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Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

BackgroundThe spatial distributions of different types of cells could reveal a cancer cell's growth pattern, its relationships with the tumor microenvironment and the immune response of the body, all of which represent key “hallmarks of cancer”. However, the process by which pathologists manually recognize and localize all the cells in pathology slides is extremely labor intensive and error prone.MethodsIn this study, we developed an automated cell type classification pipeline, ConvPath, which includes nuclei segmentation, convolutional neural network-based tumor cell, stromal cell, and lymphocyte classification, and extraction of tumor microenvironment-related features for lung cancer pathology images. To facilitate users in leveraging this pipeline for their research, all source scripts for ConvPath software are available at https://qbrc.swmed.edu/projects/cnn/.FindingsThe overall classification accuracy was 92.9% and 90.1% in training and independent testing datasets, respectively. By identifying cells and classifying cell types, this pipeline can convert a pathology image into a “spatial map” of tumor, stromal and lymphocyte cells. From this spatial map, we can extract features that characterize the tumor micro-environment. Based on these features, we developed an image feature-based prognostic model and validated the model in two independent cohorts. The predicted risk group serves as an independent prognostic factor, after adjusting for clinical variables that include age, gender, smoking status, and stage.InterpretationThe analysis pipeline developed in this study could convert the pathology image into a “spatial map” of tumor cells, stromal cells and lymphocytes. This could greatly facilitate and empower comprehensive analysis of the spatial organization of cells, as well as their roles in tumor progression and metastasis.

Purpose: To determine the intracellular water preexchange lifetime, s i , the "average residence time" of water, in the intracellular milieu of neurons and astrocytes. The preexchange lifetime is important for modeling a variety of MR data sets, including relaxation, diffusion-sensitive, and dynamic contrastenhanced data sets. Methods: Herein, s i in neurons and astrocytes is determined in a microbead-adherent, cultured cell system. In concert with thin-slice selection, rapid flow of extracellular media suppresses extracellular signal, allowing determination of the transcytolemmal-exchange-dominated, intracellular T 1 . With this knowledge, and that of the intracellular T 1 in the absence of exchange, s i can be derived. Results: Under normal culture conditions, s i for neurons is 0.75 6 0.05 s versus 0.57 6 0.03 s for astrocytes. Both neuronal and astrocytic s i s decrease within 30 min after the onset of oxygen-glucose deprivation, with the astrocytic s i showing a substantially greater decrease than the neuronal s i . Conclusions: Given an approximate intra-to extracellular volume ratio of 4:1 in the brain, these data imply that, under normal physiological conditions, an MR experimental characteristic time of less than 0.012 s is required for a nonexchanging, two-compartment (intra-and extracellular) model to be valid for MR studies. This characteristic time shortens significantly (i.e., 0.004 s) under injury conditions.

The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). To facilitate the study of cellular spatial organization and interactions, we developed Histology-based Digital-Staining, a deep learning-based computation model, to segment the nuclei of tumor, stroma, lymphocyte, macrophage, karyorrhexis, and red blood cells from standard hematoxylin and eosin-stained pathology images in lung adenocarcinoma. Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas lung adenocarcinoma dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (P ¼ 0.001), with a HR of 2.23 (1.37-3.65) after adjusting for clinical variables. Further-more, the image-derived TME features significantly correlated with the gene expression of biological pathways. For example, transcriptional activation of both the T-cell receptor and programmed cell death protein 1 pathways positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway positively correlated with the density of stromal cells. In summary, we demonstrate that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways.Significance: These findings present a deep learning-based analysis tool to study the TME in pathology images and demonstrate that the cell spatial organization is predictive of patient survival and is associated with gene expression.See related commentary by Rodriguez-Antolin, p. 1912

Hypoxic-ischemic (H-I) injury to the developing brain is a significant cause of morbidity and mortality in humans. Other than hypothermia, there is no effective treatment to prevent or lessen the consequences of neonatal H-I. Increased expression of the NAD synthesizing enzyme nicotinamide mononucleotide adenylyl transferase 1 (Nmnat1) has been shown to be neuroprotective against axonal injury in the peripheral nervous system. To investigate the neuroprotective role of Nmnat1 against acute neurodegeneration in the developing CNS, we exposed wild-type mice and mice overexpressing Nmnat1 in the cytoplasm (cytNmnat1-Tg mice) to a well-characterized model of neonatal H-I brain injury. As early as 6 h after H-I, cytNmnat1-Tg mice had strikingly less injury detected by MRI. CytNmnat1-Tg mice had markedly less injury in hippocampus, cortex, and striatum than wild-type mice as assessed by loss of tissue volume 7 d days after H-I. The dramatic protection mediated by cytNmnat1 is not mediated through modulating caspase3-dependent cell death in cytNmnat1-Tg brains. CytNmnat1 protected neuronal cell bodies and processes against NMDA-induced excitotoxicity, whereas caspase inhibition or B-cell lymphoma-extra large (Bcl-XL) protein overexpression had no protective effects in cultured cortical neurons. These results suggest that cytNmnat1 protects against neonatal HI-induced CNS injury by inhibiting excitotoxicity-induced, caspase-independent injury to neuronal processes and cell bodies. As such, the Nmnat1 protective pathway could be a useful therapeutic target for acute and chronic neurodegenerative insults mediated by excitotoxicity.

Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.

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