Monitoring the evolution of myocarditis following COVID-19 mRNA vaccination with serial cardiac magnetic resonance imaging

With the ongoing vaccination campaign against coronavirus disease-19 (COVID-19), cases of myocarditis associated with mRNA vaccination have been increasingly recognized. We describe here the case of a young male patient who developed chest pain and increased troponin 4 days after vaccination with Pfizer-BioNTech COVID-19 vaccine

Segmenting MR images by level-set algorithms for perspective colorectal cancer diagnosis

Segmentation is an essential and crucial step in interpreting medical images for possible treatment. Medical image segmentation is very chaotic procedure as medical image may have different structures of same organ in different image modalities and may also have different features in different image slices of same modality. In this work, we present a comparison of segmentation algorithms based on level set methods, viz. Caselles, Chan & Vese, Li, Lankton, Bernard, and Shi algorithms. We assessed these algorithms with our T2-weighted colorectal MR images using Dice coefficient that measures the similarity between the reference sketched by specialist and the segmentation result produced by each algorithm. In addition, computational time taken by each algorithm to perform the segmentation is also computed. Our results on average Dice coefficient and average time computation demonstrate that Bernard has the lowest average Dice coefficient and the highest computational complexity followed by Li which has second lowest Dice coefficient and highest computational complexity. Lankton has achieved satisfactory results on average Dice coefficient and computational complexity followed by Chan & Vese and Shi. Whereas, Caselles algorithm outperforms than all with respect to average Dice coefficient and computational time

HARALICK’S TEXTURE ANALYSIS APPLIED TO COLORECTAL T2-WEIGHTED MRI: A PRELIMINARY STUDY OF SIGNIFICANCE FOR CANCER EVOLUTION

Haralick’s features have been extensively used in texture analysis of medical images. In this contribution, we have applied Haralick’s to T2-weighted colorectal MRI for a possible cancer evaluation. In particular, the T2-MRI images of 8 patients with colorectal pathology were identified as early stage malignant and later stage malignant using the whole amount of follow-up exams by radiologists. 192 Haralick’s textural features were computed from normalized gray level co-occurrence matrix with respect to four different directions. Mean and standard deviation were also calculated for the extracted features to assess the statistical significance of results. Among all the extracted features, only 5 from 14 Haralick’s textural features (viz. energy, contrast, correlation, entropy and inverse difference moment (IDM)) were found as significant for colorectal cancer evaluation. In future research, these five Haralick’s textural features may be useful to detect and evaluate colorectal cancer as well as constitute a basis for predicting the prognostic trend of the disease

Automatic segmentation of colorectal cancer in 3D MRI by combining deep learning and 3D level-set algorithm-a preliminary study

In this paper, a novel method to automatically segment colorectal cancer from 3D MR images based on combination of 3D fully convolutional neural networks (3D-FCNNs) and 3D level-set is proposed. The 3D-level set is incorporated in the 3D-FCNNs aiming at: i) a fine-tuning of the training phase; ii) a refinement of the outputs during the testing phase by integrating smoothing function and prior information in a post-processing step. The proposed method is assessed and compared with 3D-FCNNs without 3D-level set (3D-FCNNs alone) in terms of Dice Similarity Coefficient (DSC) as a performance metric. The proposed method showed higher DSC than 3D-FCNNs alone on both training and testing data set as, (0.91813 vs 0.8568) and (0.9378 vs 0.86238), respectively. Our results on 3D colorectal MRI data demonstrated that the proposed method gives better and accurate segmentation results than 3D-FCNNs alone

Diffusion-weighted magnetic resonance imaging in locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy

Abstract Purpose: To analyze diffusion-weighted magnetic resonance imaging (DW-MRI) for treatment response assessment in locally advanced rectal cancer (LARC). Patients and methods: Patients with histologically proven rectal adenocarcinoma, stage IIeIII disease, were enrolled and underwent surgery following neoadjuvant chemoradiotherapy (nCRT). All patients were referred for a DW-MRI protocol on a 3 Tesla MR-system, consisting of axial T2-weighted and DWI sequences prior (I), during (II) and after (III) nCRT. Corresponding apparent diffusion coefficient (ADC) values were calculated. Results: Between February 2011 and June 2015, 37 patients participated in the study. All patients completed programmed treatment. Overall, 11 patients (29.7%) had pathologic complete response (pCR). No correlation between the mean pre- (ADC-I), during (ADC-II), post- (ADC-III) ADC and the reduction in tumor size after nCRT was recorded. No substantial difference in the ADC distribution was found between pCR and no-pCR patients. The ADC-II level significantly increased in the pCR cases (T ¼ 1.675; p < 0.05). Conclusion: ADC value could be useful for discriminating between the pCR patients and the no-pCR patients. Further studies are necessary to identify the optimal MRI parameters combination to predict tumor response to nCRT. It is hoped that these data will provide the basis for a more solid scientific evidenc

Adverse events of computed tomography colonography: an Italian National Survey

AIM: To retrospectively study the frequency and magnitude of complications associated with computed tomography (CT) colonography in clinical practice. METHODS: A questionnaire on complications of CT colonography was sent to Italian public radiology departments identified as practicing CT colonography with a reasonable level of training. The frequency of complications and possible risk factors were retrospectively determined. Responses were collated and row frequencies determined. A multivariate analysis of the factors causing adverse events was also performed. RESULTS: 40,121 examinations were performed in13 centers during the study period. No deaths were reported. Bowel perforations occurred in 0.02% (7 exams). All perforations were asymptomatic and occurred in patients undergoing manual insufflation. Five perforations (71%) occurred in procedures performed following a recent colonoscopy. There was no significant difference between perforations associated with rectal balloon (0.017%) and those that were not (0.02%). Complications related to vasovagal reaction (either with or without spasmolytic) occurred in 0.16% (63 exams). All vasovagal reactions resolved in less than 3h, without any sequelae. CONCLUSIONS: Perforation rate at CT colonography in Italy is comparable with elsewhere in the world, occurring regardless of the experience of radiology centers. Although the risk is very small, it may not be negligible when compared with the risk of diagnostic colonoscopy

Texture analysis as imaging biomarker of tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3-T magnetic resonance

PURPOSE: The aim of this study was to determine whether texture features of rectal cancer on T2-weighted (T2w) magnetic resonance images can predict tumoral response in patients treated with neoadjuvant chemoradiotherapy (CRT). MATERIALS AND METHODS: We prospectively enrolled 15 consecutive patients (6 women, 63.2 ± 13.4 years) with rectal cancer, who underwent pretreatment and midtreatment 3-T magnetic resonance imaging. Treatment protocol consisted of neoadjuvant CRT with oxaliplatin and 5-fluorouracile. Texture analysis using a filtration-histogram technique was performed using a commercial research software algorithm (TexRAD Ltd, Somerset, England, United Kingdom) on unenhanced axial T2w images by manually delineating a region of interest around the tumor outline for the largest cross-sectional area. The technique selectively filters and extracts textures at different anatomic scales followed by quantification of the histogram using kurtosis, entropy, skewness, and mean value of positive pixels. After CRT, all patients underwent complete surgical resection and the surgical specimen served as the gold standard. RESULTS: Six patients showed pathological complete response (pCR), and 4 patients, partial response (PR). Five patients were classified as nonresponders (NRs). Pretreatment medium texture-scale quantified as kurtosis was significantly lower in the pCR subgroup in comparison with the PR + NR subgroup (P = 0.01). Midtreatment kurtosis without filtration was significantly higher in pCR in comparison with PR + NR (P = 0.045). The change in kurtosis between midtreatment and pretreatment images was significantly lower in the PR + NR subgroup compared with the pCR subgroup (P = 0.038). Pretreatment area under the receiver operating characteristic curves, to discriminate between pCR and PR + NR, was significantly higher for kurtosis (0.907, P &lt; 0.001) compared with all other parameters. The optimal cutoff value for pretreatment kurtosis was 0.19 or less. Using this value, the sensitivity and specificity for pCR prediction were 100% and 77.8%, respectively. CONCLUSION: Texture parameters derived from T2w images of rectal cancer have the potential to act as imaging biomarkers of tumoral response to neoadjuvant CRT