Prognostic significance of contrast-enhanced CT attenuation value in extrahepatic cholangiocarcinoma

Asayama, Yoshiki; Nishie, Akihiro; Ishigami, Kousei; Ushijima, Yasuhiro; Takayama, Yoshiaki; Okamoto, Daisuke; Fujita, Nobuhiro; Ohtsuka, Takao; Yoshizumi, Tomoharu; Aishima, Shinichi; Oda, Yoshinao; Honda, Hiroshi

doi:10.1007/s00330-016-4621-y

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…34 Recently, studies began to explore the relationship between tumor enhancement patterns in CT images and their malignant biological behavior. [35][36][37][38][39][40] Nevertheless, few studies directly compared the genetic background of tumors with different enhancement patterns in CT. The molecular events which closely related to tumor enhancement features are largely unclear.…”

Section: Discussionmentioning

confidence: 99%

“…Tumor enhancement status on images became a distinctive way to evaluate tumor vascularity in vivo 34 . Recently, studies began to explore the relationship between tumor enhancement patterns in CT images and their malignant biological behavior 35–40 . Nevertheless, few studies directly compared the genetic background of tumors with different enhancement patterns in CT.…”

Section: Discussionmentioning

confidence: 99%

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α‐SMA⁺ cancer‐associated fibroblasts increased tumor enhancement ratio on contrast‐enhanced multidetector‐row computed tomography in stages I–III colon cancer

Wang,

Han,

et al. 2023

J of Gastro and Hepatol

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Background and AimOur prior research revealed that the tumor enhancement ratio (TER) on triphasic abdominal contrast‐enhanced MDCT (CE‐MDCT) scans was a prognostic factor for patients with stages I–III colon cancer. Building upon this finding, the present study aims to investigate the proteomic changes in colon cancer patients with varying TER values.MethodsTER was analyzed on preoperative triphasic CE‐MDCT scans of 160 stages I–III colon cancer patients. The survival outcomes of those in the low‐TER and high‐TER groups were compared. Proteomic analysis on colon cancer tissues was performed by mass spectrometry (MS) and verified by immune‐histological chemistry (IHC) assays. In vivo, mouse xenograft models were employed to test the function of target proteins identified through the MS. CE‐MDCT scans were conducted on mice xenografts, and the TER values were compared.ResultsPatients in the high‐TER group had a significantly worse prognosis than those in the low‐TER group. Proteomic analysis of colon cancer tissues revealed 153 differentially expressed proteins between the two groups. A correlation between TER and the abundance of α‐SMA protein in tumor tissue was observed. IHC assays further confirmed that α‐SMA protein expression was significantly increased in high‐TER colon cancer, predominantly in cancer‐associated fibroblasts (CAFs) within the cancer stroma. Moreover, CAFs promoted the growth of CRC xenografts in vivo and increased TER.ConclusionsOur study identified the distinct protein changes in colon cancer with low and high TER for the first time. The presence of CAFs may promote the growth of colon cancer and contribute to an increased TER.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

α‐SMA⁺ cancer‐associated fibroblasts increased tumor enhancement ratio on contrast‐enhanced multidetector‐row computed tomography in stages I–III colon cancer

Wang,

Han,

et al. 2023

J of Gastro and Hepatol

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“…Several imaging techniques could diagnose ECC, including ultrasonography (US) [ 7 ], computerized tomography (CT) [ 8 ], positron emission computerized tomography (PET-CT) [ 9 ], magnetic resonance imaging (MRI) [ 10 , 11 ], and cholangioscopy [ 12 ], etc. At present, MRI has become the imaging modality of choice for diagnosis and staging of CCA, with high soft-tissue contrast to help better detect and identify the infiltrating tumors [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma

Tang

Yang

et al. 2021

BMC Cancer

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Background Radiomics may provide more objective and accurate predictions for extrahepatic cholangiocarcinoma (ECC). In this study, we developed radiomics models based on magnetic resonance imaging (MRI) and machine learning to preoperatively predict differentiation degree (DD) and lymph node metastasis (LNM) of ECC. Methods A group of 100 patients diagnosed with ECC was included. The ECC status of all patients was confirmed by pathology. A total of 1200 radiomics features were extracted from axial T1 weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion weighted imaging (DWI), and apparent diffusion coefficient (ADC) images. A systematical framework considering combinations of five feature selection methods and ten machine learning classification algorithms (classifiers) was developed and investigated. The predictive capabilities for DD and LNM were evaluated in terms of area under precision recall curve (AUPRC), area under the receiver operating characteristic (ROC) curve (AUC), negative predictive value (NPV), accuracy (ACC), sensitivity, and specificity. The prediction performance among models was statistically compared using DeLong test. Results For DD prediction, the feature selection method joint mutual information (JMI) and Bagging Classifier achieved the best performance (AUPRC = 0.65, AUC = 0.90 (95% CI 0.75–1.00), ACC = 0.85 (95% CI 0.69–1.00), sensitivity = 0.75 (95% CI 0.30–0.95), and specificity = 0.88 (95% CI 0.64–0.97)), and the radiomics signature was composed of 5 selected features. For LNM prediction, the feature selection method minimum redundancy maximum relevance and classifier eXtreme Gradient Boosting achieved the best performance (AUPRC = 0.95, AUC = 0.98 (95% CI 0.94–1.00), ACC = 0.90 (95% CI 0.77–1.00), sensitivity = 0.75 (95% CI 0.30–0.95), and specificity = 0.94 (95% CI 0.72–0.99)), and the radiomics signature was composed of 30 selected features. However, these two chosen models were not significantly different to other models of higher AUC values in DeLong test, though they were significantly different to most of all models. Conclusion MRI radiomics analysis based on machine learning demonstrated good predictive accuracies for DD and LNM of ECC. This shed new light on the noninvasive diagnosis of ECC.

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“…In order to achieve early diagnosis, researchers have made a lot of efforts 3 . Magnetic resonance cholangiopancreatography 4 and contrast‐enhanced computed tomography 5 could identify the bile duct stricture with lesion. Positron emission tomography/computed tomography could predict the possible reason of cholangiocarcinoma on high clinical suspicion 6 .…”

Section: Introductionmentioning

confidence: 99%

The role of bile acid subtypes in the diagnosis of cholangiocarcinoma

Wang

Tian

Jin

et al. 2021

Asia-Pac J Clncl Oncology

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Objectives: To find the potential biomarkers of cholangiocarcinoma, form a biomarker package, evaluate its efficiency, and validate it. Methods: R software was used to analyze the differential expression of mRNAs between cholangiocarcinoma and adjacent nontumorous tissues, obtained from The Cancer Genome Atlas (TCGA), and enrich the KEGG pathway. Metabo-Profile Inc. performed the comprehensive bile acid profiling and quantitation. The training set concluded 20 cholangiocarcinoma and 20 nontumorous volunteers. Receiver operating characteristic (ROC) curve and accompanying area under the curve (AUC) was calculated. The top four bile acids formed a new biomarker package. The validation set included 15 cholangiocarcinoma and 15 nontumorous, and the sensitivity and specificity of the new biomarker package were tested.Results: Gene expression of 36 cholangiocarcinoma and nine adjacent nontumorous tissues was obtained in January 2020. Totally 9887 differential genes were eligible (logFC ≥ 1 or ≤ -1, P < 0.05, and adjust P < 0.01). GO analysis showed that 20 KEGG pathways were enriched, including primary bile acid biosynthesis and bile secretion. Comprehensive bile acid profiling and quantitation showed 15 differential bile acid types, and the ROC-AUC was between 0.953 and 0.750. HDCA, isoLCA, bCDCA, and DCA were selected to form a biomarker package. The Logit (p = cholangiocarcinoma) = 7.898 -3.70*(1isoLCA) -0.444*(bCDCA) + 0.415*(HDCA) + 0.041*DCA. Its ROC-AUC was 0.944. In the validation set, the sensitivity was 0.933 and the specificity was 0.867. Conclusion:Bile acid types package was efficient to distinguish nontumorous population and cholangiocarcinoma. The difference might be associated to the downregulation of primary bile acid biosynthesis and bile secretion pathway of cholangiocarcinoma.

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Prognostic significance of contrast-enhanced CT attenuation value in extrahepatic cholangiocarcinoma

Abstract: • Dynamic contrast study can evaluate the aggressiveness of extrahepatic cholangiocarcinoma. • A lower washout ratio was an independent prognostic factor for overall survival. • CT can predict survival and inform decisions on surgical options or chemotherapy.

Cited by 8 publications

References 21 publications

α‐SMA⁺ cancer‐associated fibroblasts increased tumor enhancement ratio on contrast‐enhanced multidetector‐row computed tomography in stages I–III colon cancer

α‐SMA⁺ cancer‐associated fibroblasts increased tumor enhancement ratio on contrast‐enhanced multidetector‐row computed tomography in stages I–III colon cancer

Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma

The role of bile acid subtypes in the diagnosis of cholangiocarcinoma

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Prognostic significance of contrast-enhanced CT attenuation value in extrahepatic cholangiocarcinoma

Abstract: • Dynamic contrast study can evaluate the aggressiveness of extrahepatic cholangiocarcinoma. • A lower washout ratio was an independent prognostic factor for overall survival. • CT can predict survival and inform decisions on surgical options or chemotherapy.

Cited by 8 publications

References 21 publications

α‐SMA+ cancer‐associated fibroblasts increased tumor enhancement ratio on contrast‐enhanced multidetector‐row computed tomography in stages I–III colon cancer

α‐SMA+ cancer‐associated fibroblasts increased tumor enhancement ratio on contrast‐enhanced multidetector‐row computed tomography in stages I–III colon cancer

Machine learning-based Radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma

The role of bile acid subtypes in the diagnosis of cholangiocarcinoma

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α‐SMA⁺ cancer‐associated fibroblasts increased tumor enhancement ratio on contrast‐enhanced multidetector‐row computed tomography in stages I–III colon cancer

α‐SMA⁺ cancer‐associated fibroblasts increased tumor enhancement ratio on contrast‐enhanced multidetector‐row computed tomography in stages I–III colon cancer