In pancreatic cancer, imaging plays an essential role in surveillance, diagnosis, resectability evaluation, and treatment response evaluation. Pancreatic cancer surveillance in high-risk individuals has been attempted using endoscopic ultrasound (EUS) or magnetic resonance imaging (MRI). Imaging diagnosis and resectability evaluation are the most important factors influencing treatment decisions, where computed tomography (CT) is the preferred modality. EUS, MRI, and positron emission tomography play a complementary role to CT. Treatment response evaluation is of increasing clinical importance, especially in patients undergoing neoadjuvant therapy. This review aimed to comprehensively review the role of imaging in relation to the current treatment strategy for pancreatic cancer, including surveillance, diagnosis, evaluation of resectability and treatment response, and prediction of prognosis.

The cross-sectional imaging findings play a crucial role in the diagnosis of hepatocellular carcinoma (HCC). Recent studies have shown that imaging findings of HCC are not only relevant for the diagnosis of HCC, but also for identifying genetic and pathologic characteristics and determining prognosis. Imaging findings such as rim arterial phase hyperenhancement, arterial phase peritumoral hyperenhancement, hepatobiliary phase peritumoral hypointensity, non-smooth tumor margin, low apparent diffusion coefficient, and the LR-M category of the Liver Imaging-Reporting and Data System have been reported to be associated with poor prognosis. In contrast, imaging findings such as enhancing capsule appearance, hepatobiliary phase hyperintensity, and fat in mass have been reported to be associated with a favorable prognosis. Most of these imaging findings were examined in retrospective, single-center studies that were not adequately validated. However, the imaging findings can be applied for deciding the treatment strategy for HCC, if their significance can be confirmed by a large multicenter study. In this literature, we would like to review imaging findings related to the prognosis of HCC as well as their associated clinicopathological characteristics.

Background: Cranioplasty for the treatment of cephalhematomas in small infants with limited blood volume is challenging because of massive bleeding. This study aimed to elucidate the correlation between cephalhematoma size and intraoperative blood loss and identify criteria that can predict large intraoperative blood loss. Methods: We reviewed the medical records of 120 pediatric patients aged less than 24 months who underwent cranioplasty for treatment of a cephalhematoma. The cephalhematoma sizes in preoperative brain computed tomography (CT) were measured using ImageJ. Results: Pearson correlation showed that the cephalhematoma size in the pre-operative brain CT was weakly correlated with intraoperative blood loss (Pearson coefficient = 0.192, P = 0.037). In a multivariable logistic regression analysis, a cephalhematoma size greater than 113.5 cm3 was found to be a risk factor for large blood loss. The area under the curve in the receiver operating characteristic plot of the multivariable model was 0.714 (0.619–0.809). Conclusions A cephalhematoma size cutoff value of 113.5 cm3, as measured in the preoperative CT imaging, can predict intraoperative blood loss exceeding 30% of the total body blood volume. The establishment of a transfusion strategy prior to surgery based on cephalhematoma size could be useful in pediatric cranioplasty.

Background: Cranioplasty for the treatment of cephalhematomas in small infants with limited blood volume is challenging because of massive bleeding. This study aimed to elucidate the correlation between cephalhematoma size and intraoperative blood loss and identify criteria that can predict large intraoperative blood loss. Methods: We reviewed the medical records of 120 pediatric patients aged less than 24 months who underwent cranioplasty for treatment of a cephalhematoma. The cephalhematoma sizes in preoperative brain computed tomography (CT) were measured using ImageJ. Results: Pearson correlation showed that the cephalhematoma size in the pre-operative brain CT was weakly correlated with intraoperative blood loss (Pearson coefficient = 0.192, P = 0.037). In a multivariable logistic regression analysis, a cephalhematoma size greater than 113.5 cm3 was found to be a risk factor for large blood loss. The area under the curve in the receiver operating characteristic plot of the multivariable model was 0.714 (0.619–0.809). Conclusions A cephalhematoma size cutoff value of 113.5 cm3, as measured in the preoperative CT imaging, can predict intraoperative blood loss exceeding 30% of the total body blood volume. The establishment of a transfusion strategy prior to surgery based on cephalhematoma size could be useful in pediatric cranioplasty.

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a malignant primary liver carcinoma characterized by the unequivocal presence of both hepatocytic and cholangiocytic differentiation within the same tumor. Recent research has highlighted that cHCC-CCAs are more heterogeneous than previously expected. In the updated consensus terminology and WHO 2019 classification, “classical type” and “subtypes with stem-cell features” of the WHO 2010 classification are no longer recommended. Instead, it is recommended that the presence and percentages of various histopathologic components and stem-cell features be mentioned in the pathologic report. The new terminology and classification enable the exchange of clearer and more objective information about cHCC-CCAs, facilitating multi-center and multinational research. However, there are limitations to the diagnosis of cHCC-CCA by imaging and biopsy. cHCC-CCAs showing typical imaging findings of HCC could be misdiagnosed as HCC and subjected to inappropriate treatment, if other clinical findings are not sufficiently considered. cHCC-CCAs showing at least one of the CCA-like imaging features or unusual clinical features should be subjected to biopsy. There may be a sampling error for the biopsy diagnosis of cHCC-CCA. An optimized diagnostic algorithm integrating clinical, radiological, and histopathologic information of biopsy is required to resolve these diagnostic pitfalls.

Background/Aims: Intrahepatic cholangiocarcinoma (iCCA) with a ductal plate malformation (DPM) pattern is a recently recognized rare variant. The genomic profile of iCCA with DPM pattern needs to be elucidated. Methods: Cases of iCCA with DPM pattern were retrospectively reviewed based on the medical records, pathology slides, and magnetic resonance imaging (MRI) reports collected between 2010 to 2019 at a single center. Massive parallel sequencing was performed for >500 cancerrelated genes. Results: From a total of 175 iCCAs, five (2.9%) cases of iCCA with DPM pattern were identified. All cases were of the small duct type, and background liver revealed chronic B viral or alcoholic hepatitis. Three iCCAs with DPM pattern harbored MRI features favoring the diagnosis of hepatocellular carcinoma, whereas nonspecific imaging features were observed in two cases. All patients were alive without recurrence during an average follow-up period of 57 months. Sequencing data revealed 64 mutated genes in the five cases, among which FGFR2 and PTPRT were most frequently mutated (three cases each) including an FGFR2-TNC fusion in one case. Mutations in ARID1A and CDKN2A were found in two cases, and mutations in TP53, BAP1, ATM, NF1, and STK11 were observed in one case each. No IDH1, KRAS, or PBRM1 mutations were found. Conclusions iCCAs with DPM pattern have different clinico-radio-pathologic and genetic characteristics compared to conventional iCCAs. Moreover, FGFR2 and FGFR2 variants were identified. Altogether, these findings further suggest that iCCA with DPM pattern represents a specific subtype of small duct type iCCA.

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a malignant primary liver carcinoma characterized by the unequivocal presence of both hepatocytic and cholangiocytic differentiation within the same tumor. Recent research has highlighted that cHCC-CCAs are more heterogeneous than previously expected. In the updated consensus terminology and WHO 2019 classification, “classical type” and “subtypes with stem-cell features” of the WHO 2010 classification are no longer recommended. Instead, it is recommended that the presence and percentages of various histopathologic components and stem-cell features be mentioned in the pathologic report. The new terminology and classification enable the exchange of clearer and more objective information about cHCC-CCAs, facilitating multi-center and multinational research. However, there are limitations to the diagnosis of cHCC-CCA by imaging and biopsy. cHCC-CCAs showing typical imaging findings of HCC could be misdiagnosed as HCC and subjected to inappropriate treatment, if other clinical findings are not sufficiently considered. cHCC-CCAs showing at least one of the CCA-like imaging features or unusual clinical features should be subjected to biopsy. There may be a sampling error for the biopsy diagnosis of cHCC-CCA. An optimized diagnostic algorithm integrating clinical, radiological, and histopathologic information of biopsy is required to resolve these diagnostic pitfalls.

In patients at high risk of hepatocellular carcinoma (HCC), such as those with chronic hepatitis or cirrhosis, the confirmative diagnosis of HCC can be made solely from characteristic imaging findings on contrast-enhanced CT or MR scans. However, in daily practice, HCCs showing atypical imaging features are frequently encountered. Since the criteria for diagnosis of HCC is based on dynamic contrast enhancement patterns, it is essential to thoroughly understand these patterns. In this article, we aim to use gadoxetate-enhanced MRI to comprehensively review the HCC enhancement patterns and the associated histopathologic findings with their prognostic factors.

Hepatocellular carcinoma (HCC) progresses through multiple stages of hepatocarcinogenesis, with each stage characterized by specific changes in vascular supply, drainage, and microvascular structure. These vascular changes significantly influence the imaging findings of HCC, enabling non-invasive diagnosis. Vascular changes in HCC are closely related to aggressive histological characteristics and treatment responses. Venous drainage from the tumor toward the portal vein in the surrounding liver facilitates vascular invasion, and the unique microvascular pattern of vessels that encapsulate the tumor cluster (known as a VETC pattern) promotes vascular invasion and metastasis. Systemic treatments for HCC, which are increasingly being used, primarily target angiogenesis and immune checkpoint pathways, which are closely intertwined. By understanding the complex relationship between histopathological vascular changes in hepatocarcinogenesis and their implications for imaging findings, radiologists can enhance the accuracy of imaging diagnosis and improve the prediction of prognosis and treatment response.This, in turn, will ultimately lead to better patient care.

Hepatocellular carcinoma (HCC) progresses through multiple stages of hepatocarcinogenesis, with each stage characterized by specific changes in vascular supply, drainage, and microvascular structure. These vascular changes significantly influence the imaging findings of HCC, enabling non-invasive diagnosis. Vascular changes in HCC are closely related to aggressive histological characteristics and treatment responses. Venous drainage from the tumor toward the portal vein in the surrounding liver facilitates vascular invasion, and the unique microvascular pattern of vessels that encapsulate the tumor cluster (known as a VETC pattern) promotes vascular invasion and metastasis. Systemic treatments for HCC, which are increasingly being used, primarily target angiogenesis and immune checkpoint pathways, which are closely intertwined. By understanding the complex relationship between histopathological vascular changes in hepatocarcinogenesis and their implications for imaging findings, radiologists can enhance the accuracy of imaging diagnosis and improve the prediction of prognosis and treatment response.This, in turn, will ultimately lead to better patient care.