Radiomics- and Clinical Feature-Driven Prediction of Volumetric Response in Skull-Base Meningioma after CyberKnife Radiosurgery

• URL: http://arxiv.org/abs/2604.24230v1
• Date: Mon, 27 Apr 2026 09:38:06 GMT
• Title: Radiomics- and Clinical Feature-Driven Prediction of Volumetric Response in Skull-Base Meningioma after CyberKnife Radiosurgery
• Abstract summary: We propose a radiomics- and clinical feature-driven framework for predicting volumetric response in skull-base meningiomas treated with CyberKnife.<n>Our method targets volumetric response as an indicator of treatment efficacy.
• Score: 2.2689094912853767
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Skull-base meningiomas are often characterized by favorable long-term prognosis, yet their anatomical complexity and proximity to critical neurovascular structures make treatment selection challenging. Stereotactic radiosurgery with CyberKnife represents an effective therapeutic option when surgical resection is not feasible; however, not all patients benefit equally from this treatment. Early identification of patients likely to respond to radiosurgery remains an open clinical problem. In this study, we propose a radiomics- and clinical feature-driven framework for predicting volumetric response in skull-base meningiomas treated with CyberKnife. Unlike most existing approaches that focus on progression-free survival or recurrence, our method targets volumetric response as an indicator of treatment efficacy. Pre-treatment MRI images from 104 patients were processed to extract radiomic features, which were combined with clinical variables and analyzed using six models. To ensure methodological rigor, the entire modeling process was implemented within a nested cross-validation scheme. Among the evaluated models, TabPFN achieved the best overall performance, with an AUC of 0.81 and consistently favorable classification metrics. These results suggest that advanced machine learning architectures, when combined with robust validation strategies, can effectively capture patterns associated with treatment response even in small-sample, high-dimensional settings.

Related papers

• Vision Transformers for Preoperative CT-Based Prediction of Histopathologic Chemotherapy Response Score in High-Grade Serous Ovarian Carcinoma [5.924316400347799]
  We propose a 2.5D multimodal deep learning framework that processes lesion-dense omental slices using a pre-trained Vision Transformer encoder.<n>Our model, integrating imaging and clinical data, achieved a ROC-AUC of 0.95 alongside 95% accuracy and 80% precision on the internal test cohort.<n>These preliminary results demonstrate the feasibility of transformer-based deep learning for preoperative prediction of CRS in HGSOC.
  arXiv  Detail & Related papers  (2026-04-10T10:33:07Z)
• Developing Predictive and Robust Radiomics Models for Chemotherapy Response in High-Grade Serous Ovarian Carcinoma [0.9165796164519936]
  Neoadjuvant chemotherapy (NACT) is often used to reduce tumor burden before surgery, but about 40% of patients show limited response.<n>Radiomics, combined with machine learning (ML), offers a promising non-invasive method for predicting NACT response.<n>This study aimed to improve response prediction in HGSOC patients undergoing NACT by integration different feature selection methods.
  arXiv  Detail & Related papers  (2026-01-13T11:29:02Z)
• 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)
• Predicting Brain Tumor Response to Therapy using a Hybrid Deep Learning and Radiomics Approach [4.7766856771039725]
  This paper presents an automated method for classifying the intervention response from longitudinal MRI scans.<n>We propose a novel hybrid framework that combines deep learning derived feature extraction and an extensive set of radiomics and clinically chosen features.
  arXiv  Detail & Related papers  (2025-09-08T10:15:23Z)
• A Disease-Centric Vision-Language Foundation Model for Precision Oncology in Kidney Cancer [54.58205672910646]
  RenalCLIP is a visual-language foundation model for characterization, diagnosis and prognosis of renal mass.<n>It achieved better performance and superior generalizability across 10 core tasks spanning the full clinical workflow of kidney cancer.
  arXiv  Detail & Related papers  (2025-08-22T17:48:19Z)
• A Metabolic-Imaging Integrated Model for Prognostic Prediction in Colorectal Liver Metastases [5.6492616107251274]
  This study developed and validated a robust machine learning model for predicting postoperative recurrence risk.<n>We restricted input variables to preoperative baseline clinical parameters and radiomic features from contrast-enhanced CT imaging.<n>The 3-month recurrence prediction model demonstrated optimal performance with an AUC of 0.723 in cross-validation.
  arXiv  Detail & Related papers  (2025-07-26T01:29:38Z)
• 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)
• ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
  ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations. The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations. ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
  arXiv  Detail & Related papers  (2023-10-08T17:23:17Z)
• Enhancing Clinical Support for Breast Cancer with Deep Learning Models using Synthetic Correlated Diffusion Imaging [66.63200823918429]
  We investigate enhancing clinical support for breast cancer with deep learning models. We leverage a volumetric convolutional neural network to learn deep radiomic features from a pre-treatment cohort. We find that the proposed approach can achieve better performance for both grade and post-treatment response prediction.
  arXiv  Detail & Related papers  (2022-11-10T03:02:12Z)
• Exploiting segmentation labels and representation learning to forecast therapy response of PDAC patients [60.78505216352878]
  We propose a hybrid deep neural network pipeline to predict tumour response to initial chemotherapy. We leverage a combination of representation transfer from segmentation to classification, as well as localisation and representation learning. Our approach yields a remarkably data-efficient method able to predict treatment response with a ROC-AUC of 63.7% using only 477 datasets in total.
  arXiv  Detail & Related papers  (2022-11-08T11:50:31Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)

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.