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Diagnostics
Special Issues
Radiomics in the Early Diagnosis of Lung Cancer
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Radiomics in the Early Diagnosis of Lung Cancer

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 7572

Special Issue Editor

Dr. Fu-Zong Wu

E-Mail Website
Guest Editor
1. Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
2. School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan
Interests: lung cancer; diagnosis; prognosis; radiomics; texture analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

With the recent introduction of low-dose lung computed tomography for lung cancer screening worldwide, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide manifesting with subsolid nodules, especially in Asian populations. However, there is a dilemma in the clinical management of these subsolid nodules. In recent years, radiomic analysis has played an emerging role in lung cancer diagnosis and prognosis. Over-management/over-treatment of these small SSNs could lead to overdiagnosis. In the future, to develop personalized prediction models integrated with clinical characters, texture or volumetric analyses of radiomic features and genetic information for pulmonary nodule growth prediction and lung cancer prognostic outcome are warranted. Therefore, the improvement of the early diagnosis of lung cancer is an important clinical challenge. Papers submitted to this Special Issue can be focused on the radiomic characterization of lung cancer diagnostic issues and the evaluation of prognostic factors. In this Special Issue, research on clinical and translational aspects, as well as original articles, review articles and case reports, should help to gain better insights into the current knowledge and further perspectives of research on radiomics in the early diagnosis of lung cancer.

Dr. Fu-Zong Wu
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 cancer
  • diagnosis
  • prognosis
  • radiomics
  • texture analysis

Published Papers (2 papers)

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Research

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10 pages, 1118 KiB  
Article
Radiomics Signature to Predict Prognosis in Early-Stage Lung Adenocarcinoma (≤3 cm) Patients with No Lymph Node Metastasis
by Li Zhang, Lv Lv, Lin Li, Yan-Mei Wang, Shuang Zhao, Lei Miao, Yan-Ning Gao, Meng Li and Ning Wu
Diagnostics 2022, 12(8), 1907; https://doi.org/10.3390/diagnostics12081907 - 6 Aug 2022
Cited by 3 | Viewed by 1722
Abstract
Objectives: To investigate the predictive ability of radiomics signature to predict the prognosis of early-stage primary lung adenocarcinoma (≤3 cm) with no lymph node metastasis (pathological stage I). Materials and Methods: This study included consecutive patients with lung adenocarcinoma (≤3 cm) with no [...] Read more.
Objectives: To investigate the predictive ability of radiomics signature to predict the prognosis of early-stage primary lung adenocarcinoma (≤3 cm) with no lymph node metastasis (pathological stage I). Materials and Methods: This study included consecutive patients with lung adenocarcinoma (≤3 cm) with no lymph node metastasis (pathological stage I) and divided them into two groups: good prognosis group and poor prognosis group. The association between the radiomics signature and prognosis was explored. An integrative radiomics model was constructed to demonstrate the value of the radiomics signature for individualized prognostic prediction. Results: Six radiomics features were significantly different between the two prognosis groups and were used to construct a radiomics model. On the training and test sets, the area under the receiver operating characteristic curve value of the radiomics model in discriminating between the two groups were 0.946 and 0.888, respectively, and those of the pathological model were 0.761 and 0.798, respectively. A radiomics nomogram combining sex, tumor size and rad-score was built. Conclusion: The radiomics signature has potential utility in estimating the prognosis of patients with pathological stage I lung adenocarcinoma (≤3 cm), potentially enabling a step forward in precision medicine. Full article
(This article belongs to the Special Issue Radiomics in the Early Diagnosis of Lung Cancer)
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Review

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15 pages, 1489 KiB  
Review
Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education
by Yun-Ju Wu, Fu-Zong Wu, Shu-Ching Yang, En-Kuei Tang and Chia-Hao Liang
Diagnostics 2022, 12(5), 1064; https://doi.org/10.3390/diagnostics12051064 - 24 Apr 2022
Cited by 26 | Viewed by 5076
Abstract
Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting [...] Read more.
Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient–doctor cooperation and shared decision making. Full article
(This article belongs to the Special Issue Radiomics in the Early Diagnosis of Lung Cancer)
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