Clinical trial NCT05543304: Predicting Response to Systemic Therapies for Hepatocellular Carcinoma(HCC)
Official title: Predicting Response to Systemic Therapies for Hepatocellular Carcinoma(HCC) Based on Clinical Variables and Radiomics Data With Machine Learning Methods
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Hepatocellular Carcinoma Non-resectable
Effect of Drug
Keywords: Hepatocellular Carcinoma
systemic therapy
immunotherapy
tyrosine kinase inhibitor
treatment response
radiomics
clinical characteristics
machine learning
Lead sponsor: First Affiliated Hospital of Wenzhou Medical University
Sponsor class: OTHER
Collaborators: name: The First Affiliated Hospital of Zhejiang Chinese Medical University
name: Eastern Hepatobiliary Surgery Hospital
name: Qilu Hospital of Shandong University
First posted: 2022-09-16
Enrollment: 200
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with unresectable HCC who received systemic therapies.

Brief summary: 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.

Detailed description: Although major achievements have been acquired in diagnosis and treatment, the prognosis of hepatocellular carcinoma (HCC) is still unsatisfactory. Liver resection remains the main curative treatment for HCC, but most patients are at an advanced stage when first diagnosed, leading to be not available to curative therapies. There is a variety of treatment options for advanced HCC, such as transarterial chemoembolization (TACE), hepatic artery infusion chemotherapy (HAIC), targeted therapy (sorafenib and lenvatinib), immunotherapy, and the combination of different therapies. However, due to the heterogeneity of HCC, different patients respond differently to systemic therapies. The the overall response rate (ORR) is not satisfactory and most patients can not benefit from the systemic therapies. There is an urgent need to identify patients who are likely to have positive response to systemic therapies at the beginning before treatment. Therefore ,we want to collect the clinical information of patients with advanced HCC treated with systemic therapies, including demographic data , laboratory index, histological features, radiomics data. Patients are followed-up at a interval of 1 month after treatment, and the ORR, overall survival (OS), progression-free survival (PFS) are recorded. Then the treatment response are evaluated and the relationship between the clinical data and efficacy of systemic therapies are explored by machine learning methods. Then models based on clinical features or radiomics features are developed to predict response to different systemic therapies.

Eligibility criteria: 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

Primary outcomes:
measure: Objective response rate
timeFrame: 3 months
Secondary outcomes:
measure: Overall survival
timeFrame: 1 year
measure: Progression free survival
timeFrame: 1 year
Other outcomes:
Locations:
city: Wenzhou
country: China
facility: Gang Chen
state: Zhejiang
Study officials:
affiliation: First Affiliated Hospital of Wenzhou Medical University
name: Gang Chen, MD,PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: Villanueva A. Hepatocellular Carcinoma. N Engl J Med. 2019 Apr 11;380(15):1450-1462. doi: 10.1056/NEJMra1713263. No abstract available.
citation: Llovet 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.
citation: Chen 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.
citation: Chen 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.
citation: Bruix 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.
citation: Spann 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.
citation: Lee 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.