Back to Search
Start Over
Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models
- Source :
- Br J Radiol
- Publication Year :
- 2020
-
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.
- Subjects :
- Oncology
medicine.medical_specialty
Computer science
medicine.medical_treatment
Treatment outcome
02 engineering and technology
Machine Learning
03 medical and health sciences
0302 clinical medicine
Tongue
Internal medicine
Genetic algorithm
0202 electrical engineering, electronic engineering, information engineering
medicine
Humans
Radiology, Nuclear Medicine and imaging
Throughput (business)
Probability
Principal Component Analysis
Artificial neural network
Full Paper
Probabilistic logic
Cancer
Reproducibility of Results
General Medicine
medicine.disease
Prognosis
Tongue Neoplasms
Radiation therapy
medicine.anatomical_structure
030220 oncology & carcinogenesis
Carcinoma, Squamous Cell
020201 artificial intelligence & image processing
Mouth Neoplasms
Neural Networks, Computer
Tomography, X-Ray Computed
Algorithms
Subjects
Details
- ISSN :
- 1748880X
- Volume :
- 93
- Issue :
- 1112
- Database :
- OpenAIRE
- Journal :
- The British journal of radiology
- Accession number :
- edsair.doi.dedup.....a25c4fe05044a5ed6dfa44f21e92c560