Aiqian Wu1, Yongbao Li2, Mengke Qi1, Xingyu Lu1, Qiyuan Jia1, Futong Guo1, Zhenhui Dai3, Yuliang Liu1, Chaomin Chen4, Linghong Zhou5, Ting Song6. 1. Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China. 2. Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong 510060, China. 3. Department of Radiation Oncology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510120, China. 4. Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China. Electronic address: gzccm@fimmu.edu.cn. 5. Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China. Electronic address: smart@smu.edu.cn. 6. Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong 510515, China. Electronic address: tingsong2015@smu.edu.cn.
Abstract
OBJECTIVES: To investigate whether dosiomics can benefit to IMRT treated patient's locoregional recurrences (LR) prediction through a comparative study on prediction performance inspection between radiomics methods and that integrating dosiomics in head and neck cancer cases. MATERIALS AND METHODS: A cohort of 237 patients with head and neck cancer from four different institutions was obtained from The Cancer Imaging Archive and utilized to train and validate the radiomics-only prognostic model and integrate the dosiomics prognostic model. For radiomics, the radiomics features were initially extracted from images, including CTs and PETs, and selected on the basis of their concordance index (CI) values, then condensed via principle component analysis. Lastly, multivariate Cox proportional hazards regression models were constructed with class-imbalance adjustment as the LR prediction models by inputting those condensed features. For dosiomics integration model establishment, the initial features were similar, but with additional 3-dimensional dose distribution from radiation treatment plans. The CI and the Kaplan-Meier curves with log-rank analysis were used to assess and compare these models. RESULTS: Observed from the independent validation dataset, the CI of the model for dosiomics integration (0.66) was significantly different from that for radiomics (0.59) (Wilcoxon test, p=5.9×10-31). The integrated model successfully classified the patients into high- and low-risk groups (log-rank test, p=2.5×10-02), whereas the radiomics model was not able to provide such classification (log-rank test, p=0.37). CONCLUSION: Dosiomics can benefit in predicting the LR in IMRT-treated patients and should not be neglected for related investigations.
OBJECTIVES: To investigate whether dosiomics can benefit to IMRT treated patient's locoregional recurrences (LR) prediction through a comparative study on prediction performance inspection between radiomics methods and that integrating dosiomics in head and neck cancer cases. MATERIALS AND METHODS: A cohort of 237 patients with head and neck cancer from four different institutions was obtained from The Cancer Imaging Archive and utilized to train and validate the radiomics-only prognostic model and integrate the dosiomics prognostic model. For radiomics, the radiomics features were initially extracted from images, including CTs and PETs, and selected on the basis of their concordance index (CI) values, then condensed via principle component analysis. Lastly, multivariate Cox proportional hazards regression models were constructed with class-imbalance adjustment as the LR prediction models by inputting those condensed features. For dosiomics integration model establishment, the initial features were similar, but with additional 3-dimensional dose distribution from radiation treatment plans. The CI and the Kaplan-Meier curves with log-rank analysis were used to assess and compare these models. RESULTS: Observed from the independent validation dataset, the CI of the model for dosiomics integration (0.66) was significantly different from that for radiomics (0.59) (Wilcoxon test, p=5.9×10-31). The integrated model successfully classified the patients into high- and low-risk groups (log-rank test, p=2.5×10-02), whereas the radiomics model was not able to provide such classification (log-rank test, p=0.37). CONCLUSION: Dosiomics can benefit in predicting the LR in IMRT-treated patients and should not be neglected for related investigations.
Authors: Giacomo Miserocchi; Chiara Spadazzi; Sebastiano Calpona; Francesco De Rosa; Alice Usai; Alessandro De Vita; Chiara Liverani; Claudia Cocchi; Silvia Vanni; Chiara Calabrese; Massimo Bassi; Giovanni De Luca; Giuseppe Meccariello; Toni Ibrahim; Marco Schiavone; Laura Mercatali Journal: J Pers Med Date: 2022-05-24