Artificial Intelligence in Cytopathology: A Neural Network to Identify Papillary Carcinoma on Thyroid Fine-Needle Aspiration Cytology Smears

Sanyal, Parikshit; Mukherjee, Tanushri; Barui, Sanghita; Das, Arabinda; Gangopadhyay, Prabaha

doi:10.4103/jpi.jpi_43_18

Cited by 71 publications

(42 citation statements)

References 17 publications

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“…11 Also, in cases of Thyroid Cancer, ne needle aspiration cytology diagnosis could be done with sensitivity of 90.48%. [12] But in many of these studies, the sample size in all these studies very limited, and the generalization would be doubtful.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Use for Differentiation of Low-grade vs High-Grade Glioma in Intraoperative Squash Smears

Thombre

Nandeesh

Vazhayil

et al. 2021

Preprint

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ObjectiveAutomated diagnosis using Artificial Intelligence (AI) techniques would be a useful addition to the intraoperative squash smear diagnosis. A robust diagnostic tool would enhance capabilities in centres where there is limited expertise for the diagnosis of intracranial lesions. The study aims to explore possibilities of deep learning technique-based models to classify squash smear images of glioma into high- and low-grade tumors.Methods500 Scanned images of squash smear were obtained intraoperatively and dataset was built. Image dataset was then pre-processed and fed into a CNN (Convolutional Neural Network) model for training and validation. The dataset consisted of 10,000 images of high (6000) and low (4000) grade gliomas, divided into three sets of training, validation and testing. ResultsCNN model based on deep learning algorithm was built and trained on training dataset to get accuracy of 96.2%. On a testing dataset which contains images previously unseen by trained model, it could achieve accuracies of 91% for diagnosing high grade glioma and 77% for low grade glioma. A positive predictive value of 86.6% and F1-score of 0.887 was achieved. Feature visualization technique was applied at the end to visualize regions of interest.ConclusionDeep Learning techniques can be applied as diagnostic tool if proper standardized images are obtained for reporting of squash smears of gliomas. The diagnostic accuracies of such tools can reach up to current standard diagnostic accuracies by conventional ways of reporting. Feature visualization techniques applied which can be used for rapid screening of slides or section of slide to assist in rapid diagnosis.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Use for Differentiation of Low-grade vs High-Grade Glioma in Intraoperative Squash Smears

Thombre

Nandeesh

Vazhayil

et al. 2021

Preprint

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“…Finally, in the recent years, ANNs have been again in the front line, and there are more efforts reported, for example, to distinguish follicular adenomas from follicular carcinomas [ 90 ] or papillary carcinomas [ 91 ]. Interestingly, whole slide imaging applications started to appear, and deep learning approaches have been introduced [ 92 , 93 ].…”

Section: Discussionmentioning

confidence: 99%

Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

Fragopoulos

Pouliakis

Meristoudis

et al. 2020

Journal of Thyroid Research

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Objective. This study investigates the potential of an artificial intelligence (AI) methodology, the radial basis function (RBF) artificial neural network (ANN), in the evaluation of thyroid lesions. Study Design. The study was performed on 447 patients who had both cytological and histological evaluation in agreement. Cytological specimens were prepared using liquid-based cytology, and the histological result was based on subsequent surgical samples. Each specimen was digitized; on these images, nuclear morphology features were measured by the use of an image analysis system. The extracted measurements (41,324 nuclei) were separated into two sets: the training set that was used to create the RBF ANN and the test set that was used to evaluate the RBF performance. The system aimed to predict the histological status as benign or malignant. Results. The RBF ANN obtained in the training set has sensitivity 82.5%, specificity 94.6%, and overall accuracy 90.3%, while in the test set, these indices were 81.4%, 90.0%, and 86.9%, respectively. Algorithm was used to classify patients on the basis of the RBF ANN, the overall sensitivity was 95.0%, the specificity was 95.5%, and no statistically significant difference was observed. Conclusion. AI techniques and especially ANNs, only in the recent years, have been studied extensively. The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology. The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.

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“…PTC, the most common TC (>80%), arises from abnormal growth of thyroid epithelial cells ( 28 , 38 ). In recent years, AI models with quantitative morphological features have tried to improve follicular lesions’ recognition capacity ( 55 – 57 ). Sanyal et al.…”

Section: Applications Of Ai In Cytopathological Evaluation From Fnamentioning

confidence: 99%

“…Sanyal et al. ( 55 ) obtained the nuclear morphology and papillary structure of PTC under two magnifications (×10 and ×40). CNN model selected PTC from colloid goiter, follicular neoplasms, and lymphocytic thyroiditis by right of these features.…”

Section: Applications Of Ai In Cytopathological Evaluation From Fnamentioning

confidence: 99%

Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives

Liu

et al. 2021

Front. Oncol.

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Thyroid cancers (TC) have increasingly been detected following advances in diagnostic methods. Risk stratification guided by refined information becomes a crucial step toward the goal of personalized medicine. The diagnosis of TC mainly relies on imaging analysis, but visual examination may not reveal much information and not enable comprehensive analysis. Artificial intelligence (AI) is a technology used to extract and quantify key image information by simulating complex human functions. This latent, precise information contributes to stratify TC on the distinct risk and drives tailored management to transit from the surface (population-based) to a point (individual-based). In this review, we started with several challenges regarding personalized care in TC, for example, inconsistent rating ability of ultrasound physicians, uncertainty in cytopathological diagnosis, difficulty in discriminating follicular neoplasms, and inaccurate prognostication. We then analyzed and summarized the advances of AI to extract and analyze morphological, textural, and molecular features to reveal the ground truth of TC. Consequently, their combination with AI technology will make individual medical strategies possible.

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Artificial Intelligence in Cytopathology: A Neural Network to Identify Papillary Carcinoma on Thyroid Fine-Needle Aspiration Cytology Smears

Cited by 71 publications

References 17 publications

Deep Learning Use for Differentiation of Low-grade vs High-Grade Glioma in Intraoperative Squash Smears

Deep Learning Use for Differentiation of Low-grade vs High-Grade Glioma in Intraoperative Squash Smears

Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives

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