در هنگام جستجو کلمه در قسمت عنوان میتوانید کلمات مورد جستجو را با کاراکتر (-) جدا کنید.
کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Iranian Journal of Pathology
- نوع مقاله: Journal Article
- کلمات کلیدی: Ki67,Meninigioma,Atypical meningioma,Labelling index
- چکیده:
- چکیده انگلیسی: Background & Objective: Meningiomas are the most frequently encountered primary non-glial tumors of the central nervous system (CNS). The Ki67 labelling index (Ki67LI) is a proliferation marker that may prove useful in determining the histological grade. This study aims at: 1) Studying the frequency, grade and histomorphological spectrum of meningiomas, 2) Evaluating 20 histological parameters and determining its utility in grading meningiomas and 3) Comparing the Ki67LI in the various subtypes and WHO grades.
Methods: The cases of meningiomas diagnosed in our Department from June 2009 to May 2014 were included. The clinical details, grade and 20 histological parameters: mitosis, vesicular nuclei, macronucleoli, nuclear pleomorphism, scattered bizarre nuclei, hypercellularity, sheeting, lymphocytes, small cell change, foam cells, ossification, necrosis, papillary change, lipidization, psammoma bodies, vascularization, brain invasion, dural invasion, bone invasion and other soft tissue invasion were recorded for each case. The average and highest Ki67LI was recorded as percentage and number per high power field.
Results: A total of 175 cases of meningioma were included: grade I (145), grade II (30). Atypical histological features like hypercellularity, sheeting, etc. were common in grade II tumors. Increased vascularity, lymphocytes and psammoma bodies were common in grade I tumors. Ki67LI (highest) ranged from 1-6% in grade I and 5-12% in grade II tumors.
Conclusion: Among different methods showing mitotic activity, Ki67% (highest) was the most statistically significant LI in differentiating grade I and grade II tumors. The median Ki67% (highest) was 4% for grade I and 7% for grade II tumors.- انتشار مقاله: 19-05-1398
- نویسندگان: Girish Solanke,Vidya Monappa,Ranjini Kudva
- مشاهده
- جایگاه : پژوهشی
- مجله: Iranian Journal of Pathology
- نوع مقاله: Journal Article
- کلمات کلیدی: Fasciitis,Fine-Needle Aspiration Cytological Technic
- چکیده:
- چکیده انگلیسی: Background and Objective: Nodular fasciitis (NF) is a self-limiting, transient neoplasm composed of fibroblasts and myofibroblasts. Since it regresses spontaneously, diagnosis by fine needle aspiration (FNA) cytology plays a major role in its management.
Methods: We present a series of 8 cases with either FNA or biopsy diagnosis of NF, and study the major cytological features with a review of literature on diagnostic criteria and its pitfalls.
Results and Conclusion: The 8 cases occurred in patients between the age of 14 to 72 years, with equal sex distribution. FNA diagnosis concurred in 4 cases. Causes of wrong diagnosis included lack of clinical information and paucicellular smear.
FNA cytology is an important tool in the diagnosis of nodular fasciitis, in appropriate clinico-radiological setting.- انتشار مقاله: 23-10-1396
- نویسندگان: Padmapriya Jaiprakash,Balaji Radhakrishnan,Ranjini Kudva,Manna Valiathan,Seetharam Prasad
- مشاهده
- جایگاه : پژوهشی
- مجله: Iranian Journal of Pathology
- نوع مقاله: Journal Article
- کلمات کلیدی: Fallopian tube,carcinoma-in-situ,endometrial curretage,vaginal bleeding
- چکیده:
- چکیده انگلیسی: Background and Objective: Primary fallopian tube carcinomas (PFTC) are rare tumors with non-specific clinical presentations. The current case was unique since the tumor was first detected on endometrial curettage and clinicoradiologically was misdiagnosed as endometrial carcinoma.
Case Report: A 48-year-old, post-menopausal female presented with one episode of vaginal bleeding. Endometrial curettage showed poorly differentiated carcinoma, while cervico-vaginal Papanicolaou (Pap) smear was negative for malignant cells. Right sided fallopian tube carcinoma in-situ was diagnosed on histopathological examination of surgical hysterectomy with B/L salpingo-oophorectomy specimen.
Conclusion: As observed in the current case, unusual tumor histology with broad papillary fronds lined by pleomorphic cells showing nuclear stratification and focal involvement of endometrial curettage specimen may be considered a useful pointer for tubal malignancy.- انتشار مقاله: 17-11-1395
- نویسندگان: Nilay Nishith,Vidya Monappa,Ranjini Kudva
- مشاهده
- جایگاه : پژوهشی
- مجله: Iranian Journal of Otorhinolaryngology
- نوع مقاله: Journal Article
- کلمات کلیدی: Radiotherapy,chemotherapy,Rhabdomyosarcoma,Pediatric,Temporal bone,Acute facial paralysis
- چکیده:
- چکیده انگلیسی: Introduction:
Rhabdomyosarcoma is the most frequently occurring intrusive soft tissue sarcoma in the pediatric age group. Orbit is the most common location for a pediatric rhabdomyosarcoma, but it can occur in the oral cavity, pharynx, face and neck in the descending order of incidence. Rhabdomyosarcoma in the ear is extremely rare.
Case Report:
A 5-year-old girl presented to the outpatient department of our tertiary care hospital with complaints of foul smelling, non-blood stained right ear discharge of one-month duration and deviation of angle of mouth to the left side of acute onset. Investigations revealed a diagnosis of embryonal rhabdomyosarcoma. Multimodal therapy was carried out, and the child was rendered disease-free after two years.
Conclusion:
Embryonal rhabdomyosarcoma of the head and neck mimics chronic otitis media. Early diagnosis is essential to deliver prompt treatment and prevent locoregional spread and metastasis.- انتشار مقاله: 10-12-1397
- نویسندگان: Ajay Bhandarkar,Architha Menon,Ranjini Kudva,Kailesh Pujary
- مشاهده
- جایگاه : پژوهشی
- مجله: Asian Pacific Journal of Cancer Prevention
- نوع مقاله: Journal Article
- کلمات کلیدی: deep learning,Pap smear,Cervical screening,Cell image classification,Convolution neural network
- چکیده:
- چکیده انگلیسی: Objective: Automated Pap smear cervical screening is one of the most effective imaging based cancer detection
tools used for categorizing cervical cell images as normal and abnormal. Traditional classification methods depend on
hand-engineered features and show limitations in large, diverse datasets. Effective feature extraction requires an efficient
image preprocessing and segmentation, which remains prominent challenge in the field of Pathology. In this paper, a
deep learning concept is used for cell image classification in large datasets. Methods: This relatively proposed novel
method, combines abstract and complicated representations of data acquired in a hierarchical architecture. Convolution
Neural Network (CNN) learns meaningful kernels that simulate the extraction of visual features such as edges, size,
shape and colors in image classification. A deep prediction model is built using such a CNN network to classify the
various grades of cancer: normal, mild, moderate, severe and carcinoma. It is an effective computational model which
uses multiple processing layers to learn complex features. A large dataset is prepared for this study by systematically
augmenting the images in Herlev dataset. Result: Among the three sets considered for the study, the first set of single
cell enhanced original images achieved an accuracy of 94.1% for 5 class, 96.2% for 4 class, 94.8% for 3 class and
95.7% for 2 class problems. The second set includes contour extracted images showed an accuracy of 92.14%, 92.9%,
94.7% and 89.9% for 5, 4, 3 and 2 class problems. The third set of binary images showed 85.07% for 5 class, 84%
for 4 class, 92.07% for 3 class and highest accuracy of 99.97% for 2 class problems. Conclusion: The experimental
results of the proposed model showed an effective classification of different grades of cancer in cervical cell images,
exhibiting the extensive potential of deep learning in Pap smear cell image classification.- انتشار مقاله: 10-03-1398
- نویسندگان: Shanthi P B,Faraz Faruqi,Hareesha K S,Ranjini Kudva
- مشاهده