Artificial neural network in diagnosis of urothelial cell carcinoma in urine cytology

Muralidaran, Chandrasekaran; Dey, Pranab; Nijhawan, Raje; Kakkar, Nandita

doi:10.1002/dc.23244

Cited by 32 publications

(21 citation statements)

References 26 publications

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“…ANN model is particularly helpful in differentiating the grey zone lesions where there is dilemma in the decision. ANN model was used in other problematic areas in cytology with remarkable success …”

Section: Discussionmentioning

confidence: 99%

“…ANN model was used in other problematic areas in cytology with remarkable success. [19][20][21][22] However, ANN is occasionally used to solve the specific problem of distinguishing FA and FC. 15,16 The various other workers used ANN model to identify the risk of malignancy in thyroid FNAC material, to differentiate different follicular lesions and to classify the cytology cases with indeterminate diagnosis.…”

Section: Annmentioning

confidence: 99%

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Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid

Savala

Dey

Gupta

2017

Diagnostic Cytopathology

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Section: Discussionmentioning

confidence: 99%

Section: Annmentioning

confidence: 99%

Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid

Savala

Dey

Gupta

2017

Diagnostic Cytopathology

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“…Other research groups have performed morphometric and machine-learning analyses of urine cytology specimens supporting the validity of the NC ratio as a marker of atypia and demonstrating the utility and accuracy of neural networks in relation to urinary cytology images. [15][16][17][18][19][20][21] These studies mainly demonstrate the diagnostic and prognostic utility of morphometry and machine learning in isolation.…”

Section: Comparison With Other Automated/ Semiautomated Systemsmentioning

confidence: 99%

Automating the Paris System for urine cytopathology—A hybrid deep‐learning and morphometric approach

Vaickus

Suriawinata

Wei

et al. 2019

Cancer Cytopathology

103

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Background The Paris System for Urine Cytopathology (the Paris System) has succeeded in making the analysis of liquid‐based urine preparations more reproducible. Any algorithm seeking to automate this system must accurately estimate the nuclear‐to‐cytoplasmic (N:C) ratio and produce a qualitative “atypia score.” The authors propose a hybrid deep‐learning and morphometric model that reliably automates the Paris System. Methods Whole‐slide images (WSI) of liquid‐based urine cytology specimens were extracted from 51 negative, 60 atypical, 52 suspicious, and 54 positive cases. Morphometric algorithms were applied to decompose images to their component parts; and statistics, including the NC ratio, were tabulated using segmentation algorithms to create organized data structures, dubbed rich information matrices (RIMs). These RIM objects were enhanced using deep‐learning algorithms to include qualitative measures. The augmented RIM objects were then used to reconstruct WSIs with filtering criteria and to generate pancellular statistical information. Results The described system was used to calculate the N:C ratio for all cells, generate object classifications (atypical urothelial cell, squamous cell, crystal, etc), filter the original WSI to remove unwanted objects, rearrange the WSI to an efficient, condensed‐grid format, and generate pancellular statistics containing quantitative/qualitative data for every cell in a WSI. In addition to developing novel techniques for managing WSIs, a system capable of automatically tabulating the Paris System criteria also was generated. Conclusions A hybrid deep‐learning and morphometric algorithm was developed for the analysis of urine cytology specimens that could reliably automate the Paris System and provide many avenues for increasing the efficiency of digital screening for urine WSIs and other cytology preparations.

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“…14 Until recently, based of NNs technology of AI was applied in pathologic diagnosis, for example: the early tumor screening, disease preliminary classifying and between benign and malignancy recognizing. [15][16][17][18][19][20][21][22] In the field of cytopathology, the PAPNET computer-assisted diagnosis system progressed based on "brain neural network" in 1992 greatly reduced the work of cytopathologists. [23][24][25][26] At the same time, NNs diagnosis have been applied to FNAC.…”

Section: Discussionmentioning

confidence: 99%

Automated classification of cancer from fine needle aspiration cytological image use neural networks: A meta‐analysis

Huang

Wang

2020

Diagnostic Cytopathology

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Background: The role of retrospective analysis has been evolved greatly in cancer research. We undertook this meta-analysis to evaluate the diagnostic value of Neural networks (NNs) in Fine needle aspiration cytological (FNAC) image of cancer. Methods: We systematically retrieved 396 literatures on cytodiagnosis of NNs from Cochrane, PubMed, and EMBASE. After screening, only six studies were included in metaanalysis finally. Data was comprehensively analyzed by RevMan and meta-Disc software. Results: A total of 1165 cases were extracted from six articles. Among them, 593 cases were in the abnormal/positive group and 572 cases in the normal/negative group. The pooled estimates for the NNs cytology were Area under ROC curve (AUC): 0.

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Artificial neural network in diagnosis of urothelial cell carcinoma in urine cytology

Abstract: We successfully built an ANN model in urine from the visual and morphometric data to identify the benign and malignant cases. In addition, the system can also identify the low grade and high grade UCC cases.

Cited by 32 publications

References 26 publications

Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid

Artificial neural network model to distinguish follicular adenoma from follicular carcinoma on fine needle aspiration of thyroid

Automating the Paris System for urine cytopathology—A hybrid deep‐learning and morphometric approach

Automated classification of cancer from fine needle aspiration cytological image use neural networks: A meta‐analysis

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