p16<sup>INK4a</sup> immunocytochemistry on cell blocks as an adjunct to cervical cytology: Potential reflex testing on specially prepared cell blocks from residual liquid-based cytology specimens

Shidham, Vinod B.; Mehrotra, Ravi; Varsegi, George M.; D'Amore, Krista; Hunt, Bryan; Narayan, Raj

doi:10.4103/1742-6413.76379

Cited by 29 publications

(23 citation statements)

References 23 publications

(45 reference statements)

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“…Thus helping in improving diagnostic accuracy of cervical lesion. These were consistent with findings of Siddham V. B et al [57] for p16inK4a and by Sahebali s et al [58] for KI67. (Table 9,) FIG 6 There are many studies done on markers and endometrium.…”

Section: Uses Of Cell Block Aresupporting

confidence: 82%

Manual Liquid Based Cytology in Diagnosis of Gynecologic Pathology

Manoli¹

2016

CRJ

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Abnormal uterine bleeding (AUB) is a commonly encountered complaint in gynecology department. Endometrial cancer is the fourth most common malignancy in women and the most frequent gynecological cancer in developed countries. With 5,28, 000 new cases every year, cervical cancer is the fourth most common cancer affecting women worldwide, after breast, colorectal, and lung cancers. Though the cytological examination has been the mainstay for early detection of cervical cancer, and found to be useful in detection of endometrial cancer, its widespread use has not been possible in the developing countries due to paucity of resources, man power and other facilities Its sensitivity reduces to less than 50% when there is presence of obscuring blood, inflammation or thick areas of overlapping epithelial cells. Manual Liquid Based Cytology (MLBC) is a cost effective technique that enables cells to be suspended in a monolayer and thus improve detection of precursor lesions and specimen adequacy. The residual sample can be used for other tests like Cell block and immunocytochemistry. Objectives: 1. To improve the diagnosis of gynecological cancers by a method called as Manual Liquid Based Cytology (MLBC). 2. To study a cost effective method of studying both endometrial and cervical cancer with help of ancillary techniques like cellblock, immunocytochemistry 3To compare the findings between conventional pap smear (CPS) and MLBC in detection of gynecological conditions of endometrium and cervix. Methodology: Samples were collected using Ayres spatula by split sample technique from transformation zone of cervix which included outpatients of gynecology dept. The women were aged between 20-60 years, 82 cases with bleeding history were taken to study endometrial pathology, while 100 cases of white discharge per vagina were selected to study the cervix. 60 cases were later subjected to ancillary studies like cellblock with Immunocytochemistry. Histopathological correlation was done for cases wherever possible. Results: The study on endometruim showed MLBC is more sensitive and specific than CPS in diagnosis of malignant lesions. The contingency coefficient for LBC/Histopathology V/s CPS/Histopathology was 0.572 V/s 0.556. It was observed for cervical lesion that increased detection rate was 150% for low grade intraepithelial lesion. The Positive predictive value for diagnosing neoplastic lesions on cell blocks was 75%, while Concordance Rate of CB/Histopathology Vs CPS/Histopathology was 74% vs. 54%. Conclusion: MLBC is a cost effective method for detections of cancerous lesions of endometruim and cervix.

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Section: Uses Of Cell Block Aresupporting

confidence: 82%

Manual Liquid Based Cytology in Diagnosis of Gynecologic Pathology

Manoli¹

2016

CRJ

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“…Cervical cancer is the second most commonly diagnosed [15] and fifth deadliest [16] cancer in women throughout the world. In developing countries cervical cancer has a major share in cancer mortality [17]. Every year about 500,000 new cases are diagnosed and among which about 250,000 patient die.…”

Section: Cervical Cancermentioning

confidence: 99%

Hybrid ensemble learning technique for screening of cervical cancer using Papanicolaou smear image analysis

Sarwar

Sharma

Gupta

2015

Personalized Medicine Universe

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a b s t r a c tObjective: This paper presents an innovative idea of applying a hybrid ensemble technique i.e. ensemble of ensemble methods for improving the predictive performance of Artificial intelligence based system for screening of cervical cancer by characterization and classification of Pap smear images. Methodology: Papanicolaou smear (also referred to as Pap smear) is a microscopic examination of samples of human cells scraped from the lower, narrow part of the uterus, called the cervix. A sample of cells after being stained by using Papanicolaou method is analyzed under microscope for the presence of any unusual developments indicating any precancerous and potentially precancerous changes. Abnormal findings, if observed are subjected to further precise diagnostic subroutines. Examining the cell images for abnormalities in the cervix provides grounds for provision of prompt action and thus reducing incidence and deaths from cervical cancer. It is the most popular technique used for screening of cervical cancer. Pap smear test, if done with a regular screening programs and proper follow-up, can reduce cervical cancer mortality by up to 80% [1]. The contribution of this paper is that we have pioneered to apply hybrid ensemble technique to screen cervical cancer by classification of Pap smear data. The hybrid ensemble designed in this work has also presented an idea to use an ensemble of ensemble techniques. Using such a technique, the classification potentials of individual algorithms are fused together to gain greater classification accuracy. In addition to this we have also presented a comparative analysis of various artificial intelligence based algorithms for screening of cervical cancer. Results:The results indicate that hybrid ensemble technique is an efficient method for classification of Pap smear images and hence can be effectively used for diagnosis of cervical cancer. Among all the algorithms implemented, the hybrid ensemble approach outperformed & expressed an efficiency of about 96% for 2-class problem and about 78% for 7-class problem. The results when compared with the all the standalone classifiers were significantly better for both 2-class and 7-class problems.

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“…Cervical cancer is the second most commonly diagnosed [15] and fifth deadliest [16] cancer in women throughout the world. In developing countries cervical cancer has a major share in cancer mortality [17]. Every year about 5,00,000 new cases are diagnosed and among which about 2 50 000 patient die.…”

Section: Cervical Cancermentioning

confidence: 99%

“…The Papanicolaou test (Pap smear) has been the widely used method in cervical cancer screening for many decades and has showed a dramatic lowering of incidents of cervical cancer and hence in related mortality rates in many countries [17]. In taking a Pap smear, the cells of are scraped form the outer opening of the cervix for microscopic examination and to lookup for irregularities.…”

Section: Papanicolaou Testmentioning

confidence: 99%

Novel Benchmark Database of Digitized and Calibrated Cervical Cells for Artificial Intelligence Based Screening of Cervical Cancer

Sarwar¹,

Suri²,

Sharma³

et al. 2017

Gynecol Obstet

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Objective: The primary objective of this research work is to develop a novel benchmark database of digitized and calibrated, cervical cells obtained from slides of Papanicolaou smear test, which is done for screening of cervical cancer. This database can serve as a potential tool for designing, developing, training, testing and validating various Artificial intelligence based systems for prognosis of cervical cancer by characterization and classification of Papanicolaou smear images. The database can also be used by other researchers for comparative analysis of working efficiencies of various machine learning and image processing algorithms. The database can be obtained by sending a request to the corresponding author. Besides developing a rich machine learning database we have also presented a novel artificial intelligence based hybrid ensemble technique for efficient screening of cervical cancer by automated analysis of Papanicolaou smear images. Methodology:The correct and timely diagnosis of cervical cancer is one of the major problems in the medical world. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. Papanicolaou smear (also referred to as Pap smear) is a microscopic examination of samples of human cells scraped from the lower, narrow part of the uterus, called cervix. A sample of cells after being stained by using Papanicolaou method is analyzed under microscope for the presence of any unusual developments indicating any precancerous and potentially precancerous developments. Abnormal findings, if observed are subjected to further precise diagnostic subroutines. Examining the cell images for abnormalities in the cervix provides grounds for provision of prompt action and thus reducing incidence and deaths from cervical cancer. It is the most popular technique used for screening of cervical cancer. Pap smear test, if done with a regular screening programs and proper follow-up, can reduce cervical cancer mortality by up to 80%. The contribution of this paper is that we have created a rich machine learning database of quantitatively profiled and calibrated cervical cells obtained from Papsmear test slides. The database so created consists of data of about 200 clinical cases (8091 cervical cells), which have been obtained from multiple health care centers, so as to ensure diversity in data. The slides were processed using a multi-headed digital microscope and images of cervical cells were obtained, which were passed through various data preprocessing subroutines. After preprocessing the cells were morphologically profiled and scaled to obtain separate quantitative measurements of various features of cytoplasm and nucleus respectively. The cells in the database were carefully classified in different corresponding classes according to latest 2001-Bethesda system of classification, by technicians. In addition to this, we have also pioneered to apply a novel hybrid ensemble system to this database in order to evaluate the effect...

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p16^INK4a immunocytochemistry on cell blocks as an adjunct to cervical cytology: Potential reflex testing on specially prepared cell blocks from residual liquid-based cytology specimens

Cited by 29 publications

References 23 publications

Manual Liquid Based Cytology in Diagnosis of Gynecologic Pathology

Manual Liquid Based Cytology in Diagnosis of Gynecologic Pathology

Hybrid ensemble learning technique for screening of cervical cancer using Papanicolaou smear image analysis

Novel Benchmark Database of Digitized and Calibrated Cervical Cells for Artificial Intelligence Based Screening of Cervical Cancer

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p16INK4a immunocytochemistry on cell blocks as an adjunct to cervical cytology: Potential reflex testing on specially prepared cell blocks from residual liquid-based cytology specimens

Cited by 29 publications

References 23 publications

Manual Liquid Based Cytology in Diagnosis of Gynecologic Pathology

Manual Liquid Based Cytology in Diagnosis of Gynecologic Pathology

Hybrid ensemble learning technique for screening of cervical cancer using Papanicolaou smear image analysis

Novel Benchmark Database of Digitized and Calibrated Cervical Cells for Artificial Intelligence Based Screening of Cervical Cancer

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p16^INK4a immunocytochemistry on cell blocks as an adjunct to cervical cytology: Potential reflex testing on specially prepared cell blocks from residual liquid-based cytology specimens