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Diagnostics
Special Issues
Lung Cancer Diagnosis and Prognosis Prediction
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Lung Cancer Diagnosis and Prognosis Prediction

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

Deadline for manuscript submissions: 31 January 2027 | Viewed by 1524

Editors

Dr. Jieke Liu

E-Mail Website
Guest Editor
1. School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
2. Department of Radiology, Sichuan Cancer Hospital & Institute, Chengdu, China
Interests: early-stage lung cancer diagnosis; lung cancer screening; low-dose CT; oncologic imaging; radiomics
Dr. Zhigang Chu

E-Mail Website
Guest Editor
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
Interests: lung nodule; radiomics in oncology; early lung cancer screening; low-dose CT

Special Issue Information

Dear Colleagues,

Lung cancer remains the leading cause of cancer-related mortality worldwide. The curability of early-stage lung cancer is fundamentally determined by the earliest possible detection and by the precision of biological aggressiveness estimation before treatment. For advanced-stage lung cancer, non-invasive methods to predict treatment response can be used to further personalize treatment. Over the past decade, the integration of advanced tomographic techniques, high-dimensional image analytics, and systems-level biology has transformed radiology from a purely morphologic discipline into a quantitative bioscience that non-invasively characterizes tumor phenotype, microenvironment, and evolutionary trajectories. This paradigm shift has generated a rapidly expanding array of imaging biomarkers that can now rival, complement, or even replace traditional tissue-based metrics for early detection, risk stratification, precision diagnosis, and treatment response and prognosis prediction of lung cancer.

This Special Issue invites original research articles, evidence-based reviews, and case series reports that accelerate the clinical translation of quantitative imaging biomarkers across the entire spectrum of lung cancer care. We encourage submissions based on low-dose CT, conventional standard-dose CT, spectral CT, photon-counting CT, and hybrid PET/CT. Contributions that leverage artificial intelligence, deep learning, radiomics, and habitat imaging to decode histopathological subtypes, molecular and genetic phenotypes, spatial heterogeneity, treatment response, and prognosis are of particular interest.

Dr. Jieke Liu
Dr. Zhigang Chu
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 short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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
  • CT
  • artificial intelligence
  • deep learning
  • radiomics
  • habitat imaging
  • early detection
  • precision diagnosis
  • risk stratification
  • treatment response
  • prognosis

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Published Papers (2 papers)

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Research

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16 pages, 2373 KB  
Article
Habitat-Based Radiomics for Predicting Visceral Pleural Invasion in Subpleural Nodules with Solid Component on Low-Dose CT: A Multicenter Study
by Yu Long, Xiaoyu Li, Yong Li, Yongji Zheng, Wei Lin, Peng Zhou and Jieke Liu
Diagnostics 2026, 16(8), 1191; https://doi.org/10.3390/diagnostics16081191 - 16 Apr 2026
Cited by 1 | Viewed by 519
Abstract
Objectives: Our objectives were to develop and validate the habitat model based on low-dose computed tomography (LDCT) for noninvasive prediction of the visceral pleural invasion (VPI) in subpleural nodules with solid component. Methods: A total of 313 patients with subpleural lung [...] Read more.
Objectives: Our objectives were to develop and validate the habitat model based on low-dose computed tomography (LDCT) for noninvasive prediction of the visceral pleural invasion (VPI) in subpleural nodules with solid component. Methods: A total of 313 patients with subpleural lung adenocarcinoma nodules from three centers were retrospectively enrolled and divided into training (n = 192), validation (n = 82), and external test (n = 39) sets. All patients underwent preoperative LDCT scan. The habitat model was constructed using unsupervised clustering to partition each tumor into three distinct habitats, from which radiomic features were extracted and selected. Its diagnostic performance was compared with a whole-lesion radiomic model and radiological model. Statistical analysis included receiver operating characteristic (ROC) analysis and DeLong test. Results: The habitat model significantly outperformed both the radiomic and radiological models across the validation and external test sets, with areas under the ROC curve of 0.893 and 0.908, respectively (all p < 0.05). In contrast, the radiomic model achieved 0.833 and 0.772, while the radiological model yielded 0.746 and 0.624. The corresponding software tool has been made publicly available to facilitate broader clinical application. Conclusions: The habitat imaging model based on LDCT effectively predicts the VPI in subpleural lung adenocarcinoma by quantifying intratumoral spatial heterogeneity and demonstrates promising diagnostic performance compared to conventional radiomic and radiological methods. This approach offers a noninvasive preoperative tool to assist in risk stratification and guide personalized therapeutic decision-making for subpleural nodules detected during lung cancer screening. Full article
(This article belongs to the Special Issue Lung Cancer Diagnosis and Prognosis Prediction)
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Review

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15 pages, 344 KB  
Review
Clinical Utility of Dual-Energy CT for Detection, Characterization, and Staging of Lung Tumors: A Rapid Review
by Hassibullah Sidiqy, Khalida Sidiqy, Claudia Raluca Mariean and Marian Pop
Diagnostics 2026, 16(16), 2611; https://doi.org/10.3390/diagnostics16162611 (registering DOI) - 18 Aug 2026
Viewed by 550
Abstract
Background/Objectives: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed tomography (CT) is the preferred imaging modality for evaluating pulmonary nodules because of its high spatial resolution; however, it primarily provides morphological information, including lesion size, shape, [...] Read more.
Background/Objectives: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed tomography (CT) is the preferred imaging modality for evaluating pulmonary nodules because of its high spatial resolution; however, it primarily provides morphological information, including lesion size, shape, and density. Dual-energy CT (DECT), a more recent imaging technique, uses two different energy levels to enable material decomposition and quantitative parameter assessment. These parameters may provide additional information regarding tumor perfusion, vascularization, and tissue composition. This rapid review aimed to evaluate the current evidence regarding the clinical utility of DECT in the detection, characterization, and staging of lung tumors. Methods: This rapid review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A literature search was performed in the PubMed and Cochrane Library databases for studies published between 2005 and 2026. Studies were included if they evaluated the detection, characterization, or staging of lung tumors using quantitative DECT parameters. Case reports, editorials, duplicate studies, and studies without quantitative DECT data were excluded. Descriptive data analysis was performed using Microsoft Excel. Results: A total of 24 studies were included, comprising 18 retrospective (75%) and 6 prospective studies (25%). Only one study evaluated the role of DECT in lung tumor detection, demonstrating improved detection of mixed ground-glass nodules and invasive adenocarcinoma. Significant correlations were found between iodine uptake and tumor perfusion, highlighting the potential of DECT to improve differentiation between benign and malignant lesions. Several studies also demonstrated associations between DECT parameters and tumor biomarkers, including Ki-67 Proliferation Index (Ki-67) expression, Epidermal Growth Factor Receptor (EGFR) mutation status, Programmed Death-Ligand 1 (PD-L1) expression, and treatment response in non-small cell lung cancer. In addition, DECT provided complementary metabolic information regarding tumor malignancy and showed correlations between iodine uptake and fluorodeoxyglucose (FDG) parameters. Associations between iodine volume and tumor differentiation grade were also reported. One study demonstrated the potential role of DECT in tumor staging by predicting mediastinal lymph node metastasis. Across all included studies, iodine-based parameters (50%), radiomics and material decomposition parameters (16.67% each), and spectral attenuation parameters (12.50%) were the most frequently investigated DECT metrics. Conclusions: DECT appears to be a promising complementary imaging technique that provides quantitative perfusion-related and compositional surrogate information beyond the morphological assessment offered by conventional CT. However, the current evidence remains heterogeneous and is largely based on retrospective studies with relatively small patient cohorts. Larger prospective studies with standardized imaging protocols are necessary to further establish the clinical utility of DECT in lung tumors. Full article
(This article belongs to the Special Issue Lung Cancer Diagnosis and Prognosis Prediction)
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