Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.4
5.2
Journals
Cancers
Special Issues
Innovative Prognostic Factors and Follow-Up of Localized Thoracic Malignancies
share announcement

Innovative Prognostic Factors and Follow-Up of Localized Thoracic Malignancies

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2658

Special Issue Editor

Dr. Ludovic Fournel

E-Mail Website
Guest Editor
Thoracic Surgery Department, Assistance Publique—Hopitaux de Paris, Hôpital Cochin, 75014 Paris, France
Interests: lung cancer; thoracic oncology; prognosis

Special Issue Information

Dear Colleagues, 

During the past several decades, the use of large-range therapies and restrictive tumor-based therapeutic strategies to treat thoracic malignancies have demonstrated huge limits in terms of oncological outcome and tolerability for patients. This has highlighted the major need for changes in treatment paradigms and for adopting a more personalized and precise therapeutic approach, customized according to tumor- and patient-related factors. Although thoracic malignancies, lung cancer in particular, remain leading causes of cancer-related death worldwide, significant improvements were observed in the management and survival of patients treated by tailored multidisciplinary therapies including surgery, radiation therapy and systemic treatments like targeted and immune therapies.

A challenge for multidisciplinary teams is to define the most appropriate indicators of prognosis and response to treatments, either to link to the tumor or its host. Notably, it requires the integration of mechanisms of cellular transformation and immune controls, metabolism in the broad sense, ranging from risk factors linked to lifestyle to that of the molecular mechanisms induced in tumor or immune cells. In addition, the heterogeneity of tumors should be considered, as it plays an important role in resistance to treatment and constitutes a limit to effective precision/personalized medicine. In addition, patient-tailored multidisciplinary management requires appropriate follow-up, which needs to be re-defined according to each type of malignancy and each case of patients, especially in rare tumors. The overall aim of this Special Issue is to establish original and relevant prognostic and theranostic factors, allowing the most adequate treatment assignment and choice of treatment sequence, in a wide range of thoracic malignancies. Authors will notably focus on multidisciplinary approaches, including surgical resection, neoadjuvant and adjuvant therapies, and associated follow-up requirements. We are pleased to invite you to contribute to our Special Issue entitled “Innovative prognostic factors and follow-up of localized thoracic malignancies”.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following: the prognosis and oncological management of early- and late-stage lung cancers, mesothelioma, thymoma, pulmonary neuroendocrine tumors, and chest-wall sarcoma. Among thoracic malignancies, analyses of rare tumors are particularly appreciated. Manuscripts addressing innovative methods of early postoperative and long-term follow-up are also welcome. We look forward to receiving your contributions.

Dr. Ludovic Fournel
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. Cancers 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 2900 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

  • prognosis
  • lung cancer
  • thoracic oncology
  • mesothelioma
  • neuroendocrine tumors
  • thymoma
  • sarcoma
  • surgery
  • multimodal treatment
  • rare tumors

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Review

18 pages, 1077 KiB  
Review
Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review
by Filippo Lococo, Galal Ghaly, Marco Chiappetta, Sara Flamini, Jessica Evangelista, Emilio Bria, Alessio Stefani, Emanuele Vita, Antonella Martino, Luca Boldrini, Carolina Sassorossi, Annalisa Campanella, Stefano Margaritora and Abdelrahman Mohammed
Cancers 2024, 16(10), 1832; https://doi.org/10.3390/cancers16101832 - 10 May 2024
Viewed by 729
Abstract
Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep [...] Read more.
Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue. Full article
(This article belongs to the Special Issue Innovative Prognostic Factors and Follow-Up of Localized Thoracic Malignancies)
►▼ Show Figures

Figure 1

17 pages, 339 KiB  
Review
Prognostic Factors in Advanced Non-Small Cell Lung Cancer Patients Treated with Immunotherapy
by Danilo Rocco, Luigi Della Gravara, Angela Ragone, Luigi Sapio, Silvio Naviglio and Cesare Gridelli
Cancers 2023, 15(19), 4684; https://doi.org/10.3390/cancers15194684 - 22 Sep 2023
Cited by 5 | Viewed by 1478
Abstract
Taking into account the huge epidemiologic impact of lung cancer (in 2020, lung cancer accounted for 2,206,771 of the cases and for 1,796,144 of the cancer-related deaths, representing the second most common cancer in female patients, the most common cancer in male patients, [...] Read more.
Taking into account the huge epidemiologic impact of lung cancer (in 2020, lung cancer accounted for 2,206,771 of the cases and for 1,796,144 of the cancer-related deaths, representing the second most common cancer in female patients, the most common cancer in male patients, and the second most common cancer in male and female patients) and the current lack of recommendations in terms of prognostic factors for patients selection and management, this article aims to provide an overview of the current landscape in terms of currently available immunotherapy treatments and the most promising assessed prognostic biomarkers, highlighting the current state-of-the-art and hinting at future challenges. Full article
(This article belongs to the Special Issue Innovative Prognostic Factors and Follow-Up of Localized Thoracic Malignancies)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop