25 research outputs found
An unusual spontaneous recanalization by multiple palmar arteriovenous connections of a chronically occluded radiocephalic hemodialysis fistula
No full textPreservation of a vascular access is crucial in the management of hemodialysis patients. In this regard, percutaneous transluminal angioplasty (PTA) is an effective tool if performed after an adequate understanding of preliminary fistulograms. The present case showed a chronic dysfunction of a radial-cephalic arteriovenous fistula (AVF) due to arterial occlusion and partially relieved by the spontaneous development of multiple small arteriovenous connections in the palmar region of the hand. This dense network had been so far able to ensure a sufficient retrograde blood flow for an effective hemodialytic performance. The angioplasty of the post-anastomotic stenotic segment of the radial artery was effective in restoring this neoformed AVF patency
An unusual spontaneous recanalization by multiple palmar arteriovenous connections of a chronically occluded radiocephalic hemodialysis fistula
No full textPreservation of a vascular access is crucial in the management of hemodialysis patients. In this regard, percutaneous transluminal angioplasty (PTA) is an effective tool if performed after an adequate understanding of preliminary fistulograms. The present case showed a chronic dysfunction of a radial-cephalic arteriovenous fistula (AVF) due to arterial occlusion and partially relieved by the spontaneous development of multiple small arteriovenous connections in the palmar region of the hand. This dense network had been so far able to ensure a sufficient retrograde blood flow for an effective hemodialytic performance. The angioplasty of the post-anastomotic stenotic segment of the radial artery was effective in restoring this neoformed AVF patency
Locally advanced rectal cancer: T2w-MRI-based radiomics may detect responder patients undergoing neoadjuvant chemoradiotherapy
No full textPurpose or Learning Objective
To investigate whether in locally advanced rectal cancer (LARC) treated with neo-adjuvant chemoradiotherapy (nCRT), radiomics on T2 weighted (T2w) MRI sequences can discriminate responder (R) and non-responder (NR) patients based on the Tumour Regression Grade (TRG) assigned after surgical
resection
Methods or Background
This study retrospectively enrols 40 patients undergoing pre-therapy 1.5T-MRI. Regions of Interest (ROIs) are manually outlined in all slices of the tumour’s site on T2w sequences in the oblique-axial plane, acquired with 3 mm slice thickness. Based on TRG, R patients have complete and partial nCRT response (TRG=[0,1], n°15) while NR patients have a minimal and poor nCRT response (TRG=[2,3], n°25). Eighty-four local first-order radiomic features (RFs) are extracted from tumour ROIs. To prevent overfitting, only single RFs are investigated to discriminate Rs and NRs. The most performing feature is selected through a univariate analysis guided by one-tail Wilcoxon rank-sum test (p=0.05 significance level). To assess the feature discrimination capability, ROC curve analysis is performed, through AUC computation, Youden Index (YI) for sensitivity and specificity.
Results or Findings
One RF measuring the local heterogeneity of T2w values within tumour ROIs discriminates Rs and NRs with p~10-5, AUC=0.90 (95%CI, 0.73-0.96), with YI=0.68 corresponding to sensitivity=80% and specificity=88%. The separation achieved highlights 3 false positives and 3 false negatives.
Conclusion
Pre-therapy baseline tumour heterogeneity measured from T2w-MR images has a very promising role in predicting the TRG histological classification. Patients with lower tumour heterogeneity at pre-therapy show a better response to nCRT.
Limitations
This study involves a small number of patients. However, one-only feature is considered and such a strong discrimination stresses the future role of the feature in a classification study
Uterine Artery Embolization for the Treatment of Symptomatic Uterine Fibroids of Different Sizes: A Single Center Experience
No full textThe present study aimed to evaluate the clinical and radiological 1-year outcomes of uterine artery embolization (UAE) performed in a selected population of women with symptomatic myomas and who do not wish to conceive. Between January 2004 and January 2018, a total of 62 patients with pre-menopausal status and with no wish to conceive in the future underwent UAE for the treatment of symptomatic fibroids. All the patients underwent magnetic resonance imaging (MRI) and/or transvaginal ultrasonography (TV-US) before and after the procedure at 1-year follow-up. Clinical and radiological parameters were recorded, stratifying the population into 3 groups according to the size of the dominant myoma (group 1: 80 mm). Mean fibroid diameter was significantly reduced (42.6% & PLUSMN; 21.6%) at 1-year follow-up, with excellent improvements in terms of both symptoms and quality of life. No significant difference was observed regarding baseline dimension and the number of myomas. No major complications were reported (2.5%). The present study confirms the safety and efficacy of UAE in the treatment of symptomatic fibroids in pre-menopausal women with no desire to conceive
The prominent role of percutaneous transarterial embolization in the treatment of anterior abdominal wall hematomas: the results of three high volume tertiary referral centers
No full textPurposePercutaneous transarterial embolization (PTE) represents a fast, safe and effective option for life-threatening anterior abdominal wall hematomas (AWHs) and those unresponsive to conservative treatment. Our study aims to assess cumulative results of safety, technical and clinical success of PTE performed in three high-volume tertiary referral centers and to evaluate the efficacy of the different embolic materials employed.Materials and methodsA consecutive series of 124 patients (72.8 & PLUSMN; 14.4 years) with AWHs of different etiology submitted to PTE were retrospectively collected and analyzed. Clinical success, defined as absence of recurrent bleeding within 96 h from PTE, was considered as primary endpoint. The results of the comparison of three groups based on embolic agent employed were also analyzed.ResultsSpontaneous AWHs accounted for 62.1%, iatrogenic for 21.8% and post-traumatic for 16.1% of cases. SARS-CoV-19 infection was present in 22.6% of patients. The most commonly embolized vessels were epigastric inferior artery (n = 127) and superior epigastric artery (n = 25). Technical and clinical success were 97.6 and 87.1%, respectively. Angiographic signs of active bleeding were detected in 85.5% of cases. Four (4%) major complications were reported. The comparison of the three groups of embolic agents (mechanical, particulate/fluid and combined) showed no statistically significant differences in terms of clinical success. SARS-CoV-2 infection was found to be an independent factor for recurrent bleeding and poor 30-day survival.ConclusionPTE performed with all the embolic agent employed in our centers is a safe and effective tool in the treatment of life-threatening anterior AWH of each origin
Identification of PET/CT radiomic signature for classification of locally recurrent rectal cancer: A network-based feature selection approach
No full textBackground
The modern approach to treating rectal cancer, which involves total mesorectal excision directed by imaging assessments, has significantly enhanced patient outcomes. However, locally recurrent rectal cancer (LRRC) continues to be a significant clinical issue. Identifying LRRC through imaging is complex, due to the mismatch between fibrosis and inflammatory pelvic tissue. This work aimed to develop a machine learning model for predicting LRRC using radiomic features extracted from 18F-FDG Positron Emission Tomography/Computed Tomography (PET/CT).
Methods
CT and PET images of PET/CT examinations were retrospectively collected from 44 patients, with 29 cases of recurrence (66 %) and 15 cases with no local recurrence (34 %). The whole analysis was conducted separately for CT and PET images to evaluate their different predictive power. Radiomic features were extracted from suspected lesion volumes identified by physicians and the most relevant radiomic features were selected to predict the presence or absence of LRRC. Feature selection was performed using a novel approach derived from gene expression analysis, based on the DNetPRO algorithm. The prediction was done using a Support Vector Classifier (SVC) with a 10-fold cross-validation. The efficiency of the pipeline in predicting LRRC was evaluated according to the sensitivity, specificity, Balanced Accuracy Score (BAS) and Matthews's Correlation Coefficient (MCC).
Results
CT features were found to be the most predictive, showing a sensitivity of 0.80, a specificity of 0.82, a BAS of 0.81 and an MCC of 0.61. PET features obtained a sensitivity of 0.93, a specificity of 0.61, a BAS of 0.77 and a MCC of 0.52. The combination of PET and CT features obtained a sensitivity of 0.80, a specificity of 0.75, a BAS of 0.77 and a MCC of 0.53.
Conclusions
To the best of our knowledge, the DNetPRO algorithm was applied for the first time to medical image analysis and proved suitable for the selection of radiomic features with the highest predictive power, a crucial step in a radiomic pipeline. Our results confirmed the efficiency of radiomic features in predicting LRRC, with CT features outperforming PET features in identifying the characteristic texture of LRRC. The combination of both yielded no performance improvement
Automatically extracted machine learning features from preoperative CT to early predict microvascular invasion in HCC: the role of the Zone of Transition (ZOT)
No full textMicrovascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses of radiomic analysis. Furthermore, current MVI radiomic studies have not been designed for small HCC nodules, for which a plethora of treatments exists. This study aimed to identify radiomic MVI predictors in nodules ≤3.0 cm by analysing the zone of transition (ZOT), crossing tumour and peritumour, automatically detected to face the uncertainties of radiologist’s tumour segmentation. Methods: The study considered 117 patients imaged by contrast-enhanced computed tomography; 78 patients were finally enrolled in the radiomic analysis. Radiomic features were extracted from the tumour and the ZOT, detected using an adaptive procedure based on local image contrast variations. After data oversampling, a support vector machine classifier was developed and validated. Classifier performance was assessed using receiver operating characteristic (ROC) curve analysis and related metrics. Results: The original 89 HCC nodules (32 MVI+ and 57 MVI−) became 169 (62 MVI+ and 107 MVI−) after oversampling. Of the four features within the signature, three are ZOT heterogeneity measures regarding both arterial and venous phases. On the test set (19MVI+ and 33MVI−), the classifier predicts MVI+ with area under the curve of 0.86 (95%CI (0.70–0.93), p∼10^−5), sensitivity = 79% and specificity = 82%. The classifier showed negative and positive predictive values of 87% and 71%, respectively. Conclusions: The classifier showed the highest diagnostic performance in the literature, disclosing the role of ZOT heterogeneity in predicting the MVI+ status
An Apparent Diffusion Coefficient-Based Machine Learning Model Can Improve Prostate Cancer Detection in the Grey Area of the Prostate Imaging Reporting and Data System Category 3: A Single-Centre Experience
No full textThe Prostate Imaging and Reporting Data System (PI-RADS) has a key role in the management of prostate cancer (PCa). However, the clinical interpretation of PI-RADS 3 score lesions may be challenging and misleading, thus postponing PCa diagnosis to biopsy outcome. Multiparametric magnetic resonance imaging (mpMRI) radiomic analysis may represent a stand-alone non invasive tool for PCa diagnosis. Hence, this study aims at developing a mpMRI-based radiomic PCa diagnostic model in a cohort of PI-RADS 3 lesions. We enrolled 133 patients with 155 PI-RADS 3 lesions, 84 of which had PCa confirmation by fusion biopsy. Local radiomic features were generated from apparent diffusion coefficient maps, and the four most informative were selected using LASSO, the Wilcoxon rank-sum test (p < 0.001), and support vector machines (SVMs). The selected features where augmented and used to train an SVM classifier, externally validated on a holdout subset. Linear and second-order polynomial kernels were exploited, and their predictive performance compared through receiver operating characteristics (ROC)-related metrics. On the test set, the highest performance, equally for both kernels, was specificity = 76%, sensitivity = 78%, positive predictive value = 80%, and negative predictive value = 74%. Our findings substantially improve radiologist interpretation of PI-RADS 3 lesions and let us advance towards an image-driven PCa diagnosis
Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions
No full textThe risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal–Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG[1,2] and GG ≥ 3 exist (p < 10−6). On the test set, the area under the curve = 0.88 (95% CI, 0.68–0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3
Beyond multiparametric MRI and towards radiomics to detect prostate cancer: A machine learning model to predict clinically significant lesions
No full text
