Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: physics enhances generalizability

• URL: http://arxiv.org/abs/2312.00975v1
• Date: Sat, 2 Dec 2023 00:15:44 GMT
• Title: Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: physics enhances generalizability
• Abstract summary: Prior AI-based dose prediction studies in photon and proton therapy often neglect underlying physics. Our aim is to design a physics-aware and generalizable AI-based PBSPT dose prediction method.
• Score: 18.852346492990637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Purpose: Prior AI-based dose prediction studies in photon and proton therapy often neglect underlying physics, limiting their generalizability to handle outlier clinical cases, especially for pencil beam scanning proton therapy (PBSPT). Our aim is to design a physics-aware and generalizable AI-based PBSPT dose prediction method that has the underlying physics considered to achieve high generalizability to properly handle the outlier clinical cases. Methods and Materials: This study analyzed PBSPT plans of 103 prostate and 78 lung cancer patients from our institution,with each case comprising CT images, structure sets, and plan doses from our Monte-Carlo dose engine (serving as the ground truth). Three methods were evaluated in the ablation study: the ROI-based method, the beam mask and sliding window method, and the noisy probing dose method. Twelve cases with uncommon beam angles or prescription doses tested the methods' generalizability to rare treatment planning scenarios. Performance evaluation used DVH indices, 3D Gamma passing rates (3%/2mm/10%), and dice coefficients for dose agreement. Results: The noisy probing dose method showed improved agreement of DVH indices, 3D Gamma passing rates, and dice coefficients compared to the conventional methods for the testing cases. The noisy probing dose method showed better generalizability in the 6 outlier cases than the ROI-based and beam mask-based methods with 3D Gamma passing rates (for prostate cancer, targets: 89.32%$\pm$1.45% vs. 93.48%$\pm$1.51% vs. 96.79%$\pm$0.83%, OARs: 85.87%$\pm$1.73% vs. 91.15%$\pm$1.13% vs. 94.29%$\pm$1.01%). The dose predictions were completed within 0.3 seconds. Conclusions: We've devised a novel noisy probing dose method for PBSPT dose prediction in prostate and lung cancer patients. With more physics included, it enhances the generalizability of dose prediction in handling outlier clinical cases.

Related papers

• Dose-PlanNet: Physics Based Radiotherapy Dose Prediction with Deep Learning [0.0]
  Dose-PlanNet is a physics-guided 3D deep learning architecture designed to predict dose distributions.<n>This pipeline demonstrates that physics-informed deep learning can accelerate radiotherapy while safely maintaining the stringent dosimetric quality required for high-precision clinical deployment.
  arXiv  Detail & Related papers  (2026-08-27T10:05:01Z)
• Closing the Performance Gap Between AI and Radiologists in Chest X-Ray Reporting [40.40577855417923]
  We introduce MAIRA-X, a clinically evaluated multimodal AI model for longitudinal chest X-ray report generation.<n>A novel L&T-specific metrics framework was developed to assess accuracy in reporting attributes such as type, longitudinal change and placement.<n>Our results suggest MAIRA-X can effectively assist radiologists, particularly in high-volume clinical settings.
  arXiv  Detail & Related papers  (2025-11-21T10:53:26Z)
• Fluence Map Prediction with Deep Learning: A Transformer-based Approach [4.362977319915193]
  This study presents a deep learning framework that accelerates fluence map generation while maintaining clinical quality.<n>An end-to-end 3D Swin-UNETR network was trained to predict nine-beam fluence maps directly from volumetric CT images and anatomical contours.<n>The proposed model achieved an average R2 of 0.95 +/- 0.02, MAE of 0.035 +/- 0.008, and gamma passing rate of 85 +/- 10 percent (3 percent / 3 mm) on the test set.
  arXiv  Detail & Related papers  (2025-11-10T19:24:14Z)
• A Machine Learning Approach to Volumetric Computations of Solid Pulmonary Nodules [12.415286588339901]
  We propose an advanced framework that combines a multi-scale 3D convolutional neural network (CNN) with subtype-specific bias correction for precise volume estimation.<n>Our results show a reduction in error by over 17 percentage points and a threefold acceleration in processing speed.
  arXiv  Detail & Related papers  (2025-08-26T03:36:32Z)
• Physics-Guided Radiotherapy Treatment Planning with Deep Learning [27.620227899075022]
  We propose a two-stage, physics-guided deep learning pipeline for radiotherapy planning.<n>We train and evaluate our approach on 133 prostate cancer patients treated with a uniform 2-arc VMAT protocol.
  arXiv  Detail & Related papers  (2025-06-23T19:44:56Z)
• Automatic Treatment Planning using Reinforcement Learning for High-dose-rate Prostate Brachytherapy [3.198160082615183]
  In high-dose-rate ( HDR) prostate brachytherapy procedures, the pattern of needle placement solely relies on physician experience.<n>We investigated the feasibility of using reinforcement learning (RL) to provide needle positions and dwell times based on patient anatomy during pre-planning stage.<n>This approach would reduce procedure time and ensure consistent plan quality.
  arXiv  Detail & Related papers  (2025-06-11T14:46:42Z)
• Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy [3.2139860661520347]
  Denoising low-statistics Monte Carlo (MC) dose maps is proposed to enable fast, high-quality dose generation.<n> IMPT plans and 3D CT images from 80 H&N patients were used to generate noisy and high-statistics dose maps.<n>The model achieved MAEs of 0.195 (H&N), 0.120 (lung), 0.172 (breast), and 0.376 Gy[RBE] (prostate) across all sites.
  arXiv  Detail & Related papers  (2025-06-04T21:37:15Z)
• Semi-Supervised Learning for Dose Prediction in Targeted Radionuclide: A Synthetic Data Study [42.75913504373651]
  Targeted Radionuclide Therapy (TRT) is a modern strategy in radiation oncology that aims to administer a potent radiation dose specifically to cancer cells.<n>Deep learning holds promise for personalizing TRT doses.<n>Current methods require large time series of SPECT imaging, which is hardly achievable in routine clinical practice.
  arXiv  Detail & Related papers  (2025-03-07T12:21:09Z)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Continuous sPatial-Temporal Deformable Image Registration (CPT-DIR) for motion modelling in radiotherapy: beyond classic voxel-based methods [10.17207334278678]
  We propose an implicit neural representation (INR)-based approach modelling motion continuously in both space and time, named Continues-sPatial-Temporal DIR (CPT-DIR) The DIR's performance was tested on the DIR-Lab dataset of 10 lung 4DCT cases, using metrics of landmark accuracy (TRE), contour conformity (Dice) and image similarity (MAE) The proposed CPT-DIR can reduce landmark TRE from 2.79mm to 0.99mm, outperforming B-splines' results for all cases.
  arXiv  Detail & Related papers  (2024-05-01T10:26:08Z)
• Leveraging cough sounds to optimize chest x-ray usage in low-resource settings [35.63955045151504]
  We used prospectively-collected data from 137 patients referred for chest X-ray at the Christian Medical Center and Hospital in Purnia, Bihar, India. Each patient provided at least five coughs while awaiting radiography. Three models were developed, tested and compared in their capacity to predict an abnormal result in the chest X-ray.
  arXiv  Detail & Related papers  (2024-02-13T20:54:55Z)
• Semi-supervised ViT knowledge distillation network with style transfer normalization for colorectal liver metastases survival prediction [1.283897253352624]
  We propose an end-to-end approach for automated prognosis prediction using histology slides stained with H&E and HPS. We first employ a Generative Adversarial Network (GAN) for slide normalization to reduce staining variations and improve the overall quality of the images that are used as input to our prediction pipeline. We exploit the extracted features for the metastatic nodules and surrounding tissue to train a prognosis model. In parallel, we train a vision Transformer (ViT) in a knowledge distillation framework to replicate and enhance the performance of the prognosis prediction.
  arXiv  Detail & Related papers  (2023-11-17T03:32:11Z)
• SECRETS: Subject-Efficient Clinical Randomized Controlled Trials using Synthetic Intervention [0.0]
  Cross-over trials can reduce sample size requirements by measuring the treatment effect per individual. We propose a novel framework, SECRETS, which estimates the individual treatment effect (ITE) per patient in the RCT study without using any external data. We show that SECRETS can improve the power of an RCT while maintaining comparable significance levels.
  arXiv  Detail & Related papers  (2023-05-08T22:37:16Z)
• A new methodology to predict the oncotype scores based on clinico-pathological data with similar tumor profiles [0.0]
  The Oncotype DX (ODX) test is a commercially available molecular test for breast cancer. The aim of this study is to propose a novel methodology to assist physicians in their decision-making.
  arXiv  Detail & Related papers  (2023-03-13T10:08:13Z)
• 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)
• CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis [37.41181188499616]
  Deep learning techniques can help in the faster detection of COVID-19 cases and monitoring of disease progression. Five different datasets were used to construct a representative dataset of 23 799 CXRs for model training. A U-Net-based model was developed to identify a clinically relevant region of the CXR.
  arXiv  Detail & Related papers  (2022-10-11T13:30:34Z)
• A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients [29.983525001333625]
  There is no agreed upon methodology to glean treatment effect estimation from observational data. This article showcases a pragmatic methodology to obtain preliminary estimation of treatment effect from observational studies.
  arXiv  Detail & Related papers  (2021-09-14T14:14:37Z)
• Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images [58.85481248101611]
  We propose a novel PCa detection network that incorporates a lesion-level cost-sensitive loss and an additional slice-level loss based on a lesion-to-slice mapping function. Our experiments based on 290 clinical patients concludes that 1) The lesion-level FNR was effectively reduced from 0.19 to 0.10 and the lesion-level FPR was reduced from 1.03 to 0.66 by changing the lesion-level cost.
  arXiv  Detail & Related papers  (2021-06-04T09:51:27Z)
• COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction [32.91440827855392]
  We consider the task of predicting two types of patient deterioration based on chest X-rays. Due to the relative scarcity of COVID-19 patient data, existing solutions leverage supervised pretraining on related non-COVID images. In this paper, we use self-supervised learning based on the momentum contrast (MoCo) method in the pretraining phase to learn more general image representations to use for downstream tasks.
  arXiv  Detail & Related papers  (2021-01-13T07:03:17Z)
• 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)
• Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images [0.3120960917423201]
  Non-invasively predicting the risk of cancer metastasis before surgery plays an essential role in determining optimal treatment methods. In this study, we explore a new approach to build an optimal machine learning model using small and imbalanced image datasets.
  arXiv  Detail & Related papers  (2020-09-01T19:53:09Z)
• Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
  We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
  arXiv  Detail & Related papers  (2020-07-20T19:08:50Z)
• Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning [59.17383024536595]
  Back-scatter significantly contributes to patient (skin) dose during complicated interventions. Forward-scattered radiation reduces contrast in projection images and introduces artifacts in 3-D reconstructions. We propose a novel approach combining conventional techniques with learning-based methods to simultaneously estimate the forward-scatter reaching the detector.
  arXiv  Detail & Related papers  (2020-07-08T10:47:37Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)
• Automated Quantification of CT Patterns Associated with COVID-19 from Chest CT [48.785596536318884]
  The proposed method takes as input a non-contrasted chest CT and segments the lesions, lungs, and lobes in three dimensions. The method outputs two combined measures of the severity of lung and lobe involvement, quantifying both the extent of COVID-19 abnormalities and presence of high opacities. Evaluation of the algorithm is reported on CTs of 200 participants (100 COVID-19 confirmed patients and 100 healthy controls) from institutions from Canada, Europe and the United States.
  arXiv  Detail & Related papers  (2020-04-02T21:49:14Z)

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.