Predictive value of 0.35 T magnetic resonance imaging radiomic features in stereotactic ablative body radiotherapy of pancreatic cancer: A pilot study

2-[18F]FDG PET-based quantification of lymph node metabolic heterogeneity for predicting lymph node metastasis in patients with colorectal cancer

BACKGROUND AND PURPOSE

The pre-surgical estimation of lymph node (LN) metastasis in colorectal cancer (CRC) poses a significant diagnostic predicament. The associations between LN morphology, density, and metabolic heterogeneity and LN metastasis status in CRCs have been seldomly examined through the lens of radiomics. This research aimed to assess 2-[18F]FDG PET-based quantification of intratumoral metabolic heterogeneity for predicting lymph node metastasis in patients with colorectal cancer.

MATERIALS AND METHODS

The construction of the model utilized data from 264 CRC patients, all of whom underwent preoperative 2-[18F]FDG PET/CT. Radiomic features were extracted from PET and CT images of LNs. Least absolute shrinkage and selection operator (LASSO) regression was implemented for selecting pertinent imaging features with a tenfold cross-validation. The predictive accuracy for LN metastasis status was juxtaposed against traditional methodologies (comprising CT-reported LN status and PET/CT-reported LN status) by deploying the receiver operating characteristic (ROC) curve analysis. The radiomics signature was evaluated based on discrimination, calibration, and clinical utility parameters. The model was further subjected to validation using an independent cohort of 132 patients from the period of January 2012 to June 2020.

RESULTS

The radiomics model was composed of eight significant radiomic features (five from PET and three from CT), encapsulating metabolic and density heterogeneity. The radiomics signature (area under the curve (AUC), 0.908) showcased a significantly superior performance compared to CT-reported LN status (AUC, 0.563, P < 0.001) and PET/CT-reported LN status (AUC, 0.64, P < 0.001) for predicting LN-positive or LN-negative status. The radiomics signature (AUC, 0.885) also showcased a significantly superior performance compared to CT-reported LN status (AUC, 0.587, P < 0.001) and PET/CT-reported LN status (AUC, 0.621, P < 0.001) to identify N1 and N2. This signature maintained its independence from clinical risk factors and exhibited robustness in the validation test set. Decision curve analysis attested to the clinical utility of the radiomics signature.

CONCLUSIONS

The radiomics signature based on 2-[18F]FDG PET/CT, which derived image features directly from LNs irrespective of clinical risk factors, displayed enhanced diagnostic performance compared to conventional CT or PET/CT-reported LN status. This allows for the identification of pre-surgical LN metastasis status and facilitates a patient-specific prediction of LN metastasis status in CRC patients.

Prediction of non-perfusion volume ratio for uterine fibroids treated with ultrasound-guided high-intensity focused ultrasound based on MRI radiomics combined with clinical parameters

BACKGROUND

Prediction of non-perfusion volume ratio (NPVR) is critical in selecting patients with uterine fibroids who will potentially benefit from ultrasound-guided high-intensity focused ultrasound (HIFU) treatment, as it reduces the risk of treatment failure. The purpose of this study is to construct an optimal model for predicting NPVR based on T2-weighted magnetic resonance imaging (T2MRI) radiomics features combined with clinical parameters by machine learning.

MATERIALS AND METHODS

This retrospective study was conducted among 223 patients diagnosed with uterine fibroids from two centers. The patients from one center were allocated to a training cohort (n = 122) and an internal test cohort (n = 46), and the data from the other center (n = 55) was used as an external test cohort. The least absolute shrinkage and selection operator (LASSO) algorithm was employed for feature selection in the training cohort. The support vector machine (SVM) was adopted to construct a radiomics model, a clinical model, and a radiomics-clinical model for NPVR prediction, respectively. The area under the curve (AUC) and the decision curve analysis (DCA) were performed to evaluate the predictive validity and the clinical usefulness of the model, respectively.

RESULTS

A total of 851 radiomic features were extracted from T2MRI, of which seven radiomics features were screened for NPVR prediction-related radiomics features. The radiomics-clinical model combining radiomics features and clinical parameters showed the best predictive performance in both the internal (AUC = 0.824, 95% CI 0.693-0.954) and external (AUC = 0.773, 95% CI 0.647-0.902) test cohorts, and the DCA also suggested the radiomics-clinical model had the highest net benefit.

CONCLUSIONS

The radiomics-clinical model could be applied to the NPVR prediction of patients with uterine fibroids treated by HIFU to provide an objective and effective method for selecting potential patients who would benefit from the treatment mostly.

Avoid non-diagnostic EUS-FNA: a DNN model as a possible gatekeeper to distinguish pancreatic lesions prone to inconclusive biopsy

OBJECTIVE

This work aimed to explore the utility of CT radiomics with machine learning for distinguishing the pancreatic lesions prone to non-diagnostic ultrasound-guided fine-needle aspiration (EUS-FNA).

METHODS

498 patients with pancreatic EUS-FNA were retrospectively reviewed [Development cohort: 147 pancreatic ductal adenocarcinoma (PDAC); Validation cohort: 37 PDAC]. Pancreatic lesions not PDAC were also tested exploratively. Radiomics extracted from contrast-enhanced CT was integrated with deep neural networks (DNN) after dimension reduction. The receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were performed for model evaluation. And, the explainability of the DNN model was analyzed by integrated gradients.

RESULTS

The DNN model was effective in distinguishing PDAC lesions prone to non-diagnostic EUS-FNA (Development cohort: AUC = 0.821, 95% CI: 0.742-0.900; Validation cohort: AUC = 0.745, 95% CI: 0.534-0.956). In all cohorts, the DNN model showed better utility than the logistic model based on traditional lesion characteristics with NRI >0 (p < 0.05). And, the DNN model had net benefits of 21.6% at the risk threshold of 0.60 in the validation cohort. As for the model explainability, gray-level co-occurrence matrix (GLCM) features contributed the most averagely and the first-order features were the most important in the sum attribution.

CONCLUSION

The CT radiomics-based DNN model can be a useful auxiliary tool for distinguishing the pancreatic lesions prone to nondiagnostic EUS-FNA and provide alerts for endoscopists preoperatively to reduce unnecessary EUS-FNA.

ADVANCES IN KNOWLEDGE

This is the first investigation into the utility of CT radiomics-based machine learning in avoiding non-diagnostic EUS-FNA for patients with pancreatic masses and providing potential pre-operative assistance for endoscopists.

A primer on artificial intelligence in pancreatic imaging

Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered set of self-correcting algorithms to develop a mathematical model that best fits the data. Radiomics converts imaging data into mineable features such as signal intensity, shape, texture, and higher-order features. Both methods have the potential to improve disease detection, characterization, and prognostication. This article reviews the current status of artificial intelligence in pancreatic imaging and critically appraises the quality of existing evidence using the radiomics quality score.

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.

Is the radiomics-clinical combined model helpful in distinguishing between pancreatic cancer and mass-forming pancreatitis?

PURPOSE

To develop CT-based radiomics models for distinguishing between resectable PDAC and mass-forming pancreatitis (MFP) and to provide a non-invasive tool for cases of equivocal imaging findings with EUS-FNA needed.

METHODS

A total of 201 patients with resectable PDAC and 54 patients with MFP were included. Development cohort: patients without preoperative EUS-FNA (175 PDAC cases, 38 MFP cases); validation cohort: patients with EUS-FNA (26 PDAC cases, 16 MFP cases). Two radiomic signatures (LASSOscore, PCAscore) were developed based on the LASSO model and principal component analysis. LASSOCli and PCACli prediction models were established by combining clinical features with CT radiomic features. ROC analysis and decision curve analysis (DCA) were performed to evaluate the utility of the model versus EUS-FNA in the validation cohort.

RESULTS

In the validation cohort, the radiomic signatures (LASSOscore, PCAscore) were both effective in distinguishing between resectable PDAC and MFP (AUC_LASSO = 0.743, 95% CI: 0.590-0.896; AUC_PCA = 0.788, 95% CI: 0.639-0.938) and improved the diagnostic accuracy of the baseline onlyCli model (AUC_onlyCli = 0.760, 95% CI: 0.614-0.960) after combination with variables including age, CA19-9, and the double-duct sign (AUC_PCACli = 0.880, 95% CI: 0.776-0.983; AUC_LASSOCli = 0.825, 95% CI: 0.694-0.955). The PCACli model showed comparable performance to FNA (AUC_FNA = 0.810, 95% CI: 0.685-0.935). In DCA, the net benefit of the PCACli model was superior to that of EUS-FNA, avoiding biopsies in 70 per 1000 patients at a risk threshold of 35%.

CONCLUSIONS

The PCACli model showed comparable performance with EUS-FNA in discriminating resectable PDAC from MFP.

Determining the reliable feature change in longitudinal radiomics studies: A methodological approach using the reliable change index

PURPOSE

Determination of reliable change of radiomics feature over time is essential and vital in delta-radiomics, but has not yet been rigorously examined. This study attempts to propose a methodological approach using reliable change index (RCI), a statistical metric to determine the reliability of quantitative biomarker changes by accounting for the baseline measurement standard error, in delta-radiomics. The use of RCI was demonstrated with the MRI data acquired from a group of prostate cancer (PCa) patients treated by 1.5 T MRI-guided radiotherapy (MRgRT).

METHODS

Fifty consecutive PCa patients who underwent five-fractionated MRgRT were retrospectively included, and 1023 radiomics features were extracted from the clinical target volume (CTV) and planning target volume (PTV). The two MRI datasets acquired at the first fraction (MRI11 and MRI21) were used to calculate the baseline feature reliability against image acquisition using intraclass correlation coefficient (ICC). The RCI was constructed based on the baseline feature measurement standard deviation, ICC, and feature value differences at two time points between the fifth (MRI51) and the first fraction MRI (MRI11). The reliable change of features was determined in each patient only if the calculated RCI was over 1.96 or smaller than -1.96. The feature changes between MRI51 and MRI11 were correlated to two patient-reported quality-of-life clinical endpoints of urinary domain summary score (UDSS) and bowel domain summary score (BDSS) in 35 patients using the Spearman correlation test. Only the significant correlations between a feature that was reliably changed in ≥7 patients (20%) by RCI and an endpoint were considered as true significant correlations.

RESULTS

The 352 (34.4%) and 386 (37.7%) features among all 1023 features were determined by RCI to be reliably changed in more than five (10%) patients in the CTV and PTV, respectively. Nineteen features were found reliably changed in the CTV and 31 features in the PTV, respectively, in 10 (20%) or more patients. These features were not necessarily associated with significantly different longitudinal feature values (group p-value < 0.05). Most reliably changed features in more than 10 patients had excellent or good baseline test-retest reliability ICC, while none showed poor reliability. The RCI method ruled out the features to be reliably changed when substantial feature measurement bias was presented. After applying the RCI criterion, only four and five true significant correlations were confirmed with UDSS and BDSS in the CTV, respectively, with low true significance correlation rates of 10.8% (4/37) and 17.9% (5/28). No true significant correlations were found in the PTV.

CONCLUSIONS

The RCI method was proposed for delta-radiomics and demonstrated using PCa MRgRT data. The RCI has advantages over some other statistical metrics commonly used in the previous delta-radiomics studies, and is useful to reliably identify the longitudinal radiomics feature change on an individual basis. This proposed RCI method should be helpful for the development of essential feature selection methodology in delta-radiomics.

First treatments for Lattice stereotactic body radiation therapy using magnetic resonance image guided radiation therapy

Two abdominal patients were treated with Lattice stereotactic body radiation therapy (SBRT) using magnetic resonance guided radiation therapy (MRgRT). This is one of the first reported treatments of Lattice SBRT with the use of MRgRT. A description of the treatment approach and planning considerations were incorporated into this report. MRgRT Lattice SBRT delivered similar planning quality metrics to established dosimetric parameters for Lattice SBRT. Increased signal intensity were seen in the MRI treatments for one of the patients during the course of treatment.

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

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

MRI-based delta-radiomic features for prediction of local control in liver lesions treated with stereotactic body radiation therapy

Real-time magnetic resonance image guided stereotactic ablative radiotherapy (MRgSBRT) is used to treat abdominal tumors. Longitudinal data is generated from daily setup images. Our study aimed to identify delta radiomic texture features extracted from these images to predict for local control in patients with liver tumors treated with MRgSBRT. Retrospective analysis of an IRB-approved database identified patients treated with MRgSBRT for primary liver and secondary metastasis histologies. Daily low field strength (0.35 T) images were retrieved, and the gross tumor volume was identified on each image. Next, images' gray levels were equalized, and 39 s-order texture features were extracted. Delta-radiomics were calculated as the difference between feature values on the initial scan and after delivered biological effective doses (BED, α/β = 10) of 20 Gy and 40 Gy. Then, features were ranked by the Gini Index during training of a random forest model. Finally, the area under the receiver operating characteristic curve (AUC) was estimated using a bootstrapped logistic regression with the top two features. We identified 22 patients for analysis. The median dose delivered was 50 Gy in 5 fractions. The top two features identified after delivery of BED 20 Gy were gray level co-occurrence matrix features energy and gray level size zone matrix based large zone emphasis. The model generated an AUC = 0.9011 (0.752-1.0) during bootstrapped logistic regression. The same two features were selected after delivery of a BED 40 Gy, with an AUC = 0.716 (0.600-0.786). Delta-radiomic features after a single fraction of SBRT predicted local control in this exploratory cohort. If confirmed in larger studies, these features may identify patients with radioresistant disease and provide an opportunity for physicians to alter management much sooner than standard restaging after 3 months. Expansion of the patient database is warranted for further analysis of delta-radiomic features.

Portable magnetic resonance imaging of patients indoors, outdoors and at home

Mobile medical imaging devices are invaluable for clinical diagnostic purposes both in and outside healthcare institutions. Among the various imaging modalities, only a few are readily portable. Magnetic resonance imaging (MRI), the gold standard for numerous healthcare conditions, does not traditionally belong to this group. Recently, low-field MRI technology companies have demonstrated the first decisive steps towards portability within medical facilities and vehicles. However, these scanners' weight and dimensions are incompatible with more demanding use cases such as in remote and developing regions, sports facilities and events, medical and military camps, or home healthcare. Here we present in vivo images taken with a light, small footprint, low-field extremity MRI scanner outside the controlled environment provided by medical facilities. To demonstrate the true portability of the system and benchmark its performance in various relevant scenarios, we have acquired images of a volunteer's knee in: (i) an MRI physics laboratory; (ii) an office room; (iii) outside a campus building, connected to a nearby power outlet; (iv) in open air, powered from a small fuel-based generator; and (v) at the volunteer's home. All images have been acquired within clinically viable times, and signal-to-noise ratios and tissue contrast suffice for 2D and 3D reconstructions with diagnostic value. Furthermore, the volunteer carries a fixation metallic implant screwed to the femur, which leads to strong artifacts in standard clinical systems but appears sharp in our low-field acquisitions. Altogether, this work opens a path towards highly accessible MRI under circumstances previously unrealistic.

Application of Magnetic Resonance Imaging in Neoadjuvant Treatment of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignant tumors of the human digestive system. Due to its insidious onset, many patients have already lost the opportunity for radical resection upon tumor diagnosis. In recent years, neoadjuvant treatment for patients with borderline resectable PDAC has been recommended by multiple guidelines to increase the resection rate of radical surgery and improve the postoperative survival. However, further developments are required to accurately assess the tumor response to neoadjuvant therapy and to select the population suitable for such treatment. Reductions in drug toxicity and the number of neoadjuvant cycles are also critical. At present, the clinical evaluation of neoadjuvant treatment is mainly based on several serological and imaging indicators; however, the unique characteristics of PDAC and the insufficient sensitivity and specificity of the markers render this system ineffective. The imaging evaluation system, magnetic resonance imaging (MRI), has its own unique imaging advantages compared with computed tomography (CT) and other imaging examinations. One key advantage is the ability to reflect the changes more rapidly in tumor tissue components, such as the degree of fibrosis, microvessel density, and tissue hypoxia. It can also perform multiparameter quantitative analysis of tumor tissue and changes, attributing to its increasingly important role in imaging evaluation, and potentially the evaluation of neoadjuvant treatment of pancreatic cancer, as several current articles have studied. At the same time, owing to the complexity of MRI and some of its limitations, its wider application is limited. Compared with CT imaging, few relevant studies have been conducted. In this review article, we will investigate and summarize the advantages, limitations, and future development of MRI in the evaluation of neoadjuvant treatment of PDAC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Predictive Value of Delta-Radiomics Texture Features in 0.35 Tesla Magnetic Resonance Setup Images Acquired During Stereotactic Ablative Radiotherapy of Pancreatic Cancer

Purpose

The purpose of this work is to explore delta-radiomics texture features for predicting response using setup images of pancreatic cancer patients treated with magnetic resonance image guided (MRI-guided) stereotactic ablative radiotherapy (SBRT).

Methods

The total biological effective dose (BED) was calculated for 30 patients treated with MRI-guided SBRT that delivered physical doses of 30–60 Gy in three to five fractions. Texture features were then binned into groups based upon BED per fraction by dividing BED by the number of fractions. Delta-radiomics texture features were calculated after delivery of 20 Gy BED (BED20 features) and 40 Gy BED (BED40 features). A random forest (RF) model was constructed using BED20 and then BED40 features to predict binary outcome. During model training, the Gini Index, a measure of a variable’s importance for accurate prediction, was calculated for all features, and the two features that ranked the highest were selected for internal validation. The two features selected from each bin were used in a bootstrapped logistic regression model to predict response and performance quantified using the area under the receiver operating characteristic curve (AUC). This process was an internal validation analysis.

Results

After RF model training, the Gini Index was highest for gray-level co-occurrence matrix-based (GLCM) sum average, and neighborhood gray tone difference matrix-based (NGTDM) busyness for BED20 features and gray-level size zone matrix-based (GLSZM) large zones low gray-level emphasis and gray-level run length matrix-based (GLRLM) run percentage was selected from the BED40-based features. The mean AUC obtained using the two BED20 features was AUC = 0.845 with the 2.5 percentile and 97.5 percentile values ranging from 0.794 to 0.856. Internal validation of the BED40 delta-radiomics features resulted in a mean AUC = 0.567 with a 2.5 and 97.5 percentile range of 0.502–0.675.

Conclusion

Early changes in treatment quantified with the BED20 delta-radiomics texture features in low field images acquired during MRI-guided SBRT demonstrated better performance in internal validation than features calculated later in treatment. Further analysis of delta-radiomics texture analysis in low field MRI is warranted.

Imaging-based Machine-learning Models to Predict Clinical Outcomes and Identify Biomarkers in Pancreatic Cancer: A Scoping Review

OBJECTIVE

To perform a scoping review of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC.

SUMMARY OF BACKGROUND DATA

Patients with PDAC could benefit from better selection for systemic and surgical therapy. Imaging-based machine-learning models may improve treatment selection.

METHODS

A scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses-scoping review guidelines in the PubMed and Embase databases (inception-October 2020). The review protocol was prospectively registered (open science framework registration: m4cyx). Included were studies on imaging-based machine-learning models for predicting clinical outcomes and identifying biomarkers for PDAC. The primary outcome was model performance. An area under the curve (AUC) of ≥0.75, or a P-value of ≤0.05, was considered adequate model performance. Methodological study quality was assessed using the modified radiomics quality score.

RESULTS

After screening 1619 studies, 25 studies with 2305 patients fulfilled the eligibility criteria. All but 1 study was published in 2019 and 2020. Overall, 23/25 studies created models using radiomics features, 1 study quantified vascular invasion on computed tomography, and one used histopathological data. Nine models predicted clinical outcomes with AUC measures of 0.78-0.95, and C-indices of 0.65-0.76. Seventeen models identified biomarkers with AUC measures of 0.68-0.95. Adequate model performance was reported in 23/25 studies. The methodological quality of the included studies was suboptimal, with a median modified radiomics quality score score of 7/36.

CONCLUSIONS

The use of imaging-based machine-learning models to predict clinical outcomes and identify biomarkers in patients with PDAC is increasingly rapidly. Although these models mostly have good performance scores, their methodological quality should be improved.

Robustness Assessment of Images From a 0.35T Scanner of an Integrated MRI-Linac: Characterization of Radiomics Features in Phantom and Patient Data

Purpose: Radiomics entails the extraction of quantitative imaging biomarkers (or radiomics features) hypothesized to provide additional pathophysiological and/or clinical information compared to qualitative visual observation and interpretation. This retrospective study explores the variability of radiomics features extracted from images acquired with the 0.35 T scanner of an integrated MRI-Linac. We hypothesized we would be able to identify features with high repeatability and reproducibility over various imaging conditions using phantom and patient imaging studies. We also compared findings from the literature relevant to our results. Methods: Eleven scans of a Magphan^® RT phantom over 13 months and 11 scans of a ViewRay Daily QA phantom over 11 days constituted the phantom data. Patient datasets included 50 images from ten anonymized stereotactic body radiation therapy (SBRT) pancreatic cancer patients (50 Gy in 5 fractions). A True Fast Imaging with Steady-State Free Precession (TRUFI) pulse sequence was selected, using a voxel resolution of 1.5 mm × 1.5 mm × 1.5 mm and 1.5 mm × 1.5 mm × 3.0 mm for phantom and patient data, respectively. A total of 1087 shape-based, first, second, and higher order features were extracted followed by robustness analysis. Robustness was assessed with the Coefficient of Variation (CoV < 5%). Results: We identified 130 robust features across the datasets. Robust features were found within each category, except for 2 second-order sub-groups, namely, Gray Level Size Zone Matrix (GLSZM) and Neighborhood Gray Tone Difference Matrix (NGTDM). Additionally, several robust features agreed with findings from other stability assessments or predictive performance studies in the literature. Conclusion: We verified the stability of the 0.35 T scanner of an integrated MRI-Linac for longitudinal radiomics phantom studies and identified robust features over various imaging conditions. We conclude that phantom measurements can be used to identify robust radiomics features. More stability assessment research is warranted.

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.

Delta radiomics analysis of Magnetic Resonance guided radiotherapy imaging data can enable treatment response prediction in pancreatic cancer

Background

Magnetic Resonance Image guided Stereotactic body radiotherapy (MRgRT) is an emerging technology that is increasingly used in treatment of visceral cancers, such as pancreatic adenocarcinoma (PDAC). Given the variable response rates and short progression times of PDAC, there is an unmet clinical need for a method to assess early RT response that may allow better prescription personalization. We hypothesize that quantitative image feature analysis (radiomics) of the longitudinal MR scans acquired before and during MRgRT may be used to extract information related to early treatment response.

Methods

Histogram and texture radiomic features (n = 73) were extracted from the Gross Tumor Volume (GTV) in 0.35T MRgRT scans of 26 locally advanced and borderline resectable PDAC patients treated with 50 Gy RT in 5 fractions. Feature ratios between first (F1) and last (F5) fraction scan were correlated with progression free survival (PFS). Feature stability was assessed through region of interest (ROI) perturbation.

Results

Linear normalization of image intensity to median kidney value showed improved reproducibility of feature quantification. Histogram skewness change during treatment showed significant association with PFS (p = 0.005, HR = 2.75), offering a potential predictive biomarker of RT response. Stability analyses revealed a wide distribution of feature sensitivities to ROI delineation and was able to identify features that were robust to variability in contouring.

Conclusions

This study presents a proof-of-concept for the use of quantitative image analysis in MRgRT for treatment response prediction and providing an analysis pipeline that can be utilized in future MRgRT radiomic studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01957-5.

Technical overview of magnetic resonance fingerprinting and its applications in radiation therapy

Magnetic resonance fingerprinting (MRF) is an emerging imaging technique for rapid and simultaneous quantification of multiple tissue properties. The technique has been developed for quantitative imaging of different organs. The obtained quantitative measures have the potential to improve multiple steps of a typical radiotherapy workflow and potentially further improve integration of magnetic resonance imaging guided clinical decision making. In this review paper, we first provide a technical overview of the MRF method from data acquisition to postprocessing, along with recent development in advanced reconstruction methods. We further discuss critical aspects that could influence its usage in radiation therapy, such as accuracy and precision, repeatability and reproducibility, geometric distortion, and motion robustness. Finally, future directions for MRF application in radiation therapy are discussed.

Reliability of MRI radiomics features in MR-guided radiotherapy for prostate cancer: Repeatability, reproducibility, and within-subject agreement

PURPOSE

The MR-guided radiotherapy (MRgRT) images on the integrated MRI and linear accelerator (MR-LINAC) might facilitate radiomics analysis for longitudinal treatment response assessment. However, the reliability of MRgRT radiomics features is largely unknown. This study aims to investigate MRgRT radiomics feature reliability acquired using a standardized 3D-T2W-TSE sequence in terms of repeatability, reproducibility, and within-subject feature agreement on a 1.5T MR-simulator and a 1.5T MR-LINAC for prostate cancer (PC).

METHODS

Twenty-six consecutive PC patients who underwent one MRI-simulator scan and two MR-LINAC scans before dose delivery were retrospectively included. The three MRI datasets were rigidly co-registered. 1023 first-order and texture radiomics features were extracted with different intensity bin widths for each scan in the manually segmented clinical target volume (CTV) and planning target volume (PTV) by an experienced radiation oncologist. Intraclass correlation coefficient (ICC) was used to evaluate feature repeatability between MR-LINAC scans and reproducibility between MRI-simulator and MR-LINAC scans. The within-subject feature value agreements were evaluated using Bland-Altman analysis. The impact of inter-observer segmentation on the radiomics feature reliability was also examined based on the second manual segmentation of CTV and PTV by an MRI researcher.

RESULTS

Based on the segmentation by the radiation oncologist and the default bin width of 25, 9.6%, 24.1%, 49.6%, and 16.8% of the total 1023 features exhibited excellent (ICC > 0.9), good (0.9 > ICC > 0.75), moderate (0.75 > ICC > 0.5), and poor (ICC < 0.5) repeatability in the CTV, and 9.2%, 26.8%, 50.5%, and 13.5% in the PTV, respectively. For reproducibility, the corresponding feature percentages were 8.9%, 19.7%, 41.9%, and 29.6% in the CTV, and 8.4%, 17.8%, 47.9%, and 26% in the PTV. Feature reliability was not notably influenced by intensity bin width for discretization. BA analysis revealed wide 95% limit-of-agreements and substantial biases of feature values between CTV and PTV and between any two MRI scans. The features even with excellent ICC were still subjected to considerable inter-scan feature variations in each individual subject. The analysis on the second segmentation by the MRI researcher showed insignificantly different feature repeatability and reproducibility in terms of ICC values.

CONCLUSIONS

Only a small proportion of features exhibited excellent/good repeatability and reproducibility, highlighting the importance of reliable MRgRT feature selection. The within-subject feature values were subjected to considerable inter-scan variations, imposing a challenge on the determination of the smallest detectable change in future MRgRT delta-radiomics studies.

Toward magnetic resonance fingerprinting for low-field MR-guided radiation therapy

PURPOSE

The acquisition of multiparametric quantitative magnetic resonance imaging (qMRI) is becoming increasingly important for functional characterization of cancer prior to- and throughout the course of radiation therapy. The feasibility of a qMRI method known as magnetic resonance fingerprinting (MRF) for rapid T₁ and T₂ mapping was assessed on a low-field MR-linac system.

METHODS

A three-dimensional MRF sequence was implemented on a 0.35T MR-guided radiotherapy system. MRF-derived measurements of T₁ and T₂ were compared to those obtained with gold standard single spin echo methods, and the impacts of the radiofrequency field homogeneity and scan times ranging between 6 and 48 min were analyzed by acquiring between 1 and 8 spokes per time point in a standard quantitative system phantom. The short-term repeatability of MRF was assessed over three measurements taken over a 10-h period. To evaluate transferability, MRF measurements were acquired on two additional MR-guided radiotherapy systems. Preliminary human volunteer studies were performed.

RESULTS

The phantom benchmarking studies showed that MRF is capable of mapping T₁ and T₂ values within 8% and 10% of gold standard measures, respectively, at 0.35T. The coefficient of variation of T₁ and T₂ estimates over three repeated scans was < 5% over a broad range of relaxation times. The T₁ and T₂ times derived using a single-spoke MRF acquisition across three scanners were near unity and mean percent errors in T₁ and T₂ estimates using the same phantom were < 3%. The mean percent differences in T₁ and T₂ as a result of truncating the scan time to 6 min over the large range of relaxation times in the system phantom were 0.65% and 4.05%, respectively.

CONCLUSIONS

The technical feasibility and accuracy of MRF on a low-field MR-guided radiation therapy device has been demonstrated. MRF can be used to measure accurate T₁ and T₂ maps in three dimensions from a brief 6-min scan, offering strong potential for efficient and reproducible qMRI for future clinical trials in functional plan adaptation and tumor/normal tissue response assessment.

[Artificial intelligence, radiomics and pathomics to predict response and survival of patients treated with radiations]

Artificial intelligence approaches in medicine are more and more used and are extremely promising due to the growing number of data produced and the variety of data they allow to exploit. Thus, the computational analysis of medical images in particular, radiological (radiomics), or anatomopathological (pathomics), has shown many very interesting results for the prediction of the prognosis and the response of cancer patients. Radiotherapy is a discipline that particularly benefits from these new approaches based on computer science and imaging. This review will present the main principles of an artificial intelligence approach and in particular machine learning, the principles of a radiomic and pathomic approach and the potential of their use for the prediction of the prognosis of patients treated with radiotherapy.

Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives

Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significantly improve the treatment in many situations. MRgRT systems may extend the management of inter- and intra-fraction anatomical changes, offering the possibility of online adaptation of the dose distribution according to daily patient anatomy and to directly monitor tumor motion during treatment delivery by means of a continuous cine MR acquisition. Online adaptive treatments require a multidisciplinary and well-trained team, able to perform a series of operations in a safe, precise and fast manner while the patient is waiting on the treatment couch. Artificial Intelligence (AI) is expected to rapidly contribute to MRgRT, primarily by safely and efficiently automatising the various manual operations characterizing online adaptive treatments. Furthermore, AI is finding relevant applications in MRgRT in the fields of image segmentation, synthetic CT reconstruction, automatic (on-line) planning and the development of predictive models based on daily MRI. This review provides a comprehensive overview of the current AI integration in MRgRT from a medical physicist's perspective. Medical physicists are expected to be major actors in solving new tasks and in taking new responsibilities: their traditional role of guardians of the new technology implementation will change with increasing emphasis on the managing of AI tools, processes and advanced systems for imaging and data analysis, gradually replacing many repetitive manual tasks.

Delta radiomics for rectal cancer response prediction using low field magnetic resonance guided radiotherapy: an external validation

INTRODUCTION

A recent study performed on 16 locally advanced rectal cancer (LARC) patients treated using magnetic resonance guided radiotherapy (MRgRT) has identified two delta radiomics features as predictors of clinical complete response (cCR) after neoadjuvant radio-chemotherapy (nCRT). This study aims to validate these features (ΔL_least and Δglnu) on an external larger dataset, expanding the analysis also for pathological complete response (pCR) prediction.

METHODS

A total of 43 LARC patients were enrolled: Gross Tumour Volume (GTV) was delineated on T2/T1* MR images acquired during MRgRT and the two delta features were calculated. Receiver Operating Characteristic (ROC) curve analysis was performed on the 16 cases of the original study and the best cut-off value was identified. The performance of ΔL_least and Δglnu was evaluated at the best cut-off value.

RESULTS

On the original dataset of 16 patients, ΔL_least reported an AUC of 0.81 for cCR and 0.93 for pCR, while Δglnu 0.72 and 0.54 respectively. The best cut-off values of ΔL_least was 0.73 for both outcomes, while Δglnu reported 0.54 for cCR and 0.93 for pCR. At the external validation, ΔL_least showed an accuracy of 81% for cCR and 79% for pCR, while Δglnu reported 63% for cCR and 40% for pCR.

CONCLUSION

The accuracy of ΔL_least in predicting cCR and pCR is significantly higher than those obtained considering Δglnu, but inferior if compared with other image-based biomarker, such as the early-regression index. Studies with larger cohorts of patients are recommended to further investigate the role of delta radiomic features in MRgRT.

MR-Guided Radiotherapy for Brain and Spine Tumors

MRI is the standard modality to assess anatomy and response to treatment in brain and spine tumors given its superb anatomic soft tissue contrast (e.g., T1 and T2) and numerous additional intrinsic contrast mechanisms that can be used to investigate physiology (e.g., diffusion, perfusion, spectroscopy). As such, hybrid MRI and radiotherapy (RT) devices hold unique promise for Magnetic Resonance guided Radiation Therapy (MRgRT). In the brain, MRgRT provides daily visualizations of evolving tumors that are not seen with cone beam CT guidance and cannot be fully characterized with occasional standalone MRI scans. Significant evolving anatomic changes during radiotherapy can be observed in patients with glioblastoma during the 6-week fractionated MRIgRT course. In this review, a case of rapidly changing symptomatic tumor is demonstrated for possible therapy adaptation. For stereotactic body RT of the spine, MRgRT acquires clear isotropic images of tumor in relation to spinal cord, cerebral spinal fluid, and nearby moving organs at risk such as bowel. This visualization allows for setup reassurance and the possibility of adaptive radiotherapy based on anatomy in difficult cases. A review of the literature for MR relaxometry, diffusion, perfusion, and spectroscopy during RT is also presented. These techniques are known to correlate with physiologic changes in the tumor such as cellularity, necrosis, and metabolism, and serve as early biomarkers of chemotherapy and RT response correlating with patient survival. While physiologic tumor investigations during RT have been limited by the feasibility and cost of obtaining frequent standalone MRIs, MRIgRT systems have enabled daily and widespread physiologic measurements. We demonstrate an example case of a poorly responding tumor on the 0.35 T MRIgRT system with relaxometry and diffusion measured several times per week. Future studies must elucidate which changes in MR-based physiologic metrics and at which timepoints best predict patient outcomes. This will lead to early treatment intensification for tumors identified to have the worst physiologic responses during RT in efforts to improve glioblastoma survival.

Quantitative Magnetic Resonance Imaging for Biological Image-Guided Adaptive Radiotherapy

MRI-guided radiotherapy systems have the potential to bring two important concepts in modern radiotherapy together: adaptive radiotherapy and biological targeting. Based on frequent anatomical and functional imaging, monitoring the changes that occur in volume, shape as well as biological characteristics, a treatment plan can be updated regularly to accommodate the observed treatment response. For this purpose, quantitative imaging biomarkers need to be identified that show changes early during treatment and predict treatment outcome. This review provides an overview of the current evidence on quantitative MRI measurements during radiotherapy and their potential as an imaging biomarker on MRI-guided radiotherapy systems.

Delta Radiomics Analysis for Local Control Prediction in Pancreatic Cancer Patients Treated Using Magnetic Resonance Guided Radiotherapy

The aim of this study is to investigate the role of Delta Radiomics analysis in the prediction of one-year local control (1yLC) in patients affected by locally advanced pancreatic cancer (LAPC) and treated using Magnetic Resonance guided Radiotherapy (MRgRT). A total of 35 patients from two institutions were enrolled: A 0.35 Tesla T2*/T1 MR image was acquired for each case during simulation and on each treatment fraction. Physical dose was converted in biologically effective dose (BED) to compensate for different radiotherapy schemes. Delta Radiomics analysis was performed considering the gross tumour volume (GTV) delineated on MR images acquired at BED of 20, 40, and 60 Gy. The performance of the delta features in predicting 1yLC was investigated in terms of Wilcoxon Mann-Whitney test and area under receiver operating characteristic (ROC) curve (AUC). The most significant feature in predicting 1yLC was the variation of cluster shade calculated at BED = 40 Gy, with a p-value of 0.005 and an AUC of 0.78 (0.61-0.94). Delta Radiomics analysis on low-field MR images might play a promising role in 1yLC prediction for LAPC patients: further studies including an external validation dataset and a larger cohort of patients are recommended to confirm the validity of this preliminary experience.

Impact of quantization algorithm and number of gray level intensities on variability and repeatability of low field strength magnetic resonance image-based radiomics texture features

PURPOSE

The purpose of this work was to investigate the impact of quantization preprocessing parameter selection on variability and repeatability of texture features derived from low field strength magnetic resonance (MR) images.

METHODS

Texture features were extracted from low field strength images of a daily image QA phantom with four texture inserts. Feature variability over time was quantified using all combinations of three quantization algorithms and four different numbers of gray level intensities. In addition, texture features were extracted using the same combinations from the low field strength MR images of the gross tumor volume (GTV) and left kidney of patients with repeated set up scans. The impact of region of interest (ROI) preprocessing on repeatability was investigated with a test-retest study design.

RESULTS

The phantom ROIs quantized to 64 Gy level intensities using the histogram equalization method resulted in the greatest number of features with the least variability. There was no clear method that resulted in the highest repeatability in the GTV or left kidney. However, eight texture features extracted from the GTV were repeatable regardless of ROI processing combination.

CONCLUSION

Low field strength MR images can provide a stable basis for texture analysis with ROIs quantized to 64 Gy levels using histogram equalization, but there is no clear optimal combination for repeatability.

The role of radiomics in prostate cancer radiotherapy

"Radiomics," as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.