Rationale and Objectives
This study aimed to assess whether different breast cancer subspecialty physicians can be trained to distinguish non-suspicious from suspicious areas of post-lumpectomy specimen margin in patients with breast cancer using optical coherence tomography (OCT) images (a near-infrared based imaging technique) with final histology as the reference standard.
Materials and Methods
This institutional review board-exempt, Health Insurance Portability and Accountability Act-compliant study was performed on 63 surgically excised breast specimens from 35 female patients, creating a 90-case atlas containing both non-suspicious and suspicious areas for cancer. OCT images of the specimens were performed, providing 6.5–15 µm resolution with tissue visualization 1–2 mm subsurface. From the 90-case atlas, 40 cases were chosen for training and 40 were randomly selected for reader assessment. Three breast imaging radiologists, two pathologists, two breast surgeons, and one non-clinical reader were trained and assessed for ability to distinguish non-suspicious from suspicious findings blinded to clinical data and corresponding histology slides. Duration of training and assessment, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the curve for each reader were calculated as well as averages by subspecialty.
Results
The average training time was 3.4 hours (standard deviation, 1.2). The average assessment time was 1.9 hours (standard deviation, 0.7). The overall average reader sensitivity, specificity, and accuracy for detecting suspicious findings with histologic confirmation of cancer at the surgical margin for all eight readers were 80%, 87%, and 87%, respectively. Radiologists demonstrated the highest average among the disciplines, 85%, 93%, and 94%, followed by pathologists, 79%, 90%, and 84%, and surgeons, 76%, 84%, and 82% respectively.
Conclusions
With relatively short training (3.4 hours), readers from different medical specialties were able to distinguish suspicious from non-suspicious OCT imaging findings in ex vivo breast tissue as confirmed by histology. These results support the potential of OCT as a real-time intraoperative tool for post-lumpectomy specimen margin assessment.
Introduction
Wide local excision (WLE) is a surgical technique intended to achieve complete removal of malignant neoplasms with negative margins. WLE procedures are performed as part of the preferred surgical management of solid tumors including melanomas , cancers of colon or rectum , prostate , and breast . In the context of breast cancer, WLE is also known as breast-conserving surgery (BCS) . The success of BCS has been linked with the adequacy of disease-free (negative) margin widths on the primary resected specimen . Failure to achieve negative margins during the primary surgery necessitates a re-excision surgery to remove any residual disease, so increasing health-care costs and surgery-associated physical or psychological morbidity . Currently, the average re-excision rate among the patients who undergo BCS is about 20% , with re-excision rates as high as 60% reported in the literature . The high degree of variation in re-excision rates could be reduced with a reliable real-time intraoperative tissue assessment tool for margin involvement of the tumor .
Current intraoperative breast lumpectomy assessment tools are either histopathology based (such as touch preparation cytology and frozen section ) or non-destructive imaging based (including ultrasound and specimen radiography ). Although histopathology-based methods provide high-resolution representation of the tissue and have shown high sensitivity or specificity in detecting invasive carcinoma , they are associated with a number of limitations, including being time-intensive, destructive, prone to sampling error, lacking subsurface information, low sensitivity or specificity in detecting ductal carcinoma in situ (DCIS), and requiring the presence of a pathologist in proximity to the operating room . The imaging-based margin assessment techniques have the advantage of being faster compared to the histopathology-based tools; however, it has been shown that imaging has a much lower sensitivity than histopathology in detecting breast cancer within the margin . Both imaging techniques (ie, ultrasound and specimen radiography) perform poorly in detecting DCIS without microcalcifications .
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TABLE 1
Requirements for an Ideal Clinically Useful Intraoperative Ex Vivo Breast Tissue Assessment Tool
Required Feature Description and Rationale Adjunctive information Assessment tool output can be combined with the clinician’s judgment (based on the current standard of care) and all patient-specific clinical factors Provides subsurface context Allows clinicians to review, measure, and differentiate near-surface tissue microstructures, including distinctive features and different breast tissue types, specifically: adipose tissue, fibrous stroma, breast lobules and ducts, as well as in situ and invasive carcinomas High spatial resolution Able to discern the subsurface features with 6.5–15 µm resolution (close-to-histology) Automated acquisition Standardizes data collection of the whole specimen to eliminate sampling errors, ideally with no increased operator workload (ie, from manipulating an imaging probe) or even minimizing or eliminating the need for an operator Non-destructive Preserves entire tissue sample for postoperative pathology. Does not require special subsectioning, staining, or other preparation of the tissue specimen Rapid intraoperative results Does not substantially extend the time required for the surgical procedure Useable by diverse clinicians Enables use of tool by surgeons, radiologists, or pathologists, as appropriate per institution-specific workflow needs High accuracy Provides improved quantitative clinical outcomes (ie, sensitivity, specificity, PPV, NPV, and accuracy) compared to the current standard of care
NPV, negative predictive value; PPV, positive predictive value.
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Materials and Methods
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Breast Specimen Collection and Imaging
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OCT Image Assessment Study
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Statistical Analysis
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Results
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TABLE 2
Statistical Analysis of the Results for Distinguishing Non-suspicious From Suspicious Breast Tissue
Reader Time Sensitivity 95% CI Specificity 95% CI PPV NPV Accuracy R#1 90 min 81% 0.62–1.00 92% 0.81–1.00 0.87 0.88 0.89 R#2 94 min 81% 0.62–1.00 96% 0.88–1.00 0.93 0.88 0.97 R#3 87 min 94% 0.82–1.00 92% 0.81–1.00 0.88 0.96 0.96 P#1 189 min 94% 0.82–1.00 83% 0.68–0.98 0.79 0.95 0.89 P#2 150 min 63% 0.39–0.86 96% 0.88–1.00 0.91 0.79 0.80 S#1 180 min 88% 0.71–1.00 75% 0.58–0.92 0.70 0.90 0.81 S#2 90 min 63% 0.39–0.86 92% 0.81–1.00 0.83 0.79 0.84 NC 137 min 75% 0.54–0.96 71% 0.53–0.89 0.63 0.81 0.82
CI, confidence interval; NC, non-clinician; NPV, negative predictive value; P1–P2, pathologist 1–2; PPV, positive predictive value; R1–R3, radiologist 1–3; S1–S2, surgeon 1–2.
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TABLE 3
Average Assessment Results for Distinguishing Non-suspicious From Suspicious Breast Tissue for Readers From Different Disciplines
Reader Discipline Sensitivity Specificity PPV NPV Accuracy Radiology 85% 93% 0.89 0.91 0.94 Pathology 79% 90% 0.85 0.87 0.84 Surgery 76% 84% 0.77 0.85 0.82 The 7 clinician readers 81% 89% 0.84 0.88 0.88 All 8 readers 80% 87% 0.82 0.87 0.87
NPV, negative predictive value; PPV, positive predictive value.
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Discussion
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