Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients

A nomogram based on CT texture features to predict the response of patients with advanced pancreatic cancer treated with chemotherapy

OBJECTIVE

This study aimed to evaluate the predictive value of computed tomography (CT) texture features in the treatment response of patients with advanced pancreatic cancer (APC) receiving palliative chemotherapy.

METHODS

This study enrolled 84 patients with APC treated with first-line chemotherapy and conducted texture analysis on primary pancreatic tumors. 59 patients and 25 were randomly assigned to the training and validation cohorts at a ratio of 7:3. The treatment response to chemotherapy was evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST1.1). The patients were divided into progressive and non-progressive groups. The least absolute shrinkage selection operator (LASSO) was applied for feature selection in the training cohort and a radiomics signature (RS) was calculated. A nomogram was developed based on a multivariate logistic regression model incorporating the RS and carbohydrate antigen 19-9 (CA19-9), and was internally validated using the C-index and calibration plot. We performed the decision curve analysis (DCA) and clinical impact curve analysis to reflect the clinical utility of the nomogram. The nomogram was further externally confirmed in the validation cohort.

RESULTS

The multivariate logistic regression analysis indicated that the RS and CA19-9 were independent predictors (P < 0.05), and a trend was found for chemotherapy between progressive and non-progressive groups. The nomogram incorporating RS, CA19-9 and chemotherapy showed favorable discriminative ability in the training (C-index = 0.802) and validation (C-index = 0.920) cohorts. The nomogram demonstrated favorable clinical utility.

CONCLUSION

The RS of significant texture features was significantly associated with the early treatment effect of patients with APC treated with chemotherapy. Based on the RS, CA19-9 and chemotherapy, the nomogram provided a promising way to predict chemotherapeutic effects for APC patients.

Comprehensive multimodal management of borderline resectable pancreatic cancer: Current status and progress

Borderline resectable pancreatic cancer (BRPC) is a complex clinical entity with specific biological features. Criteria for resectability need to be assessed in combination with tumor anatomy and oncology. Neoadjuvant therapy (NAT) for BRPC patients is associated with additional survival benefits. Research is currently focused on exploring the optimal NAT regimen and more reliable ways of assessing response to NAT. More attention to management standards during NAT, including biliary drainage and nutritional support, is needed. Surgery remains the cornerstone of BRPC treatment and multidisciplinary teams can help to evaluate whether patients are suitable for surgery and provide individualized management during the perioperative period, including NAT responsiveness and the selection of surgical timing.

Update on quantitative radiomics of pancreatic tumors

Radiomics is a newer approach for analyzing radiological images obtained from conventional imaging modalities such as computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography. Radiomics involves extracting quantitative data from the images and assessing them to identify diagnostic or prognostic features such as tumor grade, resectability, tumor response to neoadjuvant therapy, and survival. The purpose of this review is to discuss the basic principles of radiomics and provide an overview of the current clinical applications of radiomics in the field of pancreatic tumors.

Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review

The diagnosis, evaluation, and treatment planning of pancreatic pathologies usually require the combined use of different imaging modalities, mainly, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Artificial intelligence (AI) has the potential to transform the clinical practice of medical imaging and has been applied to various radiological techniques for different purposes, such as segmentation, lesion detection, characterization, risk stratification, or prediction of response to treatments. The aim of the present narrative review is to assess the available literature on the role of AI applied to pancreatic imaging. Up to now, the use of computer-aided diagnosis (CAD) and radiomics in pancreatic imaging has proven to be useful for both non-oncological and oncological purposes and represents a promising tool for personalized approaches to patients. Although great developments have occurred in recent years, it is important to address the obstacles that still need to be overcome before these technologies can be implemented into our clinical routine, mainly considering the heterogeneity among studies.

Radiomics in pancreatic cancer for oncologist: Present and future

Radiomics is changing the world of medicine and more specifically the world of oncology. Early diagnosis and treatment improve the prognosis of patients with cancer. After treatment, the evaluation of the response will determine future treatments. In oncology, every change in treatment means a loss of therapeutic options and this is key in pancreatic cancer. Radiomics has been developed in oncology in the early diagnosis and differential diagnosis of benign and malignant lesions, in the evaluation of response, in the prediction of possible side effects, marking the risk of recurrence, survival and prognosis of the disease. Some studies have validated its use to differentiate normal tissues from tumor tissues with high sensitivity and specificity, and to differentiate cystic lesions and pancreatic neuroendocrine tumor grades with texture parameters. In addition, these parameters have been related to survival in patients with pancreatic cancer and to response to radiotherapy and chemotherapy. This review aimed to establish the current status of the use of radiomics in pancreatic cancer and future perspectives.

A systematic review of prognosis predictive role of radiomics in pancreatic cancer: heterogeneity markers or statistical tricks?

OBJECTIVES

We aimed to systematically evaluate the prognostic prediction accuracy of radiomics features extracted from pre-treatment imaging in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

Radiomics literature on overall survival (OS) prediction of PDAC were all included in this systematic review. A further meta-analysis was performed on the effect size of first-order entropy. Methodological quality and risk of bias of the included studies were assessed by the radiomics quality score (RQS) and prediction model risk of bias assessment tool (PROBAST).

RESULTS

Twenty-three studies were finally identified in this review. Two (8.7%) studies compared prognosis prediction ability between radiomics model and TNM staging model by C-index, and both showed a better performance of the radiomics. Twenty-one (91.3%) studies reported significant predictive values of radiomics features. Nine (39.1%) studies were included in the meta-analysis, and it showed a significant correlation between first-order entropy and OS (HR 1.66, 95%CI 1.18-2.34). RQS assessment revealed validation was only performed in 5 (21.7%) studies on internal datasets and 2 (8.7%) studies on external datasets. PROBAST showed that 22 (95.7%) studies have a high risk of bias in participants because of the retrospective study design.

CONCLUSION

First-order entropy was significantly associated with OS and might improve the accuracy of PDAC prognosis prediction. Existing studies were poorly validated, and it should be noted in future studies. Modification of PROBAST for radiomics studies is necessary since the strict requirements of prospective study design may not be applicable to the demand for a large sample size in the model construction stage.

KEY POINTS

• Radiomics based on the primary lesion holds great potential for prognosis prediction. First-order entropy was significantly associated with the overall survival of PDAC and might improve the accuracy of current PDAC prognosis prediction. • We strongly recommend that at least an internal validation should be conducted in any radiomics study. Attention should be paid to the complex relationships between radiomics features. • Due to the close relationship between radiomics and big data, the strict requirement of prospective study design in PROABST may not be appropriate for radiomics studies. A balance between study types and sample sizes for radiomics studies needs to be found in the model construction stage.

Prognostic Evaluation Based on Dual-Time 18F-FDG PET/CT Radiomics Features in Patients with Locally Advanced Pancreatic Cancer Treated by Stereotactic Body Radiation Therapy

Background

¹⁸F-FDG PET/CT is widely used in the prognosis evaluation of tumor patients. The radiomics features can provide additional information for clinical prognostic assessment.

Purpose

Purpose is to explore the prognostic value of radiomics features from dual-time ¹⁸F-FDG PET/CT images for locally advanced pancreatic cancer (LAPC) patients treated with stereotactic body radiation therapy (SBRT).

Materials and Methods

This retrospective study included 70 LAPC patients who received early and delayed ¹⁸F-FDG PET/CT scans before SBRT treatment. A total of 1188 quantitative imaging features were extracted from dual-time PET/CT images. To avoid overfitting, the univariate analysis and elastic net were used to obtain a sparse set of image features that were applied to develop a radiomics score (Rad-score). Then, the Harrell consistency index (C-index) was used to evaluate the prognosis model.

Results

The Rad-score from dual-time images contains six features, including intensity histogram, morphological, and texture features. In the validation cohort, the univariate analysis showed that the Rad-score was the independent prognostic factor (p < 0.001, hazard ratio [HR]: 3.2). And in the multivariate analysis, the Rad-score was the only prognostic factor (p < 0.01, HR: 4.1) that was significantly associated with the overall survival (OS) of patients. In addition, according to cross-validation, the C-index of the prognosis model based on the Rad-score from dual-time images is better than the early and delayed images (0.720 vs. 0.683 vs. 0.583).

Conclusion

The Rad-score based on dual-time ¹⁸F-FDG PET/CT images is a promising noninvasive method with better prognostic value.

Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy

PURPOSE

To investigate the potential benefits of FDG PET radiomic feature maps (RFMs) for target delineation in non-small cell lung cancer (NSCLC) radiotherapy.

METHODS

Thirty-two NSCLC patients undergoing FDG PET/CT imaging were included. For each patient, nine grey-level co-occurrence matrix (GLCM) RFMs were generated. gross target volume (GTV) and clinical target volume (CTV) were contoured on CT (GTV_CT , CTV_CT ), PET (GTV_PET40 , CTV_PET40 ), and RFMs (GTV_RFM , CTV_RFM ,). Intratumoral heterogeneity areas were segmented as GTV_PET50-Boost and radiomic boost target volume (RTV_Boost ) on PET and RFMs, respectively. GTV_CT in homogenous tumors and GTV_PET40 in heterogeneous tumors were considered as GTV_{gold standard} (GTV_GS ). One-way analysis of variance was conducted to determine the threshold that finds the best conformity for GTV_RFM with GTV_GS . Dice similarity coefficient (DSC) and mean absolute percent error (MAPE) were calculated. Linear regression analysis was employed to report the correlations between the gold standard and RFM-derived target volumes.

RESULTS

Entropy, contrast, and Haralick correlation (H-correlation) were selected for tumor segmentation. The threshold values of 80%, 50%, and 10% have the best conformity of GTV_RFM-entropy , GTV_RFM-contrast , and GTV_{RFM-H-correlation} with GTV_GS , respectively. The linear regression results showed a positive correlation between GTV_GS and GTV_RFM-entropy (r = 0.98, p < 0.001), between GTV_GS and GTV_RFM-contrast (r = 0.93, p < 0.001), and between GTV_GS and GTV_{RFM-H-correlation} (r = 0.91, p < 0.001). The average threshold values of 45% and 15% were resulted in the best segmentation matching between CTV_RFM-entropy and CTV_RFM-contrast with CTV_GS , respectively. Moreover, we used RFM to determine RTV_Boost in the heterogeneous tumors. Comparison of RTV_Boost with GTV_PET50-Boost MAPE showed the volume error differences of 31.7%, 36%, and 34.7% in RTV_{Boost-entropy} , RTV_{Boost-contrast} , and RTV_{Boost-H-correlation} , respectively.

CONCLUSIONS

FDG PET-based radiomics features in NSCLC demonstrated a promising potential for decision support in radiotherapy, helping radiation oncologists delineate tumors and generate accurate segmentation for heterogeneous region of tumors.

Baseline CT-based Radiomic Features Aid Prediction of Nodal Positivity after Neoadjuvant Therapy in Pancreatic Cancer

Purpose To study the association between CT-derived textural features of pancreatic cancer and patient outcome. Materials and Methods This retrospective study evaluated 54 patients (median age, 62 years [range, 40-88 years]; 32 men) with pancreatic cancer who underwent chemoradiation followed by surgical resection and lymph node dissection from May 2012 to June 2016. Three-dimensional segmentation of the pancreatic tumor was performed on baseline dual-energy CT images: 70-keV pancreatic parenchymal phase (PPP) images and iodine material density images. Then, 15 and 19 radiomic features were extracted from each phase, respectively. Logistic regression with elastic net regularization was used to select textural features associated with outcome, and receiver operating characteristic analysis evaluated feature performance. Survival curves were generated using the Kaplan-Meier method. Results The feature of integral total (∫ T), representing the mean intensity in Hounsfield units times the contour volume in milliliters of PPP imaging (hereafter, "∫ T (HU·mL) (PPP)"), is inversely associated with posttherapy pathologic lymph node (ypN) category. A threshold ∫ T (HU·mL) (PPP) less than 507.85 predicted ypN1-2 classification with 96% sensitivity, 34% specificity, and area under the curve of 0.61. Patients with an ∫ T (HU·mL) (PPP) of less than 507.85 had decreased overall survival (median, 2.8 years) compared with patients with an ∫ T (HU·mL) (PPP) of 507.85 or greater (one event at 3.4 years) (P = .006). Patients with an ∫ T (HU·mL) (PPP) of less than 507.85 had decreased progression-free survival (median, 1.5 years) compared with patients with an ∫ T (HU·mL) (PPP) of 507.85 or greater (median, 2.7 years) (P = .001). Conclusion A CT-based radiomic signature may help predict ypN category in patients with pancreatic cancer. Keywords: CT-Dual Energy, Abdomen/GI, Pancreas, Tumor Response, Outcomes Analysis © RSNA, 2022 Supplemental material is available for this article.

Application of artificial intelligence to pancreatic adenocarcinoma

BACKGROUND AND OBJECTIVES

Pancreatic cancer (PC) is one of the deadliest cancers worldwide although substantial advancement has been made in its comprehensive treatment. The development of artificial intelligence (AI) technology has allowed its clinical applications to expand remarkably in recent years. Diverse methods and algorithms are employed by AI to extrapolate new data from clinical records to aid in the treatment of PC. In this review, we will summarize AI's use in several aspects of PC diagnosis and therapy, as well as its limits and potential future research avenues.

METHODS

We examine the most recent research on the use of AI in PC. The articles are categorized and examined according to the medical task of their algorithm. Two search engines, PubMed and Google Scholar, were used to screen the articles.

RESULTS

Overall, 66 papers published in 2001 and after were selected. Of the four medical tasks (risk assessment, diagnosis, treatment, and prognosis prediction), diagnosis was the most frequently researched, and retrospective single-center studies were the most prevalent. We found that the different medical tasks and algorithms included in the reviewed studies caused the performance of their models to vary greatly. Deep learning algorithms, on the other hand, produced excellent results in all of the subdivisions studied.

CONCLUSIONS

AI is a promising tool for helping PC patients and may contribute to improved patient outcomes. The integration of humans and AI in clinical medicine is still in its infancy and requires the in-depth cooperation of multidisciplinary personnel.

The impact of radiomics in diagnosis and staging of pancreatic cancer

INTRODUCTION

Pancreatic cancer (PC) is one of the most aggressive tumours, and better risk stratification among patients is required to provide tailored treatment. The meaning of radiomics and texture analysis as predictive techniques are not already systematically assessed. The aim of this study is to assess the role of radiomics in PC.

METHODS

A PubMed/MEDLINE and Embase systematic review was conducted to assess the role of radiomics in PC. The search strategy was 'radiomics [All Fields] AND ("pancreas" [MeSH Terms] OR "pancreas" [All Fields] OR "pancreatic" [All Fields])' and only original articles referred to PC in humans in the English language were considered.

RESULTS

A total of 123 studies and 183 studies were obtained using the mentioned search strategy on PubMed and Embase, respectively. After the complete selection process, a total of 56 papers were considered eligible for the analysis of the results. Radiomics methods were applied in PC for assessment technical feasibility and reproducibility aspects analysis, risk stratification, biologic or genomic status prediction and treatment response prediction.

DISCUSSION

Radiomics seems to be a promising approach to evaluate PC from diagnosis to treatment response prediction. Further and larger studies are required to confirm the role and allowed to include radiomics parameter in a comprehensive decision support system.

Radiomic analysis to predict local response in locally advanced pancreatic cancer treated with stereotactic body radiation therapy

PURPOSE

Aim of this study is to assess the ability of contrast-enhanced CT image-based radiomic analysis to predict local response (LR) in a retrospective cohort of patients affected by pancreatic cancer and treated with stereotactic body radiation therapy (SBRT). Secondary aim is to evaluate progression free survival (PFS) and overall survival (OS) at long-term follow-up.

METHODS

Contrast-enhanced-CT images of 37 patients who underwent SBRT were analyzed. Two clinical variables (BED, CTV volume), 27 radiomic features were included. LR was used as the outcome variable to build the predictive model. The Kaplan-Meier method was used to evaluate PFS and OS.

RESULTS

Three variables were statistically correlated with the LR in the univariate analysis: Intensity Histogram (StdValue feature), Gray Level Cooccurrence Matrix (GLCM25_Correlation feature) and Neighbor Intensity Difference (NID25_Busyness feature). Multivariate model showed GLCM25_Correlation (P = 0.007) and NID25_Busyness (P = 0.03) as 2 independent predictive variables for LR. The odds ratio values of GLCM25_Correlation and NID25_Busyness were 0.07 (95%CI 0.01-0.49) and 8.10 (95%CI 1.20-54.40), respectively. The area under the curve for the multivariate logistic regressive model was 0.851 (95%CI 0.724-0.978). At a median follow-up of 30 months, median PFS was 7 months (95%CI 6-NA); median OS was 11 months (95%CI 10-22 months).

CONCLUSIONS

This analysis identified a radiomic signature that correlates with LR. To confirm these results, prospective studies could identify patient sub-groups with different rates of radiation dose-response to define a more personalized SBRT approach.

The Role of Positron Emission Tomography/Computed Tomography (PET/CT) for Staging and Disease Response Assessment in Localized and Locally Advanced Pancreatic Cancer

Pancreatic Cancer (PC) has a poor prognosis, with a 5-year survival rate of only 9%. Even after radical surgical procedures, PC patients have poor survival rates, with a high chance of relapse (70-80%). Imaging is involved in all aspects of the clinical management of PC, including detection and characterization of primary tumors and their resectability, assessment of vascular, perineural and lymphatic invasion and detection of distant metastases. The role of Positron Emission Tomography/Computed Tomography (PET/CT) in detecting PC is still controversial, with the international guidelines not recommending its routine use. However, in resectable PC, PET/CT may play a role in assessing PC stage and grade and potential resectability after neoadjuvant treatment. Quantitative image analysis (radiomics) and new PET/CT radiotracers account for future developments in metabolic imaging and may further improve the relevance of this technique in several aspects of PC. In the present review, the current state of the art and future directions of PET/CT in resectable PC are presented.

Predicting Survival in Patients with Pancreatic Cancer by Integrating Bone Marrow FDG Uptake and Radiomic Features of Primary Tumor in PET/CT

Simple Summary

FDG uptake of bone marrow (BM) is known to reflect the degree of host inflammatory response to cancer cells and showed significant association with survival in diverse kinds of cancers. The aim of this retrospective study was to evaluate the prognostic significance of FDG uptake of BM and to investigate whether integrating FDG uptake of BM and radiomic features of primary tumors could improve the prediction of overall survival (OS) in patients with pancreatic cancer. In multivariable survival analysis, along with total lesion glycolysis (TLG) and first-order entropy of primary tumor lesions, FDG uptake of BM was an independent predictor of OS. We designed a PET/CT scoring system based on the cumulative scores of tumor factors (TLG and first-order entropy) and host factors (FDG uptake of BM). This scoring system was able to stratify the patients with three distinct prognostic groups independent of clinical stage and treatment modality.

Abstract

The purpose of this study was to evaluate the prognostic significance of FDG uptake of bone marrow (BM SUV) and to investigate its role combined with radiomic features of primary tumors in improving the prediction of overall survival (OS) in patients with pancreatic cancer. We retrospectively enrolled 65 pancreatic cancer patients with staging FDG PET/CT. BM SUV and conventional imaging parameters of primary tumors including total lesion glycolysis (TLG) were measured. First-order and higher-order textural features of primary cancer were extracted using PET textural analysis. Associations of PET/CT parameters of bone marrow (BM) and primary cancer with OS were assessed. BM SUV as well as TLG and first-order entropy of pancreatic cancer were significant independent predictors of OS in multivariable analysis. A PET/CT scoring system based on the cumulative scores of these three independent predictors enabled patient stratification into three distinct prognostic groups. The scoring system yielded a good prognostic stratification based on subgroup analysis irrespective of tumor stage and treatment modality. BM SUV was an independent predictor of OS in pancreatic cancer patients. The PET/CT scoring system that integrated PET/CT parameters of primary tumors and BM can provide prognostic information in pancreatic cancer independent of tumor stage and treatment.

Prognostic value of 18F-FDG PET/CT with texture analysis in patients with rectal cancer treated by surgery

PURPOSE

The aim of this study was to evaluate the ability of texture analysis using pretreatment 18F-FDG PET/CT to predict prognosis in patients with surgically treated rectal cancer.

METHODS

We analyzed 94 patients with pathologically proven rectal cancer who underwent pretreatment 18F-FDG PET/CT and were subsequently treated with surgery. The volume of interest of the primary tumor was defined using a threshold of 40% of the maximum standardized uptake value (SUVmax), and conventional (SUVmax, metabolic tumor volume [MTV], total lesion glycolysis [TLG]) and textural PET features were extracted. Harmonization of PET features was performed with the ComBat method. The study endpoints were overall survival (OS) and progression-free survival (PFS), and the prognostic value of PET features was evaluated by Cox regression analysis.

RESULTS

In the follow-up period (median 41.7 [interquartile range, 30.5-60.4] months), 21 (22.3%) and 30 (31.9%) patients had cancer-related death or disease progression, respectively. Univariate analysis revealed a significant association of (1) MTV, TLG, and gray-level co-occurrence matrix (GLCM) entropy with OS; and (2) SUVmax, MTV, TLG, and GLCM entropy with PFS. In multivariate analysis including clinical characteristics, GLCM entropy (≥ 2.13) was the only relevant prognostic PET feature for poor OS (hazard ratio [HR]: 4.16, p = 0.035) and PFS (HR: 2.70, p = 0.046).

CONCLUSION

GLCM entropy, which indicates metabolic intratumoral heterogeneity, was an independent prognostic factor in patients with surgically treated rectal cancer. Compared with conventional PET features, GLCM entropy has better predictive value and shows potential to facilitate precision medicine.

The clinical application of 18F-FDG PET/CT in pancreatic cancer: a narrative review

Pancreatic cancer is one of the worst prognoses of all malignant tumors, with an annual incidence near its annual mortality rate. To improve the prognosis of patients with pancreatic cancer, it is essential to diagnose and evaluate pancreatic cancer early. Imaging examinations play an essential role in tumor detection, staging, and surgical resection assessment and can provide reliable evidence for the diagnosis and treatment of pancreatic cancer. Currently, imaging techniques commonly used for pancreatic cancer include endoscopic ultrasound (EUS), conventional ultrasound, magnetic resonance imaging (MRI), multidetector spiral computed tomography (MDCT), positron emission tomography/computed tomography (PET/CT), and others PET/CT is a new imaging device composed of PET and CT. ¹⁸F-Fluorodeoxyglucose (¹⁸F-FDG) is a commonly used tracer in the clinic. Cancer cells are more robust than other ordinary cells in that they can ingest glucose, and the structure of glucose is similar to the structure of ¹⁸F-FDG. Therefore, after the injection of ¹⁸F-FDG, ¹⁸F-FDG in tumor cells appears very thick during PET scanning. Therefore, PET/CT can determine the metabolic capacity and anatomical position of pancreatic tumor cells in the body accurately diagnose the patient's condition and tumor location. It plays a vital role in early diagnosis and accurate staging, predicts survival, and monitors therapeutic effectiveness and pancreatic cancer recurrence. Although ¹⁸F-FDG PET/CT has limitations in identifying inflammatory diseases and tumors, it still has good development potential. This article reviews the clinical application of ¹⁸F-FDG PET/CT in pancreatic cancer.

Genomic analysis of pancreatic cancer reveals 3 molecular subtypes with different clinical outcomes

ABSTRACT

Pancreatic cancer has a very high mortality with a 5-year survival of <5%. The purpose of this study was to classify specific molecular subtypes associated with prognosis of pancreatic cancer using The Cancer Genome Atlas (TCGA) multiplatform genomic data.Multiplatform genomic data (N = 178), including gene expression, copy number alteration, and somatic mutation data, were obtained from cancer browser (https://genome-cancer.ucsc.edu, cohort: TCGA Pancreatic Cancer). Clinical data including survival results were analyzed. We also used validation cohort (GSE50827) to confirm the robustness of these molecular subtypes in pancreatic cancer.When we performed unsupervised clustering using TCGA gene expression data, we found three distinct molecular subtypes associated with different survival results. Copy number alteration and somatic mutation data showed different genomic patterns for these three subtypes. Ingenuity pathway analysis revealed that each subtype showed differentially altered pathways. Using each subtype-specific genes (200 were selected), we could predict molecular subtype in another cohort, confirming the robustness of these molecular subtypes of pancreatic cancer. Cox regression analysis revealed that molecular subtype is the only significant prognostic factor for pancreatic cancer (P = .042, 95% confidence interval 0.523-0.98).Genomic analysis of pancreatic cancer revealed 3 distinct molecular subtypes associated with different survival results. Using these subtype-specific genes and prediction model, we could predict molecular subtype associated with prognosis of pancreatic cancer.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

Prospective evaluation of metabolic intratumoral heterogeneity in patients with advanced gastric cancer receiving palliative chemotherapy

Although metabolic intratumoral heterogeneity (ITH) gives important value on treatment responses and prognoses, its association with treatment outcomes have not been reported in gastric cancer (GC). We aimed to evaluate temporal changes in metabolic ITH and the associations with treatment responses, progression-free survival (PFS), and overall survival (OS) in advanced GC patients. Eighty-five patients with unresectable, locally advanced, or metastatic GC were prospectively enrolled before the first-line palliative chemotherapy and underwent [¹⁸F]FDG PET at baseline (TP1) and the first response follow-up evaluation (TP2). Standardized uptake values (SUVs), volumetric parameters, and textural features were evaluated in primary gastric tumor at TP1 and TP2. Of 85 patients, 44 had partial response, 33 had stable disease, and 8 progressed. From TP1 to TP2, metabolic ITH was significantly reduced (P < 0.01), and the degree of the decrease was greater in responders than in non-responders (P < 0.01). Using multiple Cox regression analyses, a low SUV_max at TP2, a high kurtosis at TP2 and larger decreases in the coefficient of variance were associated with better PFS. A low SUV_max at TP2, larger decreases in the metabolic tumor volume and larger decreased in the energy were associated with better OS. Age older than 60 years and responders also showed better OS. An early reduction in metabolic ITH is useful to predict treatment outcomes in advanced GC patients.

Pancreas image mining: a systematic review of radiomics

OBJECTIVES

To systematically review published studies on the use of radiomics of the pancreas.

METHODS

The search was conducted in the MEDLINE database. Human studies that investigated the applications of radiomics in diseases of the pancreas were included. The radiomics quality score was calculated for each included study.

RESULTS

A total of 72 studies encompassing 8863 participants were included. Of them, 66 investigated focal pancreatic lesions (pancreatic cancer, precancerous lesions, or benign lesions); 4, pancreatitis; and 2, diabetes mellitus. The principal applications of radiomics were differential diagnosis between various types of focal pancreatic lesions (n = 19), classification of pancreatic diseases (n = 23), and prediction of prognosis or treatment response (n = 30). Second-order texture features were most useful for the purpose of differential diagnosis of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature), whereas filtered image features were most useful for the purpose of classification of diseases of the pancreas and prediction of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature). The median radiomics quality score of the included studies was 28%, with the interquartile range of 22% to 36%. The radiomics quality score was significantly correlated with the number of extracted radiomics features (r = 0.52, p < 0.001) and the study sample size (r = 0.34, p = 0.003).

CONCLUSIONS

Radiomics of the pancreas holds promise as a quantitative imaging biomarker of both focal pancreatic lesions and diffuse changes of the pancreas. The usefulness of radiomics features may vary depending on the purpose of their application. Standardisation of image acquisition protocols and image pre-processing is warranted prior to considering the use of radiomics of the pancreas in routine clinical practice.

KEY POINTS

• Methodologically sound studies on radiomics of the pancreas are characterised by a large sample size and a large number of extracted features. • Optimisation of the radiomics pipeline will increase the clinical utility of mineable pancreas imaging data. • Radiomics of the pancreas is a promising personalised medicine tool in diseases of the pancreas.

[Artificial intelligence and machine learning in oncologic imaging]

Machine learning (ML) is entering many areas of society, including medicine. This transformation has the potential to drastically change medicine and medical practice. These aspects become particularly clear when considering the different stages of oncologic patient care and the involved interdisciplinary and intermodality interactions. In recent publications, computers-in collaboration with humans or alone-have been outperforming humans regarding tumor identification, tumor classification, estimating prognoses, and evaluation of treatments. In addition, ML algorithms, e.g., artificial neural networks (ANNs), which constitute the drivers behind many of the latest achievements in ML, can deliver this level of performance in a reproducible, fast, and inexpensive manner. In the future, artificial intelligence applications will become an integral part of the medical profession and offer advantages for oncologic diagnostics and treatment.

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer

Patients with pancreatic cancer have a poor prognosis, therefore identifying particular tumor characteristics associated with prognosis is important. This study aims to investigate the utility of radiomics with machine learning using 18F-fluorodeoxyglucose (FDG)-PET in patients with pancreatic cancer. We enrolled 161 patients with pancreatic cancer underwent pretreatment FDG-PET/CT. The area of the primary tumor was semi-automatically contoured with a threshold of 40% of the maximum standardized uptake value, and 42 PET features were extracted. To identify relevant PET parameters for predicting 1-year survival, Gini index was measured using random forest (RF) classifier. Twenty-three patients were censored within 1 year of follow-up, and the remaining 138 patients were used for the analysis. Among the PET parameters, 10 features showed statistical significance for predicting overall survival. Multivariate analysis using Cox HR regression revealed gray-level zone length matrix (GLZLM) gray-level non-uniformity (GLNU) as the only PET parameter showing statistical significance. In RF model, GLZLM GLNU was the most relevant factor for predicting 1-year survival, followed by total lesion glycolysis (TLG). The combination of GLZLM GLNU and TLG stratified patients into three groups according to risk of poor prognosis. Radiomics with machine learning using FDG-PET in patients with pancreatic cancer provided useful prognostic information.

Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging

Background

Radiomics analyses has been proposed to interrogate the biology of tumour as well as to predict/assess response to therapy in vivo. The objective of this work was to assess the sensitivity of radiomics features to noise, resolution, and tumour volume in the context of a co-clinical trial.

Methods

Triple negative breast cancer (TNBC) patients were recruited into an ongoing co-clinical imaging trial. Sub-typed matched TNBC patient-derived tumour xenografts (PDX) were generated to investigate optimal co-clinical MR radiomic features. The MR imaging protocol included T1-weighed and T2-weighted imaging. To test the sensitivity of radiomics to resolution, PDX were imaged at three different resolutions. Multiple sets of images with varying signal-to-noise ratio (SNR) were generated, and an image independent patch-based method was implemented to measure the noise levels. Forty-eight radiomic features were extracted from manually segmented 2D and 3D segmented tumours and normal tissues of T1- and T2- weighted co-clinical MR images.

Findings

Sixteen radiomics features were identified as volume dependent and corrected for volume-dependency following normalization. Features from grey-level run-length matrix (GLRLM), grey-level size zone matrix (GLSZM) were identified as most sensitive to noise. Radiomic features Kurtosis and Run-length variance (RLV) from GLSZM were most sensitive to changes in resolution in both T1w and T2w MRI. In general, 3D radiomic features were more robust compared to 2D (single slice) measures, although the former exhibited higher variability between subjects.

Interpretation

Tumour volume, noise characteristics, and image resolution significantly impact radiomic analysis in co-clinical studies.

Prognosis prediction of pancreatic cancer after curative intent surgery using imaging parameters derived from F-18 fluorodeoxyglucose positron emission tomography/computed tomography

Imaging parameters including metabolic or textural parameters during F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) are being used for evaluation of malignancy. However, their utility for prognosis prediction has not been thoroughly investigated. Here, we evaluated the prognosis prediction ability of imaging parameters from preoperative FDGPET/CT in operable pancreatic cancer patients.Sixty pancreatic cancer patients (male:female = 36:24, age = 67.2 ± 10.5 years) who had undergone FDGPET/CT before the curative intent surgery were enrolled. Clinico-pathologic parameters, metabolic parameters from FDGPET/CT; maximal standard uptake value (SUVmax), glucose-incorporated SUVmax (GI-SUVmax), metabolic tumor volume, total-lesion glycolysis, and 53 textural parameters derived from imaging analysis software (MaZda version 4.6) were compared with overall survival.All the patients underwent curative resection. Mean and standard deviation of overall follow-up duration was 16.12 ± 9.81months. Among them, 39 patients had died at 13.46 ± 8.82 months after operation, whereas 21 patients survived with the follow-up duration of 18.56 ± 9.97 months. In the univariate analysis, Tumor diameter ≥4 cm (P = .003), Preoperative Carbohydrate antigen 19-9 ≥37 U/mL (P = .034), number of metastatic lymph node (P = .048) and GI-SUVmax (P = .004) were significant parameters for decreased overall survival. Among the textural parameters, kurtosis3D (P = .052), and skewness3D (P = .064) were potentially significant predictors in the univariate analysis. However, in multivariate analysis only GI-SUVmax (P = .026) and combined operation (P = .001) were significant independent predictors of overall survival.The current research result indicates that metabolic parameter (GI-SUVmax) from FDGPET/CT, and combined operation could predict the overall survival of surgically resected pancreatic cancer patients. Other metabolic or textural imaging parameters were not significant predictors for overall survival of localized pancreatic cancer.

Characterization of FDG PET Images Using Texture Analysis in Tumors of the Gastro-Intestinal Tract: A Review

Radiomics or textural feature extraction obtained from positron emission tomography (PET) images through complex mathematical models of the spatial relationship between multiple image voxels is currently emerging as a new tool for assessing intra-tumoral heterogeneity in medical imaging. In this paper, available literature on texture analysis using FDG PET imaging in patients suffering from tumors of the gastro-intestinal tract is reviewed. While texture analysis of FDG PET images appears clinically promising, due to the lack of technical specifications, a large variability in the implemented methodology used for texture analysis and lack of statistical robustness, at present, no firm conclusions can be drawn regarding the predictive or prognostic value of FDG PET texture analysis derived indices in patients suffering from gastro-enterologic tumors. In order to move forward in this field, a harmonized image acquisition and processing protocol as well as a harmonized protocol for texture analysis of tumor volumes, allowing multi-center studies excluding statistical biases should be considered. Furthermore, the complementary and additional value of CT-imaging, as part of the PET/CT imaging technique, warrants exploration.

Training and validation of a robust PET radiomic-based index to predict distant-relapse-free-survival after radio-chemotherapy for locally advanced pancreatic cancer

PURPOSE

To assess the value of 18F-Fluorodeoxyglucose (18F-FDG) PET Radiomic Features (RF) in predicting Distant Relapse Free Survival (DRFS) in patients with Locally AdvancedPancreaticCancer (LAPC) treated with radio-chemotherapy.

MATERIALS & METHODS

One-hundred-ninety-eight RFs were extracted using IBSI (Image Biomarker Standardization Initiative) consistent software from pre-radiotherapy images of 176 LAPC patients treated with moderate hypo-fractionation (44.25 Gy, 2.95 Gy/fr). Tumors were segmented by applying a previously validated semi-automatic method. One-hundred-twenty-six RFs were excluded due to poor reproducibility and/or repeatability and/or inter-scanner variability. The original cohort was randomly split into a training (n = 116) and a validation (n = 60) group. Multi-variable Cox regression was applied to the training group, including only independent RFs in the model. The resulting radiomic index was tested in the validation cohort. The impact of selected clinical variables was also investigated.

RESULTS

The resulting Cox model included two first order RFs: Center of Mass Shift (COMshift) and 10th Intensity percentile (P₁₀) (p = 0.0005, HR = 2.72, 95%CI = 1.54-4.80), showing worse outcomes for patients with lower COMshift and higher P₁₀. Once stratified by quartile values (<lowest quartile vs >highest quartile vs the remaining), the index properly stratified patients according to their DRFS (p = 0.0024, log-rank test). Performances were confirmed in the validation cohort (p = 0.03, HR = 2.53, 95%CI = 0.96-6.65). The addition of clinical factors did not significantly improve the models' performance.

CONCLUSIONS

A radiomic-based index including only two robust PET-RFs predicted DRFS of LAPC patients after radio-chemotherapy. The current results could find relevant applications in the treatment personalization of LAPC. A multi-institution independent validation has been planned.

Robustness of CT radiomic features against image discretization and interpolation in characterizing pancreatic neuroendocrine neoplasms

PURPOSE

To explore the variation of the discriminative power of CT radiomic features (RF) against image discretization/interpolation in characterizing pancreatic neuro-endocrine (PanNEN) neoplasms.

MATERIALS AND METHODS

Thirty-nine PanNEN patients with pre-surgical high contrast CT available were considered. Image interpolation and discretization parameters were intentionally changed, including pixel size (0.73-2.19 mm²), slice thickness (2-5 mm) and binning (32-128 grey levels) and their combination generated 27 parameter's set. The ability of 69 RF in discriminating post-surgically assessed tumor grade (>G1), positive nodes, metastases and vascular invasion was tested: AUC changes when changing the parameters were quantified for selected RF, significantly associated to each end-point. The analysis was repeated for the corresponding images with contrast medium and in a sub-group of 29/39 patients scanned on a single scanner.

RESULTS

The median tumor volume was 1.57 cm³ (16%-84% percentiles: 0.62-34.58 cm³). RF variability against discretization/interpolation parameters was large: only 21/69 RF showed %COV < 20%. Despite this variability, AUC changes were limited for all end-points: with typical AUC values around 0.75-0.85, AUC ranges for the 27 parameter's set were on average 0.062 (1SD:0.037) for all end-points with maximum %COV equal to 5.5% (mean:2.3%). Performances significantly improved when excluding the 5 mm thickness case and fixing the binning to 64 (mean AUC range: 0.036, 1SD:0.019). Using contrast images or limiting the population to single-scanner patients had limited impact on AUC variability.

CONCLUSIONS

The discriminative power of CT RF for panNEN is relatively invariant against image interpolation/discretization within a large range of voxel sizes and binning.

Artificial intelligence in pancreatic disease

In recent years, the application of artificial intelligence (AI) in radiology has been growing rapidly, fueled by the availability of large datasets, advances in computing power, and newly developed algorithms. Progress in AI applied to medical imaging analyses has transformed these images into quantitative data, termed radiomics. When combined with patients’ clinical data, these models, when developed by machine learning, have the potential to improve diagnostic, prognostic, and predictive accuracy. Currently, limited literature is available on the use of radiomics for pancreatic disease. Here, we will review recent studies in the application of AI in a variety of pancreatic diseases, mainly involving lesion detection, tumor characterization, tumor grading, response, and prognosis evaluation. Finally, we will also discuss the challenges and prospects in the field of radiomics for pancreatic disease.

Relevant Features in Nonalcoholic Steatohepatitis Determined Using Machine Learning for Feature Selection

Aim: We investigated the prevalence and the most relevant features of nonalcoholic steatohepatitis (NASH), a stage of nonalcoholic fatty liver disease, (NAFLD) in which the inflammation of hepatocytes can lead to increased cardiovascular risk, liver fibrosis, cirrhosis, and the need for liver transplant. Methods: We analyzed data from 2239 hypertensive patients using descriptive statistics and supervised machine learning algorithms, including the least absolute shrinkage and selection operator and random forest classifier, to select the most relevant features of NASH. Results: The prevalence of NASH among our hypertensive patients was 11.3%. In univariate analyses, it was associated with metabolic syndrome, type 2 diabetes, insulin resistance, and dyslipidemia. Ferritin and serum insulin were the most relevant features in the final model, with a sensitivity of 70%, specificity of 79%, and area under the curve of 0.79. Conclusion: Ferritin and insulin are significant predictors of NASH. Clinicians may use these to better assess cardiovascular risk and provide better management to hypertensive patients with NASH. Machine-learning algorithms may help health care providers make decisions.

Quantifying vascular invasion in pancreatic cancer-a contrast CT based method for surgical resectability evaluation

Pancreatic cancer (PC) is one of the most lethal cancers, with frequent local therapy resistance and dismal 5-year survival rate. To date, surgical resection remains to be the only treatment option offering potential cure. Unfortunately, at diagnosis, the majority of patients demonstrate varying levels of vascular infiltration, which can contraindicate surgical resection. Patients unsuitable for immediate resection are further divided into locally advanced (LA) and borderline resectable (BR), with different treatment goals and therapeutic designs. Accurate definition of resectability is thus critical for PC patients, yet the existing methods to determine resectability rely on descriptive abutment to surrounding vessels rather than quantitative geometric characterization. Here, we aim to introduce a novel intra-subject object-space support-vector-machine (OsSVM) method to quantitatively characterize the degree of vascular involvement -- the main factor determining the PC resectability. Intra-subject OsSVMs were applied on 107 contrast CT scans (56 LA, BR and 26 resectable (RE) PC cases) for optimized tumor-vessel separations. Nine metrics derived from OsSVM margins were calculated as indicators of the overall vascular infiltration. The combined sets of matrics selected by the elastic net yielded high classification capability between LA and BR (AUC=0.95), as well as BR and RE (AUC=0.98). The proposed OsSVM method may provide an improved quantitative imaging guideline to refine the PC resectability grading system.

Prediction of therapeutic outcome and survival in a transgenic mouse model of pancreatic ductal adenocarcinoma treated with dendritic cell vaccination or CDK inhibitor using MRI texture: a feasibility study

There is a lack of a well-established approach for assessment of early treatment outcomes for modern therapies for pancreatic ductal adenocarcinoma (PDAC) e.g. dinaciclib or dendritic cell (DC) vaccination. Here, we developed multivariate models using MRI texture features to detect treatment effects following dinaciclib drug or DC vaccine therapy in a transgenic mouse model of PDAC including 21 LSL-KrasG12D ; LSL-Trp53R172H ; Pdx-1-Cre (KPC) mice used as untreated control subjects (n=8) or treated with dinaciclib (n=7) or DC vaccine (n=6). Support vector machines (SVM) technique was performed to build a linear classifier with three variables for detection of tumor tissue changes following drug or vaccine treatments. Besides, multivariate regression models were generated with five variables to predict survival behavior and histopathological tumor markers (Fibrosis, CK19, and Ki67). The diagnostic performance was evaluated using accuracy, area under the receiver operating characteristic curve (AUC) and decision curve analyses. The regression models were evaluated with adjusted r-squared (Radj 2). SVM classifier successfully distinguished changes in tumor tissue with an accuracy of 95.24% and AUC of 0.93. The multivariate models generated with five variables were strongly associated with histopathological tumor markers, fibrosis (Radj 2=0.82, P<0.001), CK19 (Radj 2=0.92, P<0.001) and Ki67 (Radj 2=0.97, P<0.001). Furthermore, the multivariate regression model successfully predicted survival of KPC mice by interpreting tumor characteristics from MRI data (Radj 2=0.91, P<0.001). The results demonstrated that MRI texture features had great potential to generate diagnosis and prognosis models for monitoring early treatment response following dinaciclib drug or DC vaccine treatment and also predicting histopathological tumor markers and long-term clinical outcomes.

Differentiating hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma based on CT texture analysis

Background

Hypovascular pancreatic neuroendocrine tumor is usually misdiagnosed as pancreatic ductal adenocarcinoma.

Purpose

To investigate the value of texture analysis in differentiating hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma on contrast-enhanced computed tomography (CT) images.

Material and Methods

Twenty-one patients with hypovascular pancreatic neuroendocrine tumors and 63 patients with pancreatic ductal adenocarcinomas were included in this study. All patients underwent preoperative unenhanced and dynamic contrast-enhanced CT examinations. Two radiologists independently and manually contoured the region of interest of each lesion using texture analysis software on pancreatic parenchymal and portal phase CT images. Multivariate logistic regression analysis was performed to identify significant features to differentiate hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas. Receiver operating characteristic curve analysis was performed to ascertain diagnostic ability.

Results

The following CT texture features were obtained to differentiate hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: RMS (root mean square) (odds ratio [OR] = 0.50, P<0.001), Quantile50 (OR = 1.83, P<0.001), and sumAverage (OR = 0.92, P=0.007) in parenchymal images and “contrast” in portal phase images (OR = 6.08, P<0.001). The areas under the curves were 0.76 for RMS (sensitivity = 0.75, specificity = 0.67), 0.73 for Quantile50 (sensitivity = 0.60, specificity = 0.77), 0.70 for sumAverage (sensitivity = 0.65, specificity = 0.82), 0.85 for the combined texture features (sensitivity = 0.77, specificity = 0.85).

Conclusion

CT texture analysis may be helpful to differentiate hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas. The three combined texture features showed acceptable diagnostic performance.

Bridging the gap between micro- and macro-scales in medical imaging with textural analysis - A biological basis for CT radiomics classifiers?

INTRODUCTION

Studies suggest there is utility in computed tomography (CT) radiomics for pancreatic disease; however, the precise biological interpretation of its features is unclear. In this manuscript, we present a novel approach towards this interpretation by investigating sub-micron tissue structure using digital pathology.

METHODS

A classification-to attenuation (CAT) function was developed and applied to digital pathology images to create sub-micron linear attenuation maps. From these maps, grey level co-occurrence matrix (GLCM) features were extracted and compared to pathology features. To simulate the spatial frequency loss in a CT scanner, the attenuation maps were convolved with a point spread function (PSF) and subsequently down-sampled. GLCM features were extracted from these down-sampled maps to assess feature stability as a function of spatial frequency loss.

RESULTS

Two GLCM features were shown to be strongly and positively correlated (r = 0.8) with underlying characteristics of the tumor microenvironment, namely percent pimonidazole staining in the tumor. All features underwent marked change as a function of spatial frequency loss; progressively larger spatial frequency losses resulted in progressively larger inter-tumor standard deviations; two GLCM features exhibited stability up to a 100 µm pixel size.

CONCLUSION

This work represents a necessary step towards understanding the biological significance of radiomics. Our preliminary results suggest that cellular metrics of pimonidazole-detectable hypoxia correlate with sub-micron attenuation coefficient texture; however, the consistency of these textures in face of spatial frequency loss is detrimental for robust radiomics. Further study in larger data sets may elucidate additional, potentially more robust features of biologic and clinical relevance.

Predicting response to radiotherapy in tumors with PET/CT: when and how?

Radiotherapy is one of the main methods for tumor treatment, with the improved radiotherapy delivery technique to combat cancer, there is a growing interest for finding effective and feasible ways to predict tumor radiosensitivity. Based on a series of changes in metabolism, microvessel density, hypoxic microenvironment, and cytokines of tumors after radiotherapy, a variety of radiosensitivity detection methods have been studied. Among the detection methods, positron emission tomography-computed tomography (PET/CT) is a feasible tool for response evaluation following definitive radiotherapy for cancers with a high negative predictive value. The prognostic or predictive value of PET/CT is currently being studied widely. However, there are many unresolved issues, such as the optimal probe of PET/CT for radiosensitivity prediction, the selection of the most useful PET/CT parameters and their optimal cut-offs such as total lesion glycolysis (TLG), metabolic tumor volume (MTV) and standardized uptake value (SUV), and the optimal timing of PET/CT pre-treatment, during or following RT. Different radiosensitivity of tumors, modes of radiotherapy action and fraction scheduling may complicate the appropriate choice. In this study, we will discuss the diverse methods for evaluating radiosensitivity, and will also focus on the selection of the optimal probe, timing, cut-offs and parameters of PET/CT for evaluating the radiotherapy response.

Preoperative MRI-Based Radiomic Machine-Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft-Tissue Lesions: A Two-Center Study

BACKGROUND

Preoperative differentiation between malignant and benign soft-tissue masses is important for treatment decisions.

PURPOSE/HYPOTHESIS

To construct/validate a radiomics-based machine method for differentiation between malignant and benign soft-tissue masses.

STUDY TYPE

Retrospective.

POPULATION

In all, 206 cases.

FIELD STRENGTH/SEQUENCE

The T₁ sequence was acquired with the following range of parameters: relaxation time / echo time (TR/TE), 352-550/2.75-19 msec. The T₂ sequence was acquired with the following parameters: TR/TE, 700-6370/40-120 msec. The data were divided into a 3.0T training cohort, a 1.5T MR validation cohort, and a 3.0T external validationcohort.

ASSESSMENT

Twelve machine-learning methods were trained to establish classification models to predict the likelihood of malignancy of each lesion. The data of 206 cases were separated into a training set (n = 69) and two validation sets (n = 64, 73, respectively).

STATISTICAL TESTS

1) Demographic characteristics: a one-way analysis of variance (ANOVA) test was performed for continuous variables as appropriate. The χ² test or Fisher's exact test was performed for comparing categorical variables as appropriate. 2) The performance of four feature selection methods (least absolute shrinkage and selection operator [LASSO], Boruta, Recursive feature elimination [RFE, and minimum redundancy maximum relevance [mRMR]) and three classifiers (support vector machine [SVM], generalized linear models [GLM], and random forest [RF]) were compared for selecting the likelihood of malignancy of each lesion. The performance of the radiomics model was assessed using area under the receiver-operating characteristic curve (AUC) and accuracy (ACC) values.

RESULTS

The LASSO feature method + RF classifier achieved the highest AUC of 0.86 and 0.82 in the two validation cohorts. The nomogram achieved AUCs of 0.96 and 0.88, respectively, in the two validation sets, which was higher than that of the radiomic algorithm in the two validation sets and clinical model of the validation 1 set (0.92, 0.88 respectively). The accuracy, sensitivity, and specificity of the radiomics nomogram were 90.5%, 100%, and 80.6%, respectively, for validation set 1; and 80.8%, 75.8%, and 85.0% for validation set 2.

DATA CONCLUSION

A machine-learning nomogram based on radiomics was accurate for distinguishing between malignant and benign soft-tissue masses.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2 J. Magn. Reson. Imaging 2020;52:873-882.

MRI radiomics for early prediction of response to vaccine therapy in a transgenic mouse model of pancreatic ductal adenocarcinoma

BACKGROUND

There is a lack of well-established clinical tools for predicting dendritic cell (DC) vaccination response of pancreatic ductal adenocarcinoma (PDAC). DC vaccine treatment efficiency was demonstrated using histological analysis in pre-clinical studies; however, its usage was limited due to invasiveness. In this study, we aimed to investigate the potential of MRI texture features for detection of early immunotherapeutic response as well as overall survival (OS) of PDAC subjects following dendritic cell (DC) vaccine treatment in LSL-KrasG12D;LSL-Trp53R172H;Pdx-1-Cre (KPC) transgenic mouse model of pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

KPC mice were treated with DC vaccines, and tumor growth was dynamically monitored. A total of a hundred and fifty-two image features of T2-weighted MRI images were analyzed using a kernel-based support vector machine model to detect treatment effects following the first and third weeks of the treatment. Moreover, univariate analysis was performed to describe the association between MRI texture and survival of KPC mice as well as histological tumor biomarkers.

RESULTS

OS for mice in the treatment group was 54.8 ± 22.54 days while the control group had 35.39 ± 17.17 days. A subset of three MRI features distinguished treatment effects starting from the first week with increasing accuracy throughout the treatment (75% to 94%). Besides, we observed that short-run emphasis of approximate wavelet coefficients had a positive correlation with the survival of the KPC mice (r = 0.78, p < 0.001). Additionally, tissue-specific MRI texture features showed positive association with fibrosis percentage (r = 0.84, p < 0.002), CK19 positive percentage (r = - 0.97, p < 0.001), and Ki67 positive cells (r = 0.81, p < 0.02) as histological disease biomarkers.

CONCLUSION

Our results demonstrate that MRI texture features can be used as imaging biomarkers for early detection of therapeutic response following DC vaccination in the KPC mouse model of PDAC. Besides, MRI texture can be utilized to characterize tumor microenvironment reflected with histology analysis.

Logistic LASSO and Elastic Net to Characterize Vitamin D Deficiency in a Hypertensive Obese Population

Aim: The primary objective of our research was to compare the performance of data analysis to predict vitamin D deficiency using three different regression approaches and to evaluate the usefulness of incorporating machine learning algorithms into the data analysis in a clinical setting. Methods: We included 221 patients from our hypertension unit, whose data were collected from electronic records dated between 2006 and 2017. We used classical stepwise logistic regression, and two machine learning methods [least absolute shrinkage and selection operator (LASSO) and elastic net]. We assessed the performance of these three algorithms in terms of sensitivity, specificity, misclassification error, and area under the curve (AUC). Results: LASSO and elastic net regression performed better than logistic regression in terms of AUC, which was significantly better in both penalized methods, with AUC = 0.76 and AUC = 0.74 for elastic net and LASSO, respectively, than in logistic regression, with AUC = 0.64. In terms of misclassification rate, elastic net (18%) outperformed LASSO (22%) and logistic regression (25%). Conclusion: Compared with a classical logistic regression approach, penalized methods were found to have better performance in predicting vitamin D deficiency. The use of machine learning algorithms such as LASSO and elastic net may significantly improve the prediction of vitamin D deficiency in a hypertensive obese population.

Preoperative differentiation of pancreatic mucinous cystic neoplasm from macrocystic serous cystic adenoma using radiomics: Preliminary findings and comparison with radiological model

PURPOSE

To develop a radiomics model in the preoperative differentiation of mucinous cystic neoplasm (MCN) and macrocystic serous cystadenoma (MaSCA) and to compare its diagnostic performance with conventional radiological model.

METHODS

57 Patients (MCN = 31, MaSCA = 26) with preoperative multidetector computed tomography (MDCT) scans were retrospectively included in this study. A radiological model was constructed from radiological features evaluated by radiologists. A radiomics model was constructed with high-dimensional quantitative features extracted from manually segmented volume of interests (VOIs). A combined model was constructed using both radiomics features and radiological features. The diagnostic performance of three models were assessed by the area under the receiver-operating characteristic curve (AUC), sensitivity, specificity, accuracy, and the calibration curves.

RESULTS

The radiological model yielded an AUC of 0.775, sensitivity of 74.2 %, specificity of 80.8, and accuracy of 77.2 %. The radiomics model yielded an AUC of 0.989, sensitivity of 93.6 %, specificity of 96.2 %, and accuracy of 94.7 %. The combined model yielded an AUC of 0.994, sensitivity of 96.8 %, specificity of 100 %, and accuracy of 98.2 %. Both combined model and radiomics model showed higher AUC, sensitivity, and accuracy than radiological model (all P <  .05). The combined model showed higher AUC than radiomics model, though no significant difference was found (P =  .41). The combined model showed better calibration than radiomics model (P =  .91 vs. P <  .001).

CONCLUSIONS

Combined model which contained both radiomics features and radiological features outperformed radiomics model and radiological model in the preoperative differentiation of MCN and MaSCA.

Radiomics in stratification of pancreatic cystic lesions: Machine learning in action

Pancreatic cystic lesions (PCLs) are well-known precursors of pancreatic cancer. Their diagnosis can be challenging as their behavior varies from benign to malignant disease. Precise and timely management of malignant pancreatic cysts might prevent transformation to pancreatic cancer. However, the current consensus guidelines, which rely on standard imaging features to predict cyst malignancy potential, are conflicting and unclear. This has led to an increased interest in radiomics, a high-throughput extraction of comprehensible data from standard of care images. Radiomics can be used as a diagnostic and prognostic tool in personalized medicine. It utilizes quantitative image analysis to extract features in conjunction with machine learning and artificial intelligence (AI) methods like support vector machines, random forest, and convolutional neural network for feature selection and classification. Selected features can then serve as imaging biomarkers to predict high-risk PCLs. Radiomics studies conducted heretofore on PCLs have shown promising results. This cost-effective approach would help us to differentiate benign PCLs from malignant ones and potentially guide clinical decision-making leading to better utilization of healthcare resources. In this review, we discuss the process of radiomics, its myriad applications such as diagnosis, prognosis, and prediction of therapy response. We also discuss the outcomes of studies involving radiomic analysis of PCLs and pancreatic cancer, and challenges associated with this novel field along with possible solutions. Although these studies highlight the potential benefit of radiomics in the prevention and optimal treatment of pancreatic cancer, further studies are warranted before incorporating radiomics into the clinical decision support system.

Identification of clinically relevant features in hypertensive patients using penalized regression: a case study of cardiovascular events

Appropriate management of hypertensive patients relies on the accurate identification of clinically relevant features. However, traditional statistical methods may ignore important information in datasets or overlook possible interactions among features. Machine learning may improve the prediction accuracy and interpretability of regression models by identifying the most relevant features in hypertensive patients. We sought the most relevant features for prediction of cardiovascular (CV) events in a hypertensive population. We used the penalized regression models least absolute shrinkage and selection operator (LASSO) and elastic net (EN) to obtain the most parsimonious and accurate models. The clinical parameters and laboratory biomarkers were collected from the clinical records of 1,471 patients receiving care at Mostoles University Hospital. The outcome was the development of major adverse CV events. Cox proportional hazards regression was performed alone and with penalized regression analyses (LASSO and EN), producing three models. The modeling was performed using 10-fold cross-validation to fit the penalized models. The three predictive models were compared and statistically analyzed to assess their classification accuracy, sensitivity, specificity, discriminative power, and calibration accuracy. The standard Cox model identified five relevant features, while LASSO and EN identified only three (age, LDL cholesterol, and kidney function). The accuracies of the models (prediction vs. observation) were 0.767 (Cox model), 0.754 (LASSO), and 0.764 (EN), and the areas under the curve were 0.694, 0.670, and 0.673, respectively. However, pairwise comparison of performance yielded no statistically significant differences. All three calibration curves showed close agreement between the predicted and observed probabilities of the development of a CV event. Although the performance was similar for all three models, both penalized regression analyses produced models with good fit and fewer features than the Cox regression predictive model but with the same accuracy. This case study of predictive models using penalized regression analyses shows that penalized regularization techniques can provide predictive models for CV risk assessment that are parsimonious, highly interpretable, and generalizable and that have good fit. For clinicians, a parsimonious model can be useful where available data are limited, as such a model can offer a simple but efficient way to model the impact of the different features on the prediction of CV events. Management of these features may lower the risk for a CV event. Graphical Abstract In a clinical setting, with numerous biological and laboratory features and incomplete datasets, traditional statistical methods may ignore important information and overlook possible interactions among features. Our aim was to identify the most relevant features to predict cardiovascular events in a hypertensive population, using three different regression approaches for feature selection, to improve the prediction accuracy and interpretability of regression models by identifying the relevant features in these patients.

Predictive Role of Temporal Changes in Intratumoral Metabolic Heterogeneity During Palliative Chemotherapy in Patients with Advanced Pancreatic Cancer: A Prospective Cohort Study

Metabolic intratumoral heterogeneity (ITH) is known to be related to cancer treatment outcome. However, information on the temporal changes in metabolic ITH during chemotherapy and the correlations between metabolic changes and treatment outcomes in patients with pancreatic cancer is sparse. We aimed to analyze the temporal changes in metabolic ITH and the predictive role of its changes in advanced pancreatic cancer patients who underwent palliative chemotherapy. Methods: We prospectively enrolled patients with unresectable locally advanced or metastatic pancreatic cancer before first-line palliative chemotherapy. ¹⁸F-FDG PET was performed at baseline and at the first response follow-up. SUVs, volumetric parameters, and textural features of the primary pancreatic tumor were analyzed. Relationships between the parameters at baseline and first follow-up were assessed, as well as changes in the parameters with treatment response, progression-free survival (PFS), and overall survival (OS). Results: Among 63 enrolled patients, the best objective response rate was 25.8% (95% confidence interval [CI], 14.6%-37.0%). The median PFS and OS were 7.1 mo (95% CI, 5.1-9.7 mo) and 10.1 mo (95% CI, 8.6-12.7 mo), respectively. Most parameters changed significantly during the first-line chemotherapy, in a way of reducing ITH. Metabolic ITH was more profoundly reduced in responders than in nonresponders. Multiple Cox regression analysis identified high baseline compacity (P = 0.023) and smaller decreases in SUV_peak (P = 0.007) and entropy gray-level cooccurrence matrix (P = 0.033) to be independently associated with poor PFS. Patients with a high carbohydrate antigen 19-9 (P = 0.042), high pretreatment SUV_peak (P = 0.008), and high coefficient of variance at first follow-up (P = 0.04) showed worse OS. Conclusion: Reduction in metabolic ITH during palliative chemotherapy in advanced pancreatic cancer patients is associated with treatment response and might be predictive of PFS and OS.

Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative imaging biomarkers for predicting outcomes of patients treated with chemotherapy

OBJECTIVES

The primary aim of this study was to determine if computed tomographic (CT) texture analysis measurements of the tumor are independently associated with progression-free survival (PFS) and overall survival (OS) in patients with unresectable pancreatic ductal adenocarcinoma (PDAC), including both unresectable locally advanced and metastatic PDAC, who were treated with chemotherapy.

METHODS

After an institutional review board waiver was obtained, contrast material-enhanced CT studies in 41 patients with unresectable PDAC who underwent contrast-enhanced CT before chemotherapy between 2014 and 2017 were analyzed in terms of tumor texture, with quantification of mean gray-level intensity (Mean), entropy, mean of positive pixels (MPP), kurtosis, standard deviation (SD), and skewness for fine to coarse textures (spatial scaling factor (SSF) 0-6, respectively). The association between pretreatment and posttreatment texture parameters, as well as Δ value (difference between posttreatment and pretreatment texture parameters), and survival time was assessed by using Cox proportional hazards models and Kaplan-Meier analysis.

RESULTS

Findings from the multivariate Cox model indicated that tumor size, tumor SD (HR, 0.942; 95% CI: 0.898, 0.988) and skewness (HR, 0.407; 95% CI: 0.172, 0.962) measurements with SSF = 3, and tumor SD (HR, 0.958; 95% CI: 0.92, 0.997) measurements with SSF = 4 were significantly and independently associated with PFS, while tumor size and tumor SD (HR, 0.928; 95% CI: 0.882, 0.976) measurements with SSF = 3 were significantly and independently associated with OS. None of the post-therapy texture parameters or Δ value had a significant association with OS or PFS in multivariate Cox regression models. Medium SD (SSF = 3) of more than 38.38 and coarse SD (SSF = 4) of more than 40.67 were associated with longer PFS after chemotherapy (for SSF = 3, median PFS was 10.0 vs 6.0 months [P = 0.024], and for SSF = 4, median PFS was 12.0 vs 6.0 months [P = 0.003]). SD of 38.38 or greater (SSF = 3) as a dichotomized variable was a significant positive prognostic factor for OS (median OS, 20.0 vs 9.0 months [P = 0.04]). Survival models that included a combination of pretreatment SD (SSF = 3) with tumor size, had the potential to perform better than SD alone, while having no statistical significance in this study (area under the ROC curve, 0.756 vs 0.715 [P = 0.066]).

CONCLUSIONS

Pretreatment CT quantitative imaging biomarkers from texture analysis are associated with PFS and OS in patients with unresectable PDAC who were treated with chemotherapy, and the combination of pretreatment texture parameters and tumor size have the potential to perform better in survival models than imaging biomarker alone.

Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma

PURPOSE

To appraise the ability of a radiomics signature to predict clinical outcome after stereotactic body radiation therapy (SBRT) for pancreas carcinoma.

METHODS

A cohort of 100 patients was included in this retrospective, single institution analysis. Radiomics texture features were extracted from computed tomography (CT) images obtained for the clinical target volume. The cohort of patients was randomly divided into two separate groups for the training (60 patients) and validation (40 patients). Cox regression models were built to predict overall survival and local control. The significant predictors at univariate analysis were included in a multivariate model. The quality of the models was appraised by means of area under the curve and concordance index.

RESULTS

A clinical-radiomic signature associated with Overall Survival (OS) was found significant in both training and validation sets (p = 0.01 and 0.05 and concordance index 0.73 and 0.75 respectively). Similarly, a signature was found for Local Control (LC) with p = 0.007 and 0.004 and concordance index 0.69 and 0.75. In the low risk group, the median OS and LC in the validation group were 14.4 and 28.6 months while in the high-risk group were 9.0 and 17.5 months respectively.

CONCLUSION

A CT based radiomic signature was identified which correlate with OS and LC after SBRT and allowed to identify low and high-risk groups of patients.

Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer

OBJECTIVE

To develop different radiomic models based on the magnetic resonance imaging (MRI) radiomic features and machine learning methods to predict early intensity-modulated radiation therapy (IMRT) response, Gleason scores (GS) and prostate cancer (Pca) stages.

METHODS

Thirty-three Pca patients were included. All patients underwent pre- and post-IMRT T2-weighted (T2 W) and apparent diffusing coefficient (ADC) MRI. IMRT response was calculated in terms of changes in the ADC value, and patients were divided as responders and non-responders. A wide range of radiomic features from different feature sets were extracted from all T2 W and ADC images. Univariate radiomic analysis was performed to find highly correlated radiomic features with IMRT response, and a paired t test was used to find significant features between responders and non-responders. To find high predictive radiomic models, tenfold cross-validation as the criterion for feature selection and classification was applied on the pre-, post- and delta IMRT radiomic features, and area under the curve (AUC) of receiver operating characteristics was calculated as model performance value.

RESULTS

Of 33 patients, 15 patients (45%) were found as responders. Univariate analysis showed 20 highly correlated radiomic features with IMRT response (20 ADC and 20 T2). Two and fifteen T2 and ADC radiomic features were found as significant (P-value ≤ 0.05) features between responders and non-responders, respectively. Several cross-combined predictive radiomic models were obtained, and post-T2 radiomic models were found as high predictive models (AUC 0.632) followed by pre-ADC (AUC 0.626) and pre-T2 (AUC 0.61). For GS prediction, T2 W radiomic models were found as more predictive (mean AUC 0.739) rather than ADC models (mean AUC 0.70), while for stage prediction, ADC models had higher prediction performance (mean AUC 0.675).

CONCLUSIONS

Radiomic models developed by MR image features and machine learning approaches are noninvasive and easy methods for personalized prostate cancer diagnosis and therapy.

Combined morphologic and metabolic pipeline for Positron emission tomography/computed tomography based radiotherapy response evaluation in locally advanced pancreatic adenocarcinoma

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A novel morphologic and metabolic combined pipeline for PA response evaluation.

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The derived metric outperformed traditional imaging metrics in risk stratification.

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May serve as a new image biomarker to characterize heterogeneous tumor response.

Background and purpose

Adaptive radiation planning for pancreatic adenocarcinoma (PA) relies on accurate treatment response assessment, while traditional response evaluation criteria inefficiently characterize tumors with complex morphological features or intrinsically low metabolism. To better assess treatment response of PA, we quantify and compare regional morphological and metabolic features of the 3D pre- and post-radiation therapy (RT) tumor models.

Materials and methods

Thirty-one PA patients with pre and post-RT Positron emission tomography/computed tomography (PET/CT) scans were evaluated. 3D meshes of pre- and post-RT tumors were generated and registered to establish vertex-wise correspondence. To assess tumor response, Mahalanobis distances (M_dist|_Fusion) between pre- and post-RT tumor surfaces with anatomic and metabolic fused vectors were calculated for each patient. M_dist|_Fusion was evaluated by overall survival (OS) prediction and survival risk classification. As a comparison, the same analyses were conducted on traditional imaging/physiological predictors, and distances measurements based on metabolic and morphological features only.

Results

Among all the imaging/physiological parameters, M_dist|_Fusion was shown to be the best predictor of OS (HR = 0.52, p = 0.008), while other parameters failed to reach significance. Moreover, M_dist|_Fusion outperformed traditional morphologic and metabolic measurements in patient risk stratification, either alone (HR = 11.51, p < 0.001) or combined with age (HR = 9.04, p < 0.001).

Conclusions

We introduced a PET/CT-based novel morphologic and metabolic pipeline for response evaluation in locally advanced PA. The fused M_dist|_Fusion outperformed traditional morphologic, metabolic, and physiological measurements in OS prediction and risk stratification. The novel fusion model may serve as a new imaging-marker to more accurately characterize the heterogeneous tumor RT response.

Postoperative serum CA19-9, CEA and CA125 predicts the response to adjuvant chemoradiotherapy following radical resection in pancreatic adenocarcinoma

OBJECTIVE

To evaluate the prediction of benefits from adjuvant chemoradiotherapy by postoperative serum CA19-9, CA125 and CEA.

METHODS

The relations between benefits from adjuvant chemoradiotherapy and levels of postoperative serum CA19-9, CA125 and CEA were investigated in 804 pancreatic adenocarcinoma patients who received radical resection.

RESULTS

Adjuvant chemoradiotherapy was an independent factor for late recurrence [12.2 vs. 8.5 months, P = 0.001 for recurrence free survival (RFS)] and long survival [23.7 vs. 17.0 months, P < 0.001 for overall survival (OS)] in resected pancreatic adenocarcinoma. Postoperative serum CA19-9, CA125 and CEA were independent risk predictors for poor surgical outcome in pancreatic adenocarcinoma (P < 0.001 for all). Adjuvant chemradiotherapy (hazard ratio: 0.359, 95% confidence interval: 0.253-0.510, P < 0.001 for OS; hazard ratio: 0.522, 95% confidence interval: 0.387-0.705, P < 0.001 for RFS) were confirmed to improve the surgical outcome in patients with abnormal levels of any one of the three postoperative markers, but not in patients with normal levels of the three postoperative markers. In the subgroup of patients with negative lymph node, its improvement of surgical outcome was also significant in patients with abnormal levels of any one of postoperative serum CA19-9, CA125 and CEA (hazard ratio: 0.412, 95% confidence interval: 0.244-0.698, P = 0.001 for OS; hazard ratio: 0.546, 95% confidence interval: 0.352-0.847, P = 0.007 for RFS).

CONCLUSION

Postoperative serum CA19-9, CA125 and CEA could serve as predictors of response for adjuvant chemoradiotherapy even if the status of lymph nodes is negative.

Radiomics in Oncological PET/CT: Clinical Applications

18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely used for staging, evaluating treatment response, and predicting prognosis in malignant diseases. FDG uptake and volumetric PET parameters such as metabolic tumor volume have been used and are still used as conventional PET parameters to assess biological characteristics of tumors. However, in recent years, additional features derived from PET images by computational processing have been found to reflect intratumoral heterogeneity, which is related to biological tumor features, and to provide additional predictive and prognostic information, which leads to the concept of radiomics. In this review, we focus on recent clinical studies of malignant diseases that investigated intratumoral heterogeneity on PET/CT, and we discuss its clinical role in various cancers.

Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges

Radiomics is a recent area of research in precision medicine and is based on the extraction of a large variety of features from medical images. In the field of radiation oncology, comprehensive image analysis is crucial to personalization of treatments. A better characterization of local heterogeneity and the shape of the tumor, depicting individual cancer aggressiveness, could guide dose planning and suggest volumes in which a higher dose is needed for better tumor control. In addition, noninvasive imaging features that could predict treatment outcome from baseline scans could help the radiation oncologist to determine the best treatment strategies and to stratify patients as at low risk or high risk of recurrence. Nuclear medicine molecular imaging reflects information regarding biological processes in the tumor thanks to a wide range of radiotracers. Many studies involving ¹⁸F-fluorodeoxyglucose positron emission tomography suggest an added value of radiomics compared with the use of conventional PET metrics such as standardized uptake value for both tumor diagnosis and prediction of recurrence or treatment outcome. However, these promising results should not hide technical difficulties that still currently prevent the approach from being widely studied or clinically used. These difficulties mostly pertain to the variability of the imaging features as a function of the acquisition device and protocol, the robustness of the models with respect to that variability, and the interpretation of the radiomic models. Addressing the impact of the variability in acquisition and reconstruction protocols is needed, as is harmonizing the radiomic feature calculation methods, to ensure the reproducibility of studies in a multicenter context and their implementation in a clinical workflow. In this review, we explain the potential impact of positron emission tomography radiomics for radiation therapy and underline the various aspects that need to be carefully addressed to make the most of this promising approach.