Outcome Prediction

Treatment Outcome and Toxicity Prediction

Predicting treatment outcome and toxicity is critical to select personalized options for an individual cancer patient. Oncologic outcome data is often imbalanced, thus conventional algorithms based on a single objective such as accuracy during model construction may lead to low sensitivity or specificity.

To overcome the limitation of the current single-objective based predictive models, we propose a multi-objective model that explicitly considers both sensitivity and specificity during model optimization. Combined with an artificial immune-based optimization algorithm, the proposed multi-objective model can obtain a solution that balances sensitivity and specificity. 

We have developed models to predictive distant failure for lung and cervical cancer patients after radiation therapy. Additional systemic therapy for those patients at risk for distant failure may reduce the risk and improve overall survival.

We have also developed deep learning based models to predict toxicity for cervical cancer patients after radiotherapy. Special considerations will be given to those patients at high risk for development of treatment-related toxicity during the treatment planning stage.

Publications

  1. Zhou Z, Chen L, Dohopolski M, Sher D, Wang J. (2023) ARMO: Automated and reliable multi-objective model for lymph node metastasis prediction in head and neck cancer. Phys Med Biol. (journal) 
  2. Wang K, Dohopolski M, Zhang Q, Sher D, Wang J. (2023) Towards reliable head and neck cancers locoregional recurrence prediction using delta‐radiomics and learning with rejection option. Med Phys. (journal) 
  3. Zhang Q, Wang K, Zhou Z, Qin G, Wang L, Li P, Sher D, Jiang S, Wang J. (2022) Predicting local persistence/recurrence after radiation therapy for head and neck cancer from PET/CT using a multi-objective, multi-classifier radiomics model. Front Oncol. (journal) 
  4. Nguyen D, Kay F, Tan J, Yan Y, Ng YS, Iyengar P, Peshock R, Jiang S. (2021) Deep learning–based COVID-19 pneumonia classification using chest CT images: model generalizability. Front Oncol. (journal) 
  5. Shen C, Tsai MY, Chen L, Li S, Nguyen D, Wang J, Jiang S, Jia X. (2021) On the robustness of deep learning-based lung-nodule classification for CT images with respect to image noise. Phys Med Biol. (journal) 
  6. Dohopolski M, Chen L, Sher D, Wang J. (2020) Predicting lymph node metastasis in patients with oropharyngeal cancer by using a convolutional neural network with associated epistemic and aleatoric uncertainty. Phys Med Biol. (journal)
  7. Zhou Z, Wang K, Folkert M, Liu H, Jiang S, Sher D, Wang J. (2020) Multifaceted radiomics for distant metastasis prediction in head and neck cancer. Phys Med Biol. (journal) 
  8. Wang K, Zhou Z, Wang R, Chen L, Zhang Q, Sher D, Wang J. (2020) A multi‐objective radiomics model for the prediction of locoregional recurrence in head and neck squamous cell cancer. Med Phys. (journal) 
  9. Li S, Xu P, Li B, Chen L, Zhou Z, Hao H, Duan Y, Folkert M, Ma J, Huang S, Jiang D, Wang J. (2019) Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features. Phys Med Biol. (journal) 
  10. Chen L, Zhou Z, Sher D, Zhang Q, Shah J, Pham NL, ... Wang J. (2019) Combining many-objective radiomics and 3-dimensional convolutional neural network through evidential reasoning to predict lymph node metastasis in head and neck cancer. Phys Med Biol. (journal) (arXiv)
  11. Chen X, Zhou Z, Hannan R, Thomas K, Pedrosa I, Kapur P, ... Wang J. (2018) Reliable gene mutation prediction in clear cell renal cell carcinoma through multi-classifier multi-objective radiogenomics model. Phys Med Biol. (journal) (arXiv)
  12. Zhou Z, Sher D, Zhang Q, Yan P, Shah J, Pham NL, ... Wang J. (2018) Multifactorial cancer treatment outcome prediction through multifaceted radiomics. (arXiv)
  13. Zhou Z, Chen L, Sher D, Zhang Q, Shah J, Pham NL, ... Wang J. (2018) Predicting lymph node metastasis in head and neck cancer by combining many-objective radiomics and 3-dimensional convolutional neural network through evidential reasoning. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). (journal) (arXiv)
  14. Chen L, Shen C, Zhou Z, Maquilan G, Thomas K, Folkert MR, ... Wang J. (2018) Accurate segmenting of cervical tumors in PET imaging based on similarity between adjacent slices. Comput Biol Med. (journal)
  15. Hao H, Zhou Z, Li S, Maquilan G, Folkert MR, Iyengar P, ... Timmerman R. (2018) Shell feature: A new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer. Phys Med Biol. (journal) (arXiv)
  16. Zhou Z, Zhou Z J, Hao H, Li S, Chen X, Zhang Y, ... Wang J. (2017) Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion. (arXiv)
  17. Zhen X, Chen J, Zhong Z, Hrycushko B, Zhou L, Jiang S, ... Gu X. (2017) Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: A feasibility study. Phys Med Biol. (journal)
  18. Zhou Z, Folkert M, Iyengar P, Westover K, Zhang Y, Choy H, ... Wang J. (2017) Multi-objective radiomics model for predicting distant failure in lung SBRT. Phys Med Biol. (journal)
  19. Zhou Z, Folkert M, Cannon N, Iyengar P, Westover K, Zhang Y, ... Jiang S. (2016) Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters. Rad & Onc. (journal)