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Medicina
Special Issues
Thoracic Oncology: Current Challenges and Future Prospects
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Thoracic Oncology: Current Challenges and Future Prospects

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Oncology".

Deadline for manuscript submissions: 25 August 2026 | Viewed by 1070

Special Issue Editors

Dr. Luca Bertolaccini

E-Mail Website
Guest Editor
1. Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy
2. Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
Interests: lung cancer; thoracic surgery; thoracic oncology
Special Issues, Collections and Topics in MDPI journals
Dr. Claudia Bardoni

E-Mail Website
Guest Editor Assistant
Department of Thoracic Surgery, European Institute of Oncology (IEO) IRCCS, 20141 Milan, Italy
Interests: lung cancer; AI; immuno-oncology; thoracic surgery; cythopathology; global health

Special Issue Information

Dear Colleagues,

Thoracic oncology has witnessed remarkable changes as a result of advances in molecular biology, imaging technologies, personalized medicine, and minimally invasive surgery.

Thoracic malignancies, including lung cancer, mesothelioma, and mediastinal tumors, remain a substantial global health burden characterized by high incidence, late-stage diagnosis, and differential response to therapy, despite significant advances. While we are in a unique, unprecedented period of new therapies and treatment approach development, there are still multiple clinical and scientific barriers to overcome before we can achieve the genuine success of our treatment methods, including early detection, treatment resistance, best patient selection, and long-term results.

This Special Issue, titled Thoracic Oncology: Current Challenges and Future Prospects, will provide an updated overview of the current state of the art in thoracic oncology, as well as outline unmet needs and potential pathways for future research. Novel subjects include screening and early detection, biomarkers, immunotherapy and targeted therapies, perioperative treatment, "thoracic" surgery, oligometastatic disease, and multimodality therapy. Special emphasis will be dedicated to translational research, such as liquid biopsy, radiogenomics, tumor microenvironment profiling, and artificial intelligence for prediction, classification, and decision making.

The best submissions addressing mechanisms of resistance, minimal residual disease, quality-of-life and patient-reported outcomes should be at the cutting edge of research. We are also interested in novel surgical technologies that include, but are not limited to, robotic and image-guided surgical procedures.

We invite original research articles, clinical studies, systematic reviews and meta-analyses, translational studies, and high-quality narrative reviews. We also encourage multidisciplinary contributions spanning thoracic surgery, oncology, radiology, pathology, molecular science, and computational medicine.

Dr. Luca Bertolaccini
Guest Editor

Dr. Claudia Bardoni
Guest Editor Assistant

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. Medicina is an international peer-reviewed open access monthly 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 2200 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

  • thoracic oncology
  • lung cancer
  • mesothelioma
  • mediastinal tumors
  • immunotherapy
  • artificial intelligence
  • targeted therapy
  • screening
  • early detection
  • biomarkers
  • liquid biopsy
  • radiomics
  • radiogenomics
  • robotic surgery
  • multimodality treatment
  • oligometastatic disease
  • tumor microenvironment

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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

17 pages, 1538 KB  
Article
Preoperative Lactate Dehydrogenase-to-Albumin Ratio as a Tumor–Host Biomarker of Early Recurrence and Survival in Resected Pulmonary Neuroendocrine Carcinomas: A Multicenter Observational Cohort Study
by Hacer Boztepe Yesilcay, Asim Armagan Aydin, Ahmet Baklaci, Abdurrahman Aykut, Ahmet Unlu, Merve Turan, Ismail Oguz Kara, Ramazan Oguz Yuceer, Muhammed Fatih Sagiroglu, Sencan Akdag and Mustafa Yildiz
Medicina 2026, 62(5), 946; https://doi.org/10.3390/medicina62050946 - 13 May 2026
Viewed by 269
Abstract
Background and Objectives: Pulmonary neuroendocrine carcinomas (NECs) are characterized by aggressive clinical behavior and heterogeneous postoperative outcomes. Early recurrence, often reflecting occult micrometastatic disease, remains a key determinant of prognosis and is insufficiently captured by conventional staging systems. We hypothesized that the [...] Read more.
Background and Objectives: Pulmonary neuroendocrine carcinomas (NECs) are characterized by aggressive clinical behavior and heterogeneous postoperative outcomes. Early recurrence, often reflecting occult micrometastatic disease, remains a key determinant of prognosis and is insufficiently captured by conventional staging systems. We hypothesized that the lactate dehydrogenase-to-albumin ratio (LAR), as an integrative tumor–host biomarker, may provide biologically informed risk stratification in this setting. Materials and Methods: We conducted a multicenter retrospective cohort study including 88 patients with resected small cell lung cancer (SCLC) or large cell neuroendocrine carcinoma (LCNEC). Preoperative LAR and comparator inflammatory indices were evaluated. The primary endpoints were disease-free survival (DFS) and overall survival (OS), with early recurrence (≤12 months) as a prespecified secondary endpoint. Time-dependent receiver operating characteristic analyses, Cox proportional hazards models, and logistic regression analyses were applied within a predefined analytical framework. Results: Using a cut-off derived from 12-month DFS (LAR = 45.58), elevated LAR was associated with significantly shorter DFS (median 12.3 vs. 26.1 months; p = 0.018) and OS (median 20.7 vs. 52.8 months; p = 0.010). In multivariable analyses, LAR remained independently associated with both DFS (HR 1.012, 95% CI 1.001–1.023; p = 0.037) and OS (HR 1.016, 95% CI 1.005–1.027; p = 0.003). Elevated LAR was also associated with an increased risk of early recurrence (adjusted OR 4.656, 95% CI 1.520–14.262; p = 0.007). In time-dependent receiver operating characteristic (ROC) analyses, LAR demonstrated the highest overall discriminatory performance across evaluated biomarkers and showed a statistically significant advantage over neutrophil-to-lymphocyte ratio (NLR) for 24-month OS. Conclusions: Preoperative LAR captures a clinically relevant tumor–host phenotype associated with early disease progression and adverse survival outcomes in resected pulmonary NECs. As a biologically integrative and readily accessible biomarker, LAR may complement existing risk stratification strategies in this heterogeneous disease context. Prospective validation and integration into multimodal risk models are warranted. Full article
(This article belongs to the Special Issue Thoracic Oncology: Current Challenges and Future Prospects)
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20 pages, 19374 KB  
Article
Mean Corpuscular Volume as a Prognostic Marker in Patients with Non-Small Cell Lung Cancer Undergoing Surgical Resection: A Cohort Study
by Soomin An and Wankyu Eo
Medicina 2026, 62(2), 395; https://doi.org/10.3390/medicina62020395 - 18 Feb 2026
Viewed by 471
Abstract
Background and Objectives: Anatomical staging alone insufficiently explains survival heterogeneity in patients with resected non-small cell lung cancer (NSCLC). Although inflammation-based biomarkers have demonstrated prognostic value, the clinical relevance of erythrocyte-derived indices—particularly mean corpuscular volume (MCV)—remains poorly characterized in this setting. This [...] Read more.
Background and Objectives: Anatomical staging alone insufficiently explains survival heterogeneity in patients with resected non-small cell lung cancer (NSCLC). Although inflammation-based biomarkers have demonstrated prognostic value, the clinical relevance of erythrocyte-derived indices—particularly mean corpuscular volume (MCV)—remains poorly characterized in this setting. This study evaluated the prognostic significance of preoperative MCV and examined whether its integration with the Noble and Underwood (NUn) score improves survival prediction. Methods: We retrospectively analyzed patients with stage I–IIIA NSCLC who underwent complete surgical resection. Associations between clinicopathological variables and overall survival (OS) were assessed using Cox proportional hazards models. Prognostic performance was evaluated using the concordance index and the integrated time-dependent area under the curve. Continuous variables were modeled on their original scale without dichotomization. Results: Model comparison using the Akaike Information Criterion indicated that incorporation of the composite NUn–MCV index into the intermediate model—comprising age, basal metabolic rate, American Society of Anesthesiologists physical status, pleural invasion, and pathological stage—provided a superior model fit compared with inclusion of the NUn score and MCV as separate covariates. On this basis, the composite NUn–MCV model was selected as the full model. Across all evaluations, the full model demonstrated consistently greater discriminative ability for survival prediction than both the intermediate model and the baseline model based solely on pathological stage. Conclusions: Preoperative MCV independently predicts OS in patients with resected stage I–IIIA NSCLC. Integration of MCV with the NUn score into a composite index provides incremental prognostic value beyond anatomical staging and established clinical factors, supporting its use as a complementary tool for postoperative risk stratification. Full article
(This article belongs to the Special Issue Thoracic Oncology: Current Challenges and Future Prospects)
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