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The Emerging Role of Multiplexed Imaging for Cancer Diagnosis and Therapy (2nd Edition)

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 5871

Special Issue Editors

Dr. Li Liu

E-Mail Website
Guest Editor
Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX, USA
Interests: preclinical cellular and molecular multimodality imaging; applications in small animal models of breast, kidney, lung, prostate and bladder cancers
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yicheng Ni

E-Mail Website
Guest Editor
Department of Radiology, Affiliated Zhongda Hospital, Southeast University, Nanjing, China
Interests: imaging-navigated theragnostics; translational medicine; research in cancer and cardiovascular diseases; targeted contrast agents and nuclear tracers; animal models of human diseases; tumor ablation and interventional procedures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight the latest advancements and applications of multiplexed imaging techniques in cancer diagnosis and therapy. Multiplexed imaging allows for the simultaneous visualization and quantification of multiple biomarkers, enhancing the accuracy and comprehensiveness of cancer diagnostics and treatment monitoring. The scope of this Special Issue includes developments in bioluminescence, fluorescence, and photoacoustic imaging technologies, as well as advanced applications of CT, MRI, PET, and SPECT multiplexed imaging for different types of cancer treatments or theragnostics. Topics of interest also include novel contrast agents, hybrid imaging techniques, and the integration of these modalities with therapeutic interventions. Contributions exploring the clinical translation of these technologies, their impact on personalized and/or pan-anticancer therapy, and their roles in early detection, staging, and monitoring treatment response are particularly welcome.

Dr. Li Liu
Prof. Dr. Yicheng Ni
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 250 words) can be sent to the Editorial Office for assessment.

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

  • multiplexed imaging
  • cancer diagnosis
  • molecular imaging
  • novel contrast agents
  • targeted radionuclide tracers theragnostics
  • personalized cancer therapy
  • therapeutic monitoring
  • imaging biomarkers
  • clinical translation

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Published Papers (4 papers)

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Research

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16 pages, 3326 KB  
Article
CT Body Composition Changes Predict Survival in Immunotherapy-Treated Cancer Patients: A Retrospective Cohort Study
by Shlomit Tamir, Hilla Vardi Behar, Ronen Tal, Ruthy Tal Jasper, Mor Armoni, Hadar Pratt Aloni, Rotem Iris Orad, Hillary Voet, Eli Atar, Ahuva Grubstein, Salomon M. Stemmer and Gal Markel
Cancers 2026, 18(2), 341; https://doi.org/10.3390/cancers18020341 - 21 Jan 2026
Viewed by 840
Abstract
Background: Computed tomography (CT)-derived body composition parameters, including skeletal muscle and fat indices, are prognosticators in oncology. Most studies focus on baseline body-composition parameters; however, changes during treatment may provide better prognostic value. Standardized methods for measuring/reporting these parameters remain limited. Methods: This [...] Read more.
Background: Computed tomography (CT)-derived body composition parameters, including skeletal muscle and fat indices, are prognosticators in oncology. Most studies focus on baseline body-composition parameters; however, changes during treatment may provide better prognostic value. Standardized methods for measuring/reporting these parameters remain limited. Methods: This retrospective study included patients who were treated with immunotherapy for non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), or melanoma between 2017 and 2024 and had technically adequate baseline and follow-up CT scans. Body composition was analyzed using a novel, fully automated software (CompoCT) for L3 slice selection and segmentation. Body composition indices (e.g., skeletal muscle index [SMI]) were calculated by dividing the cross-sectional area by the patient’s height squared. Results: The cohort included 376 patients (mean [SD] age 66.4 [11.4] years, 67.3% male, 72.6% NSCLC, 14.6% RCC, and 12.8% melanoma). During a median follow-up of 21 months, 220 (58.5%) died. Baseline body composition parameters were not associated with mortality, except for a weak protective effect of higher SMI (HR = 0.98, p = 0.043). In contrast, longitudinal decreases were strongly associated with increased mortality. Relative decreases in SMI (HR, 1.17; 95% CI, 1.07–1.27) or subcutaneous fat index (SFI) (HR, 1.11; 95% CI, 1.07–1.15) significantly increased mortality risk. Multivariate models showed similar concordance (0.65) and identified older age, NSCLC tumor type, and relative decreases in SMI and SFI (per 5% units) as independent predictors of mortality. Conclusions: Longitudinal decreases in skeletal muscle and subcutaneous fat were independent predictors of mortality in immunotherapy-treated patients. Automated CT-based body composition analysis may support treatment decisions during immunotherapy. Full article
(This article belongs to the Special Issue The Emerging Role of Multiplexed Imaging for Cancer Diagnosis and Therapy (2nd Edition))
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Review

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13 pages, 441 KB  
Review
CT-Assessed Body Composition as Predictor of Post-Operative Complications in Lung Cancer Patients
by Stefania Rizzo and Francesco Petrella
Cancers 2026, 18(3), 431; https://doi.org/10.3390/cancers18030431 - 29 Jan 2026
Viewed by 913
Abstract
Body composition, specifically the quantification of skeletal muscle and adipose tissue using preoperative computed tomography (CT) imaging, is a clinically significant predictor of postoperative complications after lung cancer surgery. The main features of CT-derived body composition analysis are: skeletal muscle index, muscle density, [...] Read more.
Body composition, specifically the quantification of skeletal muscle and adipose tissue using preoperative computed tomography (CT) imaging, is a clinically significant predictor of postoperative complications after lung cancer surgery. The main features of CT-derived body composition analysis are: skeletal muscle index, muscle density, adipose tissue quantification and automated or semi-automated segmentation. Low skeletal muscle mass (sarcopenia) independently increases the risk of perioperative complications, including respiratory complications, and is associated with longer hospital length of stay and worse long-term survival. Sarcopenic obesity—characterized by low muscle mass in the context of high adiposity—further elevates complication risk and prolongs recovery. CT-derived measures such as muscle cross-sectional area, muscle density, and adipose tissue distribution (visceral, subcutaneous, and intramuscular) provide more precise risk stratification than BMI alone. Skeletal muscle area and density are inversely correlated with postoperative complications and recurrence risk; patients with lower muscle mass and density experience more adverse outcomes. In men, age and reduced skeletal muscle area are particularly strong predictors of complications after pneumonectomy. Obesity, when not accompanied by sarcopenia or myosteatosis, may confer a survival advantage—the so-called “obesity paradox”—but this protective effect is lost in patients with low muscle mass or poor muscle quality. Systemic inflammation and nutritional status further modulate the impact of body composition on surgical risk. This review highlights the critical role of CT-derived body composition analysis in predicting postoperative outcomes following lung cancer surgery. Full article
(This article belongs to the Special Issue The Emerging Role of Multiplexed Imaging for Cancer Diagnosis and Therapy (2nd Edition))
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30 pages, 374 KB  
Review
Redefining Prostate Cancer Precision: Radiogenomics, Theragnostics, and AI-Driven Biomarkers
by Cristina Quicios Dorado, Ana Sánchez Ramírez, Marta Pérez Pérez, Manuel Saavedra Centeno, Lira Pelari Mici, Carlos Márquez Güemez, Eduardo Albers Acosta, Guillermo Celada Luis, Martin Costal, Patricia Toquero Diez, Nuria Romero Laorden, Raquel Jover Díaz, Clara Velasco Balanza and Luis San José Manso
Cancers 2025, 17(23), 3747; https://doi.org/10.3390/cancers17233747 - 24 Nov 2025
Cited by 4 | Viewed by 1927
Abstract
Background/Objectives: Prostate cancer is the most prevalent malignancy in men and remains a leading cause of cancer-related mortality worldwide. Conventional imaging modalities exhibit limited sensitivity, particularly in the context of disease recurrence and advanced disease. Methods: A narrative review was conducted [...] Read more.
Background/Objectives: Prostate cancer is the most prevalent malignancy in men and remains a leading cause of cancer-related mortality worldwide. Conventional imaging modalities exhibit limited sensitivity, particularly in the context of disease recurrence and advanced disease. Methods: A narrative review was conducted of studies published between 2015 and 2025, identified through PubMed, Embase, and Cochrane. Eligible publications addressed advanced imaging techniques, PSMA-targeted diagnostics and therapies, radiogenomics, liquid biopsy approaches, and artificial intelligence applications and personalized medicine. Preclinical studies, single case reports, and conference abstracts without full text were excluded. Results: PSMA PET/CT outperforms conventional imaging for detection, and restaging, influencing clinical management across disease stages. Lutetium-177–PSMA-617 has become the standard radioligand therapy for metastatic castration-resistant prostate cancer, whereas alpha-emitting agents remain under clinical investigation. Radiogenomics and liquid biopsy assays (ctDNA, CTCs, AR-V7) provide complementary molecular insights. Artificial intelligence enhances imaging interpretations, standardization, and reproducibility, while multimodal data integration supports individualized risk stratification. Integrative models combining imaging, genomic, and liquid biopsy data pave the way toward precision oncology and personalized therapeutic decision-making. Conclusions: Advances in imaging and theragnostics are reshaping prostate cancer management, bridging the gap between molecular biology and clinical practice to enable precision oncology. Full article
(This article belongs to the Special Issue The Emerging Role of Multiplexed Imaging for Cancer Diagnosis and Therapy (2nd Edition))
21 pages, 555 KB  
Review
Beyond Visualization: Advanced Imaging, Theragnostics and Biomarker Integration in Urothelial Bladder Cancer
by Eduardo Albers Acosta, Lira Pelari Mici, Carlos Márquez Güemez, Clara Velasco Balanza, Manuel Saavedra Centeno, Marta Pérez Pérez, Guillermo Celada Luis, Cristina Quicios Dorado, José Daniel Subiela, Rodrigo España Navarro, Patricia Toquero Diez, Nuria Romero Laorden and Luis San José Manso
Cancers 2025, 17(19), 3261; https://doi.org/10.3390/cancers17193261 - 8 Oct 2025
Cited by 2 | Viewed by 1791
Abstract
Background/Objectives: Bladder cancer is characterized by high recurrence and progression rates, posing a challenge to current diagnostic and treatment strategies. This review aims to provide a comprehensive overview of emerging technologies, including novel PET tracers, AI-assisted cystoscopy, theragnostics, and molecular biomarkers. Methods: [...] Read more.
Background/Objectives: Bladder cancer is characterized by high recurrence and progression rates, posing a challenge to current diagnostic and treatment strategies. This review aims to provide a comprehensive overview of emerging technologies, including novel PET tracers, AI-assisted cystoscopy, theragnostics, and molecular biomarkers. Methods: We performed a narrative review of the recent literature focusing on innovations in imaging, AI, theragnostics, and biomarker research relevant to bladder cancer diagnosis and management. Results: Several novel PET tracers, such as 68Ga-PSMA and fibroblast activation protein inhibitor (FAPI), demonstrated potential in improving detection sensitivity. AI-enhanced cystoscopy has shown promise in real-time lesion detection, while theragnostic agents enable combined diagnostic and therapeutic applications. Advances in molecular biomarkers, including circulating Tumor DNA (ctDNA) and gene expression signatures, offer new avenues for patient stratification and monitoring. Conclusions: Integration of advanced imaging, AI, theragnostics, and biomarker analysis may transform bladder cancer management, supporting personalized and more effective care strategies. Full article
(This article belongs to the Special Issue The Emerging Role of Multiplexed Imaging for Cancer Diagnosis and Therapy (2nd Edition))
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