The AI Prognostic Assessment and Pathological Basis Research of Early HCC After Minimally Invasive Treatment

Study Overview
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Brief Summary
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The study evaluates artificial intelligence method based on multimodal magnetic resonance imaging (MRI) images and clinical data in preoperative prediction of prognosis in early hepatocellular carcinoma (HCC) patients treated with minimally invasive treatment. The correlation between prognosis-related MRI features and pathological features was studied through artificial intelligence method, so as to provide the interpretability of image features for predicting the prognosis of HCC patients treated with minimally invasive treatment.

Detailed Description
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The prognosis prediction of early stage hepatocellular carcinoma (HCC) after minimally invasive treatment involves clinical decision of treatment and follow-up. Magnetic resonance imaging (MRI) has become the main approach for monitoring and following up of HCC, however it's difficult to predict HCC prognosis before surgery. We found the following limitations among previous researches: multimodal MRI using different sequences shows uncertain boundaries of HCC, which makes precise segmentation more difficult, and also leads to an additional workload for extracting high throughput radiomics features, which are limited in quantity and repeatability. Regarding to prognosis aspect, the MRI images, clinical data, and follow up information have not been fully exploited yet. In addition, the prognosis result obtained by radiomics workflow is difficult to be explained and applied to clinical application. Therefore, we conduct a study to solve the problems mentioned above: (1) To explore an effective deep learning neural network method and a pre-training model for improving tumor segmentation accuracy. (2) To establish a method for extracting high-throughput multi-dimensional and multimodal MRI radiomics features related to HCC prognosis. (3) To explore a correlation between multimodal MRI based pathological features of early stage HCC and the results of multimodal MRI based prognosis depth network of early stage HCC after minimally invasive treatment. Based on above approaches, we aim to establish multimodal MRI based prognosis model of early stage HCC after minimally invasive treatment in different clinical application scenarios guiding to clinical decision-making. Moreover, we also aim to explore the correlation between MRI radiomics features and pathology, which provides theoretical foundations for the MRI radiomics based pathological researches.

Official Title
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The Artificial Intelligent Prognostic Assessment and Pathological Basis Research of Early Primary Hepatocellular Carcinoma After Minimally Invasive Treatment Based on Multimodal MRI and Clinical Big Data

Conditions
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Hepatocellular Carcinoma

Intervention / Treatment
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* Procedure: Minimally invasive treatment
* Procedure: Hepatectomy


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Hepatocellular carcinoma patients received minimally invasive treatment (transcatheter arterial chemoembolization, radiofrequency ablation, or combined) or hepatectomy; Patients received MRI examination within 1 month before treatment; Complete post-treatment prognosis information. Exclusion Criteria: local or systemic treatment before MR examination; Incomplete clinical and pathological data; Heavy image artifacts.
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Recurrence<br>All hepatocellular carcinoma (HCC) patients have been regularly monitored for recurrence via contrast CT or contrast-enhanced MRI after minimally invasive treatment or hepatectomy. The recurrence status included new intrahepatic lesions and/or extrahepatic metastasis. | Procedure: Minimally invasive treatment<br>* All hepatocellular carcinoma (HCC) patients received minimally invasive treatment, including transcatheter arterial chemoembolization (TACE), radiofrequency ablation (RFA) or combined.<br>Procedure: Hepatectomy<br>* All hepatocellular carcinoma (HCC) patients received hepatectomy.<br>|
| Non-recurrence<br>All hepatocellular carcinoma (HCC) patients have been regularly monitored for recurrence via contrast CT or contrast-enhanced MRI after minimally invasive treatment or hepatectomy. The recurrence status included new intrahepatic lesions and/or extrahepatic metastasis. | Procedure: Minimally invasive treatment<br>* All hepatocellular carcinoma (HCC) patients received minimally invasive treatment, including transcatheter arterial chemoembolization (TACE), radiofrequency ablation (RFA) or combined.<br>Procedure: Hepatectomy<br>* All hepatocellular carcinoma (HCC) patients received hepatectomy.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Three-month recurrence | All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least three months. | Three months | 
| Six-month recurrence | All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least six months. | Six months | 
| One-year recurrence | All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least one year. | One year | 
| Two-year recurrence | All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least two years. | Two years | 
| Three-year recurrence | All HCC patients have been regularly monitored for recurrence via contrast CT or MRI for at least three years. | Three years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Progression-free survival | The time between the tumor progression and initial treatment was recorded. | Three months, six months, one year, two years, and three years. | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Precision medicine information processing, Lesion recognition, Feature extraction, Deep learning, Diagnosis model