CT Texture Analysis of Perihilar Cholangiocarcinoma-Associations With Tumor Grading, Tumor Markers and Clinical Outcome.
Klatskin tumor
computed tomography
extrahepatic cholangiocarcinoma
texture analysis
Journal
Cancer reports (Hoboken, N.J.)
ISSN: 2573-8348
Titre abrégé: Cancer Rep (Hoboken)
Pays: United States
ID NLM: 101747728
Informations de publication
Date de publication:
Sep 2024
Sep 2024
Historique:
revised:
30
05
2024
received:
30
01
2024
accepted:
30
06
2024
medline:
23
9
2024
pubmed:
23
9
2024
entrez:
23
9
2024
Statut:
ppublish
Résumé
Texture analysis derived from computed tomography (CT) may provide clinically relevant imaging biomarkers associated with tumor histopathology. Perihilar cholangiocarcinoma is a malignant disease with an overall poor prognosis. The present study sought to elucidate possible associations between texture features derived from CT images with grading, tumor markers, and survival in extrahepatic, perihilar cholangiocarcinomas tumors. This retrospective study included 22 patients (10 females, 45%) with a mean age of 71.8 ± 8.7 years. Texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. Survival and clinical parameters were used as primary study outcomes. In discrimination analysis, "S(1,1)SumVarnc" was statistically significantly different between patients with long-term survival and nonlong-term survival (mean 275.8 ± 32.6 vs. 239.7 ± 26.0, p = 0.01). The first-order parameter "skewness" was associated with the tumor marker "carcinoembryonic antigen" (CEA) (r = -0.7, p = 0.01). A statistically significant correlation of the texture parameter "S(5,0)SumVarnc" with tumor grading was identified (r = -0.6, p < 0.01). Several other texture features correlated with tumor markers CA-19-9 and AFP, as well as with T and N stage of tumors. Several texture features derived from CT images were associated with tumor characteristics and survival in patients with perihilar cholangiocarcinomas. CT texture features could be used as valuable novel imaging markers in clinical routine.
Sections du résumé
BACKGROUND
BACKGROUND
Texture analysis derived from computed tomography (CT) may provide clinically relevant imaging biomarkers associated with tumor histopathology. Perihilar cholangiocarcinoma is a malignant disease with an overall poor prognosis.
AIMS
OBJECTIVE
The present study sought to elucidate possible associations between texture features derived from CT images with grading, tumor markers, and survival in extrahepatic, perihilar cholangiocarcinomas tumors.
METHODS
METHODS
This retrospective study included 22 patients (10 females, 45%) with a mean age of 71.8 ± 8.7 years. Texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. Survival and clinical parameters were used as primary study outcomes.
RESULTS
RESULTS
In discrimination analysis, "S(1,1)SumVarnc" was statistically significantly different between patients with long-term survival and nonlong-term survival (mean 275.8 ± 32.6 vs. 239.7 ± 26.0, p = 0.01). The first-order parameter "skewness" was associated with the tumor marker "carcinoembryonic antigen" (CEA) (r = -0.7, p = 0.01). A statistically significant correlation of the texture parameter "S(5,0)SumVarnc" with tumor grading was identified (r = -0.6, p < 0.01). Several other texture features correlated with tumor markers CA-19-9 and AFP, as well as with T and N stage of tumors.
CONCLUSION
CONCLUSIONS
Several texture features derived from CT images were associated with tumor characteristics and survival in patients with perihilar cholangiocarcinomas. CT texture features could be used as valuable novel imaging markers in clinical routine.
Substances chimiques
Biomarkers, Tumor
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2132Informations de copyright
© 2024 The Author(s). Cancer Reports published by Wiley Periodicals LLC.
Références
M. Incoronato, M. Aiello, T. Infante, et al., “Radiogenomic Analysis of Oncological Data: A Technical Survey,” International Journal of Molecular Sciences 18, no. 4 (2017): 805.
G. Wu, A. Jochems, T. Refaee, et al., “Structural and Functional Radiomics for Lung Cancer,” European Journal of Nuclear Medicine and Molecular Imaging 48, no. 12 (2021): 3961–3974.
N. Just, “Improving Tumour Heterogeneity MRI Assessment With Histograms,” British Journal of Cancer 111, no. 12 (2014): 2205–2213.
H. J. Meyer, J. Leonhardi, A. K. Höhn, et al., “CT Texture Analysis of Pulmonary Neuroendocrine Tumors—Associations With Tumor Grading and Proliferation,” Journal of Clinical Medicine 10, no. 23 (2021): 5571.
H. J. Meyer, G. Hamerla, A. K. Höhn, and A. Surov, “CT Texture Analysis—Correlations With Histopathology Parameters in Head and Neck Squamous Cell Carcinomas,” Frontiers in Oncology 9 (2019): 444.
H. J. Meyer, S. Schob, A. K. Höhn, and A. Surov, “MRI Texture Analysis Reflects Histopathology Parameters in Thyroid Cancer—A First Preliminary Study,” Translational Oncology 10, no. 6 (2017): 911–916.
L. G. T. Morris, N. Riaz, A. Desrichard, et al., “Pan‐Cancer Analysis of Intratumor Heterogeneity as a Prognostic Determinant of Survival,” Oncotarget 7, no. 9 (2016): 10051–10063.
A. Al Mahjoub, V. Bouvier, B. Menahem, et al., “Epidemiology of Intrahepatic, Perihilar, and Distal Cholangiocarcinoma in the French Population,” European Journal of Gastroenterology & Hepatology 31, no. 6 (2019): 678–684.
B. Blechacz, M. Komuta, T. Roskams, and G. J. Gores, “Clinical Diagnosis and Staging of Cholangiocarcinoma,” Nature Reviews. Gastroenterology & Hepatology 8, no. 9 (2011): 512–522.
C. Song, K. Kim, E. K. Chie, et al., “Nomogram Prediction of Survival and Recurrence in Patients With Extrahepatic Bile Duct Cancer Undergoing Curative Resection Followed by Adjuvant Chemoradiation Therapy,” International Journal of Radiation Oncology, Biology, Physics 87, no. 3 (2013): 499–504.
B. Groot Koerkamp, J. K. Wiggers, M. Gonen, et al., “Survival After Resection of Perihilar Cholangiocarcinoma—Development and External Validation of a Prognostic Nomogram,” Annals of Oncology 26, no. 9 (2015): 1930–1935.
H. W. Chen, A. Z. Pan, Z. J. Zhen, et al., “Preoperative Evaluation of Resectability of Klatskin Tumor With 16‐MDCT Angiography and Cholangiography,” AJR. American Journal of Roentgenology 186, no. 6 (2006): 1580–1586.
H. Y. Lee, S. H. Kim, J. M. Lee, et al., “Preoperative Assessment of Resectability of Hepatic Hilar Cholangiocarcinoma: Combined CT and Cholangiography With Revised Criteria,” Radiology 239, no. 1 (2006): 113–121.
I. Joo, J. M. Lee, and J. H. Yoon, “Imaging Diagnosis of Intrahepatic and Perihilar Cholangiocarcinoma: Recent Advances and Challenges,” Radiology 288, no. 1 (2018): 7–13.
S. M. Strasberg, “Nomenclature of Hepatic Anatomy and Resections: A Review of the Brisbane 2000 System,” Journal of Hepato‐Biliary‐Pancreatic Surgery 12, no. 5 (2005): 351–355.
H. M. Hau, M. Devantier, N. Jahn, et al., “Impact of Body Mass Index on Tumor Recurrence in Patients Undergoing Liver Resection for Perihilar Cholangiocarcinoma (pCCA),” Cancers (Basel) 13, no. 19 (2021): 4772.
M. Shimada, Y. Yamashita, S. Aishima, K. Shirabe, K. Takenaka, and K. Sugimachi, “Value of Lymph Node Dissection During Resection of Intrahepatic Cholangiocarcinoma,” British Journal of Surgery 88, no. 11 (2002): 1463–1466.
M. Strzelecki, P. Szczypinski, A. Materka, and A. Klepaczko, “A Software Tool for Automatic Classification and Segmentation of 2D/3D Medical Images,” Nuclear Instruments and Methods in Physics Research 702 (2013): 137–140.
P. M. Szczypiński, M. Strzelecki, A. Materka, and A. Klepaczko, “MaZda—A Software Package for Image Texture Analysis,” Computer Methods and Programs in Biomedicine 94, no. 1 (2009): 66–76.
J. Fruehwald‐Pallamar, C. Czerny, L. Holzer‐Fruehwald, et al., “Texture‐Based and Diffusion‐Weighted Discrimination of Parotid Gland Lesions on MR Images at 3.0 Tesla,” NMR in Biomedicine 26, no. 11 (2013): 1372–1379.
Y. E. Chung, M. J. Kim, Y. N. Park, et al., “Varying Appearances of Cholangiocarcinoma: Radiologic‐Pathologic Correlation,” Radiographics 29, no. 3 (2009): 683–700.
G. Spolverato, M. Y. Yakoob, Y. Kim, et al., “The Impact of Surgical Margin Status on Long‐Term Outcome After Resection for Intrahepatic Cholangiocarcinoma,” Annals of Surgical Oncology 22, no. 12 (2015): 4020–4028.
H. M. Hau, F. Meyer, N. Jahn, S. Rademacher, R. Sucher, and D. Seehofer, “Prognostic Relevance of the Eighth Edition of TNM Classification for Resected Perihilar Cholangiocarcinoma,” Journal of Clinical Medicine 9, no. 10 (2020): 3152.
Z. Sun, X. Sun, J. Guo, et al., “Prognostic Influence for Hilar Cholangiocarcinoma and Comparisons of Prognostic Values of Mayo Staging and TNM Staging Systems,” Medicine 101, no. 49 (2022): e32250.
H. Qin, X. Hu, J. Zhang, et al., “Machine‐Learning Radiomics to Predict Early Recurrence in Perihilar Cholangiocarcinoma After Curative Resection,” Liver International 41, no. 4 (2021): 837–850.
J. Zhao, W. Zhang, C. L. Fan, et al., “Development and Validation of Preoperative Magnetic Resonance Imaging‐Based Survival Predictive Nomograms for Patients With Perihilar Cholangiocarcinoma After Radical Resection: A Pilot Study,” European Journal of Radiology 138 (2021): 109631.
J. Zhao, W. Zhang, Y. Zhu, et al., “Development and Validation of Noninvasive MRI‐Based Signature for Preoperative Prediction of Early Recurrence in Perihilar Cholangiocarcinoma,” Journal of Magnetic Resonance Imaging 55, no. 3 (2022): 787–802.
F. Fiz, C. Masci, G. Costa, et al., “PET/CT‐Based Radiomics of Mass‐Forming Intrahepatic Cholangiocarcinoma Improves Prediction of Pathology Data and Survival,” European Journal of Nuclear Medicine and Molecular Imaging 49, no. 10 (2022): 3387–3400.
D. Mackin, X. Fave, L. Zhang, et al., “Measuring Computed Tomography Scanner Variability of Radiomics Features,” Investigative Radiology 50, no. 11 (2015): 757–765.
L. J. Jensen, D. Kim, T. Elgeti, I. G. Steffen, B. Hamm, and S. N. Nagel, “Stability of Radiomic Features Across Different Region of Interest Sizes—A CT and MR Phantom Study,” Tomography 7, no. 2 (2021): 238–252.
R. Kakino, M. Nakamura, T. Mitsuyoshi, et al., “Comparison of Radiomic Features in Diagnostic CT Images With and Without Contrast Enhancement in the Delayed Phase for NSCLC Patients,” Physica Medica 69 (2020): 176–182.
N. Rekhtman, “Lung Neuroendocrine Neoplasms: Recent Progress and Persistent Challenges,” Modern Pathology 35 (2022): 36–50.
I. S. Gruzdev, K. A. Zamyatina, V. S. Tikhonova, et al., “Reproducibility of CT Texture Features of Pancreatic Neuroendocrine Neoplasms,” European Journal of Radiology 133 (2020): 109371.