Dosimetric impact of physician style variations in contouring CTV for post-operative prostate cancer: A deep learning based simulation study

• URL: http://arxiv.org/abs/2102.01006v1
• Date: Mon, 1 Feb 2021 17:34:37 GMT
• Title: Dosimetric impact of physician style variations in contouring CTV for post-operative prostate cancer: A deep learning based simulation study
• Authors: Anjali Balagopal, Dan Nguyen, Maryam Mashayekhi, Howard Morgan, Aurelie Garant, Neil Desai, Raquibul Hannan, Mu-Han Lin, Steve Jiang
• Abstract summary: In tumor segmentation, inter-observer variation is acknowledged to be a significant problem. In this study, we analyze the impact that these physician stylistic variations have on organs-at-risk (OAR) dose by simulating the clinical workflow using deep learning.
• Score: 0.6464439696329373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In tumor segmentation, inter-observer variation is acknowledged to be a significant problem. This is even more significant in clinical target volume (CTV) segmentation, specifically, in post-operative settings, where a gross tumor does not exist. In this scenario, CTV is not an anatomically established structure but rather one determined by the physician based on the clinical guideline used, the preferred trade off between tumor control and toxicity, their experience, training background etc... This results in high inter-observer variability between physicians. Inter-observer variability has been considered an issue, however its dosimetric consequence is still unclear, due to the absence of multiple physician CTV contours for each patient and the significant amount of time required for dose planning. In this study, we analyze the impact that these physician stylistic variations have on organs-at-risk (OAR) dose by simulating the clinical workflow using deep learning. For a given patient previously treated by one physician, we use DL-based tools to simulate how other physicians would contour the CTV and how the corresponding dose distributions should look like for this patient. To simulate multiple physician styles, we use a previously developed in-house CTV segmentation model that can produce physician style-aware segmentations. The corresponding dose distribution is predicted using another in-house deep learning tool, which, averaging across all structures, is capable of predicting dose within 3% of the prescription dose on the test data. For every test patient, four different physician-style CTVs are considered and four different dose distributions are analyzed. OAR dose metrics are compared, showing that even though physician style variations results in organs getting different doses, all the important dose metrics except Maximum Dose point are within the clinically acceptable limit.

Related papers

• Leveraging Multimodal CycleGAN for the Generation of Anatomically Accurate Synthetic CT Scans from MRIs [1.779948689352186]
  We analyse the capabilities of different configurations of Deep Learning models to generate synthetic CT scans from MRI. Several CycleGAN models were trained unsupervised to generate CT scans from different MRI modalities with and without contrast agents. The results show how, depending on the input modalities, the models can have very different performances.
  arXiv  Detail & Related papers  (2024-07-15T16:38:59Z)
• FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging [68.6715007665896]
  FedMedICL is a unified framework and benchmark to holistically evaluate federated medical imaging challenges. We comprehensively evaluate several popular methods on six diverse medical imaging datasets. We find that a simple batch balancing technique surpasses advanced methods in average performance across FedMedICL experiments.
  arXiv  Detail & Related papers  (2024-07-11T19:12:23Z)
• Data-Efficient Vision Transformers for Multi-Label Disease Classification on Chest Radiographs [55.78588835407174]
  Vision Transformers (ViTs) have not been applied to this task despite their high classification performance on generic images. ViTs do not rely on convolutions but on patch-based self-attention and in contrast to CNNs, no prior knowledge of local connectivity is present. Our results show that while the performance between ViTs and CNNs is on par with a small benefit for ViTs, DeiTs outperform the former if a reasonably large data set is available for training.
  arXiv  Detail & Related papers  (2022-08-17T09:07:45Z)
• Unsupervised pre-training of graph transformers on patient population graphs [48.02011627390706]
  We propose a graph-transformer-based network to handle heterogeneous clinical data. We show the benefit of our pre-training method in a self-supervised and a transfer learning setting.
  arXiv  Detail & Related papers  (2022-07-21T16:59:09Z)
• Bayesian approaches for Quantifying Clinicians' Variability in Medical Image Quantification [0.16314780449435543]
  We show that Bayesian predictive distribution parameterized by deep neural networks could approximate the clinicians' inter-intra variability. We show a new perspective in analyzing medical images quantitatively by providing clinical measurement uncertainty.
  arXiv  Detail & Related papers  (2022-07-05T08:04:02Z)
• MetaMedSeg: Volumetric Meta-learning for Few-Shot Organ Segmentation [47.428577772279176]
  We present MetaMedSeg, a gradient-based meta-learning algorithm that redefines the meta-learning task for the volumetric medical data. In the experiments, we present an evaluation of the medical decathlon dataset by extracting 2D slices from CT and MRI volumes of different organs. Our proposed volumetric task definition leads to up to 30% improvement in terms of IoU compared to related baselines.
  arXiv  Detail & Related papers  (2021-09-18T11:13:45Z)
• PSA-Net: Deep Learning based Physician Style-Aware Segmentation Network for Post-Operative Prostate Cancer Clinical Target Volume [5.90921279461999]
  This study tries to determine if physician styles are consistent and learnable, if there is an impact of physician styles on treatment outcome and toxicity. A concept called physician style-aware (PSA) segmentation is proposed which is an encoder-multidecoder network trained with perceptual loss.
  arXiv  Detail & Related papers  (2021-02-15T22:42:52Z)
• Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning [2.231365407061881]
  We propose a supervised deep learning method for the direct estimation of aortic diameters. Our approach makes use of a 3D+2D convolutional neural network (CNN) that takes as input a 3D scan and outputs the aortic diameter at a given location. Overall, the 3D+2D CNN achieved a mean absolute error between 2.2-2.4 mm depending on the considered aortic location.
  arXiv  Detail & Related papers  (2020-09-09T18:28:00Z)
• iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry [58.943644554192936]
  This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. iPhantom precisely predicted all organ locations with good accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs and DSC of 0.3-0.9 for all other organs.
  arXiv  Detail & Related papers  (2020-08-20T01:50:49Z)
• 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)
• A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy [0.6262579136517118]
  In post-operative radiotherapy for prostate cancer, the cancerous prostate gland has been surgically removed, so the clinical target volume (CTV) to be irradiated encompasses the microscopic spread of tumor cells. In current clinical practice, physicians segment CTVs manually based on their relationship with nearby organs and other clinical information. Here, we propose a deep learning model to overcome this problem by segmenting nearby organs first, then using their relationship with the CTV to assist CTV segmentation.
  arXiv  Detail & Related papers  (2020-04-28T04:29:46Z)

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.