Verification procedures for patient-specific quality assurance (QA) in advanced radiotherapy are laborious and time-consuming. Moreover, it has been shown that these procedures cannot detect some ...inaccuracies for some particular complex cases due to tissue inhomogeneity and highly modulated plans. Secondary dose calculation verification of radiotherapy plans is an important aspect in patient-specific QA. A suitably optimized software, RadCalc equipped with 3D Monte Carlo Module (MC), was used to dosimetrically verify radiotherapy treatment plans where the measured dose distributions can be inaccurate due to the TPS dose calculation algorithm and/or treatment unit delivery uncertainties. MC Models were built using specific commissioning measurements and then the Additional Radiation to Light Field Offset parameter (Dosimetric Leaf Gap parameter) was tuned to achieve the best dose comparison agreement with phantom patient-specific QA measurements. The results showed a good agreement between the TPS and the simulations. RadCalc MC also allows to better estimate the plan doses in lung cancer patients and to detect the presence of possible inaccuracies due to tissue inhomogeneity, which is not estimable with a homogeneous phantoms.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Breast cancer represents the second leading cause of cancer-related death in the female population, despite continuing advances in treatment options that have significantly accelerated in recent ...years. Conservative treatments have radically changed the concept of healing, also focusing on the psychological aspect of oncological treatments. In this scenario, radiotherapy plays a key role. Brachytherapy is an extremely versatile radiation technique that can be used in various settings for breast cancer treatment. Although it is invasive, technically complex, and requires a long learning curve, the dosimetric advantages and sparing of organs at risk are unequivocal. Literature data support muticatheter interstitial brachytherapy as the only method with strong scientific evidence to perform partial breast irradiation and reirradiation after previous conservative surgery and external beam radiotherapy, with longer follow-up than new, emerging radiation techniques, whose effectiveness is proven by over 20 years of experience. The aim of our work is to provide a comprehensive view of the use of interstitial brachytherapy to perform breast lumpectomy boost, breast-conserving accelerated partial breast irradiation, and salvage reirradiation for ipsilateral breast recurrence, with particular focus on the implant description, limits, and advantages of the technique.
The purpose of this multi-centric work was to investigate the relationship between radiomic features extracted from pre-treatment computed tomography (CT), positron emission tomography (PET) imaging, ...and clinical outcomes for stereotactic body radiation therapy (SBRT) in early-stage non-small cell lung cancer (NSCLC). One-hundred and seventeen patients who received SBRT for early-stage NSCLC were retrospectively identified from seven Italian centers. The tumor was identified on pre-treatment free-breathing CT and PET images, from which we extracted 3004 quantitative radiomic features. The primary outcome was 24-month progression-free-survival (PFS) based on cancer recurrence (local/non-local) following SBRT. A harmonization technique was proposed for CT features considering lesion and contralateral healthy lung tissues using the LASSO algorithm as a feature selector. Models with harmonized CT features (B models) demonstrated better performances compared to the ones using only original CT features (C models). A linear support vector machine (SVM) with harmonized CT and PET features (A1 model) showed an area under the curve (AUC) of 0.77 (0.63–0.85) for predicting the primary outcome in an external validation cohort. The addition of clinical features did not enhance the model performance. This study provided the basis for validating our novel CT data harmonization strategy, involving delta radiomics. The harmonized radiomic models demonstrated the capability to properly predict patient prognosis.
Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network ...features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs.
among cardiac arrhythmias, ventricular tachycardia (VT) is one that can lead to cardiac death, although significant progress has been made in its treatment, including the use of implantable ...cardioverter-defibrillators (ICD) and radiofrequency catheter ablation. Nevertheless, long-term recurrence rates remain in about half of patients and drastically impact the patient's quality of life. Moreover, recurrent ICD shocks are painful and are associated with higher mortality and worsening of heart failure. Recently, more and more experiences are demonstrating potential efficacy in the use of stereotactic body radiotherapy (SBRT) (also called cardiac radio-ablation) to treat this condition. In this paper, we report our experience in the use of cardiac radio-ablation for the treatment of refractory ventricular tachycardia with a focus on the technique used, along with a review of the literature and technical notes.
an 81-year-old male patient with a long history of non-ischemic dilated cardiomyopathy and mechanical mitral prosthesis underwent a biventricular cardioverter defibrillator implant after atrial ventricular node ablation. At the end of 2021, the number of tachycardias increased significantly to about 10 episodes per day. After failure of medical treatment and conventional RT catheter ablation, the patient was treated with SBRT for a total dose of 25 Gy in a single session at the site of the ectopic focus. No acute toxicity was recorded. After SBRT (follow-up 7 months) no other VT episodes were recorded.
SBRT appears to be safe and leads to a rapid reduction in arrhythmic storms as treatment for VT without acute toxicity, representing one of the most promising methods for treating VT storms.
Anterior segment optical coherence tomography (AS-OCT) allows the explore not only the anterior chamber but also the front part of the vitreous cavity. Our cross-sectional single-centre study ...investigated whether AS-OCT can distinguish between vitreous involvement due to vitreoretinal lymphoma (VRL) and vitritis in uveitis. We studied AS-OCT images from 28 patients (11 with biopsy-proven VRL and 17 with differential diagnosis uveitis) using publicly available radiomics software written in MATLAB. Patients were divided into two balanced groups: training and testing. Overall, 3260/3705 (88%) AS-OCT images met our defined quality criteria, making them eligible for analysis. We studied five different sets of grey-level samplings (16, 32, 64, 128, and 256 levels), finding that 128 grey levels performed the best. We selected the five most effective radiomic features ranked by the ability to predict the class (VRL or uveitis). We built a classification model using the xgboost python function; through our model, 87% of eyes were correctly diagnosed as VRL or uveitis, regardless of exam technique or lens status. Areas under the receiver operating characteristic curves (AUC) in the 128 grey-level model were 0.95 CI 0.94, 0.96 and 0.84 for training and testing datasets, respectively. This preliminary retrospective study highlights how AS-OCT can support ophthalmologists when there is clinical suspicion of VRL.
Background: Lately, radiomic studies featuring the development of a signature to use in prediction models in diagnosis or prognosis outcomes have been increasingly published. While the results are ...shown to be promising, these studies still have many pitfalls and limitations. One of the main issues of these studies is that radiomic features depend on how the images are preprocessed before their computation. Since, in widely known and used software for radiomic features calculation, it is possible to set these preprocessing parameters before the calculation of the radiomic feature, there are ongoing studies assessing the stability and repeatability of radiomic features to find the most suitable preprocessing parameters for every used imaging modality. Materials and Methods: We performed a comprehensive literature search using four electronic databases: PubMed, Cochrane Library, Embase, and Scopus. Mesh terms and free text were modeled in search strategies for databases. The inclusion criteria were studies where preprocessing parameters’ influence on feature values and model predictions was addressed. Records lacking information on image acquisition parameters were excluded, and any eligible studies with full-text versions were included in the review process, while conference proceedings and monographs were disregarded. We used the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) tool to investigate the risk of bias. We synthesized our data in a table divided by the imaging modalities subgroups. Results: After applying the inclusion and exclusion criteria, we selected 43 works. This review examines the impact of preprocessing parameters on the reproducibility and reliability of radiomic features extracted from multimodality imaging (CT, MRI, CBCT, and PET/CT). Standardized preprocessing is crucial for consistent radiomic feature extraction. Key preprocessing steps include voxel resampling, normalization, and discretization, which influence feature robustness and reproducibility. In total, 44% of the included works studied the effects of an isotropic voxel resampling, and most studies opted to employ a discretization strategy. From 2021, several studies started selecting the best set of preprocessing parameters based on models’ best performance. As for comparison metrics, ICC was the most used in MRI studies in 58% of the screened works. Conclusions: From our work, we highlighted the need to harmonize the use of preprocessing parameters and their values, especially in light of future studies of prospective studies, which are still lacking in the current literature.
Abstract
Background and Aims
Prediction of future graft failure with non-invasive investigations is a relevant objective for transplant clinicians. We previously demonstrated a promising ...discrimination capacity for interstitial fibrosis / tubular atrophy (IFTA) >50% in kidney biopsy of a machine learning (ML) based magnetic resonance imaging (MRI) radiomic algorithm. Aim of the present study is to evaluate accuracy of MRI radiomic-based ML algorithms in predicting future graft failure.
Method
Single-center retrospective observational cohort study on a previously characterized cohort of kidney transplant recipients who underwent graft biopsy and graft MRI imaging within 6 months from biopsy, both on clinical indication, at the “Azienda Ospedaliero-Universitaria di Modena”, Italy, from 1/1/2012 to 1/3/2021. The primary outcome was to identify the best combination of MRI radiomic features for the prediction of graft failure during follow-up. Segmentation of renal parenchyma, cortex and medulla on MRI sequences was performed using the 3DSlicer software. Radiomic features were then extracted using an in-house software based on pyradiomics applying Wavelet and Gaussian filters. LASSO algorithm was employed to select correlated features with outcome and summarize them in a radiomic signature. Using radiomic signature alone and then merged with meaningful clinical data we trained ML-algorithms using 70% of cases for training/validation, with a 10-fold internal cross-validation, and 30% for model testing. Model performance was assessed using AUC with a 95% confidence interval (CI).
Results
Seventy coupled tests (63 patients) were included and randomly subdivided into a training/validation cohort (n = 50) and a test cohort (n = 20). Median follow-up was 24.73 months (interquartile range 13.64-46.57). Radiomic model had an AUC of 0.88 (95%_CI 0.70-0.97) in training and 0.57 (95%_CI 0.23-0.86) in test cohort. Radiomic-clinical mixed model had an AUC of 0.90 (95%_CI 0.73-0.98) in training and 0.66 (95%_CI 0.33-0.88) in test cohort.
Conclusion
We produced an MRI radiomic-based ML algorithm with good prediction ability for future graft failure in patients with kidney transplant. Given the limited number of enrolled patients, validation in larger (and ideally prospective) cohorts is required to confirm our findings. Comparison of radiomic-based ML with histological parameters (i.e., IFTA) for the prediction of graft failure in our cohort is currently under assessment.
Abstract
Background and Aims
Interstitial fibrosis / tubular atrophy (IFTA) is a common, irreversible and progressive form of chronic allograft injury, and it is considered a critical predictor of ...kidney allograft outcomes. Inflammation, both microvascular and interstitial, is on the contrary regarded as a reversible form of graft injury. Since treatments for rejection and other causes of graft dysfunction bear substantial toxicity and could have limited efficacy, the extent of irreversible graft scarring is a crucial information for the clinician, to evaluate risks and benefits of specific therapies. The diagnosis of kidney graft pathology is acquired through graft biopsy, which is an invasive procedure and can be subjected to sampling bias. Magnetic resonance imaging (MRI), especially with functional techniques, has emerged as a possibility for non-invasive estimation of tissue fibrosis; nevertheless, functional MRI is not widely available. Texture analysis MRI (TA-MRI) is a radiomic technique that provides a quantitative assessment of tissue heterogeneity from standard MRI images, generating features that can be fitted into a machine-learning model to assess their ability to predict clinical or histological parameters.
Method
Single-center cross-sectional observational cohort study enrolling kidney transplant recipients who underwent graft biopsy and graft MRI imaging within 6 months from biopsy, both on clinical indication, at the “Azienda Ospedaliero-Universitaria di Modena”, Italy. The study was approved by the local Ethical Committee (AOU0010167/20). The primary outcome was to identify the best TA-MRI features subset for estimation of IFTA > 50% in graft biopsy. Secondary outcomes were estimation of: IFTA > 25%, presence of total inflammation (ti) and microvascular inflammation (glomerulitis + peritubular capillaritis g+ptc). Graft biopsy was reported according to Banff 2017 system. Radiomic analysis was performed on axial T2 pre-contrast and T1 fat-suppressed post-contrast sequences. The whole renal parenchyma (PAR) was segmented and labelled on T2 and T1, renal cortex (COR) only on T2. After imaging preprocessing, PyRadiomics was used to extract radiomic features. After removal of shape features, 93 features were included and reduced using LASSO regression to produce radiomic signatures. These were introduced in Machine Learning (ML) models to test the association with outcomes. Results are reported as AUC and a value of sensitivity and specificity.
Results
Sixty patients were included in the study, and 67 graft biopsy – graft MRI pairs were available for analysis. Demographic and clinical characteristics of enrolled patients are depicted in table 1; histological diagnosis and main Banff histological parameters from graft biopsies in table 2. Among ML models, three showed an acceptable performance. T2 COR “firstorder_minimum/firstorder_range/glrlm_run_entropy” for IFTA>50% (AUC=0.77, sensitivity=73%, specificity=71%), T1 PAR “firstorder_energy” for IFTA>25% (AUC=0.71, sensitivity=74%, specificity=51%), T1 PAR “firstorder_energy/gldm_small_dependence_low_gray_level_emphasis” for g+ptc >0 (AUC=0.74, sensitivity= 78%, specificity=68%); see figures 1–3. No acceptable prediction was detected for ti >0.
Conclusion
Our study shows that TA-MRI feature signatures can predict the degree of IFTA in graft biopsies, with an acceptable diagnostic performance. These results suggest to further investigating TA-MRI from standard MRI sequences as potential tool to assess graft chronic parenchymal injury. Moreover, since graft biopsy results can be jeopardized by limited sample size, we hypothesize that evaluation of IFTA through TA-MRI could provide more comprehensive information regarding the whole parenchyma. To test this hypothesis, we are currently evaluating the association of TA-MRI radiomic features and baseline eGFR and eGFR variation over time.
The aim of the study is to use the well-known channelized Hotelling observer model (CHO) to characterize a recently installed angiography system (GE Discovery IGS 740) using sets of images of a ...contrast-detail phantom acquired with clinical protocols. A Leeds TO10 phantom was used. The phantom has 108 details: 12 diameters (size range: 0.25 mm-11 mm), each with nine contrasts (declared range: 0.012-0.930 at 70 kVp 1.00 with 1 mm Cu filtration). TO10 has been imaged between two 10 cm thick homogeneous solid water slabs. Two FOVs (32 cm and 20 cm) were used. Fluoroscopy images were taken using an abdominal protocol at two different frame rates (15 fps and 7.5 fps) and at two dose levels (low and normal); cineangiography images were acquired using an abdominal protocol at 15 fps at two dose levels (low and normal). A 40 Gabor channels CHO with internal noise was used. Human observers' studies were carried out to tune the internal noise parameter and to validate the model observer. Contrast-detail curves were obtained from the CHO output using a visibility threshold of 75% and fitted with Rose's model theory in order to characterize the angiography system. Wilcoxon rank-sum tests were performed to investigate possible differences among the different sets of images. The CHO can distinguish between the two dose levels (p -values < 0.002), while FOV and frame rate do not affect the contrast-detail curves significantly. It is important to note that the CHO does not find statistically significant differences between a fluoroscopy with FOV = 20 cm at normal dose level (17.6 mGy min−1) and a cineangiography with FOV = 32 cm low dose level (42.1 mGy min−1). This result can lead to a dose reduction of about 70% for our specific task (i.e. a static, disc shaped object at known location in homogeneous field). Given their stability in comparison to human observers, model observers provide an effective tool for image quality evaluation.
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