To investigate whether the "purfling sign," a new imaging marker, could distinguish primary central nervous system lymphoma (PCNSL) from brain gliomas, and its diagnosis value for preoperative identification of PCNSL.

Contrast-enhanced MR imaging features of 161 PCNSL and 161 glioma were evaluated by 2 independent neuroradiologists: (1) the presence/absence of the "purfling sign"; (2) the presence/absence of lesion necrosis and cystic changes; and (3) the heterogeneity of tumor parenchymal enhancement. Inter-rater agreement was assessed with Cohen's kappa (κ), and the diagnostic performance of the "purfling sign" in identifying PCNSL was investigated. Three separate institutional validation cohort (including 177 PCNSL and 177 glioma patients) was analyzed to validate the diagnostic performance of the "purfling sign."

Among the test set, the inter-rater agreement of the "purfling sign" was high (κ = 0.907), while that for the other features was only good [κ = 0.663-0.691]. The purfling sign was present in 89 (55.28%) lymphoma and 13 (8.07%) glioma cases with a specificity of 91.93%, a sensitivity of 55.28%, a positive predictive value (PPV) of 87.25%, and a negative predictive value (NPV) of 67.27% for the diagnosis of PCNSL. Furthermore, the tumors presenting with the "target sign" were all PCNSL (16/16,100%), with a specificity and PPV of 100%. Analysis with the validation cohort, 85.09% cases with a positive "purfling sign" were PCNSL (p < 0.0001; PPV = 85.09%, NPV = 66.67%, specificity = 90.40%, sensitivity = 54.80%).

With a robust inter-rater agreement, our study found that the "purfling sign" on enhanced MR represents a high specific imaging marker for the preoperative diagnosis of PCNSL. This noninvasive marker may aid in the guidance of the clinical diagnosis and treatment processes of PCNSL.

Primary large B-cell lymphoma of immune-privileged sites (IP-LBCL) encompasses a spectrum of relatively rare aggressive B-cell lymphomas, such as primary central nervous system lymphoma (PCNSL), primary testicular large B-cell lymphoma (PTL), and primary vitreoretinal large B-cell lymphoma (PVRL). Macroscopically, the development of IPI-LBCL may be associated with the dysfunction of meningeal lymphatic vessels (mLVs) and the perivascular channel system formed by astrocytes. Microscopically, mutation in MYD88 and CD79B genes plays a pivotal role in the pathogenesis of IP-LBCL. Pathological examination remains the cornerstone for establishing a diagnosis of IP-LBCL. Moreover, traditional imaging is now supplemented by a suite of advanced diagnostic methods, including cytological, genetic, immunological, multiple omics, and molecular biological, which collectively enhance the diagnostic accuracy of IP-LBCL. Despite these advancements, the high recurrence rates and attendant high mortality rates pose significant challenges to achieving long-term survival in IP-LBCL patients. However, the emergence of novel therapeutic agents, such as Bruton's tyrosine kinase inhibitors (BTKi), immune checkpoint inhibitors, immunomodulators, and anti-CD19 chimeric antigen receptor T (CAR-T) cell therapy, has offered promising new avenues for the treatment of IP-LBCL, demonstrating remarkable anti-tumor efficacy in recent years. This review delves into the epidemiology, pathogenesis mechanisms, diagnosis approaches, therapeutic strategies, and prognosis factors associated with IP-LBCL. It meticulously examines the parallels and divergences between the National Comprehensive Cancer Network (NCCN) and European Society for Medical Oncology (ESMO) guidelines, enhancing the professional comprehension of the complexities inherent to IP-LBCL.

Differentiating primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is crucial because their prognosis and treatment differ substantially. Manual examination of their histological characteristics is considered the golden standard in clinical diagnosis. However, this process is tedious and time-consuming and might lead to misdiagnosis caused by morphological similarity between their histology and tumor heterogeneity. Existing research focuses on radiological differentiation, which mostly uses multi-parametric magnetic resonance imaging. By contrast, we investigate the pathological differentiation between the two types of tumors using whole slide images (WSIs) of postoperative formalin-fixed paraffin-embedded samples.

To learn the specific and intrinsic histological feature representations from the WSI patches, a self-supervised feature extractor is trained. Then, the patch representations are fused by feeding into a weakly supervised multiple-instance learning model for the WSI classification. We validate our approach on 134 PCNSL and 526 GBM cases collected from three hospitals. We also investigate the effect of feature extraction on the final prediction by comparing the performance of applying the feature extractors trained on the PCNSL/GBM slides from specific institutions, multi-site PCNSL/GBM slides, and large-scale histopathological images.

Different feature extractors perform comparably with the overall area under the receiver operating characteristic curve value exceeding 85% for each dataset and close to 95% for the combined multi-site dataset. Using the institution-specific feature extractors generally obtains the best overall prediction with both of the PCNSL and GBM classification accuracies reaching 80% for each dataset.

The excellent classification performance suggests that our approach can be used as an assistant tool to reduce the pathologists' workload by providing an accurate and objective second diagnosis. Moreover, the discriminant regions indicated by the generated attention heatmap improve the model interpretability and provide additional diagnostic information.

Differentiating between glioblastoma (GB) with multiple foci (mGB) and multifocal central nervous system lymphoma (mCNSL) can be challenging because these cancers share several features at first appearance on magnetic resonance imaging (MRI). The aim of this study was to explore morphological differences in MRI findings for mGB versus mCNSL and to develop an interpretation algorithm with high diagnostic accuracy.

In this retrospective study, MRI characteristics were compared between 50 patients with mGB and 50 patients with mCNSL treated between 2015 and 2020. The following parameters were evaluated: size, morphology, lesion location and distribution, connections between the lesions on the fluid-attenuated inversion recovery sequence, patterns of contrast enhancement, and apparent diffusion coefficient (ADC) values within the tumor and the surrounding edema, as well as MR perfusion and susceptibility weighted imaging (SWI) whenever available.

A total of 187 mCNSL lesions and 181 mGB lesions were analyzed. The mCNSL lesions demonstrated frequently a solid morphology compared to mGB lesions, which showed more often a cystic, mixed cystic/solid morphology and a cortical infiltration. The mean measured diameter was significantly smaller for mCNSL than mGB lesions (p < 0.001). Tumor ADC ratios were significantly smaller in mCNSL than in mGB (0.89 ± 0.36 vs. 1.05 ± 0.35, p < 0.001). The ADC ratio of perilesional edema was significantly higher (p < 0.001) in mCNSL than in mGB. In SWI / T2*-weighted imaging, tumor-associated susceptibility artifacts were more often found in mCNSL than in mGB (p < 0.001).

The lesion size, ADC ratios of the lesions and the adjacent tissue as well as the vascularization of the lesions in the MR-perfusion were found to be significant distinctive patterns of mCNSL and mGB allowing a radiological differentiation of these two entities on initial MRI. A diagnostic algorithm based on these parameters merits a prospective validation.

To investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL).

MRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction.

The mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p < 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision.

In this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task.

Ki-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis.

The present study aimed to determine whether combining diffusion-weighted (DWI) and dynamic susceptibility contrast-enhanced perfusion-weighted (DSC-PWI) magnetic resonance imaging (MRI) could differentiate between primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM). The present retrospective study evaluated 45 patients with histologically confirmed brain tumors, of which 18 had PCNSLs and 27 had GBMs. All patients underwent conventional, DWI, and DSC-PWI MRIs before the surgical removal of the lesion or stereotactic biopsy. The solid tumor component, peritumoral edema, and abnormal white matter were measured in three regions of interest to evaluate relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC) and DWI. In conventional MRI, there were significant differences in tumor numbers, tumor enhancement type, tumor necrosis, hemorrhage and open-ring sign between GBM and PCNSL. Solid tumor ADC and rCBV values (ADCt and rCBVt, respectively) and their ratios with abnormal white matter amounts were significantly higher in GBM cases than in PCNSL cases (P<0.05). The rCBV value for peritumoral edema (rCBVe) and its ratio with abnormal white matter amount (rCBVe/n) were significantly higher in GBM cases than in PCNSL cases (P<0.05). However, ADC values did not differ significantly for peritumoral edema. DWI values did not differ significantly. Combining rCBVt and rCBVe/n provided a perfect area under the receiver operating characteristic curve of 1.00, with 100% sensitivity and 100% specificity for distinguishing GBM from PCNSL. In the results of the present study, the major criterion in the decision-making process distinguishing PCNSL from GBM was the combined rCBVt and rCBVe/n parameter. A minor criterion was the ADCt value of the lesion.

Primary central nervous system lymphoma (PCNSL) is a rare malignant tumor originating from the lymphatic hematopoietic system. It exhibits unique imaging manifestations due to its biological characteristics.

Magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), and magnetic resonance spectroscopy was performed. The imaging findings showed multiple space-occupying lesions with low signal on T1-weighted imaging, uniform high signal on T2-weighted imaging, and obvious enhancement on contrast-enhanced scans. DWI revealed diffusion restriction, PWI demonstrated hypoperfusion, and spectroscopy showed elevated choline peak and decreased N-acetylaspartic acid. The patient's condition significantly improved after hormone shock therapy.

This case highlights the distinctive imaging features of PCNSL and their importance in accurate diagnosis and management.

Primary central nervous system lymphoma (PCNSL) and high‑grade glioma (HGG) are distinct entities of the CNS with completely distinct treatments. The treatment of PCNSL is chemotherapy‑based, while surgery is the first choice for HGG. However, the clinical features of the two entities often overlap, and a clear pathological diagnosis is important for subsequent management, especially for the management of PCNSL. Stereotactic biopsy is recognized as one of the minimally invasive alternatives for evaluating the involvement of the CNS. However, in the case of limited tissue materials, the differential diagnosis between the two entities is still difficult. In addition, some patients are too ill to tolerate a needle biopsy. Therefore, combining imaging, histopathology and laboratory examinations is essential in order to make a clear diagnosis as soon as possible. The present study reviews the progress of comparative research on both imaging and laboratory tests based on the pathophysiological changes of the two entities, and proposes an integrative and optimized diagnostic process, with the purpose of building a better understanding for neurologists, hematologists, radiologists and pathologists.