Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

Predicting survival time of lung cancer patients using radiomic analysis

OBJECTIVES

This study investigates the prediction of Non-small cell lung cancer (NSCLC) patient survival outcomes based on radiomic texture and shape features automatically extracted from tumor image data.

MATERIALS AND METHODS

Retrospective analysis involves CT scans of 315 NSCLC patients from The Cancer Imaging Archive (TCIA). A total of 24 image features are computed from labeled tumor volumes of patients within groups defined using NSCLC subtype and TNM staging information. Spearman's rank correlation, Kaplan-Meier estimation and log-rank tests were used to identify features related to long/short NSCLC patient survival groups. Automatic random forest classification was used to predict patient survival group from multivariate feature data. Significance is assessed at P < 0.05 following Holm-Bonferroni correction for multiple comparisons.

RESULTS

Significant correlations between radiomic features and survival were observed for four clinical groups: (group, [absolute correlation range]): (large cell carcinoma (LCC) [0.35, 0.43]), (tumor size T2, [0.31, 0.39]), (non lymph node metastasis N0, [0.3, 0.33]), (TNM stage I, [0.39, 0.48]). Significant log-rank relationships between features and survival time were observed for three clinical groups: (group, hazard ratio): (LCC, 3.0), (LCC, 3.9), (T2, 2.5) and (stage I, 2.9). Automatic survival prediction performance (i.e. below/above median) is superior for combined radiomic features with age-TNM in comparison to standard TNM clinical staging information (clinical group, mean area-under-the-ROC-curve (AUC)): (LCC, 75.73%), (N0, 70.33%), (T2, 70.28%) and (TNM-I, 76.17%).

CONCLUSION

Quantitative lung CT imaging features can be used as indicators of survival, in particular for patients with large-cell-carcinoma (LCC), primary-tumor-sizes (T2) and no lymph-node-metastasis (N0).

A Containerized Software System for Generation, Management, and Exploration of Features from Whole Slide Tissue Images

Well-curated sets of pathology image features will be critical to clinical studies that aim to evaluate and predict treatment responses. Researchers require information synthesized across multiple biological scales, from the patient to the molecular scale, to more effectively study cancer. This article describes a suite of services and web applications that allow users to select regions of interest in whole slide tissue images, run a segmentation pipeline on the selected regions to extract nuclei and compute shape, size, intensity, and texture features, store and index images and analysis results, and visualize and explore images and computed features. All the services are deployed as containers and the user-facing interfaces as web-based applications. The set of containers and web applications presented in this article is used in cancer research studies of morphologic characteristics of tumor tissues. The software is free and open source. Cancer Res; 77(21); e79-82. ©2017 AACR.

Mutant-allele fraction heterogeneity is associated with non-small cell lung cancer patient survival

Genetic intratumor heterogeneity is associated with tumor occurrence, development and overall outcome. The present study aims to explore the association between mutant-allele fraction (MAF) heterogeneity and patient overall survival in lung cancer. Somatic mutation data of 939 non-small cell lung cancer (NSCLC) cases were obtained from The Cancer Genome Atlas. Entropy-based mutation allele fraction (EMAF) score was used to describe the uncertainty of individual somatic mutation patterns and to further analyze the association with patient overall survival. Results indicated that association between EMAF and overall survival was significant in the discovery set [hazard ratio (H)R=1.62; 95% confidence interval (CI): 1.08–2.41; P=0.018] and replication set (HR=1.63; 95% CI: 1.11–2.37; P=0.011). In addition, EMAF was also significantly different in lung adenocarcinoma and squamous cell carcinoma. Furthermore, a significant difference was indicated in early-stage patients. Results from c-index analysis indicated that EMAF improved the model predictive performance on the 3-year survival beyond that of traditional clinical staging, particularly in early-stage patients. In conclusion, EMAF successfully reflected MAF heterogeneity among patients with NSCLC. Additionally, EMAF improved the predictive performance in early-stage patient prognosis beyond that of traditional clinical staging. In clinical application, EMAF appears to identify a subset of early-stage patients with a poor prognosis and therefore may help inform clinical decisions regarding the application of chemotherapy after surgery.

Association of Omics Features with Histopathology Patterns in Lung Adenocarcinoma

Adenocarcinoma accounts for more than 40% of lung malignancy, and microscopic pathology evaluation is indispensable for its diagnosis. However, how histopathology findings relate to molecular abnormalities remains largely unknown. Here, we obtained H&E-stained whole-slide histopathology images, pathology reports, RNA sequencing, and proteomics data of 538 lung adenocarcinoma patients from The Cancer Genome Atlas and used these to identify molecular pathways associated with histopathology patterns. We report cell-cycle regulation and nucleotide binding pathways underpinning tumor cell dedifferentiation, and we predicted histology grade using transcriptomics and proteomics signatures (area under curve >0.80). We built an integrative histopathology-transcriptomics model to generate better prognostic predictions for stage I patients (p = 0.0182 ± 0.0021) compared with gene expression or histopathology studies alone, and the results were replicated in an independent cohort (p = 0.0220 ± 0.0070). These results motivate the integration of histopathology and omics data to investigate molecular mechanisms of pathology findings and enhance clinical prognostic prediction.

Interobserver Variation among Pathologists and Refinement of Criteria in Distinguishing Separate Primary Tumors from Intrapulmonary Metastases in Lung

Multiple tumor nodules are seen with increasing frequency in clinical practice. On the basis of the 2015 WHO classification of lung tumors, we assessed the reproducibility of the comprehensive histologic assessment to distinguish second primary lung cancers (SPLCs) from intrapulmonary metastases (IPMs), looking for the most distinctive histologic features. An international panel of lung pathologists reviewed a scanned sequential cohort of 126 tumors from 48 patients and recorded an agreed set of histologic features, including tumor typing and predominant pattern of adenocarcinoma, thereby opining whether the case was SPLC, IPM, or a combination thereof. Cohen κ statistics of 0.60 on overall assessment of SPLC or IPM indicated a good agreement. Likewise, there was good agreement (κ score 0.64, p < 0.0001) between WHO histologic pattern in individual cases and SPLC or IPM status, but the proportions diversified for histologic pattern and SPLC or IPM status (McNemar test, p < 0.0001). The strongest associations for distinguishing between SPLC and IPM were observed for nuclear pleomorphism, cell size, acinus formation, nucleolar size, mitotic rate, nuclear inclusions, intraalveolar clusters, and necrosis. Conversely, the associations for lymphocytosis, mucin content, lepidic growth, vascular invasion, macrophage response, clear cell change, acute inflammation keratinization, and emperipolesis did not reach significance with tumor extent. Comprehensive histologic assessment is recommended for distinguishing SPLC from IPM with good reproducibility among lung pathologists. In addition to main histologic type and predominant patterns of histologic subtypes, nuclear pleomorphism, cell size, acinus formation, nucleolar size, and mitotic rate strongly correlate with pathologic staging status.

Rapid Intraoperative Diagnosis of Pediatric Brain Tumors Using Stimulated Raman Histology

Accurate histopathologic diagnosis is essential for providing optimal surgical management of pediatric brain tumors. Current methods for intraoperative histology are time- and labor-intensive and often introduce artifact that limit interpretation. Stimulated Raman histology (SRH) is a novel label-free imaging technique that provides intraoperative histologic images of fresh, unprocessed surgical specimens. Here we evaluate the capacity of SRH for use in the intraoperative diagnosis of pediatric type brain tumors. SRH revealed key diagnostic features in fresh tissue specimens collected from 33 prospectively enrolled pediatric type brain tumor patients, preserving tumor cytology and histoarchitecture in all specimens. We simulated an intraoperative consultation for 25 patients with specimens imaged using both SRH and standard hematoxylin and eosin histology. SRH-based diagnoses achieved near-perfect diagnostic concordance (Cohen's kappa, κ > 0.90) and an accuracy of 92% to 96%. We then developed a quantitative histologic method using SRH images based on rapid image feature extraction. Nuclear density, tumor-associated macrophage infiltration, and nuclear morphology parameters from 3337 SRH fields of view were used to develop and validate a decision-tree machine-learning model. Using SRH image features, our model correctly classified 25 fresh pediatric type surgical specimens into normal versus lesional tissue and low-grade versus high-grade tumors with 100% accuracy. Our results provide insight into how SRH can deliver rapid diagnostic histologic data that could inform the surgical management of pediatric brain tumors.Significance: A new imaging method simplifies diagnosis and informs decision making during pediatric brain tumor surgery. Cancer Res; 78(1); 278-89. ©2017 AACR.

Convolutional Neural Network for Histopathological Analysis of Osteosarcoma

Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.

Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images

Identification of patients with early stage non-small cell lung cancer (NSCLC) with high risk of recurrence could help identify patients who would receive additional benefit from adjuvant therapy. In this work, we present a computational histomorphometric image classifier using nuclear orientation, texture, shape, and tumor architecture to predict disease recurrence in early stage NSCLC from digitized H&E tissue microarray (TMA) slides. Using a retrospective cohort of early stage NSCLC patients (Cohort #1, n = 70), we constructed a supervised classification model involving the most predictive features associated with disease recurrence. This model was then validated on two independent sets of early stage NSCLC patients, Cohort #2 (n = 119) and Cohort #3 (n = 116). The model yielded an accuracy of 81% for prediction of recurrence in the training Cohort #1, 82% and 75% in the validation Cohorts #2 and #3 respectively. A multivariable Cox proportional hazard model of Cohort #2, incorporating gender and traditional prognostic variables such as nodal status and stage indicated that the computer extracted histomorphometric score was an independent prognostic factor (hazard ratio = 20.81, 95% CI: 6.42–67.52, P < 0.001).

Regulation of genome organization and gene expression by nuclear mechanotransduction

It is well established that cells sense chemical signals from their local microenvironment and transduce them to the nucleus to regulate gene expression programmes. Although a number of experiments have shown that mechanical cues can also modulate gene expression, the underlying mechanisms are far from clear. Nevertheless, we are now beginning to understand how mechanical cues are transduced to the nucleus and how they influence nuclear mechanics, genome organization and transcription. In particular, recent progress in super-resolution imaging, in genome-wide application of RNA sequencing, chromatin immunoprecipitation and chromosome conformation capture and in theoretical modelling of 3D genome organization enables the exploration of the relationship between cell mechanics, 3D chromatin configurations and transcription, thereby shedding new light on how mechanical forces regulate gene expression.

A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome

Gene expression signatures are commonly used as predictive biomarkers, but do not capture structural features within the tissue architecture. Here we apply a 2-step machine learning framework for quantitative imaging of tumor vasculature to derive a spatially informed, prognostic gene signature. The trained algorithms classify endothelial cells and generate a vascular area mask (VAM) in H&E micrographs of clear cell renal cell carcinoma (ccRCC) cases from The Cancer Genome Atlas (TCGA). Quantification of VAMs led to the discovery of 9 vascular features (9VF) that predicted disease-free-survival in a discovery cohort (n = 64, HR = 2.3). Correlation analysis and information gain identified a 14 gene expression signature related to the 9VF's. Two generalized linear models with elastic net regularization (14VF and 14GT), based on the 14 genes, separated independent cohorts of up to 301 cases into good and poor disease-free survival groups (14VF HR = 2.4, 14GT HR = 3.33). For the first time, we successfully applied digital image analysis and targeted machine learning to develop prognostic, morphology-based, gene expression signatures from the vascular architecture. This novel morphogenomic approach has the potential to improve previous methods for biomarker development.

Quantitative multiplexed proteomics of Taenia solium cysts obtained from the skeletal muscle and central nervous system of pigs

In human and porcine cysticercosis caused by the tapeworm Taenia solium, the larval stage (cysts) can infest several tissues including the central nervous system (CNS) and the skeletal muscles (SM). The cyst’s proteomics changes associated with the tissue localization in the host tissues have been poorly studied. Quantitative multiplexed proteomics has the power to evaluate global proteome changes in response to different conditions. Here, using a TMT-multiplexed strategy we identified and quantified over 4,200 proteins in cysts obtained from the SM and CNS of pigs, of which 891 were host proteins. To our knowledge, this is the most extensive intermixing of host and parasite proteins reported for tapeworm infections.Several antigens in cysticercosis, i.e., GP50, paramyosin and a calcium-binding protein were enriched in skeletal muscle cysts. Our results suggested the occurrence of tissue-enriched antigen that could be useful in the improvement of the immunodiagnosis for cysticercosis. Using several algorithms for epitope detection, we selected 42 highly antigenic proteins enriched for each tissue localization of the cysts. Taking into account the fold changes and the antigen/epitope contents, we selected 10 proteins and produced synthetic peptides from the best epitopes. Nine peptides were recognized by serum antibodies of cysticercotic pigs, suggesting that those peptides are antigens. Mixtures of peptides derived from SM and CNS cysts yielded better results than mixtures of peptides derived from a single tissue location, however the identification of the ‘optimal’ tissue-enriched antigens remains to be discovered. Through machine learning technologies, we determined that a reliable immunodiagnostic test for porcine cysticercosis required at least five different antigenic determinants.

Human and porcine cysticercosis caused by Taenia solium is a parasite disease still endemic in developing countries. The cysts can be located in different host tissues, including different organs of the central nervous system and the skeletal muscles. The molecular mechanisms associated with the tissue localization of the cysts are not well understood. Here, we described the proteome changes of the cysts obtained from different host tissues from infected pigs using quantitative multiplex proteomics. We explored the diversity of host proteins identified in the cyst’s protein extracts and we also explored the immune-localization of several host-related proteins within the cysts, and propose their possible function. We identified several proteins and antigens enriched for a given tissue localization. Several synthetic peptides designed from these tissue-enriched antigens were tested trough ELISA. Using a combination of peptide mixtures and machine learning technologies we were able to distinguish non cysticercotic and cysticercotic pig’s sera. The tissue-enriched proteins/antigens could be useful for the development of improved immuno-diagnostic tests capable of discriminate the tissue-localization of the cysts.

The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading

Radiologists are among the first physicians to be directly affected by advances in computer technology. Computers are already capable of analyzing medical imaging data, and with decades worth of digital information available for training, will an artificial intelligence (AI) one day signal the end of the human radiologist? With the ever increasing work load combined with the looming doctor shortage, radiologists will be pushed far beyond their current estimated 3 s allotted time-of-analysis per image; an AI with super-human capabilities might seem like a logical replacement. We feel, however, that AI will lead to an augmentation rather than a replacement of the radiologist. The AI will be relied upon to handle the tedious, time-consuming tasks of detecting and segmenting outliers while possibly generating new, unanticipated results that can then be used as sources of medical discovery. This will affect not only radiologists but all physicians and also researchers dealing with medical imaging. Therefore, we must embrace future technology and collaborate interdisciplinary to spearhead the next revolution in medicine.

A Methodology for Texture Feature-based Quality Assessment in Nucleus Segmentation of Histopathology Image

CONTEXT

Image segmentation pipelines often are sensitive to algorithm input parameters. Algorithm parameters optimized for a set of images do not necessarily produce good-quality-segmentation results for other images. Even within an image, some regions may not be well segmented due to a number of factors, including multiple pieces of tissue with distinct characteristics, differences in staining of the tissue, normal versus tumor regions, and tumor heterogeneity. Evaluation of quality of segmentation results is an important step in image analysis. It is very labor intensive to do quality assessment manually with large image datasets because a whole-slide tissue image may have hundreds of thousands of nuclei. Semi-automatic mechanisms are needed to assist researchers and application developers to detect image regions with bad segmentations efficiently.

AIMS

Our goal is to develop and evaluate a machine-learning-based semi-automated workflow to assess quality of nucleus segmentation results in a large set of whole-slide tissue images.

METHODS

We propose a quality control methodology, in which machine-learning algorithms are trained with image intensity and texture features to produce a classification model. This model is applied to image patches in a whole-slide tissue image to predict the quality of nucleus segmentation in each patch. The training step of our methodology involves the selection and labeling of regions by a pathologist in a set of images to create the training dataset. The image regions are partitioned into patches. A set of intensity and texture features is computed for each patch. A classifier is trained with the features and the labels assigned by the pathologist. At the end of this process, a classification model is generated. The classification step applies the classification model to unlabeled test images. Each test image is partitioned into patches. The classification model is applied to each patch to predict the patch's label.

RESULTS

The proposed methodology has been evaluated by assessing the segmentation quality of a segmentation method applied to images from two cancer types in The Cancer Genome Atlas; WHO Grade II lower grade glioma (LGG) and lung adenocarcinoma (LUAD). The results show that our method performs well in predicting patches with good-quality segmentations and achieves F1 scores 84.7% for LGG and 75.43% for LUAD.

CONCLUSIONS

As image scanning technologies advance, large volumes of whole-slide tissue images will be available for research and clinical use. Efficient approaches for the assessment of quality and robustness of output from computerized image analysis workflows will become increasingly critical to extracting useful quantitative information from tissue images. Our work demonstrates the feasibility of machine-learning-based semi-automated techniques to assist researchers and algorithm developers in this process.

Laser-emission imaging of nuclear biomarkers for high-contrast cancer screening and immunodiagnosis

Detection of nuclear biomarkers such as nucleic acids and nuclear proteins is critical for early-stage cancer diagnosis and prognosis. Conventional methods relying on morphological assessment of cell nuclei in histopathology slides may be subjective, whereas colorimetric immunohistochemical and fluorescence-based imaging are limited by strong light absorption, broad-emission bands and low contrast. Here, we describe the development and use of a scanning laser-emission-based microscope that maps lasing emissions from nuclear biomarkers in human tissues. 41 tissue samples from 35 patients labelled with site-specific and biomarker-specific antibody-conjugated dyes were sandwiched in a Fabry-Pérot microcavity while an excitation laser beam built a laser-emission image. We observed multiple sub-cellular lasing emissions from cancer cell nuclei, with a threshold of tens of μJ/mm2, sub-micron resolution (<700 nm), and a lasing band in the few-nanometre range. Different lasing thresholds of nuclei in cancer and normal tissues enabled the identification and multiplexed detection of nuclear proteomic biomarkers, with a high sensitivity for early-stage cancer diagnosis. Laser-emission-based cancer screening and immunodiagnosis might find use in precision medicine and facilitate research in cell biology.

The role of informatics in patient-centered care and personalized medicine

The practice of cytopathology has dramatically changed due to advances in genomics and information technology. Cytology laboratories have accordingly become increasingly dependent on pathology informatics support to meet the emerging demands of precision medicine. Pathology informatics deals with information technology in the laboratory, and the impact of this technology on workflow processes and staff who interact with these tools. This article covers the critical role that laboratory information systems, electronic medical records, and digital imaging plays in patient-centered personalized medicine. The value of integrated diagnostic reports, clinical decision support, and the use of whole-slide imaging to better evaluate cytology samples destined for molecular testing is discussed. Image analysis that offers more precise and quantitative measurements in cytology is addressed, as well as the role of bioinformatics tools to cope with Big Data from next-generation sequencing. This article also highlights the barriers to the widespread adoption of these disruptive technologies due to regulatory obstacles, limited commercial solutions, poor interoperability, and lack of standardization. Cancer Cytopathol 2017;125(6 suppl):494-501. © 2017 American Cancer Society.

Inference of Cell Mechanics in Heterogeneous Epithelial Tissue Based on Multivariate Clone Shape Quantification

Cell populations in multicellular organisms show genetic and non-genetic heterogeneity, even in undifferentiated tissues of multipotent cells during development and tumorigenesis. The heterogeneity causes difference of mechanical properties, such as, cell bond tension or adhesion, at the cell–cell interface, which determine the shape of clonal population boundaries via cell sorting or mixing. The boundary shape could alter the degree of cell–cell contacts and thus influence the physiological consequences of sorting or mixing at the boundary (e.g., tumor suppression or progression), suggesting that the cell mechanics could help clarify the physiology of heterogeneous tissues. While precise inference of mechanical tension loaded at each cell–cell contacts has been extensively developed, there has been little progress on how to distinguish the population-boundary geometry and identify the cause of geometry in heterogeneous tissues. We developed a pipeline by combining multivariate analysis of clone shape with tissue mechanical simulations. We examined clones with four different genotypes within Drosophila wing imaginal discs: wild-type, tartan (trn) overexpression, hibris (hbs) overexpression, and Eph RNAi. Although the clones were previously known to exhibit smoothed or convoluted morphologies, their mechanical properties were unknown. By applying a multivariate analysis to multiple criteria used to quantify the clone shapes based on individual cell shapes, we found the optimal criteria to distinguish not only among the four genotypes, but also non-genetic heterogeneity from genetic one. The efficient segregation of clone shape enabled us to quantitatively compare experimental data with tissue mechanical simulations. As a result, we identified the mechanical basis contributed to clone shape of distinct genotypes. The present pipeline will promote the understanding of the functions of mechanical interactions in heterogeneous tissue in a non-invasive manner.

Towards Generation, Management, and Exploration of Combined Radiomics and Pathomics Datasets for Cancer Research

Cancer is a complex multifactorial disease state and the ability to anticipate and steer treatment results will require information synthesis across multiple scales from the host to the molecular level. Radiomics and Pathomics, where image features are extracted from routine diagnostic Radiology and Pathology studies, are also evolving as valuable diagnostic and prognostic indicators in cancer. This information explosion provides new opportunities for integrated, multi-scale investigation of cancer, but also mandates a need to build systematic and integrated approaches to manage, query and mine combined Radiomics and Pathomics data. In this paper, we describe a suite of tools and web-based applications towards building a comprehensive framework to support the generation, management and interrogation of large volumes of Radiomics and Pathomics feature sets and the investigation of correlations between image features, molecular data, and clinical outcome.

Fast label-free multilayered histology-like imaging of human breast cancer by photoacoustic microscopy

The goal of breast-conserving surgery is to completely remove all of the cancer. Currently, no intraoperative tools can microscopically analyze the entire lumpectomy specimen, which results in 20 to 60% of patients undergoing second surgeries to achieve clear margins. To address this critical need, we have laid the foundation for the development of a device that could allow accurate intraoperative margin assessment. We demonstrate that by taking advantage of the intrinsic optical contrast of breast tissue, photoacoustic microscopy (PAM) can achieve multilayered histology-like imaging of the tissue surface. The high correlation of the PAM images to the conventional histologic images allows rapid computations of diagnostic features such as nuclear size and packing density, potentially identifying small clusters of cancer cells. Because PAM does not require tissue processing or staining, it can be performed promptly and intraoperatively, enabling immediate directed re-excision and reducing the number of second surgeries.

Metastasis detection from whole slide images using local features and random forests

Digital pathology has led to a demand for automated detection of regions of interest, such as cancerous tissue, from scanned whole slide images. With accurate methods using image analysis and machine learning, significant speed-up, and savings in costs through increased throughput in histological assessment could be achieved. This article describes a machine learning approach for detection of cancerous tissue from scanned whole slide images. Our method is based on feature engineering and supervised learning with a random forest model. The features extracted from the whole slide images include several local descriptors related to image texture, spatial structure, and distribution of nuclei. The method was evaluated in breast cancer metastasis detection from lymph node samples. Our results show that the method detects metastatic areas with high accuracy (AUC = 0.97-0.98 for tumor detection within whole image area, AUC = 0.84-0.91 for tumor vs. normal tissue detection) and that the method generalizes well for images from more than one laboratory. Further, the method outputs an interpretable classification model, enabling the linking of individual features to differences between tissue types. © 2017 International Society for Advancement of Cytometry.

Morphoproteomic Characterization of Lung Squamous Cell Carcinoma Fragmentation, a Histological Marker of Increased Tumor Invasiveness

Accurate stratification of tumors is imperative for adequate cancer management. In addition to staging, morphologic subtyping allows stratification of patients into additional prognostic groups. In this study, we used an image-based computational method on pan-cytokeratin IHC stainings to quantify tumor fragmentation (TF), a measure of tumor invasiveness of lung squamous cell carcinoma (LSCC). In two independent clinical cohorts from tissue microarrays (TMA: n = 208 patients) and whole sections (WS: n = 99 patients), TF was associated with poor prognosis and increased risk of blood vessel infiltration. A third cohort from The Cancer Genome Atlas (TCGA: n = 335 patients) confirmed the poor prognostic value of TF using a similar human-based score on hematoxylin-eosin staining. Integration of RNA-seq data from TCGA and LC-MS/MS proteomics from WS revealed an upregulation of extracellular matrix remodeling and focal adhesion processes in tumors with high TF, supporting their increased invasive potential. This proposed histologic parameter is an independent and unfavorable prognostic marker that could be established as a new grading parameter for LSCC. Cancer Res; 77(10); 2585-93. ©2017 AACR.

Analysis of spatial heterogeneity in normal epithelium and preneoplastic alterations in mouse prostate tumor models

Cancer involves histological changes in tissue, which is of primary importance in pathological diagnosis and research. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue with all its variables. On the other hand, understanding connections between genetic alterations and histological attributes requires development of enhanced analysis methods suitable also for small sample sizes. Here, we set out to develop computational methods for early detection and distinction of prostate cancer-related pathological alterations. We use analysis of features from HE stained histological images of normal mouse prostate epithelium, distinguishing the descriptors for variability between ventral, lateral, and dorsal lobes. In addition, we use two common prostate cancer models, Hi-Myc and Pten+/- mice, to build a feature-based machine learning model separating the early pathological lesions provoked by these genetic alterations. This work offers a set of computational methods for separation of early neoplastic lesions in the prostates of model mice, and provides proof-of-principle for linking specific tumor genotypes to quantitative histological characteristics. The results obtained show that separation between different spatial locations within the organ, as well as classification between histologies linked to different genetic backgrounds, can be performed with very high specificity and sensitivity.

Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis

INTRODUCTION

Pathological examination of histopathological slides is a routine clinical procedure for lung cancer diagnosis and prognosis. Although the classification of lung cancer has been updated to become more specific, only a small subset of the total morphological features are taken into consideration. The vast majority of the detailed morphological features of tumor tissues, particularly tumor cells' surrounding microenvironment, are not fully analyzed. The heterogeneity of tumor cells and close interactions between tumor cells and their microenvironments are closely related to tumor development and progression. The goal of this study is to develop morphological feature-based prediction models for the prognosis of patients with lung cancer.

METHOD

We developed objective and quantitative computational approaches to analyze the morphological features of pathological images for patients with NSCLC. Tissue pathological images were analyzed for 523 patients with adenocarcinoma (ADC) and 511 patients with squamous cell carcinoma (SCC) from The Cancer Genome Atlas lung cancer cohorts. The features extracted from the pathological images were used to develop statistical models that predict patients' survival outcomes in ADC and SCC, respectively.

RESULTS

We extracted 943 morphological features from pathological images of hematoxylin and eosin-stained tissue and identified morphological features that are significantly associated with prognosis in ADC and SCC, respectively. Statistical models based on these extracted features stratified NSCLC patients into high-risk and low-risk groups. The models were developed from training sets and validated in independent testing sets: a predicted high-risk group versus a predicted low-risk group (for patients with ADC: hazard ratio = 2.34, 95% confidence interval: 1.12-4.91, p = 0.024; for patients with SCC: hazard ratio = 2.22, 95% confidence interval: 1.15-4.27, p = 0.017) after adjustment for age, sex, smoking status, and pathologic tumor stage.

CONCLUSIONS

The results suggest that the quantitative morphological features of tumor pathological images predict prognosis in patients with lung cancer.

Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art

With the development of functional imaging modalities we now have the ability to study the microenvironment of lung cancer and its genomic instability. Radiomics is defined as the use of automated or semi-automated post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. The automated generation of these analytical features helps to quantify a number of variables in the imaging assessment of lung malignancy. These imaging features include: tumor spatial complexity, elucidation of the tumor genomic heterogeneity and composition, subregional identification in terms of tumor viability or aggressiveness, and response to chemotherapy and/or radiation. Therefore, a radiomic approach can help to reveal unique information about tumor behavior. Currently available radiomic features can be divided into four major classes: (a) morphological, (b) statistical, (c) regional, and (d) model-based. Each category yields quantitative parameters that reflect specific aspects of a tumor. The major challenge is to integrate radiomic data with clinical, pathological, and genomic information to decode the different types of tissue biology. There are many currently available radiomic studies on lung cancer for which there is a need to summarize the current state of the art.