# Towards a deep learning approach for classifying treatment response in glioblastomas

arXiv: https://arxiv.org/abs/2504.18268 · HTML: https://mlsanity.com/paper/2504.18268/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 71 references that are in the map.
- First posted on arXiv 25 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Captum: A unified and generic model interpretability library for PyTorch](https://mlsanity.com/paper/2009.07896/) (2020)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [An overview of deep learning in medical imaging focusing on MRI](https://mlsanity.com/paper/1811.10052/) (2018)
- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [3D Deep Learning on Medical Images: A Review](https://mlsanity.com/paper/2004.00218/) (2020)
- [DC-AL GAN: Pseudoprogression and True Tumor Progression of Glioblastoma Multiform Image Classification Based on DCGAN and AlexNet](https://mlsanity.com/paper/1902.06085/) (2019)
- [A Self-supervised Multimodal Deep Learning Approach to Differentiate Post-radiotherapy Progression from Pseudoprogression in Glioblastoma](https://mlsanity.com/paper/2502.03999/) (2025)
- [Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients](https://mlsanity.com/paper/2209.01402/) (2022)

## Abstract (arXiv)

Glioblastomas are the most aggressive type of glioma, having a 5-year survival rate of 6.9%. Treatment typically involves surgery, followed by radiotherapy and chemotherapy, and frequent magnetic resonance imaging (MRI) scans to monitor disease progression. To assess treatment response, radiologists use the Response Assessment in Neuro-Oncology (RANO) criteria to categorize the tumor into one of four labels based on imaging and clinical features: complete response, partial response, stable disease, and progressive disease. This assessment is very complex and time-consuming. Since deep learning (DL) has been widely used to tackle classification problems, this work aimed to implement the first DL pipeline for the classification of RANO criteria based on two consecutive MRI acquisitions. The models were trained and tested on the open dataset LUMIERE. Five approaches were tested: 1) subtraction of input images, 2) different combinations of modalities, 3) different model architectures, 4) different pretraining tasks, and 5) adding clinical data. The pipeline that achieved the best performance used a Densenet264 considering only T1-weighted, T2-weighted, and Fluid Attenuated Inversion Recovery (FLAIR) images as input without any pretraining. A median Balanced Accuracy of 50.96% was achieved. Additionally, explainability methods were applied. Using Saliency Maps, the tumor region was often successfully highlighted. In contrast, Grad-CAM typically failed to highlight the tumor region, with some exceptions observed in the Complete Response and Progressive Disease classes, where it effectively identified the tumor region. These results set a benchmark for future studies on glioblastoma treatment response assessment based on the RANO criteria while emphasizing the heterogeneity of factors that might play a role when assessing the tumor's response to treatment.