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Cancers
Special Issues
Enhancing Precision in Cancer Treatment: AI-Driven Innovations in Imaging
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Enhancing Precision in Cancer Treatment: AI-Driven Innovations in Imaging

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2202

Special Issue Editor

Dr. Andra Valentina Krauze

E-Mail Website
Guest Editor
Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Building 10, Bethesda, MD 20892, USA
Interests: rare tumors; data science and computational biology; brain and spine cancer; head and neck cancer; genitourinary tumors; re-irradiation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are delighted to announce the call for submissions to a Special Issue of Cancers focusing on the theme of "Enhancing Precision in Cancer Treatment: AI-Driven Innovations in Imaging". In the realm of oncology, the integration of artificial intelligence (AI) into imaging technologies has revolutionized cancer diagnosis, treatment planning, and monitoring.

This Special Issue aims at exploring the intersection of AI and medical imaging in advancing precision in cancer care. We invite original research articles and reviews that delve into the following topics:

  1. Application of AI in early cancer detection and diagnosis;
  2. AI-driven innovations in radiomics and radiogenomics for personalized treatment;
  3. Role of AI in predicting treatment response and patient outcomes;
  4. Enhancing image-guided interventions through AI algorithms;
  5. Ethical considerations and challenges in implementing AI in cancer imaging.

All submissions will undergo a thorough peer-review process to ensure scientific rigor and relevance to the field. We encourage submissions from experts in medical imaging, oncology, AI, and related disciplines to share their insights and advancements in this rapidly evolving field.

By amalgamating cutting-edge research and clinical experiences, this Special Issue aims at propelling the frontiers of precision medicine in oncology and paving the way for more tailored and effective cancer treatments. Your valuable contributions are instrumental in shaping the future of cancer care through AI-powered innovations in medical imaging.

Should you have any queries, require additional information, or seek assistance during the submission process, please feel free to contact us. We are committed to facilitating your participation and ensuring the success of this Special Issue.

Thank you for your interest and dedication to advancing precision in cancer treatment through AI-driven innovations in imaging.

Warm regards,

Dr. Andra Valentina Krauze
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 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

  • precision medicine
  • artificial intelligence
  • medical imaging
  • cancer treatment
  • AI-driven innovations

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Published Papers (3 papers)

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Research

16 pages, 1493 KiB  
Article
Baseline Radiomics as a Prognostic Tool for Clinical Benefit from Immune Checkpoint Inhibition in Inoperable NSCLC Without Activating Mutations
by Fedor Moiseenko, Marko Radulovic, Nadezhda Tsvetkova, Vera Chernobrivceva, Albina Gabina, Any Oganesian, Maria Makarkina, Ekaterina Elsakova, Maria Krasavina, Daria Barsova, Elizaveta Artemeva, Valeria Khenshtein, Natalia Levchenko, Viacheslav Chubenko, Vitaliy Egorenkov, Nikita Volkov, Alexei Bogdanov and Vladimir Moiseyenko
Cancers 2025, 17(11), 1790; https://doi.org/10.3390/cancers17111790 - 27 May 2025
Abstract
Background/Objectives: Checkpoint inhibitors (ICIs) are key therapies for NSCLC, but current selection criteria, such as excluding mutation carriers and assessing PD-L1, lack sensitivity. As a result, many patients receive costly treatments with limited benefit. Therefore, this study aimed to predict which NSCLC patients [...] Read more.
Background/Objectives: Checkpoint inhibitors (ICIs) are key therapies for NSCLC, but current selection criteria, such as excluding mutation carriers and assessing PD-L1, lack sensitivity. As a result, many patients receive costly treatments with limited benefit. Therefore, this study aimed to predict which NSCLC patients would achieve durable survival (≥24 months) with immunotherapy. Methods: A comprehensive ensemble radiomics approach was applied to pretreatment CT scans to prognosticate overall survival (OS) and predict progression-free survival (PFS) in a cohort of 220 consecutive patients with inoperable NSCLC treated with first-line ICIs (pembrolizumab or atezolizumab, nivolumab or prolgolimab) as monotherapy or in combination. The radiomics pipeline evaluated four normalization methods (none, min-max, Z-score, mean), four feature selection techniques (ANOVA, RFE, Kruskal–Wallis, Relief), and ten classifiers (e.g., SVM, random forest). Using two to eight radiomics features, 1680 models were built in the Feature Explorer (FAE) Python package. Results: Three feature sets were evaluated: clinicopathological (CP) only, radiomics only, and a combined set, using 6- and 12-month PFS and 24-month OS endpoints. The top 15 models were ensembled by averaging their probability scores. The best performance was achieved at 24-month OS with the combined CP and radiomics ensemble (AUC = 0.863, accuracy = 85%), followed by radiomics-only (AUC = 0.796, accuracy = 82%) and CP-only (AUC = 0.671, accuracy = 76%). Predictive performance was lower for 6-month (AUC = 0.719) and 12-month PFS (AUC = 0.739) endpoints. Conclusions: Our radiomics pipeline improved selection of NSCLC patients for immunotherapy and could spare non-responders unnecessary toxicity while enhancing cost-effectiveness. Full article
(This article belongs to the Special Issue Enhancing Precision in Cancer Treatment: AI-Driven Innovations in Imaging)
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14 pages, 3031 KiB  
Article
Natural Language Processing of Radiology Reports to Assess Survival in Patients with Advanced Melanoma
by Jeeban P. Das, Jordan Eichholz, Varadan Sevilimedu, Natalie Gangai, Danny N. Khalil, Michael A. Postow and Richard K. G. Do
Cancers 2025, 17(9), 1595; https://doi.org/10.3390/cancers17091595 - 7 May 2025
Viewed by 235
Abstract
Background/Objectives: To use natural language processing (NLP) to extract large-scale data from the CT radiology reports of patients with advanced melanoma treated with immunotherapy and to determine whether liver metastases affect survival. Methods: Patient criteria (M1 disease subclassified into M1a, M1b, [...] Read more.
Background/Objectives: To use natural language processing (NLP) to extract large-scale data from the CT radiology reports of patients with advanced melanoma treated with immunotherapy and to determine whether liver metastases affect survival. Methods: Patient criteria (M1 disease subclassified into M1a, M1b, or M1c) as well as alternative criteria (M1 with advanced melanoma, imaged with CT chest, abdomen, and pelvis from July 2014–March 2019) were included retrospectively. NLP was used to identify metastases from CT reports, and then patients were classified according to American Joint Committee on Cancer (AJCC) staging disease subclassified into M1L+ or M1L−, indicating whether liver metastases were present or not). Statistical analysis included constructing Kaplan–Meier survival curves and calculating hazard ratios (HRs). Results: 2239 patients were included (mean age, 63 years). Whether using AJCC or alternative criteria, overall survival (OS) was poorest for M1L+ (entire cohort median OS, 0.69 years [95% CI: 0.60–0.82]; immunotherapy cohort median OS, 1.4 years [95% CI: 0.92–2.0]) compared to M1L− (entire cohort median OS, 1.8 years [95% CI: 1.4–2.2]; immunotherapy cohort median OS; M1L−, 2.9 years [95% CI: 2.3–3.9]). The median HR for M1L+ (median HR, 5.35 [95% CI: 4.59–6.24]) was higher than that for M0 (p < 0.001). The median HR for M1L+ (median HR, 2.13 [95% CI: 1.65–2.64]) was higher than that for M0 (p < 0.01). Conclusions: Patients with advanced melanoma, particularly those with liver metastases, demonstrated inferior survival, even when treated with immunotherapy. Full article
(This article belongs to the Special Issue Enhancing Precision in Cancer Treatment: AI-Driven Innovations in Imaging)
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12 pages, 1520 KiB  
Article
Incidence and Prevalence of Bone Metastases in Different Solid Tumors Determined by Natural Language Processing of CT Reports
by Niamh Long, David Woodlock, Robert D’Agostino, Gary Nguyen, Natalie Gangai, Varadan Sevilimedu and Richard Kinh Gian Do
Cancers 2025, 17(2), 218; https://doi.org/10.3390/cancers17020218 - 11 Jan 2025
Viewed by 1609
Abstract
Background/Objectives: Improved survival due to advances in medical therapy has resulted in increasing numbers of cancer patients living with bone metastases; however, our understanding of the prognostic implications of bone metastases requires larger population-based studies outlining their incidence and prevalence in different primary [...] Read more.
Background/Objectives: Improved survival due to advances in medical therapy has resulted in increasing numbers of cancer patients living with bone metastases; however, our understanding of the prognostic implications of bone metastases requires larger population-based studies outlining their incidence and prevalence in different primary cancer types, including those with lower incidence. This study aimed to evaluate the incidence and prevalence of bone metastases in solid organ tumors by analyzing reports of staging CT studies with natural language processing (NLP). Methods: In this retrospective study, 639,470 reports representing 129,326 unique patients were analyzed; 6279 randomly selected reports were manually annotated and labeled for the presence or absence of bone metastases. From these data, a BERT-based NLP model was developed and applied to the patient database. The cumulative incidence at 5 years and prevalence of bone metastases in each cancer type were calculated. Results: The accuracy of the NLP model on a validation set was 97.1%, with a positive predictive value (precision) of 88.0% and a sensitivity (recall) of 86.3%. The 5-year incidence rate of bone metastases was highest in prostate, breast, head and neck, and lung cancer (52%, 41%, 36%, 33%). Incidence was lowest in central nervous system cancer and testicular cancer (8%, 5%). Prevalence was highest in prostate, breast, and lung cancer (32%, 25% and 23%), and lowest in central nervous system cancer and testicular cancer (4%, 4%). Conclusions: NLP was utilized to demonstrate patterns of bone metastases in a broad range of cancer types and is a valuable tool in population-based assessment of bone metastases. Full article
(This article belongs to the Special Issue Enhancing Precision in Cancer Treatment: AI-Driven Innovations in Imaging)
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