Spatial and multiomics analysis of human and mouse lung adenocarcinoma precursors reveals TIM-3 as a putative target for precancer interception.

How tumor microenvironment shapes lung adenocarcinoma (LUAD) precancer evolution remains poorly understood. Spatial immune profiling of 114 human LUAD and LUAD precursors reveals a progressive increase of adaptive response and a relative decrease of innate immune response as LUAD precursors progress. The immune evasion features align the immune response patterns at various stages.

Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights.

While we recognize the prognostic importance of clinicopathological measures and circulating tumor DNA (ctDNA), the independent contribution of quantitative image markers to prognosis in non-small cell lung cancer (NSCLC) remains underexplored. In our multi-institutional study of 394 NSCLC patients, we utilize pre-treatment computed tomography (CT) and F-fluorodeoxyglucose positron emission tomography (FDG-PET) to establish a habitat imaging framework for assessing regional heterogeneity within individual tumors. This framework identifies three PET/CT subtypes, which maintain prognostic value after adjusting for clinicopathologic risk factors including tumor volume.

Single Stage Adaptive Multi-Attention Network for Image Restoration.

Recently attention-based networks have been successful for image restoration tasks. However, existing methods are either computationally expensive or have limited receptive fields, adding constraints to the model. They are also less resilient in spatial and contextual aspects and lack pixel-to-pixel correspondence, which may degrade feature representations.

Synthetic PET from CT improves diagnosis and prognosis for lung cancer: Proof of concept.

[F]Fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) are indispensable components in modern medicine. Although PET can provide additional diagnostic value, it is costly and not universally accessible, particularly in low-income countries. To bridge this gap, we have developed a conditional generative adversarial network pipeline that can produce FDG-PET from diagnostic CT scans based on multi-center multi-modal lung cancer datasets (n = 1,478).

Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis.

Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels.

Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.

Background: Only around 20-30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context.

Efficacy and clinicogenomic correlates of response to immune checkpoint inhibitors alone or with chemotherapy in non-small cell lung cancer.

The role of combination chemotherapy with immune checkpoint inhibitors (ICI) (ICI-chemo) over ICI monotherapy (ICI-mono) in non-small cell lung cancer (NSCLC) remains underexplored. In this retrospective study of 1133 NSCLC patients, treatment with ICI-mono vs ICI-chemo associate with higher rates of early progression, but similar long-term progression-free and overall survival. Sequential vs concurrent ICI and chemotherapy have similar long-term survival, suggesting no synergism from combination therapy.

Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19.

Objectives: Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients.

Artificial Intelligence in Digital Pathology to Advance Cancer Immunotherapy.

Immune-checkpoint inhibitors (ICIs) have revolutionized the treatment of many malignancies. For instance, in lung cancer, however, only 2030% of patients can achieve durable clinical benefits from ICI monotherapy. Histopathologic and molecular features such as histological type, PD-L1 expression, and tumor mutation burden (TMB), play a paramount role in selecting appropriate regimens for cancer treatment in the era of immunotherapy.

Limited Benefit from the Addition of Immunotherapy to Chemotherapy in TKI-Refractory EGFR-Mutant Lung Adenocarcinoma.

Background: The benefit of chemotherapy combined with immunotherapy in EGFR-mutant lung adenocarcinoma (LUAD) patients whose tumor developed resistance to EGFR tyrosine kinase inhibitors (TKIs) is not thoroughly investigated. The goal of this retrospective cohort study is to assess the clinical efficiency of immunotherapy alone or in combination with chemotherapy in a real-world setting.

Methods: This retrospective cohort study enrolled LUAD patients with EGFR sensitive mutations whose tumor had acquired resistance to EGFR TKIs and received systemic treatment with chemotherapy (chemo; = 84), chemotherapy combined with immunotherapy (chemoIO; = 30), chemotherapy plus bevacizumab with or without IO (withBev; = 42), and IO monotherapy (IO-mono; = 22).

Chronic Lymphocytic Leukemia Progression Diagnosis with Intrinsic Cellular Patterns via Unsupervised Clustering.

Identifying the progression of chronic lymphocytic leukemia (CLL) to accelerated CLL (aCLL) or transformation to diffuse large B-cell lymphoma (Richter transformation; RT) has significant clinical implications as it prompts a major change in patient management. However, the differentiation between these disease phases may be challenging in routine practice. Unsupervised learning has gained increased attention because of its substantial potential in data intrinsic pattern discovery.

Artificial intelligence-assisted mapping of proliferation centers allows the distinction of accelerated phase from large cell transformation in chronic lymphocytic leukemia.

Chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL) is characterized morphologically by numerous small lymphocytes and pale nodules composed of prolymphocytes and paraimmunoblasts known as proliferation centers (PCs). Patients with CLL can undergo transformation to a more aggressive lymphoma, most often diffuse large B-cell lymphoma (DLBCL), known as Richter transformation (RT). An accelerated phase of CLL (aCLL) also may be observed which correlates with subsequent transformation to DLBCL, and may represent an early stage of transformation.

Automatic Whole Slide Pathology Image Diagnosis Framework via Unit Stochastic Selection and Attention Fusion.

Pathology tissue slides are taken as the gold standard for the diagnosis of most cancer diseases. Automatic pathology slide diagnosis is still a challenging task for researchers because of the high-resolution, significant morphological variation, and ambiguity between malignant and benign regions in whole slide images (WSIs). In this study, we introduce a general framework to automatically diagnose different types of WSIs via unit stochastic selection and attention fusion.

Artificial intelligence strategy integrating morphologic and architectural biomarkers provides robust diagnostic accuracy for disease progression in chronic lymphocytic leukemia.

Artificial intelligence-based tools designed to assist in the diagnosis of lymphoid neoplasms remain limited. The development of such tools can add value as a diagnostic aid in the evaluation of tissue samples involved by lymphoma. A common diagnostic question is the determination of chronic lymphocytic leukemia (CLL) progression to accelerated CLL (aCLL) or transformation to diffuse large B-cell lymphoma (Richter transformation; RT) in patients who develop progressive disease.

Loss-Based Attention for Interpreting Image-Level Prediction of Convolutional Neural Networks.

Although deep neural networks have achieved great success on numerous large-scale tasks, poor interpretability is still a notorious obstacle for practical applications. In this paper, we propose a novel and general attention mechanism, loss-based attention, upon which we modify deep neural networks to mine significant image patches for explaining which parts determine the image decision-making. This is inspired by the fact that some patches contain significant objects or their parts for image-level decision.

Chromosome Segmentation via Data Simulation and Shape Learning.

Karyotyping, consisting of single chromosome segmentation and classification, is widely used in the cytogenetic analysis for chromosome abnormality detection. Many studies have reported automatic chromosome classification with high accuracy. Nevertheless, they usually require manual chromosome segmentation beforehand.

Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning.

Frozen sections provide a basis for rapid intraoperative diagnosis that can guide surgery, but the diagnoses often challenge pathologists. Here we propose a rule-based system to differentiate thyroid nodules from intraoperative frozen sections using deep learning techniques. The proposed system consists of three components: (1) automatically locating tissue regions in the whole slide images (WSIs), (2) splitting located tissue regions into patches and classifying each patch into predefined categories using convolutional neural networks (CNN), and (3) integrating predictions of all patches to form the final diagnosis with a rule-based system.

Interactive thyroid whole slide image diagnostic system using deep representation.

Background And Objectives: The vast size of the histopathology whole slide image poses formidable challenges to its automatic diagnosis. With the goal of computer-aided diagnosis and the insights that suspicious regions are generally easy to identify in thyroid whole slide images (WSIs), we develop an interactive whole slide diagnostic system for thyroid frozen sections based on the suspicious regions preselected by pathologists.

Methods: We propose to generate feature representations for the suspicious regions via extracting and fusing patch features using deep neural networks.

Text-Guided Neural Network Training for Image Recognition in Natural Scenes and Medicine.

Convolutional neural networks (CNNs) are widely recognized as the foundation for machine vision systems. The conventional rule of teaching CNNs to understand images requires training images with human annotated labels, without any additional instructions. In this article, we look into a new scope and explore the guidance from text for neural network training.

Correction to: Towards pixel-to-pixel deep nucleus detection in microscopy images.

Following publication of the original article [1], we have been notified of a few errors in the html version.

Towards pixel-to-pixel deep nucleus detection in microscopy images.

Background: Nucleus is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown.

Fully automatic knee osteoarthritis severity grading using deep neural networks with a novel ordinal loss.

Knee osteoarthritis (OA) is one major cause of activity limitation and physical disability in older adults. Early detection and intervention can help slow down the OA degeneration. Physicians' grading based on visual inspection is subjective, varied across interpreters, and highly relied on their experience.