Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer Prognosis
Journal:
arXiv
Published Date:
Jan 21, 2025
Abstract
Survival prognosis is crucial for medical informatics. Practitioners often
confront small-sized clinical data, especially cancer patient cases, which can
be insufficient to induce useful patterns for survival predictions. This study
deals with small sample survival analysis by leveraging transfer learning, a
useful machine learning technique that can enhance the target analysis with
related knowledge pre-learned from other data. We propose and develop various
transfer learning methods designed for common survival models. For parametric
models such as DeepSurv, Cox-CC (Cox-based neural networks), and DeepHit
(end-to-end deep learning model), we apply standard transfer learning
techniques like pretraining and fine-tuning. For non-parametric models such as
Random Survival Forest, we propose a new transfer survival forest (TSF) model
that transfers tree structures from source tasks and fine-tunes them with
target data. We evaluated the transfer learning methods on colorectal cancer
(CRC) prognosis. The source data are 27,379 SEER CRC stage I patients, and the
target data are 728 CRC stage I patients from the West China Hospital. When
enhanced by transfer learning, Cox-CC's $C^{td}$ value was boosted from 0.7868
to 0.8111, DeepHit's from 0.8085 to 0.8135, DeepSurv's from 0.7722 to 0.8043,
and RSF's from 0.7940 to 0.8297 (the highest performance). All models trained
with data as small as 50 demonstrated even more significant improvement.
Conclusions: Therefore, the current survival models used for cancer prognosis
can be enhanced and improved by properly designed transfer learning techniques.
The source code used in this study is available at
https://github.com/YonghaoZhao722/TSF.
