Towards a deep learning approach for classifying treatment response in glioblastomas

• URL: http://arxiv.org/abs/2504.18268v1
• Date: Fri, 25 Apr 2025 11:27:05 GMT
• Title: Towards a deep learning approach for classifying treatment response in glioblastomas
• Authors: Ana Matoso, Catarina Passarinho, Marta P. Loureiro, José Maria Moreira, Patrícia Figueiredo, Rita G. Nunes,
• Abstract summary: Glioblastomas are the most aggressive type of glioma, having a 5-year survival rate of 6.9%.<n> 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.<n>Since deep learning has been widely used to tackle classification problems, this work aimed to implement the first DL pipeline for the classification of RANO criteria.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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