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Cancers
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Medical Imaging and Artificial Intelligence in Cancer
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Medical Imaging and Artificial Intelligence in Cancer

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5355

Special Issue Editors

Prof. Dr. Debiao Li

E-Mail Website
Guest Editor
Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
Interests: medical imaging; magnetic resonance imaging; cancer; artificial intelligence analysis; computed tomography; cardiovascular imaging
Prof. Dr. Stephen J. Pandol

E-Mail Website
Guest Editor
Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
Interests: pancreatic cellular physiology; mechanism of pancreatic diseases; acute pancreatitis; chronic pancreatitis; pancreatic cancer; pediatric pancreatitis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Medical imaging is a critical part of cancer management and research. Despite all the advances in cancer imaging, early detection, accurate diagnosis, and tumor characterization with medical imaging remain challenging due to limitations in image spatial resolution, sensitivity, image quality variability, and image interpretation. Artificial intelligence (AI) in image acquisition, reconstruction, analysis, and diagnosis have revolutionized cancer imaging in recent years. This allows for accelerated data acquisition, enhanced signal-to-noise ratio and contrast, precise cancer prediction, diagnosis, therapeutic guidance, and outcome prediction. Major challenges related to AI in medical imaging include data availability, quality and uniformity, interpretability, bias and generalization, and legal and ethical concerns. Addressing these challenges is crucial for improving cancer diagnosis and treatment.

For this Special Issue, we solicit manuscripts on medical imaging and artificial intelligence to address important technical and clinical questions related to cancer. Topics include but are not limited to the technical development and clinical application of medical imaging and AI for cancer risk stratification, early diagnosis, treatment response prediction and assessment, and image-guided intervention. We hope that this Special Issue will introduce state-of-the art imaging and AI to the cancer research community.

Debiao Li, PhD
Director, Biomedical Imaging Research Institute
Karl Storz Chair, Minimally Invasive Surgery In Honor of Dr. George Berci
Professor, Biomedical Sciences and Imaging
Cedars-Sinai Medical Center
Professor, Medicine and Bioengineering
University of California, Los Angeles
Office: 310 423 7743
https://www.cedars-sinai.edu/research/labs/li.html
https://www.cedars-sinai.edu/research/departments-institutes/biomedical-imaging.html

Prof. Dr. Debiao Li
Prof. Dr. Stephen J. Pandol
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

  • cancer
  • medical imaging
  • magnetic resonance imaging
  • computed tomography
  • artificial intelligence
  • image analysis
  • cancer detection
  • cancer prediction
  • cancer characterization

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Published Papers (8 papers)

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Research

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14 pages, 780 KiB  
Article
Perceptions of Artificial Intelligence Among Gastroenterologists in Italy: A National Survey
by Marcello Maida, Sandro Sferrazza, Giulio Calabrese, Giovanni Marasco, Alessandro Vitello, Manuele Furnari, Ivo Boskoski, Emanuele Sinagra and Antonio Facciorusso
Cancers 2025, 17(), 1353; https://doi.org/10.3390/cancers17081353 - 17 Apr 2025
Viewed by 119
Abstract
Background: An extensive body of evidence regarding artificial intelligence (AI) in gastroenterology is currently available, but the transition of these technologies to the bedside is still in progress. This national survey aims to assess the perceptions of AI among gastroenterologists in Italy. [...] Read more.
Background: An extensive body of evidence regarding artificial intelligence (AI) in gastroenterology is currently available, but the transition of these technologies to the bedside is still in progress. This national survey aims to assess the perceptions of AI among gastroenterologists in Italy. Methods: A total of 320 Italian gastroenterologists and trainees in gastroenterology were invited to answer a web-based survey. A sub-group analysis between the two categories was performed. Results: Data from 150 respondents were analyzed. Of these, 67.3% were gastroenterologists and 32.7% trainees. Notably, 99.3% reported familiarity with AI in gastroenterology, with 49.3% currently using AI tools, indicating high levels of awareness and current use. Participants expressed low concerns regarding reliability on a 0-to-10-point scale (median = 4), legal and ethical issues (median = 5), and data protection (median 5), whereas regulatory concerns were moderate (median = 6), representing a key concern. Subgroup analysis revealed that male gastroenterologists had a higher understanding of AI (median = 7) compared to females (median 6, p = 0.036). Additionally, older gastroenterologists showed greater ease of use in AI endoscopy tools (median 8 vs. 7, p = 0.040) and raised more regulatory concerns (median 7 vs. 6, p = 0.036). Conclusions: The data from this survey show that Italian gastroenterologists have high-level awareness and a favorable perception of AI systems, with a good diffusion of AI tools across the national territory and reasonable concerns about regulatory issues, raising attention on international AI regulations. Full article
(This article belongs to the Special Issue Medical Imaging and Artificial Intelligence in Cancer)
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15 pages, 1458 KiB  
Article
FDG-PET/CT and Multimodal Machine Learning Model Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer
by David Groheux, Loïc Ferrer, Jennifer Vargas, Antoine Martineau, Adrien Borgel, Luis Teixeira, Philippe Menu, Philippe Bertheau, Olivier Gallinato, Thierry Colin and Jacqueline Lehmann-Che
Cancers 2025, 17(7), 1249; https://doi.org/10.3390/cancers17071249 - 7 Apr 2025
Viewed by 285
Abstract
Purpose: Triple-negative breast cancer (TNBC) is a biologically and clinically heterogeneous disease, associated with poorer outcomes when compared with other subtypes of breast cancer. Neoadjuvant chemotherapy (NAC) is often given before surgery, and achieving a pathological complete response (pCR) has been associated with [...] Read more.
Purpose: Triple-negative breast cancer (TNBC) is a biologically and clinically heterogeneous disease, associated with poorer outcomes when compared with other subtypes of breast cancer. Neoadjuvant chemotherapy (NAC) is often given before surgery, and achieving a pathological complete response (pCR) has been associated with patient outcomes. There is thus strong clinical interest in the ability to accurately predict pCR status using baseline data. Materials and Methods: A cohort of 57 TNBC patients who underwent FDG-PET/CT before NAC was analyzed to develop a machine learning (ML) algorithm predictive of pCR. A total of 241 predictors were collected for each patient: 11 clinical features, 11 histopathological features, 13 genomic features, and 206 PET features, including 195 radiomic features. The optimization criterion was the area under the ROC curve (AUC). Event-free survival (EFS) was estimated using the Kaplan–Meier method. Results: The best ML algorithm reached an AUC of 0.82. The features with the highest weight in the algorithm were a mix of PET (including radiomics), histopathological, genomic, and clinical features, highlighting the importance of truly multimodal analysis. Patients with predicted pCR tended to have a longer EFS than patients with predicted non-pCR, even though this difference was not significant, probably due to the small sample size and few events observed (p = 0.09). Conclusions: This study suggests that ML applied to baseline multimodal data can help predict pCR status after NAC for TNBC patients and may identify correlations with long-term outcomes. Patients predicted as non-pCR may benefit from concomitant treatment with immunotherapy or dose intensification. Full article
(This article belongs to the Special Issue Medical Imaging and Artificial Intelligence in Cancer)
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16 pages, 2063 KiB  
Article
Multi-Modal Machine Learning for Evaluating the Predictive Value of Pelvimetric Measurements (Pelvimetry) for Anastomotic Leakage After Restorative Low Anterior Resection
by Ritch T. J. Geitenbeek, Simon C. Baltus, Mark Broekman, Sander N. Barendsen, Maike C. Frieben, Ilias Asaggau, Elina Thibeau-Sutre, Jelmer M. Wolterink, Matthijs C. Vermeulen, Can O. Tan, Ivo A. M. J. Broeders and Esther C. J. Consten
Cancers 2025, 17(6), 1051; https://doi.org/10.3390/cancers17061051 - 20 Mar 2025
Viewed by 331
Abstract
Background/Objectives: Anastomotic leakage (AL) remains a major complication after restorative rectal cancer surgery, with accurate preoperative risk stratification posing a significant challenge. Pelvic measurements derived from magnetic resonance imaging (MRI) have been proposed as potential predictors of AL, but their clinical utility [...] Read more.
Background/Objectives: Anastomotic leakage (AL) remains a major complication after restorative rectal cancer surgery, with accurate preoperative risk stratification posing a significant challenge. Pelvic measurements derived from magnetic resonance imaging (MRI) have been proposed as potential predictors of AL, but their clinical utility remains uncertain. Methods: This retrospective, multicenter cohort study analyzed rectal cancer patients undergoing restorative surgery between 2013 and 2021. Pelvic dimensions were assessed using MRI-based pelvimetry. Univariate and multivariate regression analyses identified independent risk factors for AL. Subsequently, machine Learning (ML) models—logistic regression, random forest classifier, and XGBoost—were developed to predict AL using preoperative clinical data alone and in combination with pelvimetry. Model performance was evaluated using F1 scores, with the area under the receiver operating characteristic (ROC-AUC) and precision–recall curves (AUC-PR) as primary metrics. Results: Among 487 patients, the overall AL rate was 14%. Multivariate regression analysis identified distance to the anorectal junction, pelvic inlet width, and interspinous distance as independent risk factors for AL (p < 0.05). The logistic regression model incorporating pelvimetry achieved the highest predictive performance, with a mean ROC-AUC of 0.70 ± 0.09 and AUC-PR of 0.32 ± 0.10. Although predictive models that included pelvic measurements demonstrated higher ROC-AUCs compared to those without pelvimetry, the improvement was not statistically significant. Conclusions: Pelvic dimensions, specifically pelvic inlet and interspinous distance, were independently associated with an increased risk of AL. While ML models incorporating pelvimetry showed only moderate predictive performance, these measurements should be considered in developing clinical prediction tools for AL to enhance preoperative risk stratification. Full article
(This article belongs to the Special Issue Medical Imaging and Artificial Intelligence in Cancer)
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13 pages, 2181 KiB  
Article
Retrospectively Quantified T2 Improves Detection of Clinically Significant Peripheral Zone Prostate Cancer
by Haoran Sun, Lixia Wang, Timothy Daskivich, Shihan Qiu, Hsu-Lei Lee, Chang Gao, Rola Saouaf, Eric Lo, Alessandro D’Agnolo, Hyung Kim, Debiao Li and Yibin Xie
Cancers 2025, 17(3), 381; https://doi.org/10.3390/cancers17030381 - 24 Jan 2025
Viewed by 834
Abstract
Background: Multiparametric MRI (mpMRI) as a non-invasive imaging tool is important in prostate cancer (PCa) detection and localization. Combined with radiomics analysis, features extracted from mpMRI have been utilized to predict PCa aggressiveness. T2 mapping provides quantitative information in PCa diagnoses but is [...] Read more.
Background: Multiparametric MRI (mpMRI) as a non-invasive imaging tool is important in prostate cancer (PCa) detection and localization. Combined with radiomics analysis, features extracted from mpMRI have been utilized to predict PCa aggressiveness. T2 mapping provides quantitative information in PCa diagnoses but is not routinely available in clinical practice. Previous work from our group developed a deep learning-based method to estimate T2 maps from clinically acquired T1- and T2-weighted images. This study aims to evaluate the added value of the estimated T2 map by combining it with conventional T2-weighted images for detecting clinically significant PCa (csPCa). Methods: An amount of 76 peripheral zone prostate lesions, including clinically significant and insignificant cases, were retrospectively analyzed. Radiomic features were extracted from conventional T2-weighted images and deep learning-estimated T2 maps, followed by feature selection and model development using five-fold cross-validation. Logistic regression and Gaussian Process classifiers were employed to develop the prediction models, with performance evaluated by area under the curve (AUC) and accuracy metrics. Results: The model incorporating features from both T2-weighted images and estimated T2 maps achieved an AUC of 0.803, significantly outperforming the model based solely on T2-weighted image features (AUC of 0.700, p = 0.048). Conclusions: Radiomics features extracted from deep learning-estimated T2 maps provide additional quantitative information that improves the prediction of peripheral zone csPCa aggressiveness, potentially enhancing risk stratification in non-invasive PCa diagnostics. Full article
(This article belongs to the Special Issue Medical Imaging and Artificial Intelligence in Cancer)
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14 pages, 5683 KiB  
Communication
The Thermal Ablation with MRgFUS: From Physics to Oncological Applications
by Mario Leporace, Ferdinando F. Calabria, Roberto Siciliano, Carlo Capalbo, Dimitrios K. Filippiadis and Roberto Iezzi
Cancers 2025, 17(1), 36; https://doi.org/10.3390/cancers17010036 - 26 Dec 2024
Cited by 1 | Viewed by 1021
Abstract
The growing interest in minimal and non-invasive therapies, especially in the field of cancer treatment, highlights a significant shift toward safer and more effective options. Ablative therapies are well-established tools in cancer treatment, with known effects including locoregional control, while their role as [...] Read more.
The growing interest in minimal and non-invasive therapies, especially in the field of cancer treatment, highlights a significant shift toward safer and more effective options. Ablative therapies are well-established tools in cancer treatment, with known effects including locoregional control, while their role as modulators of the systemic immune response against cancer is emerging. The HIFU developed with magnetic resonance imaging (MRI) guidance enables treatment precision, improves real-time procedural control, and ensures accurate outcome assessment. Magnetic Resonance-guided Focused Ultrasound (MRgFUS) induces deep coagulation necrosis within an elliptical focal area, effectively encompassing the entire tumor site and allowing for highly targeted radical ablation. The applications of MRgFUS in oncology are rapidly expanding, offering pain relief and curative treatment options for bone metastatic lesions. Additionally, the MRgFUS plays an effective role in targeted optional therapies for early prostate and breast cancers. Emerging research also focuses on the potential uses in treating abdominal cancers and harnessing capabilities to stimulate immune responses against tumors or to facilitate the delivery of anticancer drugs. This evolving landscape presents exciting opportunities for improving patient outcomes and advancing cancer treatment methodologies. In neuro-oncology, MRgFUS utilizes low-intensity focused ultrasound (LIFU) along with intravenous microbubbles to open the blood-brain barrier (BBB) and enhance the intra-tumoral delivery of chemotherapy drugs. Full article
(This article belongs to the Special Issue Medical Imaging and Artificial Intelligence in Cancer)
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Review

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11 pages, 240 KiB  
Review
Artificial Intelligence in Gynecological Oncology from Diagnosis to Surgery
by Stefano Restaino, Maria Rita De Giorgio, Giulia Pellecchia, Martina Arcieri, Francesca Maria Vasta, Camilla Fedele, Paolo Bonome, Giuseppe Vizzielli, Sandro Pignata and Gaia Giannone
Cancers 2025, 17(7), 1060; https://doi.org/10.3390/cancers17071060 - 21 Mar 2025
Viewed by 377
Abstract
Background: The field of medicine, both clinical and surgical, has recently been overwhelmed by artificial intelligence technology, which promises countless application scenarios and, above all, implementation in clinical practice and research. Novelties are riding the wave fast, but where do we stand? A [...] Read more.
Background: The field of medicine, both clinical and surgical, has recently been overwhelmed by artificial intelligence technology, which promises countless application scenarios and, above all, implementation in clinical practice and research. Novelties are riding the wave fast, but where do we stand? A small overview in gynecological oncology of future challenges, evidence already investigated, and possible scenarios to be derived was conducted. Methods: Both diagnostic and surgical work in the field of gynecological oncology was conducted, selecting the most interesting articles on the subject. Results: From the narrative review of the literature, it emerged how much further ahead the diagnostic field is at present compared to the surgical one, which appeared to be more limited to ovarian surgery. Most current evidence focuses on the role of different biomarkers in predicting diagnostic, prognostic, and treatment-integrated patterns. Conclusions: Everything we know to date is related to a dynamic photograph that is constantly and rapidly changing as much as AI is becoming inextricably linked to our medical field. Full article
(This article belongs to the Special Issue Medical Imaging and Artificial Intelligence in Cancer)
36 pages, 4187 KiB  
Review
Advances for Managing Pancreatic Cystic Lesions: Integrating Imaging and AI Innovations
by Deniz Seyithanoglu, Gorkem Durak, Elif Keles, Alpay Medetalibeyoglu, Ziliang Hong, Zheyuan Zhang, Yavuz B. Taktak, Timurhan Cebeci, Pallavi Tiwari, Yuri S. Velichko, Cemal Yazici, Temel Tirkes, Frank H. Miller, Rajesh N. Keswani, Concetto Spampinato, Michael B. Wallace and Ulas Bagci
Cancers 2024, 16(24), 4268; https://doi.org/10.3390/cancers16244268 - 22 Dec 2024
Cited by 1 | Viewed by 1623
Abstract
Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to [...] Read more.
Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to PCL management rely heavily on radiographic imaging, and endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA), coupled with clinical and biochemical data. However, the observer-dependent nature of image interpretation and the complex morphology of PCLs can lead to diagnostic uncertainty and variability in patient management strategies. This review critically evaluates current PCL diagnosis and surveillance practices, showing features of the different lesions and highlighting the potential limitations of conventional methods. We then explore the potential of artificial intelligence (AI) to transform PCL management. AI-driven strategies, including deep learning algorithms for automated pancreas and lesion segmentation, and radiomics for analyzing heterogeneity, can improve diagnostic accuracy and risk stratification. These advanced techniques can provide more objective and reproducible assessments, aiding clinicians in decision-making regarding follow-up intervals and surgical interventions. Early results suggest that AI-driven methods can significantly improve patient outcomes by enabling earlier detection of high-risk lesions and reducing unnecessary procedures for benign cysts. Finally, this review emphasizes that AI-driven approaches could potentially reshape the landscape of PCL management, ultimately leading to improved pancreatic cancer prevention. Full article
(This article belongs to the Special Issue Medical Imaging and Artificial Intelligence in Cancer)
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Other

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12 pages, 1042 KiB  
Perspective
Can the Discovery of High-Impact Diagnostics Be Improved by Matching the Sampling Rate of Clinical Diagnostics to the Frequency Domain of Diagnostic Information?
by Steven W. Millward, Peng Wei, David Piwnica-Worms and Seth T. Gammon
Cancers 2025, 17(9), 1387; https://doi.org/10.3390/cancers17091387 - 22 Apr 2025
Abstract
Over the past 30 years, academic and industrial research investigators have developed molecular reporters to visualize cell death in complex biological systems. In parallel, clinical researchers, chemists, biochemists, and molecular biologists have endeavored to translate these molecular tools into clinical imaging agents. Despite [...] Read more.
Over the past 30 years, academic and industrial research investigators have developed molecular reporters to visualize cell death in complex biological systems. In parallel, clinical researchers, chemists, biochemists, and molecular biologists have endeavored to translate these molecular tools into clinical imaging agents. Despite these efforts, there are no clinically approved imaging methodologies with which to image cell death consistently and quantitatively. One reason may reside in the intrinsic mismatch between the sampling frequency of translational molecular imaging and the biochemical kinetics that define cell death. Beyond cell death imaging, many active research programs are now attempting to create translational diagnostic pharmaceuticals to image immunological, fibrotic, amyloidotic, and metabolic pathways. Each of these pathways is defined by a unique set of biochemical rate constants, some of which are associated with key predictive pathways. Exhaustively sampling all permutations of pathways and kinetic constants would seem to be an intractable strategy for target identification and validation. Sampling theory, if applied to these pathways, could accelerate the translation of high-impact diagnostics through prioritization of pathways for either AI enhanced diagnostic imaging or AI-enhanced wearable devices. In this perspective, we identify the Nyquist sampling rate as a key criterion for evaluating the optimal application for novel diagnostics. Sampling theory states that to fully characterize a band-limited, stationary, temporal data set, the signal must be sampled at more than twice the rate of the fastest frequency in the signal or, for diagnostics, the discriminatory signal. Through the study of the medical imaging process chain, Nyquist sampling rates of 0.25 day−1 and, more likely, slower than 0.02 day−1 were determined to provide high quality information. By prioritizing low-frequency predictive processes, or “state changes,”, imaging researchers may improve the “hit rate” of research programs by appropriately matching the rate of change in diagnostic and predictive information with the limiting sampling rate of medical imaging. Critically, however, high-frequency diagnostic information (and therefore high-frequency biological processes) need not be ignored; these processes are simply better interrogated through continuous monitoring, e.g., by wearable devices combined with machine learning or artificial intelligence. Full article
(This article belongs to the Special Issue Medical Imaging and Artificial Intelligence in Cancer)
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