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Non-Small-Cell Lung Cancer: Screening, Diagnosis and Treatment Options in the AI and Omics Era

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A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Respiratory Medicine".

Deadline for manuscript submissions: closed (25 February 2026) | Viewed by 3878

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Special Issue Editor

Dr. Miljana Tanić

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Guest Editor

1. Department of Cancer Biology, University College London Cancer Institute, London WC1E 6DD, UK
2. Experimental Oncology Deprtment, Institute for Oncology and Radiology of Serbia, 11000 Belgrade, Serbia
Interests: solid tumors; genomics; bioinformatics; cancer biomarkers; liquid biopsy

Special Issue Information

Dear Colleagues,

Lung cancer, of which non-small-cell lung cancer is the commonest form, is the leading cause of cancer deaths worldwide. Over the past decade, we have witnessed the fast pace at which technological breakthroughs, especially regarding the development and application of artificial intelligence (AI) and omics methods, combined with non-invasive or minimally invasive medical procedures, have transformed the clinical management of lung cancer to improve patient screening, diagnosis, and therapeutic strategies.

In this Special issue, we aim to highlight the use of AI, machine learning, and omics at various clinical pathway stages, including early detection, diagnosis and subtype classification, the estimation of prognosis, the prediction of treatment response, and clinical trial optimization. The topics of interest for publication include, but are not limited to, the following: pre-clinical research and the clinical implementation of molecular tumor profiling, data mining, natural language processing, the machine-learning-based modeling of both routine clinical data and blood work, as well as radiomics, digital pathology, and omics data from liquid and solid biopsies, addressing current issues and discussing further directions for the implementation of AI and omics technologies for non-small-cell lung cancer clinical workup in order to deliver improved patient care.

Dr. Miljana Tanić
Guest Editor

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Keywords

lung cancer
NSCLC
radiomics
digital pathology
electronic health records
tumor profiling
AI
machine learning
deep learning
liquid biopsy
early detection, diagnosis
prognosis
therapy
biomarkers
management

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Published Papers (4 papers)

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Research

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19 pages, 1426 KB

Open AccessArticle

Lung Cancer Screening in a Population from Northeast Italy Exposed to Both Asbestos and Smoking: A Cost-Effectiveness Analysis

by Rami Cosulich, Chloe Thomas, Fabiano Barbiero, Duncan Gillespie, Ettore Bidoli, Maria Assunta Cova, Stefano Lovadina, Alessandra Guglielmi, Luigino Dal Maso, Barbara Alessandrini, Francesca Larese Filon, Fabio Barbone and Elisa Baratella

J. Clin. Med. 2026, 15(8), 3136; https://doi.org/10.3390/jcm15083136 - 20 Apr 2026

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Abstract

Background: Past workplace exposure to asbestos in combination with tobacco smoking has increased the risk of lung cancer for some residents in an area within the Friuli Venezia Giulia region, Northeast Italy. In light of studies showing that lung cancer screening (LCS) with low-dose computed tomography (LDCT) can reduce mortality, local stakeholders and decision-makers decided to assess the potential benefits, harms and cost-effectiveness of a single round of LCS with LDCT versus standard care among people aged 55 to 80 who were formerly exposed to asbestos and with at least 10 pack-years of smoking. Methods: An economic model was developed using a decision tree connected to a Markov cohort model. The primary outcome was the incremental cost per additional quality-adjusted life year (QALY). Other outcomes included the number of life years saved, the number of deaths averted and overdiagnosis. Results: Per 10,000 people screened, the intervention led to 395 additional QALYs (95% credible interval: 129 to 831) and incremental total costs of EUR 1,086,345 (95% credible interval: −852,607 to 2,155,826). The incremental cost per QALY gained was EUR 2750. There was a probability of cost-effectiveness of 99.5% relative to a threshold of EUR 25,000. Conclusions: The model estimated that the intervention was cost-effective. The model’s simplifications and limitations should be considered when interpreting the findings in relation to policy-making decisions. Further research could include the costs and benefits of incidental findings and could assess the cost-effectiveness of repeated rounds of screening for the same population. Full article

(This article belongs to the Special Issue Non-Small-Cell Lung Cancer: Screening, Diagnosis and Treatment Options in the AI and Omics Era)

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15 pages, 3027 KB

Open AccessArticle

Artificial Intelligence as a Diagnostic Tool in Preoperative Surgical Planning for Early Non-Small Cell Lung Cancer: A Single-Center Experience

by Zeljko Garabinovic, Milan Savic, Nikola Colic, Jelena Rakocevic, Maja Ercegovac, Milos Mitrovic, Katarina Lukic, Jelica Vukmirovic, Jelena Vasic Madzarevic, Stefan Stevanovic, Gordana Bisevac Peric, Miljana Bubanja and Aleksandra Pavic

J. Clin. Med. 2025, 14(21), 7609; https://doi.org/10.3390/jcm14217609 - 27 Oct 2025

Viewed by 1006

Abstract

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for the majority of cases. Radiomics and artificial intelligence (AI) have emerged as promising tools for quantitative imaging analysis and precision staging. This study aimed to evaluate the ability of an AI-based radiomics model to preoperatively predict tumor (T) and nodal (N) stage, lymphovascular invasion (LVI), and postoperative complications in patients with early-stage NSCLC. Material and Methods: This retrospective study included 51 consecutive patients who underwent anatomical lobectomy with systematic lymph node dissection between 2019 and 2024, at the Clinic for Thoracic Surgery of the University Clinical Center of Serbia. Quantitative imaging features were extracted from preoperative CT scans using the Lesion Scout with Auto ID module (syngo.via VB50 MM, Siemens Healthineers). Radiomics and clinical predictors were analyzed using regularized logistic regression (LASSO) with five-fold cross-validation. Model performance was assessed using AUC, accuracy, sensitivity, specificity, precision, and F1 score, and calibration was evaluated using the Hosmer–Lemeshow test. Groups were compared using parametric and non-parametric tests. Correlation between the variables was assessed using Spearman’s rank correlation coefficient. All p-values less than 0.05 were considered significant. Results: The AI-based model showed excellent performance for predicting the T component (training AUC = 0.89; test AUC = 0.86; F1 = 0.81) and acceptable calibration (p = 0.41). Nodal metastasis (OR = 0.108; 95% CI: 0.011–1.069; p = 0.057) and LVI (OR = 0.519; 95% CI: 0.139–1.937; p = 0.329) were not significantly predicted. Emphysema was identified as a significant independent predictor of postoperative complications (χ² = 5.13; p = 0.024). Conclusions: The AI-driven radiomics model demonstrated strong predictive ability for the T component and identified emphysema as a clinically relevant predictor of postoperative complications. Full article

(This article belongs to the Special Issue Non-Small-Cell Lung Cancer: Screening, Diagnosis and Treatment Options in the AI and Omics Era)

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20 pages, 45835 KB

Open AccessArticle

Computer Vision-Assisted Spatial Analysis of Mitoses and Vasculature in Lung Cancer

by Anna Timakova, Alexey Fayzullin, Vladislav Ananev, Egor Zemnuhov, Vadim Alfimov, Alexey Baranov, Yulia Smirnova, Vitaly Shatalov, Natalia Konukhova, Evgeny Karpulevich, Peter Timashev and Vladimir Makarov

J. Clin. Med. 2025, 14(21), 7526; https://doi.org/10.3390/jcm14217526 - 23 Oct 2025

Viewed by 827

Abstract

Background/Objectives: Lung cancer is characterized by a significant microstructural heterogenicity among different histological types. Artificial intelligence and digital pathology instruments can facilitate morphological analysis by introducing calculated metrics allowing for the distinguishment of different tissue patterns. Methods: We used computer vision models to calculate a number of morphometric features of tumor vascularization and proliferation. We used two frameworks to process whole-slide images: (1) LVI-PathNet framework for vascular detection, based on the SegFormer architecture; and (2) Mito-PathNet framework for mitotic figure detection, based on the RetinaNet detector and an ensemble classification model. The results were visualized in the segmented and gradient heatmaps. Results: SegFormer for vessel segmentation achieved the following quality metrics: IoU = 0.96, FBeta-score = 0.98, and AUC-ROC = 0.98. RetinaNet + CNN ensemble achieved the following quality metrics: specificity = 0.96 and sensitivity = 0.97. The analysis of the obtained parameters allowed us to identify trophic patterns of lung cancer according to the degree of aggressiveness, which can serve as potential targets for therapy, including proliferative-vascular, hypoxic, proliferative, vascular, and inactive. Conclusions: The analysis of the obtained parameters allowed us to identify distinct quantitative characteristics for each histological type of lung cancer. These patterns could potentially become markers for therapeutic choices, such as antiangiogenic and hypoxia-induced factor therapy. Full article

(This article belongs to the Special Issue Non-Small-Cell Lung Cancer: Screening, Diagnosis and Treatment Options in the AI and Omics Era)

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Review

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13 pages, 535 KB

Open AccessReview

From Lung Cancer Predictive Models to MULTIPREVENTion

by Zuzanna Budzińska, Zofia Budzisz, Marta Bednarek and Joanna Bidzińska

J. Clin. Med. 2026, 15(2), 629; https://doi.org/10.3390/jcm15020629 - 13 Jan 2026

Viewed by 591

Abstract

The early diagnosis and treatment of civilizational diseases remain a significant challenge worldwide. Although advances in medical technology have led to the introduction of more screening options over time, these measures are still insufficient to effectively reduce mortality from deadly diseases such as lung cancer (LC), cardiovascular diseases (CVD), diabetes, and chronic obstructive pulmonary disease (COPD). These conditions pose a major public health burden, underlying the urgent need for more comprehensive and efficient prevention strategies. Recently, the concept of ‘multiscreening’ has emerged as a promising approach. Multiscreening involves the simultaneous screening for multiple diseases using integrated diagnostic methods, potentially improving early detection rates and optimizing resource utilization. In 2024, Rzyman W. et al. launched the MULTIPREVENT epidemiological study, which aims to develop and validate a low-dose computed tomography (LDCT)-based screening test for civilizational diseases. This study represents a step forward in the pursuit of more effective, minimally invasive diagnostic tools that could facilitate earlier intervention and improve patient outcomes. To better understand the potential of multiscreening approaches and their clinical utility, it is essential to evaluate the existing predictive models used for identifying individuals at high risk for these diseases. This narrative review focuses primarily on lung cancer risk prediction models used in LDCT screening while situating these approaches within the broader conceptual framework of the MULTIPREVENT project, aimed at future integration of multi-disease prevention strategies. With this analysis, we aim to provide insights that will guide the development of more accurate, integrative screening tools that could reduce the global burden of these diseases. Full article

(This article belongs to the Special Issue Non-Small-Cell Lung Cancer: Screening, Diagnosis and Treatment Options in the AI and Omics Era)

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