Clinical trial · Observational
Predicting Response to Systemic Therapies for Hepatocellular Carcinoma(HCC)
Predicting Response to Systemic Therapies for Hepatocellular Carcinoma(HCC) Based on Clinical Variables and Radiomics Data With Machine Learning Methods
NCT05543304CI-TRIAL-00063861unknownClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
As the most common type of primary liver cancer, hepatocellular carcinoma (HCC) has become a big challenge all over the world. Most patients are not available to curative resection when first diagnosed. There are a variety of treatment options for advanced HCC. However, due to the heterogeneity of HCC, the overall response rate (ORR) is not high for systemic therapies. Therefore, appropriate selection of patients who are suitable for individual systemic therapies is important for clinical decision-making.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Effect of Drug | — | UNRESOLVED | — |
| Hepatocellular Carcinoma Non-resectable | Hepatocellular Carcinoma | PROBABILISTIC | 0.70 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| radiological evaluation | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- label
- patients with response to systemic therapies
- description
- Patients shown complete response (CR) and partial response (PR) after treatments. The clinical data and radiomics data are collected through electronic medical record system.
- interventionNames
- Diagnostic Test: radiological evaluation
- label
- patients with no response to systemic therapies
- description
- Patients shown progressive disease (PD) and stable disease (SD) after treatments. The clinical data and radiomics data are collected through electronic medical record system.
- interventionNames
- Diagnostic Test: radiological evaluation
Primary outcomes (1)
- measure
- Objective response rate
- timeFrame
- 3 months
- description
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 80 Years
Show eligibility criteria text
Inclusion Criteria: * clinically or pathologically diagnosed HCC * Eastern Cooperative Oncology Group performance status (ECOG-PS) 0-2 * Child-Pugh score of ≤7 * complete clinical and follow-up information * evaluable efficacy after treatment * age between 18-80 years old Exclusion Criteria: * with other malignancies * Eastern Cooperative Oncology Group performance status (ECOG-PS) \>2 * Child-Pugh score of \>7 * incomplete clinical data * lost to follow up * unevaluable efficacy after treatment * age \<18 years old or \>80 years old
References
Publications (7)
- BACKGROUNDVillanueva A. Hepatocellular Carcinoma. N Engl J Med. 2019 Apr 11;380(15):1450-1462. doi: 10.1056/NEJMra1713263. No abstract available. PMID 30970190
- BACKGROUNDLlovet JM, Castet F, Heikenwalder M, Maini MK, Mazzaferro V, Pinato DJ, Pikarsky E, Zhu AX, Finn RS. Immunotherapies for hepatocellular carcinoma. Nat Rev Clin Oncol. 2022 Mar;19(3):151-172. doi: 10.1038/s41571-021-00573-2. Epub 2021 Nov 11. PMID 34764464
- BACKGROUNDChen B, Garmire L, Calvisi DF, Chua MS, Kelley RK, Chen X. Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2020 Apr;17(4):238-251. doi: 10.1038/s41575-019-0240-9. Epub 2020 Jan 3. PMID 31900465
- BACKGROUNDChen M, Cao J, Hu J, Topatana W, Li S, Juengpanich S, Lin J, Tong C, Shen J, Zhang B, Wu J, Pocha C, Kudo M, Amedei A, Trevisani F, Sung PS, Zaydfudim VM, Kanda T, Cai X. Clinical-Radiomic Analysis for Pretreatment Prediction of Objective Response to First Transarterial Chemoembolization in Hepatocellular Carcinoma. Liver Cancer. 2021 Feb;10(1):38-51. doi: 10.1159/000512028. Epub 2021 Jan 7. PMID 33708638
- BACKGROUNDBruix J, Chan SL, Galle PR, Rimassa L, Sangro B. Systemic treatment of hepatocellular carcinoma: An EASL position paper. J Hepatol. 2021 Oct;75(4):960-974. doi: 10.1016/j.jhep.2021.07.004. Epub 2021 Jul 10. PMID 34256065
- BACKGROUNDSpann A, Yasodhara A, Kang J, Watt K, Wang B, Goldenberg A, Bhat M. Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review. Hepatology. 2020 Mar;71(3):1093-1105. doi: 10.1002/hep.31103. Epub 2020 Mar 6. PMID 31907954
- BACKGROUNDLee IC, Huang JY, Chen TC, Yen CH, Chiu NC, Hwang HE, Huang JG, Liu CA, Chau GY, Lee RC, Hung YP, Chao Y, Ho SY, Huang YH. Evolutionary Learning-Derived Clinical-Radiomic Models for Predicting Early Recurrence of Hepatocellular Carcinoma after Resection. Liver Cancer. 2021 Sep 20;10(6):572-582. doi: 10.1159/000518728. eCollection 2021 Nov.