Benchmarking digital displays (monitors) for histological diagnoses: the nephropathology use case.

AIM: The digital transformation of the pathology laboratory is being continuously sustained by the introduction of innovative technologies promoting whole slide image (WSI)-based primary diagnosis. Here, we proposed a real-life benchmark of a pathology-dedicated medical monitor for the primary diagnosis of renal biopsies, evaluating the concordance between the 'traditional' microscope and commercial monitors using WSI from different scanners. METHODS: The College of American Pathologists WSI validation guidelines were used on 60 consecutive renal biopsies from three scanners (Aperio, 3DHISTECH and Hamamatsu) using pathology-dedicated medical grade (MG), professional grade (PG) and consumer-off-the-shelf (COTS) monitors, comparing results with the microscope diagnosis after a 2-week washout period. RESULTS: MG monitor was faster (1090 vs 1159 vs 1181 min, delta of 6-8%, p<0.01), with slightly better performances on the detection of concurrent diseases compared with COTS (κ=1 vs 0.96, 95% CI=0.87 to 1), but equal concordance to the commercial monitors on main diagnosis (κ=1). Minor discrepancies were noted on specific scores/classifications, with MG and PG monitors closer to the reference report (r=0.98, 95% CI=0.83 to 1 vs 0.98, 95% CI=0.83 to 1 vs 0.91, 95% CI=0.76 to 1, κ=0.93, 95% CI=077 to 1 vs 0.93, 95% CI=0.77 to 1 vs 0.86, 95% CI=0.64 to 1, κ=1 vs 0.50, 95% CI=0 to 1 vs 0.50, 95% CI=0 to 1, for IgA, antineutrophilic cytoplasmic antibody and lupus nephritis, respectively). Streamlined Pipeline for Amyloid detection through congo red fluorescence Digital Analysis detected amyloidosis on both monitors (4 of 30, 13% cases), allowing detection of minimal interstitial deposits with slight overestimation of the Amyloid Score (average 6 vs 7). CONCLUSIONS: The digital transformation needs careful assessment of the hardware component to support a smart and safe diagnostic process. Choosing the display for WSI is critical in the process and requires adequate planning.

Positron Emission Tomography-Derived Radiomics and Artificial Intelligence in Multiple Myeloma: State-of-the-Art.

Multiple myeloma (MM) is a heterogeneous neoplasm accounting for the second most prevalent hematologic disorder. The identification of noninvasive, valuable biomarkers is of utmost importance for the best patient treatment selection, especially in heterogeneous diseases like MM. Despite molecular imaging with positron emission tomography (PET) has achieved a primary role in the characterization of MM, it is not free from shortcomings. In recent years, radiomics and artificial intelligence (AI), which includes machine learning (ML) and deep learning (DL) algorithms, have played an important role in mining additional information from medical images beyond human eyes' resolving power. Our review provides a summary of the current status of radiomics and AI in different clinical contexts of MM. A systematic search of PubMed, Web of Science, and Scopus was conducted, including all the articles published in English that explored radiomics and AI analyses of PET/CT images in MM. The initial results have highlighted the potential role of such new features in order to improve the clinical stratification of MM patients, as well as to increase their clinical benefits. However, more studies are warranted before these approaches can be implemented in clinical routines.

Validation of RayStation Monte Carlo dose calculation algorithm for multiple LINACs.

PURPOSE: A photon Monte Carlo (MC) model was commissioned for flattened (FF) and flattening filter free (FFF) 6 MV beam energy. The accuracy of this model, as a single model to be used for three beam matched LINACs, was evaluated. METHODS: Multiple models were created in RayStation v.10A for three linacs equipped with Elekta "Agility" collimator. A clinically commissioned collapsed cone (CC) algorithm (GoldCC), a MC model automatically created from the CC algorithm without further optimization (CCtoMC) and an optimized MC model (GoldMC) were compared with measurements. The validation of the model was performed by following the recommendations of IAEA TRS 430 and comprised of basic validation in a water tank, validation in a heterogeneous phantom and validation of complex IMRT/VMAT paradigms using gamma analysis of calculated and measured dose maps in a 2D-Array. RESULTS: Dose calculation with the GoldMC model resulted in a confidence level of 3% for point measurements in water tank and heterogeneous phantom for measurements performed in all three linacs. The same confidence level resulted for GoldCC model. Dose maps presented an agreement for all models on par to each other with γ criteria 2%/2mm. CONCLUSIONS: The GoldMC model showed a good agreement with measured data and is determined to be accurate for clinical use for all three linacs in this study.

EFOMP policy statement 17: The role and competences of medical physicists and medical physics experts in the different stages of a medical device life cycle.

MPPs are trained in the branches of physics associated with the practice of medicine. Possessing a solid scientific background and technical skills, MPPs are well suited to play a leading role within each stage of a medical device life cycle. The various stages of the life cycle of a medical device include establishment of requirements with use-case assessment, investment planning, procurement of medical devices, acceptance testing especially regarding safety and performance, quality management, effective and safe use and maintenance, user training, interfacing with IT systems, and safe decommissioning and removal of the medical devices. Acting as an expert within the clinical staff of a healthcare organisation, the MPP can play an important role to achieve a balanced life cycle management of medical devices. Given that the functioning of medical devices and their clinical application in routine clinical practice and research is heavily physics and engineering based, the MPP is strongly associated with the hard science aspects and advanced clinical applications of medical devices and associated physical agents. Indeed, this is reflected in the mission statement of MPP professionals [1]. PURPOSE: The life cycle management of medical devices is described as well as the procedures involved. These procedures are performed by multi-disciplinary teams within a healthcare environment. The task of this workgroup was focused on clarifying and elaborating the role of the Medical Physicist and Medical Physics Expert - here collectively referred to as the Medical Physics Professional (MPP) - within these multi-disciplinary teams. This policy statement describes the role and competences of MPPs in every stage of a medical device life cycle. If MPPs are an integral part of these multi-disciplinary teams, the effective use, safety, and sustainability of the investment is likely to improve as well as the overall service quality delivered by the medical device during its life cycle. It leads to better health care quality and reduced costs. Furthermore, it gives MPPs a stronger position in health care organisations throughout Europe.

Oligorecurrent Non-Small-Cell Lung Cancer Treated by Chemo-Radiation Followed by Immunotherapy and Intracranial Radiosurgery: A Case Report and Mini Review of Literature.

Locally advanced non-small-cell lung cancer still represents a "grey zone" in terms of the best treatment choice and optimal clinical outcomes. Indeed, most patients may be suitable to receive different treatments with similar outcomes such as chemo-radiotherapy (CHT-RT) followed by immunotherapy (IO) or surgery followed by adjuvant local/systemic therapies. We report a clinical case of a patient submitted to primary thoracic surgery who developed a mediastinal nodal recurrence successfully treated by CHT-RT-IO. Subsequently, a single brain lesion was found to have been successfully treated by single fraction stereotactic ablative radiotherapy. The patient is still on follow-up and she is free from disease having a good quality of life. In this report, we also perform a mini review about the role of CHT-RT followed by IO in treating loco-regional relapse after surgery. The role of SABR after IO is also evaluated, finding that it is safe and well tolerated. More robust and larger clinical data are needed in this particular setting to better define the role of the combination of systemic and local treatments in the management of intrathoracic and intracranial relapse for patients already submitted to CHT-RT followed by immunotherapy.

PET-Derived Radiomics and Artificial Intelligence in Breast Cancer: A Systematic Review.

Breast cancer (BC) is a heterogeneous malignancy that still represents the second cause of cancer-related death among women worldwide. Due to the heterogeneity of BC, the correct identification of valuable biomarkers able to predict tumor biology and the best treatment approaches are still far from clear. Although molecular imaging with positron emission tomography/computed tomography (PET/CT) has improved the characterization of BC, these methods are not free from drawbacks. In recent years, radiomics and artificial intelligence (AI) have been playing an important role in the detection of several features normally unseen by the human eye in medical images. The present review provides a summary of the current status of radiomics and AI in different clinical settings of BC. A systematic search of PubMed, Web of Science and Scopus was conducted, including all articles published in English that explored radiomics and AI analyses of PET/CT images in BC. Several studies have demonstrated the potential role of such new features for the staging and prognosis as well as the assessment of biological characteristics. Radiomics and AI features appear to be promising in different clinical settings of BC, although larger prospective trials are needed to confirm and to standardize this evidence.

Auxetic structures used in kinesiology tapes can improve form-fitting and personalization.

Each year 65% of young athletes and 25% of physically active adults suffer from at least one musculoskeletal injury that prevents them from continuing with physical activity, negatively influencing their physical and mental well-being. The treatment of musculoskeletal injuries with the adhesive elastic kinesiology tape (KT) decreases the recovery time. Patients can thus recommence physical exercise earlier. Here, a novel KT based on auxetic structures is proposed to simplify the application procedure and allow personalization. This novel KT exploits the form-fitting property of auxetics as well as their ability to simultaneously expand in two perpendicular directions when stretched. The auxetic contribution is tuned by optimizing the structure design using analytical equations and experimental measurements. A reentrant honeycomb topology is selected to demonstrate the validity of the proposed approach. Prototypes of auxetic KT to treat general elbow pains and muscle tenseness in the forearm are developed.

Anthropometric parameters and radiation doses during percutaneous coronary procedures.

PURPOSE: Body size is a major determinant of patient's dose during percutaneous coronary interventions (PCI). Body mass index, body surface area (BSA), lean body mass and weight are commonly used estimates for body size. We aim to identify which of these measures and which procedural/clinical characteristics can better predict received dose. METHODS: Dose area product (DAP, Gycm2), fluoroscopy DAP rate (Gycm2/min), fluoroscopy DAP (Gycm2), cine-angiography DAP (Gycm2), Air Kerma (mGy) were selected as indices of patient radiation dose. Different clinical/procedural variables were analysed in multiple linear regression models with previously mentioned patient radiation dose parameters as end points. The best model for each of them was identified. RESULTS: Overall 6623 PCI were analysed, median fluoroscopy DAP rate was 35 [IQR 2.7,4.4] Gycm2, median total DAP was 62.7 [IQR 38.1,107] Gycm2. Among all anthropometric variables, BSA showed the best correlation with all radiation dose parameters considered. Every 1 m2 increment in BSA added 4.861 Gycm2/min (95% CI [4.656, 5.067]) to fluoroscopy DAP rate and 164 Gycm2 (95% CI [145.3, 182.8]) to total DAP. Height and female sex were significantly associated to a reduction in fluoroscopy DAP rate and total DAP. Coronary angioplasty, diabetes, basal creatinine and the number of treated vessels were associated to higher values. CONCLUSIONS: Main determinants of patient radiation dose are: BSA, female sex, height and number of treated vessels. In an era of increasing PCI complexity and obesity prevalence, these results can help clinicians tailoring X-ray administration to patient's size.

The impact of transfer learning on 3D deep learning convolutional neural network segmentation of the hippocampus in mild cognitive impairment and Alzheimer disease subjects.

Research on segmentation of the hippocampus in magnetic resonance images through deep learning convolutional neural networks (CNNs) shows promising results, suggesting that these methods can identify small structural abnormalities of the hippocampus, which are among the earliest and most frequent brain changes associated with Alzheimer disease (AD). However, CNNs typically achieve the highest accuracy on datasets acquired from the same domain as the training dataset. Transfer learning allows domain adaptation through further training on a limited dataset. In this study, we applied transfer learning on a network called spatial warping network segmentation (SWANS), developed and trained in a previous study. We used MR images of patients with clinical diagnoses of mild cognitive impairment (MCI) and AD, segmented by two different raters. By using transfer learning techniques, we developed four new models, using different training methods. Testing was performed using 26% of the original dataset, which was excluded from training as a hold-out test set. In addition, 10% of the overall training dataset was used as a hold-out validation set. Results showed that all the new models achieved better hippocampal segmentation quality than the baseline SWANS model (ps < .001), with high similarity to the manual segmentations (mean dice [best model] = 0.878 ± 0.003). The best model was chosen based on visual assessment and volume percentage error (VPE). The increased precision in estimating hippocampal volumes allows the detection of small hippocampal abnormalities already present in the MCI phase (SD = [3.9 ± 0.6]%), which may be crucial for early diagnosis.

Radiomics classifier to quantify automatic segmentation quality of cardiac sub-structures for radiotherapy treatment planning.

PURPOSE: A radiomics features classifier was implemented to evaluate segmentation quality of heart structures. A robust feature set sensitive to incorrect contouring would provide an ideal quantitative index to drive autocontouring optimization. METHODS: Twenty-five cardiac sub-structures were contoured as regions of interest in 36 CTs. Radiomic features were extracted from manually-contoured (MC) and Hierarchical-Clustering automatic-contouring (AC) structures. A robust feature-set was identified from correctly contoured CT datasets. Features variation was analyzed over a MC/AC dataset. A supervised-learning approach was used to train an Artificial-Intelligence (AI) classifier; incorrect contouring cases were generated from the gold-standard MC datasets with translations, expansions and contractions. ROC curves and confusion matrices were used to evaluate the AI-classifier performance. RESULTS: Twenty radiomics features, were found to be robust across structures, showing a good/excellent intra-class correlation coefficient (ICC) index comparing MC/AC. A significant correlation was obtained with quantitative indexes (Dice-Index, Hausdorff-distance). The trained AI-classifier detected correct contours (CC) and not correct contours (NCC) with an accuracy of 82.6% and AUC of 0.91. True positive rate (TPR) was 85.1% and 81.3% for CC and NCC. Detection of NCC at this point of the development still depended strongly on degree of contouring imperfection. CONCLUSIONS: A set of radiomics features, robust on "gold-standard" contour and sensitive to incorrect contouring was identified and implemented in an AI-workflow to quantify segmentation accuracy. This workflow permits an automatic assessment of segmentation quality and may accelerate expansion of an existing autocontouring atlas database as well as improve dosimetric analyses of large treatment plan databases.

Artificial intelligence in the medical physics community: An international survey.

PURPOSE: To assess current perceptions, practices and education needs pertaining to artificial intelligence (AI) in the medical physics field. METHODS: A web-based survey was distributed to the European Federation of Organizations for Medical Physics (EFOMP) through social media and email membership list. The survey included questions about education, personal knowledge, needs, research and professionalism around AI in medical physics. Demographics information were also collected. Responses were stratified and analysed by gender, type of institution and years of experience in medical physics. Statistical significance (p<0.05) was assessed using paired t-test. RESULTS: 219 people from 31 countries took part in the survey. 81% (n = 177) of participants agreed that AI will improve the daily work of Medical Physics Experts (MPEs) and 88% (n = 193) of respondents expressed the need for MPEs of specific training on AI. The average level of AI knowledge among participants was 2.3 ± 1.0 (mean ± standard deviation) in a 1-to-5 scale and 96% (n = 210) of participants showed interest in improving their AI skills. A significantly lower AI knowledge was observed for female participants (2.0 ± 1.0), compared to male responders (2.4 ± 1.0). 64% of participants indicated that they are not involved in AI projects. The percentage of female leading AI projects was significantly lower than the male counterparts (3% vs 19%). CONCLUSIONS: AI was perceived as a positive resource to support MPEs in their daily tasks. Participants demonstrated a strong interest in improving their current AI-related skills, enhancing the need for dedicated training for MPEs.

Optimal Lateral Displacement in Automatic Close-Range Photogrammetry.

Based on the use of automatic photogrammetry, different researchers made evident that the level of overlap between adjacent photographs directly affects the uncertainty of the 3D dense cloud originated by the Structure from Motion/Image Matching (SfM/IM) process. The purpose of this study was to investigate if, in the case of a convergent shooting typical of close-range photogrammetry, an optimal lateral displacement of the camera for minimizing the 3D data uncertainty could be identified. We examined five different test objects made of rock, differing in terms of stone type and visual appearance. First, an accurate reference data set was generated by acquiring each object with an active range device, based on pattern projection (σz = 18 µm). Then, each object was 3D-captured with photogrammetry, using a set of images taken radially, with the camera pointing to the center of the specimen. The camera-object minimum distance was kept at 200 mm during the shooting, and the angular displacement was as small as π/60. We generated several dense clouds by sampling the original redundant sequence at angular displacements (nπ/60, n = 1, 2, … 8). Each 3D cloud was then compared with the reference, implementing an accurate scaling protocol to minimize systematic errors. The residual standard deviation of error made consistently evident a range of angular displacements among images that appear to be optimal for reducing the measurement uncertainty, independent of each specimen shape, material, and texture. Such a result provides guidance about how best to arrange the cameras' geometry for 3D digitization of a stone cultural heritage artifact with several convergent shots. The photogrammetric tool used in the experiments was Agisoft Metashape.

Hierarchical clustering applied to automatic atlas based segmentation of 25 cardiac sub-structures.

PURPOSE: Segmentation of cardiac sub-structures for dosimetric analyses is usually performed manually in time-consuming procedure. Automatic segmentation may facilitate large-scale retrospective analysis and adaptive radiotherapy. Various approaches, among them Hierarchical Clustering, were applied to improve performance of atlas-based segmentation (ABS). METHODS: Training dataset of ABS consisted of 36 manually contoured CT-scans. Twenty-five cardiac sub-structures were contoured as regions of interest (ROIs). Five auto-segmentation methods were compared: simultaneous automatic contouring of all 25 ROIs (Method-1); automatic contouring of all 25 ROIs using lungs as anatomical barriers (Method-2); automatic contouring of a single ROI for each contouring cycle (Method-3); hierarchical cluster-based automatic contouring (Method-4); simultaneous truth and performance level estimation (STAPLE). Results were evaluated on 10 patients. Dice similarity coefficient (DSC), average Hausdorff distance (AHD), volume comparison and physician score were used as validation metrics. RESULTS: Atlas performance improved increasing number of atlases. Among the five ABS methods, Hierarchical Clustering workflow showed a significant improvement maintaining a clinically acceptable time for contouring. Physician scoring was acceptable for 70% of the ROI automatically contoured. Inter-observer evaluation showed that contours obtained by Hierarchical Clustering method are statistically comparable with them obtained by a second, independent, expert contourer considering DSC. Considering AHD, distance from the gold standard is lower for ROIs segmented by ABS. CONCLUSIONS: Hierarchical clustering resulted in best ABS results for the primarily investigated platforms and compared favorably to a second benchmark system. Auto-contouring of smaller structures, being in range of variation between manual contourers, may be ideal for large-scale retrospective dosimetric analysis.

Evaluation of the effectiveness of novel single-intervention adaptive radiotherapy strategies based on daily dose accumulation.

Parotid gland (PG) shrinkage and neck volume reduction during radiotherapy of head and neck (H&N) cancer patients is a clinical issue that has prompted interest in adaptive radiotherapy (ART). This study focuses on the difference between planned dose and delivered dose and the possible effects of an efficient replanning strategy during the course of treatment. Six patients with H&N cancer treated by tomotherapy were retrospectively enrolled. Thirty daily dose distributions (DMVCT) were calculated on pretreatment megavoltage computed tomography (MVCT) scans. Deformable Image Registration which matched daily MVCT with treatment planning kilovoltage computed tomography was performed. Using the resulting deformation vector field, all daily DMVCT were deformed to the planning kilovoltage computed tomography and resulting doses were accumulated voxel per voxel. Cumulative DMVCT was compared to planned dose distribution performing γ-analysis (2 mm, 2% of 2.2 Gy). Two single-intervention ART strategies were executed on the 18th fraction whose previous data had suggested to be a suitable timepoint for a single replanning intervention: (1) replanning on the original target and deformed organ at risks (OARs) (a "safer" approach regarding tumor coverage) and (2) replanning on both deformed target and deformed OARs. DMVCT showed differences between planned and delivered doses (3D-γ 2mm/2%-passing rate = 85 ± 1%, p < 0.001). Voxel by voxel dose accumulation showed an increase in average dose of warped PG of 3.0 Gy ± 3.3 Gy. With ART the average dose of warped PG decreased by 3.2 Gy ± 1.7 Gy in comparison to delivered dose without replanning when both target and OARs were deformed. Average dose of warped PG decreased by 2.0 Gy ± 1.4 Gy when only OARs were deformed. Anatomical variations lead to increased doses to PGs. Efficient single-intervention ART-strategies with replanning on the 18th MVCT result a reduced PG dose. A strategy with deformation of both target and OAR resulted in the lowest PG dose, while formally maintaining PTV coverage. Deformation of only OAR nevertheless reduces PG dose and has less uncertainties regarding PTV coverage.

Critical appraisal of the role of volumetric modulated arc therapy in the radiation therapy management of breast cancer.

BACKGROUND: The aim of this review is the critical appraisal of the current use of volumetric modulated arc therapy for the radiation therapy management of breast cancer. Both clinical and treatment planning studies were investigated. MATERIAL AND METHODS: A Pubmed/MEDLINE search of the National Library of Medicine was performed to identify VMAT and breast related articles. After a first order rejection of the irrelevant findings, the remaining articles were grouped according to two main categories: clinical vs. planning studies and to some sub-categories (pointing to significant technical features). Main areas of application, dosimetric and clinical findings as well as areas of innovations were defined. RESULTS: A total of 131 articles were identified and of these, 67 passed a first order selection. Six studies reported clinical results while 61 treatment dealed with treatment planning investigations. Among the innovation lines, the use of high intensity photon beams (flattening filter free), altered fractionation schemes (simultaneous integrated boost, accelerated partial breast irradiation, single fraction), prone positioning and modification of standard VMAT (use of dynamic trajectories or hybrid VMAT methods) resulted among the main relevant fields of interest. Approximately 10% of the publications reported upon respiratory gating in conjunction with VMAT. CONCLUSIONS: The role of VMAT in the radiation treatment of breast cancer seems to be consolidated in the in-silico arena while still limited evidence and only one phase II trial appeared in literature from the clinical viewpoint. More clinical reports are needed to fully proove the expected dosimetric benefits demonstrated in the planning investigations.

Expert system classifier for adaptive radiation therapy in prostate cancer.

A classifier-based expert system was developed to compare delivered and planned radiation therapy in prostate cancer patients. Its aim is to automatically identify patients that can benefit from an adaptive treatment strategy. The study predominantly addresses dosimetric uncertainties and critical issues caused by motion of hollow organs. 1200 MVCT images of 38 prostate adenocarcinoma cases were analyzed. An automatic daily re-contouring of structures (i.e. rectum, bladder and femoral heads), rigid/deformable registration and dose warping was carried out to simulate dose and volume variations during therapy. Support vector machine, K-means clustering algorithms and similarity index analysis were used to create an unsupervised predictive tool to detect incorrect setup and/or morphological changes as a consequence of inadequate patient preparation due to stochastic physiological changes, supporting clinical decision-making. After training on a dataset that was considered sufficiently dosimetrically stable, the system identified two equally sized macro clusters with distinctly different volumetric and dosimetric baseline properties and defined thresholds for these two clusters. Application to the test cohort resulted in 25% of the patients located outside the two macro clusters thresholds and which were therefore suspected to be dosimetrically unstable. In these patients, over the treatment course, mean volumetric changes of 30 and 40% for rectum and bladder were detected which possibly represents values justifying adjustment of patient preparation, frequent re-planning or a plan-of-the-day strategy. Based on our research, by combining daily IGRT images with rigid/deformable registration and dose warping, it is possible to apply a machine learning approach to the clinical setting obtaining useful information for a decision regarding an individualized adaptive strategy. Especially for treatments influenced by the movement of hollow organs, this could reduce inadequate treatments and possibly reduce toxicity, thereby increasing overall RT efficacy.

SIS epidemiological model for adaptive RT: Forecasting the parotid glands shrinkage during tomotherapy treatment.

PURPOSE: A susceptible-infected-susceptible (SIS) epidemic model was applied to radiation therapy (RT) treatments to predict morphological variations in head and neck (H&N) anatomy. METHODS: 360 daily MVCT images of 12 H&N patients treated by tomotherapy were analyzed in this retrospective study. Deformable image registration (DIR) algorithms, mesh grids, and structure recontouring, implemented in the RayStation treatment planning system (TPS), were applied to assess the daily organ warping. The parotid's warping was evaluated using the epidemiological approach considering each vertex as a single subject and its deformed vector field (DVF) as an infection. Dedicated IronPython scripts were developed to export daily coordinates and displacements of the region of interest (ROI) from the TPS. matlab tools were implemented to simulate the SIS modeling. Finally, the fully trained model was applied to a new patient. RESULTS: A QUASAR phantom was used to validate the model. The patients' validation was obtained setting 0.4 cm of vertex displacement as threshold and splitting susceptible (S) and infectious (I) cases. The correlation between the epidemiological model and the parotids' trend for further optimization of alpha and beta was carried out by Euclidean and dynamic time warping (DTW) distances. The best fit with experimental conditions across all patients (Euclidean distance of 4.09 ± 1.12 and DTW distance of 2.39 ± 0.66) was obtained setting the contact rate at 7.55 ± 0.69 and the recovery rate at 2.45 ± 0.26; birth rate was disregarded in this constant population. CONCLUSIONS: Combining an epidemiological model with adaptive RT (ART), the authors' novel approach could support image-guided radiation therapy (IGRT) to validate daily setup and to forecast anatomical variations. The SIS-ART model developed could support clinical decisions in order to optimize timing of replanning achieving personalized treatments.

Radiosurgery Using Tomotherapy for Patients with Brain Oligo-metastasis: A Retrospective Analysis on Feasibility and Tolerance.

AIM: Aim of the study was to evaluate feasibility and toxicities of exclusive radiosurgery using tomotherapy in patients with brain oligo-metastases. PATIENTS AND METHODS: Between 2008 and 2013 68 patients underwent stereotactic radiosurgery (SRS). Mean patient age was 63 years. Brain was the only site involved in 32 patients, while 36 had extracranial disease. Pre-SRS MRI 56 patients had sovratentorial lesions, 10 subtentorial and 2 patients had both. Fifty-two patients had 1 brain lesion, 11 had 2, and 5 patients had three. All patients underwent SRS using Tomotherapy. The median delivered dose was 18 Gy. RESULTS: After a mean follow-up of 13 months, 14 patients were alive, while 54 patients had died. Two patients had complete response, 32 had partial response, 21 stable disease and 13 disease progression. Overall response rate was 80.9%. One- and two-year overall survival were 41,2% and 24,7%, while local control 61.5% and 37.7%. Toxicity was acceptable. CONCLUSION: SRS using tomotherapy has been proven feasible as non-invasive exclusive treatment for oligometastatic patients with good prognostic score.

A multi-layer monitoring system for clinical management of Congestive Heart Failure.

BACKGROUND: Congestive Heart Failure (CHF) is a serious cardiac condition that brings high risks of urgent hospitalization and death. Remote monitoring systems are well-suited to managing patients suffering from CHF, and can reduce deaths and re-hospitalizations, as shown by the literature, including multiple systematic reviews. METHODS: The monitoring system proposed in this paper aims at helping CHF stakeholders make appropriate decisions in managing the disease and preventing cardiac events, such as decompensation, which can lead to hospitalization or death. Monitoring activities are stratified into three layers: scheduled visits to a hospital following up on a cardiac event, home monitoring visits by nurses, and patient's self-monitoring performed at home using specialized equipment. Appropriate hardware, desktop and mobile software applications were developed to enable a patient's monitoring by all stakeholders. For the first two layers, we designed and implemented a Decision Support System (DSS) using machine learning (Random Forest algorithm) to predict the number of decompensations per year and to assess the heart failure severity based on a variety of clinical data. For the third layer, custom-designed sensors (the Blue Scale system) for electrocardiogram (EKG), pulse transit times, bio-impedance and weight allowed frequent collection of CHF-related data in the comfort of the patient's home. We also performed a short-term Heart Rate Variability (HRV) analysis on electrocardiograms self-acquired by 15 healthy volunteers and compared the obtained parameters with those of 15 CHF patients from PhysioNet's PhysioBank archives. RESULTS: We report numerical performances of the DSS, calculated as multiclass accuracy, sensitivity and specificity in a 10-fold cross-validation. The obtained average accuracies are: 71.9% in predicting the number of decompensations and 81.3% in severity assessment. The most serious class in severity assessment is detected with good sensitivity and specificity (0.87 / 0.95), while, in predicting decompensation, high specificity combined with good sensitivity prevents false alarms. The HRV parameters extracted from the self-measured EKG using the Blue Scale system of sensors are comparable with those reported in the literature about healthy people. CONCLUSIONS: The performance of DSSs trained with new patients confirmed the results of previous work, and emphasizes the strong correlation between some CHF markers, such as brain natriuretic peptide (BNP) and ejection fraction (EF), with the outputs of interest. Comparing HRV parameters from healthy volunteers with HRV parameters obtained from PhysioBank archives, we confirm the literature that considers the HRV a promising method for distinguishing healthy from CHF patients.

A support vector machine tool for adaptive tomotherapy treatments: Prediction of head and neck patients criticalities.

PURPOSE: Adaptive radiation therapy (ART) is an advanced field of radiation oncology. Image-guided radiation therapy (IGRT) methods can support daily setup and assess anatomical variations during therapy, which could prevent incorrect dose distribution and unexpected toxicities. A re-planning to correct these anatomical variations should be done daily/weekly, but to be applicable to a large number of patients, still require time consumption and resources. Using unsupervised machine learning on retrospective data, we have developed a predictive network, to identify patients that would benefit of a re-planning. METHODS: 1200 MVCT of 40 head and neck (H&N) cases were re-contoured, automatically, using deformable hybrid registration and structures mapping. Deformable algorithm and MATLAB(®) homemade machine learning process, developed, allow prediction of criticalities for Tomotherapy treatments. RESULTS: Using retrospective analysis of H&N treatments, we have investigated and predicted tumor shrinkage and organ at risk (OAR) deformations. Support vector machine (SVM) and cluster analysis have identified cases or treatment sessions with potential criticalities, based on dose and volume discrepancies between fractions. During 1st weeks of treatment, 84% of patients shown an output comparable to average standard radiation treatment behavior. Starting from the 4th week, significant morpho-dosimetric changes affect 77% of patients, suggesting need for re-planning. The comparison of treatment delivered and ART simulation was carried out with receiver operating characteristic (ROC) curves, showing monotonous increase of ROC area. CONCLUSIONS: Warping methods, supported by daily image analysis and predictive tools, can improve personalization and monitoring of each treatment, thereby minimizing anatomic and dosimetric divergences from initial constraints.