Multi-objective radiomics model for predicting distant failure in lung SBRT

Abstract

Stereotactic body radiation therapy (SBRT) has demonstrated high local control rates in early stage non-small cell lung cancer patients who are not ideal surgical candidates. However, distant failure after SBRT is still common. For patients at high risk of early distant failure after SBRT treatment, additional systemic therapy may reduce the risk of distant relapse and improve overall survival. Therefore, a strategy that can correctly stratify patients at high risk of failure is needed. The field of radiomics holds great potential in predicting treatment outcomes by using high-throughput extraction of quantitative imaging features. The construction of predictive models in radiomics is typically based on a single objective such as overall accuracy or the area under the curve (AUC). However, because of imbalanced positive and negative events in the training datasets, a single objective may not be ideal to guide model construction. To overcome these limitations, we propose a multi-objective radiomics model that simultaneously considers sensitivity and specificity as objective functions. To design a more accurate and reliable model, an iterative multi-objective immune algorithm (IMIA) was proposed to optimize these objective functions. The multi-objective radiomics model is more sensitive than the single-objective model, while maintaining the same levels of specificity and AUC. The IMIA performs better than the traditional immune-inspired multi-objective algorithm.

Combining many-objective radiomics and 3D convolutional neural network through evidential reasoning to predict lymph node metastasis in head and neck cancer

“…The proposed algorithm consists of two phases: (1) Pareto-optimal solution generation; and (2) best solution selection. The first phase is the same as in the multi-objective algorithm [10] which includes initialization, clonal operation, mutation operation, deleting operation, population update, and termination detection. In the second phase, the final solution is selected according to accuracy and AUC.…”

“…This model aims to a two-class prediction and only uses the classification accuracy as the objective function. To build a more reliable model, our group developed a multi-objective radiomics model [10] that considered both sensitivity and specificity simultaneously as the objective functions during model training. For feature learning-based models, deep learning is a powerful method that has been used to build predictive models for cancer diagnosis.…”

“…In this work, we propose a hybrid model that combines many-objective radiomics (MO-radiomics) and three-dimensional convolutional neural network (3D-CNN) through evidential reasoning (ER) to predict LNM in HNC. Because our multi-objective radiomics model [10] can only handle binary problems, we propose a new MO-radiomics model to predict the three classes of lymph nodes: normal, suspicious, and involved. Our proposed model considers procedure accuracy (PA) and user accuracy (UA) in confusion matrix, in addition to sensitivity and specificity, as objectives.…”

Predicting Lymph Node Metastasis in Head and Neck Cancer by Combining Many-objective Radiomics and 3-dimensioal Convolutional Neural Network through Evidential Reasoning

“…In this work, we proposed a hybrid model that combines the multi-objective radiomics and 3D-CNN through evidential reasoning (ER) to predict LNM in H&N cancer. Because the multi-objective model [8] can only handle binary problems, we proposed a new MO-radiomics model that can predict the three classes of lymph nodes-normal, suspicious, and involved. Other than using sensitivity and specificity as the objectives in multi-objective model, procedure accuracy (PA) and user accuracy (UA) in confusion matrix (CM) were considered simultaneously as objectives in the proposed model.…”

Developing Predictive or Prognostic Biomarkers for Charged Particle Radiotherapy

“…In photon therapy, partially because of the large amount of accumulated data (including imaging, dosimetry, and outcome), AI has been explored for a number of applications, such as automatic treatment planning [53, 54] and treatment-outcome predictions [55–57]. Among those developments, “radiomics” has become a promising field that involves extracting large amounts of quantitative features from medical images and mining those features for clinical decision support [58–62].…”