Neural network based ensemble model to predict radiation induced lymphopenia after concurrent chemo-radiotherapy for non-small cell lung cancer from two institutions

A systematic review and meta-analysis of the utility of quantitative, imaging-based approaches to predict radiation-induced toxicity in lung cancer patients

BACKGROUND AND PURPOSE

To conduct a systematic review and meta-analysis of the performance of radiomics, dosiomics and machine learning in generating toxicity prediction in thoracic radiotherapy.

MATERIALS AND METHODS

An electronic database search was conducted and dual-screened by independent authors to identify eligible studies for systematic review and meta-analysis. Data was extracted and study quality was assessed using TRIPOD for machine learning studies, RQS for Radiomics and RoB for dosiomics.

RESULTS

10,703 studies were identified, and 5,252 entered screening. 104 studies including 23,373 patients were eligible for systematic review. Primary toxicity predicted was radiation pneumonitis (81), followed by esophagitis (12) and lymphopenia (4). Fourty-two studies studying radiation pneumonitis were eligible for meta-analysis, with pooled area-under-curve (AUC) of 0.82 (95% CI 0.79-0.85). Studies with machine learning had the best performance, with classical and deep learning models having similar performance. There is a trend towards an improvement of the performance of models with the year of publication. There is variability in study quality among the three study categories and dosiomic studies scored the highest among these. Publication bias was not observed.

CONCLUSION

The majority of existing literature using radiomics, dosiomics and machine learning has focused on radiation pneumonitis prediction. Future research should focus on toxicity prediction of other organs at risk and the adoption of these models into clinical practice.

An efficient patient's response predicting system using multi-scale dilated ensemble network framework with optimization strategy

The forecasting of a patient's response to radiotherapy and the likelihood of experiencing harmful long-term health impacts would considerably enhance individual treatment plans. Due to the continuous exposure to radiation, cardiovascular disease and pulmonary fibrosis might occur. For forecasting the response of patients to chemotherapy, the Convolutional Neural Networks (CNN) technique is widely used. With the help of radiotherapy, cancer diseases are diagnosed, but some patients suffer from side effects. The toxicity of radiotherapy and chemotherapy should be estimated. For validating the patient's improvement in treatments, a patient response prediction system is essential. In this paper, a Deep Learning (DL) based patient response prediction system is developed to effectively predict the response of patients, predict prognosis and inform the treatment plans in the early stage. The necessary data for the response prediction are collected manually. The collected data are then processed through the feature selection segment. The Repeated Exploration and Exploitation-based Coati Optimization Algorithm (REE-COA) is employed to select the features. The selected weight features are input into the prediction process. Here, the prediction is performed by Multi-scale Dilated Ensemble Network (MDEN), where we integrated Long-Short term Memory (LSTM), Recurrent Neural Network (RNN) and One-dimensional Convolutional Neural Networks (1DCNN). The final prediction scores are averaged to develop an effective MDEN-based model to predict the patient's response. The proposed MDEN-based patient's response prediction scheme is 0.79%, 2.98%, 2.21% and 1.40% finer than RAN, RNN, LSTM and 1DCNN, respectively. Hence, the proposed system minimizes error rates and enhances accuracy using a weight optimization technique.

Radiation-Induced Lymphopenia is a Causal Mediator of Survival After Chemoradiation Therapy for Esophagus Cancer

Purpose

Radiation-induced lymphopenia (RIL) is common during chemoradiation therapy. Severe lymphopenia is associated with reduced survival. Proton beam therapy (PBT), with its substantially more compact dose distributions, spares circulating lymphocytes and immune organs at risk to a greater extent than photon therapy. Recent studies comparing PBT to photon radiation therapy, specifically intensity-modulated radiation therapy (IMRT) for esophageal cancer (EC), showed that the incidence of grade 4 RIL (G4RIL) is significantly reduced among patients receiving PBT for EC. However, whether the extent of this reduction has a direct causative link with improved survival is unknown. This study applies causal mediation analysis to answer this question.

Methods and Materials

We retrospectively assessed 734 patients treated with concurrent chemoradiation therapy for biopsy-proven EC from 2004 to 2017. To address the potential for bias in the choice of radiation modality, propensity score analysis was used to evaluate and reduce imbalances between the PBT and IMRT cohorts. Causal mediation analysis was applied to decompose the total effect of radiation modality on overall survival (OS) into indirect (mediated through G4RIL) and direct effects.

Results

We found that PBT was associated with a significantly lower incidence of G4RIL and prolonged OS compared with IMRT (odds ratio, 0.41; 95% CI, 0.28-0.60; P < .001). In the propensity-matched cohort of 506 patients (253 PBT, 253 IMRT), G4RIL risk reduction with PBT versus IMRT translated into a 5% reduction in the relative rate of death (P = .032). Mediation of G4RIL explained ∼14.5% of the difference in OS.

Conclusions

G4RIL was found to mediate survival; however, a statistically significant direct effect of PBT on survival was not observed. In other words, the statistical significance of survival benefit from protons over photons in this EC cohort was lost in the absence of G4RIL risk reduction.

Early lymphocyte levels and low doses radiation exposure of lung predict lymphopenia in radiotherapy for lung cancer

Introduction

Radiation induced lymphopenia (RIL) deteriorate survival and diminishes the benefit of immune checkpoint inhibitors in combined treatment of lung cancer. Given the inconsistent data across various studies on the predictors of RIL, we aim to methodically elucidate these predictors and formulate a practical guide for clinicians.

Methods

We conducted observational cohort study in four tertiary cancer centers. Patients with non-small cell lung cancer and small cell lung cancer, without lymphopenia grade >1, who underwent standalone radiotherapy (RT) in minimum 15 fractions were eligible. Dose-volume parameters of structures and clinical factors were comprehensively analyzed using various predictors selection methods and statistical models (Linear Regressors, Elastic Net, Bayesian Regressors, Huber Regression, regression based on k-nearest neighbors, Gaussian Process Regressor, Decision Tree Regressor, Random Forest Regressor, eXtreme Gradient Boosting, Automated Machine Learning) and were ranked to predict lymphocytes count nadir (alc_nadir).

Results

Two hundred thirty eight patients (stage I-3.4%, II-17.6%, III-75.2%, IV-3.8%) who underwent RT to median dose of 60 Gy were analyzed. Median alc_nadir was 0.68K/mm3. The 60 feature sets were evaluated in 600 models (RMSE 0.27-0.41K/mm³). The most important features were baseline lymphocyte count (alc_1), mean lung_dose, lung v05, lung v10, heart v05 and effective dose to immune cells (edic). In patients with alc_1 ≤ 2.005K/mm3, median alc_nadir predictions were 0.54K/mm3 for lung_v05p > 51.8% and 0.76K/mm3 for lung_v05p ≤ 51.8%. Lymphopenia was rare in patients with alc_1 > 2.005K/mm3.

Discussion

RIL was most severe in patients with low early lymphocyte counts, primarily triggered by low RT doses in the heart and lungs.

Kaplan lecture 2023: lymphopenia in particle therapy

PURPOSE

Lymphopenia is now generally recognized as a negative prognostic factor in radiotherapy. Already at the beginning of the century we demonstrated that high-energy carbon ions induce less damage to the lymphocytes of radiotherapy patients than X-rays, even if heavy ions are more effective per unit dose in the induction of chromosomal aberrations in blood cells irradiated ex-vivo. The explanation was based on the volume effect, i.e. the sparing of larger volumes of normal tissue in Bragg peak therapy. Here we will review the current knowledge about the difference in lymphopenia between particle and photon therapy and the consequences.

CONCLUSIONS

There is nowadays an overwhelming evidence that particle therapy reduces significantly the radiotherapy-induced lymphopenia in several tumor sites. Because lymphopenia turns down the immune response to checkpoint inhibitors, it can be predicted that particle therapy may be the ideal partner for combined radiation and immunotherapy treatment and should be selected for patients where severe lymphopenia is expected after X-rays.

Modeling frameworks for radiation induced lymphopenia: A critical review

Radiation-induced lymphopenia (RIL) is a frequent, and often considered unavoidable, side effect of radiation therapy (RT), whether or not chemotherapy is included. However, in the last few years several studies have demonstrated the detrimental effect of RIL on therapeutic outcomes, with conflicting findings concerning possible inferior patient survival. In addition, since immunotherapeutic treatment has become an integral part of cancer therapy, preserving the immune system is recognized as crucial. Given this background, various research groups have reported on different frameworks for modelling RIL, frequently based on different definitions of RIL itself, and discordant results have been reported. Our aim is to critically review the current literature on RIL modelling and summarize the different approaches recently proposed to improve the prediction of RIL after RT and aimed at immunity-sparing RT. A detailed description of these approaches will be outlined and illustrated through their applications as found in the literature from the last five years. Such a critical analysis represents the necessary starting step to develop an effective strategy that ultimately could harmonize the diverse modelling methods.

Influence of treatment-related lymphopenia on the efficacy of immune checkpoint inhibitors in lung cancer: a meta-analysis

Background

Treatment-related lymphopenia (TRL) is common in patients with lung cancer, particularly in those with radiotherapy. However, the influence of TRL on the efficacy of immune checkpoint inhibitors (ICIs) for patients with lung cancer remains poorly understood. We performed a systematic review and meta-analysis to investigate the influence of TRL on survival of lung cancer patients on ICIs.

Methods

In order to accomplish the aim of the meta-analysis, a comprehensive search was conducted on databases including PubMed, Embase, Cochrane Library, and the Web of Science to identify observational studies with longitudinal follow-up. The Cochrane Q test was employed to evaluate heterogeneity among the included studies, while the I2 statistic was estimated. Random-effects models were utilized to merge the results, considering the potential impact of heterogeneity.

Results

Ten cohort studies with 1130 lung cancer patients who were treated with ICIs were included. Among them, 427 (37.8%) had TRL. Pooled results showed that compared to patients without TRL, patients with TRL were associated with poor progression-free survival (hazard ratio [HR]: 2.05, 95% confidence interval [CI]: 1.62 to 2.60, p < 0.001; I2 = 22%) and overall survival (HR: 2.69, 95% CI: 2.10 to 3.43, p < 0.001; I2 = 0%). Sensitivity analysis limited to patients with non-small cell lung cancer showed similar results (HR: 2.66 and 2.62, both p < 0.05). Moreover, subgroup analyses according to the diagnostic criteria of TRL, regression analysis model (univariate or multivariate), and indications of ICIs (for locally advanced or advanced lung cancer) showed consistent results (p for subgroup difference all > 0.05).

Conclusion

TRL was associated with poor survival of lung cancer patients who were treated with ICIs.