Pediatric non-Hodgkin lymphoma encompasses a diverse group of malignancies distinct from the adult forms of the disease. Early recognition and accurate diagnosis of NHL in children are crucial, as these factors significantly influence treatment approaches and prognostic outcomes. Radiologic assessment plays a pivotal role in this context, with various imaging modalities such as ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) being utilized to identify tumors and other related manifestations. Moreover, this review meticulously presents the visual aspects of these modalities, serving as a guide for healthcare professionals.

One notable aspect of pediatric NHL is its varied presentations, which can complicate its diagnosis. Unlike adults, where organ-specific symptoms may dominate, children may exhibit nonspecific signs. Symptoms such as fatigue, fever, and generalized lymphadenopathy often predominate. The review discusses the importance of recognizing these signs early on, suggesting that timely imaging studies can lead to quicker diagnosis and intervention, significantly enhancing patient outcomes.

The authors further elucidate the imaging characteristics of different subtypes of pediatric NHL, breaking down the typical appearances seen in various imaging modalities. For instance, CT scans often highlight enlarged lymph nodes in conventional areas, such as the cervical region, abdomen, and mediastinum, which are crucial for staging. MRI is especially useful in the assessment of central nervous system involvement, which can occur more frequently in pediatric cases, necessitating a comprehensive understanding of these features by radiologists.

In terms of ultrasound, the review indicates that this modality can provide rapid, bedside solutions for initial evaluations. The non-invasive nature and accessibility of ultrasound make it an excellent first-line tool for assessing lymphadenopathy in children. Dasic et al. highlight how this can be particularly beneficial in a pediatric setting, where concerns about radiation exposure are paramount. The review further details the sonographic characteristics found in lymphomas, allowing clinicians to differentiate them from benign lymphadenopathy and other potential diagnoses.

An essential aspect addressed in the review is the role of advanced imaging techniques, such as PET-CT, in the staging of pediatric NHL. Positron emission tomography combined with computed tomography provides unique metabolic information that can aid in assessing disease activity and treatment response. The incorporation of metabolic imaging is a significant advancement, giving clinicians a clearer understanding of tumor dynamics and their implications for prognosis and therapy.

Furthermore, Dasic et al. provide a critical overview of the complications associated with pediatric non-Hodgkin lymphoma. This includes the risk of infectious complications due to immune suppression from both the disease and its treatments. The imaging characteristics of such complications, whether they arise from neutropenic fever or cytotoxic therapy, are essential considerations that the authors meticulously outline, underscoring the need for vigilance in monitoring these at-risk patients.

An additional unique feature of this pictorial review is its focus on the differential diagnosis. The authors guide the readers through a range of conditions that may mimic or present similarly to pediatric NHL. This is particularly relevant, as misdiagnosis can lead to delays in treatment and unfavorable outcomes. By discussing entities such as infections, autoimmune disorders, and other malignancies, the review underscores the importance of a careful and methodical approach to pediatric imaging.

Another compelling angle presented is the imaging follow-up protocols for children diagnosed with NHL. Regular imaging is crucial to monitor response to treatment and detect any recurrence. The authors provide evidence-based recommendations for the frequency and type of imaging necessary at various stages of follow-up. Such insights are invaluable, as they can help streamline care pathways and reassure both patients and parents in what can be an emotionally challenging time.

Ultimately, the advent of this pictorial review marks a significant milestone in the ongoing effort to enhance the diagnostic capabilities of pediatricians and radiologists alike. By combining clinical insights with a rich array of imaging visuals, Dasic et al. equip medical professionals with the tools necessary to recognize and manage pediatric non-Hodgkin lymphoma effectively. This is not just an academic resource; it represents a lifeline for countless young patients navigating the complexities of cancer treatment.

In conclusion, the research not only highlights the significance of imaging in pediatric non-Hodgkin lymphoma but also urges further exploration into this vital area of pediatric oncology. With ongoing advancements in imaging technologies and methodologies, the potential to improve diagnostic accuracy and patient outcomes is immense. As the field continues to evolve, such comprehensive reviews will remain essential, guiding future research and clinical practices in the fight against childhood cancer.

Subject of Research: Pediatric Non-Hodgkin Lymphoma Imaging

Article Title: Imaging findings in pediatric non-Hodgkin lymphoma: a pictorial review

Article References:

Dasic, I., Littooij, A., Tolboom, N. et al. Imaging findings in paediatric non-Hodgkin lymphoma: a pictorial review. Pediatr Radiol (2025). https://doi.org/10.1007/s00247-025-06396-y

Image Credits: AI Generated

DOI: 13 November 2025

Keywords: Pediatric non-Hodgkin lymphoma, imaging findings, pediatric radiology, CT, MRI, ultrasound, PET-CT, differential diagnosis, oncology, treatment response.

Radiomics, the practice of extracting high-dimensional quantitative features from medical images, has already proven its potential in oncology by advancing personalized medicine. However, prior radiomics models in breast cancer typically leveraged only a single imaging source. The innovative aspect of this study lies in combining the radiomic data derived from multiple imaging technologies, hypothesizing that a synchronized, multi-modal analysis would offer superior clinical insights. Integrating these diverse imaging datasets allows for a multifaceted evaluation of tumor heterogeneity and biological characteristics, which are often invisible to the naked eye or single modality assessments.

The research team conducted a retrospective analysis of 89 breast cancer patients who underwent surgery following NAT between January 2019 and July 2023. The patient cohort was characterized by a pCR rate of 31.5%, which aligns with typical response rates reported in similar clinical settings. By systematically extracting radiomic features from volumes of interest across US, MM, CT, and MRI scans, the study harnessed complex image texture, shape, and intensity data reflective of tumor microenvironment dynamics and structural changes induced by therapy.

A key methodological element was the application of the least absolute shrinkage and selection operator (LASSO), a regularization technique instrumental in selecting the most robust radiomic features while mitigating overfitting risks. This step ensured that the resulting radiomic signatures for each imaging modality were both predictive and generalizable. Subsequent statistical modeling combined these signatures into a comprehensive multi-modal radiomics framework, which was further enriched by incorporating independent clinical risk factors, namely progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, and clinical tumor (T) stage.

Notably, the study reported the area under the receiver operating characteristic curve (AUC) as the primary metric for model performance. Individual imaging modalities demonstrated moderate predictive power, with CT radiomics yielding the highest single-modality AUC of 0.814, followed closely by MRI at 0.787. Mammography and ultrasound lagged slightly behind, with AUCs of 0.762 and 0.702, respectively. These results underscore the variability inherent in each imaging technique’s capacity to capture therapy-induced tumor changes.

The real breakthrough emerged when the four radiomic signatures were amalgamated into a unified multi-modal radiomics model, achieving an impressive AUC of 0.904 and a Brier score of 0.111, indicating excellent calibration and predictive accuracy. Crucially, the addition of clinical risk factors propelled performance even further—the combined model attained an outstanding AUC of 0.943 alongside a Brier score of 0.082. This synergistic integration underscores the value of combining quantitative imaging biomarkers with established pathological and clinical indicators.

To translate these advancements into clinical utility, the investigators developed a nomogram visualizing the combined model. Nomograms serve as intuitive, user-friendly tools that enable clinicians to estimate the probability of treatment response on an individual basis, thus facilitating personalized therapeutic decisions. The availability of such a tool promises to bridge the gap between sophisticated computational models and everyday clinical practice.

The implications of this study are profound and multifold. Firstly, it challenges the prevailing paradigm of relying solely on single-modality imaging in radiomics research, providing compelling evidence for a multi-modal approach. By pooling diverse imaging features, the resultant model captures complementary tumor characteristics, such as metabolic activity, vascularization, and tissue density variations, all of which are essential to comprehensively understanding the tumor’s response to NAT.

Moreover, the inclusion of clinical variables alongside radiomic data highlights a paradigm shift towards fully integrated biomarker models. This holistic approach acknowledges that while imaging can reveal structural and functional insights, molecular markers like PR and HER2 status remain indispensable in defining tumor biology and treatment responsiveness. Such integration is essential to achieving the goal of precision oncology.

Technically, this study exemplifies the growing sophistication of machine learning techniques applied to medical imaging. The use of LASSO for feature selection and rigorous five-fold cross-validation for model validation reflects best practices in reducing bias and ensuring replicability. Reproducibility remains a crucial concern in radiomics, and this study’s methodological rigor provides confidence in the robustness of its findings.

Looking ahead, this study sets the stage for the development of broadly applicable, multi-modal radiomics platforms that can be deployed in clinical workflows. Future research may extend these findings by validating the model in larger, multicenter cohorts and exploring integration with genomic and proteomic data. Additionally, the model’s applicability to other cancer types treated with neoadjuvant therapies represents an exciting avenue for exploration.

The promising results garnered from CT and MRI modalities suggest a potential prioritization in clinical imaging protocols. However, the unique advantages of ultrasound and mammography, including accessibility and cost-efficiency, remain valuable, especially in diverse healthcare settings where advanced imaging may be limited.

Importantly, the adoption of such predictive models could transform therapeutic decision-making, enabling oncologists to tailor neoadjuvant regimens based on the likelihood of complete pathological response. This could minimize overtreatment and its associated toxicities, as well as identify patients who may benefit from alternative strategies early in the treatment course.

Furthermore, the development of such multi-modal radiomics models aligns with the overarching trend towards non-invasive biomarkers in oncology. Imaging-based predictive tools offer repeatable assessments without the risks and discomfort of biopsy procedures, fostering dynamic monitoring of treatment efficacy in real time.

In conclusion, this innovative study heralds a new era in breast cancer management, harnessing the full spectrum of imaging technology combined with clinical insights to precisely predict treatment outcomes. As the oncology community moves towards increasingly personalized approaches, multi-modal radiomics models such as this will undoubtedly become invaluable assets in the clinician’s armamentarium, ultimately improving patient prognosis and quality of life.

Subject of Research: Prediction of pathological complete response to neoadjuvant treatment in breast cancer using a multi-modal radiomics model.

Article Title: Multi-modal radiomics model based on four imaging modalities for predicting pathological complete response to neoadjuvant treatment in breast cancer.

Article References:
Liang, Y., Xu, H., Lin, J. et al. Multi-modal radiomics model based on four imaging modalities for predicting pathological complete response to neoadjuvant treatment in breast cancer. BMC Cancer 25, 985 (2025). https://doi.org/10.1186/s12885-025-14407-2

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-14407-2