A radiomic approach for adaptive radiotherapy in non-small cell lung cancer patients

Abstract

The primary goal of precision medicine is to minimize side effects and optimize efficacy of treatments. Recent advances in medical imaging technology allow the use of more advanced image analysis methods beyond simple measurements of tumor size or radiotracer uptake metrics. The extraction of quantitative features from medical images to characterize tumor pathology or heterogeneity is an interesting process to investigate, in order to provide information that may be useful to guide the therapies and predict survival. This paper discusses the rationale supporting the concept of radiomics and the feasibility of its application to Non-Small Cell Lung Cancer in the field of radiation oncology research. We studied 91 stage III patients treated with concurrent chemoradiation and adaptive approach in case of tumor reduction during treatment. We considered 12 statistics features and 230 textural features extracted from the CT images. In our study, we used an ensemble learning method to classify patients’ data into either the adaptive or non-adaptive group during chemoradiation on the basis of the starting CT simulation. Our data supports the hypothesis that a specific signature can be identified (AUC 0.82). In our experience, a radiomic signature mixing semantic and image-based features has shown promising results for personalized adaptive radiotherapy in non-small cell lung cancer.

Pretreatment Prediction of Adaptive Radiation Therapy Eligibility Using MRI-Based Radiomics for Advanced Nasopharyngeal Carcinoma Patients

“…Murat et al (11) developed a decision tree for tumor shrinkage for HNC patients, incorporating initial target volumes and other clinical factors; although an accuracy of 88% was reported in predicting the tumor shrinkage in 48 patients, the validity was not tested and some of the clinical factors used may not be available in other clinics, such as tumor growth pattern (endophytic or exophytic), hindering the generalizability of the decision tree. Recently, Ramella et al (30) explored the radiomic capability for ART in lung cancer patients and reported that radiomic features extracted from planning target volume (PTV) of lung cancer on CT images were capable of distinguishing patients between ART and non-ART group with AUC of 0.82, on the ground of tumor shrinkage during treatment. To our best knowledge, this study is the first to include radiomics in predicting ART eligibility for NPC patients and its tumoral predictive biomarkers for ART has not been explored before.…”

Integrating radiomics into holomics for personalised oncology: from algorithms to bedside

“…However, cANN have been developed specifically for images and incorporating individual numerical or categorical variables (ECG signals, lab tests, pathology results) adds further complexity. More recently, ensemble learning has been introduced to combine several predictors that use different data types [37,38]. Ensemble learning is widely adopted in other domains (e.g., information retrieval, where a meta-predictor is built by voting on different predictors [39]).…”

Precision radiotherapy for non-small cell lung cancer

“…Radiomic features extracted from pre-treatment CT scans can be used to associate with clinical outcomes such as progression-free survival and overall survival [30][31][32][33][34]. Radiomic features from images were sorted into clusters and correlated with survival outcome.…”

“…Radiomics predictions were not merely applied in the SABR cohort. A study focused on stage III NSCLC patients treated with concurrent chemoradiation proposed that a specific radiomic signature can be used in prediction tumor shrinkage [31]. Specific feature pattern differences between tumor shrinkage or not during chemoradiation can help in treatment decisions for radiotherapy plan adaptation.…”