Early Response Assessment in Lung Cancer Patients using Spatio-temporal CBCT Images

• URL: http://arxiv.org/abs/2003.05408v1
• Date: Sat, 7 Mar 2020 08:20:22 GMT
• Title: Early Response Assessment in Lung Cancer Patients using Spatio-temporal CBCT Images
• Authors: Bijju Kranthi Veduruparthi, Jayanta Mukherjee, Partha Pratim Das, Mandira Saha, Sanjoy Chatterjee, Raj Kumar Shrimali, Soumendranath Ray and Sriram Prasath
• Abstract summary: We report a model to predict patient's radiological response to curative radiation therapy for non-small-cell lung cancer (NSCLC) Our model predicted clinical response with a precision of $74%$.
• Score: 3.3657245810236636
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We report a model to predict patient's radiological response to curative radiation therapy (RT) for non-small-cell lung cancer (NSCLC). Cone-Beam Computed Tomography images acquired weekly during the six-week course of RT were contoured with the Gross Tumor Volume (GTV) by senior radiation oncologists for 53 patients (7 images per patient). Deformable registration of the images yielded six deformation fields for each pair of consecutive images per patient. Jacobian of a field provides a measure of local expansion/contraction and is used in our model. Delineations were compared post-registration to compute unchanged ($U$), newly grown ($G$), and reduced ($R$) regions within GTV. The mean Jacobian of these regions $\mu_U$, $\mu_G$ and $\mu_R$ are statistically compared and a response assessment model is proposed. A good response is hypothesized if $\mu_R < 1.0$, $\mu_R < \mu_U$, and $\mu_G < \mu_U$. For early prediction of post-treatment response, first, three weeks' images are used. Our model predicted clinical response with a precision of $74\%$. Using reduction in CT numbers (CTN) and percentage GTV reduction as features in logistic regression, yielded an area-under-curve of 0.65 with p=0.005. Combining logistic regression model with the proposed hypothesis yielded an odds ratio of 20.0 (p=0.0).

Related papers

• TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI [59.86827659781022]
  A nnU-Net model (TotalSegmentator) was trained on MRI and segment 80atomic structures. Dice scores were calculated between the predicted segmentations and expert reference standard segmentations to evaluate model performance. Open-source, easy-to-use model allows for automatic, robust segmentation of 80 structures.
  arXiv  Detail & Related papers  (2024-05-29T20:15:54Z)
• 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)
• Swin transformers are robust to distribution and concept drift in endoscopy-based longitudinal rectal cancer assessment [3.0468533447146244]
  Endoscopic images are used at various stages of rectal cancer treatment to assess response and toxicity from treatments. subjective assessment is highly variable and can underestimate the degree of response in some patients. Advances in deep learning has shown the ability to produce consistent and objective response assessment for endoscopic images.
  arXiv  Detail & Related papers  (2024-05-06T18:01:13Z)
• Vestibular schwannoma growth prediction from longitudinal MRI by time conditioned neural fields [5.662694302758444]
  We introduce DeepGrowth, a deep learning method that incorporates neural fields and recurrent neural networks for prospective tumor growth prediction. The experiments on an in-house longitudinal VS dataset showed that the proposed model significantly improved the performance.
  arXiv  Detail & Related papers  (2024-04-03T10:01:23Z)
• Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph [49.11220791279602]
  It is challenging to accurately map planned paths from a generic atlas to individual patients, particularly for thoracic applications. A graph-based non-rigid registration is proposed to enable transferring planned paths from the atlas to the current setup.
  arXiv  Detail & Related papers  (2023-07-07T18:57:21Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• A Novel Mask R-CNN Model to Segment Heterogeneous Brain Tumors through Image Subtraction [0.0]
  We propose using a method performed by radiologists called image segmentation and applying it to machine learning models to prove a better segmentation. Using Mask R-CNN, its ResNet backbone being pre-trained on the RSNA pneumonia detection challenge dataset, we can train a model on the Brats 2020 Brain Tumor dataset. We can see how well the method of image subtraction works by comparing it to models without image subtraction through DICE coefficient (F1 score), recall, and precision on the untouched test set.
  arXiv  Detail & Related papers  (2022-04-04T01:45:11Z)
• Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Auto-Encoders [65.95959936242993]
  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.
  arXiv  Detail & Related papers  (2022-03-20T21:00:10Z)
• Estimating Total Lung Volume from Pixel-level Thickness Maps of Chest Radiographs Using Deep Learning [4.494514977138964]
  The total lung volume (TLV) from real and synthetic frontal chest radiographs (CXR) on a pixel level was estimated using lung thickness maps generated by a U-Net deep learning model.<n>Model performance was assessed using mean squared error (MSE), Pearson correlation coefficient (r), and two-sided Student's t-distribution.
  arXiv  Detail & Related papers  (2021-10-24T19:09:28Z)
• Osteoporosis Prescreening using Panoramic Radiographs through a Deep Convolutional Neural Network with Attention Mechanism [65.70943212672023]
  Deep convolutional neural network (CNN) with an attention module can detect osteoporosis on panoramic radiographs. dataset of 70 panoramic radiographs (PRs) from 70 different subjects of age between 49 to 60 was used.
  arXiv  Detail & Related papers  (2021-10-19T00:03:57Z)
• 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)
• 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)
• Novel Radiomic Feature for Survival Prediction of Lung Cancer Patients using Low-Dose CBCT Images [3.510687032586989]
  We present a model to take into account the heterogeneous nature of a tumor to predict survival. The tumor heterogeneity is measured in terms of its mass by combining information regarding the radiodensity obtained in images with the gross tumor volume (GTV) We propose a novel feature called Tumor Mass within a GTV (TMG), that improves the prediction of survivability, compared to existing models which use GTV.
  arXiv  Detail & Related papers  (2020-03-07T08:47:26Z)

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.