Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders

• URL: http://arxiv.org/abs/2203.10645v1
• Date: Sun, 20 Mar 2022 21:00:10 GMT
• Title: Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders
• Authors: Wei Xiong, Neil Yeung, Shubo Wang, Haofu Liao, Liyun Wang, Jiebo Luo
• Abstract summary: We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent skeletal related events (SREs) in breast cancer patients.
• Score: 65.95959936242993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Objective and Impact Statement. We adopt a deep learning model for bone osteolysis prediction on computed tomography (CT) images of murine breast cancer bone metastases. Given the bone CT scans at previous time steps, the model incorporates the bone-cancer interactions learned from the sequential images and generates future CT images. Its ability of predicting the development of bone lesions in cancer-invading bones can assist in assessing the risk of impending fractures and choosing proper treatments in breast cancer bone metastasis. Introduction. Breast cancer often metastasizes to bone, causes osteolytic lesions, and results in skeletal related events (SREs) including severe pain and even fatal fractures. Although current imaging techniques can detect macroscopic bone lesions, predicting the occurrence and progression of bone lesions remains a challenge. Methods. We adopt a temporal variational auto-encoder (T-VAE) model that utilizes a combination of variational auto-encoders and long short-term memory networks to predict bone lesion emergence on our micro-CT dataset containing sequential images of murine tibiae. Given the CT scans of murine tibiae at early weeks, our model can learn the distribution of their future states from data. Results. We test our model against other deep learning-based prediction models on the bone lesion progression prediction task. Our model produces much more accurate predictions than existing models under various evaluation metrics. Conclusion. We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent SREs in breast cancer patients.

Related papers

• Weakly-Supervised Detection of Bone Lesions in CT [48.34559062736031]
  The skeletal region is one of the common sites of metastatic spread of cancer in the breast and prostate. We developed a pipeline to detect bone lesions in CT volumes via a proxy segmentation task. Our method detected bone lesions in CT with a precision of 96.7% and recall of 47.3% despite the use of incomplete and partial training data.
  arXiv  Detail & Related papers  (2024-01-31T21:05:34Z)
• Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients [42.09584755334577]
  Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis to quantify the mortality risks of lung cancer patients. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model.
  arXiv  Detail & Related papers  (2023-03-09T15:38:16Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction [1.3503958132156484]
  We propose a deep learning-based approach to estimate necrosis ratio with outcome prediction from scanned hematoxylin and eosin whole slide images. We collected 103 osteosarcoma cases with 3134 WSIs to train our deep learning model, to validate necrosis ratio assessment, and to evaluate outcome prediction. Our study indicates deep learning can support pathologists as an objective tool to analyze osteosarcoma from histology for assessing treatment response and predicting patient outcome.
  arXiv  Detail & Related papers  (2022-08-09T17:12:27Z)
• Context-Aware Transformers For Spinal Cancer Detection and Radiological Grading [70.04389979779195]
  This paper proposes a novel transformer-based model architecture for medical imaging problems involving analysis of vertebrae. It considers two applications of such models in MR images: (a) detection of spinal metastases and the related conditions of vertebral fractures and metastatic cord compression. We show that by considering the context of vertebral bodies in the image, SCT improves the accuracy for several gradings compared to previously published model.
  arXiv  Detail & Related papers  (2022-06-27T10:31:03Z)
• Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma [2.8265965924600276]
  This study aims to use artificial intelligence to classify osteosarcoma histological images and to assess tumor survival and necrosis. We propose a typical transformer image classification framework by integrating noise reduction convolutional autoencoder and feature cross fusion learning. Our method outperforms the traditional and deep learning approaches on various evaluation metrics, with an accuracy of 99.17% to support osteosarcoma diagnosis.
  arXiv  Detail & Related papers  (2022-04-29T00:57:39Z)
• Development of the algorithm for differentiating bone metastases and trauma of the ribs in bone scintigraphy and demonstration of visual evidence of the algorithm -- Using only anterior bone scan view of thorax [0.0]
  There is no report of an AI model that determines the accumulation of ribs in bone metastases and trauma only using the anterior image of thorax of bone scintigraphy. We developed an algorithm to classify and diagnose whether RI accumulation on the ribs is bone metastasis or trauma using only anterior bone scan view of thorax.
  arXiv  Detail & Related papers  (2021-09-30T23:55:31Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)
• A Deep Learning Study on Osteosarcoma Detection from Histological Images [6.341765152919201]
  The most common type of primary malignant bone tumor is osteosarcoma. CNNs can significantly decrease surgeon's workload and make a better prognosis of patient conditions. CNNs need to be trained on a large amount of data in order to achieve a more trustworthy performance.
  arXiv  Detail & Related papers  (2020-11-02T18:16:17Z)
• Introducing Anisotropic Minkowski Functionals for Local Structure Analysis and Prediction of Biomechanical Strength of Proximal Femur Specimens [0.0]
  Bone fragility and fracture caused by osteoporosis or injury are prevalent in adults over the age of 50 and can reduce their quality of life. This study proposes a new method to predict the bone strength of proximal femur specimens from quantitative multi-detector computer tomography (MDCT) images.
  arXiv  Detail & Related papers  (2020-04-02T14:33:03Z)

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.