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Related Experiment Video

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Deep learning-based algorithm improves radiologists' performance in lung cancer bone metastases detection on computed

Tongtong Huo1,2, Yi Xie1, Ying Fang1

  • 1Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Frontiers in Oncology
|February 27, 2023
PubMed
Summary

A deep convolutional neural network (DCNN) model was developed for automatic lung cancer bone metastases detection on CT scans. This AI tool improves diagnostic accuracy and efficiency for radiologists.

Keywords:
artificial intelligencecomputer-aided diagnosisdeep convolutional neural networkdeep learninglung cancer bone metastases

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Oncology
  • Radiology

Background:

  • Bone metastases are a common complication of lung cancer, impacting patient prognosis.
  • Accurate and timely detection of bone metastases on computed tomography (CT) is crucial for treatment planning.
  • Current detection methods can be time-consuming and may benefit from AI-assisted tools.

Purpose of the Study:

  • To develop and evaluate a deep convolutional neural network (DCNN) model for automated detection and segmentation of lung cancer bone metastases on CT.
  • To assess the clinical utility of the DCNN model in collaboration with radiologists of varying experience levels.

Main Methods:

  • A retrospective study utilizing CT scans from 126 patients (76 training, 12 validation, 38 testing).
  • A DCNN model was trained to identify bone metastases in CT images.
  • Model performance was evaluated using receiver operating characteristic curves for detection and dice coefficient for segmentation.
  • An observer study assessed the DCNN's impact on junior and board-certified radiologists' performance and interpretation time.

Main Results:

  • The DCNN model achieved a detection sensitivity of 0.894 and a segmentation dice coefficient of 0.856 in the testing cohort.
  • Collaboration with the DCNN model significantly improved junior radiologists' detection accuracy (0.617 to 0.879) and sensitivity (0.680 to 0.902).
  • The mean interpretation time per case for junior radiologists decreased by 228 seconds (p = 0.045).

Conclusions:

  • The developed DCNN model effectively detects lung cancer bone metastases on CT scans.
  • The AI tool enhances diagnostic efficiency and reduces interpretation time and workload for junior radiologists.
  • This DCNN model shows promise for improving the management of lung cancer patients with bone metastases.