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Computed Tomography

Computed Tomography

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: Oct 27, 2025

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

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Automatic Extraction of Lung Cancer Staging Information From Computed Tomography Reports: Deep Learning Approach.

Danqing Hu^1,2, Huanyao Zhang^1,2, Shaolei Li³

¹College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.

JMIR Medical Informatics

|July 21, 2021

Summary

This study developed an information extraction system to automatically pull lung cancer staging details from chest CT reports. The system accurately extracts crucial data, improving clinical staging accuracy.

Keywords:

clinical staging information extraction lung cancer named entity recognition relation classification

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Medical Informatics
Natural Language Processing
Oncology

Background:

Lung cancer is a leading cause of cancer mortality globally, with accurate clinical staging critical for treatment and prognosis.
Discrepancies between clinical and pathological staging are common, impacting patient care.
Chest CT reports contain vital staging information but are unstructured, hindering automated analysis.

Purpose of the Study:

To develop an automated information extraction (IE) system for staging-related data from unstructured chest CT reports.
To enhance the accuracy of clinical lung cancer staging through automated data retrieval.

Main Methods:

Developed a three-part IE system: Named Entity Recognition (NER), Relation Classification (RC), and Postprocessing (PP).
Utilized state-of-the-art NER algorithms, including BERT, and a novel RC method with relation sign constraints (RSC).
Implemented a rule-based PP module to format extracted staging information based on TNM guidelines.

Main Results:

The BERT model achieved high performance in NER (macro-F1 of 90.06%).
The BERT-RSC model demonstrated superior performance in RC (macro-F1 of 97.13%).
The complete IE system achieved excellent accuracy (macro-F1 of 94.57%) in extracting all 22 staging-related questions.

Conclusions:

The developed IE system effectively and accurately extracts lung cancer staging information from chest CT reports.
Extracted data shows significant potential for stage verification and prediction, aiding accurate clinical staging.