Cross-Attention Multimodal Learning for Predicting Response to Neoadjuvant Imatinib in Gastrointestinal Stromal Tumors: A Multicenter Retrospective Study

• URL: http://arxiv.org/abs/2606.25579v1
• Date: Wed, 24 Jun 2026 08:53:18 GMT
• Title: Cross-Attention Multimodal Learning for Predicting Response to Neoadjuvant Imatinib in Gastrointestinal Stromal Tumors: A Multicenter Retrospective Study
• Abstract summary: Response to neoadjuvant imatinib in gastrointestinal stromal tumors (GISTs) is highly variable and cannot be reliably predicted.<n>This study developed and evaluated an explainable multimodal learning framework integrating computed tomography (CT) imaging and clinical variables.
• Score: 0.17595857197891288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Response to neoadjuvant imatinib in gastrointestinal stromal tumors (GISTs) is highly variable and cannot be reliably predicted using current clinical or molecular markers. This study developed and evaluated an explainable multimodal deep learning framework integrating computed tomography (CT) imaging and clinical variables to predict treatment response. Methods: Patients from four tertiary centers were retrospectively included between 2000-2023 in independent pretraining (n=935) and prediction (n=213) cohorts. A cross-attention framework integrating clinical variables and tumor-centered CT imaging was developed to predict response to neoadjuvant imatinib. Two training strategies were evaluated: (1) self-supervised pretraining with low-rank adaptation and (2) training from scratch. Hyperparameters were optimized using SMAC3. Performance was assessed through internal cross-validation and external testing. Ablation analyses and attention-based explanations were used to quantify modality contributions. Results: Among 213 patients (54.5% responders), responders had larger tumors (112 vs. 89 mm, P=0.026), higher mitotic index (3 vs. 0, P<0.001), and more frequent KIT mutations (69.0% vs. 56.7%, P=0.019). Cross-attention models achieved the highest internal performance (AUC up to 0.99) but lower external performance (AUC 0.60-0.63). Clinical-only performance was moderate (AUC 0.66), whereas imaging-only models showed limited generalizability (AUC 0.56-0.66). Explainability analyses identified significant differences in feature importance between responders and non-responders, including CD117, BRAF, PDGFRA, age, sex, disease status, and comorbidities (FDR-adjusted P<=0.036). Conclusion: The cross-attention framework shows potential for improving imatinib response prediction in GIST while providing interpretable insights into multimodal determinants of treatment response.

Related papers

• A Pathology Foundation Model for Gastric Cancer with Real-World Validation [47.19073124722865]
  We present GRACE, a Gastric-specific foundation model for Real-world Assessment and Clinical dEcision support.<n> GRACE was developed from multicenter gastric pathology datasets totaling 48,364 primarily HE-stained whole-slide images from 37,493 patients.<n>When calibrated to maintain non-inferior performance to senior pathologists, the AI-assisted workflow could triage 60.7% of atrophy and 82.7% of intestinal metaplasia cases.
  arXiv  Detail & Related papers  (2026-06-03T12:18:06Z)
• Predicting Response to Neoadjuvant Chemotherapy in Ovarian Cancer from CT Baseline Using Multi-Loss Deep Learning [3.7830216516108646]
  Early identification of non-responders to neoadjuvant chemotherapy is a key unmet need.<n>This work proposes a non-invasive deep learning framework to predict neoadjuvant chemotherapy response from pre-treatment contrast-enhanced CT.<n>Training combines classification loss with supervised contrastive regularization and hard-negative mining to improve separation between ambiguous responders and non-responders.
  arXiv  Detail & Related papers  (2026-05-14T15:53:57Z)
• A Breast Vision Pathology Foundation Model for Real-world Clinical Utility [65.57568187389113]
  We present bfBRAVE, a breast-adaptive pathology foundation model developed and evaluated using a total resource of 101,638 breast wholeslide images.<n>BRAVE supported practical roles in the clinical workflow, including safe exclusion of low-risk cases from routine AI-assisted second-review rescue of initially missed positives and prioritization of cases for further assessment.
  arXiv  Detail & Related papers  (2026-05-06T07:44:39Z)
• Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model [95.41752463487008]
  Parkinson's disease affects over ten million people worldwide.<n>Inter-individual variability limits empirical treatment selection, increasing non-negligible surgical risk and cost.<n>We bridge this gap with a pretraining-finetuning framework to predict outcomes directly from resting-state fMRI.
  arXiv  Detail & Related papers  (2026-03-31T02:36:10Z)
• Cerebra: A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment [56.62016795093786]
  Cerebra is an interactive multi-agent AI team that coordinates specialized agents for EHR, clinical notes, and medical imaging analysis.<n>It supports privacy-preserving deployment by operating on structured representations and remains robust when modalities are incomplete.<n>Cerebra significantly improved expert performance, increasing accuracy by 17.5 percentage points in prospective dementia risk estimation.
  arXiv  Detail & Related papers  (2026-03-23T05:46:45Z)
• AI-based Prediction of Biochemical Recurrence from Biopsy and Prostatectomy Samples [0.012216782333971603]
  Biochemical recurrence (BCR) after radical prostatectomy is a surrogate marker for aggressive prostate cancer.<n>We trained an AI-based model on diagnostic prostate biopsy slides to predict patient-specific risk of BCR.
  arXiv  Detail & Related papers  (2026-01-28T20:33:15Z)
• Neural Discrete Representation Learning for Sparse-View CBCT Reconstruction: From Algorithm Design to Prospective Multicenter Clinical Evaluation [64.42236775544579]
  Cone beam computed tomography (CBCT)-guided puncture has become an established approach for diagnosing and treating thoracic tumours.<n>DeepPriorCBCT is a three-stage deep learning framework that achieves diagnostic-grade reconstruction using only one-sixth of the conventional radiation dose.
  arXiv  Detail & Related papers  (2025-11-30T12:45:02Z)
• Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy [0.0]
  The aim of this study is to evaluate the association between MRI descriptors and complete response (pCR) to neoadjuvant chemotherapy (NAC)<n>Unifocality and non-spiculated margins are independently associated with pCR and can increase models performance to predict BC response NAC.
  arXiv  Detail & Related papers  (2025-11-21T11:25:32Z)
• Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images [2.23127246021293]
  Triple-negative breast cancer (TNBC) remains a major clinical challenge due to its aggressive behavior and lack of targeted therapies.<n>We present an attention-based multiple instance learning framework designed to predict pathologic complete response (pCR) directly from pre-treatment hematoxylin and eosin (H&E)-stained biopsy slides.
  arXiv  Detail & Related papers  (2025-05-20T02:06:34Z)
• TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs [49.69047720285225]
  We propose a novel topological approach that explicitly extracts multi-scale topological structures to better approximate breast parenchymal structures. We empirically validate emphTopoTxR using the VICTRE phantom breast dataset. Our qualitative and quantitative analyses suggest differential topological behavior of breast tissue in treatment-na"ive imaging.
  arXiv  Detail & Related papers  (2024-11-05T19:35:10Z)
• Prediction of Local Failure after Stereotactic Radiotherapy in Melanoma Brain Metastases Using Ensemble Learning on Clinical, Dosimetric, and Radiomic Data [0.7536625319739964]
  This study aimed to predict lesion-specific outcomes after stereotactic radiotherapy in patients with brain metastases from malignant melanoma.<n>RF-based models outperformed clinical models; dosimetric data alone were not predictive.
  arXiv  Detail & Related papers  (2024-05-31T14:18:37Z)
• Detection of subclinical atherosclerosis by image-based deep learning on chest x-ray [86.38767955626179]
  Deep-learning algorithm to predict coronary artery calcium (CAC) score was developed on 460 chest x-ray. The diagnostic accuracy of the AICAC model assessed by the area under the curve (AUC) was the primary outcome.
  arXiv  Detail & Related papers  (2024-03-27T16:56:14Z)

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.