Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models

Abstract

Objectives: High throughput pre-treatment imaging features may predict radiation treatment outcome and guide individualized treatment in radiotherapy (RT). Given relatively small patient sample (as compared with high dimensional imaging features), identifying potential prognostic imaging biomarkers is typically challenging. We aimed to develop robust machine learning methods for patient survival prediction using pre-treatment quantitative CT image features for a subgroup of head-and-neck cancer patients. Methods: Three neural network models, including back propagation (BP), Genetic Algorithm-Back Propagation (GA-BP), and Probabilistic Genetic Algorithm-Back Propagation (PGA-BP) neural networks were trained to simulate association between patient survival and radiomics data in radiotherapy. To evaluate the models, a subgroup of 59 head-and-neck patients with primary cancers in oral tongue area were utilized. Quantitative image features were extracted from planning CT images, a novel t-Distributed Stochastic Neighbor Embedding (t-SNE) method was used to remove irrelevant and redundant image features before fed into the network models. 80% patients were used to train the models, and remaining 20% were used for evaluation. Results: Of the three supervised machine-learning methods studied, PGA-BP yielded the best predictive performance. The reported actual patient survival interval of 30.5 ± 21.3 months, the predicted survival times were 47.3 ± 38.8, 38.5 ± 13.5 and 29.9 ± 15.3 months using the traditional PCA. Combining with the novel t-SNE dimensionality reduction algorithm, the predicted survival intervals are 35.8 ± 15.2, 32.3 ± 13.1 and 31.6 ± 15.8 months for the BP, GA-BP and PGA-BP neural network models, respectively. Conclusion: The work demonstrated that the proposed probabilistic genetic algorithm optimized neural network models, integrating with the t-SNE dimensionality reduction algorithm, achieved accurate prediction of patient survival. Advances in knowledge: The proposed PGA-BP neural network, integrating with an advanced dimensionality reduction algorithm (t-SNE), improved patient survival prediction accuracy using pre-treatment quantitative CT image features of head-and-neck cancer patients.

Cancer Detection and Prediction Using Genetic Algorithms

“…Pan et al [ 35 ] optimized the performance of a backpropagation neural network for oral cancer survival rate prediction via GAs (PGA-BP). PCA and t-SNE are compared for feature selection.…”

[Retracted] English Feature Recognition Based on GA‐BP Neural Network Algorithm and Data Mining

“…The first part is mainly about setting parameters and the design of data input function and output function, so as to determine its basic structure [ 11 ]. On the basis of structure determination, genetic algorithm is adjusted to optimize the relevant parameters [ 12 – 14 ]. In the last step of the prediction process, after the genetic algorithm optimization, the initial weight threshold assignment of the original BP neural network is optimized and the optimization process of the neural network algorithm can be completed after the input data is trained [ 15 ].…”

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

“…Five-year survival rate and survival time of cancer patients are significant indicators for cancer prognosis, as well as references for therapeutic outcomes. In a recent study, a total of 59 patients with oral tongue cancer have been examined ( Pan et al, 2020 ). All were treated with radiotherapy, and their CT images utilized and studied by computer for individual survival prediction.…”