Dataset Information

Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy.

ABSTRACT: BACKGROUND AND PURPOSE:To predict treatment response and survival of NSCLC patients receiving stereotactic body radiation therapy (SBRT), we develop an unsupervised machine learning method for stratifying patients and extracting meta-features simultaneously based on imaging data. MATERIAL AND METHODS:This study was performed based on an 18F-FDG-PET dataset of 100 consecutive patients who were treated with SBRT for early stage NSCLC. Each patient's tumor was characterized by 722 radiomic features. An unsupervised two-way clustering method was used to identify groups of patients and radiomic features simultaneously. The groups of patients were compared in terms of survival and freedom from nodal failure. Meta-features were computed for building survival models to predict survival and free of nodal failure. RESULTS:Differences were found between 2 groups of patients when the patients were clustered into 3 groups in terms of both survival (p?=?0.003) and freedom from nodal failure (p?=?0.038). Average concordance measures for predicting survival and nodal failure were 0.640±0.029 and 0.664±0.063 respectively, better than those obtained by prediction models built upon clinical variables (p?

SUBMITTER: Li H

PROVIDER: S-EPMC6261331 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy.

Li Hongming H Galperin-Aizenberg Maya M Pryma Daniel D Simone Charles B CB Fan Yong Y

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology 20180704 2

<h4>Background and purpose</h4>To predict treatment response and survival of NSCLC patients receiving stereotactic body radiation therapy (SBRT), we develop an unsupervised machine learning method for stratifying patients and extracting meta-features simultaneously based on imaging data.<h4>Material and methods</h4>This study was performed based on an <sup>18</sup>F-FDG-PET dataset of 100 consecutive patients who were treated with SBRT for early stage NSCLC. Each patient's tumor was characterize ...[more]

PMID: 30473058

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Project description:Background: To investigate the role of stereotactic body RT (SBRT) in decreased total peripheral lymphocyte count (TLC) in patients with early-stage lung cancer and to explore possible risk factors for RT-induced lymphopenia. Materials and Methods: We analyzed the TLCs and lymphocyte subsets of 76 patients in our prospective clinical database who received SBRT for early-stage lung cancer treatment. Relationships between clinical factors or dosimetric parameters and TLC were evaluated using Spearman's correlation analysis and Chi-square tests for continuous and categorical variables, respectively. Multivariate linear regression analysis was used to control for confounding factors. Kaplan-Meier analysis with a log-rank test and a multivariate Cox regression model were used for survival analysis. Results: Most patients (64/76, 84.2%) experienced decreased absolute lymphocyte counts following SBRT, as well as shifts in lymphocyte subset distributions. Spearman's correlation coefficients between post-SBRT TLC and the percentage of the lung and heart receiving 5 to 50 Gy (in 5 Gy increments) shown that most lung DVH parameters [V(10)-V(50)] were significantly negatively correlated with post-SBRT TLC, while only heart V(5), V(20), V(25), V(30), and V(45) were significant. Univariate analyses revealed that a lower Pre-SBRT TLC level, higher mean lung dose, longer treatment duration, and longer TBT were significantly associated with a lower Post-SBRT TLC level (all P < 0.05). Stepwise multivariate linear regression, which incorporated all of the significantly clinical variables and SBRT-related parameters in univariate analysis, revealed that lower pre -SBRT TLC (P < 0.001), higher heart V5 (P = 0.002), and longer total beam-on time (TBT) (P = 0.001) were the independent risk factors for decrease in post-SBRT TLC. Patients with lower post-SBRT TLC and longer TBT exhibited significantly inferior progression-free survival (PFS) (P < 0.001 and P = 0.013) and overall survival (P = 0.006 and P = 0.043). Conclusions: G2 and more severe lymphopenia after SBRT might be an independent prognostic factor for poorer outcome in early-stage lung cancer. Lowering heart V5 and TBT when designing SBRT plans may spare circulating lymphocytes and have the potential to further improve survival outcomes.

Project description:PurposeOur purpose was to develop predictive nomograms for overall survival (OS), progression-free survival (PFS), and time-to-progression (TTP) at 5 years in patients with early-stage non-small cell lung cancer (ES-NSCLC) treated with stereotactic ablative radiation therapy (SABR).Methods and materialsThe study cohort included 714 ES-NSCLC patients treated with SABR from 2004-2015 with median follow-up of 59 months, divided into training and testing sets (8:2), with the former used for nomogram development. The least absolute shrinkage and selection operator were initially employed to screen for predictors of OS, PFS, and TTP, and identified predictors were subsequently applied toward Cox proportional hazards regression modeling. Significant predictors (P < .05) on multivariable regression were then used to develop nomograms, which were validated via evaluation of concordance indexes (C-index) and calibration plots. Finally, Kaplan-Meier method and Gray's test were employed to compare and confirm differences in outcomes among various groups and explore prognostic factors associated with local versus distant disease progression.ResultsSignificant predictors of both OS and PFS at 5 years included age, sex, Charlson comorbidity index, diffusing capacity of carbon monoxide, systemic immune-inflammation index, and tumor size (P ≤ .01 for all). Eastern Cooperative Oncology Group performance status predicted for OS as well (P = .01), and both tumor size (P < .01) and minimum biological equivalent dose to 95% of planning target volume (PTV D95 BED10; P < .01) were predictive of TTP. The C-indexes for the OS, PFS, and TTP nomograms were 0.73, 0.68, and 0.60 in the training data set and 0.72, 0.66, and 0.59 in the testing data set, respectively. Tumor size > 2.45 cm and PTV D95 BED10 < 113 Gy were significantly associated with both local and distant progression.ConclusionsThese prognostic nomograms can accurately predict for OS, PFS, and TTP at 5 years after SABR for ES-NSCLC and may thus help identify high-risk patients who could benefit from additional systemic therapy.

Dataset Information

Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy.

Publications

Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy.

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