To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions
Abstract Background This study aims to introduce the concept of habitat subregions and construct an accurate prediction model by analyzing refined medical images, to predict lymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (ICC) before surgery, and to provide personalized...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
|
| Series: | Cancer Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40644-025-00842-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850237821291331584 |
|---|---|
| author | Pengyu Chen Zhenwei Yang Peigang Ning Hao Yuan Zuochao Qi Qingshan Li Bo Meng Xianzhou Zhang Haibo Yu |
| author_facet | Pengyu Chen Zhenwei Yang Peigang Ning Hao Yuan Zuochao Qi Qingshan Li Bo Meng Xianzhou Zhang Haibo Yu |
| author_sort | Pengyu Chen |
| collection | DOAJ |
| description | Abstract Background This study aims to introduce the concept of habitat subregions and construct an accurate prediction model by analyzing refined medical images, to predict lymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (ICC) before surgery, and to provide personalized support for clinical decision-making. Methods Clinical, radiological, and pathological data from ICC patients were retrospectively collected. Using information from the arterial and venous phases of multisequence CT images, tumor habitat subregions were delineated through the K-means clustering algorithm. Radiomic features were extracted and screened, and prediction models based on different subregions were constructed and compared with traditional intratumoral models. Finally, a lymph node metastasis prediction model was established by integrating the features of several subregional models, and its performance was evaluated. Results A total of 164 ICC patients were included in this study, 103 of whom underwent lymph node dissection. The patients were divided into LNM- and LNM + groups on the basis of lymph node status, and significant differences in white blood cell indicators were found between the two groups. Survival analysis revealed that patients with positive lymph nodes had significantly worse prognoses. Through cluster analysis, the optimal number of habitat subregions was determined to be 5, and prediction models based on different subregions were constructed. A comparison of the performance of each model revealed that the Habitat1 and Habitat5 models had excellent performance. The optimal model obtained by fusing the features of the Habitat1 and Habitat5 models had AUC values of 0.923 and 0.913 in the training set and validation set, respectively, demonstrating good predictive ability. Calibration curves and decision curve analysis further validated the superiority and clinical application value of the model. Conclusions This study successfully constructed an accurate prediction model based on habitat subregions that can effectively predict the lymph node metastasis of ICC patients preoperatively. This model is expected to provide personalized decision support to clinicians and help to optimize treatment plans and improve patient outcomes. |
| format | Article |
| id | doaj-art-6eff2b0e125c4d16b81a5436cb33acb9 |
| institution | OA Journals |
| issn | 1470-7330 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj-art-6eff2b0e125c4d16b81a5436cb33acb92025-08-20T02:01:39ZengBMCCancer Imaging1470-73302025-02-0125111210.1186/s40644-025-00842-8To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregionsPengyu Chen0Zhenwei Yang1Peigang Ning2Hao Yuan3Zuochao Qi4Qingshan Li5Bo Meng6Xianzhou Zhang7Haibo Yu8Department of Hepatobiliary Surgery, Henan University People’S Hospital, Henan Provincial People’S HospitalDepartment of Hepatobiliary Surgery, Henan University People’S Hospital, Henan Provincial People’S HospitalDepartment of Radiology, People’s Hospital of Zhengzhou UniversityDepartment of Hepatobiliary Surgery, Henan Provincial People’s HospitalDepartment of Hepatobiliary Surgery, Henan Provincial People’s HospitalDepartment of Hepatobiliary Surgery, Henan Provincial People’s HospitalDepartment of Hepatobiliary Surgery, Henan Cancer HospitalDepartment of Hepatobiliary Surgery, Henan Cancer HospitalDepartment of Hepatobiliary Surgery, Henan University People’S Hospital, Henan Provincial People’S HospitalAbstract Background This study aims to introduce the concept of habitat subregions and construct an accurate prediction model by analyzing refined medical images, to predict lymph node metastasis (LNM) in patients with intrahepatic cholangiocarcinoma (ICC) before surgery, and to provide personalized support for clinical decision-making. Methods Clinical, radiological, and pathological data from ICC patients were retrospectively collected. Using information from the arterial and venous phases of multisequence CT images, tumor habitat subregions were delineated through the K-means clustering algorithm. Radiomic features were extracted and screened, and prediction models based on different subregions were constructed and compared with traditional intratumoral models. Finally, a lymph node metastasis prediction model was established by integrating the features of several subregional models, and its performance was evaluated. Results A total of 164 ICC patients were included in this study, 103 of whom underwent lymph node dissection. The patients were divided into LNM- and LNM + groups on the basis of lymph node status, and significant differences in white blood cell indicators were found between the two groups. Survival analysis revealed that patients with positive lymph nodes had significantly worse prognoses. Through cluster analysis, the optimal number of habitat subregions was determined to be 5, and prediction models based on different subregions were constructed. A comparison of the performance of each model revealed that the Habitat1 and Habitat5 models had excellent performance. The optimal model obtained by fusing the features of the Habitat1 and Habitat5 models had AUC values of 0.923 and 0.913 in the training set and validation set, respectively, demonstrating good predictive ability. Calibration curves and decision curve analysis further validated the superiority and clinical application value of the model. Conclusions This study successfully constructed an accurate prediction model based on habitat subregions that can effectively predict the lymph node metastasis of ICC patients preoperatively. This model is expected to provide personalized decision support to clinicians and help to optimize treatment plans and improve patient outcomes.https://doi.org/10.1186/s40644-025-00842-8Intrahepatic cholangiocarcinomaRadiomicsLymph node metastasisTumor habitat subregionsMachine learning |
| spellingShingle | Pengyu Chen Zhenwei Yang Peigang Ning Hao Yuan Zuochao Qi Qingshan Li Bo Meng Xianzhou Zhang Haibo Yu To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions Cancer Imaging Intrahepatic cholangiocarcinoma Radiomics Lymph node metastasis Tumor habitat subregions Machine learning |
| title | To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions |
| title_full | To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions |
| title_fullStr | To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions |
| title_full_unstemmed | To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions |
| title_short | To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions |
| title_sort | to accurately predict lymph node metastasis in patients with mass forming intrahepatic cholangiocarcinoma by using ct radiomics features of tumor habitat subregions |
| topic | Intrahepatic cholangiocarcinoma Radiomics Lymph node metastasis Tumor habitat subregions Machine learning |
| url | https://doi.org/10.1186/s40644-025-00842-8 |
| work_keys_str_mv | AT pengyuchen toaccuratelypredictlymphnodemetastasisinpatientswithmassformingintrahepaticcholangiocarcinomabyusingctradiomicsfeaturesoftumorhabitatsubregions AT zhenweiyang toaccuratelypredictlymphnodemetastasisinpatientswithmassformingintrahepaticcholangiocarcinomabyusingctradiomicsfeaturesoftumorhabitatsubregions AT peigangning toaccuratelypredictlymphnodemetastasisinpatientswithmassformingintrahepaticcholangiocarcinomabyusingctradiomicsfeaturesoftumorhabitatsubregions AT haoyuan toaccuratelypredictlymphnodemetastasisinpatientswithmassformingintrahepaticcholangiocarcinomabyusingctradiomicsfeaturesoftumorhabitatsubregions AT zuochaoqi toaccuratelypredictlymphnodemetastasisinpatientswithmassformingintrahepaticcholangiocarcinomabyusingctradiomicsfeaturesoftumorhabitatsubregions AT qingshanli toaccuratelypredictlymphnodemetastasisinpatientswithmassformingintrahepaticcholangiocarcinomabyusingctradiomicsfeaturesoftumorhabitatsubregions AT bomeng toaccuratelypredictlymphnodemetastasisinpatientswithmassformingintrahepaticcholangiocarcinomabyusingctradiomicsfeaturesoftumorhabitatsubregions AT xianzhouzhang toaccuratelypredictlymphnodemetastasisinpatientswithmassformingintrahepaticcholangiocarcinomabyusingctradiomicsfeaturesoftumorhabitatsubregions AT haiboyu toaccuratelypredictlymphnodemetastasisinpatientswithmassformingintrahepaticcholangiocarcinomabyusingctradiomicsfeaturesoftumorhabitatsubregions |