Preoperative Radiologic Evaluation of Cholangiocarcinoma

Differentiating HCC from ICC and prediction of ICC grade based on MRI deep-radiomics: Using lesions and their extended regions

PURPOSE

This study aimed to evaluate the ability of MRI-based intratumoral and peritumoral radiomics features of liver tumors to differentiate between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) and to predict ICC differentiation.

METHODS

This study retrospectively collected 87 HCC patients and 75 ICC patients who were confirmed pathologically. The standard region of interest (ROI) of the lesion drawn by the radiologist manually shrank inward and expanded outward to form multiple ROI extended regions. A three-step feature selection method was used to select important radiomics features and convolution features from extended regions. The predictive performance of several machine learning classifiers on dominant feature sets was compared. The extended region performance was assessed by area under the curve (AUC), specificity, sensitivity, F1-score and accuracy.

RESULTS

The performance of the model is further improved by incorporating convolution features. Compared with the standard ROI, the extended region obtained better prediction performance, among which 6 mm extended region had the best prediction ability (Classification: AUC = 0.96, F1-score = 0.94, Accuracy: 0.94; Grading: AUC = 0.94, F1-score = 0.93, Accuracy = 0.89).

CONCLUSION

Larger extended region and fusion features can improve tumor predictive performance and have potential value in tumor radiology.

Radiomics-Based Preoperative Prediction of Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma Using Contrast-Enhanced Computed Tomography

BACKGROUND

Lymph node (LN) metastasis is significantly associated with worse prognosis for patients with intrahepatic cholangiocarcinoma (ICC). Improvement in preoperative assessment on LN metastasis helps in treatment decision-making. We aimed to investigate the role of radiomics-based method in predicting LN metastasis for patients with ICC.

METHODS

A total of 296 patients with ICC who underwent curative-intent hepatectomy and lymphadenectomy at two centers in China were analyzed. Radiomic features, including histogram- and wavelet-based features, shape and size features, and texture features were extracted from four-phase computerized tomography (CT) images. The clinical and conventional radiological variables which were independently associated with LN metastasis were also identified. A combined nomogram predicting LN metastasis was developed, and its performance was determined by discrimination, calibration, and stratification of long-term prognosis. The results were validated by the internal and external validation cohorts.

RESULTS

Twenty-four radiomic features were selected into the nomogram. The established nomogram demonstrated good discrimination and calibration, with areas under the curve (AUCs) of 0.98 [95% confidence interval (CI) 0.96-0.99], 0.93 (0.88-0.98), and 0.89 (0.81-0.96) in the training and two validation cohorts, respectively. The 5-year overall survival (OS) and recurrence-free survival (RFS) rates of patients with high risk of LN metastasis as grouped by nomogram were poorer than those of patients with low risk in the training cohort (OS 28.8% versus 53.9%, p < 0.001; RFS 26.3% versus 44.2%, p = 0.001). Similar results were observed in the two validation cohorts.

CONCLUSIONS

Radiomics-based method provided accurate prediction of LN metastasis and prognostic assessment for ICC patients, and might aid the preoperative surgical decision.

Artificial intelligence using advanced imaging techniques and cholangiocarcinoma: Recent advances and future direction

While cholangiocarcinoma represents only about 3% of all gastrointestinal tumors, it has a dismal survival rate, usually because it is diagnosed at a late stage. The utilization of Artificial Intelligence (AI) in medicine in general, and in gastroenterology has made gigantic steps. However, the application of AI for biliary disease, in particular for cholangiocarcinoma, has been sub-optimal. The use of AI in combination with clinical data, cross-sectional imaging (computed tomography, magnetic resonance imaging) and endoscopy (endoscopic ultrasound and cholangioscopy) has the potential to significantly improve early diagnosis and the choice of optimal therapeutic options, leading to a transformation in the prognosis of this feared disease. In this review we summarize the current knowledge on the use of AI for the diagnosis and management of cholangiocarcinoma and point to future directions in the field.

Challenging biliary strictures: pathophysiological features, differential diagnosis, diagnostic algorithms, and new clinically relevant biomarkers - part 1

It is frequently challenging to make the correct diagnosis in patients with biliary strictures. This is particularly important as errors may have disastrous consequences. Benign-appearing strictures treated with stents may later be revealed to be malignant and unnecessary surgery for benign strictures carries a high morbidity rate. In the first part of the review, the essential information that clinicians need to know about diseases responsible for biliary strictures is presented, with a focus on the most recent data. Then, the characteristics and pitfalls of the methods used to make the diagnosis are summarized. These include serum biomarkers, imaging studies, and endoscopic modalities. As tissue diagnosis is the only 100% specific tool, it is described in detail, including techniques for tissue acquisition and their yields, how to prepare samples, and what to expect from the pathologist. Tricks to increase diagnostic yields are described. Clues are then presented for the differential diagnosis between primary and secondary sclerosing cholangitis, IgG4-related sclerosing cholangitis, cholangiocarcinoma, pancreatic cancer, autoimmune pancreatitis, and less frequent diseases. Finally, algorithms that will help to achieve the correct diagnosis are proposed.

Intravoxel incoherent motion diffusion-weighted imaging to differentiate hepatocellular carcinoma from intrahepatic cholangiocarcinoma

The present study aimed to explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC). This study included 65 patients with malignant hepatic nodules (55 with HCC, 10 with ICC), and 17 control patients with normal livers. All patients underwent IVIM-DWI scans on a 3.0 T magnetic resonance imaging (MRI) scanner. The standard apparent diffusion coefficient (ADC), pure diffusion coefficient (Dslow), pseudo-diffusion coefficient (Dfast), and perfusion fraction (f) were obtained. Differences in the parameters among the groups were analysed using one-way ANOVA, with p < 0.05 indicating statistical significance. Receiver operating characteristic (ROC) curve analysis was used to compare the efficacy of each parameter in differentiating HCC from ICC. ADC, Dslow, Dfast, f significantly differed among the three groups. ADC and Dslow were significantly lower in the HCC group than in the ICC group, while Dfast was significantly higher in the HCC group than in the ICC group; f did not significantly differ between the HCC and ICC groups. When the cut-off values of ADC, Dslow, and Dfast were 1.27 × 10−3 mm2/s, 0.81 × 10−3 mm2/s, and 26.04 × 10−3 mm2/s, respectively, their diagnostic sensitivities for differentiating HCC from ICC were 98.18%, 58.18%, and 94.55%, their diagnostic specificities were 50.00%, 80.00%, and 80.00%, and their areas under the ROC curve (AUCs) were 0.687, 0.721, and 0.896, respectively. Dfast displayed the largest AUC value. IVIM-DWI can be used to differentiate HCC from ICC.

Intravoxel incoherent motion diffusion-weighted imaging to differentiate hepatocellular carcinoma from intrahepatic cholangiocarcinoma

The present study aimed to explore the value of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICC). This study included 65 patients with malignant hepatic nodules (55 with HCC, 10 with ICC), and 17 control patients with normal livers. All patients underwent IVIM-DWI scans on a 3.0 T magnetic resonance imaging (MRI) scanner. The standard apparent diffusion coefficient (ADC), pure diffusion coefficient (D_slow), pseudo-diffusion coefficient (D_fast), and perfusion fraction (f) were obtained. Differences in the parameters among the groups were analysed using one-way ANOVA, with p < 0.05 indicating statistical significance. Receiver operating characteristic (ROC) curve analysis was used to compare the efficacy of each parameter in differentiating HCC from ICC. ADC, D_slow, D_fast, f significantly differed among the three groups. ADC and D_slow were significantly lower in the HCC group than in the ICC group, while D_fast was significantly higher in the HCC group than in the ICC group; f did not significantly differ between the HCC and ICC groups. When the cut-off values of ADC, D_slow, and D_fast were 1.27 × 10⁻³ mm²/s, 0.81 × 10⁻³ mm²/s, and 26.04 × 10⁻³ mm²/s, respectively, their diagnostic sensitivities for differentiating HCC from ICC were 98.18%, 58.18%, and 94.55%, their diagnostic specificities were 50.00%, 80.00%, and 80.00%, and their areas under the ROC curve (AUCs) were 0.687, 0.721, and 0.896, respectively. D_fast displayed the largest AUC value. IVIM-DWI can be used to differentiate HCC from ICC.

Austrian consensus guidelines on imaging requirements prior to hepatic surgery and during follow-up in patients with malignant hepatic lesions

Rapid advances in imaging technology have improved the detection, characterization and staging of colorectal liver metastases, hepatocellular carcinoma and cholangiocarcinoma. A variety of imaging modalities are available and play a pivotal role in the work-up of patients, particularly as imaging findings determine resectability. Surgery often represents the only measure that can render long-term survival possible. Imaging is also indispensable for the assessment of responses to neoadjuvant treatment and for the detection of recurrence. At a consensus meeting held in June 2017 in Vienna, Austria, Austrian experts in the fields of surgery and radiology discussed imaging requirements prior to and after hepatic surgery for malignant liver lesions. This consensus was refined by online voting on a total of 47 items. Generally, the degree of consensus was high. The recommendations relate to the type of preferred preoperative imaging modalities, technical settings with respect to computed tomography and magnetic resonance imaging, use of contrast agents, reporting, postoperative follow-up, and long-term follow-up. Taking local resources into account, these consensus recommendations can be implemented in daily clinical practice at specialized centers as well as outpatient diagnostic institutes in Austria.

Intrahepatic cholangiocarcinoma in the setting of HBV-related cirrhosis: Differentiation with hepatocellular carcinoma by using Intravoxel incoherent motion diffusion-weighted MR imaging

Accurate preoperative differentiation of intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) in the setting of cirrhotic liver is of great clinical significance because the treatment and prognosis of these entities differ markedly. Through a retrospectively research, we sought to determine the diagnostic performances of intravoxel incoherent motion (IVIM) and diffusion weighted imaging (DWI) parameters in the differentiating of ICC and HCC. According to the results, we found that apparent diffusion coefficient (ADC) derived from mono-exponential model and true ADC (ADC^slow) derived from bi-exponential model can be used to distinguish the ICC and HCC, and ADC^slowentailed the higher diagnostic performance than ADC. However, pseudo-ADC (ADC^fast) and perfusion fraction (f) can not be used to differentiate ICC and HCC. These results suggested that IVIM and DWI parameters can be useful in differentiating ICC and HCC and might be helpful in selecting the treatment plan and predicting prognosis.

Caveolin-1: An Oxidative Stress-Related Target for Cancer Prevention

Aberrant oxidative metabolism is one of the hallmarks of cancer. Reactive species overproduction could promote carcinogenesis via inducing genetic mutations and activating oncogenic pathways, and thus, antioxidant therapy was considered as an important strategy for cancer prevention and treatment. Caveolin-1 (Cav-1), a constituent protein of caveolae, has been shown to mediate tumorigenesis and progression through oxidative stress modulation recently. Reactive species could modulate the expression, degradation, posttranslational modifications, and membrane trafficking of Cav-1, while Cav-1-targeted treatments could scavenge the reactive species. More importantly, emerging evidences have indicated that multiple antioxidants could exert antitumor activities in cancer cells and protective activities in normal cells by modulating the Cav-1 pathway. Altogether, these findings indicate that Cav-1 may be a promising oxidative stress-related target for cancer antioxidant prevention. Elucidating the underlying interaction mechanisms between oxidative stress and Cav-1 is helpful for enhancing the preventive effects of antioxidants on cancer, for improving clinical outcomes of antioxidant-related therapeutics in cancer patients, and for developing Cav-1 targeted drugs. Herein, we summarize the available evidence of the roles of Cav-1 and oxidative stress in tumorigenesis and development and shed novel light on designing strategies for cancer prevention or treatment by utilizing the interaction mode between Cav-1 and oxidative stress.