Technical note: A software tool to extract complexity metrics from radiotherapy treatment plans.

BACKGROUND: Complexity metrics are mathematical quantities designed to quantify aspects of radiotherapy treatment plans that may affect both their deliverability and dosimetric accuracy. Despite numerous studies investigating their utility, there remains a notable absence of shared tools for their extraction. PURPOSE: This study introduces UCoMX (Universal Complexity Metrics Extractor), a software package designed for the extraction of complexity metrics from the DICOM-RT plan files of radiotherapy treatments. METHODS: UCoMX is developed around two extraction engines: VCoMX (VMAT Complexity Metrics Extractor) for VMAT/IMRT plans, and TCoMX (Tomotherapy Complexity Metrics Extractor) tailored for Helical Tomotherapy plans. The software, built using Matlab, is freely available in both Matlab-based and stand-alone versions. More than 90 complexity metrics, drawn from relevant literature, are implemented in the package: 43 for VMAT/IMRT and 51 for Helical Tomotherapy. RESULTS: The package is designed to read DICOM-RT plan files generated by most commercially available Treatment Planning Systems (TPSs), across various treatment units. A reference dataset containing VMAT, IMRT, and Helical Tomotherapy plans is provided to serve as a reference for comparing UCoMX with other in-house systems available at other centers. CONCLUSION: UCoMX offers a straightforward solution for extracting complexity metrics from radiotherapy plans. Its versatility is enhanced through different versions, including Matlab-based and stand-alone, and its compatibility with a wide range of commercially available TPSs and treatment units. UCoMX presents a free, user-friendly tool empowering researchers to compute the complexity of treatment plans efficiently.

Performance evaluation of the 3D-ring cadmium-zinc-telluride (CZT) StarGuide system according to the NEMA NU 1-2018 standard.

BACKGROUND: The application of semi-conductor detectors such as cadmium-zinc-telluride (CZT) in nuclear medicine improves extrinsic energy resolution and count sensitivity due to the direct conversion of gamma photons into electric signals. A 3D-ring pixelated CZT system named StarGuide was recently developed and implemented by GE HealthCare for SPECT acquisition. The system consists of 12 detector columns with seven modules of 16 × 16 CZT pixelated crystals, each with an integrated parallel-hole tungsten collimator. The axial coverage is 27.5 cm. The detector thickness is 7.25 mm, which allows acquisitions in the energy range [40-279] keV. Since there is currently no performance characterization specific to 3D-ring CZT SPECT systems, the National Electrical Manufacturers Association (NEMA) NU 1-2018 clinical standard can be tailored to these cameras. The aim of this study was to evaluate the performance of the SPECT/CT StarGuide system according to the NEMA NU 1-2018 clinical standard specifically adapted to characterize the new 3D-ring CZT. RESULTS: Due to the integrated collimator, the system geometry and the pixelated nature of the detector, some NEMA tests have been adapted to the features of the system. The extrinsic measured energy resolution was about 5-6% for the tested isotopes (99mTc, 123I and 57Co); the maximum count rate was 760 kcps and the observed count rate at 20% loss was 917 kcps. The system spatial resolution in air extrapolated at 10 cm with 99mTc was 7.2 mm, while the SPECT spatial resolutions with scatter were 4.2, 3.7 and 3.6 mm in a central, radial and tangential direction respectively. Single head sensitivity value for 99mTc was 97 cps/MBq; with 12 detector columns, the system volumetric sensitivity reached 520 kcps MBq-1 cc-1. CONCLUSIONS: The performance tests of the StarGuide can be performed according to the NEMA NU 1-2018 standard with some adaptations. The system has shown promising results, particularly in terms of energy resolution, spatial resolution and volumetric sensitivity, potentially leading to higher quality clinical images.

Standardized diagnostic reference levels for paediatric interventional cardiology: Data from an Italian referral centre.

PURPOSE: To provide data on radiation exposure in paediatric interventional cardiology procedures, addressing the scarcity of valuable Local Diagnostic Reference Levels (LDRLs),established according to the standardized approach proposed by the Radiation Protection 185 report (RP185). METHODS: Paediatric catheterization procedures conducted at the University-Hospital of Padua from September 2019 to December 2022 were stratified by body weight (BW) classes and procedure type. LDRLs were calculated for groups with at least 20 patients as the 75th percentile of Kerma-Area Product (PKA) and Air Kerma at reference point (Ka,r) values. Kruskal-Wallis test was applied to evaluate differences in the dose-related quantities among BW groups for a selected procedure and among procedures for the same BW class. Results were compared with recent literature. RESULTS: A total of 838 procedures were analysed. LDRL were provided for five therapeutic procedures. The 75th percentile of PKA and Ka,r increases with weight, regardless procedure type. PKA and Ka,r are generally statistically different between BW groups, for both diagnostic and therapeutic procedures, and between different procedures at fixed weight group. Angioplasty and Right Ventricular Outflow Tract treatments (PVR) showed exposure values approximately doubled then other procedures. PKA/(BW·FT) is not statistically different among procedures except for Atrial Septal Defect (ASD) closures. LDRL values from this study are generally lower than the published ones. CONCLUSIONS: The study stands out as one of the few that presents a considerable number of LDRLs for weight categories and procedure types with a sample size of at least 20 patients per group, in agreement with RP185. PKA shows strong correlation with the product BW·FT.

On the necessity of specialized knowledge-based models for SBRT prostate treatments plans.

PURPOSE: Test whether a well-grounded KBP model trained on moderately hypo-fractionated prostate treatments can be used to satisfactorily drive the optimization of SBRT prostate treatments. MATERIALS AND METHODS: A KBP model (SBRT-model) was developed, trained and validated using the first forty-seven clinically treated VMAT SBRT prostate plans (42.7 Gy/7fx or 36.25 Gy/5fx). The performance and robustness of this model were compared against a high-quality KBP-model (ST-model) that was already clinically adopted for hypo-fractionated (70 Gy/28fx and 60 Gy/20fx) prostate treatments. The two models were compared in terms of their predictions robustness, and the quality of their outcomes were evaluated against a set of reference clinical SBRT plans. Plan quality was assessed using DVH metrics, blinded clinical ranking, and a dedicated Plan Quality Metric algorithm. RESULTS: The plan libraries of the two models were found to share a high degree of anatomical similarity. The overall quality (APQM%) of the plans obtained both with the ST- and SBRT-models was compatible with that of the original clinical plans, namely (93.7 ± 4.1)% and (91.6 ± 3.9)% vs (92.8.9 ± 3.6)%. Plans obtained with the ST-model showed significantly higher target coverage (PTV V95%): (97.9 ± 0.8)% vs (97.1 ± 0.9)% (p < 0.05). Conversely, plans optimized following the SBRT-model showed a small but not-clinically relevant increase in OAR sparing. ST-model generally provided more reliable predictions than SBRT-model. Two radiation oncologists judged as equivalent the plans based on the KBP prediction, which was also judged better that reference clinical plans. CONCLUSION: A KBP model trained on moderately fractionated prostate treatment plans provided optimal SBRT prostate plans, with similar or larger plan quality than an embryonic SBRT-model based on a limited number of cases.

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.

Impact of patient's habitus on image quality and quantitative metrics in 18F-FDG PET/CT images.

PURPOSE: To study how the quantitative parameters of 18F-FDG PET imaging change with the emission scan duration (ESD) and the body-mass-index (BMI) in phantom and patients on a time-of-flight (TOF)-PET/CT system. METHODS: The image-quality phantom with (b-NEMA-IQ, BMI = 29.2 kg/m2) and without (NEMA-IEC, BMI = 21.4 kg/m2) a 'belt' of water-bags was filled with 18F-FDG activities to obtain nominal standardized uptake values (SUV) of 19, 8 and 5. Patients with BMI ≤ 25 kg/m2 (L-BMI) and BMI > 25 kg/m2 (H-BMI) were enrolled in this study. Phantom and patients underwent list-mode PET acquisition at 120 s/bed-position. Images reconstructed with clinical protocol and different ESD (120, 90, 75, 60, 45, 30 s) were analysed for comparison of maximum SUV (SUVmax), maximum standardized uptake value lean-body-mass corrected (SULmax) and noise. RESULTS: 79 oncologic patients (45 L-BMI, 44 H-BMI) were analysed. From 90 s to 30 s, an increasing variation of SUVmax and SULmax with respect to the reference 120 s time was observed, from 18% to 60% and from 16% to 37% for phantom and patients, respectively. SUVmax values were significantly higher (+50%) in b-NEMA-IQ than NEMA-IQ phantom and in H-BMI (+33%) than L-BMI patients. No significant difference was found in SULmax for the two BMI categories in both phantom and patients. CV values decreased when increasing ESD, being higher in H-BMI patients (0.13-0.25) and b-NEMA-IQ phantom (0.15-0.28) than in L-BMI patients (0.11-0.21) and NEMA-IQ phantom (0.11-0.20). CONCLUSIONS: Reduction of ESD may severely impact on the variations of SUVmax and SULmax in 18F-FDG PET/CT imaging. This study confirms recommendations of using SUL for lesion uptake quantification, being unaffected by BMI variation.

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.

Effectiveness of body size stratification for patient exposure optimization in Computed Tomography.

PURPOSE: To establish size-dependent DRL and to estimate the effectiveness of the size-dependent DRLs over size-independent DRLs for a CT exposure optimization process. METHODS: The study included 16,933 adult CT body examinations of the most common CT protocols. Acquisitions were included following an image quality assessment. Patients were grouped into five different classes by means of the water equivalent diameter (Dw): 21 ≤ Dw < 25, 25 ≤ Dw < 29, 29 ≤ Dw < 33,33 ≤ Dw < 37 (in cm). CTDIvol, DLP, DLPtot. and SSDE median values were provided both for the sample as a whole (size-independent approach) and for each Dw class (size-dependent approach). The performance of the two approaches in classifying sub-optimal examinations was evaluated through the confusion matrix and Matthews Correlation Coefficient (MCC) metric. The 75th percentile of the CTDIvol distribution was arbitrarily chosen as a threshold level above which the acquisitions are considered sub-optimal. RESULTS: CTDIvol, DLP, DLPtot and SSDE typical values (median values) are statistically different across Dw groups. The confusion matrix analysis suggests that size-independent DRL could not mark potential suboptimal protocols for small and large patients. The agreement between the size-dependent and size-independent methods is strong only for the most populous classes (MCC > 0.7). For small and large patients size-independent approach fails to identify as sub-optimal around 20 % of the acquisition (MCC≪0.2). CONCLUSIONS: It was proven by means of the confusion matrix and MCC metric that stratifying DRLs by patient size, size-dependent DRL can be a powerful strategy in order to improve the dose optimization process shown that a size-independent DRL fails to identify sub-optimal examinations for small and large patients.

Updating a clinical Knowledge-Based Planning prediction model for prostate radiotherapy.

BACKGROUND AND PURPOSE: Clinical knowledge-based planning (KBP) models dedicated to prostate radiotherapy treatment may require periodical updates to remain relevant and to adapt to possible changes in the clinic. This study proposes a paired comparison of two different update approaches through a longitudinal analysis. MATERIALS AND METHODS: A clinically validated KBP model for moderately hypofractionated prostate therapy was periodically updated using two approaches: one was targeted at achieving the biggest library size (Mt), while the other one at achieving the highest mean sample quality (Rt). Four subsequent updates were accomplished. The goodness, robustness and quality of the outcomes were measured and compared to those of the common ancestor. Plan quality was assessed through the Plan Quality Metric (PQM) and plan complexity was monitored. RESULTS: Both update procedures allowed for an increase in the OARs sparing between +3.9 % and +19.2 % compared to plans generated by a human planner. Target coverage and homogeneity slightly reduced [-0.2 %;-14.7 %] while plan complexity showed only minor changes. Increasing the sample size resulted in more reliable predictions and improved goodness-of-fit, while increasing the mean sample quality improved the outcomes but slightly reduced the models reliability. CONCLUSIONS: Repeated updates of clinical KBP models can enhance their robustness, reliability and the overall quality of automatically generated plans. The periodical expansion of the model sample accompanied by the removal of the unacceptable low quality plans should maximize the benefits of the updates while limiting the associated workload.

Quantitative assessment of helical tomotherapy plans complexity.

PURPOSE: An unnecessary amount of complexity in radiotherapy plans affects the efficiency of the treatments, increasing the uncertainty of dose deposition and its susceptibility to anatomical changes or setup errors. To date, tools for quantitatively assessing the complexity of tomotherapy plans are still limited. In this study, new metrics were developed to characterize different aspects of helical tomotherapy (HT) plans, and their actual effectiveness was investigated. METHODS: The complexity of 464 HT plans delivered on a Radixact platform was evaluated. A new set of metrics was devised to assess beam geometry, leaf opening time (LOT) variability, and modulation over space and time. Sixty-five complexity metrics were extracted from the dataset using the newly in-house developed software library TCoMX: 29 metrics already proposed in the literature and 36 newly developed metrics. Their reciprocal relation is discussed. Their effectiveness was evaluated through correlation analyses with patient-specific quality assurance (PSQA) results. RESULTS: An inverse linear relation was found between the average number of closed leaves and the average number of MLC openings and closures as well as between the choice of the modulation factor and the discontinuity of the field, suggesting some intrinsic link between the LOT distribution and the geometrical complexity of the MLC openings. The newly proposed metrics were at least as correlated as the existing ones to the PSQA results. Metrics describing the geometrical complexity of the MLC openings showed the strongest connection to the PSQA results. Significant correlations were found between at least one of the new metrics and the γ index passing rate P R γ % ( 3 % G , 2 mm ) $P{R}_{\gamma}\%(3\%G,2\textit{mm})$ for six out of seven groups of plans considered. CONCLUSION: The new metrics proposed were shown to be effective to characterize more comprehensively the complexity of HT plans. A software library for their automatic extraction is described and made available.

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.

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.

Size-specific dose estimates for pediatric head CT protocols based on the AAPM report TG-293.

PURPOSE: To establish the Size Specific Dose Estimate (SSDE) typical values for pediatric head CT examinations based on the AAPM report TG-293; to provide a new stratification based on the water-equivalent diameter (Dw), given that SSDE is related to the head size. METHODS: 296 Head CT scans of pediatric patients collected using a dose monitoring software were retrospectively analysed. Typical values were derived stratifying data by age in three methods: the first proposed by the European Guidelines on Diagnostic Reference Levels for Pediatric Imaging (RP185), the second by the National Istisan Report 20/22 and a local one related to the clinical protocols (LStrata). For each scan, a self-developed Matlab routine calculated the water-equivalent diameter (Dw) and related SSDE values with the conversion factors fH16and fB16provided by the AAPM reports TG-293 and TG-204, respectively. Eventually, a Dwstratification was introduced starting from a measure of the lateral dimension of the head. RESULTS: SSDE based on TG-204 overestimatesthe dose up to 12%. Four Dwgroups were identified thanks to the good correlation between the head lateral dimension andDw: Dw < 14 cm, 14 ≤ Dw < 16 cm, 16 ≤ Dw< 17 cm, Dw≥ 17 cm. The Dw-stratified dosimetric indices presentgreater variability than those grouped by age because of the large variability of the size of the infant's head. CONCLUSIONS: The variability of the SSDE metric underlines that age-optimized protocols are not when size is considered.

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.

Analysis of clinical patient-specific pre-treatment quality assurance with the new helical tomotherapy platform, following the AAPM TG-218 report.

PURPOSE: This study presents patient-specific quality assurance (QA) results from the first 395 clinical cases for the new helical TomoTherapy® platform (Radixact) coupled with dedicated Precision TPS. METHODS: The passing rate of the Gamma Index (GP%) of 395 helical QA of patient-specific tomotherapy, acquired with ArcCHECK, is presented, analysed and correlated to various parameters of the plan. Following TG-218 recommendations, the clinic specific action limit (ALcs) and tolerance limit (TLcs) were calculated for our clinic and monitored during the analysed period. RESULTS: The mean values ​​(± 1 standard deviation) of GP% (3%/2 mm) (both global and local normalization) are: 97.6% and 90.9%, respectively. The proposed ALcs and TLcs, after a period of two years' process monitoring are 89.4% and 91.1% respectively. CONCLUSIONS: The phantom measurements closely match the planned dose distributions, demonstrating that the calculation accuracy of the new Precision TPS and the delivery accuracy of the Radixact unit are adequate, with respect to international guidelines and reports. Furthermore, a first correlation with the planning parameters was made. Action and tolerance limits have been set for the new Radixact Linac.