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Cancers
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Applications of Machine and Deep Learning in Thoracic Malignancies
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Applications of Machine and Deep Learning in Thoracic Malignancies

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 10653

Special Issue Editors

Prof. Dr. Chung-Ming Chen

E-Mail Website
Guest Editor
Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei 106319, Taiwan
Interests: artificial intelligence; medical image analysis; machine learning/deep learning for computer-aided diagnosis, treatments, and prognosis prediction; machine vision
Prof. Dr. Yeun-Chung Chang

E-Mail Website1 Website2
Guest Editor
Department of Medical Imaging, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei 100, Taiwan
Interests: thoracic imaging; computer-aided diagnosis and detection; breast imaging; thoracic percutaneous intervention
Dr. Mong-Wei Lin

E-Mail Website
Guest Editor
Department of Surgery, National Taiwan University Cancer Center, National Taiwan University College of Medicine, Taipei 106037, Taiwan
Interests: minimally invasive thoracic surgery; thoracic pathological research; thoracic radiomic research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine and deep learning for images, clinicopathological, genomic and proteomic research of thoracic malignancies have been developed for differential diagnosis, prediction of pathological features, genetic mutations, treatment response, and clinical outcomes. Recently, machine and deep learning algorithms have been applied in various clinical settings to help physicians in the diagnosis and management of thoracic malignancies. With the development of multi-omics approaches of thoracic malignancies in basic research and clinical practices, there is an urgent need for novel methodologies to improve the performance of the existing machine and deep learning methods.

We are pleased to invite you to contribute to this Special Issue. This Special Issue will mainly focus on the recent advances and applications of machine and deep learning in images, clinicopathological, genomic and proteomic research of thoracic malignancies. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: basic research for disease mechanisms of machine learning and deep learning in thoracic malignancies; clinical applications of machine learning and deep learning in thoracic malignancies; cutting-edge algorithms and methodologies of machine learning and deep learning for thoracic malignancies.

We look forward to receiving your contributions.

Prof. Dr. Chung-Ming Chen
Prof. Dr. Yeun-Chung Chang
Dr. Mong-Wei Lin
Guest Editors

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 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. Cancers 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 2900 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

  • deep learning
  • esophageal cancer
  • genomics
  • lung cancer
  • machine learning
  • radiomics
  • thoracic malignancies
  • pathology
  • proteomics
  • thymoma

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Published Papers (5 papers)

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Research

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15 pages, 4112 KiB  
Article
Deep Learning Analysis for Predicting Tumor Spread through Air Space in Early-Stage Lung Adenocarcinoma Pathology Images
by De-Xiang Ou, Chao-Wen Lu, Li-Wei Chen, Wen-Yao Lee, Hsiang-Wei Hu, Jen-Hao Chuang, Mong-Wei Lin, Kuan-Yu Chen, Ling-Ying Chiu, Jin-Shing Chen, Chung-Ming Chen and Min-Shu Hsieh
Cancers 2024, 16(11), 2132; https://doi.org/10.3390/cancers16112132 - 3 Jun 2024
Cited by 2 | Viewed by 1562
Abstract
The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated [...] Read more.
The presence of spread through air spaces (STASs) in early-stage lung adenocarcinoma is a significant prognostic factor associated with disease recurrence and poor outcomes. Although current STAS detection methods rely on pathological examinations, the advent of artificial intelligence (AI) offers opportunities for automated histopathological image analysis. This study developed a deep learning (DL) model for STAS prediction and investigated the correlation between the prediction results and patient outcomes. To develop the DL-based STAS prediction model, 1053 digital pathology whole-slide images (WSIs) from the competition dataset were enrolled in the training set, and 227 WSIs from the National Taiwan University Hospital were enrolled for external validation. A YOLOv5-based framework comprising preprocessing, candidate detection, false-positive reduction, and patient-based prediction was proposed for STAS prediction. The model achieved an area under the curve (AUC) of 0.83 in predicting STAS presence, with 72% accuracy, 81% sensitivity, and 63% specificity. Additionally, the DL model demonstrated a prognostic value in disease-free survival compared to that of pathological evaluation. These findings suggest that DL-based STAS prediction could serve as an adjunctive screening tool and facilitate clinical decision-making in patients with early-stage lung adenocarcinoma. Full article
(This article belongs to the Special Issue Applications of Machine and Deep Learning in Thoracic Malignancies)
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13 pages, 1646 KiB  
Article
Therapeutic Decision Making in Prevascular Mediastinal Tumors Using CT Radiomics and Clinical Features: Upfront Surgery or Pretreatment Needle Biopsy?
by Chao-Chun Chang, Chia-Ying Lin, Yi-Sheng Liu, Ying-Yuan Chen, Wei-Li Huang, Wu-Wei Lai, Yi-Ting Yen, Mi-Chia Ma and Yau-Lin Tseng
Cancers 2024, 16(4), 773; https://doi.org/10.3390/cancers16040773 - 13 Feb 2024
Cited by 1 | Viewed by 1643
Abstract
The study aimed to develop machine learning (ML) classification models for differentiating patients who needed direct surgery from patients who needed core needle biopsy among patients with prevascular mediastinal tumor (PMT). Patients with PMT who received a contrast-enhanced computed tomography (CECT) scan and [...] Read more.
The study aimed to develop machine learning (ML) classification models for differentiating patients who needed direct surgery from patients who needed core needle biopsy among patients with prevascular mediastinal tumor (PMT). Patients with PMT who received a contrast-enhanced computed tomography (CECT) scan and initial management for PMT between January 2010 and December 2020 were included in this retrospective study. Fourteen ML algorithms were used to construct candidate classification models via the voting ensemble approach, based on preoperative clinical data and radiomic features extracted from the CECT. The classification accuracy of clinical diagnosis was 86.1%. The first ensemble learning model was built by randomly choosing seven ML models from a set of fourteen ML models and had a classification accuracy of 88.0% (95% CI = 85.8 to 90.3%). The second ensemble learning model was the combination of five ML models, including NeuralNetFastAI, NeuralNetTorch, RandomForest with Entropy, RandomForest with Gini, and XGBoost, and had a classification accuracy of 90.4% (95% CI = 87.9 to 93.0%), which significantly outperformed clinical diagnosis (p < 0.05). Due to the superior performance, the voting ensemble learning clinical–radiomic classification model may be used as a clinical decision support system to facilitate the selection of the initial management of PMT. Full article
(This article belongs to the Special Issue Applications of Machine and Deep Learning in Thoracic Malignancies)
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15 pages, 4219 KiB  
Article
Rapid On-Site AI-Assisted Grading for Lung Surgery Based on Optical Coherence Tomography
by Hung-Chang Liu, Miao-Hui Lin, Wei-Chin Chang, Rui-Cheng Zeng, Yi-Min Wang and Chia-Wei Sun
Cancers 2023, 15(22), 5388; https://doi.org/10.3390/cancers15225388 - 13 Nov 2023
Cited by 5 | Viewed by 1876
Abstract
The determination of resection extent traditionally relies on the microscopic invasiveness of frozen sections (FSs) and is crucial for surgery of early lung cancer with preoperatively unknown histology. While previous research has shown the value of optical coherence tomography (OCT) for instant lung [...] Read more.
The determination of resection extent traditionally relies on the microscopic invasiveness of frozen sections (FSs) and is crucial for surgery of early lung cancer with preoperatively unknown histology. While previous research has shown the value of optical coherence tomography (OCT) for instant lung cancer diagnosis, tumor grading through OCT remains challenging. Therefore, this study proposes an interactive human–machine interface (HMI) that integrates a mobile OCT system, deep learning algorithms, and attention mechanisms. The system is designed to mark the lesion’s location on the image smartly and perform tumor grading in real time, potentially facilitating clinical decision making. Twelve patients with a preoperatively unknown tumor but a final diagnosis of adenocarcinoma underwent thoracoscopic resection, and the artificial intelligence (AI)-designed system mentioned above was used to measure fresh specimens. Results were compared to FSs benchmarked on permanent pathologic reports. Current results show better differentiating power among minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IA), and normal tissue, with an overall accuracy of 84.9%, compared to 20% for FSs. Additionally, the sensitivity and specificity, the sensitivity and specificity were 89% and 82.7% for MIA and 94% and 80.6% for IA, respectively. The results suggest that this AI system can potentially produce rapid and efficient diagnoses and ultimately improve patient outcomes. Full article
(This article belongs to the Special Issue Applications of Machine and Deep Learning in Thoracic Malignancies)
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17 pages, 4795 KiB  
Article
An Interpretable Three-Dimensional Artificial Intelligence Model for Computer-Aided Diagnosis of Lung Nodules in Computed Tomography Images
by Sheng-Chieh Hung, Yao-Tung Wang and Ming-Hseng Tseng
Cancers 2023, 15(18), 4655; https://doi.org/10.3390/cancers15184655 - 21 Sep 2023
Cited by 8 | Viewed by 2151
Abstract
Lung cancer is typically classified into small-cell carcinoma and non-small-cell carcinoma. Non-small-cell carcinoma accounts for approximately 85% of all lung cancers. Low-dose chest computed tomography (CT) can quickly and non-invasively diagnose lung cancer. In the era of deep learning, an artificial intelligence (AI) [...] Read more.
Lung cancer is typically classified into small-cell carcinoma and non-small-cell carcinoma. Non-small-cell carcinoma accounts for approximately 85% of all lung cancers. Low-dose chest computed tomography (CT) can quickly and non-invasively diagnose lung cancer. In the era of deep learning, an artificial intelligence (AI) computer-aided diagnosis system can be developed for the automatic recognition of CT images of patients, creating a new form of intelligent medical service. For many years, lung cancer has been the leading cause of cancer-related deaths in Taiwan, with smoking and air pollution increasing the likelihood of developing the disease. The incidence of lung adenocarcinoma in never-smoking women has also increased significantly in recent years, resulting in an important public health problem. Early detection of lung cancer and prompt treatment can help reduce the mortality rate of patients with lung cancer. In this study, an improved 3D interpretable hierarchical semantic convolutional neural network named HSNet was developed and validated for the automatic diagnosis of lung cancer based on a collection of lung nodule images. The interpretable AI model proposed in this study, with different training strategies and adjustment of model parameters, such as cyclic learning rate and random weight averaging, demonstrated better diagnostic performance than the previous literature, with results of a four-fold cross-validation procedure showing calcification: 0.9873 ± 0.006, margin: 0.9207 ± 0.009, subtlety: 0.9026 ± 0.014, texture: 0.9685 ± 0.006, sphericity: 0.8652 ± 0.021, and malignancy: 0.9685 ± 0.006. Full article
(This article belongs to the Special Issue Applications of Machine and Deep Learning in Thoracic Malignancies)
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Review

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32 pages, 6049 KiB  
Review
Deep Learning for Image Analysis in the Diagnosis and Management of Esophageal Cancer
by Charalampos Theocharopoulos, Spyridon Davakis, Dimitrios C. Ziogas, Achilleas Theocharopoulos, Dimitra Foteinou, Adam Mylonakis, Ioannis Katsaros, Helen Gogas and Alexandros Charalabopoulos
Cancers 2024, 16(19), 3285; https://doi.org/10.3390/cancers16193285 - 26 Sep 2024
Cited by 2 | Viewed by 2495
Abstract
Esophageal cancer has a dismal prognosis and necessitates a multimodal and multidisciplinary approach from diagnosis to treatment. High-definition white-light endoscopy and histopathological confirmation remain the gold standard for the definitive diagnosis of premalignant and malignant lesions. Artificial intelligence using deep learning (DL) methods [...] Read more.
Esophageal cancer has a dismal prognosis and necessitates a multimodal and multidisciplinary approach from diagnosis to treatment. High-definition white-light endoscopy and histopathological confirmation remain the gold standard for the definitive diagnosis of premalignant and malignant lesions. Artificial intelligence using deep learning (DL) methods for image analysis constitutes a promising adjunct for the clinical endoscopist that could effectively decrease BE overdiagnosis and unnecessary surveillance, while also assisting in the timely detection of dysplastic BE and esophageal cancer. A plethora of studies published during the last five years have consistently reported highly accurate DL algorithms with comparable or superior performance compared to endoscopists. Recent efforts aim to expand DL utilization into further aspects of esophageal neoplasia management including histologic diagnosis, segmentation of gross tumor volume, pretreatment prediction and post-treatment evaluation of patient response to systemic therapy and operative guidance during minimally invasive esophagectomy. Our manuscript serves as an introduction to the growing literature of DL applications for image analysis in the management of esophageal neoplasia, concisely presenting all currently published studies. We also aim to guide the clinician across basic functional principles, evaluation metrics and limitations of DL for image recognition to facilitate the comprehension and critical evaluation of the presented studies. Full article
(This article belongs to the Special Issue Applications of Machine and Deep Learning in Thoracic Malignancies)
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