cancers-logo

Journal Browser

Journal Browser

The Role of Artificial Intelligence in Thyroid, Thoracic and Gastrointestinal Tumors: Transforming the Landscape of Diagnosis and Treatment

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 655

Special Issue Editors


E-Mail Website
Guest Editor
Department of Radiotherapy/Oncology, Medical School, Democritus University of Thrace, 68100 Alexandroupolis, Greece
Interests: radiotherapy; chemotherapy; immunotherapy; hypoxia; metabolism; angiogenesis; autophagy

E-Mail
Guest Editor
Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece
Interests: colorectal cancer surgery; gastrointestinal cancer surgery; artificial intelligence; endocrine surgery; thyroid cancer; cellular metabolism in gastrointestinal cancer; laparoscopic surgery; hernia surgery

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) holds the potential to play a leading role in healthcare. To date, AI-based technology has revolutionized the diagnosis and management of cancer. Indeed, such advanced models can actually help enhance the accuracy of diagnosis for thyroid, thoracic and gastrointestinal malignancies. Apart from the fact that this novel technology can be used as a valuable tool to assist in identifying cancer earlier, it can also direct the evolution of medical imaging technology. Interestingly, AI software is particularly helpful in the treatment of these malignancies. Recently, great attention has been paid to AI applications that contribute significantly to early cancer detection and management. This Special Issue aims to highlight the role of integrating AI algorithms in clinical practice to improve diagnostic accuracy and achieve better intra- and postoperative patient outcomes. We welcome original research articles and review papers that provide insights into the emerging field of AI with advanced applications in cancer diagnosis and treatment, giving particular emphasis to thyroid, thoracic and gastrointestinal cancers. We look forward to receiving your contributions.

Prof. Dr. Michael Koukourakis
Dr. Athanasia Mitsala
Guest Editors

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 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. 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

  • artificial intelligence
  • thyroid cancer
  • thoracic cancer
  • gastrointestinal cancer
  • diagnosis
  • medical imaging
  • treatment
  • surgery

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

13 pages, 3010 KB  
Article
Improved Preoperative Diagnosis of Medullary Thyroid Carcinoma Using Dual-Mode Ultrasound Radiomics
by Luying Gao, Naishi Li, Yu Xia, Liyuan Ma, Yuang An, Jiang Ji, Jionghui Gu, Dingyue Zhang, Nengwen Luo, Yang Cao, Yijian Fan and Yuxin Jiang
Cancers 2026, 18(11), 1738; https://doi.org/10.3390/cancers18111738 - 26 May 2026
Viewed by 211
Abstract
Background: Preoperative diagnosis of medullary thyroid carcinoma (MTC) is clinically challenging due to sonographic overlap with other thyroid tumors. To address this, we aimed to develop a multi-vendor, multimodal radiomic framework for accurate MTC identification, comparing its diagnostic performance with that of [...] Read more.
Background: Preoperative diagnosis of medullary thyroid carcinoma (MTC) is clinically challenging due to sonographic overlap with other thyroid tumors. To address this, we aimed to develop a multi-vendor, multimodal radiomic framework for accurate MTC identification, comparing its diagnostic performance with that of experienced radiologists. Methods: This retrospective study included 467 pathologically confirmed thyroid nodules (94 MTCs, 373 non-MTCs) acquired across multiple ultrasound platforms. The dataset was randomly partitioned into training (80%) and internal testing (20%) sets. In total, 2250 radiomic features were extracted from grayscale and color Doppler images, followed by Z-score normalization to mitigate batch effects. A robust feature selection strategy (LASSO and recursive feature elimination) identified optimal signatures for developing machine learning classifiers (SVM, LR, RF). The optimal model was further validated on an independent, balanced cohort (n = 60; comprising 12 cases each of MTC, papillary carcinoma, follicular carcinoma, follicular adenoma, and nodular goiter) and compared with experienced radiologists across seven classification tasks. Results: The RF model achieved an AUC of 0.993 in distinguishing MTC from papillary carcinoma. The LR model showed an AUC of 0.991 for identifying MTC from all other nodules. In the independent validation cohort, the models maintained superior discriminatory ability, showing better diagnostic performance compared to the image interpretation by radiologists (AUC 0.993 vs. 0.488, p < 0.001). Conclusions: The proposed multi-vendor, multimodal radiomic system demonstrated good discriminative ability in the diagnosis and stratification of MTC. By integrating grayscale and Doppler ultrasound features while overcoming scanner variability, this model shows potential as a non-invasive adjunctive tool. Full article
Show Figures

Figure 1

Back to TopTop