Predictive Value of <sup>18</sup>F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer
We investigated predictions from <sup>18</sup>F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospe...
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MDPI AG
2022-04-01
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| Online Access: | https://www.mdpi.com/2072-6694/14/8/1987 |
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| author | Jang Yoo Jaeho Lee Miju Cheon Sang-Keun Woo Myung-Ju Ahn Hong Ryull Pyo Yong Soo Choi Joung Ho Han Joon Young Choi |
| author_facet | Jang Yoo Jaeho Lee Miju Cheon Sang-Keun Woo Myung-Ju Ahn Hong Ryull Pyo Yong Soo Choi Joung Ho Han Joon Young Choi |
| author_sort | Jang Yoo |
| collection | DOAJ |
| description | We investigated predictions from <sup>18</sup>F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospective study was conducted of 430 patients. They underwent <sup>18</sup>F-FDG PET/CT before initial treatment and after neoadjuvant CCRT followed by curative surgery. We analyzed texture features from segmented tumors and reviewed the pathologic response. The ML model employed a random forest and was used to classify the binary outcome of the pathological complete response (pCR). The predictive accuracy of the ML model for the pCR was 93.4%. The accuracy of predicting pCR using the conventional PET parameters was up to 70.9%, and the accuracy of the physicians’ assessment was 80.5%. The accuracy of the prediction from the ML model was significantly higher than those derived from conventional PET parameters and provided by physicians (<i>p</i> < 0.05). The ML model is useful for predicting pCR after neoadjuvant CCRT, which showed a higher predictive accuracy than those achieved from conventional PET parameters and from physicians. |
| first_indexed | 2024-03-09T11:03:14Z |
| format | Article |
| id | doaj.art-551b455b2cde4c48b3f00722c2d7fba6 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-09T11:03:14Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-551b455b2cde4c48b3f00722c2d7fba62023-12-01T01:07:49ZengMDPI AGCancers2072-66942022-04-01148198710.3390/cancers14081987Predictive Value of <sup>18</sup>F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung CancerJang Yoo0Jaeho Lee1Miju Cheon2Sang-Keun Woo3Myung-Ju Ahn4Hong Ryull Pyo5Yong Soo Choi6Joung Ho Han7Joon Young Choi8Department of Nuclear Medicine, Veterans Health Service Medical Center, Seoul 05368, KoreaDepartment of Preventive Medicine, Seoul National University College of Medicine, Seoul 03080, KoreaDepartment of Nuclear Medicine, Veterans Health Service Medical Center, Seoul 05368, KoreaDepartment of Nuclear Medicine, Korea Cancer Center Hospital, Korea Institute of Radiological and Medical Sciences (KIRAMS), Seoul 01812, KoreaDivision of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, KoreaDepartment of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, KoreaDepartment of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, KoreaDepartment of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, KoreaDepartment of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, KoreaWe investigated predictions from <sup>18</sup>F-FDG PET/CT using machine learning (ML) to assess the neoadjuvant CCRT response of patients with stage III non-small cell lung cancer (NSCLC) and compared them with predictions from conventional PET parameters and from physicians. A retrospective study was conducted of 430 patients. They underwent <sup>18</sup>F-FDG PET/CT before initial treatment and after neoadjuvant CCRT followed by curative surgery. We analyzed texture features from segmented tumors and reviewed the pathologic response. The ML model employed a random forest and was used to classify the binary outcome of the pathological complete response (pCR). The predictive accuracy of the ML model for the pCR was 93.4%. The accuracy of predicting pCR using the conventional PET parameters was up to 70.9%, and the accuracy of the physicians’ assessment was 80.5%. The accuracy of the prediction from the ML model was significantly higher than those derived from conventional PET parameters and provided by physicians (<i>p</i> < 0.05). The ML model is useful for predicting pCR after neoadjuvant CCRT, which showed a higher predictive accuracy than those achieved from conventional PET parameters and from physicians.https://www.mdpi.com/2072-6694/14/8/1987non-small cell lung cancerneoadjuvant concurrent chemoradiotherapy<sup>18</sup>F-FDG PET/CTmachine learningrandom forestpathologic complete response |
| spellingShingle | Jang Yoo Jaeho Lee Miju Cheon Sang-Keun Woo Myung-Ju Ahn Hong Ryull Pyo Yong Soo Choi Joung Ho Han Joon Young Choi Predictive Value of <sup>18</sup>F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer Cancers non-small cell lung cancer neoadjuvant concurrent chemoradiotherapy <sup>18</sup>F-FDG PET/CT machine learning random forest pathologic complete response |
| title | Predictive Value of <sup>18</sup>F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer |
| title_full | Predictive Value of <sup>18</sup>F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer |
| title_fullStr | Predictive Value of <sup>18</sup>F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer |
| title_full_unstemmed | Predictive Value of <sup>18</sup>F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer |
| title_short | Predictive Value of <sup>18</sup>F-FDG PET/CT Using Machine Learning for Pathological Response to Neoadjuvant Concurrent Chemoradiotherapy in Patients with Stage III Non-Small Cell Lung Cancer |
| title_sort | predictive value of sup 18 sup f fdg pet ct using machine learning for pathological response to neoadjuvant concurrent chemoradiotherapy in patients with stage iii non small cell lung cancer |
| topic | non-small cell lung cancer neoadjuvant concurrent chemoradiotherapy <sup>18</sup>F-FDG PET/CT machine learning random forest pathologic complete response |
| url | https://www.mdpi.com/2072-6694/14/8/1987 |
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