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Diagnostics
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Radiomics in Oncology
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Radiomics in Oncology

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Medical Imaging and Theranostics".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1813

Special Issue Editor

Dr. Damiano Caruso

E-Mail Website
Guest Editor
Radiology Unit, Department of Surgical and Medical Sciences and Translational Medicine, Sant’Andrea University Hospital, University of Rome Sapienza, Via di Grottarossa 1035, 00189 Rome, Italy
Interests: imaging; oncology; CT; MRI; artificial intelligence; radiomics; response to therapy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of oncologic personalized medicine, radiomics represents an emerging diagnostic tool to support clinicians in decision making, cancer detection, and treatment response assessment. Radiomics by the extraction of several quantitative features, including tumor shape and textural parameters, could provide additional information on cancer phenotype and the tumor microenvironment. Digitally coded medical images that include information related to tumor heterogeneity are transformed in quantitative and dimensional data. Radiomics-derived data, if combined with other clinical data and correlated with outcome, could support physicians in making an accurate and structured evidence-based clinical decision.

In that scenario, radiologists have the means to stratify patients at diagnosis according to tumor aggressiveness and to predict or assess the treatment response in neuro-oncology, lung cancer, gastrointestinal and hepatobiliary tumors, as well as gynecological and genitourinary cancers. Radiomics has the main advantage for physicians that it could be an additional and integrated tool in patient management workflow.

Welcome to the era of bright data!

Dr. Damiano Caruso
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. Diagnostics 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 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

  • radiomics
  • oncology
  • artificial intelligence
  • precision medicine
  • texture analysis
  • imaging

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Published Papers (2 papers)

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Research

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12 pages, 789 KiB  
Article
Comparison of K-TIRADS, EU-TIRADS and ACR-TIRADS Guidelines for Malignancy Risk Determination of Thyroid Nodules
by Eren Tobcu, Erdal Karavaş, Gülden Taşova Yılmaz and Bilgin Topçu
Diagnostics 2025, 15(8), 1015; https://doi.org/10.3390/diagnostics15081015 - 16 Apr 2025
Viewed by 330
Abstract
Background/Objectives: Thyroid nodules are commonly observed in neck ultrasonography. Most nodules are benign; hence, several nodules require biopsy to accurately identify the malignant ones. Numerous risk classification guidelines have been developed for thyroid nodules, varying in their indications for fine-needle aspiration biopsy [...] Read more.
Background/Objectives: Thyroid nodules are commonly observed in neck ultrasonography. Most nodules are benign; hence, several nodules require biopsy to accurately identify the malignant ones. Numerous risk classification guidelines have been developed for thyroid nodules, varying in their indications for fine-needle aspiration biopsy (FNAB). The aim of this study is to evaluate the performances of three internationally recognized thyroid imaging reporting and data systems (TIRADS) for risk stratification of malignancy in comparison to one another. Methods: A total of 225 thyroid nodules with definitive FNAB cytology or histopathological diagnoses were included in this study. Various ultrasound (US) features were classified into categories based on three TIRADS editions. The guidelines were assessed regarding sensitivity, specificity, predictive values, and diagnostic accuracy to compare diagnostic value. Results: The American College of Radiology (ACR)-TIRADS demonstrated the best diagnostic accuracy (63.1%), the highest specificity (58.7%) and positive predictive value (36.3%), among three different TIRADS systems. Korean (K)-TIRADS exhibited the highest sensitivity (94.2%), negative predictive value (96.1%), and the most favorable negative likelihood ratio (0.13). The European (EU)-TIRADS had a sensitivity of 90.4%, specificity of 48.6%, and diagnostic accuracy of 58.2%, ranking between the other two guidelines across most parameters. Conclusions: The rigorous use of the guidelines established by each of the three TIRADS systems would have markedly reduced the number of FNABs performed. The comparison of the three guidelines in our study indicated that they are effective screening methods for identifying malignant thyroid nodules. Among them, K-TIRADS showed the most effective diagnostic performance in sensitivity, while ACR-TIRADS yielded the best specificity. Full article
(This article belongs to the Special Issue Radiomics in Oncology)
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Review

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20 pages, 3962 KiB  
Review
Artificial Intelligence and Radiomics in Cholangiocarcinoma: A Comprehensive Review
by Marta Zerunian, Tiziano Polidori, Federica Palmeri, Stefano Nardacci, Antonella Del Gaudio, Benedetta Masci, Giuseppe Tremamunno, Michela Polici, Domenico De Santis, Francesco Pucciarelli, Andrea Laghi and Damiano Caruso
Diagnostics 2025, 15(2), 148; https://doi.org/10.3390/diagnostics15020148 - 10 Jan 2025
Viewed by 1148
Abstract
Cholangiocarcinoma (CCA) is a malignant biliary system tumor and the second most common primary hepatic neoplasm, following hepatocellular carcinoma. CCA still has an extremely high unfavorable prognosis, regardless of type and location, and complete surgical resection remains the only curative therapeutic option; however, [...] Read more.
Cholangiocarcinoma (CCA) is a malignant biliary system tumor and the second most common primary hepatic neoplasm, following hepatocellular carcinoma. CCA still has an extremely high unfavorable prognosis, regardless of type and location, and complete surgical resection remains the only curative therapeutic option; however, due to the underhanded onset and rapid progression of CCA, most patients present with advanced stages at first diagnosis, with only 30 to 60% of CCA patients eligible for surgery. Recent innovations in medical imaging combined with the use of radiomics and artificial intelligence (AI) can lead to improvements in the early detection, characterization, and pre-treatment staging of these tumors, guiding clinicians to make personalized therapeutic strategies. The aim of this review is to provide an overview of how radiological features of CCA can be analyzed through radiomics and with the help of AI for many different purposes, such as differential diagnosis, the prediction of lymph node metastasis, the defining of prognostic groups, and the prediction of early recurrence. The combination of radiomics with AI has immense potential. Still, its effectiveness in practice is yet to be validated by prospective multicentric studies that would allow for the development of standardized radiomics models. Full article
(This article belongs to the Special Issue Radiomics in Oncology)
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