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Significance: Optical coherence tomography (OCT) is widely used as a potential diagnostic tool for a variety of diseases including various types of cancer. However, sensitivity and specificity analyses of OCT in different cancers yield results varying from 11% to 100%. Hence, there is a need for more detailed statistical analysis of blinded reader studies. Aim: Extensive statistical analysis is performed on results from a blinded study involving OCT of breast tumor margins to assess the impact of reader variability on sensitivity and specificity. Approach: Five readers with varying levels of experience reading OCT images assessed 50 OCT images of breast tumor margins collected using an intraoperative OCT system. Statistical modeling and analysis was performed using the R language to analyze reader experience and variability. Results: Statistical analysis showed that the readers’ prior experience with OCT images was directly related to the probability of the readers’ assessment agreeing with histology. Additionally, results from readers with prior experience specific to OCT in breast cancer had a higher probability of agreement with histology compared to readers with experience with OCT in other (noncancer) diseases. Conclusions: The results from this study demonstrate the potential impact of reader training and experience in the assessment of sensitivity and specificity. They also demonstrate even greater potential improvement in diagnostic performance by combining results from multiple readers. These preliminary findings suggest valuable directions for further study.

Purpose:\ud \ud The pattern of dense tissue on a mammogram appears to provide additional information than overall density for risk assessment, but there has been little consistency in measures of texture identified. The purpose of this study is thus to validate a mammographic texture feature developed from a previous study in a new setting.\ud \ud Approach:\ud \ud A case–control study (316 invasive cases and 1339 controls) of women in Virginia, USA was used to validate a mammographic texture feature (MMTEXT) derived in a independent previous study. Analysis of predictive ability was adjusted for age, demographic factors, questionnaire risk factors (combined through the Tyrer-Cuzick model), and optionally BI-RADS breast density. Odds ratios per interquartile range (IQ-OR) in controls were estimated. Subgroup analysis assessed heterogeneity by mode of cancer detection (94 not detected by mammography).\ud \ud Results:\ud \ud MMTEXT was not a significant risk factor at 0.05 level after adjusting for classical risk factors (IQ-OR = 1.16, 95%CI 0.92 to 1.46), nor after further adjustment for BI-RADS density (IQ-OR = 0.92, 95%CI 0.76 to 1.10). There was weak evidence that MMTEXT was more predictive for cancers that were not detected by mammography (unadjusted for density: IQ-OR = 1.46, 95%CI 0.99 to 2.15 versus 1.03, 95%CI 0.79 to 1.35, Phet 0.10; adjusted for density: IQ-OR = 1.11, 95%CI 0.70 to 1.77 versus 0.76, 95%CI 0.55 to 1.05, Phet 0.21).\ud \ud Conclusions:\ud \ud MMTEXT is unlikely to be a useful imaging marker for invasive breast cancer risk assessment in women attending mammography screening. Future studies may benefit from a larger sample size to confirm this as well as developing and validating other measures of risk. This negative finding demonstrates the importance of external validation.

Purpose: Computer-aided detection (CAD) alerts radiologists to findings potentially associated with breast cancer but is notorious for creating false-positive marks. Although a previous paper found that radiologists took more time to interpret mammograms with more CAD marks, our impression was that this was not true in actual interpretation. We hypothesized that radiologists would selectively disregard these marks when present in larger numbers. Approach: We performed a retrospective review of bilateral digital screening mammograms. We use a mixed linear regression model to assess the relationship between number of CAD marks and ln (interpretation time) after adjustment for covariates. Both readers and mammograms were treated as random sampling units. Results: Ten radiologists, with median experience after residency of 12.5 years (range 6 to 24) interpreted 1832 mammograms. After accounting for number of images, Breast Imaging Reporting and Data System category, and breast density, the number of CAD marks was positively associated with longer interpretation time, with each additional CAD mark proportionally increasing median interpretation time by 4.35% for a typical reader. Conclusions: We found no support for our hypothesis that radiologists will selectively disregard CAD marks when they are present in larger numbers.

Significance: A key risk faced by oncological surgeons continues to be complete removal of tumor. Currently, there is no intraoperative imaging device to detect kidney tumors during excision. Aim: We are evaluating molecular chemical imaging (MCI) as a technology for real-time tumor detection and margin assessment during tumor removal surgeries. Approach: In exploratory studies, we evaluate visible near infrared (Vis-NIR) MCI for differentiating tumor from adjacent tissue in ex vivo human kidney specimens, and in anaesthetized mice with breast or lung tumor xenografts. Differentiation of tumor from nontumor tissues is made possible with diffuse reflectance spectroscopic signatures and hyperspectral imaging technology. Tumor detection is achieved by score image generation to localize the tumor, followed by application of computer vision algorithms to define tumor border. Results: Performance of a partial least squares discriminant analysis (PLS-DA) model for kidney tumor in a 22-patient study is 0.96 for area under the receiver operating characteristic curve. A PLS-DA model for in vivo breast and lung tumor xenografts performs with 100% sensitivity, 83% specificity, and 89% accuracy. Conclusion: Detection of cancer in surgically resected human kidney tissues is demonstrated ex vivo with Vis-NIR MCI, and in vivo on mice with breast or lung xenografts.

Significance: We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells’ morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies. Aim: Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies. Approach: Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning. Results: Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line. Conclusions: The proposed epithelial–mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.

Inexpensive near-infrared microscopy (NIRM) was developed as a convenient technique to detect the medulla loss of scalp hair while reducing analytical time with easy sample preparation, leading to a field screening tool for breast cancer. NIRM has been evaluated as an alternative to synchrotron-based nanoscopy and to the relatively expensive method of conventional infrared microscopy to determine the degree and pattern of medulla loss of scalp hairs of patients with breast cancer and benign diseases, as well as normal healthy individuals. NIR imaging showed a strong, scattering-based hyperintense contrast of the medulla compared to the fully attenuated cortex in medullated healthy hair. Complete medulla loss (CML) per hair strand was more extensively (60.9 ± 10.2 %) (p

Neoadjuvant chemotherapy (NACT) is routinely administered to subsets of breast cancer patients, including triple negative (TN) or human epidermal growth factor receptor 2-positive (HER2+) cancers. After NACT and subsequent surgical resection, 5% to 30% of patients have no residual invasive carcinoma, termed pathological complete response. Unfortunately, many patients experience little-to-no response after NACT and unnecessarily suffer its side effects. Methods are needed to predict an individual patient’s response to NACT. Core needle biopsies, taken before NACT, consist of tumor cells and the surrounding extracellular matrix. We performed second-harmonic generation (SHG) imaging of fibrillar collagen in core needle biopsy sections as a possible predictor of response to NACT. The ratio of forward-to-backward scattering (F/B) SHG was assessed in the “tumor bulk” and “tumor–host interface” in HER2+ and TN core needle biopsy sections. Patient response was classified post-treatment using the Residual Cancer Burden (RCB) score. In HER2+ biopsies, RCB class was associated with F/B derived from the tumor–stromal interface, but not tumor bulk. F/B was not associated with RCB class in TN biopsies. These findings suggest that F/B from needle biopsy sections may be a useful predictor of which patients will respond favorably to NACT, with the potential to help reduce overtreatment.