Label-free nonlinear optical signatures of extracellular vesicles in liquid and tissue biopsies of human breast cancer.

Extracellular vesicles (EVs) have been implicated in metastasis and proposed as cancer biomarkers. However, heterogeneity and small size makes assessments of EVs challenging. Often, EVs are isolated from biofluids, losing spatial and temporal context and thus lacking the ability to access EVs in situ in their native microenvironment. This work examines the capabilities of label-free nonlinear optical microscopy to extract biochemical optical metrics of EVs in ex vivo tissue and EVs isolated from biofluids in cases of human breast cancer, comparing these metrics within and between EV sources. Before surgery, fresh urine and blood serum samples were obtained from human participants scheduled for breast tumor surgery (24 malignant, 6 benign) or healthy participants scheduled for breast reduction surgery (4 control). EVs were directly imaged both in intact ex vivo tissue that was removed during surgery and in samples isolated from biofluids by differential ultracentrifugation. Isolated EVs and freshly excised ex vivo breast tissue samples were imaged with custom nonlinear optical microscopes to extract single-EV optical metabolic signatures of NAD(P)H and FAD autofluorescence. Optical metrics were significantly altered in cases of malignant breast cancer in biofluid-derived EVs and intact tissue EVs compared to control samples. Specifically, urinary isolated EVs showed elevated NAD(P)H fluorescence lifetime in cases of malignant cancer, serum-derived isolated EVs showed decreased optical redox ratio in stage II cancer, but not earlier stages, and ex vivo breast tissue showed an elevated number of EVs in cases of malignant cancer. Results further indicated significant differences in the measured optical metabolic signature based on EV source (urine, serum and tissue) within individuals.

Weakly supervised identification of microscopic human breast cancer-related optical signatures from normal-appearing breast tissue.

With the latest advancements in optical bioimaging, rich structural and functional information has been generated from biological samples, which calls for capable computational tools to identify patterns and uncover relationships between optical characteristics and various biomedical conditions. Constrained by the existing knowledge of the novel signals obtained by those bioimaging techniques, precise and accurate ground truth annotations can be difficult to obtain. Here we present a weakly supervised deep learning framework for optical signature discovery based on inexact and incomplete supervision. The framework consists of a multiple instance learning-based classifier for the identification of regions of interest in coarsely labeled images and model interpretation techniques for optical signature discovery. We applied this framework to investigate human breast cancer-related optical signatures based on virtual histopathology enabled by simultaneous label-free autofluorescence multiharmonic microscopy (SLAM), with the goal of exploring unconventional cancer-related optical signatures from normal-appearing breast tissues. The framework has achieved an average area under the curve (AUC) of 0.975 on the cancer diagnosis task. In addition to well-known cancer biomarkers, non-obvious cancer-related patterns were revealed by the framework, including NAD(P)H-rich extracellular vesicles observed in normal-appearing breast cancer tissue, which facilitate new insights into the tumor microenvironment and field cancerization. This framework can be further extended to diverse imaging modalities and optical signature discovery tasks.

Development of the Illinois Surgical Quality Improvement Collaborative (ISQIC): Implementing 21 Components to Catalyze Statewide Improvement in Surgical Care.

INTRODUCTION: In 2014, 56 Illinois hospitals came together to form a unique learning collaborative, the Illinois Surgical Quality Improvement Collaborative (ISQIC). Our objectives are to provide an overview of the first three years of ISQIC focused on (1) how the collaborative was formed and funded, (2) the 21 strategies implemented to support quality improvement (QI), (3) collaborative sustainment, and (4) how the collaborative acts as a platform for innovative QI research. METHODS: ISQIC includes 21 components to facilitate QI that target the hospital, the surgical QI team, and the peri-operative microsystem. The components were developed from available evidence, a detailed needs assessment of the hospitals, reviewing experiences from prior surgical and non-surgical QI Collaboratives, and interviews with QI experts. The components comprise 5 domains: guided implementation (e.g., mentors, coaches, statewide QI projects), education (e.g., process improvement (PI) curriculum), hospital- and surgeon-level comparative performance reports (e.g., process, outcomes, costs), networking (e.g., forums to share QI experiences and best practices), and funding (e.g., for the overall program, pilot grants, and bonus payments for improvement). RESULTS: Through implementation of the 21 novel ISQIC components, hospitals were equipped to use their data to successfully implement QI initiatives and improve care. Formal (QI/PI) training, mentoring, and coaching were undertaken by the hospitals as they worked to implement solutions. Hospitals received funding for the program and were able to work together on statewide quality initiatives. Lessons learned at one hospital were shared with all participating hospitals through conferences, webinars, and toolkits to facilitate learning from each other with a common goal of making care better and safer for the surgical patient in Illinois. Over the first three years, surgical outcomes improved in Illinois. DISCUSSION: The first three years of ISQIC improved care for surgical patients across Illinois and allowed hospitals to see the value of participating in a surgical QI learning collaborative without having to make the initial financial investment themselves. Given the strong support and buy-in from the hospitals, ISQIC has continued beyond the initial three years and continues to support QI across Illinois hospitals.

Changes in Surgical Outcomes in a Statewide Quality Improvement Collaborative with Introduction of Simultaneous, Comprehensive Interventions.

BACKGROUND: Surgical quality improvement collaboratives (QICs) aim to improve patient outcomes through coaching, benchmarked data reporting, and other activities. Although other regional QICs have formed organically over time, it is unknown whether a comprehensive quality improvement program implemented simultaneously across hospitals at the formation of a QIC would improve patient outcomes. STUDY DESIGN: Patients undergoing surgery at 48 hospitals in the Illinois Surgical Quality Improvement Collaborative (ISQIC) were included. Risk-adjusted rates of postoperative morbidity and mortality were compared from baseline to year 3. Difference-in-differences analyses compared ISQIC hospitals with hospitals in the NSQIP Participant Use File (PUF), which served as a control. RESULTS: There were 180,582 patients who underwent surgery at ISQIC-participating hospitals. Inpatient procedures comprised 100,219 (55.5%) cases. By year 3, risk-adjusted rates of death or serious morbidity decreased in both ISQIC (relative reduction 25.0%, p < 0.001) and PUF hospitals (7.8%, p < 0.001). Adjusted difference-in-differences analysis revealed that ISQIC participation was associated with a significantly greater reduction in death or serious morbidity (odds ratio 0.94, 95% CI 0.90 to 0.99, p = 0.01) compared with PUF hospitals. Relative reductions in risk-adjusted rates of other outcomes were also seen in both ISQIC and PUF hospitals (morbidity 22.4% vs 6.4%; venous thromboembolism 20.0% vs 5.0%; superficial surgical site infection 27.3% vs 7.7%, all p < 0.05), although these difference-in-differences did not reach statistical significance. CONCLUSIONS: Although complication rates decreased at both ISQIC and PUF hospitals, participation in ISQIC was associated with a significantly greater improvement in death or serious morbidity. These results underscore the potential of QICs to improve patient outcomes.

Differentiation of breast tissue types for surgical margin assessment using machine learning and polarization-sensitive optical coherence tomography.

We report an automated differentiation model for classifying malignant tumor, fibro-adipose, and stroma in human breast tissues based on polarization-sensitive optical coherence tomography (PS-OCT). A total of 720 PS-OCT images from 72 sites of 41 patients with H&E histology-confirmed diagnoses as the gold standard were employed in this study. The differentiation model is trained by the features extracted from both one standard OCT-based metric (i.e., intensity) and four PS-OCT-based metrics (i.e., phase difference between two channels (PD), phase retardation (PR), local phase retardation (LPR), and degree of polarization uniformity (DOPU)). Further optimized by forward searching and validated by leave-one-site-out-cross-validation (LOSOCV) method, the best feature subset was acquired with the highest overall accuracy of 93.5% for the model. Furthermore, to show the superiority of our differentiation model based on PS-OCT images over standard OCT images, the best model trained by intensity-only features (usually obtained by standard OCT systems) was also obtained with an overall accuracy of 82.9%, demonstrating the significance of the polarization information in breast tissue differentiation. The high performance of our differentiation model suggests the potential of using PS-OCT for intraoperative human breast tissue differentiation during the surgical resection of breast cancer.

Multi-institution Evaluation of Adherence to Comprehensive Postoperative VTE Chemoprophylaxis.

OBJECTIVES: The aims of this study were to: (1) measure the rate of failure to provide defect-free postoperative venous thromboembolism (VTE) chemoprophylaxis, (2) identify reasons for failure to provide defect-free VTE chemoprophylaxis, and (3) examine patient- and hospital-level factors associated with failure. SUMMARY BACKGROUND DATA: Current VTE quality measures are inadequate. VTE outcome measures are invalidated for interhospital comparison by surveillance bias. VTE process measures (e.g., SCIP-VTE-2) do not comprehensively capture failures throughout patients' entire hospitalization. METHODS: We examined adherence to a novel VTE chemoprophylaxis process measure in patients who underwent colectomies over 18 months at 36 hospitals in a statewide surgical collaborative. This measure assessed comprehensive VTE chemoprophylaxis during each patient's entire hospitalization, including reasons why chemoprophylaxis was not given. Associations of patient and hospital characteristics with measure failure were examined. RESULTS: The SCIP-VTE-2 hospital-level quality measure identified failures of VTE chemoprophylaxis in 0% to 3% of patients. Conversely, the novel measure unmasked failure to provide defect-free chemoprophylaxis in 18% (736/4086) of colectomies. Reasons for failure included medication not ordered (30.4%), patient refusal (30.3%), incorrect dosage/frequency (8.2%), and patient off-unit (3.4%). Patients were less likely to fail the chemoprophylaxis process measure if treated at nonsafety net hospitals (OR 0.62, 95% CI 0.39-0.99, P = 0.045) or Magnet designated hospitals (OR 0.45, 95% CI 0.29-0.71, P = 0.001). CONCLUSIONS: In contrast to SCIP-VTE-2, our novel quality measure unmasked VTE chemoprophylaxis failures in 18% of colectomies. Most failures were due to patient refusals or ordering errors. Hospitals should focus improvement efforts on ensuring patients receive VTE prophylaxis throughout their entire hospitalization.

Intraoperative visualization of the tumor microenvironment and quantification of extracellular vesicles by label-free nonlinear imaging.

Characterization of the tumor microenvironment, including extracellular vesicles (EVs), is important for understanding cancer progression. EV studies have traditionally been performed on dissociated cells, lacking spatial information. Since the distribution of EVs in the tumor microenvironment is associated with cellular function, there is a strong need for visualizing EVs in freshly resected tissues. We intraoperatively imaged untreated human breast tissues using a custom nonlinear imaging system. Label-free optical contrasts of the tissue, correlated with histological findings, enabled point-of-procedure characterization of the tumor microenvironment. EV densities from 29 patients with breast cancer were found to increase with higher histologic grade and shorter tumor-to-margin distance and were significantly higher than those from 7 cancer-free patients undergoing breast reduction surgery. Acquisition and interpretation of these intraoperative images not only provide real-time visualization of the tumor microenvironment but also offer the potential to use EVs as a label-free biomarker for cancer diagnosis and prognosis.

Intraoperative optical coherence tomography for assessing human lymph nodes for metastatic cancer.

BACKGROUND: Evaluation of lymph node (LN) status is an important factor for detecting metastasis and thereby staging breast cancer. Currently utilized clinical techniques involve the surgical disruption and resection of lymphatic structure, whether nodes or axillary contents, for histological examination. While reasonably effective at detection of macrometastasis, the majority of the resected lymph nodes are histologically negative. Improvements need to be made to better detect micrometastasis, minimize or eliminate lymphatic disruption complications, and provide immediate and accurate intraoperative feedback for in vivo cancer staging to better guide surgery. METHODS: We evaluated the use of optical coherence tomography (OCT), a high-resolution, real-time, label-free imaging modality for the intraoperative assessment of human LNs for metastatic disease in patients with breast cancer. We assessed the sensitivity and specificity of double-blinded trained readers who analyzed intraoperative OCT LN images for presence of metastatic disease, using co-registered post-operative histopathology as the gold standard. RESULTS: Our results suggest that intraoperative OCT examination of LNs is an appropriate real-time, label-free, non-destructive alternative to frozen-section analysis, potentially offering faster interpretation and results to empower superior intraoperative decision-making. CONCLUSIONS: Intraoperative OCT has strong potential to supplement current post-operative histopathology with real-time in situ assessment of LNs to preserve both non-cancerous nodes and their lymphatic vessels, and thus reduce the associated risks and complications from surgical disruption of lymphoid structures following biopsy.

Real-time Imaging of the Resection Bed Using a Handheld Probe to Reduce Incidence of Microscopic Positive Margins in Cancer Surgery.

Wide local excision (WLE) is a common surgical intervention for solid tumors such as those in melanoma, breast, pancreatic, and gastrointestinal cancer. However, adequate margin assessment during WLE remains a significant challenge, resulting in surgical reinterventions to achieve adequate local control. Currently, no label-free imaging method is available for surgeons to examine the resection bed in vivo for microscopic residual cancer. Optical coherence tomography (OCT) enables real-time high-resolution imaging of tissue microstructure. Previous studies have demonstrated that OCT analysis of excised tissue specimens can distinguish between normal and cancerous tissues by identifying the heterogeneous and disorganized microscopic tissue structures indicative of malignancy. In this translational study involving 35 patients, a handheld surgical OCT imaging probe was developed for in vivo use to assess margins both in the resection bed and on excised specimens for the microscopic presence of cancer. The image results from OCT showed structural differences between normal and cancerous tissue within the resection bed following WLE of the human breast. The ex vivo images were compared with standard postoperative histopathology to yield sensitivity of 91.7% [95% confidence interval (CI), 62.5%-100%] and specificity of 92.1% (95% CI, 78.4%-98%). This study demonstrates in vivo OCT imaging of the resection bed during WLE with the potential for real-time microscopic image-guided surgery.