Noninvasive Characterization of the Histopathologic Features of Pulmonary Nodules of the Lung Adenocarcinoma Spectrum using Computer-Aided Nodule Assessment and Risk Yield (CANARY)—A Pilot Study

SummaryDetermining the clinical significance of CT scan-detected subsolid pulmonary nodules requires an understanding of the molecular and cellular features that may foreshadow disease progression. We studied the alterations at the transcriptome level in both immune and non-immune cells, utilizing single-cell RNA sequencing, to compare the microenvironment of subsolid, solid, and non-involved lung tissues from surgical resection specimens. This evaluation of early spectrum lung adenocarcinoma reveals a significant decrease in the cytolytic activities of natural killer and natural killer T cells, accompanied by a reduction of effector T cells as well as an increase of CD4+ regulatory T cells in subsolid lesions. Characterization of non-immune cells revealed that both cancer-associated alveolar type 2 cells and fibroblasts contribute to the deregulation of the extracellular matrix, potentially affecting immune infiltration in subsolid lesions through ligand-receptor interactions. These findings suggest a decrement of immune surveillance in subsolid lesions.

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Computer-aided characterization of solitary pulmonary nodules (SPNs) using structural 3D, texture, and functional dynamic contrast features

10.1117/12.713232 ◽

2007 ◽

Author(s):

Yang Wang ◽

Michael F. McNitt-Gray ◽

Sumit Shah ◽

Jonathan G. Goldin ◽

Matthew S. Brown ◽

...

Keyword(s):

Pulmonary Nodules ◽

Solitary Pulmonary Nodules ◽

3D Texture ◽

Computer Aided

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- Review on Computer-Aided Detection, Diagnosis, and Characterization of Pulmonary Nodules: A Clinical Perspective

Lung Imaging and Computer Aided Diagnosis ◽

10.1201/b11106-23 ◽

2016 ◽

pp. 452-471

Keyword(s):

Pulmonary Nodules ◽

Computer Aided Detection ◽

Clinical Perspective ◽

Computer Aided

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Small Pulmonary Nodules: Effect of Two Computer-aided Detection Systems on Radiologist Performance

Yearbook of Diagnostic Radiology ◽

10.1016/s0098-1672(08)70019-5 ◽

2007 ◽

Vol 2007 ◽

pp. 20-21

Author(s):

M.L. Rosado de Christenson

Keyword(s):

Pulmonary Nodules ◽

Computer Aided Detection ◽

Detection Systems ◽

Computer Aided ◽

Small Pulmonary Nodules

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A pilot study of probe-based confocal laser endomicroscopy for computer-aided diagnosis of bile duct cancer by using the deep learning technology

10.26226/morressier.59a6b34bd462b80290b55741 ◽

2017 ◽

Author(s):

Koichi Furukawa

Keyword(s):

Deep Learning ◽

Pilot Study ◽

Bile Duct ◽

Bile Duct Cancer ◽

Confocal Laser Endomicroscopy ◽

Confocal Laser ◽

Learning Technology ◽

Computer Aided ◽

Duct Cancer ◽

Aided Diagnosis

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Improved computer-aided detection of pulmonary nodules via deep learning in the sinogram domain

Visual Computing for Industry Biomedicine and Art ◽

10.1186/s42492-019-0029-2 ◽

2019 ◽

Vol 2 (1) ◽

Cited By ~ 1

Author(s):

Yongfeng Gao ◽

Jiaxing Tan ◽

Zhengrong Liang ◽

Lihong Li ◽

Yumei Huo

Keyword(s):

Deep Learning ◽

Area Under The Curve ◽

Pulmonary Nodules ◽

Ct Image ◽

Computer Aided Detection ◽

Ct Scanner ◽

Human Visual Perception ◽

Computer Aided ◽

Detection And Diagnosis ◽

Cade System

AbstractComputer aided detection (CADe) of pulmonary nodules plays an important role in assisting radiologists’ diagnosis and alleviating interpretation burden for lung cancer. Current CADe systems, aiming at simulating radiologists’ examination procedure, are built upon computer tomography (CT) images with feature extraction for detection and diagnosis. Human visual perception in CT image is reconstructed from sinogram, which is the original raw data acquired from CT scanner. In this work, different from the conventional image based CADe system, we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain. Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain, we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram. The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database, with each case having at least one juxtapleural nodule annotation. Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve (AUC) of receiver operating characteristic based on sinogram alone, comparing to 0.89 based on CT image alone. Moreover, a combination of sinogram and CT image could further improve the value of AUC to 0.92. This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.

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Development and evaluation of two different lameness models in meat goats, a pilot study

Translational Animal Science ◽

10.1093/tas/txaa193 ◽

2020 ◽

Vol 4 (4) ◽

Author(s):

Emily J Reppert ◽

Michael D Kleinhenz ◽

Abbie Viscardi ◽

Shawnee R Montgomery ◽

Alison R Crane ◽

...

Keyword(s):

Pilot Study ◽

Plasma Cortisol ◽

Interphalangeal Joint ◽

Health Concern ◽

Distal Interphalangeal Joint ◽

Livestock Species ◽

Meat Goats ◽

Distal Interphalangeal ◽

Maximum Temperatures

Abstract Lameness is a serious health concern for livestock species. Understanding individual animal response to pain and characterization of lameness are critical when developing appropriate treatments. The objectives of this pilot study was to evaluate two different lameness models and measures for determining response to induced lameness in meat goats. Lameness was induced by intraarticular injection into the left hind lateral claw distal interphalangeal joint with either amphotericin B (Amp-B) or kaolin-carrageenan (K-C). Response to lameness was characterized by behavior scoring, visual lameness scoring (VLS), infrared thermography (IRT) of the affected digit, pressure mat gait analysis (PMT), and plasma cortisol (CORT) analysis. Lame goats had higher VLS compared to controls (P = 0.003). Maximum temperatures measured in hooves from lame vs control goats were significantly higher (P = 0.003). Pressure mat analysis demonstrated, when compared to controls, lame goats had decreased force (P = 0.013), impulse (P = 0.007), contact pressure (P = 0.007), and contact area of the left hind limb (P = 0.009). Mean CORT levels 4 and 6 h after lameness induction were higher in lame goats (P = 0.005, P = 0.01). The two lameness methods reliably induced lameness of varying severity in healthy meat goats.

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