Updates on Imaging of Liver Tumors

Imaging of pediatric liver tumors: A COG Diagnostic Imaging Committee/SPR Oncology Committee White Paper

Primary hepatic malignancies are relatively rare in the pediatric population, accounting for approximately 1%-2% of all pediatric tumors. Hepatoblastoma and hepatocellular carcinoma are the most common primary liver malignancies in children under the age of 5 years and over the age of 10 years, respectively. This paper provides consensus-based imaging recommendations for evaluation of patients with primary hepatic malignancies at diagnosis and follow-up during and after therapy.

Probe electrospray ionization mass spectrometry-based rapid diagnosis of liver tumors

BACKGROUND AND AIM

Prompt differential diagnosis of liver tumors is clinically important and sometimes difficult. A new diagnostic device that combines probe electrospray ionization-mass spectrometry (PESI-MS) and machine learning may help provide the differential diagnosis of liver tumors.

METHODS

We evaluated the diagnostic accuracy of this new PESI-MS device using tissues obtained and stored from previous surgically resected specimens. The following cancer tissues (with collection dates): hepatocellular carcinoma (HCC, 2016-2019), intrahepatic cholangiocellular carcinoma (ICC, 2014-2019), and colorectal liver metastasis (CRLM, 2014-2019) from patients who underwent hepatic resection were considered for use in this study. Non-cancerous liver tissues (NL) taken from CRLM cases were also incorporated into the analysis. Each mass spectrum provided by PESI-MS was tested using support vector machine, a type of machine learning, to evaluate the discriminatory ability of the device.

RESULTS

In this study, we used samples from 91 of 139 patients with HCC, all 24 ICC samples, and 103 of 202 CRLM samples; 80 NL from CRLM cases were also used. Each mass spectrum was obtained by PESI-MS in a few minutes and was evaluated by machine learning. The sensitivity, specificity, and diagnostic accuracy of the PESI-MS device for discriminating HCC, ICC, and CRLM from among a mix of all three tumors and from NL were 98.9%, 98.1%, and 98.3%; 87.5%, 93.1%, and 92.6%; and 99.0%, 97.9%, and 98.3%, respectively.

CONCLUSION

This study demonstrated that PESI-MS and machine learning could discriminate liver tumors accurately and rapidly.

The clinical value of dual-energy computed tomography and diffusion-weighted imaging in the context of liver cancer: A narrative review

The dual-energy computed tomography (DECT) and diffusion-weighted magnetic resonance imaging (DWI-MRI) are used to diagnose liver cancer. The clinical value of these two examination methods needs to be further summarized. We collected and summarized relevant literature published from 2011 to 2021. The diagnostic performance of DECT was assessed between conventional computed tomography and DWI-MRI. DWI-MRI had a 69% sensitivity for detecting small hepatocellular carcinoma (HCC) lesions and a 60% diagnostic specificity for differentiating between types of HCC lesions. DECT had a sensitivity to small liver lesions (<1 cm) of 69%, and the diagnostic specificity for HCC and metastasis was about 60%. DWI was more sensitive (90.3% vs. 74.9%) and accurate (91.9% vs. 76.9%) in diagnosing HCC compared with conventional MRI sequencing. With the aid of contrast media, DWI-MRI had 90.0% specificity for detecting small HCCs (smaller than 1 cm). Furthermore, DWI-MRI not only provided physicians with valuable diagnostic information but also delivered histological grading information, with 78% accuracy for all benign lesions and 71% for solid lesions. DECT had relatively high sensitivity and required a lower contrast medium dose. With standardized quantitative parameters, it can be an extremely useful tool for HCC surveillance. DWI-MRI is the preferred imaging process as it produces high-contrast images for supporting an early diagnosis (high sensitivity and specificity) and provides histological information using non-ionizing radiation.

Hepatobiliary phase images of gadoxetic acid-enhanced MRI may improve accuracy of predicting the size of hepatocellular carcinoma at pathology

BACKGROUND

Gadoxetic acid-enhanced magnetic resonance imaging (MRI) has been widely used in clinical practice. However, scientific evidence is lacking for recommending a particular sequence for measuring tumor size.

PURPOSE

To retrospectively compare the size of hepatocellular carcinoma (HCC) measured on different gadoxetic acid-enhanced MRI sequences using pathology as a reference.

MATERIAL AND METHODS

A total of 217 patients with single HCC who underwent gadoxetic acid-enhanced MRI before surgery were included. The size of the HCC was measured by two abdominal radiologists independently on the following sequences: T1-weighted; T2-weighted; b-500 diffusion-weighted imaging (DWI); and arterial, portal venous, transitional, and hepatobiliary phases. Tumor size measured on MRI was compared with pathological size by using Pearson correlation coefficient, independent-sample t test, and Bland-Altman plot. Agreement between two readers was evaluated with intraclass correlation coefficient (ICC).

RESULTS

Correlation between the MR images and pathology was high for both readers (0.899-0.955). Absolute error between MRI and pathologic assessment was lowest on hepatobiliary phase images for both readers (reader 1, 2.8±4.2 mm; reader 2, 3.2±3.4 mm) and highest on arterial phase images for reader 1 (4.9±4.4 mm) and DWI phase images for reader 2 (5.1±4.9 mm). Absolute errors were significantly different for hepatobiliary phase compared with other sequences for both readers (reader 1, P≤0.012; reader 2, P≤0.037). Inter-reader agreements for all sequence measurements were strong (0.971-0.997).

CONCLUSION

The performance of gadoxetic acid-enhanced MRI sequences varied with HCC size, and the hepatobiliary phase may be optimal among these sequences.

Comparison of multiphase data from CT perfusion vs clinical 4-phase CT scans with respect to image quality, lesion detection, and LI-RADS classification in HCC patients

Purpose

The aim of this study was to assess the image quality and diagnostic performance of reconstructed arterial (A) and portal venous (PV) phases in computed tomography perfusion (CTP) scans compared to the corresponding phases in standard 4-phase CT and to assess the utility for LI-RADS classification using CTP and 4-phase 4CT.

Methods

A total of 26 scans with each method (CTP and 4-phase CT) from 19 hepatocellular carcinoma patients were analyzed and compared. Arterial and PV phases reconstructed by advanced modeled iterative reconstruction at strength 4 (ADMIRE 4) from raw CTP data were compared with image sets from arterial and PV phases of 4-phase CT (ADMIRE 3) in the same patient with respect to image quality.

Results

Quantitative image analysis showed that reconstructed CTP datasets were equivalent to 4-phase CT image sets. Qualitative image analysis revealed similar lesion detection rates with the 2 methods for patients with an abdominal diameter ≤36 cm and body weight <90 kg, but lower detection rates with CTP for patients with an abdominal diameter >37 cm. There was no difference in Liver Imaging Reporting and Data System (LI-RADS) classifications between the 2 methods.

Conclusion

Reconstructed CTP images can potentially replace 4-phase CT images in patients weighing <90 kg and with a body diameter <37 cm, as the 2 methods are comparable in terms of quantitative image quality and ability to detect and classify lesions based on LI-RADS criteria.

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Reconstructed A- and PV-CTP images have comparable image quality to 4-phase CT.

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Reconstructed A- and PV-CTP images can be used for LI-RADS classification of HCC.

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A-/PV-CTP has the potential to reliably detect lesions in patients weighing <90 kg with body diameter ≤36 cm.

CT perfusion for response evaluation after interventional ablation of hepatocellular carcinoma: a prospective study

Background

Computed tomography (CT) perfusion was found to be useful in assessing treatment response in a variety of cancers through the evaluation in the arterial perfusion changes. We investigated the performance of CT perfusion parameters for assessment of hepatocellular carcinoma (HCC) response to radiofrequency ablation (RFA) and trans-arterial chemoembolization (TACE). We conducted a prospective diagnostic test accuracy study that recruited 70 HCC patients who were scheduled to undergo TACE or RFA. For each dynamic CT scan acquisition, four single perfusion CT image maps were generated, including functional maps of blood flow (BF), blood volume (BV), mean transit time (MTT), and permeability surface (PS).

Results

In TACE-treated lesions, the BV achieved a sensitivity and specific of 100% and 83.3%, at a cutoff level of ≤ 122 ml/min/100 gm, for responders. Likewise, at a cutoff level of > 10 s, transit time had a sensitivity of 90.5% and specificity of 100%. At a cutoff level of ≤ 14 ml/min/100 gm, the PS had a sensitivity of 100% and specificity of 83.33% for responders. In RFA-treated lesions, at a cutoff level of ≤ 170 ml/min/100 gm and ≤ 11 ml/100 gm, the BF and BV had a sensitivity of 100% and specificity 100%, respectively, for responders. At a cutoff level of ≤ 11 ml/min/100 gm, PS had a sensitivity 77.27% and specificity 80%.

Conclusions

The present study confirms the feasibility of CT perfusion for assessment of response to TACE and RFA among patients with HCC.

Laparoscopic Surgery for a Patient With Multiple Liver Metastases of Unknown Origin Concomitant With Gallbladder Tumor

BACKGROUND

The liver is the digestive organ where metastatic adenocarcinoma of unknown primary site is most often observed.

CASE REPORT

A 74-year-old man was diagnosed with a growing gallbladder tumor and multiple liver tumors limited to the left lateral sector. Liver tumors were suggested to be primary or secondary adenocarcinoma with no relation to the gallbladder tumor. Also for diagnostic purposes, laparoscopic full-thickness resection of the gallbladder, laparoscopic lateral sectionectomy and lymph node sampling were performed. The final histopathological diagnosis was hyperplastic polyp of the gallbladder and metastatic poorly differentiated adenocarcinoma of the liver. Liver tumors were suspected to originate from the stomach, duodenum, or small intestine; however, the primary sites could not be identified. The patient has been closely followed up without any chemotherapy 3 months after surgery.

CONCLUSION

Laparoscopic surgery can be strongly recommended for patients with multiple liver tumors of unknown origin concomitant with a gallbladder tumor.

Advanced CT techniques for assessing hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the sixth-most common cancer in the world, and hepatic dynamic CT studies are routinely performed for its evaluation. Ongoing studies are examining advanced imaging techniques that may yield better findings than are obtained with conventional hepatic dynamic CT scanning. Dual-energy CT-, perfusion CT-, and artificial intelligence-based methods can be used for the precise characterization of liver tumors, the quantification of treatment responses, and for predicting the overall survival rate of patients. In this review, the advantages and disadvantages of conventional hepatic dynamic CT imaging are reviewed and the general principles of dual-energy- and perfusion CT, and the clinical applications and limitations of these technologies are discussed with respect to HCC. Finally, we address the utility of artificial intelligence-based methods for diagnosing HCC.

Artificial Intelligence in Medical Imaging and Its Application in Sonography for the Management of Liver Tumor

Recent advancement in artificial intelligence (AI) facilitate the development of AI-powered medical imaging including ultrasonography (US). However, overlooking or misdiagnosis of malignant lesions may result in serious consequences; the introduction of AI to the imaging modalities may be an ideal solution to prevent human error. For the development of AI for medical imaging, it is necessary to understand the characteristics of modalities on the context of task setting, required data sets, suitable AI algorism, and expected performance with clinical impact. Regarding the AI-aided US diagnosis, several attempts have been made to construct an image database and develop an AI-aided diagnosis system in the field of oncology. Regarding the diagnosis of liver tumors using US images, 4- or 5-class classifications, including the discrimination of hepatocellular carcinoma (HCC), metastatic tumors, hemangiomas, liver cysts, and focal nodular hyperplasia, have been reported using AI. Combination of radiomic approach with AI is also becoming a powerful tool for predicting the outcome in patients with HCC after treatment, indicating the potential of AI for applying personalized medical care. However, US images show high heterogeneity because of differences in conditions during the examination, and a variety of imaging parameters may affect the quality of images; such conditions may hamper the development of US-based AI. In this review, we summarized the development of AI in medical images with challenges to task setting, data curation, and focus on the application of AI for the managements of liver tumor, especially for US diagnosis.