The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.

Dosiomic-based prediction of dysgeusia in head & neck cancer patients treated with radiotherapy.

PURPOSE: To investigate the potential of dosiomics in predicting radiotherapy-induced taste distortion (dysgeusia) in head & neck (H&N) cancer. METHODS: A cohort of 80 H&N cancer patients treated with radical or adjuvant radiotherapy and with a follow-up of at least 24 months was enrolled. Treatment information, as well as tobacco and alcohol consumption were also collected. The whole tongue was manually delineated on the planning CT and mapped to the dose map retrieved from the treatment planning system. For every patient, 6 regions of the tongue were examined; for each of them, 145 dosiomic features were extracted from the dose map and fed to a logistic regression model to predict the grade of dysgeusia at follow-up, with and without including clinical features. A mean dose-based model was considered for reference. RESULTS: Both dosiomics and mean dose models achieved good prediction performance for acute dysgeusia with AUC up to 0.88. For the dosiomic model, the central and anterior ⅔ regions of the tongue were the most predictive. For all models, a gradual reduction in the performance was observed at later times for chronic dysgeusia prediction, with higher values for dosiomics. The inclusion of smoke and alcohol habits did not improve model performances. CONCLUSION: The dosiomic analysis of the dose to the tongue identified features able to predict acute dysgeusia. Dosiomics resulted superior to the conventional mean dose-based model for chronic dysgeusia prediction. Larger, prospective studies are needed to support these results before integrating dosiomics in radiotherapy planning.

Additional Value of PET and CT Image-Based Features in the Detection of Occult Lymph Node Metastases in Lung Cancer: A Systematic Review of the Literature.

Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement serves as a crucial prognostic factor for tailoring treatment approaches. Invasive methods, such as mediastinoscopy and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), are employed for preoperative LN staging. Among the preoperative non-invasive diagnostic methods, computed tomography (CT) and, recently, positron emission tomography (PET)/CT with fluorine-18-fludeoxyglucose ([18F]FDG) are routinely recommended by several guidelines; however, they can both miss pathologically proven LN metastases, with an incidence up to 26% for patients staged with [18F]FDG PET/CT. These undetected metastases, known as occult LN metastases (OLMs), are usually cases of micro-metastasis or small LN metastasis (shortest radius below 10 mm). Hence, it is crucial to find novel approaches to increase their discovery rate. Radiomics is an emerging field that seeks to uncover and quantify the concealed information present in biomedical images by utilising machine or deep learning approaches. The extracted features can be integrated into predictive models, as numerous reports have emphasised their usefulness in the staging of lung cancer. However, there is a paucity of studies examining the detection of OLMs using quantitative features derived from images. Hence, the objective of this review was to investigate the potential application of PET- and/or CT-derived quantitative radiomic features for the identification of OLMs.

Prediction models as decision-support tools for virtual patient-specific quality assurance of helical tomotherapy plans.

Background and purpose: Prediction models may be reliable decision-support tools to reduce the workload associated with the measurement-based patient-specific quality assurance (PSQA) of radiotherapy plans. This study compared the effectiveness of three different models based on delivery parameters, complexity metrics and sinogram radiomics features as tools for virtual-PSQA (vPSQA) of helical tomotherapy (HT) plans. Materials and methods: A dataset including 881 RT plans created with two different treatment planning systems (TPSs) was collected. Sixty-five indicators including 12 delivery parameters (DP) and 53 complexity metrics (CM) were extracted using a dedicated software library. Additionally, 174 radiomics features (RF) were extracted from the plans' sinograms. Three groups of variables were formed: A (DP), B (DP + CM) and C (DP + CM + RF). Regression models were trained to predict the gamma index passing rate P R γ (3%G, 2mm) and the impact of each group of variables was investigated. ROC-AUC analysis measured the ability of the models to accurately discriminate between 'deliverable' and 'non-deliverable' plans. Results: The best performance was achieved by model C which allowed detecting around 16% and 63% of the 'deliverable' plans with 100% sensitivity for the two TPSs, respectively. In a real clinical scenario, this would have decreased the whole PSQA workload by approximately 35%. Conclusions: The combination of delivery parameters, complexity metrics and sinogram radiomics features allows for robust and reliable PSQA gamma passing rate predictions and high-sensitivity detection of a fraction of deliverable plans for one of the two TPSs. Promising yet improvable results were obtained for the other one. The results foster a future adoption of vPSQA programs for HT.

Role of radiomic analysis of [18F]fluoromethylcholine PET/CT in predicting biochemical recurrence in a cohort of intermediate and high risk prostate cancer patients at initial staging.

AIM: To study the feasibility of radiomic analysis of baseline [18F]fluoromethylcholine positron emission tomography/computed tomography (PET/CT) for the prediction of biochemical recurrence (BCR) in a cohort of intermediate and high-risk prostate cancer (PCa) patients. MATERIAL AND METHODS: Seventy-four patients were prospectively collected. We analyzed three prostate gland (PG) segmentations (i.e., PGwhole: whole PG; PG41%: prostate having standardized uptake value - SUV > 0.41*SUVmax; PG2.5: prostate having SUV > 2.5) together with three SUV discretization steps (i.e., 0.2, 0.4, and 0.6). For each segmentation/discretization step, we trained a logistic regression model to predict BCR using radiomic and/or clinical features. RESULTS: The median baseline prostate-specific antigen was 11 ng/mL, the Gleason score was > 7 for 54% of patients, and the clinical stage was T1/T2 for 89% and T3 for 9% of patients. The baseline clinical model achieved an area under the receiver operating characteristic curve (AUC) of 0.73. Performances improved when clinical data were combined with radiomic features, in particular for PG2.5 and 0.4 discretization, for which the median test AUC was 0.78. CONCLUSION: Radiomics reinforces clinical parameters in predicting BCR in intermediate and high-risk PCa patients. These first data strongly encourage further investigations on the use of radiomic analysis to identify patients at risk of BCR. CLINICAL RELEVANCE STATEMENT: The application of AI combined with radiomic analysis of [18F]fluoromethylcholine PET/CT images has proven to be a promising tool to stratify patients with intermediate or high-risk PCa in order to predict biochemical recurrence and tailor the best treatment options. KEY POINTS: • Stratification of patients with intermediate and high-risk prostate cancer at risk of biochemical recurrence before initial treatment would help determine the optimal curative strategy. • Artificial intelligence combined with radiomic analysis of [18F]fluorocholine PET/CT images allows prediction of biochemical recurrence, especially when radiomic features are complemented with patients' clinical information (highest median AUC of 0.78). • Radiomics reinforces the information of conventional clinical parameters (i.e., Gleason score and initial prostate-specific antigen level) in predicting biochemical recurrence.

Prognostic value of preoperative CT scan derived body composition measures in resected pancreatic cancer.

BACKGROUND: It remains unclear whether preoperative body composition may affect the prognosis of pancreatic cancer patients undergoing surgery. The aim of the present study was to assess the extent to which preoperative body composition impacts on postoperative complication severity and survival in patients undergoing pancreatoduodenectomy for pancreatic ductal adenocarcinoma (PDAC). METHODS: A retrospective cohort study was performed on consecutive patients who underwent pancreatoduodenectomy with preoperative CT scan imaging available. Body composition parameters including total abdominal muscle area (TAMA), visceral fat area (VFA), subcutaneous fat area and liver steatosis (LS) were assessed. Sarcopenic obesity was defined as a high VFA/TAMA ratio. Postoperative complication burden was evaluated with the comprehensive complication index (CCI). RESULTS: Overall, 371 patients were included in the study. At 90 days after surgery, 80 patients (22%) experienced severe complications. The median CCI was 20.9 (IQR 0-30). At multivariate linear regression analysis, preoperative biliary drainage, ASA score ≥3, fistula risk score and sarcopenic obesity (37% increase; 95%CI 0.06-0.74; p = 0.046) were associated to an increase in CCI. Patient characteristics associated to sarcopenic obesity were older age, male gender and preoperative LS. At a median follow-up of 25 months (IQR 18-49), median disease-free survival (DFS) was 19 months (IQR 15-22). At cox-regression analysis, only pathological features were associated with DFS, while LS and other body composition measures did not show any prognostic role. CONCLUSION: The combination of sarcopenia and visceral obesity was significantly associated with increased complication severity after pancreatoduodenectomy for cancer. Patients' body composition did not affect disease free survival after pancreatic cancer surgery.

Sarcopenia and Patient's Body Composition: New Morphometric Tools to Predict Clinical Outcome After Ivor Lewis Esophagectomy: a Multicenter Study.

BACKGROUND: The impact of preoperative body composition as independent predictor of prognosis for esophageal cancer patients after esophagectomy is still unclear. The aim of the study was to explore such a relationship. METHODS: This is a multicenter retrospective study from a prospectively maintained database. We enrolled consecutive patients who underwent Ivor-Lewis esophagectomy in four Italian high-volume centers from May 2014. Body composition parameters including total abdominal muscle area (TAMA), visceral fat area (VFA), and subcutaneous fat area (SFA) were determined based on CT images. Perioperative variables were systematically collected. RESULTS: After exclusions, 223 patients were enrolled and 24.2% had anastomotic leak (AL). Sixty-eight percent of patients were sarcopenic and were found to be more vulnerable in terms of postoperative 90-day mortality (p = 0.028). VFA/TAMA and VFA/SFA ratios demonstrated a linear correlation with the Clavien-Dindo classification (R = 0.311 and 0.239, respectively); patients with anastomotic leak (AL) had significantly higher VFA/TAMA (3.56 ± 1.86 vs. 2.75 ± 1.83, p = 0.003) and VFA/SFA (1.18 ± 0.68 vs. 0.87 ± 0.54, p = 0.002) ratios. No significant correlation was found between preoperative BMI and subsequent AL development (p = 0.159). Charlson comorbidity index correlated significantly with AL (p = 0.008): these patients had a significantly higher index (≥ 5). CONCLUSION: Analytical morphometric assessment represents a useful non-invasive tool for preoperative risk stratification. The concurrent association of sarcopenia and visceral obesity seems to be the best predictor of AL, far better than simple BMI evaluation, and potentially modifiable if targeted with prehabilitation programs.

The ImSURE phantoms: a digital dataset for radiomic software benchmarking and investigation.

In radiology and oncology, radiomic models are increasingly employed to predict clinical outcomes, but their clinical deployment has been hampered by lack of standardisation. This hindrance has driven the international Image Biomarker Standardisation Initiative (IBSI) to define guidelines for image pre-processing, standardise the formulation and nomenclature of 169 radiomic features and share two benchmark digital phantoms for software calibration. However, to better assess the concordance of radiomic tools, more heterogeneous phantoms are needed. We created two digital phantoms, called ImSURE phantoms, having isotropic and anisotropic voxel size, respectively, and 90 regions of interest (ROIs) each. To use these phantoms, we designed a systematic feature extraction workflow including 919 different feature values (obtained from the 169 IBSI-standardised features considering all possible combinations of feature aggregation and intensity discretisation methods). The ImSURE phantoms will allow to assess the concordance of radiomic software depending on interpolation, discretisation and aggregation methods, as well as on ROI volume and shape. Eventually, we provide the feature values extracted from these phantoms using five open-source IBSI-compliant software.

Image-derived Input Function in brain [18F]FDG PET data: which alternatives to the carotid siphons?

Quantification of brain [18F] fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) data requires an input function. A noninvasive alternative to gold-standard arterial sampling is the image-derived input function (IDIF), typically extracted from the internal carotid arteries (ICAs), which are however difficult to segment and subjected to spillover effects. In this work, we evaluated the feasibility of extracting the IDIF from two different vascular sites, i.e., 1) common carotids (CCA) and 2) superior sagittal sinus (SSS), other than 3) ICA in a large group of glioma patients undergoing a dynamic [18F]FDG PET acquisition on a hybrid PET/MR scanner. Comparisons are drawn between the different IDIFs in terms of peak amplitude and shape, as well as between the estimates of fractional uptake rate (Kr) obtained from the different extraction sites in terms of a) grey/white matter average absolute values, b) ratio of grey-to-white matter, and c) spatial patterns for the hemisphere contralateral to the lesion. Clinical Relevance - This work points towards new feasible IDIF extraction sites (CCA in particular) which could allow for fully noninvasive absolute PET quantification in clinical populations.

Radiomics and deep learning methods for the prediction of 2-year overall survival in LUNG1 dataset.

In this study, we tested and compared radiomics and deep learning-based approaches on the public LUNG1 dataset, for the prediction of 2-year overall survival (OS) in non-small cell lung cancer patients. Radiomic features were extracted from the gross tumor volume using Pyradiomics, while deep features were extracted from bi-dimensional tumor slices by convolutional autoencoder. Both radiomic and deep features were fed to 24 different pipelines formed by the combination of four feature selection/reduction methods and six classifiers. Direct classification through convolutional neural networks (CNNs) was also performed. Each approach was investigated with and without the inclusion of clinical parameters. The maximum area under the receiver operating characteristic on the test set improved from 0.59, obtained for the baseline clinical model, to 0.67 ± 0.03, 0.63 ± 0.03 and 0.67 ± 0.02 for models based on radiomic features, deep features, and their combination, and to 0.64 ± 0.04 for direct CNN classification. Despite the high number of pipelines and approaches tested, results were comparable and in line with previous works, hence confirming that it is challenging to extract further imaging-based information from the LUNG1 dataset for the prediction of 2-year OS.

A Novel Benchmarking Approach to Assess the Agreement among Radiomic Tools.

Background The translation of radiomic models into clinical practice is hindered by the limited reproducibility of features across software and studies. Standardization is needed to accelerate this process and to bring radiomics closer to clinical deployment. Purpose To assess the standardization level of seven radiomic software programs and investigate software agreement as a function of built-in image preprocessing (eg, interpolation and discretization), feature aggregation methods, and the morphological characteristics (ie, volume and shape) of the region of interest (ROI). Materials and Methods The study was organized into two phases: In phase I, the two Image Biomarker Standardization Initiative (IBSI) phantoms were used to evaluate the IBSI compliance of seven software programs. In phase II, the reproducibility of all IBSI-standardized radiomic features across tools was assessed with two custom Italian multicenter Shared Understanding of Radiomic Extractors (ImSURE) digital phantoms that allowed, in conjunction with a systematic feature extraction, observations on whether and how feature matches between program pairs varied depending on the preprocessing steps, aggregation methods, and ROI characteristics. Results In phase I, the software programs showed different levels of completeness (ie, the number of computable IBSI benchmark values). However, the IBSI-compliance assessment revealed that they were all standardized in terms of feature implementation. When considering additional preprocessing steps, for each individual program, match percentages fell by up to 30%. In phase II, the ImSURE phantoms showed that software agreement was dependent on discretization and aggregation as well as on ROI shape and volume factors. Conclusion The agreement of radiomic software varied in relation to factors that had already been standardized (eg, interpolation and discretization methods) and factors that need standardization. Both dependences must be resolved to ensure the reproducibility of radiomic features and to pave the way toward the clinical adoption of radiomic models. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Steiger in this issue. An earlier incorrect version appeared online and in print. This article was corrected on March 2, 2022.

Additional Value of PET Radiomic Features for the Initial Staging of Prostate Cancer: A Systematic Review from the Literature.

We performed a systematic review of the literature to provide an overview of the application of PET radiomics for the prediction of the initial staging of prostate cancer (PCa), and to discuss the additional value of radiomic features over clinical data. The most relevant databases and web sources were interrogated by using the query "prostate AND radiomic* AND PET". English-language original articles published before July 2021 were considered. A total of 28 studies were screened for eligibility and 6 of them met the inclusion criteria and were, therefore, included for further analysis. All studies were based on human patients. The average number of patients included in the studies was 72 (range 52-101), and the average number of high-order features calculated per study was 167 (range 50-480). The radiotracers used were [68Ga]Ga-PSMA-11 (in four out of six studies), [18F]DCFPyL (one out of six studies), and [11C]Choline (one out of six studies). Considering the imaging modality, three out of six studies used a PET/CT scanner and the other half a PET/MRI tomograph. Heterogeneous results were reported regarding radiomic methods (e.g., segmentation modality) and considered features. The studies reported several predictive markers including first-, second-, and high-order features, such as "kurtosis", "grey-level uniformity", and "HLL wavelet mean", respectively, as well as PET-based metabolic parameters. The strengths and weaknesses of PET radiomics in this setting of disease will be largely discussed and a critical analysis of the available data will be reported. In our review, radiomic analysis proved to add useful information for lesion detection and the prediction of tumor grading of prostatic lesions, even when they were missed at visual qualitative assessment due to their small size; furthermore, PET radiomics could play a synergistic role with the mpMRI radiomic features in lesion evaluation. The most common limitations of the studies were the small sample size, retrospective design, lack of validation on external datasets, and unavailability of univocal cut-off values for the selected radiomic features.

Exploratory Analysis of 18F-3'-deoxy-3'-fluorothymidine (18F-FLT) PET/CT-Based Radiomics for the Early Evaluation of Response to Neoadjuvant Chemotherapy in Patients With Locally Advanced Breast Cancer.

PURPOSE: The objective of this study was to evaluate a set of radiomics-based advanced textural features extracted from 18F-FLT-PET/CT images to predict tumor response to neoadjuvant chemotherapy (NCT) in patients with locally advanced breast cancer (BC). MATERIALS AND METHODS: Patients with operable (T2-T3, N0-N2, M0) or locally advanced (T4, N0-N2, M0) BC were enrolled. All patients underwent chemotherapy (six cycles every 3 weeks). Surgery was performed within 4 weeks of the end of NCT. The MD Anderson Residual Cancer Burden calculator was used to evaluate the pathological response. 18F-FLT-PET/CT was performed 2 weeks before the start of NCT and approximately 3 weeks after the first cycle. The evaluation of PET response was based on EORTC criteria. Standard uptake value (SUV) statistics (SUVmax, SUVpeak, SUVmean), together with 148 textural features, were extracted from each lesion. Indices that are robust against contour variability (ICC test) were used as independent variables to logistically model tumor response. LASSO analysis was used for variable selection. RESULTS: Twenty patients were included in the study. Lesions from 15 patients were evaluable and analyzed: 9 with pathological complete response (pCR) and 6 with pathological partial response (pPR). Concordance between PET response and histological examination was found in 13/15 patients. LASSO logistic modelling identified a combination of SUVmax and the textural feature index IVH_VolumeIntFract_90 as the most useful to classify PET response, and a combination of PET response, ID range, and ID_Coefficient of Variation as the most useful to classify pathological response. CONCLUSIONS: Our study suggests the potential usefulness of FLT-PET for early monitoring of response to NCT. A model based on PET radiomic characteristics could have good discriminatory capacity of early response before the end of treatment.

Typical values for pediatric interventional cardiology catheterizations: A standardized approach towards Diagnostic Reference Level establishment.

PURPOSE: To define weight-stratified Diagnostic Reference Levels (DRL) typical values for pediatric interventional cardiology (IC) procedures adopting standardized methodologies proposed by ICRP135 and RP185. METHODS: Procedures performed at the pediatric catheterization room of the University-Hospital of Padua were analysed. Patients were stratified into body weight (BW) classes and DRL quantities were analysed for the most performed procedures. Typical values are defined as median PKA and Ka,r. For database consistency, sampling and exclusion methods were precisely defined. The DRL-curve methodology by means of quantile regression median curves was investigated to assess the relationship between PKA and weight. A like-to-like comparison with literature was made. RESULTS: 385 procedures were analysed. A large PKA variability was observed in each weight group. PKA differences across BW groups were not always statistically significant. When stratifying by procedure, PKA variability decreased while correlations of PKA and PKA/FT with weight increased. The established typical values are generally lower than DRLs published data, whatever stratification method adopted. The highest PKA median values were observed for Angioplasty (4.9 and 11.6 Gycm2 for 5-<15 kg and 15-<30 kg, respectively). The DRL-curve approach shows promising results for Valvuloplasty and Angioplasty. CONCLUSIONS: Typical values for pediatric IC DRL quantities were determined according to ICRP135 and RP185 methodologies. Stratification by BW classification does not reduce the variability of the PKA values, unlike what happens when stratifying by procedure type. Results seem to corroborate that variability and exposure are more affected by procedure type and complexity than by patient weight. DRL-curve is a feasible approach.

Limiting treatment plan complexity by applying a novel commercial tool.

PURPOSE: A recently introduced commercial tool is tested to assess whether it is able to reduce the complexity of a treatment plan and improve deliverability without compromising overall quality. METHODS: Ten prostate and ten oropharynx plans of previously treated patients were reoptimized using the aperture shape controller (ASC) tool recently introduced in Eclipse TPS (Varian Medical Systems, Palo Alto, CA). The performance of ASC was assessed in terms of the overall plan quality using a plan quality metric, the reduction in plan complexity through the analysis of 14 of the most common plan complexity metrics, and the change in plan deliverability through 3D dosimetric measurements. Similarly, plans optimized limiting the total number of delivered monitor units was assessed and compared. The two strategies were also combined to assess their potential combination. RESULTS: The plans optimized by exploiting the ASC generally show a reduced number of total Monitor Units, a more constant gantry rotation and a MLC modulation characterized by larger and less complicated shapes with leaves traveling shorter overall lengths. CONCLUSIONS: This first experience suggests that the ASC is an effective tool to reduce the unnecessary complexity of a plan. This turns into an increased plan deliverability with no loss of plan quality.

Technical Note: An IBEX adaption toward image biomarker standardization.

PURPOSE: Interest in the field of radiomics is rapidly growing because of its potential to characterize tumor phenotype and provide predictive and prognostic information. Nevertheless, the reproducibility and robustness of radiomics studies are hampered by the lack of standardization in feature definition and calculation. In the context of the image biomarker standardization initiative (IBSI), we investigated the grade of compliance of the image biomarker explorer (IBEX), a free open-source radiomic software, and we developed and validated standardized-IBEX (S-IBEX), an adaptation of IBEX to IBSI. METHODS: Image biomarker explorer source code was checked against IBSI standard. Both the feature implementation and the overall image preprocessing chain were evaluated. Sections were re-implemented wherever differences emerged: in particular, contour-to-binary-mask conversion, image sub-portion extraction, re-segmentation, gray-level discretization and interpolation were aligned to IBSI. All reported IBSI features were implemented in S-IBEX. On a patient phantom, S-IBEX was validated by benchmarking five different preprocessing configurations proposed by IBSI. RESULTS: Most IBEX feature definitions are IBSI compliant; however, IBEX preprocessing introduces non-negligible nonconformities, resulting in feature values not aligned with the corresponding IBSI benchmarks. On the contrary, S-IBEX features are in agreement with the standard regardless of preprocessing configurations: the percentage of features equal to their benchmark values ranges from 98.1% to 99.5%, with overall maximum percentage error below 1%. Moreover, the impact of noncompliant preprocessing steps has been assessed: in these cases, the percentage of features equal to the standard drops below 35%. CONCLUSIONS: The use of standardized software for radiomic feature extraction is essential to ensure the reproducibility of results across different institutions, easing at the same time their external validation. This work presents and validates S-IBEX, a free IBSI-compliant software, developed upon IBEX, for feature extraction that is both easy to use and quantitatively accurate.

Impact of acquisition count statistics reduction and SUV discretization on PET radiomic features in pediatric 18F-FDG-PET/MRI examinations.

PURPOSE: The evaluation of features robustness with respect to acquisition and post-processing parameter changes is fundamental for the reliability of radiomics studies. The aim of this study was to investigate the sensitivity of PET radiomic features to acquisition statistics reduction and standardized-uptake-volume (SUV) discretization in PET/MRI pediatric examinations. METHODS: Twenty-seven lesions were detected from the analysis of twenty-one 18F-FDG-PET/MRI pediatric examinations. By decreasing the count-statistics of the original list-mode data (3 MBq/kg), injected activity reduction was simulated. Two SUV discretization approaches were applied: 1) resampling lesion SUV range into fixed bins numbers (FBN); 2) rounding lesion SUV into fixed bin size (FBS). One hundred and six radiomic features were extracted. Intraclass Correlation Coefficient (ICC), Spearman correlation coefficient and coefficient-of-variation (COV) were calculated to assess feature reproducibility between low tracer activities and full tracer activity feature values. RESULTS: More than 70% of Shape and first order features, and around 70% and 40% of textural features, when using FBS and FBN methods respectively, resulted robust till 1.2 MBk/kg. Differences in median features reproducibility (ICC) between FBS and FBN datasets were statistically significant for every activity level independently from bin number/size, with higher values for FBS. Differences in median Spearman coefficient (i.e. patient ranking according to feature values) were not statistically significant, varying the intensity resolution (i.e. bin number/size) for either FBS and FBN methods. CONCLUSIONS: For each simulated count-statistic level, robust PET radiomic features were determined for pediatric PET/MRI examinations. A larger number of robust features were detected when using FBS methods.

Simulation and optimization models for emergency medical systems planning.

The authors address strategic planning problems for emergency medical systems (EMS). In particular, the three following critical decisions are considered: i) how many ambulances to deploy in a given territory at any given point in time, to meet the forecasted demand, yielding an appropriate response time; ii) when ambulances should be used for serving nonurgent requests and when they should better be kept idle for possible incoming urgent requests; iii) how to define an optimal mix of contracts for renting ambulances from private associations to meet the forecasted demand at minimum cost. In particular, analytical models for decision support, based on queuing theory, discrete-event simulation, and integer linear programming were presented. Computational experiments have been done on real data from the city of Milan, Italy.