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The Future Direction of Medical Imaging in Hepato-Bilio-Pancreatic Oncology: Challenges and Opportunities

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Nuclear Medicine & Radiology".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1365

Special Issue Editor


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Guest Editor
1. Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna, Italy
2. Radiology Unit, Ospedale per gli Infermi di Faenza, AUSL Romagna, Faenza, Italy
Interests: MRI; CT; imaging; oncology; liver cancer; bilio-pancreatic imaging; liver imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the rapidly advancing field of precision oncology, medical imaging plays a pivotal role in the early detection, characterization, and personalized management of hepato-bilio-pancreatic malignancies. These tumors—arising from the liver, biliary tract, and pancreas—present unique diagnostic and therapeutic challenges that require high-resolution and functionally informative imaging approaches.

Advanced CT and MRI techniques, complemented by contrast-enhanced ultrasound (CEUS) and PET/CT when appropriate, enable detailed visualization of tumor morphology and biology, supporting early diagnosis, precise staging, and real-time evaluation of therapeutic response.

The integration of imaging data with genomics, molecular profiling, and computational approaches is transforming hepato-bilio-pancreatic oncology into a more predictive and cost-effective discipline, fostering individualized treatment planning and optimized resource allocation. Through quantitative biomarkers, radiomics, and artificial intelligence (AI), imaging is evolving from a primarily diagnostic modality into a powerful decision-support tool that informs prognosis, therapy selection, and longitudinal disease monitoring.

This Special Issue, “The Future Direction of Medical Imaging in Hepato-Bilio-Pancreatic Oncology: Challenges and Opportunities”,  aims to showcase current advances and future directions in imaging-driven management of hepato-bilio-pancreatic cancers. We invite original research and review articles focusing on innovative imaging techniques, computational and AI-based tools, and integrative, multidisciplinary strategies that enhance early detection, individualized treatment, and outcome prediction in cancer care.

By bringing together expertise from radiology, nuclear medicine, oncology, surgery, bioinformatics, and data science, this Special Issue seeks to foster collaboration and highlight how imaging can continue to drive innovation in precision hepato-bilio-pancreatic oncology, ultimately shaping the future of personalized and sustainable cancer healthcare.

Dr. Nicolò Brandi
Guest Editor

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Keywords

  • MRI
  • CT
  • molecular imaging
  • radiomics
  • imaging
  • radiogenomics
  • precision medicine
  • outcome prediction
  • cholangiocarcinoma
  • pancreatic cancer
  • imaging biomarkers
  • treatment response

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Published Papers (1 paper)

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Review

30 pages, 1058 KB  
Review
Artificial Intelligence in Hepatocellular Carcinoma: Current Applications, Clinical Performance, and Barriers to Implementation
by Sri Harsha Boppana, Aditya Chandrashekar, Gautam Maddineni, Raja Chandra Chakinala, Ritwik Raj, Rohin B. Shivaprakash, Pradeep Yarra, Venkata C. K. Sunkesula and C. David Mintz
J. Clin. Med. 2026, 15(7), 2484; https://doi.org/10.3390/jcm15072484 - 24 Mar 2026
Viewed by 1126
Abstract
Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality worldwide, and its management is limited by heterogeneous risk profiles, suboptimal surveillance performance, diagnostic uncertainty in chronically diseased livers, and difficulty individualizing prognosis after treatment. The aim of this narrative review was to [...] Read more.
Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality worldwide, and its management is limited by heterogeneous risk profiles, suboptimal surveillance performance, diagnostic uncertainty in chronically diseased livers, and difficulty individualizing prognosis after treatment. The aim of this narrative review was to critically evaluate artificial intelligence (AI) applications across the HCC care continuum, with emphasis on their intended clinical role, reported performance, evidence maturity, and barriers to implementation. A major strength of this review is that it moves beyond a descriptive catalog of models by structuring the literature around clinically relevant decision points and by explicitly distinguishing emerging proof-of-concept tools from applications with stronger translational potential. Across risk stratification, surveillance, imaging-based diagnosis, pathology, treatment-response prediction, and prognostication, we found that AI consistently demonstrates promise, particularly for identifying patients at higher future HCC risk, improving lesion detection and characterization on ultrasound, CT, MRI, and contrast-enhanced ultrasound, assisting histopathologic classification, and predicting outcomes such as microvascular invasion, recurrence, survival, and response to locoregional therapies. However, we also found that the evidence base remains highly uneven: many diagnostic studies are retrospective and lesion-enriched rather than embedded in true surveillance populations, many prognostic models lack robust external validation and calibration assessment, and reference standards, imaging protocols, and dataset composition vary substantially across studies. These findings are clinically relevant because they highlight both where AI may offer near-term value and why most published systems are not yet ready for routine use. Overall, AI in HCC should be viewed as a rapidly evolving but still transitional field. Its future impact will depend not only on higher-performing algorithms but on clearly defined clinical use cases, multicenter and prospective validation, transparent reporting, workflow-aware evaluation, and implementation strategies that support safe, equitable, and scalable adoption. Full article
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