The SurvivalNet project is creating next-generation survival modeling methods using contemporary machine learning techniques to predict disease progression, metastasis, and survival. Data produced in clinical labs or research are increasingly high-dimensional, and are outgrowing the capacity of conventional survival modeling approaches. Advanced molecular assays can generate thousands of features describing the transcriptional, genetic, and epigenetic state of a patient's disease, and new modeling capabilities are needed to realize the potential of these data for precision treatment. In addition to providing accurate predictions, these methods be transparent, and should convey their confidence to clinicians.

The methods we have developed combine conventional statistical approaches with neural networks and modern optimization techniques, creating models that can learn highly-predictive representations from censored data. We have demonstrated these models using genomic and proteomic data in breast cancer and brain tumors. These methods can also utilize convolutional networks to learn from clinical outcomes and imaging data like histology.


Safoora Yousefi, PhD
Alumni - PhD
Ramraj Chandradevan
Alumni - Student
Mohamed Amgad
PhD Candidate
Pooya Mobadersany, PhD
Alumni - PhD


Halani SH, Yousefi S, Vega JV, Rossi MR, Zhao Z, Amrollahi F, Holder CA, Baxter-Stoltzfus A, Eschbacher J, Griffith B, Olson JJ. Multi-faceted computational assessment of risk and progression in oligodendroglioma implicates NOTCH and PI3K pathways. NPJ precision oncology. 2018 Nov 6;2(1):1-9.

Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Vega JE, Brat DJ, Cooper LA. Predicting cancer outcomes from histology and genomics using convolutional networks. Proceedings of the National Academy of Sciences. 2018 Mar 27;115(13):E2970-9.

Yousefi S, Amrollahi F, Amgad M, Dong C, Lewis JE, Song C, Gutman DA, Halani SH, Vega JE, Brat DJ, Cooper LA. Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models. Scientific reports. 2017 Sep 15;7(1):1-1.

Yousefi S, Song C, Nauata N, Cooper L. Learning genomic representations to predict clinical outcomes in cancer. International Conference on Learning Representations; 2016 May; San Juan Puerto Rico. Available online.


Informatics Tools for Quantitative Digital Pathology Profiling and Integrated Prognostic Modeling
NCI U01CA220401