Applying a new bilateral mammographic density segmentation method to improve accuracy of breast cancer risk prediction

Purpose

How to optimally detect bilateral mammographic asymmetry and improve risk prediction accuracy remains a difficult and unsolved issue. Our aim was to find an effective mammographic density segmentation method to improve accuracy of breast cancer risk prediction.

Methods

A dataset including 168 negative mammography screening cases was used. We applied a mutual threshold to bilateral mammograms of left and right breasts to segment the dense breast regions. The mutual threshold was determined by the median grayscale value of all pixels in both left and right breast regions. For each case, we then computed three types of image features representing asymmetry, mean and the maximum of the image features, respectively. A two-stage classification scheme was developed to fuse the three types of features. The risk prediction performance was tested using a leave-one-case-out cross-validation method.

Results

By using the new density segmentation method, the computed area under the receiver operating characteristic curve was 0.830 ± 0.033 and overall prediction accuracy was 81.0%, significantly higher than those of 0.633 ± 0.043 and 57.1% achieved by using the previous density segmentation method (\(p<0.01\), t-test).

Conclusions

A new mammographic density segmentation method based on a bilateral mutual threshold can be used to more effectively detect bilateral mammographic density asymmetry and help significantly improve accuracy of near-term breast cancer risk prediction.

     

Computer-aided detection of breast carcinoma in standard mammographic projections with digital mammography

Purpose

A retrospective evaluation of the ability of computer-aided detection (CAD) ability to identify breast carcinoma in standard mammographic projections.

Materials and methods

Forty-five biopsy proven lesions in 44 patients imaged digitally with CAD applied at examination were reviewed. Forty-four screening BIRADS^® category 1 digital mammography examinations were randomly identified to serve as a comparative normal/control population. Data included patient age; BIRADS^® breast density; lesion type, size, and visibility; number, type, and location of CAD marks per image; CAD ability to mark lesions; needle core and surgical pathologic correlation.

Results

The CAD lesion/case sensitivity of 87% (n = 39), image sensitivity of 69% (n = 31) for mediolateral oblique view and 78% (n = 35) for the craniocaudal view was found. The average false positive rate in 44 normal screening cases was 2.0 (range 1–8). The 2.0 figure is based on 88 reported false positive CAD marks in 44 normal screening exams: 98% (n = 44) lesions proceeded to excision; initial pathology upgraded at surgical excision from in situ to invasive disease in 24% (n = 9) lesions.

Conclusion

CAD demonstrated potential to detect mammographically visible cancers in standard projections for all lesion types.     

Three-dimensional prediction of free-flap volume in autologous breast reconstruction by CT angiography imaging

Purpose

   The diagnostic use of computer tomography angiography (CTA) to identify perforating blood vessels for abdominal free-flap breast reconstruction was extended to estimate the three-dimensional (3D) preoperative flap volume and to compare it with the real intraoperative flap weights in order to (1) evaluate the accuracy of CTA-based 3D flap volume prediction, and (2) to analyze abdominal tissue estimation for required breast volume reconstruction.

Methods

   Preoperative CTA was performed in 54 patients undergoing unilateral breast reconstruction with a free, deep, inferior epigastric artery perforator flap. 3D flap volumes ( \(\hbox {cm}^{3}\) ) based on CTA data were calculated and compared with the actual intraoperative flap weight (g). In addition, a breast volume to flap volume ratio was calculated to analyze whether the estimated 3D abdominal flap volume would match that of the breast to be removed.

Results

   40 CTA data sets (74.1 %) fulfilled the technical requirements for a reliable determination of flap volume. 3D CTA flap volume prediction showed no relevant differences to the actual flap weight (p = 0.44) and high correlations (r = 0.998, \(p < 0.001\) ), allowing a prediction accuracy within 0.29 \(\pm \) 3.0 % (range: from \(-\) 8.77 to 5.67 %) of the real flap weight. Significantly larger flap volumes were harvested compared with the actually required breast volumes ( \(p < 0.001\) ), leading to an average of 21 % of the remnant flap tissue potentially being discarded.

Conclusions

   CTA-based 3D flap volume prediction provides accurate preoperative guidelines concerning the needed amount of abdominal tissue that can be harvested to achieve acceptable symmetry.     

Phantom-based comparison of conventional versus phase-contrast mammography for LCD soft-copy diagnosis

Purpose

Liquid crystal display (LCD) of mammograms provides soft-copy results that differ in conventional and phase contrast mammography (PCM). PCM potentially offers the highest quality of sharpness and graininess, an edge emphasis effect on the object, and the highest image resolution. However, when the image is displayed on an LCD, the resolution depends on the pixel pitch and the PCM image data must be diminished. We investigated the observed effect on spatial resolution and contrast when conventional or phase contrast mammograms are viewed on an LCD.

Methods

Using the tissue-equivalent phantom (Model 1011A), a conventional mammogram and a magnification radiography image were obtained with a PCM system. This phantom contains simulated fibers, microcalcifications, and masses. The PCM image was reduced 1/1.75 to render it consistent with life size mammography using the nearest neighbor, bilinear, and bicubic interpolation methods. The images were displayed on a five million (5M)-pixel LCD with 100 % magnification. Ten mammography technicians observed the reduction images displayed on LCDs and reported their results.

Results

In the detectability of the microcalcifications, there was no significant difference between conventional mammograms and reduced PCM images. Regarding fibers and masses, detectability using reduced images was higher than those of conventional images. The detectability using images reduced by the nearest-neighbor method was lower than those of images reduced by two other interpolation methods. Bilinear interpolation was affected by the smoothing effect, while CNR was increased. In addition, since the noise of PCM image was reduced by an air gap effect, high detectability of key image features was found.

Conclusions

Soft-copy display of phase-contrast mammograms is feasible with LCDs, while detectability of fibers and masses was best with bilinear interpolation and use of an air gap.     

GPU accelerated segmentation and centerline extraction of tubular structures from medical images

Purpose

   To create a fast and generic method with sufficient quality for extracting tubular structures such as blood vessels and airways from different modalities (CT, MR and US) and organs (brain, lungs and liver) by utilizing the computational power of graphic processing units (GPUs).

Methods

   A cropping algorithm is used to remove unnecessary data from the datasets on the GPU. A model-based tube detection filter combined with a new parallel centerline extraction algorithm and a parallelized region growing segmentation algorithm is used to extract the tubular structures completely on the GPU. Accuracy of the proposed GPU method and centerline algorithm is compared with the ridge traversal and skeletonization/thinning methods using synthetic vascular datasets.

Results

   The implementation is tested on several datasets from three different modalities: airways from CT, blood vessels from MR, and 3D Doppler Ultrasound. The results show that the method is able to extract airways and vessels in 3–5 s on a modern GPU and is less sensitive to noise than other centerline extraction methods.

Conclusions

   Tubular structures such as blood vessels and airways can be extracted from various organs imaged by different modalities in a matter of seconds, even for large datasets.     

Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge

Object

We investigate the use of relevance feedback (RFb) and the inclusion of expert knowledge to reduce the semantic gap in content-based image retrieval (CBIR) of mammograms.

Materials and methods

Tests were conducted with radiologists, in which their judgment of the relevance of the retrieved images was used with techniques of query-point movement to incorporate RFb. The measures of similarity of images used for CBIR were based upon textural characteristics and the distribution of density of fibroglandular tissue in the breast. The features used include statistics of the gray-level histogram, texture features based upon the gray-level co-occurrence matrix, moment-based features, measures computed in the Radon domain, and granulometric measures. Queries for CBIR with RFb were executed by three radiologists. The performance of CBIR was measured in terms of precision of retrieval and a measure of relevance-weighted precision (RWP) of retrieval.

Results

The results indicate improvement due to RFb of up to 62% in precision and 39% in RWP.

Conclusion

The gain in performance of CBIR with RFb depended upon the BI-RADS breast density index of the query mammographic image, with greater improvement in cases of mammograms with higher density.     

Computer-aided-detection marker value and breast density in the detection of invasive lobular carcinoma

Purpose

Invasive Lobular Carcinoma (ILC) is frequently a mammographic and diagnostic dilemma; thus any additional information that CAD (Computer-Aided Detection) systems can give radiologists may be helpful. Our study was to evaluate the role of CAD numeric values as indicators of malignancy and the effect of breast density in the diagnosis of ILC.

Materials and methods

Eighty consecutive biopsy-proven ILC cases with CAD (ImageChecker^®, Hologic | R2, Santa Clara, CA, versions 2.3, 3.1, 3.2, 5.0, 5.2) diagnosed between June 2002 and December 2004 were retrospectively reviewed. Data included: BIRADS^® breast density, whether CAD marked the cancer at diagnosis year or years prior, and lesion type. Study mammograms underwent additional CAD scans (Image Checker^® V5.3, V8.0, V8.1) to obtain a numeric value associated with each marker, low values represent increasingly suspicious features.

Results

CAD correctly marked 65% (52/80) of ILC cases, detection was found to decrease with increased breast density. Numeric values of CAD marks at sites of carcinoma showed median score of 171 (range 0 – 1121).

Conclusion

The CAD marker may potentially be used as an additional indicator of suspicious lesion features in all breast densities and higher likelihood that an area on the mammogram requires further investigation.     

Breast US in patients with breast cancer presenting as only microcalcifications on mammography: can US differentiate ductal carcinoma in situ from invasive cancer?

Purpose

To retrospectively review sonographic findings of breast cancers presenting as only microcalcifications on mammography and to evaluate factors essential for differentiating ductal carcinoma in situ (DCIS) from invasive cancers.

Methods

We retrospectively reviewed the medical records of 620 consecutive patients with confirmed breast cancer according to surgery performed between March 2008 and October 2011 at our institution. Of these, 53 lesions from 52 patients who had only microcalcifications without a mass or other associated findings on mammography were selected. Sonographic findings of microcalcification areas were analyzed and correlated with the histopathological findings.

Results

Of the 53 lesions, 26 (49.18 %) were classified as invasive cancer and 27 (50.9 %) as DCIS. Ultrasonography (US) showed only echogenic calcifications in five (9.4 %), calcifications within hypoechoic parenchymal thickening in 14 (26.4 %), calcifications within ductal changes in three (5.7 %), and calcifications within a mass in 14 (26.4 %). Seventeen (32.1 %) lesions were not visible on US. Negative findings in US were more frequently observed for DCIS (n = 15, 55.6 %) than for invasive cancers (n = 2, 7.7 %) (p < 0.001). Masses (n = 11, 42.3 % of invasive cancer; n = 3, 11.1 % of DCIS; p = 0.01) were more frequently observed in invasive cancers than in DCIS.

Conclusions

US findings of breast cancers presenting as only mammographic microcalcifications were significantly different between DCIS and invasive cancers. Targeted US of microcalcifications might be helpful for predicting invasive cancers and for determining the clinical preoperative work-up, including axillary staging.     

Usefulness of a glass-free medical three-dimensional autostereoscopic display in neurosurgery

Purpose

  Many stereoscopic displays require glasses that are awkward or inappropriate for use in a neurosurgical operating room. A glass-free three-dimensional autostereoscopic display (3DAD) monitor was developed and tested for neurosurgical applications.

Methods

   Our 3DAD system uses images acquired from nine directions projected into the viewer’s eyes through 1,280 lenticular lenses ( \(\text {1,280} \times 720\) pixels). The viewer interprets these as a single stereoscopic image. To evaluate the 3D visualization capabilities of the 3DAD system, 3D images of blood vessels created from brain magnetic resonance angiography were presented to 20 neurosurgeons on both a standard medical two-dimensional (2D) monitor and our 3DAD monitor. Discrimination of the positional relationships for each vessel was recorded. The observers were asked to identify blood vessels located in front of three pairs of points on each image.

Results

   The neurosurgeon observers achieved significantly higher correct responses using the 3DAD monitor compared with the 2D monitor (91.7 vs. 56.7 %, \(p<0.0001\) ). There were no reports of problems such as eye fatigue or discomfort.

Conclusion

   Displaying 3D volume rendered multimodality images with a 3DAD monitor is useful for anatomical discrimination of 3D vessels in MR angiography. This technology may be useful for a wide variety of clinical applications such as rapid and precise diagnosis, surgical simulation, and medical education.     

Predicting quality of life after breast cancer surgery using ANN-based models: performance comparison with MR

Purpose

The goal was to develop models for predicting long-term quality of life (QOL) after breast cancer surgery.

Methods

Data were obtained from 203 breast cancer patients who completed the SF-36 health survey before and 2 years after surgery. Two of the models used to predict QOL after surgery were artificial neural networks (ANNs), which included one multilayer perceptron (MLP) network and one radial basis function (RBF) network. The third model was a multiple regression (MR) model. The criteria for evaluating the accuracy of the system models were mean square error (MSE) and mean absolute percentage error (MAPE).

Results

Compared to the MR model, the ANN-based models generally had smaller MSE values and smaller MAPE values in the test data set. One exception was the second year MSE for the test value. Most MAPE values for the ANN models ranged from 10 to 20 %. The one exception was the 6-month physical component summary score (PCS), which ranged from 23.19 to 26.86 %. Comparison of criteria for evaluating system performance showed that the ANN-based systems outperformed the MR system in terms of prediction accuracy. In both the MLP and RBF networks, surgical procedure type was the most sensitive parameter affecting PCS, and preoperative functional status was the most sensitive parameter affecting mental component summary score.

Conclusion

The three systems can be combined to obtain a conservative prediction, and a combined approach is a potential supplemental tool for predicting long-term QOL after surgical treatment for breast cancer.

Relevance

Patients should also be advised that their postoperative QOL might depend not only on the success of their operations but also on their preoperative functional status.     

Catheter tip tracking for MR-guided interventions using discrete Kalman filter and mean shift localization

Purpose

This paper presents and evaluates stochastic computer algorithms used to automatically detect and track marked catheter tip during MR-guided catheterization. The algorithms developed employ extraction and matching of regional features of the catheter tip to perform the localization.

Method

To perform the tracking, a probability map that indicates the possible locations of the catheter tip in the MR images is first generated. This map is generated from the similarity to a given marker template. The method to assess the similarity between the marker template image and the different positions on each MR frame is based on speeded-up robust features extracted from the gradient image. The probability map is then used in two different stochastic localization frameworks mean shift (MS) localization and Kalman filter (KF) to track the position of the catheter using pairs of orthogonal projection of 2D MR images. The algorithm developed was tested on catheter tip marked with LC resonant circuit (of size $2\,\hbox {mm}\,\times \,2\,\hbox {cm}$ ) tuned to the Larmor frequency of the MRI scanner and for different image resolutions (1, 3, 5 and 7 mm squared pixel).

Results

The tracking performance was very robust for the two algorithms MS and KF with image resolution as low as 3 mm where the localization error was about 1 mm for KF and 0.9 mm for MS. For the 5-mm resolution images, the error was 2.2 mm for both KF and MS, and for the 7-mm resolution images, the error was 3.6 and 3.7 mm for KF and MS, respectively.

Conclusion

Both KF and MS gave comparable results when it comes to accuracy for the different image resolutions. The results showed that the two tracking algorithms tracked the catheter tip with high robustness for image resolution of 3 mm and with acceptable reliability for image resolution as poor as 5 mm with the resonant marker configuration used.     

Non-iterative partial view 3D ultrasound to CT registration in ultrasound-guided computer-assisted orthopedic surgery

Purpose

In image-guided orthopedic surgery, rigid registration of intra-operative ultrasound (US) to a pre-operative plan, developed using computed tomography (CT) scans, is an important step for providing real time surgical guidance. The ability to perform this registration accurately, automatically, and in real time is critical for enabling more effective image guidance and anatomic restoration in a number of orthopedic procedures. Several surface-based and intensity-based registration methods have been proposed before to align the US and CT data. Although relatively successful results were reported, both methods require accurate segmentation or localization of the bone surface in US data, which is a challenging task. Furthermore, typically, only partial views of the three-dimensional (3D) bone anatomy are visible in US data, and registration would only converge if a good estimation of the initial alignment between the US and CT datasets is known.

Methods

We propose a 3D rigid CT to US registration method based on the alignment of local phase bone image projections. The registration is achieved by transforming the local phase bone features, calculated using 3D Log-Gabor filters, to a projection space obtained using 3D Radon transform. Validation experiments show the capability of the method in registering partial view US volumes to full view CT volume.

Results

Feasibility experiments, carried out on a phantom and ten volunteer subjects, show an average surface registration, in the region where the US scans were acquired, of 0.42 and 0.78  mm, respectively.

Conclusions

The proposed US to CT registration method is fully automatic, non-iterative and requires no initial alignment between the two registering datasets.     

Correction of images in an open-configuration MR imaging system for radiation therapy planning and Interventional MRI

Purpose

In this paper, we present a method to remove system induced geometric distortions in MR images.

Methods

A large 3D phantom with spherical balls was used to characterize geometric distortion on an AIRIS Mate 0.2 T MR Scanner (Hitachi). MR images of the phantom were acquired in axial, sagittal planes. Using 2D Fast Spin Echo (FSE) sequence, distortions were measured at each control point. Two piecewise interpolation methods were then applied to correct distortions and greyvalues.

Results

The distortion was reduced from 16 to 1.2 mm at 180 mm from the magnet center.

Conclusion

Distortions were characterized and corrected in any axial, sagittal or coronal slice within an effective FOV of 330(LR) × 180(AP) × 210(HF) mm³. A fast and accurate method for correction of geometric distortion was performed within large distances from the magnet isocenter.     

Measures of angular spread and entropy for the detection of architectural distortion in prior mammograms

Purpose

Architectural distortion is an important sign of early breast cancer. We present methods for computer-aided detection of architectural distortion in mammograms acquired prior to the diagnosis of breast cancer in the interval between scheduled screening sessions.

Methods

Potential sites of architectural distortion were detected using node maps obtained through the application of a bank of Gabor filters and linear phase portrait modeling. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval-cancer cases, including 301 true-positive ROIs, and from 52 mammograms of 13 normal cases. Each ROI was represented by three types of entropy measures of angular histograms composed with the Gabor magnitude response, angle, coherence, orientation strength, and the angular spread of power in the Fourier spectrum, including Shannon’s entropy, Tsallis entropy for nonextensive systems, and Rényi entropy for extensive systems.

Results

Using the entropy measures with stepwise logistic regression and the leave-one-patient-out method for feature selection and cross-validation, an artificial neural network resulted in an area under the receiver operating characteristic curve of 0.75. Free-response receiver operating characteristics indicated a sensitivity of 0.80 at 5.2 false positives (FPs) per patient.

Conclusion

The proposed methods can detect architectural distortion in prior mammograms taken 15 months (on the average) before clinical diagnosis of breast cancer, with a high sensitivity and a moderate number of FPs per patient. The results are promising and may be improved with additional features to characterize subtle abnormalities and larger databases including prior mammograms.     

4D-CT Lung registration using anatomy-based multi-level multi-resolution optical flow analysis and thin-plate splines

Purpose

   The accuracy of 4D-CT registration is limited by inconsistent Hounsfield unit (HU) values in the 4D-CT data from one respiratory phase to another and lower image contrast for lung substructures. This paper presents an optical flow and thin-plate spline (TPS)-based 4D-CT registration method to account for these limitations.

Methods

   The use of unified HU values on multiple anatomy levels (e.g., the lung contour, blood vessels, and parenchyma) accounts for registration errors by inconsistent landmark HU value. While 3D multi-resolution optical flow analysis registers each anatomical level, TPS is employed for propagating the results from one anatomical level to another ultimately leading to the 4D-CT registration. 4D-CT registration was validated using target registration error (TRE), inverse consistency error (ICE) metrics, and a statistical image comparison using Gamma criteria of 1 % intensity difference in \(2\,\hbox {mm}^{3}\) window range.

Results

   Validation results showed that the proposed method was able to register CT lung datasets with TRE and ICE values \(<\) 3 mm. In addition, the average number of voxel that failed the Gamma criteria was \(<\) 3 %, which supports the clinical applicability of the propose registration mechanism.

Conclusion

   The proposed 4D-CT registration computes the volumetric lung deformations within clinically viable accuracy.     

Automatic multimodal 2D/3D breast image registration using biomechanical FEM models and intensity-based optimization

Due to their different physical origin, X-ray mammography and Magnetic Resonance Imaging (MRI) provide complementary diagnostic information. However, the correlation of their images is challenging due to differences in dimensionality, patient positioning and compression state of the breast. Our automated registration takes over part of the correlation task. The registration method is based on a biomechanical finite element model, which is used to simulate mammographic compression. The deformed MRI volume can be compared directly with the corresponding mammogram. The registration accuracy is determined by a number of patient-specific parameters. We optimize these parameters – e.g. breast rotation – using image similarity measures. The method was evaluated on 79 datasets from clinical routine. The mean target registration error was 13.2 mm in a fully automated setting. On basis of our results, we conclude that a completely automated registration of volume images with 2D mammograms is feasible. The registration accuracy is within the clinically relevant range and thus beneficial for multimodal diagnosis.     

High-resolution T1-weighted gradient echo imaging for liver MRI using parallel imaging at high-acceleration factors

Purpose

To determine whether application of a high-acceleration parallel acquisition can provide three-dimensional (3D)-fat-suppressed T1-weighted gradient-recalled-echo (T1W-GRE) imaging at 3T for liver MR imaging.

Materials and methods

This retrospective study was approved by our institutional review board. Seventy patients underwent liver MRI at a 3T scanner. After administration of a standard dose of Gadoxetic acid for 20 min, 3D-T1W-GRE images were obtained twice using sensitivity encoding with acceleration factors (AFs) 2.6 [332 × 298 matrix, 3-mm slice thickness (ST)] and 4 (380 × 320 matrix, 1.5-mm ST). The image qualities of the two image sets were graded using a five-point scale.

Results

The high-resolution (HR) 3D-T1W-GRE image sets were obtained with an AF 4 within a single breath-hold (18.5 s). It showed a better anatomic depiction than conventional 3D-T1W-GRE image sets with an AF 2.6 (p < 0.05). Although the image noise was higher on the HR image sets (p < 0.05), the HR image sets showed better lesion conspicuity and overall image quality than the conventional image sets (p < 0.05).

Conclusion

With the use of high AFs, HR 3D-T1W-GRE imaging was demonstrated to be clinically more feasible and advantageous than the conventional 3D-T1W-GRE.     

Design and initial evaluation of a treatment planning software system for MRI-guided laser ablation in the brain

Purpose

   An open-source software system for planning magnetic resonance (MR)-guided laser-induced thermal therapy (MRgLITT) in brain is presented. The system was designed to provide a streamlined and operator-friendly graphical user interface (GUI) for simulating and visualizing potential outcomes of various treatment scenarios to aid in decisions on treatment approach or feasibility.

Methods

   A portable software module was developed on the 3D Slicer platform, an open-source medical imaging and visualization framework. The module introduces an interactive GUI for investigating different laser positions and power settings as well as the influence of patient-specific tissue properties for quickly creating and evaluating custom treatment options. It also provides a common treatment planning interface for use by both open-source and commercial finite element solvers. In this study, an open-source finite element solver for Pennes’ bioheat equation is interfaced to the module to provide rapid 3D estimates of the steady-state temperature distribution and potential tissue damage in the presence of patient-specific tissue boundary conditions identified on segmented MR images.

Results

   The total time to initialize and simulate an MRgLITT procedure using the GUI was \(<\) 5 min. Each independent simulation took \(<\) 30 s, including the time to visualize the results fused with the planning MRI. For demonstration purposes, a simulated steady-state isotherm contour \((57\,^{\circ }\hbox {C})\) was correlated with MR temperature imaging (N = 5). The mean Hausdorff distance between simulated and actual contours was 2.0 mm \((\sigma \,=\,0.4\,\hbox {mm})\) , whereas the mean Dice similarity coefficient was 0.93 \((\sigma =0.026)\) .

Conclusions

   We have designed, implemented, and conducted initial feasibility evaluations of a software tool for intuitive and rapid planning of MRgLITT in brain. The retrospective in vivo dataset presented herein illustrates the feasibility and potential of incorporating fast, image-based bioheat predictions into an interactive virtual planning environment for such procedures.     

Marital status and optimism score among breast cancer survivors

Purpose

There are an increasing number of breast cancer survivors, but their psychosocial and supportive care needs are not well-understood. Recent work has found marital status, social support, and optimism to be associated with quality of life, but little research has been conducted to understand how these factors relate to one another.

Methods

Survey data from 722 breast cancer survivors were analyzed to estimate the association between marital status and optimism score, as measured using the Life Orientation Test-Revised. Linear regression was used to estimate the relationship of marital status and optimism, controlling for potential confounding variables and assessing effect modification.

Results

The results showed that the association between marital status and optimism was modified by time since breast cancer diagnosis. Specifically, in those most recently diagnosed (within 5 years), married breast cancer survivors had a 1.50 higher mean optimism score than unmarried survivors (95 % confidence interval (CI) 0.37, 2.62; p?=?0.009). The difference in optimism score by marital status was not present more than 5 years from breast cancer diagnosis.

Conclusions

Findings suggest that among breast cancer survivors within 5 years since diagnosis, those who are married have higher optimism scores than their unmarried counterparts; this association was not observed among longer-term breast cancer survivors. Future research should examine whether the difference in optimism score among this subgroup of breast cancer survivors is clinically relevant.