1,721,039 research outputs found
Identification of the most significant magnetic resonance imaging (MRI) radiomic features in oncological patients with vertebral bone marrow metastatic disease: a feasibility study
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Application of Artificial Neural Network to Preoperative 18F-FDG PET/CT for Predicting Pathological Nodal Involvement in Non-small-cell Lung Cancer Patients
Get PDFPurpose: To evaluate the performance of artificial neural networks (aNN) applied to
preoperative 18F-FDG PET/CT for predicting nodal involvement in non-small-cell lung
cancer (NSCLC) patients.
Methods: We retrospectively analyzed data from 540 clinically resectable NSCLC
patients (333 M; 67.4 ± 9 years) undergone preoperative 18F-FDG PET/CT and
pulmonary resection with hilo-mediastinal lymphadenectomy. A 3-layers NN model
was applied (dataset randomly splitted into 2/3 training and 1/3 testing). Using
histopathological reference standard, NN performance for nodal involvement (N0/N+
patient) was calculated by ROC analysis in terms of: area under the curve (AUC), accuracy
(ACC), sensitivity (SE), specificity (SP), positive and negative predictive values (PPV, NPV).
Diagnostic performance of PET visual analysis (N+ patient: at least one node with uptake
mediastinal blood-pool) and of logistic regression (LR) was evaluated.
Results: Histology proved 108/540 (20%) nodal-metastatic patients. Among all
collected data, relevant features selected as input parameters were: patients’ age, tumor
parameters (size, PET visual and semiquantitative features, histotype, grading), PET
visual nodal result (patient-based, as N0/N+ and N0/N1/N2). Training and testing NN
performance (AUC = 0.849, 0.769): ACC = 80 and 77%; SE = 72 and 58%; SP
= 81 and 81%; PPV = 50 and 44%; NPV = 92 and 89%, respectively. Visual PET
performance: ACC = 82%, SE = 32%, SP = 94%; PPV = 57%, NPV = 85%. Training
and testing LR performance (AUC = 0.795, 0.763): ACC = 75 and 77%; SE = 68
and 55%; SP = 77 and 82%; PPV = 43 and 43%; NPV = 90 and 88%, respectively..Conclusions: aNN application to preoperative 18F-FDG PET/CT provides overall good
performance for predicting nodal involvement in NSCLC patients candidate to surgery,
especially for ruling out nodal metastases, being NPV the best diagnostic result; a high
NPV was also reached by PET qualitative assessment. Moreover, in such population
with low a priori nodal involvement probability, aNN better identify the relatively few and
unexpected nodal-metastatic patients than PET analysis, so supporting the additional
aNN use in case of PET-negative images
Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma
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A Comprehensive Natural Language Processing Pipeline for the Chronic Lupus Disease
Get PDF: Electronic Health Records (EHRs) contain a wealth of unstructured patient data, making it challenging for physicians to do informed decisions. In this paper, we introduce a Natural Language Processing (NLP) approach for the extraction of therapies, diagnosis, and symptoms from ambulatory EHRs of patients with chronic Lupus disease. We aim to demonstrate the effort of a comprehensive pipeline where a rule-based system is combined with text segmentation, transformer-based topic analysis and clinical ontology, in order to enhance text preprocessing and automate rules' identification. Our approach is applied on a sub-cohort of 56 patients, with a total of 750 EHRs written in Italian language, achieving an Accuracy and an F-score over 97% and 90% respectively, in the three extracted domains. This work has the potential to be integrated with EHR systems to automate information extraction, minimizing the human intervention, and providing personalized digital solutions in the chronic Lupus disease domain
A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients
Get PDFPurposeDistant metastasis is the main cause of treatment failure in locally advanced rectal cancer (LARC) patients, despite the recent improvement in treatment strategies. This study aims to evaluate the "delta radiomics" approach in patients undergoing neoadjuvant chemoradiotherapy (nCRT) treated with 0.35-T magnetic resonance-guided radiotherapy (MRgRT), developing a logistic regression model able to predict 2-year disease-free-survival (2yDFS). MethodsPatients affected by LARC were enrolled in this multi-institutional study. A predictive model of 2yDFS was developed taking into account both clinical and radiomics variables. Gross tumour volume (GTV) was delineated on the magnetic resonance (MR) images acquired during MRgRT, and 1,067 radiomic features (RF) were extracted using the MODDICOM platform. The performance of RF in predicting 2yDFS was investigated in terms of the Wilcoxon-Mann-Whitney test and area under receiver operating characteristic (ROC) curve (AUC). Results48 patients have been retrospectively enrolled, with 8 patients (16.7%) developing distant metastases at the 2-year follow-up. A total of 1,099 variables (1,067 RF and 32 clinical variables) were evaluated in two different models: radiomics and radiomics/clinical. The best-performing 2yDFS predictive model was a delta radiomics one, based on the variation in terms of area/surface ratio between biologically effective doses (BED) at 54 Gy and simulation (AUC of 0.92). ConclusionsThe results of this study suggest a promising role of delta radiomics analysis on 0.35-T MR images in predicting 2yDFS for LARC patients. Further analyses including larger cohorts of patients and an external validation are needed to confirm these preliminary results
Germline BRCA 1-2 status prediction through ovarian ultrasound images radiogenomics: a hypothesis generating study (PROBE study)
Get PDFRadiogenomics is a specific application of radiomics where imaging features are linked to genomic profiles. We aim to develop a radiogenomics model based on ovarian US images for predicting germline BRCA1/2 gene status in women with healthy ovaries. From January 2013 to December 2017 a total of 255 patients addressed to germline BRCA1/2 testing and pelvic US documenting normal ovaries, were retrospectively included. Feature selection for univariate analysis was carried out via correlation analysis. Multivariable analysis for classification of germline BRCA1/2 status was then carried out via logistic regression, support vector machine, ensemble of decision trees and automated machine learning pipelines. Data were split into a training (75%) and a testing (25%) set. The four strategies obtained a similar performance in terms of accuracy on the testing set (from 0.54 of logistic regression to 0.64 of the auto-machine learning pipeline). Data coming from one of the tested US machine showed generally higher performances, particularly with the auto-machine learning pipeline (testing set specificity 0.87, negative predictive value 0.73, accuracy value 0.72 and 0.79 on training set). The study shows that a radiogenomics model on machine learning techniques is feasible and potentially useful for predicting gBRCA1/2 status in women with healthy ovaries
A deep learning approach to generate synthetic CT in low field MR-guided adaptive radiotherapy for abdominal and pelvic cases
No full textPurpose: Artificial intelligence (AI) can play a significant role in Magnetic Resonance guided Radiotherapy (MRgRT), especially to speed up the online adaptive workflow. The aim of this study is to set up a Deep Learning (DL) approach able to generate synthetic computed tomography (sCT) images from low field MR images in pelvis and abdomen. Methods: A conditional Generative Adversarial Network (cGAN) was used for sCT generation: a total of 120 patients treated on pelvic and abdominal sites were enrolled and divided in training (80) and test sets (40). Intensity modulated radiotherapy (IMRT) treatment plans were calculated on sCT and original CT and then compared in terms of gamma analysis and differences in Dose Volume Histogram (DVH). The two one-sided test for paired samples (TOST-P) was used to evaluate the equivalence among different DVH parameters calculated for target and organs at risks (OAR) on CT and sCT images. Results: Using a CPU architecture, the mean time required by the neural network to generate a synthetic CT was 175 ± 43 seconds (s) for pelvic cases and 110 ± 40 s for abdominal ones. Mean gamma passing rates for the three tolerance criteria analysed (1%/1 mm, 2%/2 mm and 3%/3 mm) were respectively 90.8 ± 4.5%, 98.7 ± 1.1% and 99.8 ± 0.2% for abdominal cases; 89.3 ± 4.8%, 99.0 ± 0.7% and 99.9 ± 0.2% for pelvic ones, while equivalence within 1% was observed among the DVH indicators. Conclusion: This study demonstrated that sCT generation using a DL approach is feasible for low field MR images in pelvis and abdomen, allowing a reliable calculation of IMRT plans in MRgRT
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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