Values of MRI Imaging Presentations in the Hepatobiliary Phase, DWI and T2WI Sequences in Predicting Pathological Grades of Intrahepatic Mass-Forming Cholangiocarcinoma

Xing, Li-Hong; Zhuo, Li-Yong; Wang, Jia‐Ning; Zhang, Yan; Zhu, Feng-Ying; Wang, Chu; Yin, Xiaoping; Gao, Bu-Lang

doi:10.3389/fonc.2022.867702

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…The degree of pathological differentiation is an important prognostic indicator for ICC, with poorer differentiation indicating a worse prognosis [10] . Studies on preoperative imaging to predict the degree of histological differentiation of ICC reported several independent risk factors for predicting poorly differentiated ICC, including the presence of an incomplete hypointense rim in the MRI hepatobiliary phase, intrahepatic metastases, absence of centripetal lling of contrast agent in lesions in the portal venous and delayed phases, larger tumor diameters, and higher PET/CT SUVmax and tumor-to-normal liver tissue ratio (TNR) [8,11] . However, our study found no statistically signi cant differences in CT imaging signs between ICCs with the three histological differentiations.…”

Section: Discussionmentioning

confidence: 99%

Machine learning to preoperatively determine histological differentiation of mass-forming intrahepatic cholangiocarcinoma

Zhou,

Zhong

2024

Preprint

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Objective We aim to establish a machine learning model by extracting the radiomic features from CT images and integrating clinical features to preoperatively predict the histological differentiation of mass-forming ICC. Material and Method: Clinical data and CT images were retrospectively collected from 109 ICC patients (male to female = 63:46) in our hospital from January 2013 to October 2023. The machine learning classification algorithms used in this study were random forest (RF), XGBoost (Extreme Gradient Boosting), k-nearest neighbors (KNN), and logistics regression (LR). The area under the curve of the receiver operating characteristic (AUROC) of the model on each class, as well as the macro and micro averages were calculated to comprehensively evaluate the model performance.SHapley Additive exPlanations (SHAP) was used to explain the output of the optimal model. Results Concomitant cirrhosis was more likely to occur in poorly differentiated ICC (p < 0.01), while elevated ALT and AST were more common in moderately differentiated ICC (p = 0.02). The RF model constructed based on radiomic features had moderate performance, with a macro-averaged AUC of 0.72, and an AUC of 0.69 for poorly differentiated ICC, 0.7 for moderately poorly differentiated ICC, and 0.71 for moderately differentiated ICC. Both the clinical features model and the fusion model of clinical + radiomic features performed relatively poorly, with a macro-averaged AUC of 0.51 and 0.57, respectively. Conclusion We directly classified the three histological differentiations of 109 ICC cases and found that the radiomics model performed moderately well. This suggested that a radiomics feature model alone might perform better in classification than a fusion model and that the addition of clinical features.

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Section: Discussionmentioning

confidence: 99%

Machine learning to preoperatively determine histological differentiation of mass-forming intrahepatic cholangiocarcinoma

Zhou,

Zhong

2024

Preprint

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“…Unfortunately, these findings are not specific and can be identified even in other neoplastic lesions, such as adenocarcinoma metastases [ 50 ]. For this purpose, Xing et al tried to define the MRI features predicting mass-forming iCCA in 43 patients with histopathological diagnosis and different degrees of differentiation: the presence/absence of intrahepatic metastases; the grade of the integrity of the hypointense ring in the hepatobiliary phase; and the target sign in T2-weighed sequences were highlighted as independent factors in the identification of differentiation degree of mass-forming iCCAs [ 51 ].…”

Section: Introductionmentioning

confidence: 99%

Contrast-Enhanced Imaging in the Management of Intrahepatic Cholangiocarcinoma: State of Art and Future Perspectives

Cerrito

Ainora

Borriello

et al. 2023

Cancers

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Intrahepatic cholangiocarcinoma (iCCA) represents the second most common liver cancer after hepatocellular carcinoma, accounting for 15% of primary liver neoplasms. Its incidence and mortality rate have been rising during the last years, and total new cases are expected to increase up to 10-fold during the next two or three decades. Considering iCCA’s poor prognosis and rapid spread, early diagnosis is still a crucial issue and can be very challenging due to the heterogeneity of tumor presentation at imaging exams and the need to assess a correct differential diagnosis with other liver lesions. Abdominal contrast-enhanced computed tomography (CT) and magnetic resonance imaging (MRI) plays an irreplaceable role in the evaluation of liver masses. iCCA’s most typical imaging patterns are well-described, but atypical features are not uncommon at both CT and MRI; on the other hand, contrast-enhanced ultrasound (CEUS) has shown a great diagnostic value, with the interesting advantage of lower costs and no renal toxicity, but there is still no agreement regarding the most accurate contrastographic patterns for iCCA detection. Besides diagnostic accuracy, all these imaging techniques play a pivotal role in the choice of the therapeutic approach and eligibility for surgery, and there is an increasing interest in the specific imaging features which can predict tumor behavior or histologic subtypes. Further prognostic information may also be provided by the extraction of quantitative data through radiomic analysis, creating prognostic multi-parametric models, including clinical and serological parameters. In this review, we aim to summarize the role of contrast-enhanced imaging in the diagnosis and management of iCCA, from the actual issues in the differential diagnosis of liver masses to the newest prognostic implications.

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“…Given the indispensable role of imaging in MICC management, investigators have attempted to explore non-invasive imaging biomarkers for accurate differential diagnosis as well as prediction of pathological grade and subtype. [10] , [11] , [12] Studies have also documented the potential prognostic value of radiological traits at dynamic computed tomography (CT)/magnetic resonance (MR) imaging for patients with MICC. [13] , [14] , [15] However, the progress toward clinical translation has been slower than anticipated despite extensive investigations.…”

Section: Introductionmentioning

confidence: 99%

Values of MRI Imaging Presentations in the Hepatobiliary Phase, DWI and T2WI Sequences in Predicting Pathological Grades of Intrahepatic Mass-Forming Cholangiocarcinoma

Cited by 4 publications

References 21 publications

Machine learning to preoperatively determine histological differentiation of mass-forming intrahepatic cholangiocarcinoma

Machine learning to preoperatively determine histological differentiation of mass-forming intrahepatic cholangiocarcinoma

Contrast-Enhanced Imaging in the Management of Intrahepatic Cholangiocarcinoma: State of Art and Future Perspectives

Translating imaging traits of mass-forming intrahepatic cholangiocarcinoma into the clinic: From prognostic to therapeutic insights

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