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The Development and Application of Imaging Biomarkers in Cancer

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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 16951

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Special Issue Editor

Dr. Abhishek Mahajan

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The Clatterbridge Cancer Centre NHS Foundation Trust, University of Liverpool, Liverpool, UK
Interests: head and neck cancer; thyroid cancer; neuro-oncology; cancer imaging; radiomics; artificial intelligence
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Special Issue Information

Dear Colleagues,

Imaging biomarkers have emerged as a cornerstone in the evolving landscape of oncology, offering unparalleled, non-invasive insights into the intricate dynamics of tumor biology, progression, and therapeutic responses. Advances in imaging modalities, including MRI, PET, CT, and hybrid technologies, have revolutionized cancer diagnostics by enabling the precise, quantitative evaluation of tumor characteristics at the molecular, cellular, and tissue levels.

These biomarkers have proven instrumental in diverse clinical applications such as the detection, staging, prognosis, and real-time monitoring of cancer and therapeutic efficacy. Unlike conventional tissue biopsies that provide static and localized information, imaging biomarkers offer a comprehensive, dynamic perspective on the tumor microenvironment, capturing spatial and temporal heterogeneity across the entire tumor and metastatic sites. This capability is critical in understanding the complex interplay between cancer and its surrounding tissues.

The integration of artificial intelligence (AI), deep learning, and machine learning (ML) into imaging biomarker research is significantly enhancing their diagnostic and prognostic potential. AI-powered algorithms enable the automated extraction of high-dimensional data from medical images, while deep learning models uncover complex patterns and features that may be imperceptible to the human eye. These advancements are driving innovations in radiomics, predictive analytics, and decision support systems, paving the way for earlier cancer detection, more accurate risk stratification, and the adaptive monitoring of therapy.

Furthermore, imaging biomarkers are advancing the concept of personalized medicine. By characterizing tumor-specific features, such as metabolic activity, receptor expression, and perfusion, these biomarkers inform the selection and optimization of targeted therapies, enhancing the precision of treatment and minimizing adverse effects. Emerging innovations in radiogenomics are further bridging the gap between imaging and molecular biology, offering novel insights into genotype–phenotype correlations.

This Special Issue will explore the latest advancements and applications of imaging biomarkers in cancer, emphasizing their transformative role in the early detection, treatment, and long-term management of patients with cancer. Particular attention will be paid to AI-driven approaches, highlighting how these technologies are reshaping cancer care by making it more precise, efficient, and impactful. We aim to showcase the cutting-edge research, technical innovations, and clinical applications that are driving the future of oncology, toward the ultimate goal of enhanced patient outcomes and cancer prevention.

Dr. Abhishek Mahajan
Guest Editor

Manuscript Submission Information

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Keywords

imaging biomarkers
oncology
cancer imaging
artificial intelligence

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Published Papers (7 papers)

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Research

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12 pages, 1541 KB

Open AccessArticle

Decoding Osteoradionecrosis of the Jaw: Radiological Progression and a Novel CT-Based Grading System

by Vasundhara Patil, Pritesh Shah, Abhishek Mahajan, Nilesh Sable, Anuradha Shukla, Gauri Bornak, Swapnil Rane, Sandeep Gurav, Sarbani Ghosh Laskar, Gouri Pantvaidya, Amit Janu, Suman Ankathi, Arpita Sahu, Kajari Bhattacharya, Nivedita Chakrabarty, Archi Agarwal, Prathamesh Pai, Deepa Nair, Anuja Deshmukh, Richa Vaish, Vidisha Tuljapurkar, Asawari Patil, Munita Bal, Kumar Prabhash, Vanita Noronha, Nandini Menon, Vijay Patil and Pankaj Chaturvedi Show full author list Hide full author list

Cancers 2026, 18(2), 187; https://doi.org/10.3390/cancers18020187 - 6 Jan 2026

Viewed by 896

Abstract

Background: Osteoradionecrosis (ORN) of the jaw is a severe, progressive complication of radiation therapy for head and neck malignancies. ORN features radiologically overlaps osteomyelitis and tumor recurrence. This study analyzes jaw ORN imaging characteristics and progression and proposes an ORN CT-based grading system that builds on current ClinRAD grades. Materials and Methods: A retrospective cohort study of 35 patients with biopsy-proven or clinically diagnosed ORN following radiation therapy. Initial and follow-up imaging were assessed to evaluate the radiological evolution of ORN. The imaging findings were statistically analyzed using IBM SPSS v26, and literature comparisons were made. Results: The median onset of ORN post-radiotherapy was 27–28 months (range: 2–119 months). The most common clinical presentations included non-healing ulcers (49%), pain (34%), and discharging sinuses (31%). Mandibular involvement was predominant (51%), with focal bone alterations being more frequent (63%). CT findings at clinical suspicion of ORN included resorption (100%), erosions (100%), sclerosis (86%), and fragmentation (83%). Follow-up imaging showed increased bone erosion (77%), fragmentation (92%), and sclerosis (92%). A CT-based grading system is proposed to classify ORN progression. Conclusions: ORN follows a predictable radiological progression, beginning with trabecular resorption and cortical erosion, leading to fragmentation and sequestrum formation. The proposed grading system provides a structured approach for early diagnosis. The proposed grading system provides a structured approach for diagnosis. Larger studies of imaging analyses are required to validate these findings and refine diagnostic criteria. Full article

(This article belongs to the Special Issue The Development and Application of Imaging Biomarkers in Cancer)

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15 pages, 3021 KB

Open AccessArticle

Multiparametric MRI Markers Associated with Breast Cancer Risk in Women with Dense Breasts

by Wesley Surento, Romy Fischer, Debosmita Biswas, Daniel S. Hippe, Anum S. Kazerouni, Jin You Kim, Isabella Li, John H. Gennari, Habib Rahbar and Savannah C. Partridge

Cancers 2025, 17(23), 3771; https://doi.org/10.3390/cancers17233771 - 26 Nov 2025

Viewed by 985

Abstract

Background/Objectives: This study explored the associations of normal breast tissue characteristics on multiparametric MRI with clinical assessments of breast cancer risk in women with dense breasts. Methods: Women with dense breasts who underwent multiparametric MRI were included. Breast cancer risk was determined based on Tyrer–Cuzick (TC) lifetime risk scores, categorized as high (TC ≥ 20%) or low risk. Qualitative background parenchymal enhancement (BPE) assessment was obtained from imaging reports. Quantitative imaging markers were calculated, including median BPE, median apparent diffusion coefficient, and volume measures of the whole breast, fibroglandular tissue (FGT), blood vessels, and BPE regions. The associations between imaging markers and TC risk groups were evaluated using age-adjusted logistic regression and summarized by area under the receiver operating characteristic curve (AUC). Results: Seventy-seven women were evaluated; a total of 20 (26%) were low risk, and 57 (74%) were high risk. After adjusting for age and multiple testing, BPE:breast ratio (adj. p = 0.037), FGT:breast ratio (adj. p = 0.046), and BPE:vessel ratio (adj. p = 0.037) were positively associated with risk, while qualitative BPE was not (adj. p = 0.11). Overall, risk categorizations based on imaging markers were concordant with TC score in up to 70% of women. Conclusions: In women with dense breasts, quantitative measures from multiparametric MRI (BPE:breast, FGT:breast, and BPE:vessel ratios) moderately discriminated high- and low-risk groups, warranting further investigation of their value to supplement conventional breast cancer risk assessment tools. Full article

(This article belongs to the Special Issue The Development and Application of Imaging Biomarkers in Cancer)

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13 pages, 881 KB

Open AccessArticle

Radiomics and Deep Learning Interplay for Predicting MGMT Methylation in Glioblastoma: The Crucial Role of Segmentation Quality

by Francesca Lizzi, Sara Saponaro, Alessia Giuliano, Cinzia Talamonti, Leonardo Ubaldi and Alessandra Retico

Cancers 2025, 17(21), 3417; https://doi.org/10.3390/cancers17213417 - 24 Oct 2025

Cited by 1 | Viewed by 1277

Abstract

Background/Objectives: Glioblastoma (GBM) is the most malignant subtype of glioma and shows the poorest prognosis with a median survival time of 15 months. The methylation status of the Methylguanine-DNA Methyltransferase (MGMT) was proven to be a crucial factor in selecting the most appropriate therapy. Currently, it is assessed through brain biopsy, which is a highly invasive and very expensive technique. For these reasons, in recent years, the possibility of inferring this information from multi-parametric Magnetic Resonance Imaging (mpMRI) has been widely explored. However, substantial differences in performance are reported in the literature. Methods: In this study, we developed several models based on either radiomic or deep learning approaches and a mixture of them using mpMRI for the MGMT status assessment using the public dataset UPENN-GBM, available on The Cancer Imaging Archive. Despite the tests performed using all MRI acquisitions and different methodological approaches, we did not obtain sufficiently reliable performance to direct the therapeutic path of patients. We thus investigated the impact of segmentation quality on MGMT status prediction since the UPENN-GBM dataset contains both automatic and manual refined segmentation masks. Results: We found that performance obtained through radiomic features computed on manually segmented tumors was significantly higher compared to that obtained using automatic segmentation, even when the differences between segmentation masks, measured in terms of Dice Similarity Coefficient (DSC), is not significantly different. Conclusion: This could be the reason why very different MGMT classification performance is typically reported and suggests the creation of a benchmark dataset, with high-quality segmentation masks. Full article

(This article belongs to the Special Issue The Development and Application of Imaging Biomarkers in Cancer)

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15 pages, 1034 KB

Open AccessArticle

Beyond Morphology: Quantitative MR Relaxometry in Pulmonary Lesion Classification

by Markus Graf, Alexander W. Marka, Andreas Wachter, Tristan Lemke, Nicolas Lenhart, Teresa Schredl, Jonathan Stelter, Kilian Weiss, Marcus Makowski, Dimitrios C. Karampinos, Daniela Pfeiffer, Gregor S. Zimmermann, Seyer Safi, Hans Hoffmann, Keno Bressem, Lisa Adams and Sebastian Ziegelmayer

Cancers 2025, 17(20), 3370; https://doi.org/10.3390/cancers17203370 - 18 Oct 2025

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Abstract

Background/Objectives: Lung nodules present a common diagnostic challenge, particularly when benign and malignant lesions exhibit similar imaging characteristics. Standard evaluation relies on computed tomography (CT), positron emission tomography (PET), or biopsy, all of which have limitations. Quantitative magnetic resonance (MR) relaxometry using native longitudinal relaxation time (T1) and transverse relaxation time (T2) mapping offers a radiation-free alternative reflecting tissue-specific differences. Methods: This prospective, single-center study included 64 patients with 76 histologically or radiologically confirmed lung lesions (25 primary lung cancers, 28 metastases, 9 granulomas, and 14 pneumonic infiltrates). The patients underwent T1 and T2 mapping at 3T. Two independent readers quantified the mean values for each lesion. The pre-specified primary endpoints were (1) benign versus malignant and (2) primary lung cancer versus pulmonary metastases. Results: Significant differences in T1 and T2 values were observed across lesion types. Benign lesions exhibited high T2 values (mean 213.6 ms) and low T1 values (mean 836.6 ms), whereas malignant tumors exhibited lower T2 values (~77–78 ms) and higher T1 values (~1460–1504 ms, p < 0.001). Binary classification yielded 95.7% accuracy (sensitivity 93.8% for malignant, specificity 100% for benign) in an internal 70/30 hold-out validation (no external dataset), with consistent performance confirmed by patient-level and nested cross-validation (balanced accuracy ≈ 0.92–0.94). However, malignant subtypes could not be reliably distinguished (p > 0.05), and multiclass accuracy was 60.9%. Conclusions: Quantitative MR relaxometry allows accurate, radiation-free differentiation of benign and malignant lung lesions and may help reduce unnecessary invasive procedures. Full article

(This article belongs to the Special Issue The Development and Application of Imaging Biomarkers in Cancer)

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20 pages, 4084 KB

Open AccessArticle

CT-Based Pericardial Composition Change as an Imaging Biomarker for Radiation-Induced Cardiotoxicity

by Arezoo Modiri, Ivan R. Vogelius, Cynthia Terrones Campos, Denis Kutnar, Jean Jeudy, Mette Pohl, Timm-Michael L. Dickfeld, Soren M. Bentzen, Amit Sawant and Jens Petersen

Cancers 2025, 17(16), 2635; https://doi.org/10.3390/cancers17162635 - 13 Aug 2025

Viewed by 1225

Abstract

Background/Objectives: No reliable noninvasive biomarkers are available to predict RT-induced cardiotoxicity. Because the pericardial sac is a fast responder to cardiac injury, we investigated whether RT-induced radiographic pericardial changes might serve as early imaging biomarkers for late cardiotoxicity. Methods: We performed a retrospective study of 476 patients (210 males, 266 females; median age, 69 years; median follow-up, 26.7 months) treated with chemo-RT for small cell and non-small cell lung cancers at one single institution from 2009 to 2020. The heart and its 4 mm outmost layer (representing the pericardial sac) were contoured on standard-of-care baseline CTs. Six-month post-RT follow-up CTs were deformably registered on the baseline CTs. Data were harmonized for the effect of contrast. We labeled voxels as Fat, Fluid, Heme, Fibrous, and Calcification using Hounsfield units (HUs). We studied pericardial HU-change histograms as well as volume change and voxel-based mass change in each tissue composition. Results: Pericardial HU-change histograms had skewed distributions with a mean that was significantly correlated with mean pericardial dose. Voxels within Fluid, Heme, and Fibrous had mass changes consistent with the dose. In Kaplan–Meier curves, Fibrous and Heme volume changes (translating into thickening and effusion), Fat mass change, mean doses to heart and pericardium, history of cardiac disease, and being male were significantly associated with shorter survival, whereas thickening and effusion were significantly associated with shorter time to a post-RT cardiovascular disease diagnosis. Conclusions: Pericardium composition distribution has dose-dependent changes detectable on standard-of-care CTs at around 6 months post-RT and may serve as surrogate markers for clinically relevant cardiotoxicity. The findings should be validated with additional research. Full article

(This article belongs to the Special Issue The Development and Application of Imaging Biomarkers in Cancer)

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Review

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23 pages, 16020 KB

Open AccessReview

Adrenal Mass Evaluation: Suspicious Radiological Signs of Malignancy

by Giulia Grazzini, Silvia Pradella, Federica De Litteris, Antonio Galluzzo, Matilde Anichini, Francesca Treballi, Eleonora Bicci and Vittorio Miele

Cancers 2025, 17(5), 849; https://doi.org/10.3390/cancers17050849 - 28 Feb 2025

Cited by 9 | Viewed by 9598

Abstract

An adrenal mass discovered incidentally during imaging for unrelated clinical reasons is termed an “adrenal incidentaloma” (AI). AIs can be categorized as primary or metastatic, functioning or non-functioning, and benign or malignant. The primary goal of radiological evaluation is to exclude malignancy by differentiating between benign and malignant lesions. Most AIs are benign, with adenomas and macronodular bilateral adrenal hyperplasia being the most common types. Less common benign lesions include myelolipomas, pheochromocytomas, cysts, and hematomas. Malignant adrenal masses account for less than 10% of cases and often include metastases from other cancers or primary adrenal diseases, such as adrenocortical carcinoma and pheochromocytoma. Computed Tomography (CT) remains the gold standard for diagnosing adrenal incidentalomas, while Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) are utilized for indeterminate cases. Additionally, innovative imaging techniques such as texture analysis are gaining importance, as they can assess quantitative parameters that are not visible to the human eye. This review aims to provide an updated overview of malignant adrenal lesions on CT and MRI, emphasizing key imaging features suspicious for malignancy to aid in distinguishing between benign and malignant lesions. Furthermore, it highlights the growing role of radiomics as a supportive tool for radiologists. Full article

(This article belongs to the Special Issue The Development and Application of Imaging Biomarkers in Cancer)

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Other

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15 pages, 655 KB

Open AccessSystematic Review

MRI-Based Prediction of Vestibular Schwannoma: Systematic Review

by Cheng Yang, Daniel Alvarado, Pawan Kishore Ravindran, Max E. Keizer, Koos Hovinga, Martinus P. G. Broen, Henricus P. M. Kunst and Yasin Temel

Cancers 2026, 18(2), 289; https://doi.org/10.3390/cancers18020289 - 17 Jan 2026

Viewed by 894

Abstract

Background: The vestibular schwannoma (VS) is the most common cerebellopontine angle tumor in adults, exhibiting a highly variable natural history, from stability to rapid growth. Accurate, the non-invasive prediction of tumor behavior is essential to guide personalized management and avoid overtreatment or delayed intervention. Objective: To systematically review and synthesize the evidence on MRI-based biomarkers for predicting VS growth and treatment responses. Methods: We conducted a PRISMA-compliant search of PubMed, EMBASE, and Cochrane databases for studies published between 1 January 2000 and 1 January 2025, addressing MRI predictors of VS growth. Cohort studies evaluating texture features, signal intensity ratios, perfusion parameters, and apparent diffusion coefficient (ADC) metrics were included. Study quality was assessed using the NOS (Newcastle–Ottawa Scale) score, GRADE (Grading of Recommendations, Assessment, Development and Evaluation), and ROBIS (Risk of Bias in Systematic reviews) tool. Data on diagnostic performance, including the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and p value, were extracted and descriptively analyzed. Results: Ten cohort studies (five retrospective, five prospective, total n = 525 patients) met the inclusion criteria. Texture analysis metrics, such as kurtosis and gray-level co-occurrence matrix (GLCM) features, yielded AUCs of 0.65–0.99 for predicting volumetric or linear growth thresholds. Signal intensity ratios on gadolinium-enhanced T1-weighted images for tumor/temporalis muscle achieved a 100% sensitivity and 93.75% specificity. Perfusion MRI parameters (K^trans, v_e, ASL, and DSC derived blood-flow metrics) differentiated growing from stable tumors with AUCs up to 0.85. ADC changes post-gamma knife surgery predicted a favorable response, though the baseline ADC had limited value for natural growth prediction. The heterogeneity in growth definitions, MRI protocols, and retrospective designs remains a key limitation. Conclusions: MRI-based biomarkers may provide exploratory signals associated with VS growth and treatment responses. However, substantial heterogeneity in growth definitions and MRI protocols, small single-center cohorts, and the absence of external validation currently limit clinical implementation. Full article

(This article belongs to the Special Issue The Development and Application of Imaging Biomarkers in Cancer)

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