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Predicting pathological response to neoadjuvant or conversion chemoimmunotherapy in stage IB-III non-small cell lung cancer patients using radiomic features.
Yang, Nong; Yue, Hai-Lin; Zhang, Bai-Hua; Chen, Juan; Chu, Qian; Wang, Jian-Xin; Yu, Xiao-Ping; Jian, Lian; Bin, Ya-Wen; Liu, Si-Ye; Liu, Jin; Zeng, Liang; Yang, Hai-Yan; Zhou, Chun-Hua; Jiang, Wen-Juan; Liu, Li; Zhang, Yong-Chang; Xiong, Yi; Wang, Zhan.
Afiliación
  • Yang N; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.
  • Yue HL; Lung Cancer and Gastrointestinal Unit, Department of Medical Oncology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
  • Zhang BH; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.
  • Chen J; Department of Thoracic Surgery, Hunan Cancer Hospital, Changsha, China.
  • Chu Q; Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, China.
  • Wang JX; Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yu XP; Lung Cancer and Gastrointestinal Unit, Department of Medical Oncology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
  • Jian L; Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
  • Bin YW; Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
  • Liu SY; Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Liu J; Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
  • Zeng L; Lung Cancer and Gastrointestinal Unit, Department of Medical Oncology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
  • Yang HY; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.
  • Zhou CH; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.
  • Jiang WJ; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.
  • Liu L; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.
  • Zhang YC; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.
  • Xiong Y; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.
  • Wang Z; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.
Thorac Cancer ; 14(28): 2869-2876, 2023 10.
Article en En | MEDLINE | ID: mdl-37596822
BACKGROUND: To develop a radiomics model based on chest computed tomography (CT) for the prediction of a pathological complete response (pCR) after neoadjuvant or conversion chemoimmunotherapy (CIT) in patients with non-small cell lung cancer (NSCLC). METHODS: Patients with stage IB-III NSCLC who received neoadjuvant or conversion CIT between September 2019 and July 2021 at Hunan Cancer Hospital, Xiangya Hospital, and Union Hospital were retrospectively collected. The least absolute shrinkage and selection operator (LASSO) were used to screen features. Then, model 1 (five radiomics features before CIT), model 2 (four radiomics features after CIT and before surgery) and model 3 were constructed for the prediction of pCR. Model 3 included all nine features of model 1 and 2 and was later named the neoadjuvant chemoimmunotherapy-related pathological response prediction model (NACIP). RESULTS: This study included 110 patients: 77 in the training set and 33 in the validation set. Thirty-nine (35.5%) patients achieved a pCR. Model 1 showed area under the curve (AUC) = 0.65, 64% accuracy, 71% specificity, and 50% sensitivity, while model 2 displayed AUC = 0.81, 73% accuracy, 62% specificity, and 92% sensitivity. In comparison, NACIP yielded a good predictive value, with an AUC of 0.85, 81% accuracy, 81% specificity, and 83% sensitivity in the validation set. CONCLUSION: NACIP may be a potential model for the early prediction of pCR in patients with NSCLC treated with neoadjuvant/conversion CIT.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Carcinoma de Pulmón de Células no Pequeñas / Neoplasias Pulmonares Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Thorac Cancer Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Singapur
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Carcinoma de Pulmón de Células no Pequeñas / Neoplasias Pulmonares Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Thorac Cancer Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Singapur