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

Research progress on machine algorithm prediction of liver cancer prognosis after intervention therapy

The treatment for liver cancer has transitioned from traditional surgical resection to interventional therapies, which have become increasingly popular among patients due to their minimally invasive nature and significant local efficacy. However, with advancements in treatment technologies, accurately assessing patient response and predicting long-term survival has become a crucial research topic. Over the past decade, machine algorithms have made remarkable progress in the medical field, particularly in hepatology and prognosis studies of hepatocellular carcinoma (HCC). Machine algorithms, including deep learning and machine learning, can identify prognostic patterns and trends by analyzing vast amounts of clinical data. Despite significant advancements, several issues remain unresolved in the prognosis prediction of liver cancer using machine algorithms. Key challenges and main controversies include effectively integrating multi-source clinical data to improve prediction accuracy, addressing data privacy and ethical concerns, and enhancing the transparency and interpretability of machine algorithm decision-making processes. This paper aims to systematically review and analyze the current applications and potential of machine algorithms in predicting the prognosis of patients undergoing interventional therapy for liver cancer, providing theoretical and empirical support for future research and clinical practice.

Ethiodized oil as an imaging biomarker after conventional transarterial chemoembolization

Conventional transarterial chemoembolization (cTACE) utilizing ethiodized oil as a chemotherapy carrier has become a standard treatment for intermediate-stage hepatocellular carcinoma (HCC) and has been adopted as a bridging and downstaging therapy for liver transplantation. Water-in-oil emulsion made up of ethiodized oil and chemotherapy solution is retained in tumor vasculature resulting in high tissue drug concentration and low systemic chemotherapy doses. The density and distribution pattern of ethiodized oil within the tumor on post-treatment imaging are predictive of the extent of tumor necrosis and duration of response to treatment. This review describes the multiple roles of ethiodized oil, particularly in its role as a biomarker of tumor response to cTACE. CLINICAL RELEVANCE: With the increasing complexity of locoregional therapy options, including the use of combination therapies, treatment response assessment has become challenging; Ethiodized oil deposition patterns can serve as an imaging biomarker for the prediction of treatment response, and perhaps predict post-treatment prognosis. KEY POINTS: • Treatment response assessment after locoregional therapy to hepatocellular carcinoma is fraught with multiple challenges given the varied post-treatment imaging appearance. • Ethiodized oil is unique in that its' radiopacity can serve as an imaging biomarker to help predict treatment response. • The pattern of deposition of ethiodozed oil has served as a mechanism to detect portions of tumor that are undertreated and can serve as an adjunct to enhancement in order to improve management in patients treated with intraarterial embolization with ethiodized oil.

Machine Learning Models in Prediction of Treatment Response After Chemoembolization with MRI Clinicoradiomics Features

PURPOSE

To evaluate machine learning models, created with radiomics and clinicoradiomics features, ability to predict local response after TACE.

MATERIALS AND METHODS

188 treatment-naïve patients (150 responders, 38 non-responders) with HCC who underwent TACE were included in this retrospective study. Laboratory, clinical and procedural information were recorded. Local response was evaluated by European Association for the Study of the Liver criteria at 3-months. Radiomics features were extracted from pretreatment pre-contrast enhanced T1 (T1WI) and late arterial-phase contrast-enhanced T1 (CE-T1) MRI images. After data augmentation, data were split into training and test sets (70/30). Intra-class correlations, Pearson's correlation coefficients were analyzed and followed by a sequential-feature-selection (SFS) algorithm for feature selection. Support-vector-machine (SVM) models were trained with radiomics and clinicoradiomics features of T1WI, CE-T1 and the combination of both datasets, respectively. Performance metrics were calculated with the test sets. Models' performances were compared with Delong's test.

RESULTS

1128 features were extracted. In feature selection, SFS algorithm selected 18, 12, 24 and 8 features in T1WI, CE-T1, combined datasets and clinical features, respectively. The SVM models area-under-curve was 0.86 and 0.88 in T1WI; 0.76, 0.71 in CE-T1 and 0.82, 0.91 in the combined dataset, with and without clinical features, respectively. The only significant change was observed after inclusion of clinical features in the combined dataset (p = 0.001). Higher WBC and neutrophil levels were significantly associated with lower treatment response in univariant analysis (p = 0.02, for both).

CONCLUSION

Machine learning models created with clinical and MRI radiomics features, may have promise in predicting local response after TACE.

LEVEL OF EVIDENCE

Level 4, Case-control study.

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.

Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score

Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model's efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE.

What Imaging Modality Is More Effective in Predicting Early Recurrence of Hepatocellular Carcinoma after Hepatectomy Using Radiomics Analysis: CT or MRI or Both?

BACKGROUND

It is of great importance to predict the early recurrence (ER) of hepatocellular carcinoma (HCC) after hepatectomy using preoperative imaging modalities. Nevertheless, no comparative studies have been conducted to determine which modality, CT or MRI with radiomics analysis, is more effective.

METHODS

We retrospectively enrolled 119 HCC patients who underwent preoperative CT and MRI. A total of 3776 CT features and 4720 MRI features were extracted from the whole tumor. The minimum redundancy and maximum relevance algorithm (MRMR) and least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection, then support vector machines (SVMs) were applied for model construction. Multivariable logistic regression analysis was employed to construct combined models that integrate clinical-radiological-pathological (CRP) traits and radscore. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to compare the efficacy of CT, MRI, and CT and MRI models in the test cohort.

RESULTS

The CT model and MRI model showed no significant difference in the prediction of ER in HCC patients (p = 0.911). Radiomics^CT&MRI demonstrated a superior predictive performance than either Radiomics^CT or Radiomics^MRI alone (p = 0.032, 0.039). The combined CT and MRI model can significantly stratify patients at high risk of ER (area under the curve (AUC) of 0.951 in the training set and 0.955 in the test set) than the CT model (AUC of 0.894 and 0.784) and the MRI model (AUC of 0.856 and 0.787). DCA demonstrated that the CT and MRI model provided a greater net benefit than the models without radiomics analysis.

CONCLUSIONS

No significant difference was found in predicting the ER of HCC between CT models and MRI models. However, the multimodal radiomics model derived from CT and MRI can significantly improve the prediction of ER in HCC patients after resection.

Data-Driven Assisted Decision Making for Surgical Procedure of Hepatocellular Carcinoma Resection and Prognostic Prediction: Development and Validation of Machine Learning Models

Background: Currently, surgical decisions for hepatocellular carcinoma (HCC) resection are difficult and not sufficiently personalized. We aimed to develop and validate data driven prediction models to assist surgeons in selecting the optimal surgical procedure for patients. Methods: Retrospective data from 361 HCC patients who underwent radical resection in two institutions were included. End-to-end deep learning models were built to automatically segment lesions from the arterial phase (AP) of preoperative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Clinical baseline characteristics and radiomic features were rigorously screened. The effectiveness of radiomic features and radiomic-clinical features was also compared. Three ensemble learning models were proposed to perform the surgical procedure decision and the overall survival (OS) and recurrence-free survival (RFS) predictions after taking different solutions, respectively. Results: SegFormer performed best in terms of automatic segmentation, achieving a Mean Intersection over Union (mIoU) of 0.8860. The five-fold cross-validation results showed that inputting radiomic-clinical features outperformed using only radiomic features. The proposed models all outperformed the other mainstream ensemble models. On the external test set, the area under the receiver operating characteristic curve (AUC) of the proposed decision model was 0.7731, and the performance of the prognostic prediction models was also relatively excellent. The application web server based on automatic lesion segmentation was deployed and is available online. Conclusions: In this study, we developed and externally validated the surgical decision-making procedures and prognostic prediction models for HCC for the first time, and the results demonstrated relatively accurate predictions and strong generalizations, which are expected to help clinicians optimize surgical procedures.

An AI-Powered Clinical Decision Support System to Predict Flares in Rheumatoid Arthritis: A Pilot Study

Treat-to-target (T2T) is a main therapeutic strategy in rheumatology; however, patients and rheumatologists currently have little support in making the best treatment decision. Clinical decision support systems (CDSSs) could offer this support. The aim of this study was to investigate the accuracy, effectiveness, usability, and acceptance of such a CDSS-Rheuma Care Manager (RCM)-including an artificial intelligence (AI)-powered flare risk prediction tool to support the management of rheumatoid arthritis (RA). Longitudinal clinical routine data of RA patients were used to develop and test the RCM. Based on ten real-world patient vignettes, five physicians were asked to assess patients' flare risk, provide a treatment decision, and assess their decision confidence without and with access to the RCM for predicting flare risk. RCM usability and acceptance were assessed using the system usability scale (SUS) and net promoter score (NPS). The flare prediction tool reached a sensitivity of 72%, a specificity of 76%, and an AUROC of 0.80. Perceived flare risk and treatment decisions varied largely between physicians. Having access to the flare risk prediction feature numerically increased decision confidence (3.5/5 to 3.7/5), reduced deviations between physicians and the prediction tool (20% to 12% for half dosage flare prediction), and resulted in more treatment reductions (42% to 50% vs. 20%). RCM usability (SUS) was rated as good (82/100) and was well accepted (mean NPS score 7/10). CDSS usage could support physicians by decreasing assessment deviations and increasing treatment decision confidence.

A narrative review on machine learning in diagnosis and prognosis prediction for tongue squamous cell carcinoma

Background

Tongue squamous cell carcinoma (TSCC) is the most common subtype of oral cavity squamous cell carcinoma (OCSCC), and it also has the worst prognosis. It is crucial to find an effective way to solve the challenges in diagnosis and prognosis prediction for TSCC. Machine learning (ML) has been widely used in medical research and has shown good performance. It can be used for feature extraction, feature selection, model construction, etc. Radiomics and deep learning (DL), the new components of ML, have also been utilized to explore the relationship between image features and diseases. The current study aimed to highlight the importance of ML as a potential method for addressing the challenges in diagnosis and prognosis prediction of TSCC by reviewing studies on ML in TSCC.

Methods

The studies on ML in TSCC in PubMed, Scopus, Web of Science, and China National Knowledge Infrastructure published between the dates of inception of these databases and April 30, 2022, were reviewed.

Key Content and Findings

ML (including radiomics and DL) which was used in diagnosis and prognosis prediction for TSCC, has shown promising performance.

Conclusions

Despite its limitations, ML is still a potential approach that can help to deal with the challenges in diagnosis and prognosis prediction for TSCC. Nevertheless, more efforts are needed to enhance the usefulness of ML in this field.

Machine learning predicts portal vein thrombosis after splenectomy in patients with portal hypertension: Comparative analysis of three practical models

BACKGROUND

For patients with portal hypertension (PH), portal vein thrombosis (PVT) is a fatal complication after splenectomy. Postoperative platelet elevation is considered the foremost reason for PVT. However, the value of postoperative platelet elevation rate (PPER) in predicting PVT has never been studied.

AIM

To investigate the predictive value of PPER for PVT and establish PPER-based prediction models to early identify individuals at high risk of PVT after splenectomy.

METHODS

We retrospectively reviewed 483 patients with PH related to hepatitis B virus who underwent splenectomy between July 2011 and September 2018, and they were randomized into either a training (n = 338) or a validation (n = 145) cohort. The generalized linear (GL) method, least absolute shrinkage and selection operator (LASSO), and random forest (RF) were used to construct models. The receiver operating characteristic curves (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were used to evaluate the robustness and clinical practicability of the GL model (GLM), LASSO model (LSM), and RF model (RFM).

RESULTS

Multivariate analysis exhibited that the first and third days for PPER (PPER1, PPER3) were strongly associated with PVT [odds ratio (OR): 1.78, 95% confidence interval (CI): 1.24-2.62, P = 0.002; OR: 1.43, 95%CI: 1.16-1.77, P < 0.001, respectively]. The areas under the ROC curves of the GLM, LSM, and RFM in the training cohort were 0.83 (95%CI: 0.79-0.88), 0.84 (95%CI: 0.79-0.88), and 0.84 (95%CI: 0.79-0.88), respectively; and were 0.77 (95%CI: 0.69-0.85), 0.83 (95%CI: 0.76-0.90), and 0.78 (95%CI: 0.70-0.85) in the validation cohort, respectively. The calibration curves showed satisfactory agreement between prediction by models and actual observation. DCA and CIC indicated that all models conferred high clinical net benefits.

CONCLUSION

PPER1 and PPER3 are effective indicators for postoperative prediction of PVT. We have successfully developed PPER-based practical models to accurately predict PVT, which would conveniently help clinicians rapidly differentiate individuals at high risk of PVT, and thus guide the adoption of timely interventions.