Specific immune cell and lymphatic vessel signatures identified by image analysis in renal cancer.

Anti-angiogenic therapy and immune checkpoint inhibition are novel treatment strategies for patients with renal cell carcinoma. Various components and structures of the tumor microenvironment are potential predictive biomarkers and also attractive treatment targets. Macrophages, tumor infiltrating lymphocytes, vascular and lymphatic vessels represent an important part of the tumor immune environment, but their functional phenotypes and relevance for clinical outcome are yet ill defined. We applied Tissue Phenomics methods including image analysis for the standardized quantification of specific components and structures within the tumor microenvironment to profile tissue sections from 56 clear cell renal cell carcinoma patients. A characteristic composition and unique spatial relationship of CD68+ macrophages and tumor infiltrating lymphocytes correlated with overall survival. An inverse relationship was found between vascular (CD34) and lymphatic vessel (LYVE1) density. In addition, outcome was significantly better in patients with high blood vessel density in the tumors, whereas increased lymphatic vessel density in the tumors was associated with worse outcome. The Tissue Phenomics imaging analysis approach allowed visualization and simultaneous quantification of immune environment components, adding novel contextual information, and biological insights with potential applications in treatment response prediction.

New insights into the inflamed tumor immune microenvironment of gastric cancer with lymphoid stroma: from morphology and digital analysis to gene expression.

BACKGROUND: Gastric cancer with lymphoid stroma (GCLS) is characterized by prominent stromal infiltration of T-lymphocytes. The aim of this study was to investigate GCLS biology through analysis of clinicopathological features, EBV infection, microsatellite instability (MSI), immune gene-expression profiling and PD-L1 status in neoplastic cells and tumor immune microenvironment. METHODS: Twenty-four GCLSs were analyzed by RNA in situ hybridization for EBV (EBER), PCR/fragment analysis for MSI, immunohistochemistry (PD-L1, cytokeratin, CD3, CD8), co-immunofluorescence (CK/PD-L1, CD68/PD-L1), NanoString gene-expression assay for immune-related genes and PD-L1 copy number alterations. CD3+ and CD8+ T-cell densities were calculated by digital analysis. Fifty-four non-GCLSs were used as control group. RESULTS: GCLSs displayed distinctive clinicopathological features, such as lower pTNM stage (p = 0.02) and better overall survival (p = 0.01). EBV+ or MSI-high phenotype was found in 66.7 and 16.7% cases, respectively. GCLSs harbored a cytotoxic T-cell-inflamed profile, particularly at the invasive front of tumors (p < 0.01) and in EBV+ cases (p = 0.01). EBV+ GCLSs, when compared to EBV- GCLSs, showed higher mRNA expression of genes related to Th1/cytotoxic and immunosuppressive biomarkers. PD-L1 protein expression, observed in neoplastic and immune stromal cells (33.3 and 91.7%, respectively), and PD-L1 amplification (18.8%) were restricted to EBV+/MSI-high tumors and correlated with high values of PD-L1 mRNA expression. CONCLUSIONS: This study shows that GCLS has a distinctive clinico-pathological and molecular profile. Furthermore, through an in-depth study of tumor immune microenvironment-by digital analysis and mRNA expression profiling-it highlights the role of EBV infection in promoting an inflamed tumor microenvironment, with putative therapeutic implications.

Evaluation of Simulated Lesions as Surrogates to Clinical Lesions for Thoracic CT Volumetry: The Results of an International Challenge.

RATIONALE AND OBJECTIVES: To evaluate a new approach to establish compliance of segmentation tools with the computed tomography volumetry profile of the Quantitative Imaging Biomarker Alliance (QIBA); and determine the statistical exchangeability between real and simulated lesions through an international challenge. MATERIALS AND METHODS: The study used an anthropomorphic phantom with 16 embedded physical lesions and 30 patient cases from the Reference Image Database to Evaluate Therapy Response with pathologically confirmed malignancies. Hybrid datasets were generated by virtually inserting simulated lesions corresponding to physical lesions into the phantom datasets using one projection-domain-based method (Method 1), two image-domain insertion methods (Methods 2 and 3), and simulated lesions corresponding to real lesions into the Reference Image Database to Evaluate Therapy Response dataset (using Method 2). The volumes of the real and simulated lesions were compared based on bias (measured mean volume differences between physical and virtually inserted lesions in phantoms as quantified by segmentation algorithms), repeatability, reproducibility, equivalence (phantom phase), and overall QIBA compliance (phantom and clinical phase). RESULTS: For phantom phase, three of eight groups were fully QIBA compliant, and one was marginally compliant. For compliant groups, the estimated biases were -1.8 ± 1.4%, -2.5 ± 1.1%, -3 ± 1%, -1.8 ± 1.5% (±95% confidence interval). No virtual insertion method showed statistical equivalence to physical insertion in bias equivalence testing using Schuirmann's two one-sided test (±5% equivalence margin). Differences in repeatability and reproducibility across physical and simulated lesions were largely comparable (0.1%-16% and 7%-18% differences, respectively). For clinical phase, 7 of 16 groups were QIBA compliant. CONCLUSION: Hybrid datasets yielded conclusions similar to real computed tomography datasets where phantom QIBA compliant was also compliant for hybrid datasets. Some groups deemed compliant for simulated methods, not for physical lesion measurements. The magnitude of this difference was small (<5.4%). While technical performance is not equivalent, they correlate, such that, volumetrically simulated lesions could potentially serve as practical proxies.

Qualitative and Quantitative Analysis of Cardiac Progenitor Cells in Cases of Myocarditis and Cardiomyopathy.

We aimed to identify and quantify CD117+ and CD90+ endogenous cardiac progenitor cells (CPC) in human healthy and diseased hearts. We hypothesize that these cells perform a locally acting, contributing function in overcoming medical conditions of the heart by endogenous means. Human myocardium biopsies were obtained from 23 patients with the following diagnoses: Dilatative cardiomyopathy (DCM), ischemic cardiomyopathy (ICM), myocarditis, and controls from healthy cardiac patients. High-resolution scanning microscopy of the whole slide enabled a computer-based immunohistochemical quantification of CD117 and CD90. Those signals were evaluated by Definiens Tissue Phenomics® Technology. Co-localization of CD117 and CD90 was determined by analyzing comparable serial sections. CD117+/CD90+ cardiac cells were detected in all biopsies. The highest expression of CD90 was revealed in the myocarditis group. CD117 was significantly higher in all patient groups, compared to healthy specimens (*p < 0.05). The highest co-expression was found in the myocarditis group (6.75 ± 3.25 CD90+CD117+ cells/mm2) followed by ICM (4 ± 1.89 cells/mm2), DCM (1.67 ± 0.58 cells/mm2), and healthy specimens (1 ± 0.43 cells/mm2). We conclude that the human heart comprises a fraction of local CD117+ and CD90+ cells. We hypothesize that these cells are part of local endogenous progenitor cells due to the co-expression of CD90 and CD117. With novel digital image analysis technologies, a quantification of the CD117 and CD90 signals is available. Our experiments reveal an increase of CD117 and CD90 in patients with myocarditis.

Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer.

Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low- and intermediate-risk prostate cancer patients (Gleason scores 6-7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach.

Prevalence of CD44+/CD24-/low cells in breast cancer may not be associated with clinical outcome but may favor distant metastasis.

PURPOSE: Breast cancer is composed of phenotypically diverse populations of cancer cells. The ability to form breast tumors has been shown by in vitro/in vivo studies to be restricted to epithelial tumor cells with CD44(+)/CD24(-/low) characteristics. Validation of these findings with respect to detection in clinical samples, prognosis, and clinical relevance is in demand. EXPERIMENTAL DESIGN: We investigated breast cancer tissues for the prevalence of CD44(+)/CD24(-/low) tumor cells and their prognostic value. The study included paraffin-embedded tissues of 136 patients with and without recurrences. In addition, a breast cancer progression array with normal, carcinoma in situ, and carcinoma tissues was analyzed. We applied double-staining immunohistochemistry for the detection of CD44(+)/CD24(-/low) cells. Evaluation was by microscopic pathologic inspection and automated image analysis. RESULTS: CD44(+)/CD24(-/low) cells ranged from 0% to 40% in normal breast and from 0% to 80% in breast tumor tissues. The prevalence of CD44(+)/CD24(-/low) tumor cells in 122 tumors was < or =10% in the majority (78%) of cases and >10% in the remainder. There was no significant correlation between CD44(+)/CD24(-/low) tumor cell prevalence and tumor progression. Although recurrences of tumors with high percentages of CD44(+)/CD24(-/low) tumor cells were mainly distant, preferably osseous metastasis, there was no correlation with the event-free and overall survival. There was no influence on the response to different treatment modalities. CONCLUSIONS: Our findings suggest that the prevalence of CD44(+)/CD24(-/low) tumor cells in breast cancer may not be associated with clinical outcome and survival but may favor distant metastasis.

Comparative evaluation of three proliferation markers, Ki-67, TOP2A, and RacGAP1, in bronchopulmonary neuroendocrine neoplasms: Issues and prospects.

The classification of bronchopulmonary neuroendocrine neoplasms (BP-NEN) into four tumor entities (typical carcinoids (TC), atypical carcinoids (AC), small cell lung cancers (SCLC), large cell neuroendocrine lung carcinomas (LCNEC)) is difficult to perform accurately, but important for prognostic statements and therapeutic management decisions. In this regard, we compared the expression of three proliferation markers, Ki-67, Topoisomerase II alpha (TOP2A), and RacGAP1, in a series of tumor samples from 104 BP-NEN patients (24 TC, 21 AC, 52 SCLC, 7 LCNEC) using different evaluation methods (immunohistochemistry (IHC): Average evaluation, Hotspot evaluation, digital image analysis; RT-qPCR).The results indicated that all three markers had increased protein and mRNA expression with poorer differentiation and correlated well with each other, as well as with grading, staging, and poor survival. Compared with Ki-67 and TOP2A, RacGAP1 allowed for a clearer prognostic statement. The cut-off limits obtained for Ki-67-Average (IHC) were TC-AC 1.5, AC-SCLC 19, and AC-LCNEC 23.5. The Hotspot evaluation generated equal to higher, the digital image analysis generally lower between-entity cut-off limits.All three markers enabled a clear-cut differentiation between the BP-NEN entities, and all methods evaluated were suitable for marker assessment. However, to define optimal cut-off limits, the Ki-67 evaluation methods should be standardized. RacGAP1 appeared to be a new marker with great potential.

Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge.

RATIONALE AND OBJECTIVES: Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA). MATERIALS AND METHODS: The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type. RESULTS: Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall. CONCLUSION: The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.

Inter-Method Performance Study of Tumor Volumetry Assessment on Computed Tomography Test-Retest Data.

RATIONALE AND OBJECTIVES: Tumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile. MATERIALS AND METHODS: Industry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers. RESULTS: Intra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail. CONCLUSIONS: Nine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm.

Somatostatin receptor immunohistochemistry in neuroendocrine tumors: comparison between manual and automated evaluation.

BACKGROUND: Manual evaluation of somatostatin receptor (SSTR) immunohistochemistry (IHC) is a time-consuming and cost-intensive procedure. Aim of the study was to compare manual evaluation of SSTR subtype IHC to an automated software-based analysis, and to in-vivo imaging by SSTR-based PET/CT. METHODS: We examined 25 gastroenteropancreatic neuroendocrine tumor (GEP-NET) patients and correlated their in-vivo SSTR-PET/CT data (determined by the standardized uptake values SUVmax,-mean) with the corresponding ex-vivo IHC data of SSTR subtype (1, 2A, 4, 5) expression. Exactly the same lesions were imaged by PET/CT, resected and analyzed by IHC in each patient. After manual evaluation, the IHC slides were digitized and automatically evaluated for SSTR expression by Definiens XD software. A virtual IHC score "BB1" was created for comparing the manual and automated analysis of SSTR expression. RESULTS: BB1 showed a significant correlation with the corresponding conventionally determined Her2/neu score of the SSTR-subtypes 2A (rs: 0.57), 4 (rs: 0.44) and 5 (rs: 0.43). BB1 of SSTR2A also significantly correlated with the SUVmax (rs: 0.41) and the SUVmean (rs: 0.50). Likewise, a significant correlation was seen between the conventionally evaluated SSTR2A status and the SUVmax (rs: 0.42) and SUVmean (rs: 0.62). CONCLUSION: Our data demonstrate that the evaluation of the SSTR status by automated analysis (BB1 score), using digitized histopathology slides ("virtual microscopy"), corresponds well with the SSTR2A, 4 and 5 expression as determined by conventional manual histopathology. The BB1 score also exhibited a significant association to the SSTR-PET/CT data in accordance with the high affinity profile of the SSTR analogues used for imaging.

Cognition Network Technology prototype of a CAD system for mammography to assist radiologists by finding similar cases in a reference database.

PURPOSE: We present a new approach for computer-aided detection and diagnosis in mammography based on Cognition Network Technology (CNT). Originally designed for image processing, CNT has been extended to also perform context- and knowledge-driven analysis of tabular data. For the first time using this technology, an application was created and evaluated for fully automatic searching of patient cases from a reference database of verified findings. The application aims to support radiologists in providing cases of similarity and relevance to a given query case. It adopts an extensible and knowledge-driven concept as a similarity measure. METHODS: As a preprocessing step, all input images from more than 400 patients were fully automatically segmented and the resulting objects classified--this includes the complete breast shape, the position of the mammilla, the pectoral muscle, and various potential candidate objects for suspicious mass lesions. For the similarity search, collections of object properties and metadata from many patients were combined into a single table analysis project. Extended CNT allows for a convenient implementation of knowledge-based structures, for example, by meaningfully linking detected objects in different breast views that might represent identical lesions. Objects from alternative segmentation methods are also be considered, so as to collectively become a sufficient set of base-objects for identifying suspicious mass lesions. RESULTS: For 80% of 112 patient cases with suspicious lesions, the system correctly identified at least one corresponding mass lesion as an object of interest. In this database, consisting of 1,024 images from a total of 303 patients, an average of 0.66 false-positive objects per image were detected. An additional testing database contained 480 images from 120 patients, 15 of whom were annotated with suspicious mass lesions. Here, 47% (7 out of 15) of these were detected automatically with 1.13 false-positive objects per image. A diagnosis is predicted for each patient case by applying a majority vote from the reference findings of the ten most similar cases. Two separate evaluation scenarios suggest a fraction of correct predictions of respectively 79 and 76%. CONCLUSION: Cognition Network Technology was extended to process table data, making it possible to access and relate records from different images and non-image sources, such as demographic patient data or parameters from clinical examinations. A prototypal application enables efficient searching of a patient and image database for similar patient cases. Using concepts of knowledge-driven configuration and flexible extension, the application illustrates a path to a new generation of future CAD systems.