Evaluation of tongue squamous cell carcinoma resection margins using ex-vivo MR

Contains fulltext : 174271.pdf (publisher's version ) (Open Access)PURPOSE: Purpose of this feasibility study was (1) to evaluate whether application of ex-vivo 7T MR of the resected tongue specimen containing squamous cell carcinoma may provide information on the resection margin status and (2) to evaluate the research and developmental issues that have to be solved for this technique to have the beneficial impact on clinical outcome that we expect: better oncologic and functional outcomes, better quality of life, and lower costs. METHODS: We performed a non-blinded validation of ex-vivo 7T MR to detect the tongue squamous cell carcinoma and resection margin in 10 fresh tongue specimens using histopathology as gold standard. RESULTS: In six of seven specimens with a histopathologically determined invasion depth of the tumor of [Formula: see text] mm, the tumor could be recognized on MR, with a resection margin within a 2 mm range as compared to histopathology. In three specimens with an invasion depth of [Formula: see text] mm, the tumor was not visible on MR. Technical limitations mainly included scan time, image resolution, and the fact that we used a less available small-bore 7T MR machine. CONCLUSION: Ex-vivo 7T probably will have a low negative predictive value but a high positive predictive value, meaning that in tumors thicker than a few millimeters we expect to be able to predict whether the resection margin is too small. A randomized controlled trial needs to be performed to show our hypothesis: better oncologic and functional outcomes, better quality of life, and lower costs

El narcoperiodismo de García Márquez: uma análise dos aspectos da narcoliteratura no livro-reportagem Notícia de um sequestro

Desde os anos 1970, a cobertura da mídia tradicional sobre o narcotráfico caracterizou-se pela superficialidade de suas narrativas cujo processo impossibilita a profundidade de análise. Porém, alguns repórteres foram bem-sucedidos ao aproximar o narcotráfico e o jornalismo literário, rompendo com essa barreira limitante, principalmente, a partir da produção de livros-reportagem. O tema influenciou a literatura do continente (originando termos como narcoliteratura, narconarrativa e narcocultura), bem como o contexto do tráfico de drogas proporcionou a produção editorial de obras de não ficção, a partir dos final dos anos 80, atingindo o ápice nos anos 90 e 2000. Desta forma, este artigo discute o papel do livro-reportagem para a produção cultural da narcoliteratura, a partir de uma análise de seus aspectos dentro da obra jornalística Notícia de um sequestro (1996), de Gabriel García Márquez. O artigo está apoiado nos conceitos de livro-reportagem, de Edvaldo Pereira Lima e nas discussões sobre narcocultura, de Omar Rincón e de Diana Palaversich

Computerized detection of cancer in multi-parametric prostate MRI

Contains fulltext : 134514.pdf (publisher's version ) (Open Access)Radboud Universiteit Nijmegen, 23 januari 2015Promotores : Karssemeijer, N., Barentsz, J.O. Co-promotor : Huisman, H.J

Lymph node detection in MR Lymphography: false positive reduction using multi-view convolutional neural networks

Contains fulltext : 215826.pdf (publisher's version ) (Open Access)Purpose: To investigate whether multi-view convolutional neural networks can improve a fully automated lymph node detection system for pelvic MR Lymphography (MRL) images of patients with prostate cancer. Methods: A fully automated computer-aided detection (CAD) system had been previously developed to detect lymph nodes in MRL studies. The CAD system was extended with three types of 2D multi-view convolutional neural networks (CNN) aiming to reduce false positives (FP). A 2D multi-view CNN is an efficient approximation of a 3D CNN, and three types were evaluated: a 1-view, 3-view, and 9-view 2D CNN. The three deep learning CNN architectures were trained and configured on retrospective data of 240 prostate cancer patients that received MRL images as the standard of care between January 2008 and April 2010. The MRL used ferumoxtran-10 as a contrast agent and comprised at least two imaging sequences: a 3D T1-weighted and a 3D T2*-weighted sequence. A total of 5089 lymph nodes were annotated by two expert readers, reading in consensus. A first experiment compared the performance with and without CNNs and a second experiment compared the individual contribution of the 1-view, 3-view, or 9-view architecture to the performance. The performances were visually compared using free-receiver operating characteristic (FROC) analysis and statistically compared using partial area under the FROC curve analysis. Training and analysis were performed using bootstrapped FROC and 5-fold cross-validation. Results: Adding multi-view CNNs significantly (p < 0.01) reduced false positive detections. The 3-view and 9-view CNN outperformed (p < 0.01) the 1-view CNN, reducing FP from 20.6 to 7.8/image at 80% sensitivity. Conclusion: Multi-view convolutional neural networks significantly reduce false positives in a lymph node detection system for MRL images, and three orthogonal views are sufficient. At the achieved level of performance, CAD for MRL may help speed up finding lymph nodes and assessing them for potential metastatic involvement

Automated multistructure atlas-assisted detection of lymph nodes using pelvic MR lymphography in prostate cancer patients

PURPOSE: To investigate whether atlas-based anatomical information can improve a fully automated lymph node detection system for pelvic MR lymphography (MRL) images of patients with prostate cancer. METHODS: Their data set contained MRL images of 240 prostate cancer patients who had an MRL as part of their clinical work-up between January 2008 and April 2010, with ferumoxtran-10 as contrast agent. Each MRL consisted of at least a 3D T1-weighted sequence, a 3D T2*-weighted sequence, and a FLASH-3D sequence. The reference standard was created by two expert readers, reading in consensus, who annotated and interactively segmented the lymph nodes in all MRL studies. A total of 5089 lymph nodes were annotated. A fully automated computer-aided detection (CAD) system was developed to find lymph nodes in the MRL studies. The system incorporates voxel features based on image intensities, the Hessian matrix, and spatial position. After feature calculation, a GentleBoost-classifier in combination with local maxima detection was used to identify lymph node candidates. Multiatlas based anatomical information was added to the CAD system to assess whether this could improve performance. Using histogram analysis and free-receiver operating characteristic analysis, this was compared to a strategy where relative position features were used to encode anatomical information. RESULTS: Adding atlas-based anatomical information to the CAD system reduced false positive detections both visually and quantitatively. Median likelihood values of false positives decreased significantly in all annotated anatomical structures. The sensitivity increased from 53% to 70% at 10 false positives per lymph node. CONCLUSIONS: Adding anatomical information through atlas registration significantly improves an automated lymph node detection system for MRL images

Pharmacokinetic models in clinical practice : what model to use for DCE-MRI of the breast?

Pharmacokinetic modeling is increasingly used in DCE-MRI high risk breast cancer screening. Several models are available. The most common models are the standard and extended Tofts, the shutterspeed, and the Brix model. Each model and the meaning of its parameters is explained. It was investigated which models can be used in a clinical setting by simulating a range of sampling rates and noise levels representing different MRI acquisition schemes. In addition, an investigation was performed on the errors introduced in the estimates of the pharmacokinetic parameters when using a physiologically less complex model, i.e. the standard Tofts model, to fit curves generated with more complex models. It was found that the standard Tofts model is the only model that performs within an error margin of 20% on parameter estimates over a range of sampling rates and noise levels. This still holds when small complex physiological effects are present

Predictive uncertainty estimation for out-of-distribution detection in digital pathology.

Machine learning model deployment in clinical practice demands real-time risk assessment to identify situations in which the model is uncertain. Once deployed, models should be accurate for classes seen during training while providing informative estimates of uncertainty to flag abnormalities and unseen classes for further analysis. Although recent developments in uncertainty estimation have resulted in an increasing number of methods, a rigorous empirical evaluation of their performance on large-scale digital pathology datasets is lacking. This work provides a benchmark for evaluating prevalent methods on multiple datasets by comparing the uncertainty estimates on both in-distribution and realistic near and far out-of-distribution (OOD) data on a whole-slide level. To this end, we aggregate uncertainty values from patch-based classifiers to whole-slide level uncertainty scores. We show that results found in classical computer vision benchmarks do not always translate to the medical imaging setting. Specifically, we demonstrate that deep ensembles perform best at detecting far-OOD data but can be outperformed on a more challenging near-OOD detection task by multi-head ensembles trained for optimal ensemble diversity. Furthermore, we demonstrate the harmful impact OOD data can have on the performance of deployed machine learning models. Overall, we show that uncertainty estimates can be used to discriminate in-distribution from OOD data with high AUC scores. Still, model deployment might require careful tuning based on prior knowledge of prospective OOD data

A pattern recognition approach to zonal segmentation of the prostate on MRI

Item does not contain fulltextZonal segmentation of the prostate into the central gland and peripheral zone is a useful tool in computer-aided detection of prostate cancer, because occurrence and characteristics of cancer in both zones differ substantially. In this paper we present a pattern recognition approach to segment the prostate zones. It incorporates three types of features that can differentiate between the two zones: anatomical, intensity and texture. It is evaluated against a multi-parametric multi-atlas based method using 48 multi-parametric MRI studies. Three observers are used to assess inter-observer variability and we compare our results against the state of the art from literature. Results show a mean Dice coefficient of 0.89 +/- 0.03 for the central gland and 0.75 +/- 0.07 for the peripheral zone, compared to 0.87 +/- 0.04 and 0.76 +/- 0.06 in literature. Summarizing, a pattern recognition approach incorporating anatomy, intensity and texture has been shown to give good results in zonal segmentation of the prostate