Biomarker comparison and selection for prostate cancer detection in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI)

[EN] In this work, the capability of imaging biomarkers obtained from multivariate curve resolution-alternating least squares (MCR-ALS), in combination with those obtained from first and second-generation pharmacokinetic models, have been studied for improving prostate cancer tumor depiction using partial least squares- discriminant analysis (PLS-DA). The main goal of this work is to improve tissue classification properties selecting the best biomarkers in terms of prediction. A wrapped double cross-validation method has been applied for the variable selection process. Using the best PLS-DA model, prostate tissues can be classified obtaining 13.4% of false negatives and 7.4% of false positives. Using MCR-ALS biomarkers yields the best models in terms of parsimony and classification performance.This research has been supported by "Generalitat Valenciana (Conselleria d'Educacio, Investigacio, Cultura I Esport)" under the project AICO/2016/061.Aguado-Sarrió, E.; Prats-Montalbán, JM.; Sanz-Requena, R.; Garcia-Marti, G.; Marti-Bonmati, L.; Ferrer, A. (2017). Biomarker comparison and selection for prostate cancer detection in Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI). Chemometrics and Intelligent Laboratory Systems. 165:38-45. https://doi.org/10.1016/j.chemolab.2017.04.003S384516

k-Space tutorial: an MRI educational tool for a better understanding of k-space

A main difference between Magnetic Resonance (MR) imaging and other medical imaging modalities is the control over the data acquisition and how it can be managed to finally show the adequate reconstructed image. With some basic programming adjustments, the user can modify the spatial resolution, field of view (FOV), image contrast, acquisition velocity, artifacts and so many other parameters that will contribute to form the final image. The main character and agent of all this control is called k-space, which represents the matrix where the MR data will be stored previously to a Fourier transformation to obtain the desired image

A practical solution to estimate the sample size required for clinical prediction models generated from observational research on data

[EN] Background Estimating the required sample size is crucial when developing and validating clinical prediction models. However, there is no consensus about how to determine the sample size in such a setting. Here, the goal was to compare available methods to define a practical solution to sample size estimation for clinical predictive models, as applied to Horizon 2020 PRIMAGE as a case study. Methods Three different methods (Riley's; "rule of thumb" with 10 and 5 events per predictor) were employed to calculate the sample size required to develop predictive models to analyse the variation in sample size as a function of different parameters. Subsequently, the sample size for model validation was also estimated. Results To develop reliable predictive models, 1397 neuroblastoma patients are required, 1060 high-risk neuroblastoma patients and 1345 diffuse intrinsic pontine glioma (DIPG) patients. This sample size can be lowered by reducing the number of variables included in the model, by including direct measures of the outcome to be predicted and/or by increasing the follow-up period. For model validation, the estimated sample size resulted to be 326 patients for neuroblastoma, 246 for high-risk neuroblastoma, and 592 for DIPG. Conclusions Given the variability of the different sample sizes obtained, we recommend using methods based on epidemiological data and the nature of the results, as the results are tailored to the specific clinical problem. In addition, sample size can be reduced by lowering the number of parameter predictors, by including direct measures of the outcome of interest.This work is funded by the HORIZON2020 PRIMAGE project (RIA, topic SC1DTH 07-2018), from the EU Framework Programme for Research and Innovation of the European Commission.Baeza-Delgado, C.; Cerdá Alberich, L.; Carot Sierra, JM.; Veiga-Canuto, D.; Martinez De Las Heras, B.; Raza, B.; Marti-Bonmati, L. (2022). A practical solution to estimate the sample size required for clinical prediction models generated from observational research on data. European Radiology Experimental. 6(1). https://doi.org/10.1186/s41747-022-00276-y6

Machine Learning-Based Integration of Prognostic Magnetic Resonance Imaging Biomarkers for Myometrial Invasion Stratification in Endometrial Cancer

[EN] Background: Estimation of the depth of myometrial invasion (MI) in endometrial cancer is pivotal in the preoperatively staging. Magnetic resonance (MR) reports suffer from human subjectivity. Multiparametric MR imaging radiomics and parameters may improve the diagnostic accuracy. Purpose: To discriminate between patients with MI ¿ 50% using a machine learning-based model combining texture features and descriptors from preoperatively MR images. Study Type: Retrospective. Population: One hundred forty-three women with endometrial cancer were included. The series was split into training (n = 107, 46 with MI ¿ 50%) and test (n = 36, 16 with MI ¿ 50%) cohorts. Field Strength/Sequences: Fast spin echo T2-weighted (T2W), diffusion-weighted (DW), and T1-weighted gradient echo dynamic contrast-enhanced (DCE) sequences were obtained at 1.5 or 3 T magnets. Assessment: Tumors were manually segmented slice-by-slice. Texture metrics were calculated from T2W and ADC map images. Also, the apparent diffusion coefficient (ADC), wash-in slope, wash-out slope, initial area under the curve at 60 sec and at 90 sec, initial slope, time to peak and peak amplitude maps from DCE sequences were obtained as parameters. MR diagnostic models using single-sequence features and a combination of features and parameters from the three sequences were built to estimate MI using Adaboost methods. The pathological depth of MI was used as gold standard. Statistical Test: Area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, precision and recall were computed to assess the Adaboost models performance. Results: The diagnostic model based on the features and parameters combination showed the best performance to depict patient with MI ¿ 50% in the test cohort (accuracy = 86.1% and AUROC = 87.1%). The rest of diagnostic models showed a worse accuracy (accuracy = 41.67%¿63.89% and AUROC = 41.43%¿63.13%). Data Conclusion: The model combining the texture features from T2W and ADC map images with the semi-quantitative parameters from DW and DCE series allow the preoperative estimation of myometrial invasion. Evidence Level: 4 Technical Efficacy: Stage 3This study received funding from the Global Investigator Initiated Research Committee (GIIRC) research program by Bracco S.p.A (2015/0724). The funders had no role in study design, data collection and analysis and preparation of the manuscript.Rodriguez Ortega, A.; Alegre, A.; Lago, V.; Carot Sierra, JM.; Ten-Esteve, A.; Montoliu, G.; Domingo, S.... (2021). Machine Learning-Based Integration of Prognostic Magnetic Resonance Imaging Biomarkers for Myometrial Invasion Stratification in Endometrial Cancer. Journal of Magnetic Resonance Imaging. 54(3):987-995. https://doi.org/10.1002/jmri.27625S98799554

Undifferentiated liver sarcoma – rare entity: a case report and review of the literature

<p>Abstract</p> <p>Introduction</p> <p>Undifferentiated Liver Sarcoma, also known as Undifferentiated Embryonal Sarcoma of the Liver, is a rare, highly malignant neoplasm which affects mostly the pediatric population, although a few cases have been reported in adults. It accounts for about 13% of pediatric hepatic malignancies.</p> <p>Case presentation</p> <p>We report a case of undifferentiated liver sarcoma in a 14-year-old Chinese boy who presented with non-specific right hypochondriac pain. Exploratory laparotomy with tumor resection was performed, followed by adjuvant chemotherapy.</p> <p>Conclusion</p> <p>Undifferentiated Liver Sarcoma is a rare, highly malignant hepatic neoplasm affecting almost exclusively the pediatric population. The prognosis is poor but recent evidence shows that long-term survival is possible after complete surgical resection and postoperative chemotherapy.</p

Safety of meglumine gadoterate (Gd-DOTA)-enhanced MRI compared to unenhanced MRI in patients with chronic kidney disease (RESCUE study)

OBJECTIVE: To prospectively compare the renal safety of meglumine gadoterate (Gd-DOTA)-enhanced magnetic resonance imaging (MRI) to a control group (unenhanced MRI) in high-risk patients. METHODS: Patients with chronic kidney disease (CKD) scheduled for MRI procedures were screened. The primary endpoint was the percentage of patients with an elevation of serum creatinine levels, measured 72 ± 24 h after the MRI procedure, by at least 25 % or 44.2 μmol/l (0.5 mg/dl) from baseline. A non-inferiority margin of the between-group difference was set at −15 % for statistical analysis of the primary endpoint. Main secondary endpoints were the variation in serum creatinine and eGFR values between baseline and 72 ± 24 h after MRI and the percentage of patients with a decrease in eGFR of at least 25 % from baseline. Patients were screened for signs of nephrogenic systemic fibrosis (NSF) at 3-month follow-up. RESULTS: Among the 114 evaluable patients, one (1.4 %) in the Gd-DOTA-MRI group and none in the control group met the criteria of the primary endpoint [Δ = −1.4 %, 95%CI = (−7.9 %; 6.7 %)]. Non-inferiority was therefore demonstrated (P = 0.001). No clinically significant differences were observed between groups for the secondary endpoints. No serious safety events (including NSF) were noted. CONCLUSION: Meglumine gadoterate did not affect renal function and was a safe contrast agent in patients with CKD. KEY POINTS: • Contrast-induced nephropathy (CIN) is a potential problem following gadolinium administration for MRI. • Meglumine gadoterate (Gd-DOTA) appears safe, even in patients with chronic kidney disease. • Gd-DOTA only caused a temporary creatinine level increase in 1/70 such patients. • No case or sign of NSF was detected at 3-month follow-up

Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images

[EN] Simple Summary Tumor segmentation is a key step in oncologic imaging processing and is a time-consuming process usually performed manually by radiologists. To facilitate it, there is growing interest in applying deep-learning segmentation algorithms. Thus, we explore the variability between two observers performing manual segmentation and use the state-of-the-art deep learning architecture nnU-Net to develop a model to detect and segment neuroblastic tumors on MR images. We were able to show that the variability between nnU-Net and manual segmentation is similar to the inter-observer variability in manual segmentation. Furthermore, we compared the time needed to manually segment the tumors from scratch with the time required for the automatic model to segment the same cases, with posterior human validation with manual adjustment when needed. Tumor segmentation is one of the key steps in imaging processing. The goals of this study were to assess the inter-observer variability in manual segmentation of neuroblastic tumors and to analyze whether the state-of-the-art deep learning architecture nnU-Net can provide a robust solution to detect and segment tumors on MR images. A retrospective multicenter study of 132 patients with neuroblastic tumors was performed. Dice Similarity Coefficient (DSC) and Area Under the Receiver Operating Characteristic Curve (AUC ROC) were used to compare segmentation sets. Two more metrics were elaborated to understand the direction of the errors: the modified version of False Positive (FPRm) and False Negative (FNR) rates. Two radiologists manually segmented 46 tumors and a comparative study was performed. nnU-Net was trained-tuned with 106 cases divided into five balanced folds to perform cross-validation. The five resulting models were used as an ensemble solution to measure training (n = 106) and validation (n = 26) performance, independently. The time needed by the model to automatically segment 20 cases was compared to the time required for manual segmentation. The median DSC for manual segmentation sets was 0.969 (+/- 0.032 IQR). The median DSC for the automatic tool was 0.965 (+/- 0.018 IQR). The automatic segmentation model achieved a better performance regarding the FPRm. MR images segmentation variability is similar between radiologists and nnU-Net. Time leverage when using the automatic model with posterior visual validation and manual adjustment corresponds to 92.8%.This study was funded by PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, empowered by imaging biomarkers), a Horizon 2020 | RIA project (Topic SC1-DTH-07-2018), grant agreement no: 826494.Veiga-Canuto, D.; Cerdà-Alberich, L.; Sangüesa Nebot, C.; Martínez De Las Heras, B.; Pötschger, U.; Gabelloni, M.; Carot Sierra, JM.... (2022). Comparative Multicentric Evaluation of Inter-Observer Variability in Manual and Automatic Segmentation of Neuroblastic Tumors in Magnetic Resonance Images. Cancers. 14(15):1-15. https://doi.org/10.3390/cancers14153648115141

Intrabiliary rupture of liver hydatid cyst: a case report and review of the literature

Herein, we report a 66 year old woman who was diagnosed to have intrabiliary rupture of liver hydatid cyst with demonstrative computed tomography, magnetic resonance imaging, and magnetic resonance cholangiopancreatography findings, with a review of the literature