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Related Concept Videos

Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Related Experiment Video

Updated: Sep 22, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Knowledge-guided multi-task attention network for survival risk prediction using multi-center computed tomography

Liwen Zhang1, Lianzhen Zhong1, Cong Li2

  • 1CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 23, 2022
PubMed
Summary

This study introduces a new deep learning network for predicting cancer survival risk from CT scans. The method improves accuracy by simultaneously predicting clinical stages, aiding personalized cancer treatment.

Keywords:
Computed tomography (CT)Deep learningNeural networkOverall survival

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Oncology
  • Computational Pathology

Background:

  • Accurate preoperative prediction of overall survival (OS) risk in human cancers using CT images is crucial for personalized treatment strategies.
  • Deep learning (DL) methods have shown promise in automated OS risk prediction, but existing approaches face limitations in accuracy.
  • There is a need for advanced methods to better capture survival-related information from medical imaging data.

Purpose of the Study:

  • To develop a novel knowledge-guided multi-task network for improved OS risk prediction in cancer patients.
  • To simultaneously predict clinical stages alongside OS risk to enhance predictive accuracy.
  • To leverage shared information across multiple learning tasks for better survival prediction.

Main Methods:

  • Proposed a knowledge-guided multi-task network incorporating tailored attention modules.
  • The network was designed to simultaneously perform OS risk prediction and clinical stage prediction.
  • Evaluated the network on three multi-center datasets: two gastric cancer datasets (459 patients) and one public lung cancer dataset (422 patients).

Main Results:

  • The proposed network demonstrated improved performance by effectively capturing and sharing information from clinical stage predictions.
  • Outperformed state-of-the-art methods, achieving the highest geometrical metric.
  • Showcased superior prognostic value with the highest hazard ratio for patient risk stratification.

Conclusions:

  • The developed knowledge-guided multi-task network offers a significant advancement in predicting cancer OS risk from CT images.
  • The method's ability to integrate information from clinical stage prediction enhances its accuracy and prognostic capability.
  • This approach holds potential as a valuable tool for improving personalized cancer treatment strategies.