Artefatti di ricostruzione nella tomografia computerizzata con raggi X

Gli artefatti da ricostruzione sono problemi comuni nella tomografia computerizzata con raggi X. Essi possono degradare la qualità di una immagine al punto da renderla inutilizzabile. Scopo dell’elaborato è l’analisi di diversi tipi di artefatti, a partire dai fenomeni che li causano, fino ad arrivare ai metodi impiegati per eliminarli o quantomeno ridurli. A tal fine si è proceduto ad una approfondita ricerca bibliografica e allo studio dei testi scientifici fondamentali riguardo all’argomento. Particolare attenzione è stata rivolta al fenomeno fisico del Beam Hardening, che è tra le maggiori cause di artefatti grafici, e di cui si sono viste diverse metodologie di correzione. Sono stati poi trattati gli artefatti dovuti all’apparato di misura e gli artefatti dovuti al paziente. Per quanto riguarda le tecniche di correzione, oltre ai procedimenti di più vecchia concezione e ormai consolidati, si è dato largo spazio alle metodologie innovative, di recente applicazione e ancora oggetto di studio e ricerca, come la Dual-Energy CT e la ricostruzione iterativa. Si è visto come la ricostruzione iterativa garantisca risultati di gran lunga superiori in merito alla qualità dell’immagine ricostruita, rispetto alla canonica Filtered Back-Projection, in particolare per quanto riguarda la Low-dose CT, di grande interesse in campo medico

Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer

Background: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC). Method: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor’s zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification. Results: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17. Conclusions: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models

Minimally invasive vs. open segmental resection of the splenic flexure for cancer: a nationwide study of the Italian Society of Surgical Oncology-Colorectal Cancer Network (SICO-CNN)

Background Evidence on the efficacy of minimally invasive (MI) segmental resection of splenic flexure cancer (SFC) is not available, mostly due to the rarity of this tumor. This study aimed to determine the survival outcomes of MI and open treatment, and to investigate whether MI is noninferior to open procedure regarding short-term outcomes. Methods This nationwide retrospective cohort study included all consecutive SFC segmental resections performed in 30 referral centers between 2006 and 2016. The primary endpoint assessing efficacy was the overall survival (OS). The secondary endpoints included cancer-specific mortality (CSM), recurrence rate (RR), short-term clinical outcomes (a composite of Clavien-Dindo > 2 complications and 30-day mortality), and pathological outcomes (a composite of lymph nodes removed >= 12, and proximal and distal free resection margins length >= 5 cm). For these composites, a 6% noninferiority margin was chosen based on clinical relevance estimate. Results A total of 606 patients underwent either an open (208, 34.3%) or a MI (398, 65.7%) SFC segmental resection. At univariable analysis, OS and CSM were improved in the MI group (log-rank test p = 0.004 and Gray's tests p = 0.004, respectively), while recurrences were comparable (Gray's tests p = 0.434). Cox multivariable analysis did not support that OS and CSM were better in the MI group (p = 0.109 and p = 0.163, respectively). Successful pathological outcome, observed in 53.2% of open and 58.3% of MI resections, supported noninferiority (difference 5.1%; 1-sided 95%CI - 4.7% to infinity). Successful short-term clinical outcome was documented in 93.3% of Open and 93.0% of MI procedures, and supported noninferiority as well (difference - 0.3%; 1-sided 95%CI - 5.0% to infinity). Conclusions Among patients with SFC, the minimally invasive approach met the criterion for noninferiority for postoperative complications and pathological outcomes, and was found to provide results of OS, CSM, and RR comparable to those of open resection

Colorectal Cancer Stage at Diagnosis Before vs During the COVID-19 Pandemic in Italy

IMPORTANCE Delays in screening programs and the reluctance of patients to seek medical attention because of the outbreak of SARS-CoV-2 could be associated with the risk of more advanced colorectal cancers at diagnosis. OBJECTIVE To evaluate whether the SARS-CoV-2 pandemic was associated with more advanced oncologic stage and change in clinical presentation for patients with colorectal cancer. DESIGN, SETTING, AND PARTICIPANTS This retrospective, multicenter cohort study included all 17 938 adult patients who underwent surgery for colorectal cancer from March 1, 2020, to December 31, 2021 (pandemic period), and from January 1, 2018, to February 29, 2020 (prepandemic period), in 81 participating centers in Italy, including tertiary centers and community hospitals. Follow-up was 30 days from surgery. EXPOSURES Any type of surgical procedure for colorectal cancer, including explorative surgery, palliative procedures, and atypical or segmental resections. MAIN OUTCOMES AND MEASURES The primary outcome was advanced stage of colorectal cancer at diagnosis. Secondary outcomes were distant metastasis, T4 stage, aggressive biology (defined as cancer with at least 1 of the following characteristics: signet ring cells, mucinous tumor, budding, lymphovascular invasion, perineural invasion, and lymphangitis), stenotic lesion, emergency surgery, and palliative surgery. The independent association between the pandemic period and the outcomes was assessed using multivariate random-effects logistic regression, with hospital as the cluster variable. RESULTS A total of 17 938 patients (10 007 men [55.8%]; mean [SD] age, 70.6 [12.2] years) underwent surgery for colorectal cancer: 7796 (43.5%) during the pandemic period and 10 142 (56.5%) during the prepandemic period. Logistic regression indicated that the pandemic period was significantly associated with an increased rate of advanced-stage colorectal cancer (odds ratio [OR], 1.07; 95%CI, 1.01-1.13; P = .03), aggressive biology (OR, 1.32; 95%CI, 1.15-1.53; P < .001), and stenotic lesions (OR, 1.15; 95%CI, 1.01-1.31; P = .03). CONCLUSIONS AND RELEVANCE This cohort study suggests a significant association between the SARS-CoV-2 pandemic and the risk of a more advanced oncologic stage at diagnosis among patients undergoing surgery for colorectal cancer and might indicate a potential reduction of survival for these patients

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Prediction of cancer trajectories by statistical learning on radiomic features

Radiomics refers to the analysis of quantitative features extracted from medical images including Positron Emission Tomography (PET), Computerized Tomography (CT), Magnetic Resonance Imaging (MRI), and other medical imaging techniques. Radiomic features can be used to build models providing diagnostic, prognostic, and predictive information. The aim of this work was to build a machine learning model able to predict survival probability in 85 cervical cancer patients, using the radiomics features extracted from CT and PET medical images. A thorough feature selection process was conducted employing different techniques to select the best predictors among the original features in the dataset. In particular, the Genetic Algorithm revealed to be the best of the feature selection methods employed, with promising applications in the field of radiomics. Two different survival models have been developed, a Cox proportional hazard model and a Random Survival Forest. A Decision Tree Classifier was also implemented as a further model to evaluate. All the models were trained on 80% of the available data and tested on the remaining 20%. The Concordance Index (CI) was used as the evaluation metric for the two survival models, while the area under the Receiver Operating Characteristic curve (ROC AUC) was used as the evaluation metric for the classifier. The Cox model trained using 9 selected CT features was superior to all the other models tested. It achieved a Concordance Index score of 0.71 on the test set, showing promising predictive capabilities on external data. Finally, the recurrence outcome was used as an additional feature, producing a general improvement of all the models

Sediment core analysis using artificial intelligence

Abstract Subsurface stratigraphic modeling is crucial for a variety of environmental, societal, and economic challenges. However, the need for specific sedimentological skills in sediment core analysis may constitute a limitation. Methods based on Machine Learning and Deep Learning can play a central role in automatizing this time-consuming procedure. In this work, using a robust dataset of high-resolution digital images from continuous sediment cores of Holocene age that reflect a wide spectrum of continental to shallow-marine depositional environments, we outline a novel deep-learning-based approach to perform automatic semantic segmentation directly on core images, leveraging the power of convolutional neural networks. To optimize the interpretation process and maximize scientific value, we use six sedimentary facies associations as target classes in lieu of ineffective classification methods based uniquely on lithology. We propose an automated model that can rapidly characterize sediment cores, allowing immediate guidance for stratigraphic correlation and subsurface reconstructions

Automated Wound Image Segmentation: Transfer Learning from Human to Pet via Active Semi-Supervised Learning

Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications regarding wound management for pets. Precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for chronic wounds. In this work, we introduced a novel pipeline for the segmentation of pet wound images. Starting from a model pre-trained on human-based wound images, we applied a combination of transfer learning (TL) and active semi-supervised learning (ASSL) to automatically label a large dataset. Additionally, we provided a guideline for future applications of TL+ASSL training strategy on image datasets. We compared the effectiveness of the proposed training strategy, monitoring the performance of an EfficientNet-b3 U-Net model against the lighter solution provided by a MobileNet-v2 U-Net model. We obtained 80% of correctly segmented images after five rounds of ASSL training. The EfficientNet-b3 U-Net model significantly outperformed the MobileNet-v2 one. We proved that the number of available samples is a key factor for the correct usage of ASSL training. The proposed approach is a viable solution to reduce the time required for the generation of a segmentation dataset

The impact of Distributed Generation on the European Power System - the Italian Experience

In response to the challenges of security of supply, environmental sustainability and competitiveness, which underpin the European Union (EU) integrated climate change and energy policy, the electric power sector in Europe is facing several modifications and developments towards targets for 2020 and beyond. In this context, the Distributed Generation (DG) technologies - often based on Renewable Energy Sources (RES) or cogeneration (Combined Heat and Power, CHP) - here defined according to the EU regulation as the electric power sources connected to distribution grids, are expected to play an increasingly important role throughout Europe. The present paper, after an overview on the EU policy background, reviews the current state and penetration of RES, CHP and DG technologies deployed around the EU Member States; then, it investigates the main technical issues towards the DG integration into the European power systems and their respective impacts. Afterwards, a revision of the impact of the current power system “status quo” is made and the grid planning issues that arise due to the DG presence are put forward. The paper subsequently introduces the specific planning and development issues related to the Italian high voltage network, a 60-150 kV system, in presence of DG. The Italian TSO approach to this situation, described on this paper and named “Power Collector Method” is a method that allows the connection of a sizeable amount of DG, reducing congestions by reinforcing a limited length of the 150kV network and constructing new 380/150kV substations.JRC.F.3-Energy securit

Machine and Deep Learning Algorithms for COVID-19 Mortality Prediction Using Clinical and Radiomic Features

Aim: Machine learning (ML) and deep learning (DL) predictive models have been employed widely in clinical settings. Their potential support and aid to the clinician of providing an objective measure that can be shared among different centers enables the possibility of building more robust multicentric studies. This study aimed to propose a user-friendly and low-cost tool for COVID-19 mortality prediction using both an ML and a DL approach. Method: We enrolled 2348 patients from several hospitals in the Province of Reggio Emilia. Overall, 19 clinical features were provided by the Radiology Units of Azienda USL-IRCCS of Reggio Emilia, and 5892 radiomic features were extracted from each COVID-19 patient’s high-resolution computed tomography. We built and trained two classifiers to predict COVID-19 mortality: a machine learning algorithm, or support vector machine (SVM), and a deep learning model, or feedforward neural network (FNN). In order to evaluate the impact of the different feature sets on the final performance of the classifiers, we repeated the training session three times, first using only clinical features, then employing only radiomic features, and finally combining both information. Results: We obtained similar performances for both the machine learning and deep learning algorithms, with the best area under the receiver operating characteristic (ROC) curve, or AUC, obtained exploiting both clinical and radiomic information: 0.803 for the machine learning model and 0.864 for the deep learning model. Conclusions: Our work, performed on large and heterogeneous datasets (i.e., data from different CT scanners), confirms the results obtained in the recent literature. Such algorithms have the potential to be included in a clinical practice framework since they can not only be applied to COVID-19 mortality prediction but also to other classification problems such as diabetic prediction, asthma prediction, and cancer metastases prediction. Our study proves that the lesion’s inhomogeneity depicted by radiomic features combined with clinical information is relevant for COVID-19 mortality prediction