# Post-hoc Overall Survival Time Prediction from Brain MRI

arXiv: https://arxiv.org/abs/2102.10765 · HTML: https://mlsanity.com/paper/2102.10765/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [Brain Tumor Segmentation and Survival Prediction](https://mlsanity.com/paper/1909.09399/) (2019)
- [Pre and Post-hoc Diagnosis and Interpretation of Malignancy from Breast DCE-MRI](https://mlsanity.com/paper/1809.09404/) (2018)
- [Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction](https://mlsanity.com/paper/1911.08483/) (2019)
- [M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients](https://mlsanity.com/paper/2006.10135/) (2020)

## Built on by (most cited first, 3 of 3)

- [Multi-View Hypercomplex Learning for Breast Cancer Screening](https://mlsanity.com/paper/2204.05798/) (2022)
- [Towards a Medical AI Scientist](https://mlsanity.com/paper/2603.28589/) (2026)
- [Censor-aware Semi-supervised Learning for Survival Time Prediction from Medical Images](https://mlsanity.com/paper/2205.13226/) (2022)

## Abstract (arXiv)

Overall survival (OS) time prediction is one of the most common estimates of the prognosis of gliomas and is used to design an appropriate treatment planning. State-of-the-art (SOTA) methods for OS time prediction follow a pre-hoc approach that require computing the segmentation map of the glioma tumor sub-regions (necrotic, edema tumor, enhancing tumor) for estimating OS time. However, the training of the segmentation methods require ground truth segmentation labels which are tedious and expensive to obtain. Given that most of the large-scale data sets available from hospitals are unlikely to contain such precise segmentation, those SOTA methods have limited applicability. In this paper, we introduce a new post-hoc method for OS time prediction that does not require segmentation map annotation for training. Our model uses medical image and patient demographics (represented by age) as inputs to estimate the OS time and to estimate a saliency map that localizes the tumor as a way to explain the OS time prediction in a post-hoc manner. It is worth emphasizing that although our model can localize tumors, it uses only the ground truth OS time as training signal, i.e., no segmentation labels are needed. We evaluate our post-hoc method on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019 data set and show that it achieves competitive results compared to pre-hoc methods with the advantage of not requiring segmentation labels for training.