A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy

Predicting overall survival in hepatocellular carcinoma patients via a combined MRI radiomics and pathomics signature

OBJECTIVE

This study aims to develop and validate a radiopathomics model that integrates radiomic and pathomic features to predict overall survival (OS) in hepatocellular carcinoma (HCC) patients.

MATERIALS AND METHODS

This study involved 126 HCC patients who underwent hepatectomy and were followed for more than 5 years. Radiomic features were extracted from arterial-phase (AP) and portal venous-phase (PVP) MRI scans, whereas pathomic features were obtained from whole-slide images (WSIs) of the HCC patients. Using LASSO Cox regression, both radiomics and pathomics signatures were established. A combined radiopathomics nomogram for predicting OS was constructed and validated. The correlation between the radiopathomics nomogram and OS prediction was evaluated, demonstrating its potential clinical utility in prognosis assessment.

RESULTS

We selected four radiomic features from the AP and PVP MRI scans to construct a signature, achieving a concordance index (C-index) of 0.739 in the training cohort and 0.724 in the validation cohort; these results indicate favourable 5-year OS prediction. Similarly, from 1,141 pathomics features extracted from WSIs, 15 were chosen for a pathomics signature, which had C-indexes of 0.821 and 0.808 in the training and validation cohorts, respectively. The most robust performance was delivered by a radiopathomics nomogram, with C-index values of 0.840 in the training cohort and 0.875 in the validation cohort. Decision curve analysis (DCA) confirmed the highest net benefit achievable by the combined radiopathomics nomogram.

CONCLUSION

Our findings indicate that the radiopathomics nomogram can serve as a predictive marker for hepatectomy prognosis in HCC patients and has the potential to enhance personalized therapeutic approaches.

Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning

BACKGROUND

Accurate mortality risk quantification is crucial for the management of hepatocellular carcinoma (HCC); however, most scoring systems are subjective.

PURPOSE

To develop and independently validate a machine learning mortality risk quantification method for HCC patients using standard-of-care clinical data and liver radiomics on baseline magnetic resonance imaging (MRI).

METHODS

This retrospective study included all patients with multiphasic contrast-enhanced MRI at the time of diagnosis treated at our institution. Patients were censored at their last date of follow-up, end-of-observation, or liver transplantation date. The data were randomly sampled into independent cohorts, with 85% for development and 15% for independent validation. An automated liver segmentation framework was adopted for radiomic feature extraction. A random survival forest combined clinical and radiomic variables to predict overall survival (OS), and performance was evaluated using Harrell's C-index.

RESULTS

A total of 555 treatment-naïve HCC patients (mean age, 63.8 years ± 8.9 [standard deviation]; 118 females) with MRI at the time of diagnosis were included, of which 287 (51.7%) died after a median time of 14.40 (interquartile range, 22.23) months, and had median followed up of 32.47 (interquartile range, 61.5) months. The developed risk prediction framework required 1.11 min on average and yielded C-indices of 0.8503 and 0.8234 in the development and independent validation cohorts, respectively, outperforming conventional clinical staging systems. Predicted risk scores were significantly associated with OS (p < .00001 in both cohorts).

CONCLUSIONS

Machine learning reliably, rapidly, and reproducibly predicts mortality risk in patients with hepatocellular carcinoma from data routinely acquired in clinical practice.

CLINICAL RELEVANCE STATEMENT

Precision mortality risk prediction using routinely available standard-of-care clinical data and automated MRI radiomic features could enable personalized follow-up strategies, guide management decisions, and improve clinical workflow efficiency in tumor boards.

KEY POINTS

• Machine learning enables hepatocellular carcinoma mortality risk prediction using standard-of-care clinical data and automated radiomic features from multiphasic contrast-enhanced MRI. • Automated mortality risk prediction achieved state-of-the-art performances for mortality risk quantification and outperformed conventional clinical staging systems. • Patients were stratified into low, intermediate, and high-risk groups with significantly different survival times, generalizable to an independent evaluation cohort.

An MRI-based radiomics nomogram to predict progression-free survival in patients with endometrial cancer

Aim: This study aimed to explore the importance of an MRI-based radiomics nomogram in predicting the progression-free survival (PFS) of endometrial cancer.Methods: Based on clinicopathological and radiomic characteristics, we established three models (clinical, radiomics and combined model) and developed a nomogram for the combined model. The Kaplan-Meier method was utilized to evaluate the association between nomogram-based risk scores and PFS.Results: The nomogram had a strong predictive ability in calculating PFS with areas under the curve (ROC) of 0.905 and 0.901 at 1 and 3 years, respectively. The high-risk groups identified by the nomogram-based scores had shorter PFS compared with the low-risk groups.Conclusion: The radiomics nomogram has the potential to serve as a noninvasive imaging biomarker for predicting individual PFS of endometrial cancer.

Prediction power of radiomics in early recurrence of hepatocellular carcinoma: A systematic review and meta-analysis

BACKGROUND

Raiomics is an emerging auxiliary diagnostic tool, but there are still differences in whether it can be applied to predict early recurrence of hepatocellular carcinoma (HCC). The purpose of this meta-analysis was to systematically evaluate the predictive power of radiomics in the early recurrence (ER) of HCC.

METHODS

Comprehensive studies on the application of radiomics to predict ER in HCC patients after hepatectomy or curative ablation were systematically screened in Embase, PubMed, and Web of Science.

RESULTS

Ten studies which is involving a total of 1929 patients were reviewed. The overall estimates of radiomic models for sensitivity and specificity in predicting the ER of HCC were 0.79 (95% confidence interval [CI]: 0.68-0.87) and 0.83 (95% CI: 0.73-0.90), respectively. The area under the summary receiver operating characteristic curve (SROC) was 0.88 (95% CI: 0.85-0.91).

CONCLUSIONS

The imaging method is a reliable method for diagnosing HCC. Radiomics, which is based on medical imaging, has excellent power in predicting the ER of HCC. With the help of radiomics, we can predict the recurrence of HCC after surgery more effectively and provide a useful reference for clinical practice.

Deep learning model based on contrast-enhanced MRI for predicting post-surgical survival in patients with hepatocellular carcinoma

Objective

To develop a deep learning model based on contrast-enhanced magnetic resonance imaging (MRI) data to predict post-surgical overall survival (OS) in patients with hepatocellular carcinoma (HCC).

Methods

This bi-center retrospective study included 564 surgically resected patients with HCC and divided them into training (326), testing (143), and external validation (95) cohorts. This study used a three-dimensional convolutional neural network (3D-CNN) ResNet to learn features from the pretreatment MR images (T1WIpre, late arterial phase, and portal venous phase) and got the deep learning score (DL score). Three cox regression models were established separately using the DL score (3D-CNN model), clinical features (clinical model), and a combination of above (combined model). The concordance index (C-index) was used to evaluate model performance.

Results

We trained a 3D-CNN model to get DL score from samples. The C-index of the 3D-CNN model in predicting 5-year OS for the training, testing, and external validation cohorts were 0.746, 0.714, and 0.698, respectively, and were higher than those of the clinical model, which were 0.675, 0.674, and 0.631, respectively (P = 0.009, P = 0.204, and P = 0.092, respectively). The C-index of the combined model for testing and external validation cohorts was 0.750 and 0.723, respectively, significantly higher than the clinical model (P = 0.017, P = 0.016) and the 3D-CNN model (P = 0.029, P = 0.036).

Conclusions

The combined model integrating the DL score and clinical factors showed a higher predictive value than the clinical and 3D-CNN models and may be more useful in guiding clinical treatment decisions to improve the prognosis of patients with HCC.

Current use of artificial intelligence in the diagnosis and management of acute appendicitis

INTRODUCTION

Acute appendicitis is a common and time-sensitive surgical emergency, requiring rapid and accurate diagnosis and management to prevent complications. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering significant potential to improve the diagnosis and management of acute appendicitis. This review provides an overview of the evolving role of AI in the diagnosis and management of acute appendicitis, highlighting its benefits, challenges, and future perspectives.

EVIDENCE ACQUISITION

We performed a literature search on articles published from 2018 to September 2023. We included only original articles.

EVIDENCE SYNTHESIS

Overall, 121 studies were examined. We included 32 studies: 23 studies addressed the diagnosis, five the differentiation between complicated and uncomplicated appendicitis, and 4 studies the management of acute appendicitis.

CONCLUSIONS

AI is poised to revolutionize the diagnosis and management of acute appendicitis by improving accuracy, speed and consistency. It could potentially reduce healthcare costs. As AI technologies continue to evolve, further research and collaboration are needed to fully realize their potential in the diagnosis and management of acute appendicitis.

Clinical prediction of microvascular invasion in hepatocellular carcinoma using an MRI-based graph convolutional network model integrated with nomogram

OBJECTIVES

Based on enhanced MRI, a prediction model of microvascular invasion (MVI) for hepatocellular carcinoma (HCC) was developed using graph convolutional network (GCN) combined nomogram.

METHODS

We retrospectively collected 182 HCC patients confirmed histopathologically, all of them performed enhanced MRI before surgery. The patients were randomly divided into training and validation groups. Radiomics features were extracted from the arterial phase (AP), portal venous phase (PVP), and delayed phase (DP), respectively. After removing redundant features, the graph structure by constructing the distance matrix with the feature matrix was built. Screening the superior phases and acquired GCN Score (GS). Finally, combining clinical, radiological and GS established the predicting nomogram.

RESULTS

27.5% (50/182) patients were with MVI positive. In radiological analysis, intratumoural artery (P = 0.007) was an independent predictor of MVI. GCN model with grey-level cooccurrence matrix-grey-level run length matrix features exhibited area under the curves of the training group was 0.532, 0.690, and 0.885 and the validation group was 0.583, 0.580, and 0.854 for AP, PVP, and DP, respectively. DP was selected to develop final model and got GS. Combining GS with diameter, corona enhancement, mosaic architecture, and intratumoural artery constructed a nomogram which showed a C-index of 0.884 (95% CI: 0.829-0.927).

CONCLUSIONS

The GCN model based on DP has a high predictive ability. A nomogram combining GS, clinical and radiological characteristics can be a simple and effective guiding tool for selecting HCC treatment options.

ADVANCES IN KNOWLEDGE

GCN based on MRI could predict MVI on HCC.

Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis

BACKGROUND

The prognosis for hepatocellular carcinoma (HCC) in the presence of cirrhosis is unfavourable, primarily attributable to the high incidence of recurrence.

AIM

To develop a machine learning model for predicting early recurrence (ER) of post-hepatectomy HCC in patients with cirrhosis and to stratify patients' overall survival (OS) based on the predicted risk of recurrence.

METHODS

In this retrospective study, 214 HCC patients with cirrhosis who underwent curative hepatectomy were examined. Radiomics feature selection was conducted using the least absolute shrinkage and selection operator and recursive feature elimination methods. Clinical-radiologic features were selected through univariate and multivariate logistic regression analyses. Five machine learning methods were used for model comparison, aiming to identify the optimal model. The model's performance was evaluated using the receiver operating characteristic curve [area under the curve (AUC)], calibration, and decision curve analysis. Additionally, the Kaplan-Meier (K-M) curve was used to evaluate the stratification effect of the model on patient OS.

RESULTS

Within this study, the most effective predictive performance for ER of post-hepatectomy HCC in the background of cirrhosis was demonstrated by a model that integrated radiomics features and clinical-radiologic features. In the training cohort, this model attained an AUC of 0.844, while in the validation cohort, it achieved a value of 0.790. The K-M curves illustrated that the combined model not only facilitated risk stratification but also exhibited significant discriminatory ability concerning patients' OS.

CONCLUSION

The combined model, integrating both radiomics and clinical-radiologic characteristics, exhibited excellent performance in HCC with cirrhosis. The K-M curves assessing OS revealed statistically significant differences.

Machine Learning-Based Nomogram for Predicting Overall Survival in Elderly Patients with Cirrhotic Hepatocellular Carcinoma Undergoing Ablation Therapy

Purpose

The aim of the study is to identify and evaluate multifaceted factors impacting the survival of elderly cirrhotic HCC patients following ablation therapy, with the goal of constructing a nomogram to predict their 3-, 5-, and 8-year overall survival (OS).

Patients and Methods

A retrospective analysis was conducted on 736 elderly cirrhotic HCC patients who underwent ablation therapy between 2014 and 2022. LASSO regression, random survival forest (RSF), and multivariate Cox analyses were employed to identify independent prognostic factors for OS, followed by the development and validation of a predictive nomogram. Harrell's concordance index (C-index), calibration plot and decision curve analysis (DCA) were used to assess the performance of the nomogram. The nomogram was finally utilized to stratify patients into low-, intermediate-, and high-risk groups, aiming to assess its efficacy in precisely discerning individuals with diverse overall survival outcomes.

Results

Alcohol drinking, tumor number, globulin (Glob) and prealbumin (Palb) were identified and integrated to establish a novel prognostic nomogram. The nomogram exhibited strong discriminative ability with C-indices of 0.723 (training cohort) and 0.693 (validation cohort), along with significant Area Under the Curve (AUC) values for 3-year, 5-year, and 8-year OS in both cohorts (0.758, 0.770, and 0.811 for training cohort; 0.744, 0.699 and 0.737 for validation cohort). Calibration plots substantiated its consistency, while DCA curves corroborated its clinical utility. The nomogram further demonstrated exceptional effectiveness in discerning distinct risk populations, highlighting its robust applicability for prognostic stratification.

Conclusion

Our study successfully developed and validated a robust nomogram model based on four key clinical parameters for predicting 3-, 5- and 8-year OS among elderly cirrhotic HCC patients following ablation therapy. The nomogram exhibited a remarkable capability in identifying high-risk patients, furnishing clinicians with invaluable insights for postoperative surveillance and tailored therapeutic interventions.

Utilization of Radiomics Features Extracted From Preoperative Medical Images to Detect Metastatic Lymph Nodes in Cholangiocarcinoma and Gallbladder Cancer Patients: A Systemic Review and Meta-analysis

OBJECTIVES

This study aimed to determine the methodological quality and evaluate the diagnostic performance of radiomics features in detecting lymph node metastasis on preoperative images in patients with cholangiocarcinoma and gallbladder cancer.

METHODS

Publications between January 2005 and October 2022 were considered for inclusion. Databases such as Pubmed/Medline, Scopus, Embase, and Google Scholar were searched for relevant studies. The quality of the methodology of the manuscripts was determined using the Radiomics Quality Score and Quality Assessment of Diagnostic Accuracy Studies 2. Pooled results with corresponding 95% confidence intervals (CIs) were calculated using the DerSimonian-Liard method (random-effect model). Forest plots were used to visually represent the diagnostic profile of radiomics signature in each of the data sets pertaining to each study. Fagan plot was used to determine clinical applicability.

RESULTS

Overall sensitivity was 0.748 (95% CI, 0.703-0.789). Overall specificity was 0.795 (95% CI, 0.742-0.839). The combined negative likelihood ratio was 0.299 (95% CI, 0.266-0.350), and the positive likelihood ratio was 3.545 (95% CI, 2.850-4.409). The combined odds ratio of the studies was 12.184 (95% CI, 8.477-17.514). The overall summary receiver operating characteristics area under the curve was 0.83 (95% CI, 0.80-0.86). Three studies applied nomograms to 8 data sets and achieved a higher pooled sensitivity and specificity (0.85 [0.80-0.89] and 0.85 [0.71-0.93], respectively).

CONCLUSIONS

The pooled analysis showed that predictive models fed with radiomics features achieve good sensitivity and specificity in detecting lymph node metastasis in computed tomography and magnetic resonance imaging images. Supplementation of the models with biological correlates increased sensitivity and specificity in all data sets.

Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma

To examine the comparative robustness of computed tomography (CT)-based conventional radiomics and deep-learning convolutional neural networks (CNN) to predict overall survival (OS) in HCC patients. Retrospectively, 114 HCC patients with pretherapeutic CT of the liver were randomized into a development (n = 85) and a validation (n = 29) cohort, including patients of all tumor stages and several applied therapies. In addition to clinical parameters, image annotations of the liver parenchyma and of tumor findings on CT were available. Cox-regression based on radiomics features and CNN models were established and combined with clinical parameters to predict OS. Model performance was assessed using the concordance index (C-index). Log-rank tests were used to test model-based patient stratification into high/low-risk groups. The clinical Cox-regression model achieved the best validation performance for OS (C-index [95% confidence interval (CI)] 0.74 [0.57-0.86]) with a significant difference between the risk groups (p = 0.03). In image analysis, the CNN models (lowest C-index [CI] 0.63 [0.39-0.83]; highest C-index [CI] 0.71 [0.49-0.88]) were superior to the corresponding radiomics models (lowest C-index [CI] 0.51 [0.30-0.73]; highest C-index [CI] 0.66 [0.48-0.79]). A significant risk stratification was not possible (p > 0.05). Under clinical conditions, CNN-algorithms demonstrate superior prognostic potential to predict OS in HCC patients compared to conventional radiomics approaches and could therefore provide important information in the clinical setting, especially when clinical data is limited.

Efficacy of Radiomics in Predicting Oncologic Outcome of Liver-Directed Combined Radiotherapy in Locally Advanced Hepatocellular Carcinoma

PURPOSE

We investigated whether radiomic features extracted from three-phase dynamic contrast-enhanced computed tomography (CECT) can be used to predict clinical outcomes, including objective treatment response (OR) and in-field failure-free survival rate (IFFR), in patients with hepatocellular carcinoma (HCC) who received liver-directed combined radiotherapy (LD-CRT).

METHODS

We included 409 patients, and they were randomly divided into training (n = 307) and validation (n = 102) cohorts. For radiomics models, we extracted 116 radiomic features from the region of interest on the CECT images. Significant clinical prognostic factors are identified to predict the OR and IFFR in the clinical models. We developed clinical models, radiomics models, and a combination of both features (CCR model).

RESULTS

Among the radiomic models evaluated for OR, the OR-PVP-Peri-1cm model showed favorable predictive performance with an area under the curve (AUC) of 0.647. The clinical model showed an AUC of 0.729, whereas the CCR model showed better performance (AUC 0.759). For the IFFR, the IFFR-PVP-Peri-1cm model showed an AUC of 0.673, clinical model showed 0.687, and the CCR model showed 0.736. We also developed and validated a prognostic nomogram based on CCR models.

CONCLUSION

In predicting the OR and IFFR in patients with HCC undergoing LD-CRT, CCR models performed better than clinical and radiomics models. Moreover, the constructed nomograms based on these models may provide valuable information on the prognosis of these patients.

Performances and variability of CT radiomics for the prediction of microvascular invasion and survival in patients with HCC: a matter of chance or standardisation?

OBJECTIVES

To measure the performance and variability of a radiomics-based model for the prediction of microvascular invasion (MVI) and survival in patients with resected hepatocellular carcinoma (HCC), simulating its sequential development and application.

METHODS

This study included 230 patients with 242 surgically resected HCCs who underwent preoperative CT, of which 73/230 (31.7%) were scanned in external centres. The study cohort was split into training set (158 patients, 165 HCCs) and held-out test set (72 patients, 77 HCCs), stratified by random partitioning, which was repeated 100 times, and by a temporal partitioning to simulate the sequential development and clinical use of the radiomics model. A machine learning model for the prediction of MVI was developed with least absolute shrinkage and selection operator (LASSO). The concordance index (C-index) was used to assess the value to predict the recurrence-free (RFS) and overall survivals (OS).

RESULTS

In the 100-repetition random partitioning cohorts, the radiomics model demonstrated a mean AUC of 0.54 (range 0.44-0.68) for the prediction of MVI, mean C-index of 0.59 (range 0.44-0.73) for RFS, and 0.65 (range 0.46-0.86) for OS in the held-out test set. In the temporal partitioning cohort, the radiomics model yielded an AUC of 0.50 for the prediction of MVI, a C-index of 0.61 for RFS, and 0.61 for OS, in the held-out test set.

CONCLUSIONS

The radiomics models had a poor performance for the prediction of MVI with a large variability in the model performance depending on the random partitioning. Radiomics models demonstrated good performance in the prediction of patient outcomes.

CLINICAL RELEVANCE STATEMENT

Patient selection within the training set strongly influenced the performance of the radiomics models for predicting microvascular invasion; therefore, a random approach to partitioning a retrospective cohort into a training set and a held-out set seems inappropriate.

KEY POINTS

• The performance of the radiomics models for the prediction of microvascular invasion and survival widely ranged (AUC range 0.44-0.68) in the randomly partitioned cohorts. • The radiomics model for the prediction of microvascular invasion was unsatisfying when trying to simulate its sequential development and clinical use in a temporal partitioned cohort imaged with a variety of CT scanners. • The performance of the radiomics models for the prediction of survival was good with similar performances in the 100-repetition random partitioning and temporal partitioning cohorts.

CT Radiomics Features of Abdominal Adipose and Muscle Tissues Can Predict the Postoperative Early Recurrence of Hepatocellular Carcinoma

RATIONALE AND OBJECTIVES

To investigate the potential of computed tomography radiomics features extracted from abdominal adipose and muscle in predicting early recurrence (ER) of hepatocellular carcinoma (HCC) after surgery.

MATERIALS AND METHODS

This retrospective study enrolled 252 patients with HCC who underwent curative resection from two independent institutions. In the training cohort of 178 patients from institution A, radiomics signatures extracted from abdominal visceral adipose, subcutaneous adipose, and muscle were applied to establish the radiomics score using the least absolute shrinkage and selection operator regression. Using multivariable Cox regression analysis, two models were developed: one incorporated preoperative variables, and the other incorporated both pre- and postoperative variables. The external validation of the two models was conducted at institution B with 74 patients.

RESULTS

The preoperative model incorporated tumor size, alpha-fetoprotein, body mass index, and radiomics score, whereas the postoperative model additionally integrated Edmondson-Steiner grade on the basis of the aforementioned parameters. In both cohorts, both models demonstrated superior performance to traditional staging systems and corresponding clinical models (P < 0.01), with time-dependent area under the curve exceeding 0.81 and concordance indices exceeding 0.72. Furthermore, the two models exhibited lower prediction errors (integrated Brier score < 0.19), well-calibrated calibration curves, and greater net clinical benefits. Finally, the two radiomics-based models facilitated risk stratification by accurately distinguishing the high-, intermediate-, and low-risk groups for ER (P < 0.01).

CONCLUSION

Statistical models integrating the radiomics data of abdominal adipose and muscle can provide accurate and reliable predictions of postoperative ER for patients with HCC.

Effect of ABO blood group on postoperative overall survival and recurrence-free survival rate in patients with hepatocellular carcinoma after hepatectomy: a multi-center retrospective cohort study

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. The survival rate after hepatectomy as the first line of treatment for HCC depends on various factors. This study evaluated the association of the ABO blood group and Rh with overall survival (OS) and Recurrence-free survival (RFS) rate after hepatectomy.

METHODS

This multicenter retrospective cohort study reviewed the medical files of 639 HCC patients who underwent hepatectomy from 2010 to 2022 in three medical centers affiliated with the Iran University of Medical Sciences. Patient data, including demographic, clinical, tumor characteristics, and post-surgery outcomes, were collected by referring to the patient's medical profiles. The Cox proportional hazard investigated the relationship between ABO blood group type and OS and RFS rate after hepatectomy.

RESULTS

The five-year OS and RFS rates were 25.4% and 18.7%, respectively. The five-year OS (Lok rank:40.89, P:0.001) and RFS rate in patients with blood type A were significantly lower than in non-A patients. (Lok rank:10.8, P:0.001) The multivariate Cox analysis showed that blood type A, age < 45 years, tumor size > 5 cm, Poor tumor differentiation, presence of metastasis, The number of involved lymph nodes ≤ 2, and serum Alpha-Fetoprotein)AFP( level ≥ 400 were significantly related to the decreased survival rate of HCC patients after hepatectomy (P < 0.05) There was no significant association between Rh with OS and RFS (P > 0.05).

CONCLUSION

Blood group type A, compared to non-A, can be associated with decreased OS and RFS rates in patients with HCC after hepatectomy.

Radiomics features from 3D-MPRAGE imaging can differentiate temporal-plus epilepsy from temporal lobe epilepsy

OBJECTIVE

This study aimed to differentiate temporal-plus epilepsy (TPE) from temporal lobe epilepsy (TLE) using extraction of radiomics features from three-dimensional magnetization-prepared rapid acquisition gradient echo (3D-MPRAGE) imaging data.

METHODS

Data from patients with TLE or TPE who underwent epilepsy surgery between January 2019 and January 2021 were retrospectively analyzed. Thirty-three regions of interest in the affected hemisphere of each patient were defined on 3D-MPRAGE images. A total of 3531 image features were extracted from each patient. Four feature selection methods and 10 machine learning algorithms were used to build 40 differentiation models. Model performance was evaluated using receiver operating characteristic analysis.

RESULTS

Eighty-two patients were included for analysis, 47 with TLE and 35 with TPE. The model combining logistic regression and the relief selection method had the best performance (area under the receiver operating characteristic curve, .779; accuracy, .875; sensitivity, .800; specificity, .929; positive predictive value, .889; negative predictive value, .867).

SIGNIFICANCE

Radiomics analysis can differentiate TPE from TLE. The logistic regression classifier trained with radiomics features extracted from 3D-MPRAGE images had the highest accuracy and best performance.

Fusion-based tensor radiomics using reproducible features: Application to survival prediction in head and neck cancer

BACKGROUND

Numerous features are commonly generated in radiomics applications as applied to medical imaging, and identification of robust radiomics features (RFs) can be an important step to derivation of reliable, reproducible solutions. In this work, we utilize a tensor radiomics (TR) framework, where numerous fusions are explored, to generate different flavours of RFs, and we aimed to identify RFs that are robust to fusion techniques in head and neck cancer. Overall, we aimed to predict progression-free survival (PFS) using Hybrid Machine Learning Systems (HMLS) and reproducible RFs.

METHODS

The study was performed on 408 patients with head and neck cancer from The Cancer Imaging Archive. After image preprocessing, 15 fusion techniques were employed to combine Positron Emission Tomography (PET) and Computed Tomography (CT) images. Subsequently, 215 RFs were extracted through a standardized radiomics software, with 17 'flavours' generated using PET-only, CT-only, and 15 fused PET&CT images. The variability of RFs across flavours was studied using the Intraclass Correlation Coefficient (ICC). Furthermore, the features were categorized into seven reliability groups, 106 reproducible RFs with ICC>0.75 were selected, highly correlated flavours were removed, Principal Component Analysis was used to convert 17 flavours to 1 attribute, the polynomial function was utilized to increase RFs, and Analysis of variance (ANOVA) was used to select the relevant attributes. Finally, 3 classifiers including Random Forest (RFC), Logistic regression (LR), and Multi-layer perceptron were applied to the preselected relevant attributes to predict binary PFS. In 5-fold cross-validation, 80% of 4 divisions were utilized to train the model, and the remaining 20% was utilized to evaluate the model. Further, the remaining fold was used for external nested testing.

RESULTS

Reliability analysis indicated that most morphological features belong to the high-reliability category. By contrast, local intensity and statistical features extracted from images belong to the low-reliability category. In the tensor framework, the highest 5-fold cross-validation accuracy of 76.7%±3.3% with an external nested testing of 70.6%±6.7% resulted from the reproducible TR+polynomial function+ANOVA+LR algorithm while the accuracy of 70.0%±4.2% with the external nested testing of 67.7%±4.9% was achieved through the PCA fusion+RFC (non-tensor paradigm).

CONCLUSIONS

This study demonstrated that using reproducible RFs as utilized within a tensor fusion radiomics framework, linked with ANOVA and LR, added value to prediction of progression-free survival outcome in head and neck cancer patients.

A Nomogram Prognostic Model for Advanced Hepatocellular Carcinoma Based on the Interaction Between CD8+T Cell Counts and Age

Objective

CD8+T cells are essential components of the adaptive immune system and are crucial in the body's immune system. This study aimed to investigate how the prognosis of patients with advanced hepatocellular carcinoma (HCC) was affected by their CD8+ T cell counts and age and established an effective nomogram model to predict the overall survival (OS).

Methods

A total of 427 patients with advanced HCC from Beijing Ditan Hospital, Capital Medical University, were enrolled in this study and randomly divided into training and validation groups, with 300 and 127 individuals in each group, respectively. Cox regression analysis was used to screen for independent risk factors for advanced HCC, and the interactive relationship between CD8+T cells and patient age was examined to establish a nomogram prediction model.

Results

Cox multivariate regression and interaction analyses indicated that tumor number, tumor size, aspartate aminotransferase (AST), C-reactive protein (CRP), relationship of CD8+T cell counts and age were independent predictors of 6-month OS in patients with advanced HCC, and the nomogram model was established based on these factors. The area under the receiver operating characteristic curve (AUC) of the nomogram model for predicting the 3-month, 6-month, and 12-month OS rates were 0.821, 0.802, and 0.756, respectively. Moreover, in clinical practice, patients with true-positive survival benefit more than true-positive death, therefore, we selected 25% as the clinical decision threshold probability based on probability density functions (PDFs) and clinical utility curves (CUCs), which can distinguish approximately 92% of patients who died and 37% of patients who survived.

Conclusion

The nomogram model based on CD8+T cell counts and age accurately assessed the prognosis of patients with advanced HCC and suggested that high CD8+T cell levels are beneficial to the survival of patients with advanced HCC.

[TMEM64 is highly expressed in hepatocellular carcinoma and promotes tumor cell proliferation and invasion]

OBJECTIVE

To analyze the expression of TMEM64 in hepatocellular carcinoma (HCC) and investigate the effect of TMEM64 expression level on proliferation and invasion of HCC cells in vitro.

METHODS

We analyzed the expression level of TMEM64 in HCC and adjacent tissues based on data from TCGA and GTEx databases. The prognostic value of TMEM64 for HCC patients was examined using Kaplan-Meier survival analysis and a Cox regression model, and a nomogram was constructed based on TMEM64 expression and clinical characteristics of the patients. Functional enrichment analysis was performed to explore the potential signaling pathways, and immune cell infiltration was assessed using single sample gene set enrichment analysis. We also performed cell experiment to observe the changes in proliferation, migration, and invasion in HCCLM3 cells with TMEM64 knockdown and in Huh7 cells with TMEM64 overexpression using CCK-8, EdU, colony formation, Transwell, and wound healing assays.

RESULTS

The expression level of TMEM64 was significantly higher in HCC than in the adjacent tissues (P < 0.05). Kaplan-Meier analysis suggested that a high expression of TMEM64 was associated with poor outcomes of the patients (P < 0.05). Multivariate Cox regression analysis indicated that a high TMEM64 expression was an independent risk factor for overall survival of HCC patients (P < 0.05). TMEM64 expression level was negatively correlated with the levels of immune cell infiltration by NK cells, CD8 + T cells, and plasma pDCs cells (P < 0.05). GO, KEGG, and GSEA enrichment analyses showed that TMEM64 was significantly enriched with tumor invasion and metastasis pathways. The nomogram and calibration curves indicated a moderate prediction reliability of the model. In the cell experiment, TMEM64 knockdown obviously suppressed and TMEM64 overexpression markedly promoted the proliferation, migration, and invasion of HCC cells (P < 0.01).

CONCLUSION

A high TMEM64 expression may serve as an independent risk factor for poor prognosis of HCC and promotes proliferation, migration, and invasion of HCC cells in vitro.

Radiomics model based on contrast-enhanced computed tomography to predict early recurrence in patients with hepatocellular carcinoma after radical resection

BACKGROUND

Radical resection remains an effective strategy for patients with hepatocellular carcinoma (HCC). Unfortunately, the postoperative early recurrence (recurrence within 2 years) rate is still high.

AIM

To develop a radiomics model based on preoperative contrast-enhanced computed tomography (CECT) to evaluate early recurrence in HCC patients with a single tumour.

METHODS

We enrolled a total of 402 HCC patients from two centres who were diagnosed with a single tumour and underwent radical resection. First, the features from the portal venous and arterial phases of CECT were extracted based on the region of interest, and the early recurrence-related radiomics features were selected via the least absolute shrinkage and selection operator proportional hazards model (LASSO Cox) to determine radiomics scores for each patient. Then, the clinicopathologic data were combined to develop a model to predict early recurrence by Cox regression. Finally, we evaluated the prediction performance of this model by multiple methods.

RESULTS

A total of 1915 radiomics features were extracted from CECT images, and 31 of them were used to determine the radiomics scores, which showed a significant difference between the early recurrence and nonearly recurrence groups. Univariate and multivariate Cox regression analyses showed that radiomics scores and serum alpha-fetoprotein were independent indicators, and they were used to develop a combined model to predict early recurrence. The area under the receiver operating characteristic curve values for the training and validation cohorts were 0.77 and 0.74, respectively, while the C-indices were 0.712 and 0.674, respectively. The calibration curves and decision curve analysis showed satisfactory accuracy and clinical utilities. Kaplan-Meier curves based on recurrence-free survival and overall survival showed significant differences.

CONCLUSION

The preoperative radiomics model was shown to be effective for predicting early recurrence among HCC patients with a single tumour.

Radiomics in the diagnosis and treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor. At present, early diagnosis of HCC is difficult and therapeutic methods are limited. Radiomics can achieve accurate quantitative evaluation of the lesions without invasion, and has important value in the diagnosis and treatment of HCC. Radiomics features can predict the development of cancer in patients, serve as the basis for risk stratification of HCC patients, and help clinicians distinguish similar diseases, thus improving the diagnostic accuracy. Furthermore, the prediction of the treatment outcomes helps determine the treatment plan. Radiomics is also helpful in predicting the HCC recurrence, disease-free survival and overall survival. This review summarized the role of radiomics in the diagnosis, treatment and prognosis of HCC.

Integrative Nomogram of Computed Tomography Radiomics, Clinical, and Tumor Immune Features for Analysis of Disease-Free Survival of NSCLC Patients with Surgery

To improve prognosis of cancer patients and determine the integrative value for analysis of disease-free survival prediction, a clinic investigation was performed involving with 146 non-small cell lung cancer (NSCLC) patients (83 men and 73 women; mean age: 60.24 years ± 8.637) with a history of surgery. Their computed tomography (CT) radiomics, clinical records, and tumor immune features were firstly obtained and analyzed in this study. Histology and immunohistochemistry were also performed to establish a multimodal nomogram through the fitting model and cross-validation. Finally, Z test and decision curve analysis (DCA) were performed to evaluate and compare the accuracy and difference of each model. In all, seven radiomics features were selected to construct the radiomics score model. The clinicopathological and immunological factors model, including T stage, N stage, microvascular invasion, smoking quantity, family history of cancer, and immunophenotyping. The C-index of the comprehensive nomogram model on the training set and test set was 0.8766 and 0.8426 respectively, which was better than that of the clinicopathological-radiomics model (Z test, P =0.041<0.05), radiomics model and clinicopathological model (Z test, P =0.013<0.05 and P =0.0097<0.05). Integrative nomogram based on computed tomography radiomics, clinical and immunophenotyping can be served as effective imaging biomarker to predict DFS of hepatocellular carcinoma after surgical resection.

Association of Multi-Phasic MR-Based Radiomic and Dosimetric Features with Treatment Response in Unresectable Hepatocellular Carcinoma Patients following Novel Sequential TACE-SBRT-Immunotherapy

This study aims to investigate the association of pre-treatment multi-phasic MR-based radiomics and dosimetric features with treatment response to a novel sequential trans-arterial chemoembolization (TACE) plus stereotactic body radiotherapy (SBRT) plus immunotherapy regimen in unresectable Hepatocellular Carcinoma (HCC) sub-population. Twenty-six patients with unresectable HCC were retrospectively analyzed. Radiomic features were extracted from 42 lesions on arterial phase (AP) and portal-venous phase (PVP) MR images. Delta-phase (DeltaP) radiomic features were calculated as AP-to-PVP ratio. Dosimetric data of the tumor was extracted from dose-volume-histograms. A two-sided independent Mann-Whitney U test was used to assess the clinical association of each feature, and the classification performance of each significant independent feature was assessed using logistic regression. For the 3-month timepoint, four DeltaP-derived radiomics that characterize the temporal change in intratumoral randomness and uniformity were the only contributors to the treatment response association (p-value = 0.038-0.063, AUC = 0.690-0.766). For the 6-month timepoint, DeltaP-derived radiomic features (n = 4) maintained strong clinical associations with the treatment response (p-value = 0.047-0.070, AUC = 0.699-0.788), additional AP-derived radiomic features (n = 4) that reflect baseline tumoral arterial-enhanced signal pattern and tumor morphology (n = 1) that denotes initial tumor burden were shown to have strong associations with treatment response (p-value = 0.028-0.074, AUC = 0.719-0.773). This pilot study successfully demonstrated associations of pre-treatment multi-phasic MR-based radiomics with tumor response to the novel treatment regimen.

Radiomics: A radiological evidence-based artificial intelligence technique to facilitate personalized precision medicine in hepatocellular carcinoma

The high postoperative recurrence rates in hepatocellular carcinoma (HCC) remain a major hurdle in its management. Appropriate staging and treatment selection may alleviate the extent of fatal recurrence. However, effective methods to preoperatively evaluate pathophysiologic and molecular characteristics of HCC are lacking. Imaging plays a central role in HCC diagnosis and stratification due to the non-invasive diagnostic criteria. Vast and crucial information is hidden within image data. Other than providing a morphological sketch for lesion diagnosis, imaging could provide new insights to describe the pathophysiological and genetic landscape of HCC. Radiomics aims to facilitate diagnosis and prognosis of HCC using artificial intelligence techniques to harness the immense information contained in medical images. Radiomics produces a set of archetypal and robust imaging features that are correlated to key pathological or molecular biomarkers to preoperatively risk-stratify HCC patients. Inferred with outcome data, comprehensive combination of radiomic, clinical and/or multi-omics data could also improve direct prediction of response to treatment and prognosis. The evolution of radiomics is changing our understanding of personalized precision medicine in HCC management. Herein, we review the key techniques and clinical applications in HCC radiomics and discuss current limitations and future opportunities to improve clinical decision making.

Survival prediction for patients with glioblastoma multiforme using a Cox proportional hazards denoising autoencoder network

Objectives

This study aimed to establish and validate a prognostic model based on magnetic resonance imaging and clinical features to predict the survival time of patients with glioblastoma multiforme (GBM).

Methods

In this study, a convolutional denoising autoencoder (DAE) network combined with the loss function of the Cox proportional hazard regression model was used to extract features for survival prediction. In addition, the Kaplan-Meier curve, the Schoenfeld residual analysis, the time-dependent receiver operating characteristic curve, the nomogram, and the calibration curve were performed to assess the survival prediction ability.

Results

The concordance index (C-index) of the survival prediction model, which combines the DAE and the Cox proportional hazard regression model, reached 0.78 in the training set, 0.75 in the validation set, and 0.74 in the test set. Patients were divided into high- and low-risk groups based on the median prognostic index (PI). Kaplan-Meier curve was used for survival analysis (p = < 2e-16 in the training set, p = 3e-04 in the validation set, and p = 0.007 in the test set), which showed that the survival probability of different groups was significantly different, and the PI of the network played an influential role in the prediction of survival probability. In the residual verification of the PI, the fitting curve of the scatter plot was roughly parallel to the x-axis, and the p-value of the test was 0.11, proving that the PI and survival time were independent of each other and the survival prediction ability of the PI was less affected than survival time. The areas under the curve of the training set were 0.843, 0.871, 0.903, and 0.941; those of the validation set were 0.687, 0.895, 1.000, and 0.967; and those of the test set were 0.757, 0.852, 0.683, and 0.898.

Conclusion

The survival prediction model, which combines the DAE and the Cox proportional hazard regression model, can effectively predict the prognosis of patients with GBM.

Radiomic Analysis Based on Magnetic Resonance Imaging for Predicting PD-L2 Expression in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the sixth most common malignant tumour and the third leading cause of cancer death in the world. The emerging field of radiomics involves extracting many clinical image features that cannot be recognized by the human eye to provide information for precise treatment decision making. Radiomics has shown its importance in HCC identification, histological grading, microvascular invasion (MVI) status, treatment response, and prognosis, but there is no report on the preoperative prediction of programmed death ligand-2 (PD-L2) expression in HCC. The purpose of this study was to investigate the value of MRI radiomic features for the non-invasive prediction of immunotherapy target PD-L2 expression in hepatocellular carcinoma (HCC). A total of 108 patients with HCC confirmed by pathology were retrospectively analysed. Immunohistochemical analysis was used to evaluate the expression level of PD-L2. 3D-Slicer software was used to manually delineate volumes of interest (VOIs) and extract radiomic features on preoperative T2-weighted, arterial-phase, and portal venous-phase MR images. Least absolute shrinkage and selection operator (LASSO) was performed to find the best radiomic features. Multivariable logistic regression models were constructed and validated using fivefold cross-validation. The area under the receiver characteristic curve (AUC) was used to evaluate the predictive performance of each model. The results show that among the 108 cases of HCC, 50 cases had high PD-L2 expression, and 58 cases had low PD-L2 expression. Radiomic features correlated with PD-L2 expression. The T2-weighted, arterial-phase, and portal venous-phase and combined MRI radiomics models showed AUCs of 0.789 (95% CI: 0.702-0.875), 0.727 (95% CI: 0.632-0.823), 0.770 (95% CI: 0.682-0.875), and 0.871 (95% CI: 0.803-0.939), respectively. The combined model showed the best performance. The results of this study suggest that prediction based on the radiomic characteristics of MRI could noninvasively predict the expression of PD-L2 in HCC before surgery and provide a reference for the selection of immune checkpoint blockade therapy.

Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma

Purpose

Using an ensemble machine learning technique that incorporates the results of multiple machine learning algorithms, the study's objective is to build a reliable model to predict the early mortality among hepatocellular carcinoma (HCC) patients with bone metastases.

Methods

We extracted a cohort of 124,770 patients with a diagnosis of hepatocellular carcinoma from the Surveillance, Epidemiology, and End Results (SEER) program and enrolled a cohort of 1897 patients who were diagnosed as having bone metastases. Patients with a survival time of 3 months or less were considered to have had early death. To compare patients with and without early mortality, subgroup analysis was used. Patients were randomly divided into two groups: a training cohort (n = 1509, 80%) and an internal testing cohort (n = 388, 20%). In the training cohort, five machine learning techniques were employed to train and optimize models for predicting early mortality, and an ensemble machine learning technique was used to generate risk probability in a way of soft voting, and it was able to combine the results from the multiply machine learning algorithms. The study employed both internal and external validations, and the key performance indicators included the area under the receiver operating characteristic curve (AUROC), Brier score, and calibration curve. Patients from two tertiary hospitals were chosen as the external testing cohorts (n = 98). Feature importance and reclassification were both operated in the study.

Results

The early mortality was 55.5% (1052/1897). Eleven clinical characteristics were included as input features of machine learning models: sex (p = 0.019), marital status (p = 0.004), tumor stage (p = 0.025), node stage (p = 0.001), fibrosis score (p = 0.040), AFP level (p = 0.032), tumor size (p = 0.001), lung metastases (p < 0.001), cancer-directed surgery (p < 0.001), radiation (p < 0.001), and chemotherapy (p < 0.001). Application of the ensemble model in the internal testing population yielded an AUROC of 0.779 (95% confidence interval [CI]: 0.727-0.820), which was the largest AUROC among all models. Additionally, the ensemble model (0.191) outperformed the other five machine learning models in terms of Brier score. In terms of decision curves, the ensemble model also showed favorable clinical usefulness. External validation showed similar results; with an AUROC of 0.764 and Brier score of 0.195, the prediction performance was further improved after revision of the model. Feature importance demonstrated that the top three most crucial features were chemotherapy, radiation, and lung metastases based on the ensemble model. Reclassification of patients revealed a substantial difference in the two risk groups' actual probabilities of early mortality (74.38% vs. 31.35%, p < 0.001). Patients in the high-risk group had significantly shorter survival time than patients in the low-risk group (p < 0.001), according to the Kaplan-Meier survival curve.

Conclusions

The ensemble machine learning model exhibits promising prediction performance for early mortality among HCC patients with bone metastases. With the aid of routinely accessible clinical characteristics, this model can be a trustworthy prognostic tool to predict the early death of those patients and facilitate clinical decision-making.

CT radiomics combined with clinical variables for predicting the overall survival of hepatocellular carcinoma patients after hepatectomy

PURPOSE

To establish a model for assessing the overall survival (OS) of the hepatocellular carcinoma (HCC) patients after hepatectomy based on the clinical and radiomics features.

METHODS

This study recruited a total of 267 patients with HCC, which were randomly divided into the training (N = 188) and validation (N = 79) cohorts. In the training cohort, radiomic features were selected with the intra-reader and inter-reader correlation coefficient (ICC), Spearman's correlation coefficient, and the least absolute shrinkage and selection operator (LASSO). The radiomics signatures were built by COX regression analysis and compared the predictive potential in the different phases (arterial, portal, and double-phase) and regions of interest (tumor, peritumor 3 mm, peritumor 5 mm). A clinical-radiomics model (CR model) was established by combining the radiomics signatures and clinical risk factors. The validation cohort was used to validate the proposed models.

RESULTS

A total of 267 patients 86 (45.74%) and 37 (46.84%) patients died in the training and validation cohorts, respectively. Among all the radiomics signatures, those based on the tumor and peritumor (5 mm) (AP-TP5-Signature) showed the best prognostic potential (training cohort 1-3 years AUC:0.774-0.837; validation cohort 1-3 years AUC:0.754-0.810). The CR model showed better discrimination, calibration, and clinical applicability as compared to the clinical model and radiomics features. In addition, the CR model could perform risk-stratification and also allowed for significant discrimination between the Kaplan-Meier curves in most of the subgroups.

CONCLUSIONS

The CR model could predict the OS of the HCC patients after hepatectomy.

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.

AutoRadiomics: A Framework for Reproducible Radiomics Research

Purpose

Machine learning based on radiomics features has seen huge success in a variety of clinical applications. However, the need for standardization and reproducibility has been increasingly recognized as a necessary step for future clinical translation. We developed a novel, intuitive open-source framework to facilitate all data analysis steps of a radiomics workflow in an easy and reproducible manner and evaluated it by reproducing classification results in eight available open-source datasets from different clinical entities.

Methods

The framework performs image preprocessing, feature extraction, feature selection, modeling, and model evaluation, and can automatically choose the optimal parameters for a given task. All analysis steps can be reproduced with a web application, which offers an interactive user interface and does not require programming skills. We evaluated our method in seven different clinical applications using eight public datasets: six datasets from the recently published WORC database, and two prostate MRI datasets-Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-UCLA) and PROSTATEx.

Results

In the analyzed datasets, AutoRadiomics successfully created and optimized models using radiomics features. For WORC datasets, we achieved AUCs ranging from 0.56 for lung melanoma metastases detection to 0.93 for liposarcoma detection and thereby managed to replicate the previously reported results. No significant overfitting between training and test sets was observed. For the prostate cancer detection task, results were better in the PROSTATEx dataset (AUC = 0.73 for prostate and 0.72 for lesion mask) than in the Prostate-UCLA dataset (AUC 0.61 for prostate and 0.65 for lesion mask), with external validation results varying from AUC = 0.51 to AUC = 0.77.

Conclusion

AutoRadiomics is a robust tool for radiomic studies, which can be used as a comprehensive solution, one of the analysis steps, or an exploratory tool. Its wide applicability was confirmed by the results obtained in the diverse analyzed datasets. The framework, as well as code for this analysis, are publicly available under https://github.com/pwoznicki/AutoRadiomics.

Application of a nomogram to radiomics labels in the treatment prediction scheme for lumbar disc herniation

Objective

To investigate and verify the efficiency and effectiveness of a nomogram based on radiomics labels in predicting the treatment of lumbar disc herniation (LDH).

Methods

By reviewing medical records that were analysed over the past three years, clinical and imaging data of 200 lumbar disc patients at the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine were obtained. The collected cases were randomly divided into a training group (n = 140) and a testing group (n = 60) at a ratio of 7:3. Two radiologists with experience in reading orthopaedics images independently segmented the ROIs. The whole intervertebral disc with the most obvious protrusion in the sagittal plane T2WI lumbar MRI as a mask (ROI) is sketched. The LASSO (Least Absolute Shrinkage And Selection Operator) algorithm was used to filter the features after extracting the radiomics features. The multivariate logistic regression model was used to construct a quantitative imaging Rad‑Score for the selected features with nonzero coefficients. The radiomics labels and nomogram were evaluated using the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The calibration curve was used to evaluate the consistency between the nomogram prediction and the actual treatment plan. The DCA decision curve was used to evaluate the clinical applicability of the nomogram.

Result

Following feature extraction, 11 radiomics features were used to construct the radiomics label for predicting the treatment plan of LDH. A nomogram was then constructed. The AUC was 0.93 (95% CI: 0.90–0.97), with a sensitivity of 89%, a specificity of 91%, a positive predictive value of 92.7%, a negative predictive value of 89.4%, and an accuracy of 91%. The calibration curve showed that there was good consistency between the prediction and the actual observation. The DCA decision curve analysis showed that the nomogram of the imaging group has great potential for clinical application when the risk threshold is between 5 and 72%.

Conclusion

A nomogram based on radiomics labels has good predictive value for the treatment of LDH and can be used as a reference for clinical decision-making.

A combined postoperative nomogram for survival prediction in clear cell renal carcinoma

PURPOSE

To investigate and validate the prognostic value of nomogram models for predicting disease-free survival (DFS) and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).

METHODS

In this retrospective study, 223 patients (age 54.38 ± 10.93 years) with pathologically confirmed ccRCC who underwent resection and lymph node dissection between March 2010 and September 2018 were investigated. All patients were randomly divided into training (n = 155) and validation (n = 68) cohorts. Radiomics features were extracted from computed tomography (CT) images in the unenhanced, corticomedullary, and nephrographic phases. Radiomic score was calculated and combined with clinicopathological factors for model construction and nomogram development. Clinicopathological factors and imaging features were collected at initial diagnosis. Univariate and multivariate Cox proportional hazards regression analyses were used to evaluate the relationship between the radiomics signature and prognosis outcomes.

RESULTS

There were four prognostic factors for predicting DFS and five factors for predicting OS in our nomogram model (P < 0.05). The radiomics signature correlated independently with DFS (hazard ratio = 27; P < 0.001) and OS (hazard ratio = 25; P < 0.001). The nomogram showed excellent performance (C-index = 0.825) for predicting DFS. The combined nomogram also showed the highest C-index for OS (C-index = 0.943), which was verified in the validation dataset.

CONCLUSION

The combined nomogram model based on radiomics, clinicopathological factors, and preoperative CT features can accurately perform prognosis and survival analysis and can potentially be used for preoperative non-invasive survival prediction in ccRCC patients.

Prediction and verification of survival in patients with non-small-cell lung cancer based on an integrated radiomics nomogram

AIM

To develop and validate a nomogram to predict 1-, 2-, and 5-year survival in patients with non-small-cell lung cancer (NSCLC) by combining optimised radiomics features, clinicopathological factors, and conventional image features extracted from three-dimensional (3D) computed tomography (CT) images.

MATERIALS AND METHODS

A total of 172 patients with NSCLC were selected to construct the model, and 74 and 72 patients were selected for internal validation and external testing, respectively. A total of 828 radiomics features were extracted from each patient's 3D CT images. Univariable Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to select features and generate a radiomics signature (radscore). The performance of the nomogram was evaluated by calibration curves, clinical practicability, and the c-index. Kaplan-Meier (KM) analysis was used to compare the overall survival (OS) between the two subgroups.

RESULT

The radiomics features of the NSCLC patients correlated significantly with survival time. The c-indexes of the nomogram in the training cohort, internal validation cohort, and external test cohort were 0.670, 0.658, and 0.660, respectively. The calibration curves showed that the predicted survival time was close to the actual survival time. Decision curve analysis shows that the nomogram could be useful in the clinic. According to KM analysis, the 1-, 2- and 5-year survival rates of the low-risk group were higher than those of the high-risk group.

CONCLUSION

The nomogram, combining the radscore, clinicopathological factors, and conventional CT parameters, can improve the accuracy of survival prediction in patients with NSCLC.

Pulmonary tuberculosis diagnosis, differentiation and disease management: A review of radiomics applications

Pulmonary tuberculosis is a worldwide epidemic that can only be fought effectively with early and accurate diagnosis and proper disease management. The means of diagnosis and disease management should be easily accessible, cost effective and be readily available in the high tuberculosis burdened countries where it is most needed. Fortunately, the fast development of computer science in recent years has ensured that medical images can accurately be quantified. Radiomics is one such tool that can be used to quantify medical images. This review article focuses on the literature currently available on the application of radiomics explicitly for the purpose of diagnosis, differentiation from other pulmonary diseases and disease management of pulmonary tuberculosis. Despite using a formal search strategy, only five articles could be found on the application of radiomics to pulmonary tuberculosis. In all five articles reviewed, radiomic feature extraction was successfully used to quantify digital medical images for the purpose of comparing, or differentiating, pulmonary tuberculosis from other pulmonary diseases. This demonstrates that the use of radiomics for the purpose of tuberculosis disease management and diagnosis remains a valuable data mining opportunity not yet realised.

Radiomics in hepatocellular carcinoma: A state-of-the-art review

Hepatocellular carcinoma (HCC) is the most common cancer and the second major contributor to cancer-related mortality. Radiomics, a burgeoning technology that can provide invisible high-dimensional quantitative and mineable data derived from routine-acquired images, has enormous potential for HCC management from diagnosis to prognosis as well as providing contributions to the rapidly developing deep learning methodology. This article aims to review the radiomics approach and its current state-of-the-art clinical application scenario in HCC. The limitations, challenges, and thoughts on future directions are also summarized.

Machine learning model development for quantitative analysis of CT heterogeneity in canine hepatic masses may predict histologic malignancy

Tumor heterogeneity is a well-established marker of biologically aggressive neoplastic processes and is associated with local recurrence and distant metastasis. Quantitative analysis of CT textural features is an indirect measure of tumor heterogeneity and therefore may help predict malignant disease. The purpose of this retrospective, secondary analysis study was to quantitatively evaluate CT heterogeneity in dogs with histologically confirmed liver masses to build a predictive model for malignancy. Forty dogs with liver tumors and corresponding histopathologic evaluation from a previous prospective study were included. Triphasic image acquisition was standardized across dogs and whole liver and liver mass were contoured on each precontrast and delayed postcontrast dataset. First-order and second-order indices were extracted from contoured regions. Univariate analysis identified potentially significant indices that were subsequently used for top-down model construction. Multiple quadratic discriminatory models were constructed and tested, including individual models using both postcontrast and precontrast whole liver or liver mass volumes. The best performing model utilized the CT features voxel volume and uniformity from postcontrast mass contours; this model had an accuracy of 0.90, sensitivity of 0.67, specificity of 1.0, positive predictive value of 1.0, negative predictive value of 0.88, and precision of 1.0. Heterogeneity indices extracted from delayed postcontrast CT hepatic mass contours were more informative about tumor type compared to indices from whole liver contours, or from precontrast hepatic mass and whole liver contours. Results demonstrate that CT radiomic feature analysis may hold clinical utility as a noninvasive method of predicting hepatic malignancy and may influence diagnostic or therapeutic approaches.

Hepatocellular Carcinoma Drug-Eluting Bead Transarterial Chemoembolization (DEB-TACE): Outcome Analysis Using a Model Based On Pre-Treatment CT Texture Features

(1) Introduction and Aim: The aim of this study is to investigate the prognostic value, in terms of response and survival, of CT-based radiomics features for patients with HCC undergoing drug-eluting beads transarterial chemoembolization (DEB-TACE). (2) Materials and Methods: Pre-treatment CT examinations of 50 patients with HCC, treated with DEB-TACE were manually segmented to obtain the tumor volumetric region of interest, extracting radiomics features with TexRAD. Response to therapy evaluation was performed basing on post-procedural CT examination compared to pre-procedural CT, using modified RECIST criteria for HCC. The prognostic value of texture analysis was evaluated, investigating the correlation between radiomics features, response to therapy and overall survival. Three models based on texture and clinical variables and a combination of them were finally built; (3) Results: Entropy, skewness, MPP and kurtosis showed a significant correlation with complete response (CR) to TACE (all p < 0.001). A predictive model to identify patients with a high and low probability of CR was evaluated with an ROC curve, with an AUC of 0.733 (p < 0.001). The three models built for survival prediction yielded an HR of 2.19 (95% CI: 2.03-2.35) using texture features, of 1.7 (95% CI: 1.54-1.9) using clinical data and of 4.61 (95% CI: 4.24-5.01) combining both radiomics and clinical data (all p < 0.0001). (4) Conclusion: Texture analysis based on pre-treatment CT examination is associated with response to therapy and survival in patients with HCC undergoing DEB-TACE, especially if combined with clinical data.