A Machine Learning-Based Model to Predict Survival After Transarterial Chemoembolization for BCLC Stage B Hepatocellular Carcinoma

Development and Validation of Deep Learning Model for Intermediate-Stage Hepatocellular Carcinoma Survival with Transarterial Chemoembolization (MC-hccAI 002): a Retrospective, Multicenter, Cohort Study

Background: There are few effective prediction models for intermediate-stage hepatocellular carcinoma (IM-HCC) patients treated with transarterial chemoembolization (TACE) to predict overall survival (OS) is available. The learning survival neural network (DeepSurv) was developed to showed a better performance than cox proportional hazards model in prediction of OS. This study aimed to develop a deep learning-based prediction model to predict individual OS. Methods: This multicenter, retrospective, cohort study examined data from the electronic medical record system of four hospitals in China between January 1, 2007, to December 31, 2016. Patients were divided into a training set(n=1075) and a test set(n=269) at a ratio of 8:2 to develop a deep learning-based algorithm (deepHAP IV). The deepHAP IV model was externally validated on an independent cohort(n=414) from the other three centers. The concordance index, the area under the receiver operator characteristic curves, and the calibration curve were used to assess the performance of the models. Results: The deepHAP IV model had a c-index of 0.74, whereas AUROC for predicting survival outcomes of 1-, 3-, and 5-year reached 0.80, 0.76, and 0.74 in the training set. Calibration graphs showed good consistency between the actual and predicted OS in the training set and the validation cohort. Compared to the other five Cox proportional-hazards models, the model this study conducted had a better performance. Patients were finally classified into three groups by X-tile plots with predicted 3-year OS rate (low: ≤ 0.11; middle: > 0.11 and ≤ 0.35; high: >0.35). Conclusion: The deepHAP IV model can effectively predict the OS of patients with IM-HCC, showing a better performance than previous Cox proportional hazards models.

Current Status and Analysis of Machine Learning in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common tumor. Although the diagnosis and treatment of HCC have made great progress, the overall prognosis remains poor. As the core component of artificial intelligence, machine learning (ML) has developed rapidly in the past decade. In particular, ML has become widely used in the medical field, and it has helped in the diagnosis and treatment of cancer. Different algorithms of ML have different roles in diagnosis, treatment, and prognosis. This article reviews recent research, explains the application of different ML models in HCC, and provides suggestions for follow-up research.

Development and Validation of a Prediction Model for Hepatitis B Virus-Related Hepatocellular Carcinoma Patients Receiving Postoperative Adjuvant Transarterial Chemoembolization

Background

Hepatocellular carcinoma (HCC) patients who are at significant risk of tumor recurrence and mortality can benefit from postoperative adjuvant transarterial chemoembolization (PA-TACE). However, the benefits of PA-TACE remain unclear. Herein, we aimed to develop a model for predicting the prognosis of HBV-related patients who undergo PA-TACE and endeavored to guide individualized clinical treatment.

Methods

We included 432 HBV-related patients who underwent PA-TACE after curative resection were included. The dataset was divided into a training set (n=216) and an internal validation set (n=216). For identifying independent risk factors, the least absolute shrinkage and selection operator and univariate and multivariate Cox analyses were performed. We derived a prognostic model from the training set that was internally validated. The concordance index (C-index), receiver operating characteristic (ROC) curve, calibration curve, and risk stratification were used to evaluate the performance of the nomogram.

Results

Patients undergoing PA-TACE had significantly longer overall survival (OS) than those who did not undergo PA-TACE. Age, albumin levels, macrovascular invasion, tumor size, and, stages of Barcelona Clinic Liver Cancer were identified as independent risk variables and concluded into the nomogram to predict the OS of HBV-related patients who received PA-TACE. The nomogram's C-index values OS were 0.710 and 0.652 in the training and internal validation sets, respectively. Both time-dependent AUC and the calibration curve showed good discrimination and model fitness. The risk score -0.12 was kept as the cut-off value that would accurately divide patients into high-risk and low-risk groups; furthermore, the Kaplan-Meier curve showed a high discriminative ability of the model.

Conclusion

We developed a predictive model. comprising a formula and nomogram to predict the OS and provide risk stratification for HBV-related patients undergoing PA-TACE, which could contribute to suitable treatment options for this patient population.

Survival Prediction After Transarterial Chemoembolization for Hepatocellular Carcinoma: a Deep Multitask Survival Analysis Approach

The accurate prediction of postoperative survival time of patients with Barcelona Clinic Liver Cancer (BCLC) stage B hepatocellular carcinoma (HCC) is important for postoperative health care. Survival analysis is a common method used to predict the occurrence time of events of interest in the medical field. At present, the mainstream survival analysis models, such as the Cox proportional risk model, should make strict assumptions about the potential random process to solve the censored data, thus potentially limiting their application in clinical practice. In this paper, we propose a novel deep multitask survival model (DMSM) to analyze HCC survival data. Specifically, DMSM transforms the traditional survival time prediction problem of patients with HCC into a survival probability prediction problem at multiple time points and applies entropy regularization and ranking loss to optimize a multitask neural network. Compared with the traditional methods of deleting censored data and strong hypothesis, DMSM makes full use of all the information in the censored data but does not need to make any assumption. In addition, we identify the risk factors affecting the prognosis of patients with HCC and visualize the importance of ranking these factors. On the basis of the analysis of a real dataset of patients with BCLC stage B HCC, experimental results on three different validation datasets show that the DMSM achieves competitive performance with concordance index of 0.779, 0.727, and 0.780 and integrated Brier score (IBS) of 0.172, 0.138, and 0.135, respectively. Our DMSM has a comparatively small standard deviation (0.002, 0.002, and 0.003) for IBS of bootstrapping 100 times. The DMSM we proposed can be utilized as an effective survival analysis model and provide an important means for the accurate prediction of postoperative survival time of patients with BCLC stage B HCC.

MRI-based random survival Forest model improves prediction of progression-free survival to induction chemotherapy plus concurrent Chemoradiotherapy in Locoregionally Advanced nasopharyngeal carcinoma

Background

The present study aimed to explore the application value of random survival forest (RSF) model and Cox model in predicting the progression-free survival (PFS) among patients with locoregionally advanced nasopharyngeal carcinoma (LANPC) after induction chemotherapy plus concurrent chemoradiotherapy (IC + CCRT).

Methods

Eligible LANPC patients underwent magnetic resonance imaging (MRI) scan before treatment were subjected to radiomics feature extraction. Radiomics and clinical features of patients in the training cohort were subjected to RSF analysis to predict PFS and were tested in the testing cohort. The performance of an RSF model with clinical and radiologic predictors was assessed with the area under the receiver operating characteristic (ROC) curve (AUC) and Delong test and compared with Cox models based on clinical and radiologic parameters. Further, the Kaplan-Meier method was used for risk stratification of patients.

Results

A total of 294 LANPC patients (206 in the training cohort; 88 in the testing cohort) were enrolled and underwent magnetic resonance imaging (MRI) scans before treatment. The AUC value of the clinical Cox model, radiomics Cox model, clinical + radiomics Cox model, and clinical + radiomics RSF model in predicting 3- and 5-year PFS for LANPC patients was [0.545 vs 0.648 vs 0.648 vs 0.899 (training cohort), and 0.566 vs 0.736 vs 0.730 vs 0.861 (testing cohort); 0.556 vs 0.604 vs 0.611 vs 0.897 (training cohort), and 0.591 vs 0.661 vs 0.676 vs 0.847 (testing cohort), respectively]. Delong test showed that the RSF model and the other three Cox models were statistically significant, and the RSF model markedly improved prediction performance (P < 0.001). Additionally, the PFS of the high-risk group was lower than that of the low-risk group in the RSF model (P < 0.001), while comparable in the Cox model (P > 0.05).

Conclusion

The RSF model may be a potential tool for prognostic prediction and risk stratification of LANPC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09832-6.

CircRNAs: novel therapeutic targets in multiple myeloma

BACKGROUND

Circular RNA (circRNA) is a type of non-coding RNA that has recently attracted the attention of researchers. Multiple myeloma (MM) is a hematological malignancy with a dismal prognosis that indicates a pressing need for better treatment alternatives, particularly in terms of biological indicators. According to recent research findings, the presence of circRNA is also closely related to the incidence and progression of malignant hemopathy. There have been, however, only a few investigations of circRNA in MM.

MATERIAL AND METHODS

This review will be on the biological properties and functions of circRNA in MM and a discussion of the clinical utility of circRNA in the diagnosis, prognosis, and treatment of MM.

CONCLUSIONS

CircRNA is involved in gene transcription, translation, and epigenetic modification as well as the regulation of cancer cell proliferation, invasion, and metastasis, and hence, promotes or inhibits the occurrence and progression of MM. Therefore, circRNA holds promise as a potential future MM biomarker.

Serum hsa_circ_0087776 as a new oncologic marker for the joint diagnosis of multiple myeloma

Multiple myeloma (MM) is a hematologic malignancy caused by abnormal proliferation of bone marrow plasma cells, which lacks diagnostic markers and has a general prognosis. At present, the understanding of its pathogenesis provides the basis for the combined diagnosis and new targeted therapy of the disease. In this study, quantitative real-time PCR was used to detect 136 MM patients and 74 healthy controls, and the clinical application value of hsa_circ_0087776 as a new tumor marker and combined diagnosis was evaluated. The results showed that the expression of hsa_circ_0087776 was significantly lower in serum of MM patients (P-value < 0.0001), and the expression was consistent in MM cells. In the analysis of clinicopathological parameters, it was found that there were significant statistical differences with MM stage and renal injury. In addition, it significantly increased the sensitivity with ALB, β₂-MG joint diagnosis, to provide a basis for diagnosis, improve the prognosis of the disease, improve the survival of patients and quality of life. These studies suggest that hsa_circ_0087776 can be used as a new oncology marker for the combined diagnosis of MM.

Impact statement: Various evidences have shown that the role of circRNA in the occurrence and development of diseases is potentially unknown and untapped. Therefore, it has a broad prospect to find circRNA specifically expressed in MM patients for combined diagnosis and targeted therapy of MM. However, MM lacks such specific tumor markers. Therefore, the discovery of new specific tumor markers for combined diagnosis is an important milestone in the development of medical history. In the research, we founded hsa_circ_0087776 can be used as a new oncology marker for combined diagnosis of MM.