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Life
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Innovations in Thoracic Surgery and Medicine: Advancements and Challenges
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Innovations in Thoracic Surgery and Medicine: Advancements and Challenges

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".

Deadline for manuscript submissions: 18 May 2025 | Viewed by 4305

Special Issue Editor

Dr. Paolo Scanagatta

E-Mail Website
Guest Editor
Division of Thoracic Surgery, ASST Valtellina e Alto Lario, "Eugenio Morelli" Hospital, Sondalo, Italy
Interests: lung cancer; pulmonary metastases; surgical oncology; VATS; minimally invasive thoracic surgery; innovative surgical techniques; artificial intelligence; development of medical apps; pleural mesothelioma; respiration physiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Topic background and history:

The evolution of medicine in recent years has been strongly linked to technological evolution, which has allowed the development of medical devices and surgical instruments capable of improving the patients’ quality of life in the daily management of chronic diseases, such as diabetes, incontinence or cardiovascular pathologies, and of making surgery less invasive with video-assisted surgery and robotics, which also guarantees more complex interventions with shorter recovery times.

We can now foresee new scenarios in which robotic surgery will have more accessible costs and will become the ideal interface for the entry of Artificial Intelligence into the surgical field. From this perspective, professional training is essential to control and exploit all the potential that technological innovation represents, thus improving the benefit of patients.

Aim and scope of the Special Issue:

This Special Issue aims to understand what could happen in future years, attempting to foresee the trends for innovative technologies and future surgical treatments.

We also seek to understand how technology could improve MDT, preoperative studies, anesthesia, postoperative rehabilitation, palliation and follow-up.

Furthermore, we ask the question of Artificial Intelligences’ role. 

Cutting-edge research:

  • What does innovation mean today?
    Innovation means accessibility to the most innovative technologies for the greatest number of patients, the optimization of clinical results in terms of outcome, expectancy and quality of life and the ability to reconcile expenses with the effectiveness of treatments. In the surgical field, these factors demand minimally invasive interventions that guarantee conservative procedures, fewer post-operative complications and reduced recovery times, as well as a revolution that allows us to treat even fragile patients for whom a traditional, so-called open surgery could involve very high risks. In the clinical approach, however, medical devices capable of meeting the physical and psychological needs of patients, like augmented reality or 3D reconstruction planning tools, are significant innovations. These innovations also allow us to review the concept of prevention, and a non-invasive investigation ensures many more people are screened for oncological diseases. It is, therefore, important that research is increasingly oriented towards precision medicine that makes use of all the tools necessary to improve treatment paths and the quality of life of patients, combining concerns regarding the sustainability of the health system—at a time when the spending review calls for a review of the distribution of funds—and accessibility to innovations for all patients.
  • What kind of papers we are soliciting?
    We are soliciting clinical trial reports or projects, study designs, case series, technical “how-to-do-it” articles, case perspectives, narrative and systematic reviews and expert commentaries.

Dr. Paolo Scanagatta
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. Life 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 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

  • thoracic medicine
  • thoracic surgery
  • lung cancer
  • artificial intelligence (AI)
  • VATS
  • thoracic anesthesia
  • innovations

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Published Papers (4 papers)

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Research

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15 pages, 1599 KiB  
Article
Optimizing Lung Cancer Diagnostics: Insights from a Fast-Track Program in a Complex Healthcare Setting
by Paolo Scanagatta, Alessandro Bertolini, Giuseppe Naldi, Francesca Antoniazzi, Francesco Inzirillo, Casimiro Eugenio Giorgetta, Eugenio Ravalli, Gianluca Ancona, Sara Cagnetti, Claudio Barbonetti and Fabiano Stangoni
Life 2025, 15(3), 362; https://doi.org/10.3390/life15030362 - 25 Feb 2025
Viewed by 476
Abstract
Lung cancer remains a leading cause of cancer-related mortality, with diagnostic delays significantly impacting patient outcomes. Despite advancements in diagnostic strategies, inefficiencies persist, particularly in geographically complex regions with limited healthcare resources. The Fast-Track Program was developed to address these challenges in lung [...] Read more.
Lung cancer remains a leading cause of cancer-related mortality, with diagnostic delays significantly impacting patient outcomes. Despite advancements in diagnostic strategies, inefficiencies persist, particularly in geographically complex regions with limited healthcare resources. The Fast-Track Program was developed to address these challenges in lung cancer diagnostics within the geographically complex and resource-limited Valtellina region. This prospective observational study compared patients managed under the Fast-Track pathway (May–August 2024) with those following standard diagnostic procedures (January–April 2024). The program integrated structured, pre-scheduled diagnostic slots, a rotating Case Manager role, and weekly multidisciplinary team (MDT) discussions to enhance coordination and reduce diagnostic timelines. Results showed a significant reduction in the mean time to definitive diagnosis from 42.9 days (95% CI: 35.6–50.3) in the control group to 25.0 days (95% CI: 20.8–29.3) in the Fast-Track cohort (p < 0.001). Patient adherence to diagnostic pathways improved from 71% to 92% (p < 0.05), while satisfaction scores increased from 64% to 89%, with patients rating their experience as “very good” or “excellent” (p < 0.05). Although the predefined clinical significance criteria were not fully met, the program demonstrated a favorable trend toward improved efficiency and patient-centered care. These findings support the feasibility and scalability of structured diagnostic workflows in streamlining lung cancer diagnostics, with potential implications for broader oncological and chronic disease management in resource-constrained healthcare settings. Full article
(This article belongs to the Special Issue Innovations in Thoracic Surgery and Medicine: Advancements and Challenges)
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11 pages, 552 KiB  
Article
Development of Imaging Complexity Biomarkers for Prediction of Symptomatic Radiation Pneumonitis in Patients with Non-Small Cell Lung Cancer, Focusing on Underlying Lung Disease
by Jeongeun Hwang, Hakyoung Kim, Joon-Young Moon, Sun Myung Kim and Dae Sik Yang
Life 2024, 14(11), 1497; https://doi.org/10.3390/life14111497 - 17 Nov 2024
Cited by 1 | Viewed by 1047
Abstract
Objectives: We aimed to develop imaging biomarkers to predict radiation pneumonitis (RP) in non-small cell lung cancer (NSCLC) patients undergoing thoracic radiotherapy. We hypothesized that measuring morphometric complexity in the lung using simulation computed tomography may provide objective imaging biomarkers for lung parenchyma [...] Read more.
Objectives: We aimed to develop imaging biomarkers to predict radiation pneumonitis (RP) in non-small cell lung cancer (NSCLC) patients undergoing thoracic radiotherapy. We hypothesized that measuring morphometric complexity in the lung using simulation computed tomography may provide objective imaging biomarkers for lung parenchyma integrity, potentially forecasting the risk of RP. Materials and Methods: A retrospective study was performed on medical records of 175 patients diagnosed with NSCLC who had received thoracic radiotherapy. Three indices were utilized to measure the morphometric complexity of the lung parenchyma: box-counting fractal dimension, lacunarity, and minimum spanning tree (MST) fractal dimension. Patients were dichotomized into two groups at median values. Cox proportional hazard models were constructed to estimate the hazard ratios for grade ≥ 2 or grade ≥ 3 RP. Results and Conclusions: We found significant associations between lung parenchymal morphometric complexity and RP incidence. In univariate Cox-proportional hazard analysis, patients with a lower MST fractal dimension had a significantly higher hazard ratio of 2.296 (95% CI: 1.348–3.910) for grade ≥ 2 RP. When adjusted for age, sex, smoking status, category of the underlying lung disease, category of radiotherapy technique, clinical stage, histology, and DLCO, patients with a lower MST fractal dimension showed a significantly higher hazard ratio of 3.292 (95% CI: 1.722–6.294) for grade ≥ 2 RP and 7.952 (95% CI: 1.722 36.733) for grade ≥ 3 RP than those with a higher MST fractal dimension. Patients with lower lacunarity exhibited a significantly lower hazard ratio of 0.091 (95% CI: 0.015–0.573) for grade ≥ 3 RP in the adjusted model. We speculated that the lung tissue integrity is captured by morphometric complexity measures, particularly by the MST fractal dimension. We suggest the MST fractal dimension as an imaging biomarker for predicting the occurrence of symptomatic RP after thoracic radiotherapy. Full article
(This article belongs to the Special Issue Innovations in Thoracic Surgery and Medicine: Advancements and Challenges)
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Review

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12 pages, 476 KiB  
Review
New Perspectives on Lung Cancer Screening and Artificial Intelligence
by Leonardo Duranti, Luca Tavecchio, Luigi Rolli and Piergiorgio Solli
Life 2025, 15(3), 498; https://doi.org/10.3390/life15030498 - 19 Mar 2025
Viewed by 877
Abstract
Lung cancer is the leading cause of cancer-related death worldwide, with 1.8 million deaths annually. Early detection is vital for improving patient outcomes; however, survival rates remain low due to late-stage diagnoses. Accumulating data supports the idea that screening methods are useful for [...] Read more.
Lung cancer is the leading cause of cancer-related death worldwide, with 1.8 million deaths annually. Early detection is vital for improving patient outcomes; however, survival rates remain low due to late-stage diagnoses. Accumulating data supports the idea that screening methods are useful for improving early diagnosis in high-risk patients. However, several barriers limit the application of lung cancer screening in real-world settings. The widespread diffusion of artificial intelligence (AI), radiomics, and machine learning has dramatically changed the current diagnostic landscape. This review explores the potential of AI and biomarker-driven methods, particularly liquid biopsy, in enhancing early lung cancer detection. We report the findings of major randomized controlled trials, cohort studies, and research on AI algorithms that use multi-modal imaging (e.g., CT and PET scans) and liquid biopsy to identify early molecular alterations. AI algorithms enhance diagnostic accuracy by automating image analysis and reducing inter-reader variability. Biomarker-driven methods identify molecular alterations in patients before imaging signs of cancer are evident. Both AI and liquid biopsy show the potential to improve sensitivity and specificity, enabling the detection of early-stage cancers that traditional methods, like low-dose CT (LDCT) scans, might miss. Integrating AI and biomarker-driven methods offers significant promise for transforming lung cancer screening. These technologies could enable earlier, more accurate detection, ultimately improving survival outcomes. AI-driven lung cancer screening can achieve over 90% sensitivity, compared to 70–80% with traditional methods, and can reduce false positives by up to 30%. AI also boosts specificity to 85–90%, with faster processing times (a few minutes vs. 30–60 min for radiologists). However, challenges remain in standardizing these approaches and integrating them into clinical practice. Ongoing research is essential to fully realize their clinical benefits and enhance timely interventions. Full article
(This article belongs to the Special Issue Innovations in Thoracic Surgery and Medicine: Advancements and Challenges)
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Other

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17 pages, 4384 KiB  
Case Report
Surgical Treatment and Targeted Therapy for a Large Metastatic Malignant Peripheral Nerve Sheath Tumor: A Case Report and Literature Review
by Patryk Skórka, Dawid Kordykiewicz, Andrzej Ilków, Konrad Ptaszyński, Janusz Wójcik, Wiktoria Skórka and Małgorzata Edyta Wojtyś
Life 2024, 14(12), 1648; https://doi.org/10.3390/life14121648 - 12 Dec 2024
Cited by 1 | Viewed by 1035
Abstract
Neurofibromatosis type 1 (NF1) significantly increases the risk of malignant peripheral nerve sheath tumors (MPNST), a rare and aggressive malignancy for which treatment is clinically challenging. This paper presents the case of a 24-year-old male with an NF1 who developed MPNST with lung [...] Read more.
Neurofibromatosis type 1 (NF1) significantly increases the risk of malignant peripheral nerve sheath tumors (MPNST), a rare and aggressive malignancy for which treatment is clinically challenging. This paper presents the case of a 24-year-old male with an NF1 who developed MPNST with lung metastases. Due to the limited effectiveness of systemic therapy in the treatment of MPNST, the patient underwent radical surgical resection and radiotherapy. Pathological evaluation confirmed high-grade MPNST, and PET-CT imaging revealed further metastatic progression. The treatment results for our patient are compared with those of other patients with NF1 who also developed MPNST with lung metastases in the literature. The findings suggest the need for further research into personalized treatment strategies that may improve prognosis and overall survival in patients with NF1 and MPNST, with immunotherapy being a promising therapeutic option. Full article
(This article belongs to the Special Issue Innovations in Thoracic Surgery and Medicine: Advancements and Challenges)
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