Prediction of Consolidation Tumor Ratio on Planning CT Images of Lung Cancer Patients Treated with Radiotherapy Based on Deep Learning Article Swipe
Yizhi Tong
,
Hidetaka Arimura
,
Tadamasa Yoshitake
,
Yunhao Cui
,
Takumi Kodama
,
Yoshiyuki Shioyama
,
Ronnie Wirestam
,
Hidetake Yabuuchi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14083275
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14083275
This study aimed to propose an automated prediction approach of the consolidation tumor ratios (CTRs) of part-solid tumors of patients treated with radiotherapy on treatment planning computed tomography images using deep learning segmentation (DLS) models. For training the DLS model for cancer regions, a total of 115 patients with non-small cell lung cancer (NSCLC) who underwent stereotactic body radiation therapy were selected as the training dataset, including solid, part-solid, and ground-glass opacity tumors. For testing the automated prediction approach of CTRs based on segmented tumor regions, 38 patients with part-solid tumors were selected as an internal test dataset A (IN) from a same institute as the training dataset, and 49 patients as an external test dataset (EX) from a public database. The CTRs for part-solid tumors were predicted as ratios of the maximum diameters of solid components to those of whole tumors. Pearson correlations between reference and predicted CTRs for the two test datasets were 0.953 (IN) and 0.926 (EX) for one of the DLS models (p < 0.01). Intraclass correlation coefficients between reference and predicted CTRs for the two test datasets were 0.943 (IN) and 0.904 (EX) for the same DLS models. The findings suggest that the automated prediction approach could be robust in calculating the CTRs of part-solid tumors.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app14083275
- https://www.mdpi.com/2076-3417/14/8/3275/pdf?version=1713237703
- OA Status
- gold
- Cited By
- 1
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394821320
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394821320Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/app14083275Digital Object Identifier
- Title
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Prediction of Consolidation Tumor Ratio on Planning CT Images of Lung Cancer Patients Treated with Radiotherapy Based on Deep LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-13Full publication date if available
- Authors
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Yizhi Tong, Hidetaka Arimura, Tadamasa Yoshitake, Yunhao Cui, Takumi Kodama, Yoshiyuki Shioyama, Ronnie Wirestam, Hidetake YabuuchiList of authors in order
- Landing page
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https://doi.org/10.3390/app14083275Publisher landing page
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https://www.mdpi.com/2076-3417/14/8/3275/pdf?version=1713237703Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/14/8/3275/pdf?version=1713237703Direct OA link when available
- Concepts
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Lung cancer, Ground-glass opacity, Artificial intelligence, Computer science, Radiation therapy, Deep learning, Cancer, Medicine, Nuclear medicine, Radiology, Internal medicine, AdenocarcinomaTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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