Background:Colorectal liver metastasis(CRLM)has a poor prognosis,and traditional prognostic models have certain limitations in clinical application.This study aims to evaluate the prognostic value of CT-based habitat ...Background:Colorectal liver metastasis(CRLM)has a poor prognosis,and traditional prognostic models have certain limitations in clinical application.This study aims to evaluate the prognostic value of CT-based habitat analysis in CRLM patients and compare it with existing traditional prognostic models to provide more evidence for individualized treatment of CRLM patients.Methods:This retrospective study included 197 patients with CRLM whose preoperative contrast-enhanced CT images and corresponding DICOM Segmentation Objects(DSOs)were obtained from The Cancer Imaging Archive(TCIA).Tumor regions were segmented,and habitat features representing distinct subregions were extracted.An unsupervised K-means clustering algorithm classified the tumors into two clusters based on their habitat characteristics.Kaplan–Meier analysis was used to evaluate overall survival(OS),disease-free survival(DFS),and liver-specific DFS.The habitat model's predictive performance was compared with the Clinical Risk Score(CRS)and Tumor Burden Score(TBS)using the concordance index(C-index),Integrated Brier Score(IBS),and time-dependent area under the curve(AUC).Results:The habitat model identified two distinct patient clusters with significant differences in OS,DFS,and liverspecific DFS(p<0.01).Compared with CRS and TBS,the habitat model demonstrated superior predictive accuracy,particularly for DFS and liver-specific DFS,with higher time-dependent AUC values and improved model calibration(lower IBS).Conclusions:CT-based habitat analysis captures spatial tumor heterogeneity and provides enhanced prognostic stratification in CRLM.The method outperforms conventional models and offers potential for more personalized treatment planning.展开更多
With the development of artificial intelligence(AI)technologies,biomedical imaging data play an important role in scientific research and clinical application,but the available resources are limited.Here we present Op...With the development of artificial intelligence(AI)technologies,biomedical imaging data play an important role in scientific research and clinical application,but the available resources are limited.Here we present Open Biomedical Imaging Archive(OBIA),a repository for archiving biomedical imaging and related clinical data.OBIA adopts five data objects(Collection,Individual,Study,Series,and Image)for data organization,and accepts the submission of biomedical images of multiple modalities,organs,and diseases.In order to protect personal privacy,OBIA has formulated a unified de-identification and quality control process.In addition,OBIA provides friendly and intuitive web interfaces for data submission,browsing,and retrieval,as well as image retrieval.As of September 2023,OBIA has housed data for a total of 937 individuals,4136 studies,24,701 series,and 1,938,309 images covering 9 modalities and 30 anatomical sites.Collectively,OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world.OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.展开更多
基金supported by Henan University Interdisciplinary Advanced Research Institute Construction Project(CX3070A0970005)Key Scientific Research Project of Higher Education Institutions(25A320014)+1 种基金Henan Provincial Medical Science and Technology Public Relations Program Provincial Ministerial Co-Construction Key Project(SBGJ202302093)Henan Provincial Medical Science and Technology Research Project(LHGJ20240406).
文摘Background:Colorectal liver metastasis(CRLM)has a poor prognosis,and traditional prognostic models have certain limitations in clinical application.This study aims to evaluate the prognostic value of CT-based habitat analysis in CRLM patients and compare it with existing traditional prognostic models to provide more evidence for individualized treatment of CRLM patients.Methods:This retrospective study included 197 patients with CRLM whose preoperative contrast-enhanced CT images and corresponding DICOM Segmentation Objects(DSOs)were obtained from The Cancer Imaging Archive(TCIA).Tumor regions were segmented,and habitat features representing distinct subregions were extracted.An unsupervised K-means clustering algorithm classified the tumors into two clusters based on their habitat characteristics.Kaplan–Meier analysis was used to evaluate overall survival(OS),disease-free survival(DFS),and liver-specific DFS.The habitat model's predictive performance was compared with the Clinical Risk Score(CRS)and Tumor Burden Score(TBS)using the concordance index(C-index),Integrated Brier Score(IBS),and time-dependent area under the curve(AUC).Results:The habitat model identified two distinct patient clusters with significant differences in OS,DFS,and liverspecific DFS(p<0.01).Compared with CRS and TBS,the habitat model demonstrated superior predictive accuracy,particularly for DFS and liver-specific DFS,with higher time-dependent AUC values and improved model calibration(lower IBS).Conclusions:CT-based habitat analysis captures spatial tumor heterogeneity and provides enhanced prognostic stratification in CRLM.The method outperforms conventional models and offers potential for more personalized treatment planning.
基金supported by grants from the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB38050300)the Genomics Data Center Operation and Maintenance of Chinese Academy of Sciences(Grant No.CAS-WX2022SDC-XK05)the Key Technology Talent Program of the Chinese Academy of Sciences,China.
文摘With the development of artificial intelligence(AI)technologies,biomedical imaging data play an important role in scientific research and clinical application,but the available resources are limited.Here we present Open Biomedical Imaging Archive(OBIA),a repository for archiving biomedical imaging and related clinical data.OBIA adopts five data objects(Collection,Individual,Study,Series,and Image)for data organization,and accepts the submission of biomedical images of multiple modalities,organs,and diseases.In order to protect personal privacy,OBIA has formulated a unified de-identification and quality control process.In addition,OBIA provides friendly and intuitive web interfaces for data submission,browsing,and retrieval,as well as image retrieval.As of September 2023,OBIA has housed data for a total of 937 individuals,4136 studies,24,701 series,and 1,938,309 images covering 9 modalities and 30 anatomical sites.Collectively,OBIA provides a reliable platform for biomedical imaging data management and offers free open access to all publicly available data to support research activities throughout the world.OBIA can be accessed at https://ngdc.cncb.ac.cn/obia.
