Soft Tissue Monitoring of the Surgical Field: Detection and Tracking of Breast Surface Deformations.

OBJECTIVE: Deformable object tracking is common in the computer vision field, with applications typically focusing on nonrigid shape detection and usually not requiring specific three-dimensional point localization. In surgical guidance however, accurate navigation is intrinsically linked to precise correspondence of tissue structure. This work presents a contactless, automated fiducial acquisition method using stereo video of the operating field to provide reliable three-dimensional fiducial localization for an image guidance framework in breast conserving surgery. METHODS: On n = 8 breasts from healthy volunteers, the breast surface was measured throughout the full range of arm motion in a supine mock-surgical position. Using hand-drawn inked fiducials, adaptive thresholding, and KAZE feature matching, precise three-dimensional fiducial locations were detected and tracked through tool interference, partial and complete marker occlusions, significant displacements and nonrigid shape distortions. RESULTS: Compared to digitization with a conventional optically tracked stylus, fiducials were automatically localized with 1.6 ± 0.5 mm accuracy and the two measurement methods did not significantly differ. The algorithm provided an average false discovery rate <0.1% with all cases' rates below 0.2%. On average, 85.6 ± 5.9% of visible fiducials were automatically detected and tracked, and 99.1 ± 1.1% of frames provided only true positive fiducial measurements, which indicates the algorithm achieves a data stream that can be used for reliable on-line registration. CONCLUSIONS: Tracking is robust to occlusions, displacements, and most shape distortions. SIGNIFICANCE: This work-flow friendly data collection method provides highly accurate and precise three-dimensional surface data to drive an image guidance system for breast conserving surgery.

Incorporating heterogeneity and anisotropy for surgical applications in breast deformation modeling.

BACKGROUND: Simulating soft-tissue breast deformations is of interest for many applications including image fusion, longitudinal registration, and image-guided surgery. For the surgical use case, positional changes cause breast deformations that compromise the use of preoperative imaging to inform tumor excision. Even when acquiring imaging in the supine position, which better reflects surgical presentation, deformations still occur due to arm motion and orientation changes. A biomechanical modeling approach to simulate supine breast deformations for surgical applications must be both accurate and compatible with the clinical workflow. METHODS: A supine MR breast imaging dataset from n = 11 healthy volunteers was used to simulate surgical deformations by acquiring images in arm-down and arm-up positions. Three linear-elastic modeling approaches with varying levels of complexity were used to predict deformations caused by this arm motion: a homogeneous isotropic model, a heterogeneous isotropic model, and a heterogeneous anisotropic model using a transverse-isotropic constitutive model. FINDINGS: The average target registration errors for subsurface anatomical features were 5.4 ± 1.5 mm for the homogeneous isotropic model, 5.3 ± 1.5 mm for the heterogeneous isotropic model, and 4.7 ± 1.4 mm for the heterogeneous anisotropic model. A statistically significant improvement in target registration error was observed between the heterogeneous anisotropic model and both the homogeneous and the heterogeneous isotropic models (P < 0.01). INTERPRETATION: While a model that fully incorporates all constitutive complexities of anatomical structure likely achieves the best accuracy, a computationally tractable heterogeneous anisotropic model provided significant improvement and may be applicable for image-guided breast surgeries.

Supine magnetic resonance image registration for breast surgery: insights on material mechanics.

Purpose: Breast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, is being explored since it more closely represents the surgical presentation compared to conventional diagnostic imaging positions. Despite this preoperative imaging position, deformation is still present between the supine imaging and surgical state. As a result, a fast and accurate image-to-physical registration approach is needed to realize image-guided breast surgery. Approach: In this study, three registration methods were investigated on healthy volunteers' breasts ( n = 11 ) with the supine arm-down position simulating preoperative imaging and supine arm-up position simulating intraoperative presentation. The registration methods included (1) point-based rigid registration using synthetic fiducials, (2) nonrigid biomechanical model-based registration using sparse data, and (3) a data-dense three-dimensional diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. Additionally, deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field to better understand breast material mechanics. Results: The average target registration errors (TRE) were 10.4 ± 2.3 , 6.4 ± 1.5 , and 2.8 ± 1.3 mm (mean ± standard deviation) and the average fiducial registration errors (FRE) were 7.8 ± 1.7 , 2.5 ± 1.1 , and 3.1 ± 1.1 mm for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. The mechanics-based deformation metrics revealed an overall anisotropic tissue behavior and a statistically significant difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy. Conclusions: Overall, registration accuracy significantly improved with increasingly flexible and data-dense registration methods. Analysis of these outcomes may inform the future development of image guidance systems for lumpectomy procedures.

Tumor deformation correction for an image guidance system in breast conserving surgery.

Breast cancer is the most common cancer in women, and surgical resection is standard of care for the majority of breast cancer patients. Unfortunately, current reoperation rates are 10-29%. Uncertainty in lesion localization is one of the main factors contributing to these high reoperation rates. This work uses the linearized iterative boundary reconstruction approach to model patient breast deformation due to abduction of the ipsilateral arm. A preoperative supine magnetic resonance (MR) image was obtained with the patient's arms down near the torso. A mock intraoperative breast shape was measured from a supine MR image obtained with the patient's arm up near the head. Sparse data was subsampled from the full volumetric image to represent realistic intraoperative data collection: surface fiducial points, the intra-fiducial skin surface, and the chest wall as measured with 7 tracked ultrasound images. The deformed preoperative arm-down data was compared to the ground truth arm-up data. From rigid registration to model correction the tumor centroid distance improves from 7.3 mm to 3.3 mm, average surface fiducial error across 9 synthetic fiducials and the nipple improves from 7.4 ± 2.2 to 1.3 ± 0.7, and average subsurface error across 14 corresponding features improves from 6.2 ± 1.4 mm to 3.5 ± 1.1 mm. Using preoperative supine MR imaging and sparse data in the deformed position, this modeling framework can correct for breast shape changes between imaging and surgery to more accurately predict intraoperative position of the tumor as well as 10 surface fiducials and 14 subsurface features.

Breast image registration for surgery: Insights on material mechanics modeling.

Breast conserving surgery (BCS) is a common procedure for early-stage breast cancer patients. Supine preoperative magnetic resonance (MR) breast imaging for visualizing tumor location and extent, while not standard for procedural guidance, more closely represents the surgical presentation compared to conventional diagnostic pendant positioning. Optimal utilization for surgical guidance, however, requires a fast and accurate image-to-physical registration from preoperative imaging to intraoperative surgical presentation. In this study, three registration methods were investigated on healthy volunteers' breasts (n=11) with the arm-down position simulating preoperative imaging and arm-up position simulating intraoperative data. The registration methods included: (1) point-based rigid registration using synthetic fiducials, (2) non-rigid biomechanical model-based registration using sparse data, and (3) a data-dense 3D diffeomorphic image-based registration from the Advanced Normalization Tools (ANTs) repository. The average target registration errors (TRE) were 10.4 ± 2.3, 6.4 ± 1.5, and 2.8 ± 1.3 mm (mean ± standard deviation) and the average fiducial registration errors (FRE) were 7.8 ± 1.7, 2.5 ± 1.1, and 3.1 ± 1.1 mm (mean ± standard deviation) for the point-based rigid, nonrigid biomechanical, and ANTs registrations, respectively. Additionally, common mechanics-based deformation metrics (volume change and anisotropy) were calculated from the ANTs deformation field. The average metrics revealed anisotropic tissue behavior and a statistical difference in volume change between glandular and adipose tissue, suggesting that nonrigid modeling methods may be improved by incorporating material heterogeneity and anisotropy. Overall, registration accuracy significantly improved with increasingly flexible registration methods, which may inform future development of image guidance systems for lumpectomy procedures.

Computational Imaging to Compensate for Soft-Tissue Deformations in Image-Guided Breast Conserving Surgery.

OBJECTIVE: During breast conserving surgery (BCS), magnetic resonance (MR) images aligned to accurately display intraoperative lesion locations can offer improved understanding of tumor extent and position relative to breast anatomy. Unfortunately, even under consistent supine conditions, soft tissue deformation compromises image-to-physical alignment and results in positional errors. METHODS: A finite element inverse modeling technique has been developed to nonrigidly register preoperative supine MR imaging data to the surgical scene for improved localization accuracy during surgery. Registration is driven using sparse data compatible with acquisition during BCS, including corresponding surface fiducials, sparse chest wall contours, and the intra-fiducial skin surface. Deformation predictions were evaluated at surface fiducial locations and subsurface tissue features that were expertly identified and tracked. Among n = 7 different human subjects, an average of 22 ± 3 distributed subsurface targets were analyzed in each breast volume. RESULTS: The average target registration error (TRE) decreased significantly when comparing rigid registration to this nonrigid approach (10.4 ± 2.3 mm vs 6.3 ± 1.4 mm TRE, respectively). When including a single subsurface feature as additional input data, the TRE significantly improved further (4.2 ± 1.0 mm TRE), and in a region of interest within 15 mm of a mock biopsy clip TRE was 3.9 ± 0.9 mm. CONCLUSION: These results demonstrate accurate breast deformation estimates based on sparse-data-driven model predictions. SIGNIFICANCE: The data suggest that a computational imaging approach can account for image-to-surgery shape changes to enhance surgical guidance during BCS.

Impact of deformation on a supine-positioned image-guided breast surgery approach.

PURPOSE: To reduce reoperation rates for image-guided breast-conserving surgery, the enhanced sensitivity of magnetic resonance (MR) supine imaging may be leveraged. However, accurate tissue correspondence between images and their physical counterpart in the surgical presentation is challenging due to breast deformations (e.g., from patient/arm position changes, and operating room table rotation differences). In this study, standard rigid registration methods are employed and tissue deformation is characterized. METHODS: On n = 10 healthy breasts, surface displacements were measured by comparing intraoperative fiducial locations as the arm was moved from conventional MR scanning positions (arm-down and arm-up) to the laterally extended surgical configuration. Supine MR images in the arm-down and arm-up positions were registered to mock intraoperative presentations. RESULTS: Breast displacements from a supine MR imaging configuration to a mock surgical presentation were 28.9 ± 9.2 mm with shifts occurring primarily in the inferior/superior direction. With respect to supine MR to surgical alignment, the average fiducial, target, and maximum target registration errors were 9.0 ± 1.7 mm, 9.3 ± 1.7 mm, and 20.0 ± 7.6 mm, respectively. Even when maintaining similar arm positions in the MR image and mock surgery, the respective averages were 6.0 ± 1.0 mm, 6.5 ± 1.1 mm, and 12.5 ± 2.8 mm. CONCLUSION: From supine MR positioning to surgical presentation, the breast undergoes large displacements (9.9-70.1 mm). The data also suggest that significant nonrigid deformations (9.3 ± 1.7 mm with 20.0 mm average maximum) exist that need to be considered in image guidance and modeling applications.

Metabolic Heterogeneity in Patient Tumor-Derived Organoids by Primary Site and Drug Treatment.

New tools are needed to match cancer patients with effective treatments. Patient-derived organoids offer a high-throughput platform to personalize treatments and discover novel therapies. Currently, methods to evaluate drug response in organoids are limited because they overlook cellular heterogeneity. In this study, non-invasive optical metabolic imaging (OMI) of cellular heterogeneity was characterized in breast cancer (BC) and pancreatic cancer (PC) patient-derived organoids. Baseline heterogeneity was analyzed for each patient, demonstrating that single-cell techniques, such as OMI, are required to capture the complete picture of heterogeneity present in a sample. Treatment-induced changes in heterogeneity were also analyzed, further demonstrating that these measurements greatly complement current techniques that only gauge average cellular response. Finally, OMI of cellular heterogeneity in organoids was evaluated as a predictor of clinical treatment response for the first time. Organoids were treated with the same drugs as the patient's prescribed regimen, and OMI measurements of heterogeneity were compared to patient outcome. OMI distinguished subpopulations of cells with divergent and dynamic responses to treatment in living organoids without the use of labels or dyes. OMI of organoids agreed with long-term therapeutic response in patients. With these capabilities, OMI could serve as a sensitive high-throughput tool to identify optimal therapies for individual patients, and to develop new effective therapies that address cellular heterogeneity in cancer.

Textual fiducial detection in breast conserving surgery for a near-real time image guidance system.

Breast cancer is the most common cancer in American women, and is the second most deadly. Current guidance approaches for breast cancer surgery provide distance to a seed implanted near the tumor centroid. Large deformations between preoperative imaging and surgical presentation, coupled with the lack of tumor extent information leads to difficulty in ensuring complete tumor resection. Here we propose a novel image guidance platform that utilizes character-based fiducials for easy detection and small fiducial points for precise and accurate localization. Our system is work-flow friendly, and near-real time with use of stereo cameras for surface acquisition. Using simple image processing techniques, the proposed technique can localize fiducials and character labels, providing updates without relying on video history. Character based fiducial labels can be recognized and used to determine correspondence between left and right images in a pair of stereo cameras, and frame to frame in a sequence of images during a procedure. Letters can be recognized with 89% accuracy using the MATLAB built in optical character recognition function, and an average of 81% of points can be accurately labeled and localized. The stereo camera system can determine surface points with accuracy below 2mm when compared to optically tracked stylus points. These surface points are incorporated to a four-panel guidance display that includes preoperative supine MR, tracked ultrasound, and a model view of the breast and tumor with respect to optically tracked instrumentation.

Toward quantitative quasistatic elastography with a gravity-induced deformation source for image-guided breast surgery.

Biomechanical breast models have been employed for applications in image registration and diagnostic analysis, breast augmentation simulation, and for surgical and biopsy guidance. Accurate applications of stress-strain relationships of tissue within the breast can improve the accuracy of biomechanical models that attempt to simulate breast deformations. Reported stiffness values for adipose, glandular, and cancerous tissue types vary greatly. Variations in reported stiffness properties have been attributed to differences in testing methodologies and assumptions, measurement errors, and natural interpatient differences in tissue elasticity. Therefore, the ability to determine patient-specific in vivo breast tissue properties would be advantageous for these procedural applications. While some in vivo elastography methods are not quantitative and others do not measure material properties under deformation conditions that are appropriate to the application of concern, in this study, we developed an elasticity estimation method that is performed using deformations representative of supine therapeutic procedures. More specifically, reconstruction of mechanical properties appropriate for the standard-of-care supine lumpectomy was performed by iteratively fitting two anatomical images before and after deformations taking place in the supine breast configuration. The method proposed is workflow-friendly, quantitative, and uses a noncontact, gravity-induced deformation source.

A system for automatic monitoring of surgical instruments and dynamic, non-rigid surface deformations in breast cancer surgery.

When negative tumor margins are achieved at the time of resection, breast conserving therapy (lumpectomy followed with radiation therapy) offers patients improved cosmetic outcomes and quality of life with equivalent survival outcomes to mastectomy. However, high reoperation rates ranging 10-59% continue to challenge adoption and suggest that improved intraoperative tumor localization is a pressing need. We propose to couple an optical tracker and stereo camera system for automated monitoring of surgical instruments and non-rigid breast surface deformations. A bracket was designed to rigidly pair an optical tracker with a stereo camera, optimizing overlap volume. Utilizing both devices allowed for precise instrument tracking of multiple objects with reliable, workflow friendly tracking of dynamic breast movements. Computer vision techniques were employed to automatically track fiducials, requiring one-time initialization with bounding boxes in stereo camera images. Point based rigid registration was performed between fiducial locations triangulated from stereo camera images and fiducial locations recorded with an optically tracked stylus. We measured fiducial registration error (FRE) and target registration error (TRE) with two different stereo camera devices using a phantom breast with five fiducials. Average FREs of 2.7 ± 0.4 mm and 2.4 ± 0.6 mm with each stereo-camera device demonstrate considerable promise for this approach in monitoring the surgical field. Automated tracking was shown to reduce error when compared to manually selected fiducial locations in stereo camera image-based localization. The proposed instrumentation framework demonstrated potential for the continuous measurement of surgical instruments in relation to the dynamic deformations of a breast during lumpectomy.

Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation.

Tissue stiffness interrogation is fundamental in breast cancer diagnosis and treatment. Furthermore, biomechanical models for predicting breast deformations have been created for several breast cancer applications. Within these applications, constitutive mechanical properties must be defined and the accuracy of this estimation directly impacts the overall performance of the model. In this study, we present an image-derived computational framework to obtain quantitative, patient specific stiffness properties for application in image-guided breast cancer surgery and interventions. The method uses two MR acquisitions of the breast in different supine gravity-loaded configurations to fit mechanical properties to a biomechanical breast model. A reproducibility assessment of the method was performed in a test-retest study using healthy volunteers and was further characterized in simulation. In five human data sets, the within subject coefficient of variation ranged from 10.7% to 27% and the intraclass correlation coefficient ranged from 0.91-0.944 for assessment of fibroglandular and adipose tissue stiffness. In simulation, fibroglandular content and deformation magnitude were shown to have significant effects on the shape and convexity of the objective function defined by image similarity. These observations provide an important step forward in characterizing the use of nonrigid image registration methodologies in conjunction with biomechanical models to estimate tissue stiffness. In addition, the results suggest that stiffness estimation methods using gravity-induced excitation can reliably and feasibly be implemented in breast cancer surgery/intervention workflows.

Bilateral Changes in Deep Tissue Environment After Manual Lymphatic Drainage in Patients with Breast Cancer Treatment-Related Lymphedema.

BACKGROUND: Breast cancer treatment-related lymphedema (BCRL) arises from a mechanical insufficiency following cancer therapies. Early BCRL detection and personalized intervention require an improved understanding of the physiological processes that initiate lymphatic impairment. Here, internal magnetic resonance imaging (MRI) measures of the tissue microenvironment were paired with clinical measures of tissue structure to test fundamental hypotheses regarding structural tissue and muscle changes after the commonly used therapeutic intervention of manual lymphatic drainage (MLD). METHODS AND RESULTS: Measurements to identify lymphatic dysfunction in healthy volunteers (n = 29) and patients with BCRL (n = 16) consisted of (1) limb volume, tissue dielectric constant, and bioelectrical impedance (i.e., non-MRI measures); (2) qualitative 3 Tesla diffusion-weighted, T1-weighted and T2-weighted MRI; and (3) quantitative multi-echo T2 MRI of the axilla. Measurements were repeated in patients immediately following MLD. Normative control and BCRL T2 values were quantified and a signed Wilcoxon Rank-Sum test was applied (significance: two-sided p < 0.05). Non-MRI measures yielded significant capacity for discriminating between arms with versus without clinical signs of BCRL, yet yielded no change in response to MLD. Alternatively, a significant increase in deep tissue T2 on the involved (pre T2 = 0.0371 ± 0.003 seconds; post T2 = 0.0389 ± 0.003; p = 0.029) and contralateral (pre T2 = 0.0365 ± 0.002; post T2 = 0.0395 ± 0.002; p < 0.01) arms was observed. Trends for larger T2 increases on the involved side after MLD in patients with stage 2 BCRL relative to earlier stages 0 and 1 BCRL were observed, consistent with tissue composition changes in later stages of BCRL manifesting as breakdown of fibrotic tissue after MLD in the involved arm. Contrast consistent with relocation of fluid to the contralateral quadrant was observed in all stages. CONCLUSION: Quantitative deep tissue T2 MRI values yielded significant changes following MLD treatment, whereas non-MRI measurements did not vary. These findings highlight that internal imaging measures of tissue composition may be useful for evaluating how current and emerging therapies impact tissue function.

A Randomized Phase II Neoadjuvant Study of Cisplatin, Paclitaxel With or Without Everolimus in Patients with Stage II/III Triple-Negative Breast Cancer (TNBC): Responses and Long-term Outcome Correlated with Increased Frequency of DNA Damage Response Gene Mutations, TNBC Subtype, AR Status, and Ki67.

Purpose: Because of inherent disease heterogeneity, targeted therapies have eluded triple-negative breast cancer (TNBC), and biomarkers predictive of treatment response have not yet been identified. This study was designed to determine whether the mTOR inhibitor everolimus with cisplatin and paclitaxel would provide synergistic antitumor effects in TNBC.Methods: Patients with stage II/III TNBC were enrolled in a randomized phase II trial of preoperative weekly cisplatin, paclitaxel and daily everolimus or placebo for 12 weeks, until definitive surgery. Tumor specimens were obtained at baseline, cycle 1, and surgery. Primary endpoint was pathologic complete response (pCR); secondary endpoints included clinical responses, breast conservation rate, safety, and discovery of molecular features associated with outcome.Results: Between 2009 and 2013, 145 patients were accrued; 36% of patients in the everolimus arm and 49% of patients in the placebo arm achieved pCR; in each arm, 50% of patients achieved complete responses by imaging. Higher rates of neutropenia, mucositis, and transaminase elevation were seen with everolimus. Clinical response to therapy and long-term outcome correlated with increased frequency of DNA damage response (DDR) gene mutations, Basal-like1 and Mesenchymal TNBC-subtypes, AR-negative status, and high Ki67, but not with tumor-infiltrating lymphocytes.Conclusions: The paclitaxel/cisplatin combination was well tolerated and active, but addition of everolimus was associated with more adverse events without improvement in pCR or clinical response. However, discoveries made from correlative studies could lead to predictive TNBC biomarkers that may impact clinical decision-making and provide new avenues for mechanistic exploration that could lead to clinical utility. Clin Cancer Res; 23(15); 4035-45. ©2017 AACR.

Prognostic Impact of 21-Gene Recurrence Score in Patients With Stage IV Breast Cancer: TBCRC 013.

PURPOSE: The objective of this study was to determine whether the 21-gene Recurrence Score (RS) provides clinically meaningful information in patients with de novo stage IV breast cancer enrolled in the Translational Breast Cancer Research Consortium (TBCRC) 013. PATIENTS AND METHODS: TBCRC 013 was a multicenter prospective registry that evaluated the role of surgery of the primary tumor in patients with de novo stage IV breast cancer. From July 2009 to April 2012, 127 patients from 14 sites were enrolled; 109 (86%) patients had pretreatment primary tumor samples suitable for 21-gene RS analysis. Clinical variables, time to first progression (TTP), and 2-year overall survival (OS) were correlated with the 21-gene RS by using log-rank, Kaplan-Meier, and Cox regression. RESULTS: Median patient age was 52 years (21 to 79 years); the majority had hormone receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative (72 [66%]) or hormone receptor-positive/HER2-positive (20 [18%]) breast cancer. At a median follow-up of 29 months, median TTP was 20 months (95% CI, 16 to 26 months), and median survival was 49 months (95% CI, 40 months to not reached). An RS was generated for 101 (93%) primary tumor samples: 22 (23%) low risk (< 18), 29 (28%) intermediate risk (18 to 30); and 50 (49%) high risk (≥ 31). For all patients, RS was associated with TTP (P = .01) and 2-year OS (P = .04). In multivariable Cox regression models among 69 patients with estrogen receptor (ER)-positive/HER2-negative cancer, RS was independently prognostic for TTP (hazard ratio, 1.40; 95% CI, 1.05 to 1.86; P = .02) and 2-year OS (hazard ratio, 1.83; 95% CI, 1.14 to 2.95; P = .013). CONCLUSION: The 21-gene RS is independently prognostic for both TTP and 2-year OS in ER-positive/HER2-negative de novo stage IV breast cancer. Prospective validation is needed to determine the potential role for this assay in the clinical management of this patient subset.

Breast Cancer Risk Reduction, Version 2.2015.

Breast cancer is the most frequently diagnosed malignancy in women in the United States and is second only to lung cancer as a cause of cancer death. To assist women who are at increased risk of developing breast cancer and their physicians in the application of individualized strategies to reduce breast cancer risk, NCCN has developed these guidelines for breast cancer risk reduction.

Realization of a biomechanical model-assisted image guidance system for breast cancer surgery using supine MRI.

PURPOSE: Unfortunately, the current re-excision rates for breast conserving surgeries due to positive margins average 20-40 %. The high re-excision rates arise from difficulty in localizing tumor boundaries intraoperatively and lack of real-time information on the presence of residual disease. The work presented here introduces the use of supine magnetic resonance (MR) images, digitization technology, and biomechanical models to investigate the capability of using an image guidance system to localize tumors intraoperatively. METHODS: Preoperative supine MR images were used to create patient-specific biomechanical models of the breast tissue, chest wall, and tumor. In a mock intraoperative setup, a laser range scanner was used to digitize the breast surface and tracked ultrasound was used to digitize the chest wall and tumor. Rigid registration combined with a novel nonrigid registration routine was used to align the preoperative and intraoperative patient breast and tumor. The registration framework is driven by breast surface data (laser range scan of visible surface), ultrasound chest wall surface, and MR-visible fiducials. Tumor localizations by tracked ultrasound were only used to evaluate the fidelity of aligning preoperative MR tumor contours to physical patient space. The use of tracked ultrasound to digitize subsurface features to constrain our nonrigid registration approach and to assess the fidelity of our framework makes this work unique. Two patient subjects were analyzed as a preliminary investigation toward the realization of this supine image-guided approach. RESULTS: An initial rigid registration was performed using adhesive MR-visible fiducial markers for two patients scheduled for a lumpectomy. For patient 1, the rigid registration resulted in a root-mean-square fiducial registration error (FRE) of 7.5 mm and the difference between the intraoperative tumor centroid as visualized with tracked ultrasound imaging and the registered preoperative MR counterpart was 6.5 mm. Nonrigid correction resulted in a decrease in FRE to 2.9 mm and tumor centroid difference to 5.5 mm. For patient 2, rigid registration resulted in a FRE of 8.8 mm and a 3D tumor centroid difference of 12.5 mm. Following nonrigid correction for patient 2, the FRE was reduced to 7.4 mm and the 3D tumor centroid difference was reduced to 5.3 mm. CONCLUSION: Using our prototype image-guided surgery platform, we were able to align intraoperative data with preoperative patient-specific models with clinically relevant accuracy; i.e., tumor centroid localizations of approximately 5.3-5.5 mm.

Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer.

OBJECTIVES: The purpose of this study was to determine whether multiparametric magnetic resonance imaging (MRI) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI), obtained before and after the first cycle of neoadjuvant chemotherapy (NAC), is superior to single-parameter measurements for predicting pathologic complete response (pCR) in patients with breast cancer. MATERIALS AND METHODS: Patients with stage II/III breast cancer were enrolled in an institutional review board-approved study in which 3-T DCE-MRI and DWI data were acquired before (n = 42) and after 1 cycle (n = 36) of NAC. Estimates of the volume transfer rate (K), extravascular extracellular volume fraction (ve), blood plasma volume fraction (vp), and the efflux rate constant (kep = K/ve) were generated from the DCE-MRI data using the Extended Tofts-Kety model. The apparent diffusion coefficient (ADC) was estimated from the DWI data. The derived parameter kep/ADC was compared with single-parameter measurements for its ability to predict pCR after the first cycle of NAC. RESULTS: The kep/ADC after the first cycle of NAC discriminated patients who went on to achieve a pCR (P < 0.001) and achieved a sensitivity, specificity, positive predictive value, and area under the receiver operator curve (AUC) of 0.92, 0.78, 0.69, and 0.88, respectively. These values were superior to the single parameters kep (AUC, 0.76) and ADC (AUC, 0.82). The AUCs between kep/ADC and kep were significantly different on the basis of the bootstrapped 95% confidence intervals (0.018-0.23), whereas the AUCs between kep/ADC and ADC trended toward significance (-0.11 to 0.24). CONCLUSIONS: The multiparametric analysis of DCE-MRI and DWI was superior to the single-parameter measurements for predicting pCR after the first cycle of NAC.

LYN-activating mutations mediate antiestrogen resistance in estrogen receptor-positive breast cancer.

Estrogen receptor-positive (ER(+)) breast cancers adapt to hormone deprivation and become resistant to antiestrogen therapy. Here, we performed deep sequencing on ER(+) tumors that remained highly proliferative after treatment with the aromatase inhibitor letrozole and identified a D189Y mutation in the inhibitory SH2 domain of the SRC family kinase (SFK) LYN. Evaluation of 463 breast tumors in The Cancer Genome Atlas revealed four LYN mutations, two of which affected the SH2 domain. In addition, LYN was upregulated in multiple ER(+) breast cancer lines resistant to long-term estrogen deprivation (LTED). An RNAi-based kinome screen revealed that LYN is required for growth of ER(+) LTED breast cancer cells. Kinase assays and immunoblot analyses of SRC substrates in transfected cells indicated that LYN(D189Y) has higher catalytic activity than WT protein. Further, LYN(D189Y) exhibited reduced phosphorylation at the inhibitory Y507 site compared with LYN(WT). Other SH2 domain LYN mutants, E159K and K209N, also exhibited higher catalytic activity and reduced inhibitory site phosphorylation. LYN(D189Y) overexpression abrogated growth inhibition by fulvestrant and/or the PI3K inhibitor BKM120 in 3 ER(+) breast cancer cell lines. The SFK inhibitor dasatinib enhanced the antitumor effect of BKM120 and fulvestrant against estrogen-deprived ER(+) xenografts but not LYN(D189Y)-expressing xenografts. These results suggest that LYN mutations mediate escape from antiestrogens in a subset of ER(+) breast cancers.

DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: pilot study findings.

PURPOSE: The purpose of this pilot study is to determine (1) if early changes in both semiquantitative and quantitative DCE-MRI parameters, observed after the first cycle of neoadjuvant chemotherapy in breast cancer patients, show significant difference between responders and nonresponders and (2) if these parameters can be used as a prognostic indicator of the eventual response. METHODS: Twenty-eight patients were examined using DCE-MRI pre-, post-one cycle, and just prior to surgery. The semiquantitative parameters included longest dimension, tumor volume, initial area under the curve, and signal enhancement ratio related parameters, while quantitative parameters included K(trans), v(e), k(ep), v(p), and τ(i) estimated using the standard Tofts-Kety, extended Tofts-Kety, and fast exchange regime models. RESULTS: Our preliminary results indicated that the signal enhancement ratio washout volume and k(ep) were significantly different between pathologic complete responders from nonresponders (P < 0.05) after a single cycle of chemotherapy. Receiver operator characteristic analysis showed that the AUC of the signal enhancement ratio washout volume was 0.75, and the AUCs of k(ep) estimated by three models were 0.78, 0.76, and 0.73, respectively. CONCLUSION: In summary, the signal enhancement ratio washout volume and k(ep) appear to predict breast cancer response after one cycle of neoadjuvant chemotherapy. This observation should be confirmed with additional prospective studies.