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Cancers
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The Applications of Radiomics in Precision Diagnosis and Treatment of...
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The Applications of Radiomics in Precision Diagnosis and Treatment of Solid and Hematological Tumors

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 (15 December 2023) | Viewed by 6635

Special Issue Editors

Prof. Dr. Luigi Mansi

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Guest Editor
Section Health and Development, Interuniversity Research Center for Sustainability (CIRPS), 00038 Rome, Italy
Interests: diagnostic imaging; molecular imaging; nuclear medicine; theragnostics
Special Issues, Collections and Topics in MDPI journals
Dr. Laura Evangelista

E-Mail Website
Guest Editor
Department of Nuclear Medicine, Humanitas University, Pieve Emanuele, 20072 Milan, Italy
Interests: PET/CT; molecular imaging; radiopharmaceuticals; prostate cancer; PET/MRI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Radiomics and artificial intelligence are largely expanded in health care systems, mainly in the oncological field and in the imaging setting. Their role as biomarkers for adding new information to the diagnosis of cancer and to monitor the response to therapy in solid and hematological disease is continuously increasing.

  • To evaluate the role of radiomics and artificial intelligence in the diagnosis of solid and hematological cancers.
  • To evaluate the role of radiomics and artificial intelligence in monitoring the response to therapy in solid and hematological cancers.

Prof. Dr. Luigi Mansi
Dr. Laura Evangelista
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 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

  • radiomics
  • artificial intelligence
  • cancers
  • nuclear imaging
  • radiological imaging

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Published Papers (3 papers)

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Research

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15 pages, 1862 KiB  
Article
Evaluating the Potential of Delta Radiomics for Assessing Tyrosine Kinase Inhibitor Treatment Response in Non-Small Cell Lung Cancer Patients
by Ting-Wei Wang, Heng-Sheng Chao, Hwa-Yen Chiu, Yi-Hui Lin, Hung-Chun Chen, Chia-Feng Lu, Chien-Yi Liao, Yen Lee, Tsu-Hui Shiao, Yuh-Min Chen, Jing-Wen Huang and Yu-Te Wu
Cancers 2023, 15(21), 5125; https://doi.org/10.3390/cancers15215125 - 24 Oct 2023
Cited by 5 | Viewed by 1767
Abstract
Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating [...] Read more.
Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal. Full article
(This article belongs to the Special Issue The Applications of Radiomics in Precision Diagnosis and Treatment of Solid and Hematological Tumors)
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15 pages, 13167 KiB  
Article
Unsupervised Machine Learning of MRI Radiomics Features Identifies Two Distinct Subgroups with Different Liver Function Reserve and Risks of Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma
by Qiang Wang, Changfeng Li, Geng Chen, Kai Feng, Zhiyu Chen, Feng Xia, Ping Cai, Leida Zhang, Ernesto Sparrelid, Torkel B. Brismar and Kuansheng Ma
Cancers 2023, 15(12), 3197; https://doi.org/10.3390/cancers15123197 - 15 Jun 2023
Cited by 3 | Viewed by 1879
Abstract
Objective: To identify subgroups of patients with hepatocellular carcinoma (HCC) with different liver function reserves using an unsupervised machine-learning approach on the radiomics features from preoperative gadoxetic-acid-enhanced MRIs and to evaluate their association with the risk of post-hepatectomy liver failure (PHLF). Methods: Clinical [...] Read more.
Objective: To identify subgroups of patients with hepatocellular carcinoma (HCC) with different liver function reserves using an unsupervised machine-learning approach on the radiomics features from preoperative gadoxetic-acid-enhanced MRIs and to evaluate their association with the risk of post-hepatectomy liver failure (PHLF). Methods: Clinical data from 276 consecutive HCC patients who underwent liver resections between January 2017 and March 2019 were retrospectively collected. Radiomics features were extracted from the non-tumorous liver tissue at the gadoxetic-acid-enhanced hepatobiliary phase MRI. The reproducible and non-redundant features were selected for consensus clustering analysis to detect distinct subgroups. After that, clinical variables were compared between the identified subgroups to evaluate the clustering efficacy. The liver function reserve of the subgroups was compared and the correlations between the subgroups and PHLF, postoperative complications, and length of hospital stay were evaluated. Results: A total of 107 radiomics features were extracted and 37 were selected for unsupervised clustering analysis, which identified two distinct subgroups (138 patients in each subgroup). Compared with subgroup 1, subgroup 2 had significantly more patients with older age, albumin–bilirubin grades 2 and 3, a higher indocyanine green retention rate, and a lower indocyanine green plasma disappearance rate (all p < 0.05). Subgroup 2 was also associated with a higher risk of PHLF, postoperative complications, and longer hospital stays (>18 days) than that of subgroup 1, with an odds ratio of 2.83 (95% CI: 1.58–5.23), 2.41(95% CI: 1.15–5.35), and 2.14 (95% CI: 1.32–3.47), respectively. The odds ratio of our method was similar to the albumin–bilirubin grade for postoperative complications and length of hospital stay (2.41 vs. 2.29 and 2.14 vs. 2.16, respectively), but was inferior for PHLF (2.83 vs. 4.55). Conclusions: Based on the radiomics features of gadoxetic-acid-enhanced MRI, unsupervised clustering analysis identified two distinct subgroups with different liver function reserves and risks of PHLF in HCC patients. Future studies are required to validate our findings. Full article
(This article belongs to the Special Issue The Applications of Radiomics in Precision Diagnosis and Treatment of Solid and Hematological Tumors)
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Review

Jump to: Research

15 pages, 457 KiB  
Review
PET Radiomics and Response to Immunotherapy in Lung Cancer: A Systematic Review of the Literature
by Laura Evangelista, Francesco Fiz, Riccardo Laudicella, Francesco Bianconi, Angelo Castello, Priscilla Guglielmo, Virginia Liberini, Luigi Manco, Viviana Frantellizzi, Alessia Giordano, Luca Urso, Stefano Panareo, Barbara Palumbo and Luca Filippi
Cancers 2023, 15(12), 3258; https://doi.org/10.3390/cancers15123258 - 20 Jun 2023
Cited by 7 | Viewed by 2392
Abstract
The aim of this review is to provide a comprehensive overview of the existing literature concerning the applications of positron emission tomography (PET) radiomics in lung cancer patient candidates or those undergoing immunotherapy. Materials and Methods: A systematic review was conducted on databases [...] Read more.
The aim of this review is to provide a comprehensive overview of the existing literature concerning the applications of positron emission tomography (PET) radiomics in lung cancer patient candidates or those undergoing immunotherapy. Materials and Methods: A systematic review was conducted on databases and web sources. English-language original articles were considered. The title and abstract were independently reviewed to evaluate study inclusion. Duplicate, out-of-topic, and review papers, or editorials, articles, and letters to editors were excluded. For each study, the radiomics analysis was assessed based on the radiomics quality score (RQS 2.0). The review was registered on the PROSPERO database with the number CRD42023402302. Results: Fifteen papers were included, thirteen were qualified as using conventional radiomics approaches, and two used deep learning radiomics. The content of each study was different; indeed, seven papers investigated the potential ability of radiomics to predict PD-L1 expression and tumor microenvironment before starting immunotherapy. Moreover, two evaluated the prediction of response, and four investigated the utility of radiomics to predict the response to immunotherapy. Finally, two papers investigated the prediction of adverse events due to immunotherapy. Conclusions: Radiomics is promising for the evaluation of TME and for the prediction of response to immunotherapy, but some limitations should be overcome. Full article
(This article belongs to the Special Issue The Applications of Radiomics in Precision Diagnosis and Treatment of Solid and Hematological Tumors)
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