Gender differences in long term sickness absence.

PURPOSE: Sickness absence is a major public health problem, given its high cost and negative impact on employee well-being. Understanding sickness absence duration and recovery rates among different groups is useful to develop effective strategies for enhancing recovery and reducing costs related to sickness absence. METHODS: Our study analyzed data from a large occupational health service, including over 5 million sick-listed employees from 2010 to 2020, out of which almost 600,000 cases were diagnosed by an occupational health physician. We classified each case according to diagnosis and gender, and performed descriptive statistical analysis for each category. In addition, we used survival analysis to determine recovery rates for each group. RESULTS: Mean sickness duration and recovery rate both differ significantly among groups. Mental and musculoskeletal disorders had the longest absence duration. Recovery rates differed especially during the first months of sickness absence. For men the recovery rate was nearly constant during the first 1.5 year, for women the recovery rate was relatively low in the first three months, and then stayed nearly constant for 1.5 year. CONCLUSION: Across almost all diagnostic classes, it was consistently observed that women had longer average sickness absence durations than to men. Considering mental disorders and diseases of the musculoskeletal system, women had relatively lower recovery rates during the initial months compared to men. As time progressed, the recovery rates of both genders converged and became more similar.

Computer-aided diagnosis with temporal analysis to improve radiologists' interpretation of mammographic mass lesions.

The purpose of this study was to evaluate the effect of independent reading with computer-aided diagnosis (CAD) and independent double reading on radiologists' performance to characterize mass lesions on serial mammograms. Six radiologists rated 198 cases, 99 benign and 99 malignant. For each case, the mammograms from two consecutive screening rounds were available. The mass was visible on the prior view in 40% of the cases. Independently, a CAD programe also rated each mass lesion making use of information from prior and current views. The following reading situations were compared: single reading, independent reading with CAD, and independent double reading. Independent reading with CAD was implemented by averaging the scaled ratings from each radiologist and the scaled CAD scores. We implemented independent double reading by averaging the scaled scores from two radiologists. Results were evaluated using receiver-operating characteristic (ROC) methodology and multiple reader multiple case analysis. The average performance, measured as the area under the ROC curve (A(z) value), was 0.80 for the single-reading mode. For independent double reading, the average performance improved to 0.81. This improvement was not significant. For independent interpretation with CAD, the average performance significantly increased to 0.83 (P < 0.05). We conclude that CAD technology with temporal analysis has the potential to help radiologists with the task of discriminating between benign and malignant masses.

Temporal change analysis for characterization of mass lesions in mammography.

In this paper, we present a fully automated computer-aided diagnosis (CAD) program to detect temporal changes in mammographic masses between two consecutive screening rounds. The goal of this work was to improve the characterization of mass lesions by adding information about the tumor behavior over time. Towards this goal we previously developed a regional registration technique that finds for each mass lesion on the current view a location on the prior view where the mass was most likely to develop. For the task of interval change analysis, we designed two kinds of temporal features: difference features and similarity features. Difference features indicate the (relative) change in feature values determined on prior and current views. These features may be especially useful for lesions that are visible on both views. Similarity features measure whether two regions are comparable in appearance and may be useful for lesions that are visible on the prior view as well as for newly developing lesions. We evaluated the classification performance with and without the use of temporal features on a dataset consisting of 465 temporal mammogram pairs, 238 benign, and 227 malignant. We used cross validation to partition the dataset into a training set and a test set. The training set was used to train a support vector machine classifier and the test set to evaluate the classifier. The average A(z) value (area under the receiver operating characteristic curve) for classifying each lesion was 0.74 without temporal features and 0.77 with the use of temporal features. The improvement obtained by adding temporal features was statistically significant (P = 0.005). In particular, similarity features contributed to this improvement. Furthermore, we found that the improvement was comparable for masses that were visible and for masses that were not visible on the prior view. These results show that the use of temporal features is an effective approach to improve the characterization of masses.

Finding corresponding regions of interest in mediolateral oblique and craniocaudal mammographic views.

In this paper we present a method to link potentially suspicious mass regions detected by a Computer-Aided Detection (CAD) scheme in mediolateral oblique (MLO) and craniocaudal (CC) mammographic views of the breast. For all possible combinations of mass candidate regions, a number of features are determined. These features include the difference in the radial distance from the candidate regions to the nipple, the gray scale correlation between both regions, and the mass likelihood of the regions determined by the single view CAD scheme. Linear Discriminant Analysis (LDA) is used to discriminate between correct and incorrect links. The method was tested on a set of 412 cancer cases. In each case a malignant mass, architectural distortion, or asymmetry was annotated. In 92% of these cases the candidate mass detections by CAD included the cancer regions in both views. It was found that in 82% of the cases a correct link between the true positive regions in both views could be established by our method. Possible applications of the method may be found in multiple view analysis to improve CAD results, and for the presentation of CAD results to the radiologist on a mammography workstation.

Interval change analysis to improve computer aided detection in mammography.

We are developing computer aided diagnosis (CAD) techniques to study interval changes between two consecutive mammographic screening rounds. We have previously developed methods for the detection of malignant masses based on features extracted from single mammographic views. The goal of the present work was to improve our detection method by including temporal information in the CAD program. Toward this goal, we have developed a regional registration technique. This technique links a suspicious location on the current mammogram with a corresponding location on the prior mammogram. The novelty of our method is that the search for correspondence is done in feature space. This has the advantage that very small lesions and architectural distortions may be found as well. Following the linking process several features are calculated for the current and prior region. Temporal features are obtained by combining the feature values from both regions. We evaluated the detection performance with and without the use of temporal features on a data set containing 2873 temporal film pairs from 938 patients. There were 589 cases in which the current mammogram contained exactly one malignant mass. Cross validation was used to partition the data set into a train set and a test set. The train set was used for feature selection and classifier training, the test set for classifier evaluation. FROC (free response operating characteristic) analysis showed an improvement in detection performance with the use of temporal features.

A regional registration method to find corresponding mass lesions in temporal mammogram pairs.

In this paper we develop an automatic regional registration method to find corresponding masses on prior and current mammograms. The method contains three steps. In the first, we globally align both images. Then, for each mass lesion on the current view, we define a search area on the prior view, which is likely to contain the same mass lesion. Third, at each location in this search area we calculate a registration measure to quantify how well this location matches the mass lesion on the current view. Finally we select the best location. To determine the performance of our method we compare it to several other registration methods. On a dataset of 389 temporal mass pairs our method correctly links 82% of prior and current mass lesions, whereas other methods achieve at most 72%.

A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography.

Mass segmentation plays a crucial role in computer-aided diagnosis (CAD) systems for classification of suspicious regions as normal, benign, or malignant. In this article we present a robust and automated segmentation technique--based on dynamic programming--to segment mass lesions from surrounding tissue. In addition, we propose an efficient algorithm to guarantee resulting contours to be closed. The segmentation method based on dynamic programming was quantitatively compared with two other automated segmentation methods (region growing and the discrete contour model) on a dataset of 1210 masses. For each mass an overlap criterion was calculated to determine the similarity with manual segmentation. The mean overlap percentage for dynamic programming was 0.69, for the other two methods 0.60 and 0.59, respectively. The difference in overlap percentage was statistically significant. To study the influence of the segmentation method on the performance of a CAD system two additional experiments were carried out. The first experiment studied the detection performance of the CAD system for the different segmentation methods. Free-response receiver operating characteristics analysis showed that the detection performance was nearly identical for the three segmentation methods. In the second experiment the ability of the classifier to discriminate between malignant and benign lesions was studied. For region based evaluation the area Az under the receiver operating characteristics curve was 0.74 for dynamic programming, 0.72 for the discrete contour model, and 0.67 for region growing. The difference in Az values obtained by the dynamic programming method and region growing was statistically significant. The differences between other methods were not significant.