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Related Concept Videos

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Radiological investigations are paramount in the diagnosis and management of various pulmonary diseases. Two essential investigations are the Pulmonary Angiogram and the Positron Emission Tomography (PET) Scan.
Pulmonary Angiogram
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Related Experiment Video

Updated: Aug 10, 2025

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Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma.

Sneha Mithun1,2,3, Ashish Kumar Jha4,5,6, Umesh B Sherkhane4,5

  • 1Department of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229 ET, Maastricht, The Netherlands. s.mithun@maastrichtuniversity.nl.

Journal of Digital Imaging
|February 14, 2023
PubMed
Summary

A new pipeline effectively classifies lung carcinoma radiology reports using Natural Language Processing (NLP). A rule-based algorithm achieved the best performance, aiding in automated report annotation and data extraction for cancer research.

Keywords:
Artificial IntelligenceBig data analyticsClinical concept extractionDeep learningElectronic medical recordsLung carcinomaNamed entity recognitionNatural Language ProcessingRadiology reports

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Oncology

Background:

  • The rising incidence of cancer necessitates efficient data extraction from unstructured medical reports.
  • Radiology reports contain crucial information on disease characteristics, treatment, and outcomes, but manual extraction is time-consuming.
  • Natural Language Processing (NLP) offers a solution for automating information extraction from free-text clinical data.

Purpose of the Study:

  • To develop and compare NLP models for classifying lung carcinoma radiology reports based on clinical concepts.
  • To evaluate the performance of rule-based and machine learning models for this classification task.
  • To assess the utility of the developed pipeline for automated annotation and data analysis of lung cancer reports.

Main Methods:

  • A clinical concept-based classification pipeline was created for lung carcinoma radiology reports.
  • Rule-based, XGBoost, and Bidirectional Long Short-Term Memory (Bi-LSTM) deep learning models were developed and compared.
  • The models were trained and tested on 1700 radiology reports (CT and PET/CT) and validated on 501 reports from the MIMIC-III database.

Main Results:

  • The rule-based algorithm with expert input achieved the highest performance, with an F1 score of 0.94 internally and 0.74 on external validation.
  • Among machine learning models, Bi-LSTM_dropout outperformed XGBoost and Bi-LSTM_simple on the internal dataset.
  • On external validation, Bi-LSTM_simple showed relatively better performance compared to the other two machine learning models.

Conclusions:

  • The developed NLP pipeline demonstrates effectiveness in classifying lung carcinoma radiology reports.
  • Rule-based approaches, leveraging expert knowledge, show strong performance in this specific application.
  • The pipeline can facilitate automated annotation and efficient analysis of large volumes of lung cancer radiology reports.