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Diagnostics
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Advances in Lung Cancer Diagnosis
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Advances in Lung Cancer Diagnosis

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

Deadline for manuscript submissions: closed (30 April 2025) | Viewed by 2827

Special Issue Editor

Dr. Houssein Youness

E-Mail Website
Guest Editor
Section of Pulmonary, Critical Care and Sleep Medicine, Oklahoma University Health Sciences Center, Oklahoma City, OK, USA
Interests: lung cancer; diagnosis; biomarkers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a comprehensive overview of the latest advancements and innovations in the diagnosis of lung cancer. Lung cancer remains one of the most challenging cancers to diagnose and treat, necessitating continual improvements in diagnostic methodologies to enhance early detection, accuracy, and personalized care. This Special Issue will explore a wide range of cutting-edge topics, from the integration of artificial intelligence and robotic technologies in diagnostic procedures to the development of novel biomarkers and imaging techniques.

This collection of articles aims to inform and guide healthcare professionals, researchers, and policymakers in their efforts to enhance lung cancer diagnostic practices and ultimately improve patient outcomes.

The scope of this Special Issue includes, but is not limited to, the following:

  1. Developments in low-dose CT screening;
  2. Biomarkers for nodule risk assessment;
  3. Artificial intelligence in lung cancer diagnosis;
  4. Robotic bronchoscopy;
  5. Intra-procedural bronchoscopic imaging;
  6. Advances in endobronchial ultrasound (EBUS);
  7. Quality indicators in lung cancer diagnosis;
  8. Molecular and genetic profiling for personalized diagnosis;
  9. Optimizing tissue acquisition for molecular and genetic profiling of lung cancer.

Dr. Houssein Youness
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
  • biomarkers
  • artificial intelligence
  • machine learning
  • robotic bronchoscopy
  • bronchoscopic imaging
  • endobronchial ultrasound (EBUS)
  • quality indicators
  •  molecular profiling
  •  genetic profiling
  •  next-generation sequencing (NGS)
  •  tissue acquisition
  •  low-dose CT screening
  •  early detection
  •  risk assessment
  •  personalized medicine
  •  diagnostic accuracy
  •  medical imaging
  •  lung nodules

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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17 pages, 1990 KiB  
Article
Circulating Tumor DNA and [18F]FDG-PET for Early Response Assessment in Patients with Advanced NSCLC
by Heidi Ryssel, Lise Barlebo Ahlborn, Danijela Dejanovic, Sune Hoegild Keller, Mette Pøhl, Olga Østrup, Annika Loft, Barbara Malene Fischer, Seppo Wang Langer, Andreas Kjaer and Tine Nøhr Christensen
Diagnostics 2025, 15(3), 247; https://doi.org/10.3390/diagnostics15030247 - 22 Jan 2025
Viewed by 1163
Abstract
Background/Objectives: Identifying treatment failure at earlier time points to could spare cancer patients from ineffective treatment and side effects. In this study, circulating tumor DNA (ctDNA) and [18F]FDG-PET/CT were investigated during the first cycle of anticancer therapy in patients with [...] Read more.
Background/Objectives: Identifying treatment failure at earlier time points to could spare cancer patients from ineffective treatment and side effects. In this study, circulating tumor DNA (ctDNA) and [18F]FDG-PET/CT were investigated during the first cycle of anticancer therapy in patients with advanced non-small cell lung cancer (NSCLC) to explore their potential for early response evaluation. Methods: Patients with advanced NSCLC receiving first-line therapy with immune checkpoint inhibitors and/or chemotherapy were included. CtDNA and [18F]FDG-PET/CT assessments were conducted before treatment and at weeks 1 and 3 during the first cycle of therapy. ctDNA quantification was performed using a targeted next-generation sequencing (NGS) panel, and the least favorable change in any mutated allele frequency at a given time was used for analysis. [18F]FDG-PET/CT was quantified using sumSULpeak and metabolic tumor volume (MTV4.0). Early changes in ctDNA levels and [18F]FDG-PET parameters were compared with final treatment response, measured by RECIST after 12 weeks, as well as progression-free survival and overall survival. Results: Of the sixteen included patients, eight were non-responders. ctDNA mutations were detected in baseline blood samples in eight patients. Changes in ctDNA level, MTV4.0, and sumSULpeak at week 3 indicated response in 7 out of 8 patients, 13 out of 15 patients, and 9 out of 15 patients, respectively. At week 3, no false increases were seen with ctDNA and MTV4.0. Conclusions: These results suggest that early changes in ctDNA and [18F]FDG-PET/CT at 3 weeks of treatment could be used to early assess treatment response. Increased levels of ctDNA and MTV4.0 at week 3 were only observed in patients with treatment failure. Full article
(This article belongs to the Special Issue Advances in Lung Cancer Diagnosis)
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Review

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33 pages, 20646 KiB  
Review
Clinical TNM Lung Cancer Staging: A Diagnostic Algorithm with a Pictorial Review
by Ivana Kuhtić, Tinamarel Mandić Paulić, Lucija Kovačević, Sonja Badovinac, Marko Jakopović, Margareta Dobrenić and Maja Hrabak-Paar
Diagnostics 2025, 15(7), 908; https://doi.org/10.3390/diagnostics15070908 - 1 Apr 2025
Viewed by 595
Abstract
Lung cancer is a prevalent malignant disease with the highest mortality rate among oncological conditions. The assessment of its clinical TNM staging primarily relies on contrast-enhanced computed tomography (CT) of the thorax and proximal abdomen, sometimes with the addition of positron emission tomography/CT [...] Read more.
Lung cancer is a prevalent malignant disease with the highest mortality rate among oncological conditions. The assessment of its clinical TNM staging primarily relies on contrast-enhanced computed tomography (CT) of the thorax and proximal abdomen, sometimes with the addition of positron emission tomography/CT scans, mainly for better evaluation of mediastinal lymph node involvement and detection of distant metastases. The purpose of TNM staging is to establish a universal nomenclature for the anatomical extent of lung cancer, facilitating interdisciplinary communication for treatment decisions and research advancements. Recent studies utilizing a large international database and multidisciplinary insights indicate a need to update the TNM classification to enhance the anatomical categorization of lung cancer, ultimately optimizing treatment strategies. The eighth edition of the TNM classification, issued by the International Association for the Study of Lung Cancer (IASLC), transitioned to the ninth edition on 1 January 2025. Key changes include a more detailed classification of the N and M descriptor categories, whereas the T descriptor remains unchanged. Notably, the N2 category will be split into N2a and N2b based on the single-station or multi-station involvement of ipsilateral mediastinal and/or subcarinal lymph nodes, respectively. The M1c category will differentiate between single (M1c1) and multiple (M1c2) organ system involvement for extrathoracic metastases. This review article emphasizes the role of radiologists in implementing the updated TNM classification through CT imaging for correct clinical lung cancer staging and optimal patient management. Full article
(This article belongs to the Special Issue Advances in Lung Cancer Diagnosis)
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14 pages, 1170 KiB  
Review
Outcomes of Robot-Assisted Transbronchial Biopsies of Pulmonary Nodules: A Review
by Peter A. Ebeling, Salim Daouk, Jean I. Keddissi and Houssein A. Youness
Diagnostics 2025, 15(4), 450; https://doi.org/10.3390/diagnostics15040450 - 13 Feb 2025
Viewed by 674
Abstract
Background/Objectives: Robot-assisted bronchoscopy (RAB) is a novel platform for sampling peripheral pulmonary nodules (PPNs). To further clarify the role robot-assisted platforms have in diagnosing PPNs, we performed a review of the recent literature. Methods: A systematic review was performed in Medline [...] Read more.
Background/Objectives: Robot-assisted bronchoscopy (RAB) is a novel platform for sampling peripheral pulmonary nodules (PPNs). To further clarify the role robot-assisted platforms have in diagnosing PPNs, we performed a review of the recent literature. Methods: A systematic review was performed in Medline from 2019 to 2024 using the search terms “robotic bronchoscopy”, “diagnostic yield”, “sensitivity”, and “positive predictive value”, alone and in combination. Studies that focused on earlier electromagnetic bronchoscopies were excluded. The patient demographic information, nodule characteristics, intra-procedure imaging modality, biopsy methods, diagnostic yield, sensitivity for malignancy, and adverse outcomes were analyzed. A total of 22 studies were available for the analyses. Results: The diagnostic yield was variable and ranged from 69 to 93%, with a median of 86%. The sensitivity ranged from 69% to 91.7%, with a median of 85%. The effect of the nodule size on the diagnostic yield was variable across the literature. Obtaining an eccentric or concentric view on a radial endobronchial ultrasound (rEBUS) was associated with a higher diagnostic yield than obtaining no view. A nodule appearance on CT imaging and the location were not definitively associated with a higher diagnostic yield. Fine needle aspiration usage ranged from 93.5 to 100%, with a median of 96.95%, while the use of biopsy forceps ranged from 2.7 to 96%, with a median of 69.9%. The most common complication was a pneumothorax, which occurred in 1–5.7% of cases, with a median of 1.6%. Conclusions: Robot-assisted transbronchial biopsies produce diagnostic yields that approach those of transthoracic needle aspirations. The nodule location and appearance may not affect the diagnostic yield. Obtaining a concentric or eccentric view on rEBUS is likely associated with an increased diagnostic yield. Additional prospective studies would better inform practitioners as this technology becomes more widespread. Full article
(This article belongs to the Special Issue Advances in Lung Cancer Diagnosis)
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