A Flexible Three-Dimensional Hetero-phase Computed Tomography
Hepatocellular Carcinoma (HCC) Detection Algorithm for Generalizable and
Practical HCC Screening
- URL: http://arxiv.org/abs/2108.07492v1
- Date: Tue, 17 Aug 2021 08:14:29 GMT
- Title: A Flexible Three-Dimensional Hetero-phase Computed Tomography
Hepatocellular Carcinoma (HCC) Detection Algorithm for Generalizable and
Practical HCC Screening
- Authors: Chi-Tung Cheng, Jinzheng Cai, Wei Teng, Youjing Zheng, YuTing Huang,
Yu-Chao Wang, Chien-Wei Peng, Youbao Tang, Wei-Chen Lee, Ta-Sen Yeh, Jing
Xiao, Le Lu, Chien-Hung Liao, Adam P. Harrison
- Abstract summary: Hepatocellular carcinoma (HCC) can be potentially discovered from abdominal computed tomography (CT) studies.
We develop a flexible three-dimensional deep algorithm, called hetero-phase volumetric detection (HPVD)
HPVD can accept any combination of contrast-phase inputs and with adjustable sensitivity depending on the clinical purpose.
- Score: 18.78910829126741
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Hepatocellular carcinoma (HCC) can be potentially discovered from abdominal
computed tomography (CT) studies under varied clinical scenarios, e.g., fully
dynamic contrast enhanced (DCE) studies, non-contrast (NC) plus venous phase
(VP) abdominal studies, or NC-only studies. We develop a flexible
three-dimensional deep algorithm, called hetero-phase volumetric detection
(HPVD), that can accept any combination of contrast-phase inputs and with
adjustable sensitivity depending on the clinical purpose. We trained HPVD on
771 DCE CT scans to detect HCCs and tested on external 164 positives and 206
controls, respectively. We compare performance against six clinical readers,
including two radiologists, two hepato-pancreatico-biliary (HPB) surgeons, and
two hepatologists. The area under curve (AUC) of the localization receiver
operating characteristic (LROC) for NC-only, NC plus VP, and full DCE CT
yielded 0.71, 0.81, 0.89 respectively. At a high sensitivity operating point of
80% on DCE CT, HPVD achieved 97% specificity, which is comparable to measured
physician performance. We also demonstrate performance improvements over more
typical and less flexible non hetero-phase detectors. Thus, we demonstrate that
a single deep learning algorithm can be effectively applied to diverse HCC
detection clinical scenarios.
Related papers
- Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction [47.7045293755736]
Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life.
This study demonstrates the potential of retinal optical coherence tomography ( OCT) imaging combined with fundus photographs for identifying future adverse cardiac events.
We propose a novel binary classification network based on a Multi-channel Variational Autoencoder (MCVAE), which learns a latent embedding of patients' fundus and OCT images to classify individuals into two groups: those likely to develop CVD in the future and those who are not.
arXiv Detail & Related papers (2024-10-18T12:37:51Z) - Improved Esophageal Varices Assessment from Non-Contrast CT Scans [15.648325577912608]
Esophageal varices (EV) is a serious health concern resulting from portal hypertension.
Despite non-contrast computed tomography (NC-CT) imaging being a less expensive and non-invasive imaging modality, it has yet to gain full acceptance as a primary clinical diagnostic tool for EV evaluation.
We present the Multi-Organ-cOhesion-Network (MOON), a novel framework enhancing the analysis of critical organ features in NC-CT scans for effective assessment of EV.
arXiv Detail & Related papers (2024-07-18T06:49:10Z) - Non-invasive Liver Fibrosis Screening on CT Images using Radiomics [0.0]
The aim of this study was to develop and evaluate a radiomics machine learning model for detecting liver fibrosis on CT of the liver.
The combination and selected features with the highest AUC were used to develop a final liver fibrosis screening model.
arXiv Detail & Related papers (2022-11-25T22:33:22Z) - 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) - A deep learning pipeline for localization, differentiation, and
uncertainty estimation of liver lesions using multi-phasic and multi-sequence
MRI [15.078841623264543]
We propose a fully-automatic computer-aided diagnosis (CAD) solution for liver lesion characterization.
We enroll 400 patients who had either liver resection or a biopsy and was diagnosed with either liver carcinoma (HCC), intrahepatic cholangiocarcinoma, or secondary metastasis.
We propose a fully-automatic deep CAD pipeline that localizes lesions from 3D MRI studies using key-slice parsing and provides a confidence measure for its diagnoses.
arXiv Detail & Related papers (2021-10-17T13:19:00Z) - Ensemble machine learning approach for screening of coronary heart
disease based on echocardiography and risk factors [19.076443235356873]
We develop a machine learning approach that integrates a number of popular classification methods together by model stacking.
We improve the CHD classification accuracy from around 70% to 87.7% on the testing set.
arXiv Detail & Related papers (2021-05-20T11:04:58Z) - COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19
from Chest CT Images Through Bigger, More Diverse Learning [70.92379567261304]
We introduce COVID-Net CT-2, enhanced deep neural networks for COVID-19 detection from chest CT images.
We leverage explainability to investigate the decision-making behaviour of COVID-Net CT-2.
Results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 assessment.
arXiv Detail & Related papers (2021-01-19T03:04:09Z) - Performance of Dual-Augmented Lagrangian Method and Common Spatial
Patterns applied in classification of Motor-Imagery BCI [68.8204255655161]
Motor-imagery based brain-computer interfaces (MI-BCI) have the potential to become ground-breaking technologies for neurorehabilitation.
Due to the noisy nature of the used EEG signal, reliable BCI systems require specialized procedures for features optimization and extraction.
arXiv Detail & Related papers (2020-10-13T20:50:13Z) - A comparative study of 2D image segmentation algorithms for traumatic
brain lesions using CT data from the ProTECTIII multicenter clinical trial [0.0]
We have tried to segment different phenotypes of hemorrhagic lesions found after traumatic brain injury (TBI)
These include: intraparenchymal hemorrhage (IPH), subdural hematoma (SDH), epidural hematoma (EDH), and traumatic contusions.
We were able to achieve an optimal Dice Coefficient1 score of 0.94 using UNet++ 2D Architecture with Focal Tversky Loss Function.
We were also able to achieve the Dice Coefficient score of 0.90 and 0.86 in cases of Extra-Axial bleeds and Traumatic contusions, respectively.
arXiv Detail & Related papers (2020-06-01T21:00:20Z) - Co-Heterogeneous and Adaptive Segmentation from Multi-Source and
Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion
Segmentation [48.504790189796836]
We present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe)
We propose a versatile framework that fuses appearance based semi-supervision, mask based adversarial domain adaptation, and pseudo-labeling.
CHASe can further improve pathological liver mask Dice-Sorensen coefficients by ranges of $4.2% sim 9.4%$.
arXiv Detail & Related papers (2020-05-27T06:58:39Z) - Detecting Pancreatic Ductal Adenocarcinoma in Multi-phase CT Scans via
Alignment Ensemble [77.5625174267105]
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers among the population.
Multiple phases provide more information than single phase, but they are unaligned and inhomogeneous in texture.
We suggest an ensemble of all these alignments as a promising way to boost the performance of PDAC detection.
arXiv Detail & Related papers (2020-03-18T19:06:27Z)
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.