Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma

Zhao, Ying; Wang, Nan; Wu, Jingjun; Zhang, Qinhe; Lin, Ting‐Tse; Yu, Yao; Chen, Zhebin; Wang, Man; Sheng, Liuji; Liu, Jinghong; Song, Qingwei; Wang, Feng; An, Xiangbo; Guo, Yan; Li, Xin; Wu, Tingfan; Liu, Ai Lian

doi:10.3389/fonc.2021.582788

Cited by 25 publications

(28 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…7 Recently, several radiomic models have been developed to estimate the response of TACE treatments that have shown promising results. [8][9][10] Chen et al built a clinical radiomics model that showed good performance in predicting the response of treatment to the first TACE in patients with intermediate-stage HCC, with the area under the receiver operator characteristic curve (AUC) reaching up to 0.94. 8 Kong et al developed an MRI-based radiomics model to predict tumor response after TACE in intermediateadvanced patients with HCC.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography

Dong¹,

Lin²,

Lin³

et al. 2021

JHC

View full text Add to dashboard Cite

The treatment response to initial conventional transarterial chemoembolization (cTACE) is essential for the prognosis of patients with hepatocellular carcinoma (HCC). This study explored and verified the feasibility of machine-learning models based on clinical data and contrast-enhanced computed tomography (CT) image findings to predict early responses of HCC patients after initial cTACE treatment. Patients and Methods: Overall, 110 consecutive unresectable HCC patients who were treated with cTACE for the first time were retrospectively enrolled. Clinical data and imaging features based on contrast-enhanced CT were collected for the selection of characteristics. Treatment responses were evaluated based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) by postoperative CT examination within 2 months after the procedure. Python (version 3.70) was used to develop machine learning models. Least absolute shrinkage and selection operator (LASSO) algorithm was applied to select features with the impact on predicting treatment response after the first TACE procedure. Six machine learning algorithms were used to build predictive models, including XGBoost, decision tree, support vector machine, random forest, k-nearest neighbor, and fully convolutional networks, and their performances were compared using receiver operator characteristic (ROC) curves to determine the best performing model. Results: Following TACE, 31 patients (28.2%) were described as responsive to TACE, while 72 patients (71.8%) were nonresponsive to TACE. Portal vein tumor thrombosis type, albumin level, and distribution of tumors within the liver were selected for predictive model building. Among the models, the RF model showed the best performance, with area under the curve (AUC), accuracy, sensitivity, and specificity of 0.802, 0.784, 0.904, and 0.480, respectively. Conclusion: Machine learning models can provide an accurate prediction of the early response of initial TACE treatment for HCC, which can help in individualizing clinical decision-making and modification of further treatment strategies for patients with unresectable HCC.

show abstract

Section: Introductionmentioning

confidence: 99%

Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography

Dong¹,

Lin²,

Lin³

et al. 2021

JHC

View full text Add to dashboard Cite

show abstract

“…TACE is recognized as an effective treatment for advanced HCC ( 125 ), but its long-term efficacy needs to be further improved ( 139 – 141 ). MRI radiomics can be used to predict the response to TACE treatment and provide a reference for the formulation of individualized treatment plans ( 21 , 22 , 142 – 148 ). Sun et al ( 142 )predicted the risk of early postoperative progression based on multiparameter MRI data before TACE.…”

Section: Prediction Of Response To Tacementioning

confidence: 99%

“…TACE is recognized as an effective treatment for advanced HCC (125), but its long-term efficacy needs to be further improved (139)(140)(141). MRI radiomics can be used to predict the response to TACE treatment and provide a reference for the formulation of individualized treatment plans (21,22,(142)(143)(144)(145)(146)(147)(148)…”

Section: Prediction Of Response To Tacementioning

confidence: 99%

“…Radiomics, a new technology, can transform the potential histopathological and physiological information in images into high-dimensional quantitative image features that can be mined (6,7).The study of radiomics will contribute to the early diagnosis and treatment of HCC and ultimately improve survival (8,9). In recent years, many studies have confirmed the application values of magnetic resonance imaging (MRI) radiomics in the diagnosis and differentiation (10,11), histological grading (12,13), microvascular invasion (MVI) assessment (14,15), radiogenomics (16,17),prediction of relapse and prognosis after surgical resection (18)(19)(20), response to transarterial chemoembolization(TACE) (21,22) and systemic treatment efficacy of HCC (23).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Progress of MRI Radiomics in Hepatocellular Carcinoma

Gong

Tao

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

BackgroundHepatocellular carcinoma (HCC) is the sixth most common cancer in the world and the third leading cause of cancer-related death. Although the diagnostic scheme of HCC is currently undergoing refinement, the prognosis of HCC is still not satisfactory. In addition to certain factors, such as tumor size and number and vascular invasion displayed on traditional imaging, some histopathological features and gene expression parameters are also important for the prognosis of HCC patients. However, most parameters are based on postoperative pathological examinations, which cannot help with preoperative decision-making. As a new field, radiomics extracts high-throughput imaging data from different types of images to build models and predict clinical outcomes noninvasively before surgery, rendering it a powerful aid for making personalized treatment decisions preoperatively.ObjectiveThis study reviewed the workflow of radiomics and the research progress on magnetic resonance imaging (MRI) radiomics in the diagnosis and treatment of HCC.MethodsA literature review was conducted by searching PubMed for search of relevant peer-reviewed articles published from May 2017 to June 2021.The search keywords included HCC, MRI, radiomics, deep learning, artificial intelligence, machine learning, neural network, texture analysis, diagnosis, histopathology, microvascular invasion, surgical resection, radiofrequency, recurrence, relapse, transarterial chemoembolization, targeted therapy, immunotherapy, therapeutic response, and prognosis.ResultsRadiomics features on MRI can be used as biomarkers to determine the differential diagnosis, histological grade, microvascular invasion status, gene expression status, local and systemic therapeutic responses, and prognosis of HCC patients.ConclusionRadiomics is a promising new imaging method. MRI radiomics has high application value in the diagnosis and treatment of HCC.

show abstract

“…Random forest (RF) is regarded as one of the most promising ML algorithm 10 , and it is consisted of an ensemble learning approach of multiple unique decision trees. Although RF algorithm has previously been utilized to predict the prognosis of HCC patients after various treatment modalities 11 - 15 , it has not yet been used to predict the early TACE refractoriness. Therefore, the purpose of the present study is to develop and validate a predictive model of early TACE refractoriness based on an RF algorithm.…”

Section: Introductionmentioning

confidence: 99%

Predicting early refractoriness of transarterial chemoembolization in patients with hepatocellular carcinoma using a random forest algorithm: A pilot study

Zou

et al. 2021

J. Cancer

View full text Add to dashboard Cite

Purpose: To develop and validate a random forest (RF) based predictive model of early refractoriness to transarterial chemoembolization (TACE) in patients with unresectable hepatocellular carcinoma (HCC). Methods: A total of 227 patients with unresectable HCC who initially treated with TACE from three independent institutions were retrospectively included. Following a random split, 158 patients (70%) were assigned to a training cohort and the remaining 69 patients (30%) were assigned to a validation cohort. The process of variables selection was based on the importance variable scores generated by RF algorithm. A RF predictive model incorporating the selected variables was developed, and five-fold cross-validation was performed. The discrimination and calibration of the RF model were measured by a receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow test. Results: The potential variables selected by RF algorithm for developing predictive model of early TACE refractoriness included patients' age, number of tumors, tumor distribution, platelet count (PLT), and neutrophil-to-lymphocyte ratio (NLR). The results showed that the RF predictive model had good discrimination ability, with an area under curve (AUC) of 0.863 in the training cohort and 0.767 in the validation cohort, respectively. In Hosmer-Lemeshow test, the RF model had a satisfactory calibration with P values of 0.538 and 0.068 in training cohort and validation cohort, respectively. Conclusion: The RF algorithm-based model has a good predictive performance in the prediction of early TACE refractoriness, which may easily be deployed in clinical routine and help to determine the optimal patient of care.

show abstract

Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma

Cited by 25 publications

References 45 publications

Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography

Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography

Progress of MRI Radiomics in Hepatocellular Carcinoma

Predicting early refractoriness of transarterial chemoembolization in patients with hepatocellular carcinoma using a random forest algorithm: A pilot study

Contact Info

Product

Resources

About