SynthesisJournal of medical systems2019
An Appraisal of Nodule Diagnosis for Lung Cancer in CT Images.
Synthesis in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Construction and validation of a prediction model for malignant pulmonary nodules based on imaging, demographic, and laboratory features.Frontiers in oncology · 2026Article
- Challenging the significance of SUV-based parameters in a large-scale retrospective study on lung lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Article
- A Lower False Positive Pulmonary Nodule Detection Approach for Early Lung Cancer Screening.Diagnostics (Basel, Switzerland) · 2022Article
- Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images.Sensors (Basel, Switzerland) · 2021Article
- Article
- Combining multi-scale feature fusion with multi-attribute grading, a CNN model for benign and malignant classification of pulmonary nodules.Journal of digital imaging · 2020Article
- Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features.Journal of imaging · 2020Article
- Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems.BioMed research international · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
As "the second eyes" of radiologists, computer-aided diagnosis systems play a significant role in nodule detection and diagnosis for lung cancer. In this paper, we aim to provide a systematic survey of state-of-the-art techniques (both traditional techniques and deep learning techniques) for nodule diagnosis from computed tomography images. This review first introduces the current progress and the popular structure used for nodule diagnosis. In particular, we provide a detailed overview of the five major stages in the computer-aided diagnosis systems: data acquisition, nodule segmentation, feature extraction, feature selection and nodule classification. Second, we provide a detailed report of the selected works and make a comprehensive comparison between selected works. The selected papers are from the IEEE Xplore, Science Direct, PubMed, and Web of Science databases up to December 2018. Third, we discuss and summarize the better techniques used in nodule diagnosis and indicate the existing future challenges in this field, such as improving the area under the receiver operating characteristic curve and accuracy, developing new deep learning-based diagnosis techniques, building efficient feature sets (fusing traditional features and deep features), developing high-quality labeled databases with malignant and benign nodules and promoting the cooperation between medical organizations and academic institutions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.