1 Introduction Patient with rheumatic diseases are at increased risk of malignancies. The most widely known malignancies associated with rheumatic diseases other than inflammatory myositis include lymphoproliferative disorders and skin cancer. Treatment options of rheumatic diseases such as disease-modifying anti-rheumatic drugs (DMARDs) and biologics may also increase the risks of different malignancies. It is important to be aware of the association of malignancy with rheumatic diseases and anti-rheumatic agents, in order to guide our decision of treatment in
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Introduction Neurofibromatosis type 1 (NF1) is one of the most common genetically determined disease and is characterized by 5–15% higher risk of malignant tumor formation than the general population [ 1 ]. The NF1-associated NF1 gene is the tumor suppressor gene. The mutations occurring in NF1-patients cause the inactivation of the gene, predisposing to certain types of tumors arising from the embryonic neural crest [ 2 , 3 ]. Therefore, there is an association between NF1 and the following malignancy: gliomas, malignant peripheral nerve sheath tumors (MPNSTs
Peripheral pulmonary tumours are often quite difficult to diagnose and treat. Their detection brings immediately the problem of whether clinicians should just wait and observe or operate the patients.
The AIM of this study was to determine if there is a direct correlation between tumour size and the risk for malignancy and whether the tumor size should be considered a risk factor for malignancy.
PATIENTS AND METHODS: Between 1997 and 2009, 145 patients with peripheral pulmonary tumours of less than 3 cm in diameter underwent video-assisted thoracoscopic (VATS) resection for the purpose of histologic examination of the tumor.
RESULTS: The mean age of the patients was 62.60 ± 0.95 years. The youngest patient was 17 years old and the oldest - 82. The study sample included 61 women and 84 men; the men were statistically signifi cantly more than the women (57.3% and 42.07%, respectively) (t = 2.74 , P < 0.01). The total number of patients we operated were 145 with 198 resected tumours. The diameter of the lesions ranged between 0.30 cm and 3 cm (mean 1.41 ± 0.06 cm). We found that 108 (54.55%) of the tumours were malignant, and 90 (45.45%) were benign, the difference between them failing to reach statistical signifi cance (t = 1.82, P > 0.05). The mean size of malignant lesions was statistically signifi cantly greater than that of benign tumours (1.62 ± 0.08 cm vs 1.15 ± 0.06 cm).
CONCLUSION: The results of this study suggest that the bigger the diameter of the nodule, the greater the percentage share of malignant tumours, which means that the size of the tumour is an important risk factor for malignancy.
Background: There is a challenge in diagnosing cancer in patients with exudative plural effusion using a noninvasive and accurate method.
Objective: We developed artificial neural network (ANN), as a nonlinear model, to discriminate malignant exudative plural effusion from nonmalignant based on routine pleural fluid findings.
Methods: The plural fluid parameters including total and differential cell counts, total proteins, lactate dehydrogenase (LDH), glucose, adenosine deaminase (ADA), as well as age and sex of 114 patients with exudative plural effusion were applied by models as input. The output was supposed to be the presence or absence of the cancer.
Results: The accuracy, sensitivity and specificity of ANN for predicting malignancy were 89.7%, 86.7%, and 91.7%, respectively. In addition, the neural network significantly outperformed the logistic regression model, as a linear model, (AUC: 0.892 vs. 0.633, respectively, p < 0.001).
Conclusion: The ANN is a novel accurate and noninvasive method that can be used clinically to diagnose malignancy in patients with exudative plural effusion.
Classification of Breast Cancer Malignancy Using Cytological Images of Fine Needle Aspiration Biopsies
According to the World Health Organization (WHO), breast cancer (BC) is one of the most deadly cancers diagnosed among middle-aged women. Precise diagnosis and prognosis are crucial to reduce the high death rate. In this paper we present a framework for automatic malignancy grading of fine needle aspiration biopsy tissue. The malignancy grade is one of the most important factors taken into consideration during the prediction of cancer behavior after the treatment. Our framework is based on a classification using Support Vector Machines (SVM). The SVMs presented here are able to assign a malignancy grade based on preextracted features with the accuracy up to 94.24%. We also show that SVMs performed best out of four tested classifiers.
Prognostic Index (FLIPI) prognostic index score was calculated as “3: high risk”, and rituximab was added to the treatment protocol. After four cycles of CHOP plus the monoclonal antibody rituximab (R-CHOP), the patient achieved complete response with no pathological 18-FDG involvement on PET/CT and continued with rituximab maintenance therapy. The patient was disease free after 1-year follow-up with complete response. Discussion The frequency of occurrence of synchronous multiple malignancies is very low among the population. After the treatment of primary solid tumors