Multi-institutional prognostic modeling of survival outcomes in NSCLC patients treated with first-line immunotherapy using radiomics

BACKGROUND

Immune checkpoint inhibitors (ICIs) have emerged as one of the most promising first-line therapeutics in the management of non-small cell lung cancer (NSCLC). However, only a subset of these patients responds to ICIs, highlighting the clinical need to develop better predictive and prognostic biomarkers. This study will leverage pre-treatment imaging profiles to develop survival risk models for NSCLC patients treated with first-line immunotherapy.

METHODS

Advanced NSCLC patients (n = 149) were retrospectively identified from two institutions who were treated with first-line ICIs. Radiomics features extracted from pretreatment imaging scans were used to build the predictive models for progression-free survival (PFS) and overall survival (OS). A compendium of five feature selection methods and seven machine learning approaches were utilized to build the survival risk models. The concordance index (C-index) was used to evaluate model performance.

RESULTS

From our results, we found several combinations of machine learning algorithms and feature selection methods to achieve similar performance. K-nearest neighbourhood (KNN) with ReliefF (RL) feature selection was the best-performing model to predict PFS (C-index = 0.61 and 0.604 in discovery and validation cohorts), while XGBoost with Mutual Information (MI) feature selection was the best-performing model for OS (C-index = 0.7 and 0.655 in discovery and validation cohorts).

CONCLUSION

The results of this study highlight the importance of implementing an appropriate feature selection method coupled with a machine learning strategy to develop robust survival models. With further validation of these models on external cohorts when available, this can have the potential to improve clinical decisions by systematically analyzing routine medical images.

Imaging-Based Biomarkers Predict Programmed Death-Ligand 1 and Survival Outcomes in Advanced NSCLC Treated With Nivolumab and Pembrolizumab: A Multi-Institutional Study

Background

Although the immune checkpoint inhibitors, nivolumab and pembrolizumab, were found to be promising in patients with advanced NSCLC, some of them either do not respond or have recurrence after an initial response. It is still unclear who will benefit from these therapies, and, hence, there is an unmet clinical need to build robust biomarkers.

Methods

Patients with advanced NSCLC (N = 323) who were treated with pembrolizumab or nivolumab were retrospectively identified from two institutions. Radiomics features extracted from baseline pretreatment computed tomography scans along with the clinical variables were used to build the predictive models for overall survival (OS), progression-free survival (PFS), and programmed death-ligand 1 (PD-L1). To develop the imaging and integrative clinical-imaging predictive models, we used the XGBoost learning algorithm with ReliefF feature selection method and validated them in an independent cohort. The concordance index for OS, PFS, and area under the curve for PD-L1 was used to evaluate model performance.

Results

We developed radiomics and the ensemble radiomics-clinical predictive models for OS, PFS, and PD-L1 expression. The concordance indices of the radiomics model were 0.60 and 0.61 for predicting OS and PFS and area under the curve was 0.61 for predicting PD-L1 in the validation cohort, respectively. The combined radiomics-clinical model resulted in higher performance with 0.65, 0.63, and 0.68 to predict OS, PFS, and PD-L1 in the validation cohort, respectively.

Conclusions

We found that pretreatment computed tomography imaging along with clinical data can aid as predictive biomarkers for PD-L1 and survival end points. These imaging-driven approaches may prove useful to expand the therapeutic options for nonresponders and improve the selection of patients who would benefit from immune checkpoint inhibitors.

A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study

BACKGROUND

Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy.

METHODS

This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort.

RESULTS

We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively.

CONCLUSIONS

We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted.

Generalization optimizing machine learning to improve CT scan radiomics and assess immune checkpoint inhibitors' response in non-small cell lung cancer: a multicenter cohort study

Background

Recent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations in image acquisition and reconstruction. Using radiomics, we investigated the importance of scan normalization as part of a broader machine learning framework to enable model external generalizability to predict ICI response in non-small cell lung cancer (NSCLC) patients across different centers.

Methods

Radiomics features were extracted and compared from 642 advanced NSCLC patients on pre-ICI scans using established open-source PyRadiomics and a proprietary DeepRadiomics deep learning technology. The population was separated into two groups: a discovery cohort of 512 NSCLC patients from three academic centers and a validation cohort that included 130 NSCLC patients from a fourth center. We harmonized images to account for variations in reconstruction kernel, slice thicknesses, and device manufacturers. Multivariable models, evaluated using cross-validation, were used to estimate the predictive value of clinical variables, PD-L1 expression, and PyRadiomics or DeepRadiomics for progression-free survival at 6 months (PFS-6).

Results

The best prognostic factor for PFS-6, excluding radiomics features, was obtained with the combination of Clinical + PD-L1 expression (AUC = 0.66 in the discovery and 0.62 in the validation cohort). Without image harmonization, combining Clinical + PyRadiomics or DeepRadiomics delivered an AUC = 0.69 and 0.69, respectively, in the discovery cohort, but dropped to 0.57 and 0.52, in the validation cohort. This lack of generalizability was consistent with observations in principal component analysis clustered by CT scan parameters. Subsequently, image harmonization eliminated these clusters. The combination of Clinical + DeepRadiomics reached an AUC = 0.67 and 0.63 in the discovery and validation cohort, respectively. Conversely, the combination of Clinical + PyRadiomics failed generalizability validations, with AUC = 0.66 and 0.59.

Conclusion

We demonstrated that a risk prediction model combining Clinical + DeepRadiomics was generalizable following CT scan harmonization and machine learning generalization methods. These results had similar performances to routine oncology practice using Clinical + PD-L1. This study supports the strong potential of radiomics as a future non-invasive strategy to predict ICI response in advanced NSCLC.

Radiomics approaches to predict PD-L1 and PFS in advanced non-small cell lung patients treated with immunotherapy: a multi-institutional study

With the increasing use of immune checkpoint inhibitors (ICIs), there is an urgent need to identify biomarkers to stratify responders and non-responders using programmed death-ligand (PD-L1) expression, and to predict patient-specific outcomes such as progression free survival (PFS). The current study is aimed to determine the feasibility of building imaging-based predictive biomarkers for PD-L1 and PFS through systematically evaluating a combination of several machine learning algorithms with different feature selection methods. A retrospective, multicenter study of 385 advanced NSCLC patients amenable to ICIs was undertaken in two academic centers. Radiomic features extracted from pretreatment CT scans were used to build predictive models for PD-L1 and PFS (short-term vs. long-term survivors). We first employed the LASSO methodology followed by five feature selection methods and seven machine learning approaches to build the predictors. From our analyses, we found several combinations of feature selection methods and machine learning algorithms to achieve a similar performance. Logistic regression with ReliefF feature selection (AUC = 0.64, 0.59 in discovery and validation cohorts) and SVM with Anova F-test feature selection (AUC = 0.64, 0.63 in discovery and validation datasets) were the best-performing models to predict PD-L1 and PFS. This study elucidates the application of suitable feature selection approaches and machine learning algorithms to predict clinical endpoints using radiomics features. Through this study, we identified a subset of algorithms that should be considered in future investigations for building robust and clinically relevant predictive models.

Single-cell spatial landscape of immunotherapy response reveals mechanisms of CXCL13 enhanced antitumor immunity

BACKGROUND

Immunotherapy has revolutionized clinical outcomes for patients suffering from lung cancer, yet relatively few patients sustain long-term durable responses. Recent studies have demonstrated that the tumor immune microenvironment fosters tumorous heterogeneity and mediates both disease progression and response to immune checkpoint inhibitors (ICI). As such, there is an unmet need to elucidate the spatially defined single-cell landscape of the lung cancer microenvironment to understand the mechanisms of disease progression and identify biomarkers of response to ICI.

METHODS

Here, in this study, we applied imaging mass cytometry to characterize the tumor and immunological landscape of immunotherapy response in non-small cell lung cancer by describing activated cell states, cellular interactions and neighborhoods associated with improved efficacy. We functionally validated our findings using preclinical mouse models of cancer treated with anti-programmed cell death protein-1 (PD-1) immune checkpoint blockade.

RESULTS

We resolved 114,524 single cells in 27 patients treated with ICI, enabling spatial resolution of immune lineages and activation states with distinct clinical outcomes. We demonstrated that CXCL13 expression is associated with ICI efficacy in patients, and that recombinant CXCL13 potentiates anti-PD-1 response in vivo in association with increased antigen experienced T cell subsets and reduced CCR2+ monocytes.

DISCUSSION

Our results provide a high-resolution molecular resource and illustrate the importance of major immune lineages as well as their functional substates in understanding the role of the tumor immune microenvironment in response to ICIs.

Efficacy of immune checkpoint inhibitors in older patients with non-small cell lung cancer: Real-world data from multicentric cohorts in Canada and France

BACKGROUND

Age-related immune remodelling is thought to be associated with resistance to immune checkpoint inhibitors (ICIs) in cancer. Patients older than 70 years, representing >50% of the population with non-small cell lung cancer (NSCLC) according to SEER database, are underrepresented in clinical trials exploring ICIs. The objective of this study was to determine if patients with NSCLC older than ≥70 years had inferior clinical outcomes with ICIs.

METHODS

We conducted a retrospective analysis of 381 patients treated with anti-PD-(L)1 ICI for advanced NSCLC at the Dijon Cancer Center (n = 177), University of Montreal Hospital (n = 106) and Quebec Heart and Lung Institute (n = 98). Age was considered as a categorical variable. Patients' baseline characteristics were compared using the Chi-squared test. Survival curves were estimated by the Kaplan-Meier method and compared with the Log-rank test in a univariate analysis. Multivariate cox regression model was used to determine hazard ratios and 95% confidence intervals for progression-free survival (PFS) and overall survival (OS) between the groups, adjusting for other clinicopathologic features.

RESULTS

Among 381 patients included, 335 (88%) received ICI after platinum chemotherapy. The median age was 66 (range 37-89) and 33% were older than 70 years of age. Considering age as a categorical variable, differences in age were not associated with PFS or OS. Subgroup analysis and multivariate cox regression did not reveal significant interaction of age with outcomes. ECOG performance status was the only significant factor in the three cohorts.

CONCLUSIONS

Unlike previously described in the era of chemotherapy, age was not associated with outcomes in NSCLC patients treated with ICI.

Detection of iron atoms by emission spectroscopy and laser-induced fluorescence in solid propellant flames

Planar laser-induced fluorescence on atomic iron is investigated in this paper, and a measurement strategy is proposed to monitor the fluorescence of iron atoms with good sensitivity. A model is proposed to fit the experimental fluorescence spectra, and good agreement is found between simulated and experimental spectra. Emission and laser-induced fluorescence measurements are performed in the flames of ammonium perchlorate composite propellants containing iron-based catalysts. A fluorescence signal from iron atoms after excitation at 248 nm is observed for the first time in propellant flames. Images of the spatial distribution of iron atoms are recorded in the flame in which turbulent structures are generated. Iron fluorescence is detected up to 1.0 MPa, which opens the way to application in propellant combustion.

Abstract 4339: Prognostic significance of copy number alteration burden in unfavorable intermediate-risk prostate cancers harboring intraductal carcinoma and cribriform architecture

Aim: Men with intermediate-risk prostate cancer represent a heterogeneous group of patients with varying prognoses. Intraductal carcinoma (IDC), cribriform architecture (CA), and high copy number alteration burden are novel pathological and genomic indices that predict aggressive disease and inferior clinical outcomes in patients with localised prostate cancer. We aimed to test the independent prognostic effect of these indices in a cohort of intermediate-risk prostate cancers.

Experimental methods: We defined a set of clinical and genomic indices to test for their prognostic significance in a cohort of individuals with NCCN-defined intermediate-risk prostate cancer, who were treated with radical prostatectomy (RadP) or radiotherapy (RT) (N = 173, RadP; N = 358, RT). Clinical indices include primary T-category, pre-treatment PSA level, Gleason's score and pattern, and presence of IDC and/or CA. Pathological features were reviewed centrally by two expert pathologists. Copy number alteration burden was assessed in 215 tumours of the cohort using SNP array profiling (Affymetrix Oncoscan), and reported as percent genome aberration (PGA). Our primary endpoint was to test if a model incorporating IDC/CA and PGA stratifies patients with intermediate-risk disease for risk of biochemical relapse after primary treatment.

Results: Biochemical relapse was associated with PGA on univariable and multivariable analysis for the sub-cohort of 215 patients (HR of High vs Low PGA defined by median = 1.61, 95% CI = 1.04-2.49, Wald's p = 0.033). Based on modelling of the clinical indices, presence of IDC/CA was associated with biochemical relapse-free rate (bRFR) on univariable and multivariable analyses (HR = 1.90, 95% CI = 1.34-2.69, p = 0.00034), along with PSA level. Risk stratification considering both IDC/CA and PGA indicated an additive prognostic effect of IDC/CA to PGA for early biochemical relapse, with 18-month bRFR of 83% in High PGA, present IDC/CA vs 94% in Low PGA, absent IDC/CA subgroups (HR = 2.55, 95% CI = 1.49-4.39, p = 0.00069). A comparison of risk stratification models revealed that inclusion of PGA to IDC/CA yielded the strongest model for predicting 18-month bRFR in patients with intermediate-risk prostate cancer following treatment (area under the curve, AUC = 0.580, 95% CI = 0.456-0.675 [T-category, PSA, and IDC/CA] vs 0.649, 95% CI = 0.476-0.776 [T-category, PSA, IDC/CA, and PGA]).

Conclusions: We herein demonstrate for the first time a novel risk stratification model integrating pathological (IDC/CA) and genomic (PGA) indices to identify patients with unfavourable intermediate-risk prostate cancer, who may benefit from intensification to conventional definitive treatment.

Citation Format: Melvin Lee Kiang Chua, Jure Murgic, Melania Pintilie, Emilie Lalonde, Charlotte Kweldam, Winnie Lo, Alejandro Berlin, Alan Dal Pra, Michele Orain, Valerie Picard, Helene Hovington, Alain Begeron, Yves Fradet, Bernard Tetu, Julie Livingstone, Alice Meng, Jun Yan Zhang, Gaetano Zafarana, Neil Fleshner, Mike Fraser, Paul Boutros, Robert Bristow, Theodorus van der Kwast. Prognostic significance of copy number alteration burden in unfavorable intermediate-risk prostate cancers harboring intraductal carcinoma and cribriform architecture. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4339.