Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma
Surgical decision-making on advanced laryngeal carcinoma is heavily depended on the identification of preoperative T category (T3 vs. T4), which is challenging for surgeons. A T category prediction radiomics (TCPR) model would be helpful for subsequent surgery. A total of 211 patients with locally a...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-10-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fonc.2019.01064/full |
| _version_ | 1818578724281384960 |
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| author | Fei Wang Bin Zhang Bin Zhang Xiangjun Wu Xiangjun Wu Lizhi Liu Jin Fang Qiuying Chen Qiuying Chen Minmin Li Minmin Li Zhuozhi Chen Zhuozhi Chen Yueyue Li Di Dong Di Dong Jie Tian Jie Tian Jie Tian Shuixing Zhang |
| author_facet | Fei Wang Bin Zhang Bin Zhang Xiangjun Wu Xiangjun Wu Lizhi Liu Jin Fang Qiuying Chen Qiuying Chen Minmin Li Minmin Li Zhuozhi Chen Zhuozhi Chen Yueyue Li Di Dong Di Dong Jie Tian Jie Tian Jie Tian Shuixing Zhang |
| author_sort | Fei Wang |
| collection | DOAJ |
| description | Surgical decision-making on advanced laryngeal carcinoma is heavily depended on the identification of preoperative T category (T3 vs. T4), which is challenging for surgeons. A T category prediction radiomics (TCPR) model would be helpful for subsequent surgery. A total of 211 patients with locally advanced laryngeal cancer who had undergone total laryngectomy were randomly classified into the training cohort (n = 150) and the validation cohort (n = 61). We extracted 1,390 radiomic features from the contrast-enhanced computed tomography images. Interclass correlation coefficient and the least absolute shrinkage and selection operator (LASSO) analyses were performed to select features associated with pathology-confirmed T category. Eight radiomic features were found associated with preoperative T category. The radiomic signature was constructed by Support Vector Machine algorithm with the radiomic features. We developed a nomogram incorporating radiomic signature and T category reported by experienced radiologists. The performance of the model was evaluated by the area under the curve (AUC). The T category reported by radiologists achieved an AUC of 0.775 (95% CI: 0.667–0.883); while the radiomic signature yielded a significantly higher AUC of 0.862 (95% CI: 0.772–0.952). The predictive performance of the nomogram incorporating radiomic signature and T category reported by radiologists further improved, with an AUC of 0.892 (95% CI: 0.811–0.974). Consequently, for locally advanced laryngeal cancer, the TCPR model incorporating radiomic signature and T category reported by experienced radiologists have great potential to be applied for individual accurate preoperative T category. The TCPR model may benefit decision-making regarding total laryngectomy or larynx-preserving treatment. |
| first_indexed | 2024-12-16T06:50:21Z |
| format | Article |
| id | doaj.art-410a7729c4e9458980d13885dc3afb99 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-16T06:50:21Z |
| publishDate | 2019-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-410a7729c4e9458980d13885dc3afb992022-12-21T22:40:25ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2019-10-01910.3389/fonc.2019.01064493053Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal CarcinomaFei Wang0Bin Zhang1Bin Zhang2Xiangjun Wu3Xiangjun Wu4Lizhi Liu5Jin Fang6Qiuying Chen7Qiuying Chen8Minmin Li9Minmin Li10Zhuozhi Chen11Zhuozhi Chen12Yueyue Li13Di Dong14Di Dong15Jie Tian16Jie Tian17Jie Tian18Shuixing Zhang19Department of Radiology, The First Affiliated Hospital, Jinan University, Guangzhou, ChinaDepartment of Radiology, The First Affiliated Hospital, Jinan University, Guangzhou, ChinaFirst Clinical Medical College, Jinan University, Guangzhou, ChinaCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaCollege of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, ChinaState Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, ChinaDepartment of Radiology, The First Affiliated Hospital, Jinan University, Guangzhou, ChinaDepartment of Radiology, The First Affiliated Hospital, Jinan University, Guangzhou, ChinaFirst Clinical Medical College, Jinan University, Guangzhou, ChinaDepartment of Radiology, The First Affiliated Hospital, Jinan University, Guangzhou, ChinaFirst Clinical Medical College, Jinan University, Guangzhou, ChinaDepartment of Radiology, The First Affiliated Hospital, Jinan University, Guangzhou, ChinaFirst Clinical Medical College, Jinan University, Guangzhou, ChinaDepartment of Radiology, The First Affiliated Hospital, Jinan University, Guangzhou, ChinaCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaCollege of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, ChinaCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaCollege of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, ChinaBeijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, ChinaDepartment of Radiology, The First Affiliated Hospital, Jinan University, Guangzhou, ChinaSurgical decision-making on advanced laryngeal carcinoma is heavily depended on the identification of preoperative T category (T3 vs. T4), which is challenging for surgeons. A T category prediction radiomics (TCPR) model would be helpful for subsequent surgery. A total of 211 patients with locally advanced laryngeal cancer who had undergone total laryngectomy were randomly classified into the training cohort (n = 150) and the validation cohort (n = 61). We extracted 1,390 radiomic features from the contrast-enhanced computed tomography images. Interclass correlation coefficient and the least absolute shrinkage and selection operator (LASSO) analyses were performed to select features associated with pathology-confirmed T category. Eight radiomic features were found associated with preoperative T category. The radiomic signature was constructed by Support Vector Machine algorithm with the radiomic features. We developed a nomogram incorporating radiomic signature and T category reported by experienced radiologists. The performance of the model was evaluated by the area under the curve (AUC). The T category reported by radiologists achieved an AUC of 0.775 (95% CI: 0.667–0.883); while the radiomic signature yielded a significantly higher AUC of 0.862 (95% CI: 0.772–0.952). The predictive performance of the nomogram incorporating radiomic signature and T category reported by radiologists further improved, with an AUC of 0.892 (95% CI: 0.811–0.974). Consequently, for locally advanced laryngeal cancer, the TCPR model incorporating radiomic signature and T category reported by experienced radiologists have great potential to be applied for individual accurate preoperative T category. The TCPR model may benefit decision-making regarding total laryngectomy or larynx-preserving treatment.https://www.frontiersin.org/article/10.3389/fonc.2019.01064/fulladvanced laryngeal cancercomputed tomographyradiomicsT categorynomogram |
| spellingShingle | Fei Wang Bin Zhang Bin Zhang Xiangjun Wu Xiangjun Wu Lizhi Liu Jin Fang Qiuying Chen Qiuying Chen Minmin Li Minmin Li Zhuozhi Chen Zhuozhi Chen Yueyue Li Di Dong Di Dong Jie Tian Jie Tian Jie Tian Shuixing Zhang Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma Frontiers in Oncology advanced laryngeal cancer computed tomography radiomics T category nomogram |
| title | Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma |
| title_full | Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma |
| title_fullStr | Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma |
| title_full_unstemmed | Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma |
| title_short | Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma |
| title_sort | radiomic nomogram improves preoperative t category accuracy in locally advanced laryngeal carcinoma |
| topic | advanced laryngeal cancer computed tomography radiomics T category nomogram |
| url | https://www.frontiersin.org/article/10.3389/fonc.2019.01064/full |
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