Integrative genomics identifies SHPRH as a tumor suppressor gene in lung adenocarcinoma that regulates DNA damage response.

BACKGROUND: Identification of driver mutations and development of targeted therapies has considerably improved outcomes for lung cancer patients. However, significant limitations remain with the lack of identified drivers in a large subset of patients. Here, we aimed to assess the genomic landscape of lung adenocarcinomas (LUADs) from individuals without a history of tobacco use to reveal new genetic drivers of lung cancer. METHODS: Integrative genomic analyses combining whole-exome sequencing, copy number, and mutational information for 83 LUAD tumors was performed and validated using external datasets to identify genetic variants with a predicted functional consequence and assess association with clinical outcomes. LUAD cell lines with alteration of identified candidates were used to functionally characterize tumor suppressive potential using a conditional expression system both in vitro and in vivo. RESULTS: We identified 21 genes with evidence of positive selection, including 12 novel candidates that have yet to be characterized in LUAD. In particular, SNF2 Histone Linker PHD RING Helicase (SHPRH) was identified due to its frequency of biallelic disruption and location within the familial susceptibility locus on chromosome arm 6q. We found that low SHPRH mRNA expression is associated with poor survival outcomes in LUAD patients. Furthermore, we showed that re-expression of SHPRH in LUAD cell lines with inactivating alterations for SHPRH reduces their in vitro colony formation and tumor burden in vivo. Finally, we explored the biological pathways associated SHPRH inactivation and found an association with the tolerance of LUAD cells to DNA damage. CONCLUSIONS: These data suggest that SHPRH is a tumor suppressor gene in LUAD, whereby its expression is associated with more favorable patient outcomes, reduced tumor and mutational burden, and may serve as a predictor of response to DNA damage. Thus, further exploration into the role of SHPRH in LUAD development may make it a valuable biomarker for predicting LUAD risk and prognosis.

Three-Dimension Epithelial Segmentation in Optical Coherence Tomography of the Oral Cavity Using Deep Learning.

This paper aims to simplify the application of optical coherence tomography (OCT) for the examination of subsurface morphology in the oral cavity and reduce barriers towards the adoption of OCT as a biopsy guidance device. The aim of this work was to develop automated software tools for the simplified analysis of the large volume of data collected during OCT. Imaging and corresponding histopathology were acquired in-clinic using a wide-field endoscopic OCT system. An annotated dataset (n = 294 images) from 60 patients (34 male and 26 female) was assembled to train four unique neural networks. A deep learning pipeline was built using convolutional and modified u-net models to detect the imaging field of view (network 1), detect artifacts (network 2), identify the tissue surface (network 3), and identify the presence and location of the epithelial-stromal boundary (network 4). The area under the curve of the image and artifact detection networks was 1.00 and 0.94, respectively. The Dice similarity score for the surface and epithelial-stromal boundary segmentation networks was 0.98 and 0.83, respectively. Deep learning (DL) techniques can identify the location and variations in the epithelial surface and epithelial-stromal boundary in OCT images of the oral mucosa. Segmentation results can be synthesized into accessible en face maps to allow easier visualization of changes.

DNA Ploidy as a Potential Adjunct Prognostic Marker of Low-Risk Prostate Cancer Progression after Radical Prostatectomy.

PURPOSE: Post prostatectomy PSA kinetics and General Grade Groups (GGG) are the strongest prognostic markers of biochemical recurrence (BCR) and prostate cancer (PCa)-specific mortality after radical prostatectomy. Despite having low-risk PCa, some patients will experience BCR, for some, clinically significant BCR. There is a need for an objective prognostic marker at the time of prostatectomy to improve risk stratification within this population. In this study, we investigated the prognostic potential of DNA ploidy. MATERIALS AND METHODS: Prostatectomy samples from 97 patients with GGG1 and GGG2 with a low-risk CAPRA-S score were included in this study. PCa tissue with the worst Gleason pattern underwent tissue disaggregation, cell isolation and staining with a DNA stoichiometric stain. Using image cytometry, DNA ploidy was measured and a Ploidy Score (PS) was generated. RESULTS: Among the 97 patients, 79 had no BCR, 18 experienced BCR, of which 14 had a PSA doubling time (PSA-DT) >1 year (low-risk group) and 4 had a PSA-DT of <1 year (high-risk group). Using Logistic regression analysis, only pathological T stage (pT) and PS independently predicted BCR with PS being the most significant (p = 0.001). The number of aneuploid cells was significantly higher in the high-risk group compared to the other groups (p = 1.7x10-11). PS combined with GGG diagnosis further stratified risk groups of biochemical recurrence free survival within CAPRA-S low-risk cohort. CONCLUSION: DNA ploidy is an independent prognostic marker of BCR in low-risk PCa after radical prostatectomy, which could early on identify potentially aggressive PCa recurrences and introduce a more personalized approach to salvage treatments.

Tensor radiomics: paradigm for systematic incorporation of multi-flavoured radiomics features.

Background: Radiomics features hold significant value as quantitative imaging biomarkers for diagnosis, prognosis, and treatment response assessment. To generate radiomics features and ultimately develop signatures, various factors can be manipulated, including image discretization parameters (e.g., bin number or size), convolutional filters, segmentation perturbation, or multi-modality fusion levels. Typically, only one set of parameters is employed, resulting in a single value or "flavour" for each radiomics feature. In contrast, we propose "tensor radiomics" (TR) where tensors of features calculated using multiple parameter combinations (i.e., flavours) are utilized to optimize the creation of radiomics signatures. Methods: We provide illustrative instances of TR implementation in positron emission tomography-computed tomography (PET-CT), magnetic resonance imaging (MRI), and CT by leveraging machine learning (ML) and deep learning (DL) methodologies, as well as reproducibility analyses: (I) to predict overall survival (OS) in lung cancer (CT) and head and neck cancer (PET-CT), TR was employed by varying bin sizes. This approach involved use of a hybrid deep neural network called 'TR-Net' and two ML-based techniques for combining different flavours. (II) TR was constructed by incorporating different segmentation perturbations and various bin sizes to classify the response of late-stage lung cancer to first-line immunotherapy using CT images. (III) In MRI of glioblastoma (GBM), TR was implemented to generate multi-flavour radiomics features, enabling enhanced analysis and interpretation. (IV) TR was employed via multiple PET-CT fusions in head and neck cancer. Flavours based on different fusions were created using Laplacian pyramids and wavelet transforms. Results: Our findings demonstrated that TR outperformed conventional radiomics features in lung cancer CT and head and neck cancer PET-CT images, significantly enhancing OS prediction accuracy. TR also improved classification of lung cancer response to therapy and exhibited notable advantages in reproducibility compared to single-flavour features in MR imaging of GBM. Moreover, in head and neck cancer, TR through multiple PET-CT fusions exhibited improved performance in predicting OS. Conclusions: We conclude that the proposed TR paradigm has significant potential to improve performance in different medical imaging tasks. By incorporating multiple flavours of radiomics features, TR overcomes limitations associated with individual features and shows promise in enhancing prognostic capabilities in clinical settings.

Delineating spatial cell-cell interactions in the solid tumour microenvironment through the lens of highly multiplexed imaging.

The growth and metastasis of solid tumours is known to be facilitated by the tumour microenvironment (TME), which is composed of a highly diverse collection of cell types that interact and communicate with one another extensively. Many of these interactions involve the immune cell population within the TME, referred to as the tumour immune microenvironment (TIME). These non-cell autonomous interactions exert substantial influence over cell behaviour and contribute to the reprogramming of immune and stromal cells into numerous pro-tumourigenic phenotypes. The study of some of these interactions, such as the PD-1/PD-L1 axis that induces CD8+ T cell exhaustion, has led to the development of breakthrough therapeutic advances. Yet many common analyses of the TME either do not retain the spatial data necessary to assess cell-cell interactions, or interrogate few (<10) markers, limiting the capacity for cell phenotyping. Recently developed digital pathology technologies, together with sophisticated bioimage analysis programs, now enable the high-resolution, highly-multiplexed analysis of diverse immune and stromal cell markers within the TME of clinical specimens. In this article, we review the tumour-promoting non-cell autonomous interactions in the TME and their impact on tumour behaviour. We additionally survey commonly used image analysis programs and highly-multiplexed spatial imaging technologies, and we discuss their relative advantages and limitations. The spatial organization of the TME varies enormously between patients, and so leveraging these technologies in future studies to further characterize how non-cell autonomous interactions impact tumour behaviour may inform the personalization of cancer treatment.​.

Prediction of Disease Progression to Upfront Pembrolizumab Monotherapy in Advanced Non-Small-Cell Lung Cancer with High PD-L1 Expression Using Baseline CT Disease Quantification and Smoking Pack Years.

Health Canada approved pembrolizumab in the first-line setting for advanced non-small-cell lung cancer with PD-L1 ≥ 50% and no EGFR/ALK aberration. The keynote 024 trial showed 55% of such patients progress with pembrolizumab monotherapy. We propose that the combination of baseline CT and clinical factors can help identify those patients who may progress. In 138 eligible patients from our institution, we retrospectively collected their baseline variables, including baseline CT findings (primary lung tumor size and metastatic site), smoking pack years, performance status, tumor pathology, and demographics. The treatment response was assessed via RECIST 1.1 using the baseline and first follow-up CT. Associations between the baseline variables and progressive disease (PD) were tested by logistic regression analyses. The results showed 46/138 patients had PD. The baseline CT "number of involved organs" by metastasis and smoking pack years were independently associated with PD (p < 0.05), and the ROC analysis showed a good performance of the model that integrated these variables in predicting PD (AUC: 0.79). This pilot study suggests that the combination of baseline CT disease and smoking PY can identify who may progress on pembrolizumab monotherapy and can potentially facilitate decision-making for the optimal first-line treatment in the high PD-L1 cohort.

Automated PD-L1 Scoring for Non-Small Cell Lung Carcinoma Using Open-Source Software.

PD-L1 expression in non-small cell lung cancer (NSCLC) is predictive of response to immunotherapy, but scoring of PD-L1 immunohistochemistry shows considerable interobserver variability. Automated methods may allow more consistent and expedient PD-L1 scoring. We aimed to assess the technical concordance of PD-L1 scores produced using free open source QuPath software with the manual scores of three pathologists. A classifier for PD-L1 scoring was trained using 30 NSCLC image patches. A separate test set of 207 image patches from 69 NSCLC resection cases was used for comparison of automated and manual scores. Automated and average manual scores showed excellent correlation (concordance correlation coeffecient = 0.925), though automated scoring resulted in significantly more 1-49% scores than manual scoring (p = 0.012). At both 1% and 50% thresholds, automated scores showed a level of concordance with our 'gold standard' (the average of three pathologists' manual scores) similar to that of individual pathologists. Automated scoring showed high sensitivity (95%) but lower specificity (84%) at a 1% threshold, and excellent specificity (100%) but lower sensitivity (71%) at a 50% threshold. We conclude that our automated PD-L1 scoring system for NSCLC has an accuracy similar to that of individual pathologists. The detailed protocol we provide for free open source scoring software and our discussion of the limitations of this technology may facilitate more effective integration of automated scoring into clinical workflows.

An improved algorithm using a Health Canada-approved DNA-image cytometry system for non-invasive screening of high-grade oral lesions.

BACKGROUND: DNA-image cytometry (DNA-ICM) is able to detect gross alterations of cellular DNA-content representing aneuploidy, a biomarker of malignancy. A Health Canada-approved DNA-ICM system, ClearCyte® in combination with a cytopathologist's review, has demonstrated high sensitivity (89%) and specificity (97%) in identifying high-grade oral lesions. The study objective was to create an improved automated algorithm (iClearcyte) and test its robustness in differentiating high grade from benign reactive oral lesions without a cytopathologist's input. METHODS: A set of 214 oral brushing samples of oral cancer (n = 92), severe dysplasia (n = 20), reactive lesions (n = 52), and normal samples (n = 50) were spun down onto slides and stained using Feulgen-Thionin reaction. Following ClearCyte® scan, nuclear features were calculated, and nuclei categorized into "diploid," "hyperdiploid," "tetraploid," and "aneuploid" DNA ploidy groups by the ClearCyte® software. The samples were randomized into training and test sets (70:30) based on patient's age, sex, tobacco use, and lesion site risk. The training set was used to create a new algorithm which was then validated using the remaining samples in the test set, where sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. RESULTS: The proposed iClearCyte algorithm (>1 "aneuploid" cell or ≥ 1.7% combined "hyperdiploid" and "tetraploid" nuclei frequency) identified high-grade samples with sensitivity, specificity, PPV, and NPV of 100.0%, 86.7%, 89.7%, and 100.0%, respectively, in the test set. CONCLUSION: The iClearCyte test has potential to serve as a robust non-invasive automated oral cancer screening tool promoting early oral cancer detection and decreasing the number of unnecessary invasive biopsies.

Imaging intracellular motion with dynamic micro-optical coherence tomography.

This paper describes a new technology that uses 1-µm-resolution optical coherence tomography (µOCT) to obtain cross-sectional images of intracellular dynamics with dramatically enhanced image contrast. This so-called dynamic µOCT (d-µOCT) is accomplished by acquiring a time series of µOCT images and conducting power frequency analysis of the temporal fluctuations that arise from intracellular motion on a pixel-per-pixel basis. Here, we demonstrate d-µOCT imaging of freshly excised human esophageal and cervical biopsy samples. Depth-resolved d-µOCT images of intact tissue show that intracellular dynamics provides a new contrast mechanism for µOCT that highlights subcellular morphology and activity in epithelial surface maturation patterns.

Miniaturized multimodal multiphoton microscope for simultaneous two-photon and three-photon imaging with a dual-wavelength Er-doped fiber laser.

A multimodal multiphoton microscopy (MPM) is developed to acquire both two-photon microscopy (2PM) and three-photon microscopy (3PM) signals. A dual-wavelength Er-doped fiber laser is used as the light source, which provides the fundamental pulse at 1580 nm to excite third harmonic generation (THG) and the frequency-doubled pulse at 790 nm to excite intrinsic two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG). Due to their different contrast mechanisms, the TPEF, SHG, and THG images can acquire complementary information about tissues, including cells, collagen fibers, lipids, and interfaces, all label-free. The compact MPM imaging probe is developed using miniature objective lens and a micro-electro-mechanical scanner. Furthermore, the femtosecond laser pulses are delivered by a single mode fiber and the signals are collected by a multimode fiber, which makes the miniaturized MPM directly fiber-coupled, compact, and portable. Design considerations on using the dual excitation wavelengths are discussed. Multimodal and label-free imaging by TPEF, SHG, and THG are demonstrated on biological samples. The miniaturized multimodal MPM is shown to have great potential for label-free imaging of thick and live tissues.

Cervical cytology reproducibility and associated clinical and demographic factors.

BACKGROUND: Although the Pap test has been the standard screening method for cervical precancer/cancer detection, it has been criticized for having a relatively low sensitivity and a low reproducibility between pathologists. There is limited knowledge about inter-rater agreement and what clinical and demographic factors are associated with disagreements between pathologists reading the same Pap smear. METHODS: This study aimed to assess inter- and intra- rater agreement of the Pap smear in 1619 cytologic slides with biopsy confirmation, using kappa statistics. Clinical and demographic factors associated with higher odds of inter-rater agreement were also examined and stratified by histologic diagnosis grade. RESULTS: Using a five grade classification system, the overall kappa statistics for total, inter-rater, and intra-rater samples were 0.62, 0.57, and 0.88 (unweighted) and 0.83, 0.81, and 0.95 (weighted), respectively. In stratified analyses by histologic grade, total kappas ranged from 0.40 (atypia) to 0.64 (human papilloma virus/CIN 1). Factors such as referral for abnormal Pap test (diagnostic vs screening population), recruiting site, and parity were found to be associated with higher agreement between the two cytologic readings. CONCLUSIONS: We observed relatively higher levels of agreement compared with other studies. However, variability was considerable and agreement was generally moderate, suggesting that cervical screening test accuracy and reproducibility needs to be improved.

Epithelial tumor suppressor ELF3 is a lineage-specific amplified oncogene in lung adenocarcinoma.

Gene function in cancer is often cell type-specific. The epithelial cell-specific transcription factor ELF3 is a documented tumor suppressor in many epithelial tumors yet displays oncogenic properties in others. Here, we show that ELF3 is an oncogene in the adenocarcinoma subtype of lung cancer (LUAD), providing genetic, functional, and clinical evidence of subtype specificity. We discover a region of focal amplification at chromosome 1q32.1 encompassing the ELF3 locus in LUAD which is absent in the squamous subtype. Gene dosage and promoter hypomethylation affect the locus in up to 80% of LUAD analyzed. ELF3 expression was required for tumor growth and a pan-cancer expression network analysis supports its subtype and tissue specificity. We further show that ELF3 displays strong prognostic value in LUAD but not LUSC. We conclude that, contrary to many other tumors of epithelial origin, ELF3 is an oncogene and putative therapeutic target in LUAD.

Submillimeter diameter rotary-pullback fiber-optic endoscope for narrowband red-green-blue reflectance, optical coherence tomography, and autofluorescence in vivo imaging.

A fiber-based endoscopic imaging system combining narrowband red-green-blue (RGB) reflectance with optical coherence tomography (OCT) and autofluorescence imaging (AFI) has been developed. The system uses a submillimeter diameter rotary-pullback double-clad fiber imaging catheter for sample illumination and detection. The imaging capabilities of each modality are presented and demonstrated with images of a multicolored card, fingerprints, and tongue mucosa. Broadband imaging, which was done to compare with narrowband sources, revealed better contrast but worse color consistency compared with narrowband RGB reflectance. The measured resolution of the endoscopic system is 25 µm in both the rotary direction and the pullback direction. OCT can be performed simultaneously with either narrowband RGB reflectance imaging or AFI.

Hyperspectral cell sociology reveals spatial tumor-immune cell interactions associated with lung cancer recurrence.

BACKGROUND: The tumor microenvironment (TME) is a complex mixture of tumor epithelium, stroma and immune cells, and the immune component of the TME is highly prognostic for tumor progression and patient outcome. In lung cancer, anti-PD-1 therapy significantly improves patient survival through activation of T cell cytotoxicity against tumor cells. Direct contact between CD8+ T cells and target cells is necessary for CD8+ T cell activity, indicating that spatial organization of immune cells within the TME reflects a critical process in anti-tumor immunity. Current immunohistochemistry (IHC) imaging techniques identify immune cell numbers and densities, but lack assessment of cell-cell spatial relationships (or "cell sociology"). Immune functionality, however, is often dictated by cell-to-cell contact and cannot be resolved by simple metrics of cell density (for example, number of cells per mm2). To address this issue, we developed a Hyperspectral Cell Sociology technology platform for the analysis of cell-cell interactions in multi-channel IHC-stained tissue. METHODS: Tissue sections of primary tumors from lung adenocarcinoma patients with known clinical outcome were stained using multiplex IHC for CD3, CD8, and CD79a, and hyperspectral image analysis determined the phenotype of all cells. A Voronoi diagram for each cell was used to approximate cell boundaries, and the cell type of all neighboring cells was identified and quantified. Monte Carlo analysis was used to assess whether cell sociology patterns were likely due to random distributions of the cells. RESULTS: High density of intra-tumoral CD8+ T cells was significantly associated with non-recurrence of tumors. A cell sociology pattern of CD8+ T cells surrounded by tumor cells was more significantly associated with non-recurrence compared to CD8+ T cell density alone. CD3+ CD8- T cells surrounded by tumor cells was also associated with non-recurrence, but at a similar significance as cell density alone. Cell sociology metrics improved recurrence classifications of 12 patients. Monte Carlo re-sampling analysis determined that these cell sociology patterns were non-random. CONCLUSION: Hyperspectral Cell Sociology expands our understanding of the complex interplay between tumor cells and immune infiltrate. This technology could improve predictions of responses to immunotherapy and lead to a deeper understanding of anti-tumor immunity.

Depth-multiplexed optical coherence tomography dual-beam manually-actuated distortion-corrected imaging (DMDI) with a micromotor catheter.

We present a new micromotor catheter implementation of dual-beam manually-actuated distortion-corrected imaging (DMDI). The new catheter called a depth-multiplexed dual-beam micromotor catheter, or mDBMC, maintains the primary advantage of unlimited field-of-view distortion-corrected imaging along the catheter axis. The mDBMC uses a polarization beam splitter and cube mirror to create two beams that scan circularly with approximately constant separation at the catheter surface. This arrangement also multiplexes both imaging channels into a single optical coherence tomography channel by offsetting them in depth, requiring half the data bandwidth compared to previous DMDI demonstrations that used two parallel image acquisition systems. Furthermore, the relatively simple scanning pattern of the two beams enables a straightforward automated distortion correction algorithm. We demonstrate the imaging capabilities of this catheter with a printed paper phantom and in a section of dragon fruit.

Dual-beam manually actuated distortion-corrected imaging (DMDI): two dimensional scanning with a single-axis galvanometer.

We recently demonstrated a new two-dimensional imaging paradigm called dual-beam manually actuated distortion-corrected imaging (DMDI). This technique uses a single mechanical scanner and two spatially separated beams to determine relative sample velocity and simultaneously corrects image distortions due to manual actuation. DMDI was first demonstrated using a rotating dual-beam micromotor catheter. Here, we present a new implementation of DMDI using a single axis galvanometer to scan a pair of beams in approximately parallel lines onto a sample. Furthermore, we present a method for automated distortion correction based on frame co-registration between images acquired by the two beams. Distortion correction is possible for manually actuated motion both perpendicular and parallel to the galvanometer-scanned lines. Using en face OCT as the imaging modality, we demonstrate DMDI and the automated distortion correction algorithm for imaging a printed paper phantom, a dragon fruit, and a fingerprint.

Using quantitative tissue phenotype to assess the margins of surgical samples from a pan-Canadian surgery study.

BACKGROUND: The purpose of this study was to use quantitative tissue phenotype (QTP) to assess the surgical margins to examine if a fluorescence visualization-guided surgical approach produces a shift in the surgical field by sparing normal tissue while catching high-risk tissue. METHODS: Using our QTP to calculate the degree of nuclear chromatin abnormalities, Nuclear Phenotypic Score (NPS), we analyzed 1290 biopsy specimens taken from surgical samples of 248 patients enrolled in the Efficacy of Optically-guided Surgery in the Management of Early-staged Oral Cancer (COOLS) trial. Multiple margin specimens were collected from each surgical specimen according to the presence of fluorescence visualization alterations and the distance to the surgical margins. RESULTS: The NPS in fluorescence visualization-altered (fluorescence visualization-positive) samples was significantly higher than that in fluorescence visualization-retained (fluorescence visualization-negative) samples. There was a constant trend of decreasing NPS of margin samples from non-adjacent-fluorescence visualization margins to adjacent-fluorescence visualization margins. CONCLUSION: Our results suggested that using fluorescence visualization to guide surgery has the potential to spare more normal tissue at surgical margins.

Correction of motion artifacts in endoscopic optical coherence tomography and autofluorescence images based on azimuthal en face image registration.

We present a method for the correction of motion artifacts present in two- and three-dimensional in vivo endoscopic images produced by rotary-pullback catheters. This method can correct for cardiac/breathing-based motion artifacts and catheter-based motion artifacts such as nonuniform rotational distortion (NURD). This method assumes that en face tissue imaging contains slowly varying structures that are roughly parallel to the pullback axis. The method reduces motion artifacts using a dynamic time warping solution through a cost matrix that measures similarities between adjacent frames in en face images. We optimize and demonstrate the suitability of this method using a real and simulated NURD phantom and in vivo endoscopic pulmonary optical coherence tomography and autofluorescence images. Qualitative and quantitative evaluations of the method show an enhancement of the image quality.

Quantification of large scale DNA organization for predicting prostate cancer recurrence.

This study investigates whether Genomic Organization at Large Scales (which we propose to call GOALS) as quantified via nuclear phenotype characteristics and cell sociology features (describing cell organization within tissue) collected from prostate tissue microarrays (TMAs) can separate biochemical failure from biochemical nonevidence of disease (BNED) after radical prostatectomy (RP). Of the 78 prostate cancer tissue cores collected from patients treated with RP, 16 who developed biochemical relapse (failure group) and 16 who were BNED patients (nonfailure group) were included in the analyses (36 cores from 32 patients). A section from this TMA was stained stoichiometrically for DNA using the Feulgen-Thionin methodology, and scanned with a Pannoramic MIDI scanner. Approximately 110 nuclear phenotypic features, predominately quantifying large scale DNA organization (GOALS), were extracted from each segmented nuclei. In addition, the centers of these segmented nuclei defined a Voronoi tessellation and subsequent architectural analysis. Prostate TMA core classification as biochemical failure or BNED after RP using GOALS features was conducted (a) based on cell type and cell position within the epithelium (all cells, all epithelial cells, epithelial >2 cell layers away from basement membrane) from all cores, and (b) based on epithelial cells more than two cell layers from the basement membrane using a Classifier trained on Gleason 6, 8, 9 (16 cores) only and applied to a Test set consisting of the Gleason 7 cores (20 cores). Successful core classification as biochemical failure or BNED after RP by a linear classifier was 75% using all cells, 83% using all epithelial cells, and 86% using epithelial >2 layers. Overall success of predicted classification by the linear Classifier of (b) was 87.5% using the Training Set and 80% using the Test Set. Overall success of predicted progression using Gleason score alone was 75% for Gleason >7 as failures and 69% for Gleason >6 as failures. © 2017 International Society for Advancement of Cytometry.

Dual-beam manually-actuated distortion-corrected imaging (DMDI) with micromotor catheters.

We present a new paradigm for performing two-dimensional scanning called dual-beam manually-actuated distortion-corrected imaging (DMDI). DMDI operates by imaging the same object with two spatially-separated beams that are being mechanically scanned rapidly in one dimension with slower manual actuation along a second dimension. Registration of common features between the two imaging channels allows remapping of the images to correct for distortions due to manual actuation. We demonstrate DMDI using a 4.7 mm OD rotationally scanning dual-beam micromotor catheter (DBMC). The DBMC requires a simple, one-time calibration of the beam paths by imaging a patterned phantom. DMDI allows for distortion correction of non-uniform axial speed and rotational motion of the DBMC. We show the utility of this technique by demonstrating en face OCT image distortion correction of a manually-scanned checkerboard phantom and fingerprint scan.