Conditional Diffusion Model with Anatomical-Dose Dual Constraints for End-to-End Multi-Tumor Dose Prediction

• URL: http://arxiv.org/abs/2508.02043v1
• Date: Mon, 04 Aug 2025 04:25:47 GMT
• Title: Conditional Diffusion Model with Anatomical-Dose Dual Constraints for End-to-End Multi-Tumor Dose Prediction
• Authors: Hui Xie, Haiqin Hu, Lijuan Ding, Qing Li, Yue Sun, Tao Tan,
• Abstract summary: ADDiff-Dose is an Anatomical-Dose Dual Constraints Diffusion Model for end-to-end multi-tumor dose prediction.<n>It incorporates conditional features via a multi-head attention mechanism and utilizes a composite loss function combining MSE, conditional terms, and KL divergence.<n>It significantly outperforms traditional baselines, achieving an MAE of 0.101-0.154, a DICE coefficient of 0.927, and limiting spinal cord maximum dose error to within 0.1 Gy.
• Score: 13.716930604289924
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Radiotherapy treatment planning often relies on time-consuming, trial-and-error adjustments that heavily depend on the expertise of specialists, while existing deep learning methods face limitations in generalization, prediction accuracy, and clinical applicability. To tackle these challenges, we propose ADDiff-Dose, an Anatomical-Dose Dual Constraints Conditional Diffusion Model for end-to-end multi-tumor dose prediction. The model employs LightweightVAE3D to compress high-dimensional CT data and integrates multimodal inputs, including target and organ-at-risk (OAR) masks and beam parameters, within a progressive noise addition and denoising framework. It incorporates conditional features via a multi-head attention mechanism and utilizes a composite loss function combining MSE, conditional terms, and KL divergence to ensure both dosimetric accuracy and compliance with clinical constraints. Evaluation on a large-scale public dataset (2,877 cases) and three external institutional cohorts (450 cases in total) demonstrates that ADDiff-Dose significantly outperforms traditional baselines, achieving an MAE of 0.101-0.154 (compared to 0.316 for UNet and 0.169 for GAN models), a DICE coefficient of 0.927 (a 6.8% improvement), and limiting spinal cord maximum dose error to within 0.1 Gy. The average plan generation time per case is reduced to 22 seconds. Ablation studies confirm that the structural encoder enhances compliance with clinical dose constraints by 28.5%. To our knowledge, this is the first study to introduce a conditional diffusion model framework for radiotherapy dose prediction, offering a generalizable and efficient solution for automated treatment planning across diverse tumor sites, with the potential to substantially reduce planning time and improve clinical workflow efficiency.

Related papers

• 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)
• Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging [26.589728923739596]
  We evaluate and compare the transferability of Convolutional Neural Networks and Foundation Models in predicting clinical outcomes in COVID-19 patients.<n>The evaluations were conducted across multiple learning paradigms, including both extensive full-data scenarios and more clinically realistic Few-Shot Learning settings.
  arXiv  Detail & Related papers  (2025-06-23T09:16:04Z)
• Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-Series [4.101915841246237]
  This study presents the Multi-source Irregular Time-Series Transformer (MITST), designed to predict blood glucose levels in ICU patients.<n>MITST integrates diverse clinical data--including laboratory results, medications, and vital signs--without predefined aggregation.<n>MITST achieves a statistically significant ( p 0.001 ) average improvement of 1.7 percentage points (pp) in AUROC and 1.8 pp in AUPRC over a state-of-the-art random forest baseline.
  arXiv  Detail & Related papers  (2024-11-03T03:03:11Z)
• ISLES'24: Final Infarct Prediction with Multimodal Imaging and Clinical Data. Where Do We Stand? [5.354756727899756]
  ISLES24 challenge focuses on the prediction of final infarct volumes from pre-interventional acute stroke imaging and clinical data.<n>Top-performing model, a multimodal nnU-Net-based architecture, achieved a Dice score of 0.285 on hidden test set of 98 cases.
  arXiv  Detail & Related papers  (2024-08-20T16:01:05Z)
• Deep State-Space Generative Model For Correlated Time-to-Event Predictions [54.3637600983898]
  We propose a deep latent state-space generative model to capture the interactions among different types of correlated clinical events. Our method also uncovers meaningful insights about the latent correlations among mortality and different types of organ failures.
  arXiv  Detail & Related papers  (2024-07-28T02:42:36Z)
• Enhancing clinical decision support with physiological waveforms -- a multimodal benchmark in emergency care [0.9503773054285559]
  We present a dataset and benchmarking protocol designed to advance multimodal decision support in emergency care.<n>Our models utilize demographics, biometrics, vital signs, laboratory values, and electrocardiogram (ECG) waveforms as inputs to predict both discharge diagnoses and patient deterioration.
  arXiv  Detail & Related papers  (2024-07-25T08:21:46Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge [45.3253187215396]
  The 2024 Brain Tumor Meningioma Radiotherapy (BraTS-MEN-RT) challenge aimed to advance automated segmentation algorithms.<n>We describe the design and results from the BraTS-MEN-RT challenge.
  arXiv  Detail & Related papers  (2024-05-28T17:25:43Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks [47.187609203210705]
  We aim to develop and validate an automated computational framework for patient-specific deposition modelling. An image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images.
  arXiv  Detail & Related papers  (2023-03-02T07:47:07Z)
• Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG [56.155331323304]
  Deep learning based electroencephalogram channels' feature level fusion is carried out in this work. Channel selection, fusion, and classification procedures were optimized by two optimization algorithms.
  arXiv  Detail & Related papers  (2021-12-18T14:17:49Z)
• Deformation Driven Seq2Seq Longitudinal Tumor and Organs-at-Risk Prediction for Radiotherapy [12.05638699290782]
  We present a novel 3D sequence-to-sequence model based on Convolution Long Short Term Memory (ConvLSTM) It predicts future anatomical deformations and changes in gross tumor volume as well as critical OARs. We validated our model on two radiotherapy datasets.
  arXiv  Detail & Related papers  (2021-06-16T18:29:16Z)
• Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score [59.75318183140857]
  Estimating causal effects from randomized experiments is central to clinical research. Most methods for historical borrowing achieve reductions in variance by sacrificing strict type-I error rate control.
  arXiv  Detail & Related papers  (2020-12-17T21:10:10Z)
• Ensemble model for pre-discharge icd10 coding prediction [45.82374977939355]
  We propose an ensemble model incorporating multiple clinical data sources for accurate code predictions. We obtain multi-label classification accuracies of 0.73 and 0.58 for average precision, 0.56 and 0.35 for F1-scores and 0.71 and 0.4 accuracy in predicting principal diagnosis for inpatient and outpatient datasets respectively.
  arXiv  Detail & Related papers  (2020-12-16T07:02:56Z)

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.