Apparent Diffusion Coefficient in Invasive Ductal Breast Carcinoma: Correlation with Detailed Histologic Features and the Enhancement Ratio on Dynamic Contrast-Enhanced MR Images

Purpose. To investigate the correlation of Apperent Diffusion Coefficient (ADC) values in invasive ductal breast carcinomas with detailed histologic features and enhancement ratios on dynamic contrast-enhanced MRI. Methods and Materials. Dynamic MR images and diffusion-weighted images (DWIs) of invasive ductal breast carcinomas were reviewed in 25 (26 lesions) women. In each patient, DWI, T2WI, T1WI, and dynamic images were obtained. The ADC values of the 26 carcinomas were calculated with b-factors of 0 and 1000 s/mm(2) using echoplanar DWI. Correlations of the ADC values were examined on dynamic MRI with enhancement ratios (early to delayed phase: E/D ratio) and detailed histologic findings for each lesion, including cellular density, the size of cancer nests, and architectural features of the stroma (broad, narrow, and delicate) between cancer nests. Results. The mean ADC was 0.915 ± 0.151 × 10(−3) mm(2)/sec. Cellular density was significantly correlated with ADC values (P = .0184) and E/D ratios (P = .0315). The ADC values were also significantly correlated to features of the stroma (broad to narrow, P = .0366). Conclusion. The findings suggest that DWIs reflect the growth patterns of carcinomas, including cellular density and architectural features of the stroma, and E/D ratios may also be closely correlated to cellular density

Etiological factors in primary hepatic B-cell lymphoma

Sixty-four cases of malignant lymphoma involving the liver were examined. Of these, 20 cases were histologically confirmed to be primary hepatic B-cell lymphoma. Twelve of these 20 cases were diffuse large B-cell lymphoma (DLBCL) and eight cases were mucosa-associated lymphoid tissue (MALT) lymphoma. Of the 12 cases of DLBCL, six were immunohistologically positive for CD10 and/or Bcl6 (indicating a germinal center phenotype), six were positive for Bcl2, and five were positive for CD25. Eight of the 12 DLBCL cases (66.7%) and two of the eight MALT lymphoma cases (25%) had serum anti-hepatitis C virus (HCV) antibodies and HCV RNA. The incidence of HCV infection was significantly higher in the hepatic DLBCL cases than in systemic intravascular large B-cell cases with liver involvement (one of 11 cases, 9.1%) and T/NK-cell lymphoma cases (one of 19 cases, 5.3%) (p < 0.01 for both). Two hepatic DLBCL cases (16.7%) had rheumatoid arthritis treated with methotrexate, and four MALT lymphoma cases (50%) had Sjögren’s syndrome, primary biliary cirrhosis, or autoimmune hepatitis; one case in each of these two groups was complicated by chronic HCV-seropositive hepatitis. Although primary hepatic lymphoma is rare, persistent inflammatory processes associated with HCV infection or autoimmune disease may play independent roles in the lymphomagenesis of hepatic B cells

Immunosuppressant therapy successfully improved regorafenib-induced severe hepatic injury in a patient with metastatic gastrointestinal stromal tumor: A case report

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Weakly-supervised learning for lung carcinoma classification using deep learning

Abstract Lung cancer is one of the major causes of cancer-related deaths in many countries around the world, and its histopathological diagnosis is crucial for deciding on optimum treatment strategies. Recently, Artificial Intelligence (AI) deep learning models have been widely shown to be useful in various medical fields, particularly image and pathological diagnoses; however, AI models for the pathological diagnosis of pulmonary lesions that have been validated on large-scale test sets are yet to be seen. We trained a Convolution Neural Network (CNN) based on the EfficientNet-B3 architecture, using transfer learning and weakly-supervised learning, to predict carcinoma in Whole Slide Images (WSIs) using a training dataset of 3,554 WSIs. We obtained highly promising results for differentiating between lung carcinoma and non-neoplastic with high Receiver Operator Curve (ROC) area under the curves (AUCs) on four independent test sets (ROC AUCs of 0.975, 0.974, 0.988, and 0.981, respectively). Development and validation of algorithms such as ours are important initial steps in the development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists

Methylation of drug resistance‐related genes in chemotherapy‐sensitive Epstein–Barr virus‐associated gastric cancer

Epstein–Barr virus (EBV)‐associated gastric cancer (GC) is associated with a high degree of DNA methylation. However, the association between chemotherapy susceptibility and tumor DNA methylation in advanced diseases remains unclear. The comprehensive DNA methylation status of GC cells obtained from an advanced EBV‐associated GC (EBVGC) case, in which complete response to S‐1 plus cisplatin chemotherapy was achieved, was analyzed using a DNA methylation microarray. We compared DNA methylation of GC cells with public data and identified genes with higher methylation in EBVGC cell lines than in normal gastric cells, and genes in which methylation was increased by EBV. Of these genes, ABCG2, AHNAK2, BCL2, FZD1, and TP73 are associated with published evidence for resistance to 5‐fluorouracil and cisplatin. Silencing of these genes may be associated with hypersensitivity to chemotherapy