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Zhang YZ, Liu YC, Su T, Shi JN, Huang Y, Liang B. Current advances and future directions in combined hepatocellular and cholangiocarcinoma. Gastroenterol Rep (Oxf) 2024; 12:goae031. [PMID: 38628397 PMCID: PMC11018545 DOI: 10.1093/gastro/goae031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 03/17/2024] [Accepted: 03/24/2024] [Indexed: 04/19/2024] Open
Abstract
The low incidence of combined hepatocellular cholangiocarcinoma (cHCC-CCA) is an important factor limiting research progression. Our study extensively included nearly three decades of relevant literature and assembled the most comprehensive database comprising 5,742 patients with cHCC-CCA. We summarized the characteristics, tumor markers, and clinical features of these patients. Additionally, we present the evolution of cHCC-CCA classification and explain the underlying rationale for these classification standards. We reviewed cHCC-CCA diagnostic advances using imaging features, tumor markers, and postoperative pathology, as well as treatment options such as surgical, adjuvant, and immune-targeted therapies. In addition, recent advances in more effective chemotherapeutic regimens and immune-targeted therapies were explored. Furthermore, we described the molecular mutation features and potential specific markers of cHCC-CCA. The prognostic value of Nestin has been proven, and we speculate that Nestin will also play a role in classification and diagnosis. However, further research is needed. Moreover, we believe that the possibility of using machine learning liquid biopsy for preoperative diagnosis and establishing a scoring system are directions for future research.
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Affiliation(s)
- Yu-Zhu Zhang
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Jiangxi, Nanchang, Jiangxi, P. R. China
- The Second Clinical Medical College of Nanchang University, The Second Affiliated Hospital of Nanchang University, Jiangxi, Nanchang, Jiangxi, P. R. China
| | - Yu-Chen Liu
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Jiangxi, Nanchang, Jiangxi, P. R. China
- Queen Mary School, Jiangxi Medical College of Nanchang University, Nanchang, Jiangxi, P. R. China
| | - Tong Su
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Jiangxi, Nanchang, Jiangxi, P. R. China
- The Second Clinical Medical College of Nanchang University, The Second Affiliated Hospital of Nanchang University, Jiangxi, Nanchang, Jiangxi, P. R. China
| | - Jiang-Nan Shi
- The Second Clinical Medical College of Nanchang University, The Second Affiliated Hospital of Nanchang University, Jiangxi, Nanchang, Jiangxi, P. R. China
| | - Yi Huang
- The Second Clinical Medical College of Nanchang University, The Second Affiliated Hospital of Nanchang University, Jiangxi, Nanchang, Jiangxi, P. R. China
| | - Bo Liang
- Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Jiangxi, Nanchang, Jiangxi, P. R. China
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Salehi MA, Harandi H, Mohammadi S, Shahrabi Farahani M, Shojaei S, Saleh RR. Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01058-1. [PMID: 38438694 DOI: 10.1007/s10278-024-01058-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/18/2024] [Accepted: 02/19/2024] [Indexed: 03/06/2024]
Abstract
Due to the increasing interest in the use of artificial intelligence (AI) algorithms in hepatocellular carcinoma detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI and to compare them with clinicians' performance. A search in PubMed and Scopus was performed in January 2024 to find studies that evaluated and/or validated an AI algorithm for the detection of HCC. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the modality of imaging and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST) reporting guidelines. Out of 3177 studies screened, 44 eligible studies were included. The pooled sensitivity and specificity for internally validated AI algorithms were 84% (95% CI: 81,87) and 92% (95% CI: 90,94), respectively. Externally validated AI algorithms had a pooled sensitivity of 85% (95% CI: 78,89) and specificity of 84% (95% CI: 72,91). When clinicians were internally validated, their pooled sensitivity was 70% (95% CI: 60,78), while their pooled specificity was 85% (95% CI: 77,90). This study implies that AI can perform as a diagnostic supplement for clinicians and radiologists by screening images and highlighting regions of interest, thus improving workflow.
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Affiliation(s)
| | - Hamid Harandi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Antibiotic Stewardship and Antimicrobial Resistance, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Soheil Mohammadi
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | | | - Shayan Shojaei
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Ramy R Saleh
- Department of Oncology, McGill University, Montreal, QC, H3A 0G4, Canada
- Division of Medical Oncology, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada
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Yang J, Zhang Y, Bao WYG, Chen YD, Jiang H, Huang JY, Zeng KY, Song B, Huang ZX, Lu Q. Comparison contrast-enhanced CT with contrast-enhanced US in diagnosing combined hepatocellular-cholangiocarcinoma: a propensity score-matched study. Insights Imaging 2024; 15:44. [PMID: 38353807 PMCID: PMC10866845 DOI: 10.1186/s13244-023-01576-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/25/2023] [Indexed: 02/17/2024] Open
Abstract
OBJECTIVES To develop and compare noninvasive models for differentiating between combined hepatocellular-cholangiocarcinoma (cHCC-CCA) and HCC based on serum tumor markers, contrast-enhanced ultrasound (CEUS), and computed tomography (CECT). METHODS From January 2010 to December 2021, patients with pathologically confirmed cHCC-CCA or HCC who underwent both preoperative CEUS and CECT were retrospectively enrolled. Propensity scores were calculated to match cHCC-CCA and HCC patients with a near-neighbor ratio of 1:2. Two predicted models, a CEUS-predominant (CEUS features plus tumor markers) and a CECT-predominant model (CECT features plus tumor markers), were constructed using logistic regression analyses. Model performance was evaluated by the area under the curve (AUC), sensitivity, specificity, and accuracy. RESULTS A total of 135 patients (mean age, 51.3 years ± 10.9; 122 men) with 135 tumors (45 cHCC-CCA and 90 HCC) were included. By logistic regression analysis, unclear boundary in the intratumoral nonenhanced area, partial washout on CEUS, CA 19-9 > 100 U/mL, lack of cirrhosis, incomplete tumor capsule, and nonrim arterial phase hyperenhancement (APHE) volume < 50% on CECT were independent factors for a diagnosis of cHCC-CCA. The CECT-predominant model showed almost perfect sensitivity for cHCC-CCA, unlike the CEUS-predominant model (93.3% vs. 55.6%, p < 0.001). The CEUS-predominant model showed higher diagnostic specificity than the CECT-predominant model (80.0% vs. 63.3%; p = 0.020), especially in the ≤ 5 cm subgroup (92.0% vs. 70.0%; p = 0.013). CONCLUSIONS The CECT-predominant model provides higher diagnostic sensitivity than the CEUS-predominant model for CHCC-CCA. Combining CECT features with serum CA 19-9 > 100 U/mL shows excellent sensitivity. CRITICAL RELEVANCE STATEMENT Combining lack of cirrhosis, incomplete tumor capsule, and nonrim arterial phase hyperenhancement (APHE) volume < 50% on CECT with serum CA 19-9 > 100 U/mL shows excellent sensitivity in differentiating cHCC-CCA from HCC. KEY POINTS 1. Accurate differentiation between cHCC-CCA and HCC is essential for treatment decisions. 2. The CECT-predominant model provides higher accuracy than the CEUS-predominant model for CHCC-CCA. 3. Combining CECT features and CA 19-9 levels shows a sensitivity of 93.3% in diagnosing cHCC-CCA.
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Affiliation(s)
- Jie Yang
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yun Zhang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Wu-Yong-Ga Bao
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yi-di Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Hanyu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Jia-Yan Huang
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Ke-Yu Zeng
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
- Department of Radiology, Sanya People's Hospital, Hainan, China
| | - Zi-Xing Huang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
- Department of Radiology, West China Tianfu hospital of Sichuan University, Sichuan, China.
| | - Qiang Lu
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
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Huang J, Bai X, Qiu Y, He X. Application of AI on cholangiocarcinoma. Front Oncol 2024; 14:1324222. [PMID: 38347839 PMCID: PMC10859478 DOI: 10.3389/fonc.2024.1324222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/08/2024] [Indexed: 02/15/2024] Open
Abstract
Cholangiocarcinoma, classified as intrahepatic, perihilar, and extrahepatic, is considered a deadly malignancy of the hepatobiliary system. Most cases of cholangiocarcinoma are asymptomatic. Therefore, early detection of cholangiocarcinoma is significant but still challenging. The routine screening of a tumor lacks specificity and accuracy. With the application of AI, high-risk patients can be easily found by analyzing their clinical characteristics, serum biomarkers, and medical images. Moreover, AI can be used to predict the prognosis including recurrence risk and metastasis. Although they have some limitations, AI algorithms will still significantly improve many aspects of cholangiocarcinoma in the medical field with the development of computing power and technology.
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Affiliation(s)
| | | | | | - Xiaodong He
- Department of General Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
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