Recent advances in blood-based and artificial intelligence-enhanced approaches for gastrointestinal cancer diagnosis

Gastrointestinal (GI) cancers are among the most common cancer types and leading causes of cancer-related deaths worldwide. There is a tremendous clinical need for effective early diagnosis for better healthcare of GI cancer patients. In this article, we provide a short overview of the recent advances in GI cancer diagnosis. In the first part, we discuss the applications of blood-based biomarkers, such as plasma circulating cell-free DNA, circulating tumor cells, extracellular vesicles, and circulating cell-free RNA, for cancer liquid biopsies. In the second part, we review the current trends of artificial intelligence (AI) for pathology image and tissue biopsy analysis for GI cancer, as well as deep learning-based approaches for purity assessment of tissue biopsies. We further provide our opinions on the future directions in blood-based and AI-enhanced approaches for GI cancer diagnosis, and we think that these fields will have more intensive integrations with clinical needs in the near future.

Early gastrointestinal cancer: The application of artificial intelligence

Early gastrointestinal (GI) cancer has been the core of clinical endoscopic work. Its early detection and treatment are tightly associated with patients’ prognoses. As a novel technology, artificial intelligence has been improved and applied in the field of endoscopy. Studies on detection, diagnosis, risk, and prognosis evaluation of diseases in the GI tract have been in development, including precancerous lesions, adenoma, early GI cancers, and advanced GI cancers. In this review, research on esophagus, stomach, and colon was concluded, and associated with the process from precancerous lesions to early GI cancer, such as from Barrett’s esophagus to early esophageal cancer, from dysplasia to early gastric cancer, and from adenoma to early colonic cancer. A status quo of research on early GI cancers and artificial intelligence was provided.

Association between Chromatin Structural Organization of Peripheral Blood Neutrophils and Self-Perceived Mental Stress: Gray-Level Co-occurrence Matrix Analysis

Methods based on the evaluation of textural patterns in microscopy, such as the “gray-level co-occurrence matrix” (GLCM) analysis are modern and innovative computer and mathematical algorithms that can be used to quantify subtle structural changes in cells and their organelles. Potential application of GLCM method in the fields of psychophysiology and psychiatry to this date has not been systematically investigated. The main objective of our study was to test the existence and strength of the association between chromatin structural organization of peripheral blood neutrophils and levels of self-perceived mental stress. The research was done on a sample of 100 healthy student athletes, and the Depression, Anxiety, and Stress Scales (DASS-21) were used for the estimation of psychological distress. Chromatin textural homogeneity and uniformity were negatively correlated (p < 0.01) with mental distress and had relatively good discriminatory power in differentiating participants with normal and elevated stress levels. As an addition, we propose the creation of a machine learning model based on binomial logistic regression that uses these and other GLCM features to predict stress elevation. To the best of our knowledge, these results are one of the first to establish the link between neutrophil chromatin structural organization quantified by the GLCM method and indicators of normal psychological functioning.

CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance

Most oncological cases can be detected by imaging techniques, but diagnosis is based on pathological assessment of tissue samples. In recent years, the pathology field has evolved to a digital era where tissue samples are digitised and evaluated on screen. As a result, digital pathology opened up many research opportunities, allowing the development of more advanced image processing techniques, as well as artificial intelligence (AI) methodologies. Nevertheless, despite colorectal cancer (CRC) being the second deadliest cancer type worldwide, with increasing incidence rates, the application of AI for CRC diagnosis, particularly on whole-slide images (WSI), is still a young field. In this review, we analyse some relevant works published on this particular task and highlight the limitations that hinder the application of these works in clinical practice. We also empirically investigate the feasibility of using weakly annotated datasets to support the development of computer-aided diagnosis systems for CRC from WSI. Our study underscores the need for large datasets in this field and the use of an appropriate learning methodology to gain the most benefit from partially annotated datasets. The CRC WSI dataset used in this study, containing 1,133 colorectal biopsy and polypectomy samples, is available upon reasonable request.

Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology

Tremendous advances in artificial intelligence (AI) in medical image analysis have been achieved in recent years. The integration of AI is expected to cause a revolution in various areas of medicine, including gastrointestinal (GI) pathology. Currently, deep learning algorithms have shown promising benefits in areas of diagnostic histopathology, such as tumor identification, classification, prognosis prediction, and biomarker/genetic alteration prediction. While AI cannot substitute pathologists, carefully constructed AI applications may increase workforce productivity and diagnostic accuracy in pathology practice. Regardless of these promising advances, unlike the areas of radiology or cardiology imaging, no histopathology-based AI application has been approved by a regulatory authority or for public reimbursement. Thus, implying that there are still some obstacles to be overcome before AI applications can be safely and effectively implemented in real-life pathology practice. The challenges have been identified at different stages of the development process, such as needs identification, data curation, model development, validation, regulation, modification of daily workflow, and cost-effectiveness balance. The aim of this review is to present challenges in the process of AI development, validation, and regulation that should be overcome for its implementation in real-life GI pathology practice.

State of machine and deep learning in histopathological applications in digestive diseases

Machine learning (ML)- and deep learning (DL)-based imaging modalities have exhibited the capacity to handle extremely high dimensional data for a number of computer vision tasks. While these approaches have been applied to numerous data types, this capacity can be especially leveraged by application on histopathological images, which capture cellular and structural features with their high-resolution, microscopic perspectives. Already, these methodologies have demonstrated promising performance in a variety of applications like disease classification, cancer grading, structure and cellular localizations, and prognostic predictions. A wide range of pathologies requiring histopathological evaluation exist in gastroenterology and hepatology, indicating these as disciplines highly targetable for integration of these technologies. Gastroenterologists have also already been primed to consider the impact of these algorithms, as development of real-time endoscopic video analysis software has been an active and popular field of research. This heightened clinical awareness will likely be important for future integration of these methods and to drive interdisciplinary collaborations on emerging studies. To provide an overview on the application of these methodologies for gastrointestinal and hepatological histopathological slides, this review will discuss general ML and DL concepts, introduce recent and emerging literature using these methods, and cover challenges moving forward to further advance the field.

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cognitive tasks, matching or even beating those provided by human performance. One of the benefits of DL is the ability to learn massive amounts of data. The DL field has grown fast in the last few years and it has been extensively used to successfully address a wide range of traditional applications. More importantly, DL has outperformed well-known ML techniques in many domains, e.g., cybersecurity, natural language processing, bioinformatics, robotics and control, and medical information processing, among many others. Despite it has been contributed several works reviewing the State-of-the-Art on DL, all of them only tackled one aspect of the DL, which leads to an overall lack of knowledge about it. Therefore, in this contribution, we propose using a more holistic approach in order to provide a more suitable starting point from which to develop a full understanding of DL. Specifically, this review attempts to provide a more comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field. In particular, this paper outlines the importance of DL, presents the types of DL techniques and networks. It then presents convolutional neural networks (CNNs) which the most utilized DL network type and describes the development of CNNs architectures together with their main features, e.g., starting with the AlexNet network and closing with the High-Resolution network (HR.Net). Finally, we further present the challenges and suggested solutions to help researchers understand the existing research gaps. It is followed by a list of the major DL applications. Computational tools including FPGA, GPU, and CPU are summarized along with a description of their influence on DL. The paper ends with the evolution matrix, benchmark datasets, and summary and conclusion.

High Expression of PD-L1 Is Associated with Better Survival in Pancreatic/Periampullary Cancers and Correlates with Epithelial to Mesenchymal Transition

Periampullary cancers (PACs) are characterized by tumor-infiltrating lymphocytes (TILs), severe fibrosis, and epithelial to mesenchymal transition (EMT). The immune checkpoint marker programmed death-1 (PD-1) and its ligands 1 and 2 have gained popularity in cancers with TILs. Evidence suggests a strong relationship between immune checkpoint markers and EMT in cancers. Here, we evaluated the expression and prognostic significance of immune checkpoint and EMT markers in PAC using an automated image analyzer. Formalin-fixed, paraffin-embedded surgically excised PAC tissues from laboratory archives (1998-2014) were evaluated by immunohistochemical staining for PD-1, PD-L1, and PD-L2 in a tissue microarray. In total, 115 PAC patients (70 males and 45 females) with an average age of 63 years were analyzed. Location, gross type, size, radial resection margin, N-M stage, lymphatic invasion, vascular invasion, perineural invasion, histologically well-differentiated severe inflammation, and high PD-L1 expression were significantly associated with recurrence. Higher PD-L1 expression, but not PD-1 and PD-L2, was significantly related to better overall survival (OS) and disease-free survival (DFS). PD-L1 and PD-L2 were significantly related to EMT markers. Aside from other clinicopathologic parameters, high PD-L1 expression was significantly related to better OS and DFS of PAC patients. Moreover, immune checkpoint markers were significantly associated with EMT markers. Therefore, PD-L1 expression can be a good prognostic marker to guide future immune target-based therapies in PAC patients.

Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation

BACKGROUND

Immunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and subtyping, and in creating personalized treatment plans. However, the interpretation of IHC results presents challenges in complicated cases. Furthermore, rapidly increasing amounts of IHC data are making it even harder for pathologists to reach to definitive conclusions.

METHODS

We developed ImmunoGenius, a machine-learning-based expert system for the pathologist, to support the diagnosis of tumors of unknown origin. Based on Bayesian theorem, the most probable diagnoses can be drawn by calculating the probabilities of the IHC results in each disease. We prepared IHC profile data of 584 antibodies in 2009 neoplasms based on the relevant textbooks. We developed the reactive native mobile application for iOS and Android platform that can provide 10 most possible differential diagnoses based on the IHC input.

RESULTS

We trained the software using 562 real case data, validated it with 382 case data, tested it with 164 case data and compared the precision hit rate. Precision hit rate was 78.5, 78.0 and 89.0% in training, validation and test dataset respectively. Which showed no significant difference. The main reason for discordant precision was lack of disease-specific IHC markers and overlapping IHC profiles observed in similar diseases.

CONCLUSION

The results of this study showed a potential that the machine-learning algorithm based expert system can support the pathologic diagnosis by providing second opinion on IHC interpretation based on IHC database. Incorporation with contextual data including the clinical and histological findings might be required to elaborate the system in the future.

Epithelial-mesenchymal transition in undifferentiated carcinoma of the pancreas with and without osteoclast-like giant cells

Undifferentiated carcinoma (UC) and undifferentiated carcinoma with osteoclast-like giant cells (UCOGC) are peculiar variants of pancreatic ductal adenocarcinoma (PDAC), characterized by hypercellularity and absence of glandular patterns. The inflammatory microenvironment is peculiar in UCOGC, since it is dominated by macrophages and osteoclast-like giant cells. However, from a molecular point of view, both UC and UCOGC are very similar to conventional PDAC, sharing alterations of the most common genetic drivers. Clinically, UC usually show a worse prognosis, whereas UCOGC may show a better prognosis if it is not associated with a PDAC component. To highlight potential biological differences between these entities, we investigated the role of the epithelial to mesenchymal transition (EMT) in UC and UCOGC. Specifically, we analyzed the immunohistochemical expression of three well-known EMT markers, namely Twist1, Snai2, and E-cadherin, in 16 cases of UCOGC and 10 cases of UC. We found that EMT is more frequently activated in UC (10/10 cases) than in UCOGC (8/16 cases; p = 0.05). Furthermore, in UCOGC, EMT was activated with a higher frequency in cases with an associated PDAC component. Snai2 was the most frequently and strongly expressed marker in both tumor types (10/10 UC, 8/16 UCOGC), and its expression was higher in UC than in UCOGC (mean immunohistochemical score: 4.8 in UC vs. 2.1 in UCOGC, p < 0.01). Our results shed new light on the biology of UC and UCOGC: EMT appeared as a more important process in UC, and Snai2 emerged as a central EMT effector in this setting.

Automated Classification and Segmentation in Colorectal Images Based on Self-Paced Transfer Network

Colorectal imaging improves on diagnosis of colorectal diseases by providing colorectal images. Manual diagnosis of colorectal disease is labor-intensive and time-consuming. In this paper, we present a method for automatic colorectal disease classification and segmentation. Because of label unbalanced and difficult colorectal data, the classification based on self-paced transfer VGG network (STVGG) is proposed. ImageNet pretraining network parameters are transferred to VGG network with training colorectal data to acquire good initial network performance. And self-paced learning is used to optimize the network so that the classification performance of label unbalanced and difficult samples is improved. In order to assist the colonoscopist to accurately determine whether the polyp needs surgical resection, feature of trained STVGG model is shared to Unet segmentation network as the encoder part and to avoid repeat learning of polyp segmentation model. The experimental results on 3061 colorectal images illustrated that the proposed method obtained higher classification accuracy (96%) and segmentation performance compared with a few other methods. The polyp can be segmented accurately from around tissues by the proposed method. The segmentation results underpin the potential of deep learning methods for assisting colonoscopist in identifying polyps and enabling timely resection of these polyps at an early stage.

Nodal EBV-positive polymorphic B cell lymphoproliferative disorder with plasma cell differentiation: clinicopathological analysis of five cases

Plasma cell differentiation (PCD) is frequently observed in some entities of non-Hodgkin B cell lymphoma, including both low-grade and high-grade lymphomas. However, except for plasmablastic lymphoma and primary effusion lymphoma, EBV⁺ B cell lymphoproliferative disorder (LPD) with PCD has not been well addressed due to its rarity. We clinicopathologically examined five cases of nodal EBV⁺ polymorphic B cell LPD with PCD (PBLPD-PCD) initially diagnosed as polymorphic EBV⁺ diffuse large B cell lymphoma, not otherwise specified (DLBCL-NOS) with PCD (n = 3) and methotrexate-associated B cell LPD (MTX-associated B-LPD) (n = 2). One case had a concomitant brain lesion which was clinically diagnosed as EBV-related encephalitis. This patient received therapy with vidarabine, and both the brain lesion and the nodal EBV⁺ PBLPD-PCD lesions disappeared. Another case was characterized by Mott cell differentiation. This case was the first reported case of EBV⁺ B cell lymphoma or LPD with Mott cell differentiation. The two cases of MTX-associated B cell LPD which arose in patients with rheumatoid arthritis spontaneously regressed after MTX cessation. TCRγ and IGH PCR analysis was performed in four cases. Two cases had TCRγ rearrangements, but no IGH rearrangements. The other two cases had no rearrangements in these genes. We concluded that nodal EBV⁺ PBLPD-PCD is rare, with heterogeneous characteristics. PCR analysis revealed that nodal EBV⁺ PBLPD-PCD may have only TCR clonality and no IGH clonality. Considering the partial or complete loss of CD20 expression on the tumor cells, this result may be confusing for accurate diagnosis of EBV⁺ PBLPD-PCD, and pathologists need to be aware of this phenomenon to avoid misdiagnosis.

Colon Cancer Biomarkers: Implications for Personalized Medicine

The heterogeneity of colon cancers and their reactions presents both a challenge and promise for personalized medicine. The challenge is to develop effective biologically personalized therapeutics guided by predictive and prognostic biomarkers. Presently, there are several classes of candidate biomarkers, including genomic probes, inhibitory RNAs, assays for immunity dysfunction and, not to be forgotten, specific histopathologic and histochemical features. To develop effective therapeutics, candidate biomarkers must be qualified and validated in comparable independent cohorts, no small undertaking. This process and subsequent deployment in clinical practice involves not only the strong association of the biomarker with the treatment but also careful attention to the prosaic aspects of representative tumor site selection, obtaining a fully adequate sample which is preserved and prepared to optimize high quality analysis. In the future, the clinical utility of biomarker analytical results will benefit from associated clinical and basic science data with the assistance of artificial intelligence techniques. By application of an individualized, selected suite of biomarkers, comprehensively interpreted, individualized, more effective and less toxic therapy for colon cancer will be enabled, thereby fulfilling the promise of personalized medicine.

A familial case of Kikuchi-Fujimoto disease in dizygotic twins

BACKGROUND

Kikuchi-Fujimoto disease (KFD) or necrotizing histiocytic lymphadenitis, was described separately by both Kikuchi and Fujimoto in Japan in the early 1970's. Despite its rarity in the pediatric population, it is an important differential in persistent lymphadenopathy. Familial cases of KFD in the literature are rare. Here we describe the first reported case of KFD in non-identical twin sisters.

CASE PRESENTATION

Twin 1 presented with a 3-week history of worsening right-sided cervical lymphadenopathy, daily fevers, significant lethargy, weight loss and arthralgia of her knees and ankles at the age of 12 years in 2015. She had had an unremarkable medical history. A biopsy of her lymph nodes showed histiocytic necrosis consistent with KFD. Twin 2 presented with a three-week history of lethargy, fatigue, weight loss and left-sided posterior cervical chain lymphadenopathy at 16 years of age in 2018. She had a history of frequently relapsing nephrotic syndrome and celiac disease. A biopsy of her lymph nodes was undertaken and showed histiocytic necrosis consistent with KFD.

CONCLUSIONS

KFD is a rare but self-limiting pathological process of necrotizing histiocytic lymphadenitis. Although further research is needed, there is an increasing amount of evidence which suggests a multifactorial pathological basis of disease. The two cases we document here are the first reported cases of familial KFD in dizygotic HLA-identical twins which reinforces the likely HLA-linkage in the etiology of KFD.

Viral metagenomics in Brazilian multiply transfused patients with sickle cell disease as an indicator for blood transfusion safety

BACKGROUND AND AIM

Patients with sickle cell disease (SCD) are submitted to multiple transfusions in order to increase the oxygen capacity of the blood, decrease blood viscosity, and suppress the sickling of the cells. Multiply transfused patients with SCD represent significant risk of acquiring parenterally transmitted infections. The analysis of the virome profile of high-risk multiply transfused patients with SCD can reveal the presence of parenterally transmitted viruses and therefore be used an indirect approach for evaluation of blood transfusion safety.

MATERIALS AND METHODS

Blood samples were collected from 45 patients with SCD receiving multiple transfusions and analyzed by metagenomic analyses. The samples were assembled in pools f which were submitted to nucleic acids extraction and sequencing by Illumina NextSeq 550 equipment. For bioinformatic analysis, we used a specific in-house developed pipeline specialized in identification of emerging viruses.

RESULTS

The virome composition of SCD patients revealed the presence of commensal viruses represented by anelloviruses and Human Pegivirus-1 (HPgV-1, GB virus C). Contaminant viral sequences belonging to human lentiviruses (rev, env genes), cytomegalovirus and murine leukemia virus were also identified and are attributed to vectors used in the laboratory practice. No novel or unsuspected pathogenic viruses were identified.

CONCLUSION

This study evaluates for the first time the virome of multiply transfused patients with SCD. Exclusively genetic material of commensal viruses was annotated. Therefore, we believe that viral metagenomics applied in patients with high risk for acquiring parenterally transmitted infections can serve as a direct indicator for evaluation of transfusion safety.

Identification of Torque Teno Virus/Torque Teno-Like Minivirus in the Cervical Lymph Nodes of Kikuchi-Fujimoto Lymphadenitis Patients (Histiocytic Necrotizing Lymphadenitis): A Possible Key to Idiopathic Disease

Kikuchi-Fujimoto disease (KFD) is rare, and many infectious agents have been suspected for its etiology. This report presents an interesting case of KFD found with torque teno virus/torque teno minivirus (TTV/TTMV), which closely resembles the circovirus that causes necrotizing lymphadenitis in pigs. Three Korean patients showed several enlarged lymph nodes in their neck. Quantitative polymerase chain reaction (qPCR) and subsequent DNA sequencing for TTV/TTMV using formalin-fixed paraffin-embedded tissue were performed. Histologic examination demonstrated typical features of KFD. qPCR showed successful amplification of TTV/TTMV, and DNA sequencing confirmed the results. It is the first report of TTV/TTMV presence in three patients with KFD.

Histopathologic Analysis of Conjunctival Lymphoproliferative Disease After Topical Brimonidine Use

Purpose: To describe of histopathological findings of conjunctival lymphoproliferative disease (CLD) after topical brimonidine use. Methods: This is a retrospective medical record review study, including histopathologic description. We reviewed the medical records of 208 patients (415 eyes) who were diagnosed with glaucoma and who were treated with topical brimonidine only for a minimum of 6 months. Of these, the medical records of 19 patients with suspected CLD clinical features were reviewed in detail. When CLD was suspected due to administration of brimonidine, histopathological analysis was performed by biopsy of these lesions. In addition, immunohistochemical staining was performed to analyze lymphocyte markers in some pathological tissues. Results: Nineteen patients had suspected CLD without definite irritative symptoms. Diffuse elevated (11 patients) or follicular lesion (8 patients) of salmon pink appearance was observed in inferior palpebral conjunctiva. Among these patients, 5 patients who agreed to conjunctival biopsy had histopathological findings of CLD such as reactive lymphoid hyperplasia (LH) (2 cases) or atypical LH (2 cases). The mean duration of brimonidine use was 29.00 ± 20.25 months (6-76 months). And follow-up period after discontinuation of brimonidine was 27.93 ± 11.87 months (12-58 months). At the last visit, complete resolution of the lesion was seen in 13 patients, and partial improvement was observed in 6 patients. Conclusions: We found 4 cases of CLD following long-term administration of brimonidine. However, large-scale additional studies should be performed to establish causality, to determine whether these novel side effects were caused by long-term brimonidine treatment.

Gastric MALT Lymphoma with Increased Plasma Cell Differentiation Showing Unique Endoscopic Features

A 62-year-old woman was diagnosed with extranodal marginal zone lymphoma of mucosa-associated lymphoid tissue (MALT lymphoma) with increased plasma cell differentiation of the stomach. Esophagogastroduodenoscopy showed slightly elevated, whitish lesions in the gastric body. Magnifying endoscopic observation revealed that the gastric surface epithelium was swollen, but the structure was not destroyed or diminished. Elongated, tortuous vasculature was observed on the surface of the whitish lesions. The patient underwent eradication treatment for Helicobacter pylori, which resulted in complete remission. Although the appearance of abnormal vessels and the destruction of gastric epithelial structure are the typical features of gastric MALT lymphoma during magnifying endoscopy, the present case showed different features, which were rather similar to those observed in a previously reported case of gastric plasmacytoma. The current case indicates that magnifying endoscopic features are not uniform among gastric MALT lymphomas.

Detection of MYD88 L265P in patients with lymphoplasmacytic lymphoma/Waldenstrom macroglobulinemia and other B-cell non-Hodgkin lymphomas

Background

Recent studies have identified a high prevalence of the MYD88 L265P mutation in lymphoplasmacytic lymphoma (LPL)/Waldenstrom macroglobulinemia (WM) cases, whereas low frequencies have been observed in other B cell non-Hodgkin lymphomas (NHLs).

Methods

We evaluated the sensitivity of the mutant enrichment 3'-modified oligonucleotide (MEMO)-PCR technique, a new detection method. We examined the MYD88 L265P mutation in a series of Korean patients with LPL/WM and other B cell NHLs in bone marrow aspirates, using the MEMO-PCR technique.

Results

The sensitivity of MEMO-PCR was estimated to be approximately 10-16.7%. MYD88 L265P was detected in 21 of 28 LPL cases (75%) and only three of 69 B cell NHL cases (4.3%).

Conclusion

Although MEMO-PCR had relatively low sensitivity, we confirmed the high prevalence of the MYD88 L265P mutation in Korean LPL patients. Our study suggests the diagnostic value of MYD88 L265P for differentiating B-cell NHLs.

Clinical manifestations of autoimmune disease-related non-Hodgkin lymphoma: a Korean single-center, retrospective clinical study

BACKGROUND/AIMS

Recently, large cohort studies regarding associations between autoimmune disease and lymphomas have been reported in a few Western countries. However, Asian data concerning autoimmune-related lymphomas are limited. Therefore, we evaluated the clinical characteristics and prognostic factors of patients with autoimmune disease-related non-Hodgkin lymphoma (NHL) in a single center in Korea.

METHODS

We analyzed the data from 11 patients with autoimmune-related NHL. Patients were categorized into two groups, those with rheumatoid arthritis (RA) and those with non-RA-related NHL. Then patients were re-categorized into a group with methotrexate (MTX) usage and a MTX non-usage group. Histological subtype, MTX duration, autoimmune disease duration, treatment modalities, and other data were collected and analyzed.

RESULTS

Our study revealed that older RA patients have a greater likelihood of occurrence of NHL (p = 0.042). We confirmed that MTX duration and cumulative dose of MTX have no significant correlation with autoimmune disease and NHL (p = 0.073). In the management of autoimmune disease-related NHL, all patients were directly treated with systemic chemotherapy instead of employing a wait and watch approach. Overall survival (OS) and progression-free survival (PFS) in all autoimmune disease-related NHL were 100% and 87.5%, with no treatment-related mortality during the 2-year follow-up period of our study.

CONCLUSIONS

Our study suggests that patients with RA-NHL are characterized by older age at onset compared to those with non-RA-NHL. Also considering of OS and PFS, intensive treatment strategy instead of delayed watchful managements may be required for autoimmune disease-related NHL including of old age group.

[Marginal zone lymphoma with monoclonal immunoglobulin: three cases report and literatures review]

OBJECTIVE

To investigate the clinical features and treatment in patients of marginal zone lymphoma (MZL)with monoclonal immunoglobulin (McIg).

METHODS

The clinical data of MZL patients with McIg, including 3 cases diagnosed and treated in Beijing Anzhen Hospital from Jan 2007 to Dec 2014 were retrospectively studied, meanwhile 36 patients searched from literatures were reviewed.

RESULTS

Of a total of 39 patients, the ratio of male and female was 1.05∶1 with an average age of 65.1± 12.3 years old. 28 cases (71.8%)were with mucosa associated lymphoid tissue lymphomas (MALTL), 9 cases (23.1% )with nodal marginal zone lymphoma, and 2 cases (5.1%)with splenic marginal zone lymphoma. Nine cases (23.1% )were in the early stage, 30 cases (76.9%)in the advanced stage. The common initial symptom was non-mass lesions (65.5%), such as skin purpura, peripheral neuropathy; 13 patients (33.3% )were accompanied by autoimmune phenomenon, and most were with Sjogren's syndrome. Among MALTL patients, the common primary lesion was in non- gastrointestinal tract (17 cases, 60.7%). Most of patients with McIg were one with McIgM (82.0%); the others with McIgA, Mcκ-light chain, McIgG and double McIg. The level of plasma McIgM was (25.55±21.31)g/L, which was higher in advanced stage patients than in early stage ones [(29.85±20.60)g/Lvs (3.23±2.95)g/L,P= 0.008]. The complete remission (CR)rate was 56.0% and the overall response rate (ORR)92.0%, respectively in 30 patients treated by chemotherapy. At a median follow- up of 10 months, the 3- year progression free survival and the 3-year overall survival were 44.7% and 76.5%, respectively. The rates of ORR and CR in the patients received rituximab- included regimen were seemly better than those without rituximab one (100.0%vs 78.6%, 63.6%vs 50.0%;P>0.05), but no statistic differences were found. The CR rate in patients with McIgM was significantly higher than that with non- McIgM (P=0.026). The plasma McIgM level decreased after chemotherapy (P=0.002).

CONCLUSION

The MZL with McIg, perhaps a kind of unique subtype, usually occurred in 60 years or older patients. It was often diagnosed in patients of advanced stage and susceptible to autoimmune phenomenon. MALTL in non- gastrointestinal tract was more prone to find McIg. In MZL patients with McIg, McIgM was more common and other McIg rare. Rituximab-included regimen produced a better therapeutic response.