Ling Xiao Wu, Ning Ding, Yi Ding Ji, Yi Chi Zhang, Meng Juan Li, Jia Cheng Shen, Hai Tao Hu, Long Jin, Sheng Nan Yin

Department of Medical Imaging, Suzhou Ninth People’s Hospital, Wujiang, Suzhou, Jiangsu, People’s Republic of China

Correspondence: Long Jin; Sheng Nan Yin, Department of Medical Imaging, Suzhou Ninth People’s Hospital, Wujiang, Suzhou, Jiangsu, People’s Republic of China, Tel +8618550232113; +8618862301079, Email 402588941@qq.com

Abstract: Radiomics received a lot of attention because of its potential to provide personalized medicine in a non-invasive manner, usually focusing on the analysis of the entire lesion. A new method called habitat can identify subregional phenotypic changes within the lesion, thereby improving the ability to distinguish heterogeneity. The clustering method can be applied to multiple measurement parameters to separate different tumor habitats by segmentation. A data-driven repeatable voxel clustering method to identify subregions reflecting live tumors will be valuable for clinical diagnosis and further treatment. In this review, we aim to briefly summarize the widely used cluster analysis algorithms in subregion segmentation and the application of habitat analysis in tumor imaging. By analyzing many literatures, the commonly used K-means algorithm and other algorithms such as hierarchical clustering and consensus clustering are summarized. By identifying intratumoral heterogeneity, the key findings of habitat analysis in oncology are described, such as tumor differentiation, grading, and gene expression status. The latest progress and innovations in predicting tumor therapeutic effects and prognosis using habitat analysis are reviewed, including multimodal imaging data fusion, integration with artificial intelligence technologies, and non-invasive diagnostic methods. The limitations and challenges of habitat analysis in tumor imaging are also discussed, such as dependence on image quality and imaging techniques, insufficient automation and standardization, difficulties in biological interpretation, and lack of clinical validation. Finally, future directions for increasing the level of automation and standardization of habitat analysis to improve its accuracy and efficiency and reduce reliance on expert intervention are proposed. Habitat analysis represents a significant advancement in radiomics, offering a nuanced understanding of tumor heterogeneity. By leveraging sophisticated clustering algorithms and integrating multimodal imaging data, habitat analysis has the potential to transform clinical decision-making, enabling more precise diagnostics and personalized treatment strategies, ultimately advancing the field of precision medicine.

Keywords: habitat, cluster analysis, tumor imaging, K-means, radiomics