Deep radiomic model based on the sphere–shell partition for predicting treatment response to chemotherapy in lung cancer
Background: The prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients. Objectives: To develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images. Materials and Methods: This retrospe...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Translational Oncology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1936523323001055 |
| _version_ | 1797778355212255232 |
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| author | Runsheng Chang Shouliang Qi Yanan Wu Yong Yue Xiaoye Zhang Yubao Guan Wei Qian |
| author_facet | Runsheng Chang Shouliang Qi Yanan Wu Yong Yue Xiaoye Zhang Yubao Guan Wei Qian |
| author_sort | Runsheng Chang |
| collection | DOAJ |
| description | Background: The prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients. Objectives: To develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images. Materials and Methods: This retrospective multicenter study enrolled 485 patients with NSCLC who received chemotherapy alone as a first-line treatment. Two integrated models were developed using radiomic and deep-learning-based features. First, we partitioned pre-chemotherapy CT images into spheres and shells with different radii around the tumor (0–3, 3–6, 6–9, 9–12, 12–15 mm) containing intratumoral and peritumoral regions. Second, we extracted radiomic and deep-learning-based features from each partition. Third, using radiomic features, five sphere–shell models, one feature fusion model, and one image fusion model were developed. Finally, the model with the best performance was validated in two cohorts. Results: Among the five partitions, the model of 9–12 mm achieved the highest area under the curve (AUC) of 0.87 (95% confidence interval: 0.77–0.94). The AUC was 0.94 (0.85–0.98) for the feature fusion model and 0.91 (0.82–0.97) for the image fusion model. For the model integrating radiomic and deep-learning-based features, the AUC was 0.96 (0.88–0.99) for the feature fusion method and 0.94 (0.85–0.98) for the image fusion method. The best-performing model had an AUC of 0.91 (0.81–0.97) and 0.89 (0.79–0.93) in two validation sets, respectively. Conclusions: This integrated model can predict the response to chemotherapy in NSCLC patients and assist physicians in clinical decision-making. |
| first_indexed | 2024-03-12T23:17:03Z |
| format | Article |
| id | doaj.art-13f85238cb1d421dbd620c1cc758d3a6 |
| institution | Directory Open Access Journal |
| issn | 1936-5233 |
| language | English |
| last_indexed | 2024-03-12T23:17:03Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Translational Oncology |
| spelling | doaj.art-13f85238cb1d421dbd620c1cc758d3a62023-07-17T04:07:33ZengElsevierTranslational Oncology1936-52332023-09-0135101719Deep radiomic model based on the sphere–shell partition for predicting treatment response to chemotherapy in lung cancerRunsheng Chang0Shouliang Qi1Yanan Wu2Yong Yue3Xiaoye Zhang4Yubao Guan5Wei Qian6College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China; Corresponding author at :College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaDepartment of Radiology, Shengjing Hospital of China Medical University, Shenyang, ChinaDepartment of Oncology, Shengjing Hospital of China Medical University, Shenyang, ChinaDepartment of Radiology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, ChinaCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang, ChinaBackground: The prognosis of chemotherapy is important in clinical decision-making for non-small cell lung cancer (NSCLC) patients. Objectives: To develop a model for predicting treatment response to chemotherapy in NSCLC patients from pre-chemotherapy CT images. Materials and Methods: This retrospective multicenter study enrolled 485 patients with NSCLC who received chemotherapy alone as a first-line treatment. Two integrated models were developed using radiomic and deep-learning-based features. First, we partitioned pre-chemotherapy CT images into spheres and shells with different radii around the tumor (0–3, 3–6, 6–9, 9–12, 12–15 mm) containing intratumoral and peritumoral regions. Second, we extracted radiomic and deep-learning-based features from each partition. Third, using radiomic features, five sphere–shell models, one feature fusion model, and one image fusion model were developed. Finally, the model with the best performance was validated in two cohorts. Results: Among the five partitions, the model of 9–12 mm achieved the highest area under the curve (AUC) of 0.87 (95% confidence interval: 0.77–0.94). The AUC was 0.94 (0.85–0.98) for the feature fusion model and 0.91 (0.82–0.97) for the image fusion model. For the model integrating radiomic and deep-learning-based features, the AUC was 0.96 (0.88–0.99) for the feature fusion method and 0.94 (0.85–0.98) for the image fusion method. The best-performing model had an AUC of 0.91 (0.81–0.97) and 0.89 (0.79–0.93) in two validation sets, respectively. Conclusions: This integrated model can predict the response to chemotherapy in NSCLC patients and assist physicians in clinical decision-making.http://www.sciencedirect.com/science/article/pii/S1936523323001055Non-small cell lung cancerTreatment response to chemotherapySphere–shell partitionRadiomicsDeep learning |
| spellingShingle | Runsheng Chang Shouliang Qi Yanan Wu Yong Yue Xiaoye Zhang Yubao Guan Wei Qian Deep radiomic model based on the sphere–shell partition for predicting treatment response to chemotherapy in lung cancer Translational Oncology Non-small cell lung cancer Treatment response to chemotherapy Sphere–shell partition Radiomics Deep learning |
| title | Deep radiomic model based on the sphere–shell partition for predicting treatment response to chemotherapy in lung cancer |
| title_full | Deep radiomic model based on the sphere–shell partition for predicting treatment response to chemotherapy in lung cancer |
| title_fullStr | Deep radiomic model based on the sphere–shell partition for predicting treatment response to chemotherapy in lung cancer |
| title_full_unstemmed | Deep radiomic model based on the sphere–shell partition for predicting treatment response to chemotherapy in lung cancer |
| title_short | Deep radiomic model based on the sphere–shell partition for predicting treatment response to chemotherapy in lung cancer |
| title_sort | deep radiomic model based on the sphere shell partition for predicting treatment response to chemotherapy in lung cancer |
| topic | Non-small cell lung cancer Treatment response to chemotherapy Sphere–shell partition Radiomics Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S1936523323001055 |
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