Insulinoma‐associated protein 1 is a novel diagnostic marker of small cell lung cancer in bronchial brushing and cell block cytology from pleural effusions: Validity and reliability with cutoff value

Abe, Hideyuki; Takase, Yoshiaki; Sadashima, Eiji; Fukumitsu, Chihiro; Murata, Kazuya; Ito, Takaaki; Kawahara, Akihiko; Naito, Yoshiki; Akiba, Jun

doi:10.1002/cncy.22177

Cited by 14 publications

(6 citation statements)

References 25 publications

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“…Cell-block preparations have been used as a complementary technique for increasing diagnostic accuracy in many fields 49 , such as endometrial cytology, malignant pleural effusion, and needle aspiration cytology of thyroid gland 50,51 . We first proposed a cell-block technique for fixation of fNRBC samples.…”

Section: Discussionmentioning

confidence: 99%

A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cell With Convolutional Neural Network

Sun¹,

Wang²,

Zhao³

et al. 2021

Preprint

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Background: The rapid recognition of fetal nucleated red blood cells (fNRBCs) present considerable challenges.Objective: To establish a computer-aided diagnosis system (CAD) for rapid recognition of fNRBCs by a convolutional neural network (CNN). Methods: We adopted density gradient centrifugation and magnetic-activated cell sorting to extract fNRBCs from umbilical cord blood samples. A cell-block method was used to embed fNRBCs for routine formalin-fixed paraffin sectioning and hematoxylin-eosin stains. Then we proposed a CAD-based on CNN to automatically learn discriminative features and recognize fNRBCs. Region of interest 1 extraction methods were used to automatically segment individual cells in cell slices. The discriminant information from ROIs was encoded into a feature vector. The prediction network provided a pathological diagnosis. Results: Totally, 4760 pictures of fNRBCs from 260 cell-slides of 4 umbilical cord blood samples were collected. On the premise of 100% accuracy in the training set (3720 pictures) the sensitivity, specificity, and accuracy of cellular intelligent recognition were 96.5%, 100%, and 98.5% in the test set (1040 pictures).Conclusion: We present a CAD system for effective and accurate fNRBCs recognition based on CNN.

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Section: Discussionmentioning

confidence: 99%

A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cell With Convolutional Neural Network

Sun¹,

Wang²,

Zhao³

et al. 2021

Preprint

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“…The patients diagnosed with benign endometrium by D&C and malignant lesions by Li Brush samplers were not further explored for the reasons and followed up. Third, cell block has been used as a complementary technique for increasing diagnostic accuracy in many fields (38,39). The endometrial specimens sampled by Li Brush can be processed into both cell smears and cell blocks.…”

Section: Discussionmentioning

confidence: 99%

Clinical Evaluation of Li Brush Endometrial Samplers for Diagnosing Endometrial Lesions in Women With Intrauterine Devices

Han

Zhao

et al. 2020

Front. Med.

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Background: For women with intrauterine devices (IUDs), it is difficult to sample the endometrium when abnormal uterine bleeding occurs or when regular screening of endometrial cancer is proposed. The purpose of this study is to evaluate the validity of endometrial sampling using Li Brush in IUD users.Methods: This study was a prospective cohort study and conducted in two parts. Part I was to assess the impact of Li Brush on the position of IUDs. Transvaginal ultrasound was used to locate IUDs before and after sampling. Part II was to explore the diagnostic accuracy of Li Brush in detecting endometrial lesions. IUD users with irregular uterine bleeding were recruited in the IUD group and IUD non-users who arranged for dilatation and curettage (D&C) were recruited in the control group. The endometrium was sampled by Li Brush for cells and by D&C for tissues in both groups. The satisfactoriness of sampling and validity of Li Brush were evaluated.Results: Seventeen cases in part I confirmed no significant difference in the position of IUDs before and after sampling (p = 0.20). 112 IUD users and 139 IUD non-users were recruited in part II. Li Brush achieved 94.64 and 92.09% satisfactory sampling rates in the IUD group and control group, respectively, without statistically significant difference between the two groups (p = 0.42). The Sensitivity and specificity of Li Brush for detection of endometrial lesions in IUD group were 95.35 and 87.76% respectively.Conclusions: Li Brush used for endometrial biopsy did not affect the position of IUDs and had high yield of satisfactory samples and good validity for endometrial diagnoses. It was feasible to screen endometrial lesions by Li Brush for women with IUDs.

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“…Cell block preparations have been used as a complementary technique for increasing diagnostic accuracy in many fields 42 , such as endometrial cytology, malignant pleural effusion, and needle aspiration cytology of thyroid gland 43,44 . In this study, we first proposed a cell block technique for fixation of fNRBC samples.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based adaptive detection of fetal nucleated red blood cells

Sun

Wang

Zhao

et al. 2020

Preprint

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15Aim: this study, we established an artificial intelligence system for rapid 16 identification of fetal nucleated red blood cells (fNRBCs). 17Method: Density gradient centrifugation and magnetic-activated cell sorting were 18 used for the separation of fNRBCs from umbilical cord blood. The cell block 19 technique was used for fixation. We proposed a novel preprocessing method based on 20 imaging characteristics of fNRBCs for region of interest (ROI) extraction, which 21 automatically segmented individual cells in peripheral blood cell smears. The 22 discriminant information from ROIs was encoded into a feature vector and 23 pathological diagnosis were provided by the prediction network. 24Results: Four umbilical cord blood samples were collected and validated based on a 25 large dataset containing 260 samples. Finally, the dataset was classified into 3,720 and 26 1,040 slides for training and testing, respectively. In the test set, classifier obtained 27 98.5% accuracy and 96.5% sensitivity. 28Conclusion: Therefore, this study offers an effective and accurate method for 29 fNRBCs preservation and identification. 30 Keywords: fetal nucleated red blood cells, cell-block, deep learning, non-invasive 31 prenatal diagnosis 32 Introduction 33The clinical application of fNRBCs during pregnancy could be classified into two 34 main categories 1,2 . One is the prognosis of possible diseases in pregnant women by 35 counting fNRBCs in umbilical cord blood. Chronic tissue hypoxia results in increased 36 levels of erythropoietin, which, in turn, leads to stimulation of erythropoiesis and 37 increased numbers of circulating nucleated red blood cells (NRBCs) 1,3-5 . Increased 38 umbilical cord levels of erythropoietin have been reported in pregnancies complicated 39 by intrauterine growth restriction, maternal hypertension, preeclampsia, maternal 40 smoking, Rh isoimmunization, and maternal diabetes 5-8 . As expected, each of these 41 conditions has been associated with increased NRBCs in the newborn 9 . The other 42 objective is to screen and extract fNRBCs from maternal peripheral blood for 43 non-invasive prenatal diagnosis (NIPD) 10-12 . The choice of fNRBCs as ideal target 44 cells is based on the following parameters 13,14 : (1) presence of intact nuclei containing 45 the complete fetal genome in fNRBCs, which is a prerequisite for prenatal analysis; (2) 46 limited life span of fNRBCs in the maternal circulation, which can be differentiated 47 55 cells) 16,17 . Several fNRBC enrichment methods based on different principles have 56 been reported, such as density gradient centrifugation (DGC) 13,18 , 57 fluorescence-activated cell sorting (FACS) 19 , and magnetic-activated cell sorting 58 (MACS) 20 , dielectrophoresis, and microfluidics based technologies 21,22 . Nevertheless, 59 long-term preservation of samples and rapid identification of target cells (fNRBCs) 60 still present considerable challenges. 61 Since the identification of fNRBCs in large number of cell block slices represent a 62 huge manual burden on p...

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Insulinoma‐associated protein 1 is a novel diagnostic marker of small cell lung cancer in bronchial brushing and cell block cytology from pleural effusions: Validity and reliability with cutoff value

Cited by 14 publications

References 25 publications

A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cell With Convolutional Neural Network

A Computer-Aided Diagnosis System of Fetal Nucleated Red Blood Cell With Convolutional Neural Network

Clinical Evaluation of Li Brush Endometrial Samplers for Diagnosing Endometrial Lesions in Women With Intrauterine Devices

Deep learning-based adaptive detection of fetal nucleated red blood cells

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