Dosiomics improves prediction of locoregional recurrence for intensity modulated radiotherapy treated head and neck cancer cases. (May 2020)

• Record Type: Journal Article
• Authors: Wu, Aiqian; Li, Yongbao; Qi, Mengke; Lu, Xingyu; Jia, Qiyuan; Guo, Futong; Dai, Zhenhui; Liu, Yuliang; Chen, Chaomin; Zhou, Linghong; Song, Ting
• Is Part Of: Oral oncology. Volume 104(2020)
• Publication Date: 2020-05
• Subjects: Dosiomics -- Radiomics -- Prognosis -- Locoregional recurrences -- Intensity-modulated radiotherapy -- 3D dose distribution -- Head and neck cancer; Mouth -- Cancer -- Periodicals; Mouth -- Tumors -- Periodicals; Mouth Diseases -- Periodicals; Mouth Neoplasms -- Periodicals; Bouche -- Cancer -- Périodiques; Bouche -- Tumeurs -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9943105
• DOI: 10.1016/j.oraloncology.2020.104625
• Languages: English
• ISSNs: 1368-8375

Abstract

Highlights: First study evolves RT 3D dose distribution to cancer therapy prognosis analysis. Dosiomics improves predicting LR for HNSCC and should be recommended correlatively. Dosiomics marker LGLE_GLDM_GTV0 could be a potential LR prognostic factor for HNSCC. Dosiomics is general and suitable for other tumor site and prognosis scenarios. 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.