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Diagnostics
Special Issues
Advancements and Innovations in the Diagnosis of Lung Cancer
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Advancements and Innovations in the Diagnosis of Lung Cancer

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Diagnosis and Prognosis".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2346

Special Issue Editor

Prof. Dr. Houssein A. Youness

E-Mail Website
Guest Editor
1. Interventional Pulmonary Program, Section of Pulmonary, Critical Care and Sleep Medicine, University of Oklahoma, Oklahoma City, OK 73104, USA
2. Section of Pulmonary, Critical Care and Sleep Medicine, The Oklahoma City VA Healthcare System, Oklahoma City, OK 73104, 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:

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

Prof. Dr. Houssein A. 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 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-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
  • robotic bronchoscopy
  • bronchoscopic imaging
  • endobronchial ultrasound
  • molecular profiling
  • genetic profiling
  • next-generation sequencing
  • tissue acquisition
  • low-dose CT screening
  • personalized medicine
  • medical imaging
  • lung nodules

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

15 pages, 1808 KB  
Article
Investigation of the Prevalence of Associated Genetic Mutations (Co-Mutations) in Patients with Actionable Driver Mutations in Lung Cancer: A Retrospective Study
by Abed Agbarya, Walid Shalata, Edmond Sabo, Leonard Saiegh, Yuval Shaham, Haitam Nasrallah, Kamel Mhameed, Salam Mazareb, Mohammad Sheikh-Ahmad and Dan Levy Faber
Diagnostics 2026, 16(7), 1106; https://doi.org/10.3390/diagnostics16071106 - 7 Apr 2026
Viewed by 328
Abstract
Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality globally. Approximately 45% of these tumors harbor oncogenic mutations that drive carcinogenesis and are amenable to targeted therapies. Other predictive biomarkers—e.g., PD-L1, TMB, and MSI—play a crucial role in patients’ management. This [...] Read more.
Background/Objectives: Lung cancer remains the leading cause of cancer-related mortality globally. Approximately 45% of these tumors harbor oncogenic mutations that drive carcinogenesis and are amenable to targeted therapies. Other predictive biomarkers—e.g., PD-L1, TMB, and MSI—play a crucial role in patients’ management. This study aims to investigate the existence of mutation clusters (co-mutations) and evaluate the correlation of these clusters with various clinical and laboratory parameters. Methods: A retrospective study was conducted utilizing pathological samples from lung cancer patients harboring mutations in EGFR, KRAS, ALK, BRAF, MET, HER2, ROS1, NTRK, and NRG1. Data were collected from the Institute of Pathology at Carmel Medical Center between the years 2022 and 2024. Patients were stratified using a Two-Step Cluster Analysis algorithm based on actionable mutations and co-mutations. Heatmaps and dendrograms were generated to assess the correlation between these genomic clusters, clinical metrics, and predictive biomarkers. Results: The study cohort included 129 patients with actionable mutations. Five distinct clusters were identified: Clusters 1, 2, and 3 exhibited a high expression of STK11 and TP53 co-mutations alongside KRAS drivers (n = 38, n = 12, and n = 23, respectively). Clusters 4 and 5 demonstrated high expression of ALK alterations and tumor suppressor gene mutations (n = 31 and n = 25, respectively). Cluster comparisons demonstrated statistically significant differences between clusters regarding age, gender, PD-L1 expression, and tumor mutational burden. No significant associations were found regarding ethnicity or microsatellite instability status. Conclusions: By constructing clusters based on the aggregate of genomic alterations in patients with actionable mutations, it is possible to predict associations with distinct demographic and clinical characteristics. Future research should apply this analytical approach to larger cohorts to further characterize these subgroups and investigate potential correlations with therapeutic efficacy. Full article
(This article belongs to the Special Issue Advancements and Innovations in the Diagnosis of Lung Cancer)
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14 pages, 2429 KB  
Article
Identifying a Critical Blind Spot: How Commercial AI (CAD) Systems Fail to Detect Faint Ground-Glass Opacities at −730 HU on Low-Dose CT
by Shan Liang, Jia Wang, Wentao Fu and Yali Wang
Diagnostics 2026, 16(7), 1014; https://doi.org/10.3390/diagnostics16071014 - 27 Mar 2026
Viewed by 317
Abstract
Objective: The integration of artificial intelligence (AI) into computer-aided detection (CAD) is a major innovation in lung cancer diagnosis. However, its reliability in detecting the earliest radiographic sign—faint ground-glass opacities (GGOs) indicating pre-invasive adenocarcinoma—remains a critical, unquantified gap. This study aimed to perform [...] Read more.
Objective: The integration of artificial intelligence (AI) into computer-aided detection (CAD) is a major innovation in lung cancer diagnosis. However, its reliability in detecting the earliest radiographic sign—faint ground-glass opacities (GGOs) indicating pre-invasive adenocarcinoma—remains a critical, unquantified gap. This study aimed to perform a rigorous failure analysis to define the specific conditions under which commercial AI/CAD systems fail in a low-dose CT (LDCT) screening setting. Methods: In this retrospective diagnostic accuracy study, a primary cohort of 100 patients and an external validation cohort of 50 patients with moderate/low-risk nodules on LDCT were included. An expert reference standard was established by a consensus panel of three thoracic radiologists. Two independent, commercially deployed AI/CAD systems from different vendors (Vendor A & Vendor B) processed all cases. Nodules confirmed by experts but missed by AI were analyzed. Their morphology was categorized, and their mean CT attenuation (HU) was measured via manual region-of-interest placement. Results: The AI systems demonstrated significant and comparable false negative rates in the combined cohort: 12.7% for Vendor A and 14.7% for Vendor B. The vast majority of missed nodules were GGOs (92.3% and 78.6%, respectively, in the primary cohort). Crucially, quantitative analysis revealed a consistent density threshold for AI failure: the mean CT value of missed GGOs was −737 ± 51.50 HU for Vendor A and −727 ± 70.07 HU for Vendor B. This algorithmic blind spot was fully corroborated by the external validation cohort (−741 ± 48.2 HU and −733 ± 62.5 HU, respectively). Anatomical complexity (juxta-pleural/endobronchial location) was a secondary failure factor. Conclusions: This study identifies a quantifiable “−730 HU blind spot” as a common limitation of current commercial AI/CAD systems in diagnosing early lung adenocarcinoma. This finding represents a pivotal advancement in understanding AI’s role in diagnostics: it is not infallible. To innovate and safeguard screening efficacy, radiologists must adopt a human–AI collaborative model with mandated manual verification targeting low-attenuation opacities, ensuring this diagnostic innovation fulfills its promise while mitigating the risks of overdiagnosis. Full article
(This article belongs to the Special Issue Advancements and Innovations in the Diagnosis of Lung Cancer)
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