Quantitative Analysis of (18)F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

Temporal Validation of an FDG-PET-Radiomic Model for Distant-Relapse-Free-Survival After Radio-Chemotherapy for Pancreatic Adenocarcinoma

Background/Objectives: Pancreatic cancer is a very aggressive disease with a poor prognosis, even when diagnosed at an early stage. This study aimed to validate and refine a radiomic-based [18F]FDG-PET model to predict distant relapse-free survival (DRFS) in patients with unresectable locally advanced pancreatic cancer (LAPC). Methods: A Cox regression model incorporating two radiomic features (RFs) and cancer stage (III vs. IV) was temporally validated using a larger cohort (215 patients treated between 2005-2022). Patients received concurrent chemoradiotherapy with capecitabine and hypo-fractionated Intensity Modulated Radiotherapy (IMRT). Data were split into training (145 patients, 2005-2017) and validation (70 patients, 2017-2022) groups. Seventy-eight RFs were extracted, harmonized, and analyzed using machine learning to develop refined models. Results: The model incorporating Statistical-Percentile10, Morphological-ComShift, and stage demonstrated moderate predictive accuracy (training: C-index = 0.632; validation: C-index = 0.590). When simplified to include only Statistical-Percentile10, performance improved slightly in the validation group (C-index = 0.601). Adding GLSZM3D-grayLevelVariance to Statistical-Percentile10, while excluding Morphological-ComShift, further enhanced accuracy (training: C-index = 0.654; validation: C-index = 0.623). Despite these refinements, all versions showed similar moderate ability to stratify patients into risk classes. Conclusions: [18F]FDG-PET radiomic features are robust predictors of DRFS after chemoradiotherapy in LAPC. Despite moderate performance, these models hold promise for patient risk stratification. Further validation with external cohorts is ongoing.

Joint segmentation of tumors in 3D PET-CT images with a network fusing multi-view and multi-modal information

Objective. Joint segmentation of tumors in positron emission tomography-computed tomography (PET-CT) images is crucial for precise treatment planning. However, current segmentation methods often use addition or concatenation to fuse PET and CT images, which potentially overlooks the nuanced interplay between these modalities. Additionally, these methods often neglect multi-view information that is helpful for more accurately locating and segmenting the target structure. This study aims to address these disadvantages and develop a deep learning-based algorithm for joint segmentation of tumors in PET-CT images.Approach. To address these limitations, we propose the Multi-view Information Enhancement and Multi-modal Feature Fusion Network (MIEMFF-Net) for joint tumor segmentation in three-dimensional PET-CT images. Our model incorporates a dynamic multi-modal fusion strategy to effectively exploit the metabolic and anatomical information from PET and CT images and a multi-view information enhancement strategy to effectively recover the lost information during upsamping. A Multi-scale Spatial Perception Block is proposed to effectively extract information from different views and reduce redundancy interference in the multi-view feature extraction process.Main results. The proposed MIEMFF-Net achieved a Dice score of 83.93%, a Precision of 81.49%, a Sensitivity of 87.89% and an IOU of 69.27% on the Soft Tissue Sarcomas dataset and a Dice score of 76.83%, a Precision of 86.21%, a Sensitivity of 80.73% and an IOU of 65.15% on the AutoPET dataset.Significance. Experimental results demonstrate that MIEMFF-Net outperforms existing state-of-the-art models which implies potential applications of the proposed method in clinical practice.

Preliminary study on the ability of the machine learning models based on 18F-FDG PET/CT to differentiate between mass-forming pancreatic lymphoma and pancreatic carcinoma

PURPOSE

The objective of this study was to preliminarily assess the ability of metabolic parameters and radiomics derived from 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) to distinguish mass-forming pancreatic lymphoma from pancreatic carcinoma using machine learning.

METHODS

A total of 88 lesions from 86 patients diagnosed as mass-forming pancreatic lymphoma or pancreatic carcinoma were included and randomly divided into a training set and a validation set at a 4-to-1 ratio. The segmentation of regions of interest was performed using ITK-SNAP software, PET metabolic parameters and radiomics features were extracted using 3Dslicer and PYTHON. Following the selection of optimal metabolic parameters and radiomics features, Logistic regression (LR), support vector machine (SVM), and random forest (RF) models were constructed for PET metabolic parameters, CT radiomics, PET radiomics, and PET/CT radiomics. Model performance was assessed in terms of area under the curve (AUC), accuracy, sensitivity, and specificity in both the training and validation sets.

RESULTS

Strong discriminative ability observed in all models, with AUC values ranging from 0.727 to 0.978. The highest performance exhibited by the combined PET and CT radiomics features. AUC values for PET/CT radiomics models in the training set were LR 0.994, SVM 0.994, RF 0.989. In the validation set, AUC values were LR 0.909, SVM 0.883, RF 0.844.

CONCLUSION

Machine learning models utilizing the metabolic parameters and radiomics of 18F-FDG PET/CT show promise in distinguishing between pancreatic carcinoma and mass-forming pancreatic lymphoma. Further validation on a larger cohort is necessary before practical implementation in clinical settings.

Predicting the Efficacy of SBRT for Lung Cancer with 18F-FDG PET/CT Radiogenomics

Purpose: to develop a radiogenomic model on the basis of 18F-FDG PET/CT radiomics and clinical-parameter EGFR for predicting PFS stratification in lung-cancer patients after SBRT treatment. Methods: A total of 123 patients with lung cancer who had undergone 18F-FDG PET/CT examination before SBRT from September 2014 to December 2021 were retrospectively analyzed. All patients’ PET/CT images were manually segmented, and the radiomic features were extracted. LASSO regression was used to select radiomic features. Logistic regression analysis was used to screen clinical features to establish the clinical EGFR model, and a radiogenomic model was constructed by combining radiomics and clinical EGFR. We used the receiver operating characteristic curve and calibration curve to assess the efficacy of the models. The decision curve and influence curve analysis were used to evaluate the clinical value of the models. The bootstrap method was used to validate the radiogenomic model, and the mean AUC was calculated to assess the model. Results: A total of 2042 radiomics features were extracted. Five radiomic features were related to the PFS stratification of lung-cancer patients with SBRT. T-stage and overall stages (TNM) were independent factors for predicting PFS stratification. AUCs under the ROC curve of the radiomics, clinical EGFR, and radiogenomic models were 0.84, 0.67, and 0.86, respectively. The calibration curve shows that the predicted value of the radiogenomic model was in good agreement with the actual value. The decision and influence curve showed that the model had high clinical application values. After Bootstrap validation, the mean AUC of the radiogenomic model was 0.850(95%CI 0.849–0.851). Conclusions: The radiogenomic model based on 18F-FDG PET/CT radiomics and clinical EGFR has good application value in predicting the PFS stratification of lung-cancer patients after SBRT treatment.

Prognostic analysis of curatively resected pancreatic cancer using harmonized positron emission tomography radiomic features

BACKGROUND

Texture features reflecting tumour heterogeneity enable us to investigate prognostic factors. The R package ComBat can harmonize the quantitative texture features among several positron emission tomography (PET) scanners. We aimed to identify prognostic factors among harmonized PET radiomic features and clinical information from pancreatic cancer patients who underwent curative surgery.

METHODS

Fifty-eight patients underwent preoperative enhanced dynamic computed tomography (CT) scanning and fluorodeoxyglucose PET/CT using four PET scanners. Using LIFEx software, we measured PET radiomic parameters including texture features with higher order and harmonized these PET parameters. For progression-free survival (PFS) and overall survival (OS), we evaluated clinical information, including age, TNM stage, and neural invasion, and the harmonized PET radiomic features based on univariate Cox proportional hazard regression. Next, we analysed the prognostic indices by multivariate Cox proportional hazard regression (1) by using either significant (p < 0.05) or borderline significant (p = 0.05-0.10) indices in the univariate analysis (first multivariate analysis) or (2) by using the selected features with random forest algorithms (second multivariate analysis). Finally, we checked these multivariate results by log-rank test.

RESULTS

Regarding the first multivariate analysis for PFS after univariate analysis, age was the significant prognostic factor (p = 0.020), and MTV and GLCM contrast were borderline significant (p = 0.051 and 0.075, respectively). Regarding the first multivariate analysis of OS, neural invasion, Shape sphericity and GLZLM LZLGE were significant (p = 0.019, 0.042 and 0.0076). In the second multivariate analysis, only MTV was significant (p = 0.046) for PFS, whereas GLZLM LZLGE was significant (p = 0.047), and Shape sphericity was borderline significant (p = 0.088) for OS. In the log-rank test, age, MTV and GLCM contrast were borderline significant for PFS (p = 0.08, 0.06 and 0.07, respectively), whereas neural invasion and Shape sphericity were significant (p = 0.03 and 0.04, respectively), and GLZLM LZLGE was borderline significant for OS (p = 0.08).

CONCLUSIONS

Other than the clinical factors, MTV and GLCM contrast for PFS and Shape sphericity and GLZLM LZLGE for OS may be prognostic PET parameters. A prospective multicentre study with a larger sample size may be warranted.

MFCNet: A multi-modal fusion and calibration networks for 3D pancreas tumor segmentation on PET-CT images

In clinical diagnosis, positron emission tomography and computed tomography (PET-CT) images containing complementary information are fused. Tumor segmentation based on multi-modal PET-CT images is an important part of clinical diagnosis and treatment. However, the existing current PET-CT tumor segmentation methods mainly focus on positron emission tomography (PET) and computed tomography (CT) feature fusion, which weakens the specificity of the modality. In addition, the information interaction between different modal images is usually completed by simple addition or concatenation operations, but this has the disadvantage of introducing irrelevant information during the multi-modal semantic feature fusion, so effective features cannot be highlighted. To overcome this problem, this paper propose a novel Multi-modal Fusion and Calibration Networks (MFCNet) for tumor segmentation based on three-dimensional PET-CT images. First, a Multi-modal Fusion Down-sampling Block (MFDB) with a residual structure is developed. The proposed MFDB can fuse complementary features of multi-modal images while retaining the unique features of different modal images. Second, a Multi-modal Mutual Calibration Block (MMCB) based on the inception structure is designed. The MMCB can guide the network to focus on a tumor region by combining different branch decoding features using the attention mechanism and extracting multi-scale pathological features using a convolution kernel of different sizes. The proposed MFCNet is verified on both the public dataset (Head and Neck cancer) and the in-house dataset (pancreas cancer). The experimental results indicate that on the public and in-house datasets, the average Dice values of the proposed multi-modal segmentation network are 74.14% and 76.20%, while the average Hausdorff distances are 6.41 and 6.84, respectively. In addition, the experimental results show that the proposed MFCNet outperforms the state-of-the-art methods on the two datasets.

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.

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.

Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging

Simple Summary

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, associated with a 98% loss of life expectancy and a 30% increase in disability-adjusted life years. Image-based artificial intelligence (AI) can help improve outcomes for PDAC given that current clinical guidelines are non-uniform and lack evidence-based consensus. However, research on image-based AI for PDAC is too scattered and lacking in sufficient quality to be incorporated into clinical workflows. In this review, an international, multi-disciplinary team of the world’s leading experts in pancreatic cancer breaks down the patient pathway and pinpoints the current clinical touchpoints in each stage. The available PDAC imaging AI literature addressing each pathway stage is then rigorously analyzed, and current performance and pitfalls are identified in a comprehensive overview. Finally, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.

Abstract

Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.

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.

Nasopharyngeal Carcinoma Radiomic Evaluation with Serial PET/CT: Exploring Features Predictive of Survival in Patients with Long-Term Follow-Up

PURPOSE

We aim determine the value of PET and CT radiomic parameters on survival with serial follow-up PET/CT in patients with nasopharyngeal carcinoma (NPC) for which curative intent therapy is undertaken.

METHODS

Patients with NPC and available pre-treatment as well as follow up PET/CT were included from 2005 to 2006 and were followed to 2021. Baseline demographic, radiological and outcome data were collected. Univariable Cox proportional hazard models were used to evaluate features from baseline and follow-up time points, and landmark analyses were performed for each time point.

RESULTS

Sixty patients were enrolled, and two-hundred and seventy-eight (278) PET/CT were at baseline and during follow-up. Thirty-eight percent (38%) were female, and sixty-two patients were male. All patients underwent curative radiation or chemoradiation therapy. The median follow-up was 11.72 years (1.26-14.86). Five-year and ten-year overall survivals (OSs) were 80.0% and 66.2%, and progression-free survival (PFS) was 90.0% and 74.4%. Time-dependent modelling suggested that, among others, PET gray-level zone length matrix (GLZLM) gray-level non-uniformity (GLNU) (HR 2.74 95% CI 1.06, 7.05) was significantly associated with OS. Landmark analyses suggested that CT parameters were most predictive at 15 month, whereas PET parameters were most predictive at time points 3, 6, 9 and 15 month.

CONCLUSIONS

This study with long-term follow up data on NPC suggests that mainly PET-derived radiomic features are predictive for OS but not PFS in a time-dependent evaluation. Furthermore, CT radiomic measures may predict OS and PFS best at initial and long-term follow-up time points and PET measures may be more predictive in the interval. These modalities are commonly used in NPC surveillance, and prospective validation should be considered.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 2, Infradiaphragmatic Cancers, Blood Malignancies, Melanoma and Musculoskeletal Cancers

The objective of this review was to summarize published radiomics studies dealing with infradiaphragmatic cancers, blood malignancies, melanoma, and musculoskeletal cancers, and assess their quality. PubMed database was searched from January 1990 to February 2022 for articles performing radiomics on PET imaging of at least 1 specified tumor type. Exclusion criteria includd: non-oncological studies; supradiaphragmatic tumors; reviews, comments, cases reports; phantom or animal studies; technical articles without a clinically oriented question; studies including <30 patients in the training cohort. The review database contained PMID, first author, year of publication, cancer type, number of patients, study design, independent validation cohort and objective. This database was completed twice by the same person; discrepant results were resolved by a third reading of the articles. A total of 162 studies met inclusion criteria; 61 (37.7%) studies included >100 patients, 13 (8.0%) were prospective and 61 (37.7%) used an independent validation set. The most represented cancers were esophagus, lymphoma, and cervical cancer (n = 24, n = 24 and n = 19 articles, respectively). Most studies focused on 18F-FDG, and prognostic and response to treatment objectives. Although radiomics and artificial intelligence are technically challenging, new contributions and guidelines help improving research quality over the years and pave the way toward personalized medicine.

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 Value of 18F-FDG PET/CT in Diagnosing Pancreatic Lesions: Comparison With CA19-9, Enhanced CT or Enhanced MR

Objective: To investigate the value of ¹⁸F-FDG PET/CT in diagnosing pancreatic lesions, and compare it with CA19-9, contrast-enhanced CT (CECT), and contrast-enhanced MRI (CEMR). Methods: Cases of patients with suspected pancreatic lesions examined between January 1, 2011 and June 30, 2017 were retrospectively analyzed. CA19-9, CECT and CEMR within 2 weeks of PET/CT were evaluated. We compared the diagnostic efficacy of PET/CT with CA19-9, CECT and CEMR as well as combined tests. Results: A total of 467 cases were examined in this study, including 293 males and 174 females, with an average age of 57.79 ± 12.68 y (16-95 y). Cases in the malignant group (n = 248) had significantly higher SUVmax (7.34 ± 4.17 vs. 1.70 ± 2.68, P < 0.001) and CA19-9 (663.21 ± 531.98 vs. 87.80 ± 218.47, P < 0.001) than those in the benign group (n = 219). The sensitivity, specificity and accuracy of PET/CT were 91.9, 96.3, and 94.0%, respectively. Those for CECT were 83.6, 77.8, 81.2%, respectively; and 91.2, 75.0, 81.7% were for CEMR. PET/CT corrected 14.7% (28/191) CECT diagnoses and 12.2% (10/82) CEMR diagnoses. Although the diagnostic efficiency of CA19-9 was acceptable (80.0, 69.0, 74.9% respectively), the joint application of PET/CT and CA19-9 could significantly enhance the diagnostic efficiency compared with PET/CT alone (sen 97.4 vs. 90.5%, P = 0.0003; spe 100.0 vs. 95.2%, P = 0.0047). Conclusions: PET/CT has sensitivity similar to CECT, CEMR and significantly higher specificity and accuracy, helping reduce false diagnoses of morphological images. Combining PET/CT with CA19-9 could enhance diagnostic efficiency.

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.

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.

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.

Quantitative imaging for radiotherapy purposes

Quantitative imaging biomarkers show great potential for use in radiotherapy. Quantitative images based on microscopic tissue properties and tissue function can be used to improve contouring of the radiotherapy targets. Furthermore, quantitative imaging biomarkers might be used to predict treatment response for several treatment regimens and hence be used as a tool for treatment stratification, either to determine which treatment modality is most promising or to determine patient-specific radiation dose. Finally, patient-specific radiation doses can be further tailored to a tissue/voxel specific radiation dose when quantitative imaging is used for dose painting. In this review, published standards, guidelines and recommendations on quantitative imaging assessment using CT, PET and MRI are discussed. Furthermore, critical issues regarding the use of quantitative imaging for radiation oncology purposes and resultant pending research topics are identified.

Advanced imaging techniques for chronic pancreatitis

MRI and MRCP play an important role in the diagnosis of chronic pancreatitis (CP) by imaging pancreatic parenchyma and ducts. MRI/MRCP is more widely used than computed tomography (CT) for mild to moderate CP due to its increased sensitivity for pancreatic ductal and gland changes; however, it does not detect the calcifications seen in advanced CP. Quantitative MR imaging offers potential advantages over conventional qualitative imaging, including simplicity of analysis, quantitative and population-based comparisons, and more direct interpretation of detected changes. These techniques may provide quantitative metrics for determining the presence and severity of acinar cell loss and aid in the diagnosis of chronic pancreatitis. Given the fact that the parenchymal changes of CP precede the ductal involvement, there would be a significant benefit from developing MRI/MRCP-based, more robust diagnostic criteria combining ductal and parenchymal findings. Among cross-sectional imaging modalities, multi-detector CT (MDCT) has been a cornerstone for evaluating chronic pancreatitis (CP) since it is ubiquitous, assesses primary disease process, identifies complications like pseudocyst or vascular thrombosis with high sensitivity and specificity, guides therapeutic management decisions, and provides images with isotropic resolution within seconds. Conventional MDCT has certain limitations and is reserved to provide predominantly morphological (e.g., calcifications, organ size) rather than functional information. The emerging applications of radiomics and artificial intelligence are poised to extend the current capabilities of MDCT. In this review article, we will review advanced imaging techniques by MRI, MRCP, CT, and ultrasound.

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.

A Preliminary Study of CT Texture Analysis for Characterizing Epithelial Tumors of the Parotid Gland

Objective

The aim of this study was to explore and validate the diagnostic performance of whole-volume CT texture features in differentiating the common benign and malignant epithelial tumors of the parotid gland.

Materials and Methods

Contrast-enhanced CT images of 83 patients with common benign and malignant epithelial tumors of the parotid gland confirmed by histopathology were retrospectively analyzed, including 50 patients with pleomorphic adenoma (PA) and 33 patients with malignant epithelial tumors. Quantitative texture features of tumors were extracted from CT images of arterial phase. The diagnostic performance of texture features was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC). The specificity and sensitivity were respectively discussed by the maximum Youden’s index.

Results

All the texture features were subject to normal distribution and homoscedasticity. Energy, mean, correlation, and sum entropy of epithelial malignancy group were significantly higher than those of PA group (P<0.05). There were no statistically significant differences between PA group and epithelial malignancy group in uniformity, entropy, skewness, kurtosis, contrast, and difference entropy (P>0.05). The AUC of each texture feature and joint diagnostic model was 0.887 (energy), 0.734 (mean), 0.739 (correlation), 0.623 (sum entropy), 0.888 (energy-mean), 0.883 (energy-correlation), 0.784 (mean-correlation). The diagnostic efficiency of energy-mean was the best. Based on the maximum Youden’s index, the specificity of energy-correlation was the highest (97%) and the sensitivity of energy was the highest (97%).

Conclusion

Energy, mean, correlation, and sum entropy can be the effective quantitative texture features to differentiate the benign and malignant epithelial tumors of the parotid gland. With higher AUC, energy and energy-mean are superior to other indexes or joint diagnostic models in differentiating the benign and malignant epithelial tumors of the parotid gland. CT texture analysis can be used as a noninvasive and valuable means of preoperative assessment of parotid epithelial tumors without additional cost to the patients.

Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters

Background

Our study aims to develop and validate diagnostic models of the common parotid tumors based on whole-volume CT textural image biomarkers (IBMs) in combination with clinical parameters at a single institution.

Methods

The study cohort was composed of 51 pleomorphic adenoma (PA) patients and 42 Warthin tumor (WT) patients. Clinical parameters and conventional image features were scored retrospectively and textural IBMs were extracted from CT images of arterial phase. Independent-samples t test or Chi-square test was used for evaluating the significance of the difference among clinical parameters, conventional CT image features, and textural IBMs. The diagnostic performance of univariate model and multivariate model was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC).

Results

Significant differences were found in clinical parameters (age, gender, disease duration, smoking), conventional image features (site, maximum diameter, time-density curve, peripheral vessels sign) and textural IBMs (mean, uniformity, energy, entropy) between PA group and WT group (P<0.05). ROC analysis showed that clinical parameter (age) and quantitative textural IBMs (mean, energy, entropy) were able to categorize the patients into PA group and WT group, with the AUC of 0.784, 0.902, 0.910, 0.805, respectively. When IBMs were added in clinical model, the multivariate models including age-mean and age-energy performed significantly better than the univariate models with the improved AUC of 0.940, 0.944, respectively (P<0.001).

Conclusions

Both clinical parameter and CT textural IBMs can be used for the preoperative, noninvasive diagnosis of parotid PA and WT. The diagnostic performance of textural IBM model was obviously better than that of clinical model and conventional image model in this study. While the multivariate model consisted of clinical parameter and textural IBM had the optimal diagnostic performance, which would contribute to the better selection of individualized surgery program.

Radiomics: A primer for the radiation oncologist

PURPOSE

Radiomics are a set of methods used to leverage medical imaging and extract quantitative features that can characterize a patient's phenotype. All modalities can be used with several different software packages. Specific informatics methods can then be used to create meaningful predictive models. In this review, we will explain the major steps of a radiomics analysis pipeline and then present the studies published in the context of radiation therapy.

METHODS

A literature review was performed on Medline using the search engine PubMed. The search strategy included the search terms "radiotherapy", "radiation oncology" and "radiomics". The search was conducted in July 2019 and reference lists of selected articles were hand searched for relevance to this review.

RESULTS

A typical radiomics workflow always includes five steps: imaging and segmenting, data curation and preparation, feature extraction, exploration and selection and finally modeling. In radiation oncology, radiomics studies have been published to explore different clinical outcome in lung (n=5), head and neck (n=5), esophageal (n=3), rectal (n=3), pancreatic (n=2) cancer and brain metastases (n=2). The quality of these retrospective studies is heterogeneous and their results have not been translated to the clinic.

CONCLUSION

Radiomics has a great potential to predict clinical outcome and better personalize treatment. But the field is still young and constantly evolving. Improvement in bias reduction techniques and multicenter studies will hopefully allow more robust and generalizable models.

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 SUVpeak (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 SUVpeak (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.

Radiomics Facilitates Candidate Selection for Irradiation Stents Among Patients With Unresectable Pancreatic Cancer

Purpose: To develop a model to select appropriate candidates for irradiation stent placement among patients with unresectable pancreatic cancer with malignant biliary obstruction (UPC-MBO). Methods: This retrospective study included 106 patients treated with an irradiation stent for UPC-MBO. These patients were randomly divided into a training group (74 patients) and a validation group (32 patients). A clinical model for predicting restenosis-free survival (RFS) was developed with clinical predictors selected by univariate and multivariate analyses. After integrating the radiomics signature, a combined model was constructed to predict RFS. The predictive performance was evaluated with the concordance index (C-index) in both the training and validation groups. The median risk score of progression in the training group was used to divide patients into high- and low-risk subgroups. Results: Radiomics features were integrated with clinical predictors to develop a combined model. The predictive performance was better in the combined model (C-index, 0.791 and 0.779 in the training and validation groups, respectively) than in the clinical model (C-index, 0.673 and 0.667 in the training and validation groups, respectively). According to the median risk score of 1.264, the RFS was significantly different between the high- and low-risk groups (p < 0.001 for the training group, and p = 0.016 for the validation group). Conclusions: The radiomics-based model had good performance for RFS prediction in patients with UPC-MBO who received an irradiation stent. Patients with slow progression should consider undergoing irradiation stent placement for a longer RFS.

18F-Fluorodeoxyglucose Positron Emission Tomography Predicts Treatment Efficacy and Clinical Outcome for Patients With Pancreatic Carcinoma: A Meta-analysis

OBJECTIVES

F-Fluorodeoxyglucose positron emission tomography (FDG-PET) has been an important modality for detecting malignancies. Recently, an increasing number of studies reported the utility of FDG-PET parameters in predicting clinical outcomes and treatment assessment in variety of cancers. We aimed at clarifying both the prognostic role and assessment value of FDG-PET in pancreatic carcinoma.

METHODS

We systematically searched electronic databases of PubMed, Embase, Cochrane Library, and Web of Science to identify relevant studies to conduct this meta-analysis. Comparative analyses of the pooled hazard ratio (HR) for overall survival were performed to assess the utility of FDG-PET parameters in prognosis evaluation and treatment assessment by random-effect model.

RESULTS

Twenty-three studies with 1762 patients met the inclusion criteria of this meta-analysis. The pooled results revealed that greater maximum standardized uptake value of the primary tumor was significantly correlated with poorer overall survival (HR, 1.31; 95% confidence interval, 1.15-1.50; P < 0.001). Besides, greater reduction of maximum standardized uptake value after treatments indicated significant better overall survival (HR, 0.68; 95% confidence interval, 0.47-0.98; P = 0.037).

CONCLUSIONS

F-Fluorodeoxyglucose positron emission tomography parameters might be helpful not only for predicting survival outcome but also for selecting potentially efficacious treatments in patients with pancreatic carcinoma.

Selection Between Liver Resection Versus Transarterial Chemoembolization in Hepatocellular Carcinoma: A Multicenter Study

OBJECTIVES

Models should be developed to assist choice between liver resection (LR) and transarterial chemoembolization (TACE) for hepatocellular carcinoma.

METHODS

After separating 520 cases from 5 hospitals into training (n = 302) and validation (n = 218) data sets, we weighted the cases to control baseline difference and ensured the causal effect between treatments (LR and TACE) and estimated progression-free survival (PFS) difference. A noninvasive PFS model was constructed with clinical factors, radiological characteristics, and radiomic features. We compared our model with other 4 state-of-the-art models. Finally, patients were classified into subgroups with and without significant PFS difference between treatments.

RESULTS

Our model included treatments, age, sex, modified Barcelona Clinic Liver Cancer stage, fusion lesions, hepatocellular carcinoma capsule, and 3 radiomic features, with good discrimination and calibrations (area under the curve for 3-year PFS was 0.80 in the training data set and 0.75 in the validation data set; similar results were achieved in 1- and 2-year PFS). The model had better accuracy than the other 4 models. A nomogram was built, with different scores assigned for LR and TACE. Separated by the threshold of score difference between treatments, for some patients, LR provided longer PFS and might be the better option (training: hazard ratio [HR] = 0.50, P = 0.014; validation: HR = 0.52, P = 0.026); in the others, LR provided similar PFS with TACE (training: HR = 0.84, P = 0.388; validation: HR = 1.14, P = 0.614). TACE may be better because it was less invasive.

DISCUSSION

We propose an individualized model predicting PFS difference between LR and TACE to assist in the optimal treatment choice.

The prognostic value of CT-based image-biomarkers for head and neck cancer patients treated with definitive (chemo-)radiation

OBJECTIVES

The aim of this study was to investigate whether quantitative CT image-biomarkers (IBMs) can improve the prediction models with only classical prognostic factors for local-control (LC), regional-control (RC), distant metastasis-free survival (DMFS) and disease-free survival (DFS) for head and neck cancer (HNC) patients.

MATERIALS AND METHODS

The cohort included 240 and 204 HNC patients in the training and validation analysis, respectively. Clinical variables were scored prospectively and IBMs of the primary tumor and lymph nodes were extracted from planning CT-images. Clinical, IBM and combined models were created from multivariable Cox proportional-hazard analyses based on clinical features, IBMs, and both for LC, RC, DMFS and DFS.

RESULTS

Clinical variables identified in the multivariable analysis included tumor-site, WHO performance-score, tumor-stage and age. Bounding-box-volume describing the tumor volume and irregular shape, IBM correlation representing radiological heterogeneity, and LN_major-axis-length showing the distance between lymph nodes were included in the IBM models. The performance of IBM LC, RC, DMFS and DFS models (c-index(validated):0.62, 0.80, 0.68 and 0.65) were comparable to that of the clinical models (0.62, 0.76, 0.70 and 0.66). The combined DFS model (0.70) including clinical features and IBMs performed significantly better than the clinical model. Patients stratified with the combined models revealed larger differences between risk groups in the validation cohort than with clinical models for LC, RC and DFS. For DMFS, the differences were similar to the clinical model.

CONCLUSION

For prediction of HNC treatment outcomes, image-biomarkers performed as good as or slightly better than clinical variables.

A review on radiomics and the future of theranostics for patient selection in precision medicine

The growing complexity and volume of clinical data and the associated decision-making processes in oncology promote the advent of precision medicine. Precision (or personalised) medicine describes preventive and/or treatment procedures that take individual patient variability into account when proscribing treatment, and has been hindered in the past by the strict requirements of accurate, robust, repeatable and preferably non-invasive biomarkers to stratify both the patient and the disease. In oncology, tumour subtypes are traditionally measured through repeated invasive biopsies, which are taxing for the patient and are cost and labour intensive. Quantitative analysis of routine clinical imaging provides an opportunity to capture tumour heterogeneity non-invasively, cost-effectively and on large scale. In current clinical practice radiological images are qualitatively analysed by expert radiologists whose interpretation is known to suffer from inter- and intra-operator variability. Radiomics, the high-throughput mining of image features from medical images, provides a quantitative and robust method to assess tumour heterogeneity, and radiomics-based signatures provide a powerful tool for precision medicine in cancer treatment. This study aims to provide an overview of the current state of radiomics as a precision medicine decision support tool. We first provide an overview of the requirements and challenges radiomics currently faces in being incorporated as a tool for precision medicine, followed by an outline of radiomics' current applications in the treatment of various types of cancer. We finish with a discussion of possible future advances that can further develop radiomics as a precision medicine tool.

Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis

Background

To investigate the effect of machine learning methods on predicting the Overall Survival (OS) for non-small cell lung cancer based on radiomics features analysis.

Methods

A total of 339 radiomic features were extracted from the segmented tumor volumes of pretreatment computed tomography (CT) images. These radiomic features quantify the tumor phenotypic characteristics on the medical images using tumor shape and size, the intensity statistics and the textures. The performance of 5 feature selection methods and 8 machine learning methods were investigated for OS prediction. The predicted performance was evaluated with concordance index between predicted and true OS for the non-small cell lung cancer patients. The survival curves were evaluated by the Kaplan-Meier algorithm and compared by the log-rank tests.

Results

The gradient boosting linear models based on Cox’s partial likelihood method using the concordance index feature selection method obtained the best performance (Concordance Index: 0.68, 95% Confidence Interval: 0.62~ 0.74).

Conclusions

The preliminary results demonstrated that certain machine learning and radiomics analysis method could predict OS of non-small cell lung cancer accuracy.

Electronic supplementary material

The online version of this article (10.1186/s13014-018-1140-9) contains supplementary material, which is available to authorized users.

Can physician gestalt predict survival in patients with resectable pancreatic adenocarcinoma?

PURPOSE

Clinician gestalt may hold unexplored information that can be capitalized upon to improve existing nomograms. The study objective was to evaluate physician ability to predict 2-year overall survival (OS) in resected pancreatic ductal adenocarcinoma (PDAC) patients based on pre-operative clinical characteristics and routine CT imaging.

METHODS

Ten surgeons and two radiologists were provided with a clinical vignette (including age, gender, presenting symptoms, and pre-operative CA19-9 when available) and pre-operative CT scan for 20 resected PDAC patients and asked to predict the probability of each patient reaching 2-year OS. Receiver operating characteristic curves were used to assess agreement and to compare performance with an established institutional nomogram.

RESULTS

Ten surgeons and 2 radiologists participated in this study. The area under the curve (AUC) for all physicians was 0.707 (95% CI 0.642-0.772). Attending physicians with > 5 years experience performed better than physicians with < 5 years of clinical experience since completion of post-graduate training (AUC = 0.710, 95% CI [0.536-0.884] compared to AUC = 0.662, 95% CI [0.398-0.927]). Radiologists performed better than surgeons (AUC = 0.875, 95% CI [0.765-0.985] compared to AUC = 0.656, 95% CI [0.580-0.732]). All but one physician outperformed the clinical nomogram (AUC = 0.604).

CONCLUSIONS

This pilot study demonstrated significant promise in the quantification of physician gestalt. While PDAC remains a difficult disease to prognosticate, physicians, particularly those with more clinical experience and radiologic expertise, are able to perform with higher accuracy than existing nomograms in predicting 2-year survival.

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.

Quantifying the robustness of [ 18 F]FDG-PET/CT radiomic features with respect to tumor delineation in head and neck and pancreatic cancer patients

PURPOSE

To investigate the robustness of PET radiomic features (RF) against tumour delineation uncertainty in two clinically relevant situations.

METHODS

Twenty-five head-and-neck (HN) and 25 pancreatic cancer patients previously treated with ¹⁸F-Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT)-based planning optimization were considered. Seven FDG-based contours were delineated for tumour (T) and positive lymph nodes (N, for HN patients only) following manual (2 observers), semi-automatic (based on SUV maximum gradient: PET_Edge) and automatic (40%, 50%, 60%, 70% SUV_max thresholds) methods. Seventy-three RF (14 of first order and 59 of higher order) were extracted using the CGITA software (v.1.4). The impact of delineation on volume agreement and RF was assessed by DICE and Intra-class Correlation Coefficients (ICC).

RESULTS

A large disagreement between manual and SUV_max method was found for thresholds  ≥50%. Inter-observer variability showed median DICE values between 0.81 (HN-T) and 0.73 (pancreas). Volumes defined by PET_Edge were better consistent with the manual ones compared to SUV40%. Regarding RF, 19%/19%/47% of the features showed ICC < 0.80 between observers for HN-N/HN-T/pancreas, mostly in the Voxel-alignment matrix and in the intensity-size zone matrix families. RFs with ICC < 0.80 against manual delineation (taking the worst value) increased to 44%/36%/61% for PET_Edge and to 69%/53%/75% for SUV40%.

CONCLUSIONS

About 80%/50% of 72 RF were consistent between observers for HN/pancreas patients. PET_edge was sufficiently robust against manual delineation while SUV40% showed a worse performance. This result suggests the possibility to replace manual with semi-automatic delineation of HN and pancreas tumours in studies including PET radiomic analyses.

Radiomics and radiogenomics for precision radiotherapy

Imaging plays an important role in the diagnosis and staging of cancer, as well as in radiation treatment planning and evaluation of therapeutic response. Recently, there has been significant interest in extracting quantitative information from clinical standard-of-care images, i.e. radiomics, in order to provide a more comprehensive characterization of image phenotypes of the tumor. A number of studies have demonstrated that a deeper radiomic analysis can reveal novel image features that could provide useful diagnostic, prognostic or predictive information, improving upon currently used imaging metrics such as tumor size and volume. Furthermore, these imaging-derived phenotypes can be linked with genomic data, i.e. radiogenomics, in order to understand their biological underpinnings or further improve the prediction accuracy of clinical outcomes. In this article, we will provide an overview of radiomics and radiogenomics, including their rationale, technical and clinical aspects. We will also present some examples of the current results and some emerging paradigms in radiomics and radiogenomics for clinical oncology, with a focus on potential applications in radiotherapy. Finally, we will highlight the challenges in the field and suggest possible future directions in radiomics to maximize its potential impact on precision radiotherapy.

Clinical significance of defining borderline resectable pancreatic cancer

Since the introduction of the concept of borderline resectable pancreatic cancer (BRPC), various definitions of this disease entity have been suggested. However, there are several obstacles in defining this disease category. The current diagnostic criteria of BRPC mainly focuses on its expanded 'technical resectability'; however, they are difficult to interpret because of their ambiguity using potential subjective or arbitrary terminology, In addition, limitations in current imaging technology and a lack of evidence in radiological-pathological-clinical correlation make it difficult to refine the criteria. On the other hand, neoadjuvant treatment is usually applied to increase the R0 resection rate of BRPC focusing on the 'oncological curability'. However, evidence is needed concerning the effect of neoadjuvant treatment by quality-controlled prospective randomized clinical trials based on a standardized radiologic and pathologic reporting system. In conclusion, there are two aspects in the current concept of BRPC, which are technical resectability and oncological curability. Although the recent evolution of surgical techniques is expanding the scope of technical resectability, it should not be overlooked that the disease entity must be defined based on the evidence of oncological curability.

Radiomics: the bridge between medical imaging and personalized medicine

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

Prognostic value of 18F-FDG-PET/CT parameters in patients with pancreatic carcinoma: A systematic review and meta-analysis

BACKGROUND

The identification of pancreatic carcinoma (PC) patients with poor prognosis is a priority in clinical oncology because of their high 5-year mortality. However, the prognostic value of pretreatment F-fluorodeoxyglucose (F-FDG)- positron emission tomography (PET)/computed tomography (CT) parameters in PC patients is controversial and no consensus exists as to its predictive capability. This meta-analysis was performed to comprehensively explore the prognostic significance of F-FDG-PET/CT parameters in patients with pancreatic carcinoma.

METHODS

Extensive literature searches of the PubMed, Embase, Web of Science, and Cochrane Library databases were conducted to identify literature published until March 5, 2017. Comparative analyses of the pooled hazard ratios (HRs) for event-free survival (EFS) and overall survival (OS) were performed to assess their correlations with pretreatment maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). Either the fixed- or the random-effects model was adopted, depending on the heterogeneity observed across studies. Subgroup and sensitivity analyses were performed to assess the robustness of the results.

RESULTS

Sixteen studies including 1146 patients were identified. The pooled HRs for the probability of EFS were 1.90 (95% confidential interval (CI): 1.48-2.45) for SUVmax, 1.76 (95% CI: 1.20-2.58) for MTV, and 1.81 (95% CI: 1.27-2.58) for TLG. The pooled HRs for the probability of OS were 1.21 (95% CI: 1.12-1.31) for SUVmax, 1.56 (95% CI: 1.13-2.16) for MTV, and 1.70 (95% CI: 1.25-2.30) for TLG. A slight publication bias was detected using Begg test. After adjustment using the trim and fill procedure, the corrected HRs were not significantly different. The results of the subgroup analyses by SUVmax, MTV, and TLG showed that these factors may have similar prognostic significance.

CONCLUSION

F-FDG-PET/CT parameters, such as SUVmax, MTV, and TLG, may be significant prognostic factors in patients with pancreatic carcinoma. F-FDG-PET/CT imaging could be a promising tool to provide prognostic information for these patients.

Improving the prediction of overall survival for head and neck cancer patients using image biomarkers in combination with clinical parameters

PURPOSE

To develop and validate prediction models of overall survival (OS) for head and neck cancer (HNC) patients based on image biomarkers (IBMs) of the primary tumor and positive lymph nodes (Ln) in combination with clinical parameters.

MATERIAL AND METHODS

The study cohort was composed of 289 nasopharyngeal cancer (NPC) patients from China and 298 HNC patients from the Netherlands. Multivariable Cox-regression analysis was performed to select clinical parameters from the NPC and HNC datasets, and IBMs from the NPC dataset. Final prediction models were based on both IBMs and clinical parameters.

RESULTS

Multivariable Cox-regression analysis identified three independent IBMs (tumor Volume-density, Run Length Non-uniformity and Ln Major-axis-length). This IBM model showed a concordance(c)-index of 0.72 (95%CI: 0.65-0.79) for the NPC dataset, which performed reasonably with a c-index of 0.67 (95%CI: 0.62-0.72) in the external validation HNC dataset. When IBMs were added in clinical models, the c-index of the NPC and HNC datasets improved to 0.75 (95%CI: 0.68-0.82; p=0.019) and 0.75 (95%CI: 0.70-0.81; p<0.001), respectively.

CONCLUSION

The addition of IBMs from the primary tumor and Ln improved the prognostic performance of the models containing clinical factors only. These combined models may improve pre-treatment individualized prediction of OS for HNC patients.

Integration of Stereotactic Body Radiation Therapy into the Multidisciplinary Management of Pancreatic Cancer

Although most patients with pancreatic cancer die of metastatic disease, an autopsy study showed that up to one-third of patients die of predominantly local disease. This patient population stands to benefit the most from radiation, surgery, or both. Unfortunately, however, single-agent chemotherapy has had minimal benefit in pancreatic cancer, and most patients progress distantly before receiving radiation therapy (RT). With the addition of multiagent chemotherapy, patients are living longer, and RT has emerged as an important modality in preventing local progression. Standard chemoradiation delivered over 5-6 weeks has been shown to improve local control, but this approach delays full-dose systemic therapy and increases toxicity when compared to chemotherapy alone. Stereotactic body RT (SBRT) delivered in 3-5 fractions can be used to accurately target the pancreatic tumor with small margins and limited acute treatment-related toxicity. Given the favorable toxicity profile, SBRT can easily be integrated with other therapies in all stages of pancreatic cancer. However, future studies are necessary to determine optimal dose or fractionation regimens and sequencing with targeted therapies and immunotherapy. The purpose of this review is to discuss our current understanding of SBRT in the multidisciplinary management of patients with pancreatic cancer and future implications.