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Diagnostics
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Artificial Intelligence as a Diagnostic Tool for Lung Nodule Evaluation
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Artificial Intelligence as a Diagnostic Tool for Lung Nodule Evaluation

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 17603

Special Issue Editor

Dr. I-Shiang Tzeng

E-Mail Website
Guest Editor
Department of Research, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City 23142, Taiwan
Interests: genetic risk score; differentially expressed genes; data mining and analysis; machine learning; artificial intelligence; epidemiology; biostatistics; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence refers to the development and simulation of human intelligence processes by computer algorithms (systems) in diagnostic medicine, biology, public health, and other life sciences. A lung (pulmonary) nodule is an abnormal growth that forms in a lung and can then become cancerous. The clinical diagnosis of lung nodules is mainly based on imaging, including chest X-ray, computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI). Nevertheless, blood tests may identify benign lung nodules. This Special Issue invites scholars to apply up-to-date Artificial Intelligence methods to detect lung nodules in pulmonary medicine, thereby improving the efficacy and accuracy of clinical decisions.

Dr. I-Shiang Tzeng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • lung nodule
  • pulmonary diseases
  • lung scan
  • artificial intelligence
  • machine learning
  • deep learning
  • chest X-ray
  • computed tomography
  • positron emission tomography
  • magnetic resonance imaging
  • laboratory test
  • diagnosis

Published Papers (6 papers)

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Research

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14 pages, 1467 KiB  
Article
Radiomics and Artificial Intelligence Can Predict Malignancy of Solitary Pulmonary Nodules in the Elderly
by Stefano Elia, Eugenio Pompeo, Antonella Santone, Rebecca Rigoli, Marcello Chiocchi, Alexandro Patirelis, Francesco Mercaldo, Leonardo Mancuso and Luca Brunese
Diagnostics 2023, 13(3), 384; https://doi.org/10.3390/diagnostics13030384 - 19 Jan 2023
Cited by 3 | Viewed by 1669
Abstract
Solitary pulmonary nodules (SPNs) are a diagnostic and therapeutic challenge for thoracic surgeons. Although such lesions are usually benign, the risk of malignancy remains significant, particularly in elderly patients, who represent a large segment of the affected population. Surgical treatment in this subset, [...] Read more.
Solitary pulmonary nodules (SPNs) are a diagnostic and therapeutic challenge for thoracic surgeons. Although such lesions are usually benign, the risk of malignancy remains significant, particularly in elderly patients, who represent a large segment of the affected population. Surgical treatment in this subset, which usually presents several comorbidities, requires careful evaluation, especially when pre-operative biopsy is not feasible and comorbidities may jeopardize the outcome. Radiomics and artificial intelligence (AI) are progressively being applied in predicting malignancy in suspicious nodules and assisting the decision-making process. In this study, we analyzed features of the radiomic images of 71 patients with SPN aged more than 75 years (median 79, IQR 76–81) who had undergone upfront pulmonary resection based on CT and PET-CT findings. Three different machine learning algorithms were applied—functional tree, Rep Tree and J48. Histology was malignant in 64.8% of nodules and the best predictive value was achieved by the J48 model (AUC 0.9). The use of AI analysis of radiomic features may be applied to the decision-making process in elderly frail patients with suspicious SPNs to minimize the false positive rate and reduce the incidence of unnecessary surgery. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Diagnostic Tool for Lung Nodule Evaluation)
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20 pages, 3075 KiB  
Article
A Lower False Positive Pulmonary Nodule Detection Approach for Early Lung Cancer Screening
by Shaohua Zheng, Shaohua Kong, Zihan Huang, Lin Pan, Taidui Zeng, Bin Zheng, Mingjing Yang and Zheng Liu
Diagnostics 2022, 12(11), 2660; https://doi.org/10.3390/diagnostics12112660 - 01 Nov 2022
Cited by 4 | Viewed by 1842
Abstract
Pulmonary nodule detection with low-dose computed tomography (LDCT) is indispensable in early lung cancer screening. Although existing methods have achieved excellent detection sensitivity, nodule detection still faces challenges such as nodule size variation and uneven distribution, as well as excessive nodule-like false positive [...] Read more.
Pulmonary nodule detection with low-dose computed tomography (LDCT) is indispensable in early lung cancer screening. Although existing methods have achieved excellent detection sensitivity, nodule detection still faces challenges such as nodule size variation and uneven distribution, as well as excessive nodule-like false positive candidates in the detection results. We propose a novel two-stage nodule detection (TSND) method. In the first stage, a multi-scale feature detection network (MSFD-Net) is designed to generate nodule candidates. This includes a proposed feature extraction network to learn the multi-scale feature representation of candidates. In the second stage, a candidate scoring network (CS-Net) is built to estimate the score of candidate patches to realize false positive reduction (FPR). Finally, we develop an end-to-end nodule computer-aided detection (CAD) system based on the proposed TSND for LDCT scans. Experimental results on the LUNA16 dataset show that our proposed TSND obtained an excellent average sensitivity of 90.59% at seven predefined false positives (FPs) points: 0.125, 0.25, 0.5, 1, 2, 4, and 8 FPs per scan on the FROC curve introduced in LUNA16. Moreover, comparative experiments indicate that our CS-Net can effectively suppress false positives and improve the detection performance of TSND. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Diagnostic Tool for Lung Nodule Evaluation)
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18 pages, 950 KiB  
Article
Prediction of Lung Nodule Progression with an Uncertainty-Aware Hierarchical Probabilistic Network
by Xavier Rafael-Palou, Anton Aubanell, Mario Ceresa, Vicent Ribas, Gemma Piella and Miguel A. González Ballester
Diagnostics 2022, 12(11), 2639; https://doi.org/10.3390/diagnostics12112639 - 31 Oct 2022
Cited by 1 | Viewed by 1591
Abstract
Predicting whether a lung nodule will grow, remain stable or regress over time, especially early in its follow-up, would help doctors prescribe personalized treatments and better surgical planning. However, the multifactorial nature of lung tumour progression hampers the identification of growth patterns. In [...] Read more.
Predicting whether a lung nodule will grow, remain stable or regress over time, especially early in its follow-up, would help doctors prescribe personalized treatments and better surgical planning. However, the multifactorial nature of lung tumour progression hampers the identification of growth patterns. In this work, we propose a deep hierarchical generative and probabilistic network that, given an initial image of the nodule, predicts whether it will grow, quantifies its future size and provides its expected semantic appearance at a future time. Unlike previous solutions, our approach also estimates the uncertainty in the predictions from the intrinsic noise in medical images and the inter-observer variability in the annotations. The evaluation of this method on an independent test set reported a future tumour growth size mean absolute error of 1.74 mm, a nodule segmentation Dice’s coefficient of 78% and a tumour growth accuracy of 84% on predictions made up to 24 months ahead. Due to the lack of similar methods for providing future lung tumour growth predictions, along with their associated uncertainty, we adapted equivalent deterministic and alternative generative networks (i.e., probabilistic U-Net, Bayesian test dropout and Pix2Pix). Our method outperformed all these methods, corroborating the adequacy of our approach. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Diagnostic Tool for Lung Nodule Evaluation)
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Review

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16 pages, 801 KiB  
Review
Additional Value of PET and CT Image-Based Features in the Detection of Occult Lymph Node Metastases in Lung Cancer: A Systematic Review of the Literature
by Priscilla Guglielmo, Francesca Marturano, Andrea Bettinelli, Matteo Sepulcri, Giulia Pasello, Michele Gregianin, Marta Paiusco and Laura Evangelista
Diagnostics 2023, 13(13), 2153; https://doi.org/10.3390/diagnostics13132153 - 23 Jun 2023
Cited by 1 | Viewed by 1499
Abstract
Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement serves as a crucial prognostic factor for tailoring treatment approaches. Invasive methods, such as mediastinoscopy and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), are employed for preoperative LN staging. Among [...] Read more.
Lung cancer represents the second most common malignancy worldwide and lymph node (LN) involvement serves as a crucial prognostic factor for tailoring treatment approaches. Invasive methods, such as mediastinoscopy and endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA), are employed for preoperative LN staging. Among the preoperative non-invasive diagnostic methods, computed tomography (CT) and, recently, positron emission tomography (PET)/CT with fluorine-18-fludeoxyglucose ([18F]FDG) are routinely recommended by several guidelines; however, they can both miss pathologically proven LN metastases, with an incidence up to 26% for patients staged with [18F]FDG PET/CT. These undetected metastases, known as occult LN metastases (OLMs), are usually cases of micro-metastasis or small LN metastasis (shortest radius below 10 mm). Hence, it is crucial to find novel approaches to increase their discovery rate. Radiomics is an emerging field that seeks to uncover and quantify the concealed information present in biomedical images by utilising machine or deep learning approaches. The extracted features can be integrated into predictive models, as numerous reports have emphasised their usefulness in the staging of lung cancer. However, there is a paucity of studies examining the detection of OLMs using quantitative features derived from images. Hence, the objective of this review was to investigate the potential application of PET- and/or CT-derived quantitative radiomic features for the identification of OLMs. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Diagnostic Tool for Lung Nodule Evaluation)
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13 pages, 1353 KiB  
Review
Artificial Intelligence-Assisted Chest X-ray for the Diagnosis of COVID-19: A Systematic Review and Meta-Analysis
by I-Shiang Tzeng, Po-Chun Hsieh, Wen-Lin Su, Tsung-Han Hsieh and Sheng-Chang Chang
Diagnostics 2023, 13(4), 584; https://doi.org/10.3390/diagnostics13040584 - 05 Feb 2023
Cited by 4 | Viewed by 2539
Abstract
Because it is an accessible and routine image test, medical personnel commonly use a chest X-ray for COVID-19 infections. Artificial intelligence (AI) is now widely applied to improve the precision of routine image tests. Hence, we investigated the clinical merit of the chest [...] Read more.
Because it is an accessible and routine image test, medical personnel commonly use a chest X-ray for COVID-19 infections. Artificial intelligence (AI) is now widely applied to improve the precision of routine image tests. Hence, we investigated the clinical merit of the chest X-ray to detect COVID-19 when assisted by AI. We used PubMed, Cochrane Library, MedRxiv, ArXiv, and Embase to search for relevant research published between 1 January 2020 and 30 May 2022. We collected essays that dissected AI-based measures used for patients diagnosed with COVID-19 and excluded research lacking measurements using relevant parameters (i.e., sensitivity, specificity, and area under curve). Two independent researchers summarized the information, and discords were eliminated by consensus. A random effects model was used to calculate the pooled sensitivities and specificities. The sensitivity of the included research studies was enhanced by eliminating research with possible heterogeneity. A summary receiver operating characteristic curve (SROC) was generated to investigate the diagnostic value for detecting COVID-19 patients. Nine studies were recruited in this analysis, including 39,603 subjects. The pooled sensitivity and specificity were estimated as 0.9472 (p = 0.0338, 95% CI 0.9009–0.9959) and 0.9610 (p < 0.0001, 95% CI 0.9428–0.9795), respectively. The area under the SROC was 0.98 (95% CI 0.94–1.00). The heterogeneity of diagnostic odds ratio was presented in the recruited studies (I2 = 36.212, p = 0.129). The AI-assisted chest X-ray scan for COVID-19 detection offered excellent diagnostic potential and broader application. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Diagnostic Tool for Lung Nodule Evaluation)
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36 pages, 11742 KiB  
Review
A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography
by Adnane Ait Nasser and Moulay A. Akhloufi
Diagnostics 2023, 13(1), 159; https://doi.org/10.3390/diagnostics13010159 - 03 Jan 2023
Cited by 17 | Viewed by 7641
Abstract
Chest X-ray radiography (CXR) is among the most frequently used medical imaging modalities. It has a preeminent value in the detection of multiple life-threatening diseases. Radiologists can visually inspect CXR images for the presence of diseases. Most thoracic diseases have very similar patterns, [...] Read more.
Chest X-ray radiography (CXR) is among the most frequently used medical imaging modalities. It has a preeminent value in the detection of multiple life-threatening diseases. Radiologists can visually inspect CXR images for the presence of diseases. Most thoracic diseases have very similar patterns, which makes diagnosis prone to human error and leads to misdiagnosis. Computer-aided detection (CAD) of lung diseases in CXR images is among the popular topics in medical imaging research. Machine learning (ML) and deep learning (DL) provided techniques to make this task more efficient and faster. Numerous experiments in the diagnosis of various diseases proved the potential of these techniques. In comparison to previous reviews our study describes in detail several publicly available CXR datasets for different diseases. It presents an overview of recent deep learning models using CXR images to detect chest diseases such as VGG, ResNet, DenseNet, Inception, EfficientNet, RetinaNet, and ensemble learning methods that combine multiple models. It summarizes the techniques used for CXR image preprocessing (enhancement, segmentation, bone suppression, and data-augmentation) to improve image quality and address data imbalance issues, as well as the use of DL models to speed-up the diagnosis process. This review also discusses the challenges present in the published literature and highlights the importance of interpretability and explainability to better understand the DL models’ detections. In addition, it outlines a direction for researchers to help develop more effective models for early and automatic detection of chest diseases. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Diagnostic Tool for Lung Nodule Evaluation)
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