traditional treatment with chemotherapy.

Prognostic factors for survival in patients with metastatic lung adenocarcinoma: An analysis of the SEER database

BACKGROUND

Lung adenocarcinoma (ADC) is the main cause of death related to lung cancer. The aim of this study was to identify poor prognostic factors for overall survival (OS) in patients with stage IV lung ADC in real-world clinical practice.

METHODS

Patients were selected from the Surveillance Epidemiology and End Results (SEER) database. Chi-square bivariate analysis was used for the association of binary qualitative variables. A multivariate Cox regression analysis was performed to determine the impact of these prognostic factors on OS.

RESULTS

A total of 46 030 patients were included (51.3% men, mean age 67.03 ± 11.6), of whom 41.3% presented with metastases in bone, 28.9% in brain, 17.1% in liver and 31.8% in lung. Patients with liver metastases presented with two or more metastatic sites more frequently than patients without liver metastases (P < 0.001). Male sex (HR 0.78, 95% CI: 0.76-0.80), age ≥ 65 years (HR 1.37, 95% CI: 1.33-1.40), lack of family support (HR 0.80, 95% CI: 0.78-0.81) and presence of liver (HR 1.45, 95% CI: 1.40-1.50), bone (HR 1.21, 95% CI: 1.18-1.24) or brain metastases (HR 1.18, 95% CI: 1.15-1.21) were identified as poor prognostic factors for OS. Patients with liver metastasis showed the highest hazard ratio value (P < 0.001).

CONCLUSIONS

The presence of liver metastases was the worst prognostic factor for patients with metastatic lung ADC. This factor should be considered as a stratification factor for future studies evaluating new cancer treatments including immunotherapy.

KEY POINTS

SIGNIFICANT FINDINGS OF THE STUDY: Regression analysis identified poor prognostic factors for overall survival. Factors were male sex, age ≥ 65 years, lack of family support and presence of liver, bone and brain metastases. Patients with liver metastasis showed the highest HR (HR = 1.45 95% CI: 1.40-1.50). This study included the highest number of adenocarcinoma patients analyzed so far (N = 46 030). What this study adds The presence of liver metastases should be considered as a stratification factor for future studies evaluating new cancer treatments including immunotherapy.

Development of an Individualized Prediction Calculator for the Benefit of Postoperative Radiotherapy in Patients with Surgically Resected De Novo Stage IV Breast Cancer

PURPOSE

Locoregional treatment has been increasingly adopted for metastatic breast cancer at presentation. This study aims to develop an individualized calculator to predict the benefit of postoperative radiotherapy (PORT) for patients with surgically resected de novo stage IV breast cancer.

METHODS AND MATERIALS

We searched the Surveillance, Epidemiology, and End Results (SEER) database for patients diagnosed with stage IV breast cancer between 2010 and 2014. After applying exclusion criteria, a total of 4473 patients were included in the analysis. Propensity score matching was used to balance the individual characteristics of the patients. After identifying the significant prognosticators, a nomogram was developed using multivariate regression models and internally validated. A web-based calculator was then constructed using a fitted survival prediction model.

RESULTS

With a median follow-up of 34 months, the three-year overall survival (OS) rates were 54.1% in the surgery alone group and 63.5% in the surgery + PORT group (p < 0.001). The survival benefit of PORT was maintained after propensity score matching (p < 0.001). Interaction testing of the prognostic variables found significant interactions between PORT and the presence of brain metastasis (p = 0.001), and between PORT and hormonal receptor expression (p = 0.018). After reviewing the performance of various models, a log-normal distributed survival model was adopted, with a C-index of 0.695. A calibration plot verified that the predicted survival rates were strongly correlated with the actual OS rates. A web-based survival calculator was constructed to provide individualized estimates of survival according to PORT.

CONCLUSION

PORT significantly improved OS rates, though the individual benefit was affected by a number of factors. We successfully developed a nomogram and web-based calculator that predicted the prognosis according to PORT in patients with surgically resected de novo stage IV breast cancer. These tools are expected to be useful in clinical practice and in the design of related trials.

Construction of a Nomogram After Using Propensity Score Matching to Reveal the Prognostic Benefit of Tumor Resection of Stage IV M1a Nonsmall Cell Lung Cancer Patients

The aim of this work was to determine whether tumor resection could improve the prognosis of M1a nonsmall-cell lung cancer (NSCLC) patients. We obtained patient data from the Surveillance, Epidemiology, and End Results (SEER) database and used propensity score matching (PSM) to reduce the influence of confounding variables. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors, and the prediction results were visualized using the nomogram. A total of 772 patients with and without tumor resection were enrolled after PSM, and the nomogram combined with independent prognostic factors including age, sex, histological type, grade, T stage, N stage, chemotherapy, and surgery showed great prediction and discriminatory ability. Tumor resection is possibly a better choice for these patients.

Establishment and Validation of Nomogram Model Integrated With Inflammation-Based Factors for the Prognosis of Advanced Non-Small Cell Lung Cancer

OBJECTS

Inflammation is one of the hallmarks of cancer. Tumor-associated inflammatory response plays a crucial role in enhancing tumorigenesis. This study aimed to establish an effective predictive nomogram based on inflammation factors in patients with advanced non-small cell lung cancer (NSCLC).

METHODS

We retrospectively evaluated 887 patients with advanced NSCLC between November 2004 and December 2015 and randomly divided them into primary (n = 520) and validation cohorts (n = 367). Cox regression analysis was used to identify prognostic factors for building the nomogram. The predictive accuracy and discriminative ability of the nomogram were determined using a concordance index (C-index), calibration plot, and decision curve analysis and were compared to the TNM staging system.

RESULTS

The nomogram was established using independent risk factors (P < 0.05): age, TNM stage, C reaction protein-to-albumin ratio (CAR), and neutrophils (NEU). The C-index of the model for predicting OS had a superior discrimination power compared to that of the TNM staging system both in the primary [0.711 (95% CI: 0.675-0.747) vs 0.531 (95% CI: 0.488-0.574), P < 0.01] and validation cohorts [0.703, 95% CI: 0.671 -0.735 vs 0.582, 95% CI: 0.545-0.619, P < 0.01]. Decision curves also demonstrated that the nomogram had higher overall net benefits than that of the TNM staging system. Subgroup analyses revealed that the nomogram was a favorable prognostic parameter in advanced NSCLC (P < 0.05). The results were internally validated using the validation cohorts.

CONCLUSIONS

The proposed nomogram with inflammatory factors resulted in an accurate prognostic prediction in patients with advanced NSCLC.

Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab

Immune checkpoint blockade is an emerging anticancer strategy, and Nivolumab is a human mAb to PD-1 that is used in the treatment of a number of different malignancies, including non-small cell lung cancer (NSCLC), kidney cancer, urothelial carcinoma and melanoma. Although the use of Nivolumab prolongs survival in a number of patients, this treatment is hampered by high cost. Therefore, the identification of predictive markers of response to treatment in patients is required. In this context, PD-1/PDL1 blockade antitumor effects occur through the reactivation of a pre-existing immune response, and the efficacy of these effects is strictly associated with the presence of necrosis, hypoxia and inflammation at the tumour sites. It has been indicated that these events can be evaluated by specific assessments using a computed tomography (CT) texture analysis (TA) or radiomics. Therefore, a retrospective study was performed, which aimed to evaluate the potential use of this analysis in the identification of patients with NSCLC who may benefit from Nivolumab treatment. A retrospective analysis was performed of 59 patients with metastatic NSCLC who received Nivolumab treatment between January 2015 and July 2017 at Siena University Hospital (35 patients, training dataset), Catanzaro University Hospital and Reggio Calabria Grand Metropolitan Hospital, Italy (24 patients, validation dataset). Pre- and post-contrast CT sequences were used to contour the gross tumour volume (GTV) of the target lesions prior to Nivolumab treatment. The impact of variations on contouring was analysed using two delineations, which were performed on each patient, and the TA parameters were tested for reliability using the Intraclass Coefficient Correlation method (ICC). All analyses for the current study were performed using LifeX Software^©. Imaging, clinical and pathological parameters were correlated with progression free survival and overall survival (OS) using Kaplan Meier analysis. An external validation testing was performed for the TA Score using the validation dataset. A total of 59 patients were included in the analysis of the present study. The reliability ICC analysis of 14 TA parameters indicated a highly reproducibility (ICC >0.70, single measure) in 12 (85%) pre- contrast and 13 (93%) post-contrast exams. A specific cut-off was detected for each of the following parameters: volume (score 1 >36 ml), histogram entropy (score 1 > 1.30), compacity (score 1 <3), gray level co-occurrence matrix (GLCM)-entropy (score 1 >1.80), GLCM-Dissimilarity (score 1 >5) and GLCM-Correlation (score 1<0.54). The global texture score allowed the classification of two subgroups of Low (Score 0-1; 36 patients; 61%) and High Risk patients (Score >1; 23 patients; 39%) that respectively, showed a median OS of 26 (mean +/- SD: 18 +/- 1.98 months; 95% CI 14-21 months) and 5 months (mean +/- SD: 6 +/- 0.99 months; 95% CI: 4-8 months; P=0.002). The current study indicated that TA parameters can identify patients that will benefit from PD-1 blockage by defining the radiological settings that are potentially suggestive of an active immune response. These results require further confirmation in prospective trials.