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 777

Special Issue Editor


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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
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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

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Keywords

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

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Published Papers (1 paper)

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Research

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
Viewed by 389
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
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