CT Texture Analysis in Breast Cancer Patients Undergoing CT-Guided Bone Biopsy: Correlations With Histopathology
Background: Texture analysis has the potential to deliver quantitative imaging markers. Patients receiving computed tomography (CT)-guided percutaneous bone biopsies could be characterized using texture analysis derived from CT. Especially for breast cancer (BC) patients, it could be crucial to bett...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-01-01
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| Series: | Breast Cancer: Basic and Clinical Research |
| Online Access: | https://doi.org/10.1177/11782234241305886 |
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| author | Silvio Wermelskirchen Jakob Leonhardi Anne-Kathrin Höhn Georg Osterhoff Nikolas Schopow Susanne Briest Timm Denecke Hans-Jonas Meyer |
| author_facet | Silvio Wermelskirchen Jakob Leonhardi Anne-Kathrin Höhn Georg Osterhoff Nikolas Schopow Susanne Briest Timm Denecke Hans-Jonas Meyer |
| author_sort | Silvio Wermelskirchen |
| collection | DOAJ |
| description | Background: Texture analysis has the potential to deliver quantitative imaging markers. Patients receiving computed tomography (CT)-guided percutaneous bone biopsies could be characterized using texture analysis derived from CT. Especially for breast cancer (BC) patients, it could be crucial to better predict the outcome of the biopsy to better reflect the immunohistochemistry status of the tumor. Objectives: The present study examined the relationship between texture features and outcomes in patients with BC receiving CT-guided bone biopsies. Design: This study is based on a retrospective analysis. Methods: The present study included a total of 66 patients. All patients proceeded to undergo a CT-guided percutaneous bone biopsy, using an 11-gauge coaxial needle. Clinical and imaging characteristics as well as CT texture analysis were included in the analysis. Logistic regression analysis was performed to predict negative biopsy results. Results: Overall, 33 patients had osteolytic metastases (50%) and 33 had osteoblastic metastases (50%). The overall positivity rate for the biopsy was 75%. The clinical model exhibited a predictive accuracy for a positive biopsy result, as indicated by an area under the curve (AUC) of 0.73 [95% confidence interval (CI) = 0.63-0.83]. Several CT texture features were different between Luminal A and Luminal B cancers; the best discrimination was reached for “WavEnHH_s-3” with a P -value of .002. When comparing triple-negative to non–triple-negative cancers, several CT texture features were different, the best discrimination achieved “S(5,5)SumVarnc” with a P -value of .01. For the Her 2 discrimination, only 3 parameters reached statistical significance, “S(4,-4)SumOfSqs” with a P -value of .01. Conclusions: The utilization of CT texture features may facilitate a more accurate characterization of bone metastases in patients with BC. There is the potential to predict the immunohistochemical subtype with a high degree of accuracy. The identified parameters may prove useful in clinical decision-making and could help to identify patients at risk of a negative biopsy result. |
| format | Article |
| id | doaj-art-d7f77963b1e644558589893da2f4c9a4 |
| institution | Kabale University |
| issn | 1178-2234 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Breast Cancer: Basic and Clinical Research |
| spelling | doaj-art-d7f77963b1e644558589893da2f4c9a42025-01-29T15:03:23ZengSAGE PublishingBreast Cancer: Basic and Clinical Research1178-22342025-01-011910.1177/11782234241305886CT Texture Analysis in Breast Cancer Patients Undergoing CT-Guided Bone Biopsy: Correlations With HistopathologySilvio Wermelskirchen0Jakob Leonhardi1Anne-Kathrin Höhn2Georg Osterhoff3Nikolas Schopow4Susanne Briest5Timm Denecke6Hans-Jonas Meyer7Department of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, GermanyDepartment of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, GermanyDepartment of Pathology, University Hospital Leipzig, University of Leipzig, Leipzig, GermanyDepartment of Orthopaedics, Trauma and Reconstructive Surgery, University Hospital Leipzig, Leipzig, GermanyDepartment of Orthopaedics, Trauma and Reconstructive Surgery, University Hospital Leipzig, Leipzig, GermanyDepartment of Gynaecology, University Hospital Leipzig, Leipzig, GermanyDepartment of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, GermanyDepartment of Diagnostic and Interventional Radiology, University of Leipzig, Leipzig, GermanyBackground: Texture analysis has the potential to deliver quantitative imaging markers. Patients receiving computed tomography (CT)-guided percutaneous bone biopsies could be characterized using texture analysis derived from CT. Especially for breast cancer (BC) patients, it could be crucial to better predict the outcome of the biopsy to better reflect the immunohistochemistry status of the tumor. Objectives: The present study examined the relationship between texture features and outcomes in patients with BC receiving CT-guided bone biopsies. Design: This study is based on a retrospective analysis. Methods: The present study included a total of 66 patients. All patients proceeded to undergo a CT-guided percutaneous bone biopsy, using an 11-gauge coaxial needle. Clinical and imaging characteristics as well as CT texture analysis were included in the analysis. Logistic regression analysis was performed to predict negative biopsy results. Results: Overall, 33 patients had osteolytic metastases (50%) and 33 had osteoblastic metastases (50%). The overall positivity rate for the biopsy was 75%. The clinical model exhibited a predictive accuracy for a positive biopsy result, as indicated by an area under the curve (AUC) of 0.73 [95% confidence interval (CI) = 0.63-0.83]. Several CT texture features were different between Luminal A and Luminal B cancers; the best discrimination was reached for “WavEnHH_s-3” with a P -value of .002. When comparing triple-negative to non–triple-negative cancers, several CT texture features were different, the best discrimination achieved “S(5,5)SumVarnc” with a P -value of .01. For the Her 2 discrimination, only 3 parameters reached statistical significance, “S(4,-4)SumOfSqs” with a P -value of .01. Conclusions: The utilization of CT texture features may facilitate a more accurate characterization of bone metastases in patients with BC. There is the potential to predict the immunohistochemical subtype with a high degree of accuracy. The identified parameters may prove useful in clinical decision-making and could help to identify patients at risk of a negative biopsy result.https://doi.org/10.1177/11782234241305886 |
| spellingShingle | Silvio Wermelskirchen Jakob Leonhardi Anne-Kathrin Höhn Georg Osterhoff Nikolas Schopow Susanne Briest Timm Denecke Hans-Jonas Meyer CT Texture Analysis in Breast Cancer Patients Undergoing CT-Guided Bone Biopsy: Correlations With Histopathology Breast Cancer: Basic and Clinical Research |
| title | CT Texture Analysis in Breast Cancer Patients Undergoing CT-Guided Bone Biopsy: Correlations With Histopathology |
| title_full | CT Texture Analysis in Breast Cancer Patients Undergoing CT-Guided Bone Biopsy: Correlations With Histopathology |
| title_fullStr | CT Texture Analysis in Breast Cancer Patients Undergoing CT-Guided Bone Biopsy: Correlations With Histopathology |
| title_full_unstemmed | CT Texture Analysis in Breast Cancer Patients Undergoing CT-Guided Bone Biopsy: Correlations With Histopathology |
| title_short | CT Texture Analysis in Breast Cancer Patients Undergoing CT-Guided Bone Biopsy: Correlations With Histopathology |
| title_sort | ct texture analysis in breast cancer patients undergoing ct guided bone biopsy correlations with histopathology |
| url | https://doi.org/10.1177/11782234241305886 |
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