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MRI radiomics may predict early tumor recurrence in patients with sinonasal squamous cell carcinoma.
Park, Chae Jung; Choi, Seo Hee; Kim, Dain; Kim, Si Been; Han, Kyunghwa; Ahn, Sung Soo; Lee, Won Hee; Choi, Eun Chang; Keum, Ki Chang; Kim, Jinna.
Afiliação
  • Park CJ; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Choi SH; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Kim D; Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, Republic of Korea.
  • Kim SB; Undergraduate School of Biomedical Engineering, Korea University College of Health Science, Seoul, Republic of Korea.
  • Han K; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
  • Ahn SS; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea.
  • Lee WH; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Choi EC; Department of Otorhinolaryngology, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Keum KC; Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
  • Kim J; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea. jinna@yuhs.ac.
Eur Radiol ; 2023 Nov 06.
Article em En | MEDLINE | ID: mdl-37926740
OBJECTIVES: Sinonasal squamous cell carcinoma (SCC) follows a poor prognosis with high tendency for local recurrence. We aimed to evaluate whether MRI radiomics can predict early local failure in sinonasal SCC. METHODS: Sixty-eight consecutive patients with node-negative sinonasal SCC (January 2005-December 2020) were enrolled, allocated to the training (n = 47) and test sets (n = 21). Early local failure, which occurred within 12 months of completion of initial treatment, was the primary endpoint. For clinical features (age, location, treatment modality, and clinical T stage), binary logistic regression analysis was performed. For 186 extracted radiomic features, different feature selections and classifiers were combined to create two prediction models: (1) a pure radiomics model; and (2) a combined model with clinical features and radiomics. The areas under the receiver operating characteristic curves (AUCs) were calculated and compared using DeLong's method. RESULTS: Early local failure occurred in 38.3% (18/47) and 23.8% (5/21) in the training and test sets, respectively. We identified several radiomic features which were strongly associated with early local failure. In the test set, both the best-performing radiomics model and the combined model (clinical + radiomic features) yielded higher AUCs compared to the clinical model (AUC, 0.838 vs. 0.438, p = 0.020; 0.850 vs. 0.438, p = 0.016, respectively). The performances of the best-performing radiomics model and the combined model did not differ significantly (AUC, 0.838 vs. 0.850, p = 0.904). CONCLUSION: MRI radiomics integrated with a machine learning classifier may predict early local failure in patients with sinonasal SCC. CLINICAL RELEVANCE STATEMENT: MRI radiomics intergrated with machine learning classifiers may predict early local failure in sinonasal squamous cell carcinomas more accurately than the clinical model. KEY POINTS: • A subset of radiomic features which showed significant association with early local failure in patients with sinonasal squamous cell carcinomas was identified. • MRI radiomics integrated with machine learning classifiers can predict early local failure with high accuracy, which was validated in the test set (area under the curve = 0.838). • The combined clinical and radiomics model yielded superior performance for early local failure prediction compared to that of the radiomics (area under the curve 0.850 vs. 0.838 in the test set), without a statistically significant difference.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article