DAX-Net: A dual-branch dual-task adaptive cross-weight feature fusion network for robust multi-class cancer classification in pathology images.

BACKGROUND AND OBJECTIVE: Multi-class cancer classification has been extensively studied in digital and computational pathology due to its importance in clinical decision-making. Numerous computational tools have been proposed for various types of cancer classification. Many of them are built based on convolutional neural networks. Recently, Transformer-style networks have shown to be effective for cancer classification. Herein, we present a hybrid design that leverages both convolutional neural networks and transformer architecture to obtain superior performance in cancer classification. METHODS: We propose a dual-branch dual-task adaptive cross-weight feature fusion network, called DAX-Net, which exploits heterogeneous feature representations from the convolutional neural network and Transformer network, adaptively combines them to boost their representation power, and conducts cancer classification as categorical classification and ordinal classification. For an efficient and effective optimization of the proposed model, we introduce two loss functions that are tailored to the two classification tasks. RESULTS: To evaluate the proposed method, we employed colorectal and prostate cancer datasets, of which each contains both in-domain and out-of-domain test sets. For colorectal cancer, the proposed method obtained an accuracy of 88.4%, a quadratic kappa score of 0.945, and an F1 score of 0.831 for the in-domain test set, and 84.4%, 0.910, and 0.768 for the out-of-domain test set. For prostate cancer, it achieved an accuracy of 71.6%, a kappa score of 0.635, and an F1 score of 0.655 for the in-domain test set, 79.2% accuracy, 0.721 kappa score, and 0.686 F1 score for the first out-of-domain test set, and 58.1% accuracy, 0.564 kappa score, and 0.493 F1 score for the second out-of-domain test set. It is worth noting that the performance of the proposed method outperformed other competitors by significant margins, in particular, with respect to the out-of-domain test sets. CONCLUSIONS: The experimental results demonstrate that the proposed method is not only accurate but also robust to varying conditions of the test sets in comparison to several, related methods. These results suggest that the proposed method can facilitate automated cancer classification in various clinical settings.

Clear Cell Adenocarcinoma of Urethra: Clinical and Pathologic Implications and Characterization of Molecular Aberrations.

PURPOSE: This study aimed to evaluate the molecular features of clear cell adenocarcinoma (CCA) of the urinary tract and investigate its pathogenic pathways and possible actionable targets. MATERIALS AND METHODS: We retrospectively collected the data of patients with CCA between January 1999 and December 2016; the data were independently reviewed by two pathologists. We selected five cases of urinary CCA, based on the clinicopathological features. We analyzed these five cases by whole exome sequencing (WES) and subsequent bioinformatics analyses to determine the mutational spectrum and possible pathogenic pathways. RESULTS: All patients were female with a median age of 62 years. All tumors were located in the urethra and showed aggressive behavior with disease progression. WES revealed several genetic alterations, including driver gene mutations (AMER1, ARID1A, CHD4, KMT2D, KRAS, PBRM1, and PIK3R1) and mutations in other important genes with tumor-suppressive and oncogenic roles (CSMD3, KEAP1, SMARCA4, and CACNA1D). We suggest putative pathogenic pathways (chromatin remodeling pathway, mitogen-activated protein kinase signaling pathway, phosphoinositide 3-kinase/AKT/mammalian target of rapamycin pathway, and Wnt/ß-catenin pathway) as candidates for targeted therapies. CONCLUSION: Our findings shed light on the molecular background of this extremely rare tumor with poor prognosis and can help improve treatment options.

Multi-cell type and multi-level graph aggregation network for cancer grading in pathology images.

In pathology, cancer grading is crucial for patient management and treatment. Recent deep learning methods, based upon convolutional neural networks (CNNs), have shown great potential for automated and accurate cancer diagnosis. However, these do not explicitly utilize tissue/cellular composition, and thus difficult to incorporate the existing knowledge of cancer pathology. In this study, we propose a multi-cell type and multi-level graph aggregation network (MMGA-Net) for cancer grading. Given a pathology image, MMGA-Net constructs multiple cell graphs at multiple levels to represent intra- and inter-cell type relationships and to incorporate global and local cell-to-cell interactions. In addition, it extracts tissue contextual information using a CNN. Then, the tissue and cellular information are fused to predict a cancer grade. The experimental results on two types of cancer datasets demonstrate the effectiveness of MMGA-Net, outperforming other competing models. The results also suggest that the information fusion of multiple cell types and multiple levels via graphs is critical for improved pathology image analysis.

Comparison of the Characteristics of Three Acellular Dermal Matrices Subjected to Distinct Processing Methods Using Five Types of Histochemical Staining.

BACKGROUND: Acellular dermal matrix (ADM) is treated using various devitalization and aseptic processing methods. The processing effects on ADM were evaluated by histochemical tests. METHODS: From January 2014 to December 2016, 18 patients [average age, 43.0 (range, 30-54) years] who underwent breast reconstruction with an ADM and tissue expander were prospectively enrolled. During the permanent implant replacement, a biopsy of the ADM was performed. We used three different human-derived products, namely, Alloderm®, Allomend®, and Megaderm®. Hematoxylin and eosin, CD68, CD3, CD31, and smooth muscle actin were used to evaluate the collagen structure, inflammation, angiogenesis, and myofibroblast infiltration. Each ADM was semi-quantitatively analyzed. RESULTS: Significant differences in collagen degradation, acute inflammation, and myofibroblast infiltration were observed among the ADMs. Collagen degeneration (p<0.001) and myofibroblast infiltration (smooth muscle actin-positive, p=0.018; CD31-negative, p=0.765) were the most severe in Megaderm®. Acute inflammation, represented by CD68, was most severe in Alloderm® (p=0.024). Both radiation and freeze-drying treatment physically damaged the collagen structure. Collagen degeneration was most severe in Megaderm®, followed by Allomend® and Alloderm®. Since Alloderm® is treated using chemicals, an assessment of the chemical irritation is warranted. CONCLUSIONS: The biopsy results were inconclusive. Therefore, to better interpret processing, more large-scale, serial, histochemical studies of each ADM are needed. LEVEL OF EVIDENCE IV: This journal requires that authors 38 assign a level of evidence to each article. For a full 39 description of these Evidence-Based Medicine ratings, 40 please refer to the Table of Contents or the online 41 Instructions to Authors www.springer.com/00266 .

Prevalence of Incidentally Detected Spondylolysis in Children.

Purpose: To assess the prevalence of incidentally detected lumbar spondylolysis in children. Materials and Methods: We retrospectively reviewed the data of 809 patients under the age of 11 years (mean age, 7.0 ± 2.7 years; boys:girls = 479:330) who underwent abdominal and pelvic CT between March 2014 and December 2018. We recorded the presence, level, and laterality (unilateral or bilateral) of spondylolysis. Patients were divided into two groups based on the presence of spondylolysis: the spondylolysis (SP) and non-SP groups. Results: In total, 21 cases of spondylolysis were detected in 20 patients (20/809, 2.5%). The mean age of the SP group was higher than that of the non-SP group (7.8 ± 1.8 vs. 6.9 ± 2.7 years, p > 0.05). The prevalence of spondylolysis in boys was higher than that in girls (15/479 [3.1%] vs. 5/330 [1.5%], p > 0.05). The prevalence of spondylolysis in school-age children (6-10 year olds) was higher than that in preschool-age children (0-5 year olds) (17/538 [3.2%] vs. 3/271 [1.1%], p > 0.05). L5 was the most common level of spondylolysis (76.2%); one 8-year-old boy had twolevel spondylolysis. One case of isthmic spondylolisthesis was detected in a 10-year-old boy (1/809, 0.1%). There were 11 unilateral spondylolysis cases (11/21, 52.4%). Conclusion: In our study, the prevalence of spondylolysis in children under the age of 11 was 2.5%. The prevalence was higher in boys than in girls and in school-age than in preschool-age children, despite the lack of any statistically significant differences.

Prediction of Epstein-Barr Virus Status in Gastric Cancer Biopsy Specimens Using a Deep Learning Algorithm.

Importance: Epstein-Barr virus (EBV)-associated gastric cancer (EBV-GC) is 1 of 4 molecular subtypes of GC and is confirmed by an expensive molecular test, EBV-encoded small RNA in situ hybridization. EBV-GC has 2 histologic characteristics, lymphoid stroma and lace-like tumor pattern, but projecting EBV-GC at biopsy is difficult even for experienced pathologists. Objective: To develop and validate a deep learning algorithm to predict EBV status from pathology images of GC biopsy. Design, Setting, and Participants: This diagnostic study developed a deep learning classifier to predict EBV-GC using image patches of tissue microarray (TMA) and whole slide images (WSIs) of GC and applied it to GC biopsy specimens from GCs diagnosed at Kangbuk Samsung Hospital between 2011 and 2020. For a quantitative evaluation and EBV-GC prediction on biopsy specimens, the area of each class and the fraction in total tissue or tumor area were calculated. Data were analyzed from March 5, 2021, to February 10, 2022. Main Outcomes and Measures: Evaluation metrics of predictive model performance were assessed on accuracy, recall, precision, F1 score, area under the receiver operating characteristic curve (AUC), and κ coefficient. Results: This study included 137 184 image patches from 16 TMAs (708 tissue cores), 24 WSIs, and 286 biopsy images of GC. The classifier was able to classify EBV-GC image patches from TMAs and WSIs with 94.70% accuracy, 0.936 recall, 0.938 precision, 0.937 F1 score, and 0.909 κ coefficient. The classifier was used for predicting and measuring the area and fraction of EBV-GC on biopsy tissue specimens. A 10% cutoff value for the predicted fraction of EBV-GC to tissue (EBV-GC/tissue area) produced the best prediction results in EBV-GC biopsy specimens and showed the highest AUC value (0.8723; 95% CI, 0.7560-0.9501). That cutoff also obtained high sensitivity (0.895) and moderate specificity (0.745) compared with experienced pathologist sensitivity (0.842) and specificity (0.854) when using the presence of lymphoid stroma and a lace-like pattern as diagnostic criteria. On prediction maps, EBV-GCs with lace-like pattern and lymphoid stroma showed the same prediction results as EBV-GC, but cases lacking these histologic features revealed heterogeneous prediction results of EBV-GC and non-EBV-GC areas. Conclusions and Relevance: This study showed the feasibility of EBV-GC prediction using a deep learning algorithm, even in biopsy samples. Use of such an image-based classifier before a confirmatory molecular test will reduce costs and tissue waste.

Colorectal cancers with a residual adenoma component: Clinicopathologic features and KRAS mutation.

BACKGROUND/AIM: Colorectal cancer is well known for its "adenoma-carcinoma" sequential carcinogenesis. Some colorectal cancers demonstrate a residual adenoma component during progression from adenoma to invasive carcinoma. However, the clinicopathological significance of residual adenoma component remains unclear. In this study, we aimed to investigate the clinicopathologic and molecular characteristics including the KRAS mutation in colorectal cancers containing a residual adenoma component. MATERIALS AND METHODS: In this study, 498 surgically resected colorectal cancer patients were enrolled. Their detailed clinicopathologic features and results of molecular study including KRAS mutation test and microsatellite instability were analyzed. RESULTS: A residual adenoma component was identified in 42 (8.4%) patients with colorectal cancer. The presence of a residual adenoma component was associated with a high frequency of the KRAS mutation (65%, p = 0.031) as well as indolent clinicopathological features, including polypoid gross type (p < 0.001), well-differentiated histology (p < 0.001), low pT (p < 0.001) and pN stage (p = 0.003), absence of vascular invasion (p = 0.005), and a better progression-free prognosis (p = 0.029). The cases with an adenoma component had a 35.7% discordance rate on the KRAS mutation tests in their adenoma and carcinoma regions. CONCLUSION: In conclusion, colorectal cancer with a residual adenoma component showed indolent clinicopathologic features and frequent KRAS mutations. Due to the discordance in the incidence of the KRAS mutation between the adenoma and carcinoma components, the adenoma component should be documented in the pathology report, and care should be taken not to include the adenoma component when collecting samples for molecular testing.

SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images.

Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task still remains challenging due to the intrinsic variations in size, intensity, and morphology of different types of nuclei. Herein, we propose a self-guided ordinal regression neural network for simultaneous nuclear segmentation and classification that can exploit the intrinsic characteristics of nuclei and focus on highly uncertain areas during training. The proposed network formulates nuclei segmentation as an ordinal regression learning by introducing a distance decreasing discretization strategy, which stratifies nuclei in a way that inner regions forming a regular shape of nuclei are separated from outer regions forming an irregular shape. It also adopts a self-guided training strategy to adaptively adjust the weights associated with nuclear pixels, depending on the difficulty of the pixels that is assessed by the network itself. To evaluate the performance of the proposed network, we employ large-scale multi-tissue datasets with 276349 exhaustively annotated nuclei. We show that the proposed network achieves the state-of-the-art performance in both nuclei segmentation and classification in comparison to several methods that are recently developed for segmentation and/or classification.

Multi-Scale Binary Pattern Encoding Network for Cancer Classification in Pathology Images.

Multi-scale approaches have been widely studied in pathology image analysis. These offer an ability to characterize tissues in an image at various scales, in which the tissues may appear differently. Many of such methods have focused on extracting multi-scale hand-crafted features and applied them to various tasks in pathology image analysis. Even, several deep learning methods explicitly adopt the multi-scale approaches. However, most of these methods simply merge the multi-scale features together or adopt the coarse-to-fine/fine-to-coarse strategy, which uses the features one at a time in a sequential manner. Utilizing the multi-scale features in a cooperative and discriminative fashion, the learning capabilities could be further improved. Herein, we propose a multi-scale approach that can identify and leverage the patterns of the multiple scales within a deep neural network and provide the superior capability of cancer classification. The patterns of the features across multiple scales are encoded as a binary pattern code and further converted to a decimal number, which can be easily embedded in the current framework of the deep neural networks. To evaluate the proposed method, multiple sets of pathology images are employed. Under the various experimental settings, the proposed method is systematically assessed and shows an improved classification performance in comparison to other competing methods.

Venous Invasion and Perineural Invasion as Upstaging and Poor Prognostic Factors in N0 Gastric Cancers.

BACKGROUND/AIM: Lymph node metastasis is an important prognostic factor in gastric cancer patients. In node-negative (N0) gastric cancer patients, additional prognostic factors are needed to reinforce TNM staging. PATIENTS AND METHODS: We semi-quantitatively recorded the presence of lymphatic, venous, and perineural invasion and evaluated the possibility that they could be used as upstaging factors in N0 gastric cancer by comparing N0 gastric cancer cases with N1 cases. RESULTS: Venous (p<0.001) and perineural (p<0.001) invasion were important factors in the relapse-free survival of N0 patients, but lymphatic invasion was not. N0 cases with venous or perineural invasion had survival curves similar to those of N1 patients. In addition, the number of invasive features (lymphatic, venous, or perineural) was an important factor in predicting poor patient survival. CONCLUSION: Venous and perineural invasion were significant prognostic factors in N0 gastric cancer cases. It is necessary to record lymphatic, venous, and perineural invasion separately in the pathology report, especially in cases of N0 gastric cancer.

Joint categorical and ordinal learning for cancer grading in pathology images.

Cancer grading in pathology image analysis is one of the most critical tasks since it is related to patient outcomes and treatment planning. Traditionally, it has been considered a categorical problem, ignoring the natural ordering among the cancer grades, i.e., the higher the grade is, the more aggressive it is, and the worse the outcome is. Herein, we propose a joint categorical and ordinal learning framework for cancer grading in pathology images. The approach simultaneously performs both categorical classification and ordinal classification and aims to leverage the distinctive features from the two tasks. Moreover, we propose a new loss function for the ordinal classification task that offers an improved contrast between the correctly classified examples and misclassified examples. The proposed method is evaluated on multiple collections of colorectal and prostate pathology images that underwent different acquisition and processing procedures. Both quantitative and qualitative assessments of the experimental results confirm the effectiveness and robustness of the proposed method in comparison to other competing methods. The results suggest that the proposed approach could permit improved histopathologic analysis of cancer grades in pathology images.

Prognostic significance of microscopic tumor extension in local recurrence of myxofibrosarcoma and undifferentiated pleomorphic sarcoma.

Myxofibrosarcoma (MFS) and undifferentiated pleomorphic sarcoma (UPS) frequently display infiltrative growth into the adjacent normal soft tissue. In this study, we aimed to determine whether the microscopic extension into surrounding normal tissue can influence the local recurrence of MFS and UPS. A total of 42 cases (22 MFS and 20 UPS) were examined. The length of the microscopic extension was measured from the edge of the main tumor mass to the end of infiltration. The length of 5.5 mm was selected as the optimal cut-off value that could predict local recurrence using the receiver operating characteristic (ROC) curve and Youden index. Longer length of microscopic extension was significantly correlated with the status of resection margin (P = 0.032). The group with longer length of microscopic extension (>5.5 mm) had significantly worse recurrence-free survival than the group with shorter length of microscopic extension (≤5.5 mm) (P = 0.000). Multivariate analysis revealed that the length of microscopic extension was independent predictors of recurrence-free survival (P = 0.018). In conclusion, microscopic extensions at the edge of the main mass into the surrounding normal tissue were observed in most MFS and UPS patients, and the length of microscopic extension was associated with local recurrence.

Clinicopathologic Features of Benign Neurogenic Tumor of Urinary Bladder.

BACKGROUND: Benign neurogenic tumor involving the urinary bladder is a very rare and heterogeneous disease group. The clinical and radiological diagnosis may be difficult because of the disease's rarity and the histological similarities of each disease especially in needle biopsy specimens. However, accurate diagnosis is very important because the clinical course of each disease, even within the same diseases, is quite variable. In this study, we investigated 7 benign neurogenic tumors to better understand the rare disease entity in the urinary bladder by analyzing histological and immunohistochemical findings and comparing clinicopathologic features. METHODS: We collected the cases by searching the medical records database of Seoul National University Hospital from 2000 to 2016. RESULTS: We identified 3 ganglioneuromas, 2 schwannomas, 1 neurofibroma, and 1 granular cell tumor involving the urinary bladder. There were some limitations for the initial clinical and radiological diagnosis and even pathologic diagnosis using needle biopsy specimens. One infant patient was diagnosed radiologically with rhabdomyosarcoma, but the final diagnosis changed to ganglioneuromatosis. The initial needle biopsy diagnosis of 2 ganglioneuroma cases showed neurofibroma. All patients underwent a local resection, and 1 granular cell tumor patient suffered with pain because of a recurrent tumor. One neurofibromatosis patient had a lesion appear 34 months after the bladder operation, so he underwent repeated debulking operations, but he was diagnosed with malignant transformation after 8 years. CONCLUSIONS: An understanding of benign neurogenic tumors involving the urinary bladder and the sharing of rare experiences surrounding them are required to provide accurate diagnoses.

Intraosseous Hibernoma: A Rare and Unique Intraosseous Lesion.

BACKGROUND: Hibernoma is a rare benign tumor of adults that is composed of multivacuolated adipocytes resembling brown fat cells. Hibernoma typically occurs in soft tissue, and intraosseous examples are very rare. Intraosseous hibernomas can radiologically mimic metastatic carcinoma and other tumorous conditions. METHODS: To collect the intraosseous hibernomas, we searched the pathologic database and reviewed the hematoxylin and eosin (H&E)-stained slides of bone biopsy samples performed to differentiate radiologically abnormal bone lesions from 2006 to 2016. A total of six intraosseous hibernoma cases were collected, and clinical and radiological information was verified from electronic medical records. H&E slide review and immunohistochemical staining for CD68, pan-cytokeratin, and S-100 protein were performed. RESULTS: Magnetic resonance imaging of intraosseous hibernomas showed low signal intensity with slightly hyperintense foci on T1 and intermediate to high signal intensity on T2 weighted images. Intraosseous hibernomas appeared as heterogeneous sclerotic lesions with trabecular thickening on computed tomography scans and revealed mild hypermetabolism on positron emission tomography scans. Histopathologically, the bone marrow space was replaced by sheets of multivacuolated, foamy adipocytes resembling brown fat cells, without destruction of bone trabeculae. In immunohistochemical analysis, the tumor cells were negative for CD68 and pan-cytokeratin and positive for S-100 protein. CONCLUSIONS: Intraosseous hibernoma is very rare. This tumor can be overlooked due to its rarity and resemblance to bone marrow fat. Pathologists need to be aware of this entity to avoid misdiagnosis of this rare lesion.

Implication of PHF2 Expression in Clear Cell Renal Cell Carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (CCRCC) is presumed to be associated with adipogenic differentiation. Histone modification is known to be important for adipogenesis, and the function of histone demethylase plant homeodomain finger 2 (PHF2) has been noted. In addition, PHF2 may act as a tumor suppressor via epigenetic regulation of p53 and is reported to be reduced in colon cancer and stomach cancer tissues. In this study, we examined PHF2 expression in CCRCC specimens by immunohistochemistry. METHODS: We studied 254 CCRCCs and 56 non-neoplastic renal tissues from patients who underwent radical or partial nephrectomy between 2000 and 2003 at the Seoul National University Hospital. Tissue microarray blocks were prepared, and immunohistochemical staining for PHF2 was performed. RESULTS: Among 254 CCRCC cases, 150 cases (59.1%) showed high expression and 104 cases (40.1%) showed low expression. High expression of PHF2 was significantly correlated with a low Fuhrman nuclear grade (p < .001), smaller tumor size (p < .001), low overall stage (p = .003), longer cancer-specific survival (p = .002), and progression-free survival (p < .001) of the patients. However, it was not an independent prognostic factor in multivariate analysis adjusted for Fuhrman nuclear grade and overall stage. CONCLUSIONS: Our study showed that low expression of PHF2 is associated with aggressiveness and poor prognosis of CCRCC.

Comparison of the FDA and ASCO/CAP Criteria for HER2 Immunohistochemistry in Upper Urinary Tract Urothelial Carcinoma.

BACKGROUND: Human epidermal growth factor receptor 2 (HER2) is one of the known oncogenes in urothelial carcinoma. However, the association between HER2 and the prognosis of upper urinary tract urothelial carcinoma (UUTUC) has not yet been fully clarified. The aim of this study was to evaluate HER2 expression using the United States Food and Drug Administration (FDA) criteria and American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) criteria and compare their prognostic significance in UUTUC. METHODS: HER2 expression was evaluated in 144 cases of UUTUC by immunohistochemistry (IHC) using tissue microarrays. We separately analyzed HER2 expression using the FDA and ASCO/CAP criteria. The IHC results were categorized into low (0, 1+) and high (2+, 3+) groups. RESULTS: Using the FDA criteria, 94 cases were negative, 38 cases were 1+, nine cases were 2+, and three cases were 3+. Using the ASCO/CAP criteria, 94 cases were negative, 34 cases were 1+, 13 cases were 2+, and three cases were 3+. Four cases showing 2+ according to the ASCO/CAP criteria were reclassified as 1+ by the FDA criteria. High HER2 expression by both the FDA criteria and ASCO/CAP criteria was significantly associated with International Society of Urological Pathology high grade (p = .001 and p < .001). The high HER2 expression group classified with the FDA criteria showed significantly shorter cancer-specific survival (p = .004), but the HER2 high and low expression groups classified with the ASCO/CAP criteria did not show significant differences (p = .161) in cancer-specific survival. CONCLUSIONS: HER2 high expression groups were significantly associated with shorter cancer-specific survival, and our study revealed that the FDA criteria are more suitable for determining HER2 expression in UUTUC.

Renal epithelioid angiomyolipoma: Histopathologic review, immunohistochemical evaluation and prognostic significance.

Epithelioid angiomyolipoma (EAML) is considered to be a potentially malignant tumor and requires a differential diagnosis from renal cell carcinoma. In this study, we assessed the clinicopathologic features of renal EAML and evaluated the prognostic significance. Among 78 angiomyolipoma (AML) patients, a total of 5 EAMLs were identified, accounting for 6.4% of the total AML cases. The mean age was 41.4 years, and the average tumor size was 12.7 cm in diameter. Association of tuberous sclerosis complex was identified in two cases. One EAML case showed malignant behavior with local recurrence and distant metastasis. The malignant EAML had a larger tumor size, a higher percentage of epithelioid component and atypical epithelioid cells, ≥2 mitoses per 10 high power fields with atypical mitosis, necrosis, extrarenal extension, and carcinoma-like growth pattern. Furthermore, the malignant case revealed p53 immunoreactivity and decreased membranous E-cadherin expression. Pathologic evaluation of adverse prognostic factors will be helpful for risk stratification and prognosis estimation of EAML patients.

HER2 Protein Overexpression and Gene Amplification in Plasmacytoid Urothelial Carcinoma of the Urinary Bladder.

AIM: HER2 overexpression has been reported in a minority of urothelial carcinomas, but little is known about HER2 protein expression and gene alterations in plasmacytoid urothelial carcinoma, a rare and aggressive variant. The aim of this study was to clarify the HER2 status in plasmacytoid urothelial carcinomas. METHODS: Six cases of plasmacytoid urothelial carcinoma were included, in which we evaluated HER2 protein expression by immunohistochemistry (IHC) and HER2 gene amplification by fluorescence in situ hybridization (FISH). RESULTS: The patients' ages ranged from 57 to 83 years (mean age, 71 years). Five patients were male and one was female. The ratio of the plasmacytoid component ranged from 30% to 100% (mean, 77%). HER2 expression score was 3+ in 4 cases, 2+ in one case, and negative in one case. HER2 gene amplification was positive in 3 cases, of which 2 cases showed a 3+ HER2 IHC score but one case was negative for HER2 IHC. Another 2 cases showed equivocal HER2 FISH results, and one remaining case was negative for HER2 FISH. CONCLUSION: Our observation that plasmacytoid urothelial carcinomas frequently demonstrated HER2 protein overexpression provides supporting evidence that HER2 may be a potential therapeutic target for plasmacytoid urothelial carcinoma.

Injectable in situ-forming hydrogel for cartilage tissue engineering.

Methoxy polyethylene glycol-poly(ε-caprolactone) (MPEG-PCL; MP) diblock copolymers undergo a solution-to-gel phase transition at body temperature and serve as ideal biomaterials for drug delivery and tissue engineering. Here, we examined the potential use of a chondrocyte-loaded MP solution as an injectable, in situ-forming hydrogel for cartilage regeneration. The chondrocyte-MP solution underwent a temperature-dependent solution-to-gel phase transition in vitro, as shown by an increase in viscosity from 1 cP at 20-30 °C to 1.6 × 105 cP at 37 °C. The chondrocytes readily attached to and proliferated on the MP hydrogel in vitro. The chondrocyte-MP solution transitioned to a hydrogel immediately after subcutaneous injection into mice, and formed an interconnected pore structure required to support the growth, proliferation, and differentiation of the chondrocytes. The chondrocyte-MP hydrogels formed cartilage in vivo, as shown by the histological and immunohistochemical staining of glycosaminoglycans, proteoglycans, and type II collagen, the major components of cartilage. Cartilage formation increased with hydrogel implantation time, and the expression of glycosaminoglycans, and type II collagen reached maximal levels at 6 weeks post-implantation. Collectively, these data suggest that in situ-forming chondrocyte-MP hydrogels have potential as non-invasive alternatives for tissue-engineered cartilage formation.