A Lesion-Based Response Prediction Model Using Pretherapy PET/CT Image Features for Y90 Radioembolization to Hepatic Malignancies

Radiomics Features Extracted From Pre- and Postprocedural Imaging in Early Prediction of Treatment Response in Patients Undergoing Transarterial Radioembolization of Hepatic Lesions: A Systematic Review, Meta-Analysis, and Quality Appraisal Study

INTRODUCTION

Transarterial radioembolization (TARE) is one of the most promising therapeutic options for hepatic masses. Radiomics features, which are quantitative numeric features extracted from medical images, are considered to have potential in predicting treatment response in TARE. This article aims to provide meta-analytic evidence and critically appraise the methodology of radiomics studies published in this regard.

METHODS

A systematic search was performed on PubMed, Scopus, Embase, and Web of Science. All relevant articles were retrieved, and the characteristics of the studies were extracted. The Radiomics Quality Score and Checklist for Evaluation of Radiomics Research were used to assess the methodologic quality of the studies. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve in predicting objective response were determined.

RESULTS

The systematic review included 15 studies. The average Radiomics Quality Score of these studies was 11.4 ± 2.1, and the average Checklist for Evaluation of Radiomics Research score was 33± 6.7. There was a notable correlation (correlation coefficient = 0.73) between the two metrics. Adherence to quality measures differed considerably among the studies and even within different components of the same studies. The pooled sensitivity and specificity of the radiomics models in predicting complete or partial response were 83.5% (95% confidence interval 76%-88.9%) and 86.7% (95% confidence interval 78%-92%), respectively.

CONCLUSION

Radiomics models show great potential in predicting treatment response in TARE of hepatic lesions. However, the heterogeneity seen between the methodologic quality of studies may limit the generalizability of the results. Future initiatives should aim to develop radiomics signatures using multiple external datasets and adhere to quality measures in radiomics methodology.

Advancements and Future Outlook of PET/CT-Guided Interventions

Advancements in minimally invasive technology, coupled with imaging breakthroughs, have empowered the field of interventional radiology to achieve unparalleled precision in image-guided diagnosis and treatment while simultaneously reducing periprocedural morbidity. Molecular imaging, which provides valuable physiological and metabolic information alongside anatomical localization, can expand the capabilities of image-guided interventions. Among various molecular imaging techniques, positron emission tomography (PET) stands out for its superior spatial resolution and ability to acquire quantitative data. PET has emerged as a crucial tool for oncologic imaging and plays a pivotal role in both staging and the assessment of treatment responses. Typically used in combination with computed tomography (CT) (PET/CT) and occasionally with magnetic resonance imaging MRI (PET/MRI), PET as a hybrid imaging approach offers enhanced insights into disease progression and response. In recent years, PET has also found its way into image-guided interventions, especially within the rapidly expanding field of interventional oncology. This review aims to explore the current and evolving role of metabolic imaging, specifically PET, in interventional oncology. By delving into the unique advantages and applications of PET in guiding oncological interventions and assessing response, we seek to highlight the increasing significance of this modality in the realm of interventional radiology.

FFCD 1709-SIRTCI phase II trial: Selective internal radiation therapy plus Xelox, Bevacizumab and Atezolizumab in liver-dominant metastatic colorectal cancer

Immune checkpoint inhibitors (ICI) have high efficacy in metastatic colorectal cancer (mCRC) with microsatellite instability (MSI) but not in microsatellite stable (MSS) tumour due to the low tumour mutational burden. Selective internal radiation therapy (SIRT) could enhance neoantigen production thus triggering systemic anti-tumoral immune response (abscopal effect). In addition, Oxalipatin can induce immunogenic cell death and Bevacizumab can decrease the exhaustion of tumour infiltrating lymphocyte. In combination, these treatments could act synergistically to sensitize MSS mCRCs to ICI SIRTCI is a prospective, multicentre, open-label, phase II, non-comparative single-arm study evaluating the efficacy and safety of SIRT plus Xelox, Bevacizumab and Atezolizumab (anti-programmed death-ligand 1) in patients with liver-dominant MSS mCRC. The primary objective is progression-free survival at 9 months. The main inclusion criteria are patients with MSS mCRC with liver-dominant disease, initially unresectable disease and with no prior oncologic treatment for metastatic disease. The trial started in November 2020 and has included 10 out of the 52 planned patients.

The role of volumetric and textural analysis of pretreatment 18F-fluorodeoxyglucose PET/computerized tomography images in predicting complete response to transarterial radioembolization in hepatocellular cancer

OBJECTIVE

This study evaluates the role of pretreatment 18F-FDG PET/CT in predicting the response to treatment in patients with hepatocellular cancer (HCC) who applied transarterial radioembolization (TARE) via the volumetric and texture features extracted from 18F-FDG PET/CT images.

METHODS

Thirty-three patients with HCC who had applied TARE [lobar (LT) or superselective (ST)] after 18F-FDG PET/CT were included in the study. Response to the treatment was evaluated from posttherapy magnetic resonance (MR). Patients were divided into two groups: the responder group (RG) (complete responders) and non-RG (NRG) (including partial response, stabile, and progressive). Metabolic tumor volume (MTV) and total lesion glycolysis (TLG) and texture features were extracted from PET/CT images. The differences among MTV, TLG, and texture features between response groups were analyzed with the Mann-Whitney U test. ROC analysis was performed for features with P < 0.05. Spearman correlation analysis was used, and features with correlation coefficient < 0.8 were evaluated with the logistic regression analysis.

RESULTS

Significant differences were detected in TLG, MTV, SHAPE_compacity, GLCM_correlation, GLRLM_GLNU, GLRLM_RLNU, NGLDM_coarseness, NGLDM_busyness, GLZLM_LZHGE, GLZLM_GLNU, and GLZLM_ZLNU between RG and NRG. Multivariate analysis demonstrated that MTV was the only meaningful parameter with an AUC of 0.827 (P = 0.002; 95% CI, 0.688-0.966). The best cutoff value was determined as 74.11 ml with 78.9% sensitivity and 78.6% specificity in discriminating nonresponders.

CONCLUSION

In predicting the curative effect of TARE, multivariate analysis results demonstrated that MTV was the only independent predictor, and MTV higher than 74.11 ml were determined the best predictor of nonresponders.

Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients

PURPOSE

To examine the usefulness of machine learning to predict prognosis in cervical cancer using clinical and radiomic features of 2-deoxy-2-[¹⁸F]fluoro-D-glucose (¹⁸F-FDG) positron emission tomography/computed tomography (CT) (¹⁸F-FDG-PET/CT).

METHODS

This retrospective study included 50 cervical cancer patients who underwent ¹⁸F-FDG-PET/CT before treatment. Four clinical (age, histology, stage, and treatment) and 41 ¹⁸F-FDG-PET-based radiomic features were ranked and a subset of useful features for association with disease progression was selected based on decrease of the Gini impurity. Six machine learning algorithms (random forest, neural network, k-nearest neighbors, naive Bayes, logistic regression, and support vector machine) were compared using the areas under the receiver operating characteristic curve (AUC). Progression-free survival (PFS) was assessed using Cox regression analysis.

RESULTS

The five top predictors of disease progression were: stage, surface area, metabolic tumor volume, gray-level run length non-uniformity (GLRLM_RLNU), and gray-level non-uniformity for run (GLRLM_GLNU). The naive Bayes model was the best-performing classifier for predicting disease progression (AUC = 0.872, accuracy = 0.780, F1 score = 0.781, precision = 0.788, and recall = 0.780). In the naive Bayes model, 5-year PFS was significantly higher in predicted non-progression than predicted progression (80.1% vs. 9.1%, p < 0.001) and was only the independent factor for PFS in multivariate analysis (HR, 6.89; 95% CI, 1.92-24.69; p = 0.003).

CONCLUSION

A machine learning approach based on clinical and pretreatment ¹⁸F-FDG PET-based radiomic features may be useful for predicting tumor progression in cervical cancer patients.

Fluorodeoxyglucose-PET for Ablation Treatment Planning, Intraprocedural Monitoring, and Response

PET has become an essential tool for staging and response assessment in oncologic imaging. Over the past decade it has also evolved into a tool for image-guided interventions, specifically in the rapidly growing field of interventional oncology. PET-guided biopsies have greater sensitivity and diagnostic yield for fluorodeoxyglucose-avid lesions. Real-time PET imaging can also provide valuable image guidance during therapeutic minimally invasive procedures such as ablation of PET-avid tumors. The increasing use of PET in the assessment of therapeutic response results in earlier identification of disease that is amenable to image-guided therapies.

Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins

Function annotation efforts provide a foundation to our understanding of cellular processes and the functioning of the living cell. This motivates high-throughput computational methods to characterize new protein members of a particular function. Research work has focused on discriminative machine-learning methods, which promise to make efficient, de novo predictions of protein function. Furthermore, available function annotation exists predominantly for individual proteins rather than residues of which only a subset is necessary for the conveyance of a particular function. This limits discriminative approaches to predicting functions for which there is sufficient residue-level annotation, e.g., identification of DNA-binding proteins or where an excellent global representation can be divined. Complete understanding of the various functions of proteins requires discovery and functional annotation at the residue level. Herein, we cast this problem into the setting of multiple-instance learning, which only requires knowledge of the protein’s function yet identifies functionally relevant residues and need not rely on homology. We developed a new multiple-instance leaning algorithm derived from AdaBoost and benchmarked this algorithm against two well-studied protein function prediction tasks: annotating proteins that bind DNA and RNA. This algorithm outperforms certain previous approaches in annotating protein function while identifying functionally relevant residues involved in binding both DNA and RNA, and on one protein-DNA benchmark, it achieves near perfect classification.