Search Results (47)

Background: Mycosis fungoides (MF) and Sézary syndrome (SS) are the predominant cutaneous T-cell lymphomas. Although malignant T cells in MF/SS overexpress CCR4 and respond to the anti-CCR4 antibody mogamulizumab, skin response rates vary. We hypothesized that immune components within the tumor microenvironment contribute to differential outcomes. Methods: Imaging mass cytometry with a 37-antibody panel was used to characterize immune and structural elements in FFPE tissues from sixteen MF/SS patients (6 MF, 10 SS) treated with Mogamulizumab, including seven skin responders and nine non-responders. Single-cell phenotyping and spatial analyses were performed using the Visiopharm^® Phenoplex™ platform, with supervision. Results: We identified 68,974 cells pre-treatment and 58,852 cells post-treatment. Malignant CD4⁺ T cells showed reduced baseline CD27, CD103, CD25, and ICOS expression compared with non-malignant CD4⁺ cells. Baseline MF lesions were enriched for IL-13⁺ and CD103⁺ malignant T cells, whereas SS lesions contained higher proportions of CD27⁺ and LAG3⁺ cells. IL 13⁺ malignant cells decreased after treatment, most prominently in MF. Myeloid profiles differed by disease and response: MF lesions exhibited baseline enrichment of M1-like macrophages (CD86⁺, HLA-DR⁺), while SS lesions were predominantly M2-polarized macrophages (CD163⁺, CD206⁺). Responders showed increased M1-like macrophages, whereas non-responders displayed reduced M1-features. An increase in DC3-like cells was observed in non-responders following treatment. Conclusions: This single-cell spatial atlas reveals shared and subtype-specific immune features in MF/SS. Th2-skewed malignant T-cell status and myeloid polarization correlate with clinical response, supporting their potential as spatial biomarkers for patient stratification in mogamulizumab therapy. Full article

Advanced-stage classic Hodgkin lymphoma (cHL) represents one of the major success stories in modern oncology, with long-term survival now exceeding 80% for most patients. In this review, we examine the evolution of frontline therapy for advanced-stage cHL, tracing the transition from empiric combination chemotherapy to contemporary, biologically informed treatment strategies. We begin by revisiting the early development of multiagent chemotherapy regimens, including MOPP and ABVD. These regimens established, for the first time, that advanced lymphoma could be cured with systemic therapy. We then discuss efforts to improve outcomes through treatment intensification, which culminated in the development of BEACOPP-based approaches that improved disease control at the cost of substantial acute and long-term toxicity. Subsequently, the incorporation of functional imaging ushered in the era of PET-adapted therapy, enabling dynamic treatment modification based on early response and providing a framework to better balance efficacy with toxicity reduction. Finally, we review the integration of novel agents, including brentuximab vedotin and PD-1 blockade, which have reshaped the frontline treatment landscape and further improved outcomes for high-risk patients while challenging historical chemotherapy paradigms. Collectively, the treatment history of advanced-stage cHL reflects a broader evolution in oncology: from maximizing cytotoxic intensity toward increasingly personalized strategies designed to optimize cure while minimizing long-term harm. Ongoing efforts focused on biomarker-driven risk stratification and the utilization of circulating tumor DNA are poised to further refine this balance in the coming decade. Full article

Classic Hodgkin lymphoma (cHL) has traditionally been conceptualized as a malignancy confined to lymphoid tissues, with disease extent defined primarily by anatomical staging systems. While this framework has guided clinical management for decades, it incompletely captures the biological complexity of cHL. Emerging evidence from molecular, immunological, and translational studies supports a reinterpretation of cHL as a systemic immunobiological disease rather than a purely nodal malignancy. A defining feature of cHL is the rarity of malignant Hodgkin and Reed–Sternberg (HRS) cells, which orchestrate a highly structured tumor microenvironment through constitutive activation of signaling pathways, including NF-κB and JAK/STAT, and through expression of immune checkpoint ligands. Beyond local effects, HRS cells secrete cytokines, chemokines, and extracellular vesicles that enter the systemic circulation, promoting widespread immune reprogramming. This includes T-cell exhaustion, expansion of regulatory T cells, and activation of immunosuppressive myeloid populations, which collectively shape host immunity beyond the lymph node. Circulating tumor DNA (ctDNA) and soluble mediators such as thymus and activation-regulated chemokine (TARC/CCL17) provide measurable evidence of systemic disease activity and enable dynamic monitoring of tumor burden. These biological insights help explain key clinical features of cHL, including constitutional (“B”) symptoms, extranodal involvement, and heterogeneous patterns of treatment response and resistance. Importantly, integration of ctDNA kinetics, peripheral immune profiling, and functional imaging offers a multidimensional framework for disease assessment that overcomes the limitations of conventional staging systems. Therapeutically, the efficacy of immune checkpoint inhibitors underscores the central role of systemic immune dysregulation, while emerging biomarker-driven strategies support adaptive and personalized approaches to treatment. Collectively, these findings support a paradigm shift toward understanding cHL as a systemic immunobiological disease. This framework has important implications for disease monitoring, therapeutic decision-making, and future research, paving the way for biology-driven, precision medicine approaches in cHL. Full article

Background: Artificial intelligence (AI) shows promising results in lymphoma detection, prediction, and classification. However, translating these findings into practice requires a rigorous assessment of potential biases, clinical utility, and further validation of research models. Objective: The goal of this study was to summarize existing studies on artificial intelligence models for the histopathological detection of lymphoma. Design: This study adhered to the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines. A systematic search was conducted across three major databases (Scopus, PubMed, Web of Science) for English-language articles and reviews published between 2016 and 2025. Seven precise search queries were applied to identify relevant publications, accounting for variations in study modality, algorithmic architectures, and disease-specific terminology. Results: The search identified 612 records, of which 36 articles met the inclusion criteria. These studies presented 36 AI models, comprising 30 diagnostic and six prognostic applications, with Convolutional Neural Networks (CNNs) being the predominant architecture. Regarding data sources, 83% (30/36) of datasets utilized Hematoxylin and Eosin (H&E)-stained images, while the remainder relied on diverse modalities, including IHC-stained slides, bone marrow smears, and other tissue preparations. Studies predominantly utilized retrospective, private cohorts with sample sizes typically ranging from 50 to 400 patients; only a minority leveraged open-access repositories (e.g., Kaggle, TCGA). The primary application was slide-level multi-class classification, distinguishing between specific lymphoma subtypes and non-neoplastic controls. Beyond diagnosis, a subset of studies explored advanced prognostic tasks, such as predicting chemotherapy response and disease progression (e.g., in CLL), as well as automated biomarker quantification (c-MYC, BCL2, PD-L1). Reported diagnostic performance was generally high, with accuracy ranging from 60% to 100% (clustering around 90%) and AUC values spanning 0.70 to 0.99 (predominantly >0.90). Conclusions: While AI models demonstrate high diagnostic accuracy, their translation into practice is limited by unstandardized protocols, morphological complexity, and the “black box” nature of algorithms. Critical issues regarding data provenance, image noise, and lack of representativeness raise risks of systematic bias, hence the need for rigorous validation in diverse clinical environments. Full article

Background: Vitreoretinal lymphoma (VRL) often presents with features resembling uveitis and is commonly associated with central nervous system lymphoma (CNSL). Intravitreal methotrexate (IVMTX) is widely used as local therapy; however, objective markers for treatment response and prognosis remain limited. This study investigated choroidal structural changes after IVMTX via enhanced depth imaging optical coherence tomography (EDI-OCT) and explored prognostic indicators for subsequent CNSL development. Methods: This retrospective study included 18 patients (27 eyes) with VRL treated with IVMTX at Tokushima University Hospital between 2006 and 2021. EDI-OCT was conducted at baseline and at 1 and 3 months after IVMTX. Choroidal thickness and luminal and stromal areas were quantified through image binarization. The stromal/choroidal area (S/C) ratio and its association with CNSL onset were statistically analyzed. Results: The mean number of IVMTX injections administered over 3 months was 5.9 ± 1.3. Foveal retinal thickness did not significantly change, whereas foveal choroidal thickness significantly decreased from 275.8 ± 15.8 µm at baseline to 257.5 ± 14.7 µm at 1 month (p < 0.01). Total choroidal and stromal areas, particularly in the outer choroidal layer, were significantly decreased after IVMTX (p < 0.0001), whereas the luminal area in the inner layer modestly reduced (p < 0.05). The S/C ratio significantly declined at 1 month post-treatment (p < 0.001). Patients who developed CNSL within 2 years of VRL onset demonstrated higher baseline S/C ratios (p < 0.05). Conclusions: IVMTX induces measurable reductions in choroidal areas and stromal proportion, indicating decreased inflammatory infiltration. The baseline S/C ratio observed on EDI-OCT is a potential noninvasive biomarker of VRL activity and a prognostic indicator for early CNSL development. Full article

The integration of molecular features into histopathological diagnoses has become central to the World Health Organization (WHO) classification of central nervous system (CNS) tumors, improving prognostic accuracy and supporting precision medicine. However, unequal access to molecular testing limits the universal application of integrated diagnosis. To address this, artificial intelligence (AI) models are being developed to predict molecular alterations directly from histological data. In gliomas, deep learning applied to whole-slide images (WSIs) of permanent sections achieves neuropathologist-level accuracy in predicting biomarkers such as IDH mutation and 1p/19q co-deletion, as well as in molecular subtype classification and outcome prediction. Recent advances extend these approaches to intraoperative cryosections, enabling real-time glioma grading, molecular prediction, and label-free tissue analysis using modalities such as stimulated Raman histology and domain-adaptive image translation. Beyond gliomas, AI-powered histology is being explored in other brain tumors, including morphology-based molecular classification of spinal cord ependymomas and intraoperative discrimination of gliomas from primary CNS lymphomas. This review summarizes current progress in AI-assisted molecular profiling prediction of brain tumors from tissue, highlighting opportunities for rapid, accurate, and globally accessible diagnostics. The integration of histology and computational methods holds promise for the development of smart AI-assisted neuro-oncology. Full article

Objectives: The potential use of electron density (ED) and effective atomic number (Zeff) derived from dual-energy computed tomography (DECT) as novel quantitative imaging biomarkers for differentiating malignant brain tumors was investigated. Methods: Data pertaining to 136 patients with a pathological diagnosis of brain metastasis (BM), glioblastoma, and primary central nervous system lymphoma (PCNSL) were retrospectively reviewed. The 10th percentile, mean and 90th percentile values of conventional 120-kVp CT value (CTconv), ED, Zeff, and relative apparent diffusion coefficient derived from diffusion-weighted magnetic resonance imaging (rADC: ADC of lesion divided by ADC of normal-appearing white matter) within the contrast-enhanced tumor region were compared across the three groups. Furthermore, machine learning (ML)-based diagnostic models were developed to maximize diagnostic performance for each tumor classification using the indices of DECT parameters and rADC. Machine learning models were developed using the AutoGluon-Tabular framework with rigorous patient-level data splitting into training (60%), validation (20%), and independent test sets (20%). Results: The 10th percentile of Zeff was significantly higher in glioblastomas than in BMs (p = 0.02), and it was the only index with a significant difference between BMs and glioblastomas. In the comparisons including PCNSLs, all indices of CTconv, Zeff, and rADC exhibited significant differences (p < 0.001–0.02). DECT-based ML models exhibited high area under the receiver operating characteristic curves (AUC) for all pairwise differentiations (BMs vs. Glioblastomas: AUC = 0.83; BMs vs. PCNSLs: AUC = 0.91; Glioblastomas vs. PCNSLs: AUC = 0.82). Combined models of DECT and rADC demonstrated excellent diagnostic performance between BMs and PCNSLs (AUC = 1) and between Glioblastomas and PCNSLs (AUC = 0.93). Conclusion: This study suggested the potential of DECT-derived ED and Zeff as novel quantitative imaging biomarkers for differentiating malignant brain tumors. Full article

We aimed to establish non-invasive diagnostic models comparable to pathology testing and explore reliable digital imaging biomarkers to classify diffuse large B-cell lymphoma (DLBCL) and invasive ductal carcinoma (IDC). Our study enrolled 386 breast nodules from 279 patients with DLBCL and IDC, which were pathologically confirmed and underwent ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) examination. Patients from two centers were separated into internal and external cohorts. Notably, we introduced 2.5D deep learning and machine learning to extract features, develop models, and discover biomarkers. Performances were assessed using the area under curve (AUC) and confusion matrix. Additionally, the Shapley additive explanation (SHAP) and local interpretable model-agnostic explanations (LIME) techniques were employed to interpret the model. On the internal cohort, the optimal model PT_TDC_SVM achieved an accuracy of 0.980 (95% confidence interval (CI): 0.957–0.991) and an AUC of 0.992 (95% CI: 0.946–0.998), surpassing the other models. On the external cohort, the accuracy was 0.975 (95% CI: 0.913–0.993) and the AUC was 0.996 (95% CI: 0.972–0.999). The optimal imaging biomarker PET_LBP-2D_gldm_DependenceEntropy demonstrated an average accuracy of 0.923/0.937 on internal/external testing. Our study presented an innovative automated model for DLBCL and IDC, identifying reliable digital imaging biomarkers with significant potential. Full article

In this review, we explore the complexities of objectively assessing the response to immunotherapy in mycosis fungoides (MF), a prevalent form of cutaneous T-cell lymphoma. The core challenge lies in distinguishing between reactive and malignant lymphocytes amidst treatment, particularly given the absence of uniform pathological biomarkers for MF. We highlight the vital role of emerging histological technologies, such as multispectral imaging and spatial transcriptomics, in offering a more profound insight into the tumor microenvironment (TME) and its dynamic response to immunomodulatory therapies. Drawing on parallels with melanoma—another immunogenic skin cancer—our review suggests that methodologies and insights from melanoma could be instrumental in refining the approach to MF. We specifically focus on the prognostic implications of various TME cell types, including CD8+ tumor-infiltrating lymphocytes, natural killer (NK) cells, and histiocytes, in predicting therapy responses. The review culminates in a discussion about adapting and evolving treatment response quantification strategies from melanoma research to the distinct context of MF, advocating for the implementation of novel techniques like high-throughput T-cell receptor gene rearrangement analysis. This exploration underscores the urgent need for continued innovation and standardization in evaluating responses to immunotherapies in MF, a field rapidly evolving with new therapeutic strategies. Full article

Background/Objective: Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) has affected more than 1700 women with textured breast implants. About 80% of patients present with fluid (seroma) around their implant. BIA-ALCL can be cured by surgery alone when confined to the seroma and lining of the peri-implant capsule. To address the need for early detection, we developed a rapid point of care (POC) lateral flow assay (LFA) to identify lymphoma in seromas. Methods: We compared 28 malignant seromas to 23 benign seromas using both ELISA and LFA. LFA test lines (TL) and control lines (CL) were visualized and measured with imaging software and the TL/CL ratio for each sample was calculated. Results: By visual exam, the sensitivity for detection of CA9 was 93% and specificity 78%, while the positive predictive value was 84% and negative predictive value 90%. Quantitative image analysis increased the positive predictive value to 96% while the negative predictive value reduced to 79%. Conclusions: We conclude that CA9 is a sensitive biomarker for detection and screening of patients for BIA-ALCL in patients who present with seromas of unknown etiology. The CA9 LFA can potentially replace ELISA, flow cytometry and other tests requiring specialized equipment, highly trained personnel, larger amounts of fluid and delay in diagnosis of BIA-ALCL. Full article

Background: Diffuse large B-cell lymphomas (DLBCLs) are the most common, aggressive disease form that accounts for 30% of all lymphoma cases. Identifying patients who will respond to these advanced cell-based therapies is an unaddressed challenge. Methods: We propose to develop a radiomics- (quantitative image metric) based signature on the patients’ imaging scans (positron emission tomography/computed tomography, PET/CT) and use these metrics to prognosticate response to axi-cel (axicabtagene ciloleucel), autologous CD19 chimeric antigen receptor (CAR) T-cell (CAR-T) therapy. We curated a cohort of 155 patients with relapsed/refractory (R/R) DLBCL who were treated with axi-cel. Using their baseline image scan (PET/CT), the largest lesions related to nodal/extra-nodal disease were identified and characterized using imaging metrics (radiomics). We used principal component (PC) analysis to reduce the dimensionality of these features across the functional categories (size, shape, and texture). We evaluated the prognostic ability of radiomic-based PC to treatment response (1-year), measured by overall survival (OS) and progression-free survival (PFS). Results: We found that radiomic PC was prognostic of overall survival (Shape-PC, q < 0.013/0.0108, Size-PC, q < 0.003/0.0088), in CT/PET, respectively. In comparison, the metabolic tumor volume (MTV) was prognostic (q < 0.0002/0.0007). The radiomic PCs across the functional categories showed moderate to weak correlation with MTV, Spearman’s ρ of 0.44/0.35/0.27, and 0.45/0.36/0.55 for Size/Shape/Texture-PC1 obtained on PET and CT, respectively. Conclusions: We found radiomic PC based on size and shape metrics that are able to prognosticate treatment response to CAR-T therapy. Full article

Background/Objectives: Circulating tumor DNA (ctDNA) has emerged as a promising biomarker for non-invasive tumor monitoring in diffuse large B-cell lymphoma (DLBCL). Methods: In this study, 52 patients with newly diagnosed advanced-stage DLBCL treated with R-CHOP underwent serial ctDNA analysis at baseline, interim (after three cycles), and end of treatment. The prognostic significance of ctDNA dynamics was evaluated, and its predictive value was compared with the PET/CT response. Results: Targeted next-generation sequencing revealed baseline ctDNA in 98.1% of patients, with 74.7% concordance to tumor tissue genotyping. Higher baseline ctDNA levels correlated with elevated LDH, older age, and high IPI scores. A ≥2-log reduction in ctDNA at interim was significantly associated with improved overall survival (p = 0.004), though not with progression-free survival. Notably, combining interim ctDNA dynamics with PET/CT results enhanced the predictive accuracy for treatment outcomes, particularly among patients with partial metabolic responses. Conclusions: These findings support the clinical utility of ctDNA for dynamic risk assessment in DLBCL, and suggest that integrating ctDNA with imaging biomarkers may guide more personalized therapeutic strategies. Further validation using highly sensitive ctDNA assays is warranted to optimize its role in routine clinical practice. Full article

Background/Objectives: Performing hematoxylin and eosin (H&E) staining and immunohistochemistry (IHC) on the same specimen slide provides advantages that include specimen conservation and the ability to combine the H&E context with biomarker expression at the individual cell level. We previously used invisible deposited chromogens and dual-camera imaging, including monochrome and color cameras, to implement simultaneous H&E and IHC. Using this approach, conventional H&E staining could be simultaneously viewed in color on a computer monitor alongside a monochrome video of the invisible IHC staining, while manually scanning the specimen. Methods: We have now simplified the microscope system to a single camera and increased the IHC multiplexing to four biomarkers using translational assays. The color camera used in this approach also enabled multispectral imaging, similar to monochrome cameras. Results: Application is made to several clinically relevant specimens, including breast cancer (HER2, ER, and PR), prostate cancer (PSMA, P504S, basal cell, and CD8), Hodgkin’s lymphoma (CD15 and CD30), and melanoma (LAG3). Additionally, invisible chromogenic IHC was combined with conventional DAB IHC to present a multiplex IHC assay with unobscured DAB staining, suitable for visual interrogation. Conclusions: Simultaneous staining and detection, as described here, provides the pathologist a means to evaluate complex multiplexed assays, while seated at the microscope, with the added multispectral imaging capability to support digital pathology and artificial intelligence workflows of the future. Full article

Background: Lung malignancies, including cancerous lymphangitis and lymphomas, can mimic interstitial lung diseases like cryptogenic organizing pneumonia (COP) on imaging, leading to diagnostic delays. We aimed to identify potential biomarkers to distinguish between these conditions. Methods: We analyzed bronchoalveolar lavage fluid from 8 patients (4 COP, mean age 59.8 ± 13.5 years; 4 lung malignancies including 2 cancerous lymphangitis, 1 MALT lymphoma, and 1 diffuse large B cell lymphoma, mean age 67.8 ± 4.5 years) using mass cytometry with 35 T cell markers. Data were analyzed using principal component analysis (PCA) and unsupervised Citrus clustering. Results: PCA of T cell marker intensities effectively separated the two groups, with IL-2Rα, PD-L2, CD45RA, CD44, and OX40 being the top discriminating markers. Citrus analysis showed a significant increase in the CD16+ CD4+ and CD16+ CD8+ T cell populations in the COP group compared to lung malignancies. Conclusions: Our findings reveal distinct T cell immunophenotypes in COP versus lung malignancies, particularly increased CD16+ T cells in COP, which could serve as potential diagnostic biomarkers. Full article

Purpose: The current guidelines for initial cross-sectional imaging in pediatric lymphomas involve computed tomography (CT) of the chest, abdomen, and pelvis. However, whole-body magnetic resonance imaging (MRI) can be favored over CT for diagnosing and staging the disease, given its lack of ionizing radiation and its higher tissue contrast. Imaging characteristics of lymphoid tissue on MRI include a high T2/short tau inversion recovery (STIR) signal. A low or intermediate signal of lymphadenopathy on T2 and STIR images is an unexpected finding, noted anecdotally in nodular sclerosing Hodgkin lymphoma. This signal may be characteristic of a histological subtype of the disease and, if confirmed, could potentially be used to avoid biopsy. In this study, we aimed to review signal characteristics of lymphadenopathy in patients with biopsy-confirmed nodular sclerosing Hodgkin lymphoma. Methods: We undertook a retrospective review of relevant MR studies of patients with nodular sclerosing Hodgkin lymphoma. Studies were reviewed by an experienced pediatric radiologist regarding lymph node signal, especially on T2/STIR. Results: Eleven children with nodular sclerosing Hodgkin lymphoma were included. Median age at the time of MRI was 14.3 (IQR: 13.9–16.1) years, and nine were boys. Five patients showed some lymphadenopathy with a low T2/STIR signal, and six showed an intermediate T2/STIR signal. Central gadolinium non-enhancement was observed in four patients. Conclusions: All eleven patients (100%) with a diagnosis of nodular sclerosing Hodgkin lymphoma showed some lymphadenopathy with a low or intermediate T2/STIR signal, and five children (45.5%) showed a frank low signal of some lymphadenopathy, a feature which may prove to be a biomarker for this histology. Full article