A Foundation Model Framework for Multi-View MRI Classification of Extramural Vascular Invasion and Mesorectal Fascia Invasion in Rectal Cancer

• URL: http://arxiv.org/abs/2505.18058v1
• Date: Fri, 23 May 2025 16:04:27 GMT
• Title: A Foundation Model Framework for Multi-View MRI Classification of Extramural Vascular Invasion and Mesorectal Fascia Invasion in Rectal Cancer
• Authors: Yumeng Zhang, Zohaib Salahuddin, Danial Khan, Shruti Atul Mali, Henry C. Woodruff, Sina Amirrajab, Eduardo Ibor-Crespo, Ana Jimenez-Pastor, Luis Marti-Bonmati, Philippe Lambin,
• Abstract summary: Extramural vascular invasion (EVI) and mesorectal fascia invasion (MFI) is pivotal for risk-stratified management of rectal cancer.<n>Foundation model features, harmonization, and multi-view fusion significantly enhance diagnostic performance in rectal MRI.
• Score: 3.9216006287929086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Accurate MRI-based identification of extramural vascular invasion (EVI) and mesorectal fascia invasion (MFI) is pivotal for risk-stratified management of rectal cancer, yet visual assessment is subjective and vulnerable to inter-institutional variability. Purpose: To develop and externally evaluate a multicenter, foundation-model-driven framework that automatically classifies EVI and MFI on axial and sagittal T2-weighted MRI. Methods: This retrospective study used 331 pre-treatment rectal cancer MRI examinations from three European hospitals. After TotalSegmentator-guided rectal patch extraction, a self-supervised frequency-domain harmonization pipeline was trained to minimize scanner-related contrast shifts. Four classifiers were compared: ResNet50, SeResNet, the universal biomedical pretrained transformer (UMedPT) with a lightweight MLP head, and a logistic-regression variant using frozen UMedPT features (UMedPT_LR). Results: UMedPT_LR achieved the best EVI detection when axial and sagittal features were fused (AUC = 0.82; sensitivity = 0.75; F1 score = 0.73), surpassing the Chaimeleon Grand-Challenge winner (AUC = 0.74). The highest MFI performance was attained by UMedPT on axial harmonized images (AUC = 0.77), surpassing the Chaimeleon Grand-Challenge winner (AUC = 0.75). Frequency-domain harmonization improved MFI classification but variably affected EVI performance. Conventional CNNs (ResNet50, SeResNet) underperformed, especially in F1 score and balanced accuracy. Conclusion: These findings demonstrate that combining foundation model features, harmonization, and multi-view fusion significantly enhances diagnostic performance in rectal MRI.

Related papers

• DeSamba: Decoupled Spectral Adaptive Framework for 3D Multi-Sequence MRI Lesion Classification [0.6749750044497732]
  DeSamba is a framework designed to extract decoupled representations and adaptively fuse spatial and spectral features for lesion classification.<n>DeSamba achieves 62.10% Top-1 accuracy, 63.62% F1-score, 87.71% AUC, and 93.55% Top-3 accuracy on an external validation set.
  arXiv  Detail & Related papers  (2025-07-21T10:42:21Z)
• Mitigating Multi-Sequence 3D Prostate MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection [1.6508709227918446]
  Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machine learning development for medical image interpretation.<n>We propose CCELLA++ to address these limitations and improve clinical utility.
  arXiv  Detail & Related papers  (2025-07-08T20:38:10Z)
• Explainable Anatomy-Guided AI for Prostate MRI: Foundation Models and In Silico Clinical Trials for Virtual Biopsy-based Risk Assessment [3.5408411348831232]
  We present a fully automated, anatomically guided deep learning pipeline for prostate cancer (PCa) risk stratification using routine MRI.<n>The pipeline integrates three key components: an nnU-Net module for segmenting the prostate gland and its zones on axial T2-weighted MRI; a classification module based on the DiceedPT Swin Transformer foundation model, fine-tuned on 3D patches with optional anatomical priors and clinical data; and a VAE-GAN framework for generating counterfactual heatmaps that localize decision-driving image regions.
  arXiv  Detail & Related papers  (2025-05-23T14:40:09Z)
• UMambaAdj: Advancing GTV Segmentation for Head and Neck Cancer in MRI-Guided RT with UMamba and nnU-Net ResEnc Planner [0.04924932828166548]
  Magnetic Resonance Imaging (MRI) plays a crucial role in adaptive radiotherapy for head and neck cancer (HNC) due to its superior soft-tissue contrast. accurately segmenting the gross tumor volume (GTV), which includes both the primary tumor (GTVp) and lymph nodes (GTVn) remains challenging. Recently, two deep learning segmentation innovations have shown great promise: UMamba, which effectively captures long-range dependencies, and the nnU-Net Residual (ResEnc) which enhances feature extraction through multistage residual blocks.
  arXiv  Detail & Related papers  (2024-10-16T18:26:27Z)
• Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge [44.76736949127792]
  We describe the design and results from the BraTS 2023 Intracranial Meningioma Challenge.<n>The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas.<n>The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor.
  arXiv  Detail & Related papers  (2024-05-16T03:23:57Z)
• Pre-examinations Improve Automated Metastases Detection on Cranial MRI [36.39673740985943]
  Automated MM detection on contrast-enhanced T1-weighted images performed with high sensitivity. Highest diagnostic performance was achieved by inclusion of only the contrast-enhanced T1-weighted images of the diagnosis and of a prediagnosis MRI.
  arXiv  Detail & Related papers  (2024-03-13T06:18:08Z)
• Brain Tumor Radiogenomic Classification [1.8276368987462532]
  The RSNA-MICCAI brain tumor radiogenomic classification challenge aimed to predict MGMT biomarker status in glioblastoma through binary classification. The dataset is splitted into three main cohorts: training set, validation set which were used during training, and the testing were only used during final evaluation.
  arXiv  Detail & Related papers  (2024-01-11T10:30:09Z)
• Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks [33.51490233427579]
  Two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.
  arXiv  Detail & Related papers  (2023-04-18T10:14:45Z)
• Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis [0.0]
  MRI protocols may change over time or differ between centres resulting in low-quality sCT. domain randomisation (DR) to increase the generalisation of a DL model for brain sCT generation.
  arXiv  Detail & Related papers  (2023-03-17T18:45:05Z)
• Perfusion imaging in deep prostate cancer detection from mp-MRI: can we take advantage of it? [0.0]
  We evaluate strategies to integrate information from perfusion imaging in deep neural architectures. Perfusion maps from dynamic contrast enhanced MR exams are shown to positively impact segmentation and grading performance of PCa lesions.
  arXiv  Detail & Related papers  (2022-07-06T07:55:46Z)
• CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans [36.093580055848186]
  Lung Adenocarcinoma (LAUC) has recently been the most prevalent. Timely and accurate knowledge of the invasiveness of lung nodules leads to a proper treatment plan and reduces the risk of unnecessary or late surgeries. The primary imaging modality to assess and predict the invasiveness of LAUCs is the chest CT. In this paper, a predictive transformer-based framework, referred to as the "CAE-Transformer", is developed to classify LAUCs.
  arXiv  Detail & Related papers  (2021-10-17T04:37:24Z)
• Vision Transformers for femur fracture classification [59.99241204074268]
  The Vision Transformer (ViT) was able to correctly predict 83% of the test images. Good results were obtained in sub-fractures with the largest and richest dataset ever.
  arXiv  Detail & Related papers  (2021-08-07T10:12:42Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.