Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning

Li, Yuan; Chen, Pingjun; Li, Zhiyuan; Su, Hua; Yang, Lin; Zhong, Dingrong

doi:10.1016/j.artmed.2020.101918

Cited by 31 publications

(30 citation statements)

References 34 publications

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“…Owing to the limited number of patients, data enhancement was used to provide more data and expand the dataset The ResNet50 model was trained using pathological images and misdiagnosed 64 cases. An analysis of pathological images and a literature review showed the following results: 1) a nodular goiter may be confused with PTC because of the nodular changes and sometimes papillary structures that can be observed microscopically; 11 2) adenomas often exhibit follicular enlargement and fusion, forming a cystic structure, while some PTC cases may also form a cystic structure, resulting in misdiagnosis between the two; 12,13 3) both FTC and PTC are differentiated thyroid carcinomas (DTCs) from follicular epithelial cells and have similar pathological manifestations; additionally, there is a special type of PTC called follicular papillary thyroid carcinoma (FPTC) that exhibits manifestations similar to those of FTC, and FTC also has papillary structures, so DNN models easily misdiagnose the two; 14 and 4) some tumor cells in MTC may be arranged in papillary or follicular shapes, causing misdiagnoses of PTC by DNN models. 15 The RF model was trained by the text dataset and misdiagnosed 81 auxiliary examination results.…”

Section: Discussionmentioning

confidence: 99%

Differential diagnostic value of the ResNet50, random forest, and DS ensemble models for papillary thyroid carcinoma and other thyroid nodules

et al. 2022

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Objective To explore the differential diagnostic efficiency of the residual network (ResNet)50, random forest (RF), and DS ensemble models for papillary thyroid carcinoma (PTC) and other pathological types of thyroid nodules. Methods This study retrospectively analyzed 559 patients with thyroid nodules and collected thyroid pathological images and auxiliary examination results (laboratory and ultrasound results) to construct datasets. The pathological image dataset was used to train a ResNet50 model, the text dataset was used to train a random forest (RF) model, and a DS ensemble model was constructed from the results of the two models. The differential diagnostic values of the three models for PTC and other types of thyroid nodules were then compared. Results The DS ensemble model had the highest sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (85.87%, 97.18%, 93.77%, and 0.982, respectively). Conclusions Compared with Resnet50 and the RF models trained only on imaging data or text information, respectively, the DS ensemble model showed better diagnostic value for PTC.

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Section: Discussionmentioning

confidence: 99%

Differential diagnostic value of the ResNet50, random forest, and DS ensemble models for papillary thyroid carcinoma and other thyroid nodules

et al. 2022

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“…Moreover, the methodology resulted in diagnosis of a typical Whole Slide Image in less than one min. 45 Paeng's presentation covers limitations of pathology and relative advantages of DP of reproducibility, accuracy, and workload reduction. Key applications of DP are a) Tumor proliferation score prediction -breast resection, and b) Gleason score prediction -prostate biopsy.…”

Section: D) Digital Pathology (Dp)mentioning

confidence: 99%

Artificial Intelligence (AI) in Pathology – A Summary and Challenges

Buch¹,

Kulkarni²

2021

GJMR

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This bibliographic study covers Artificial Intelligence (AI)theory and its applications from the healthcare field and in particular from the discipline of pathology. This review includes basics of AI, supervised and unsupervised machine learning (ML), various supervised ML algorithms, and their applications in healthcare and pathology. Digital Pathology with Deep Machine Learning is more advantageous over traditional pathology that is based on ‘physical slide on a physical microscope’. However, various implementation challenges of cost, data quality, multi-center validation, bias, and regulatory approval issues for AI in clinical practice still remain, which are also described in this study.

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“…7,8 For instance, with a deep learning method, the malignancy of thyroid lesions could be determined with frozen section images. 9 Images of magnetic resonance, ultrasonic, and cytopathology may be treated similarly. [10][11][12][13] Mass spectrometry had also been used in diagnosis and discovery of biomarkers of thyroid related diseases.…”

Section: Introductionmentioning

confidence: 99%

Rapid diagnosis of papillary thyroid carcinoma with machine learning and probe electrospray ionization mass spectrometry

et al. 2022

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Frozen section examination could provide pathological diagnosis for surgery of thyroid nodules, which is time‐consuming, skill‐ and experience‐dependent. This study developed a rapid classification method for thyroid nodules and machine learning. Total 69 tissues were collected including 43 nodules and 26 nodule‐adjacent tissues. Intraoperative frozen section was first performed to give accurate diagnosis, and the rest frozen specimen were pretreated for probe electrospray ionization mass measurement. By multivariate analysis of mass scan data, a series compounds were found downregulated in the extraction solution of papillary thyroid carcinoma (PTC), but some were found upregulated by mass spectrometry imaging. m/z 758.5713 ([PC[34:2] + H]+), m/z 772.5845 ([PC[32:0] + K]+), and m/z 786.6037 ([PC[36:2] + H]+) were firstly identified as potential biomarkers for nodular goiter (NG). Machine learning was employed by means of support vector machine (SVM) and random forest (RF) algorithms. For classification of PTC from NG, SVM and RF algorithms exhibited the same performance and the concordance was 94.2% and 94.4% between prediction and pathological diagnosis with positive and negative mass dataset, respectively. For the classification of PTC from PTC adjacent tissues, SVM was better than RF and the concordance was 93.8% and 83.3% with positive and negative mass dataset, respectively. With the identified compounds as training features, the sensitivity and specificity are 87.5% and 88.9% for the test set. The developed method could also correctly predict the malignancy of one medullary thyroid carcinoma and one adenomatous goiter (benign). The diagnosis time is about 10 min for one specimen, and it is very promising for the intraoperative diagnosis of papillary thyroid carcinoma.

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Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning

Cited by 31 publications

References 34 publications

Differential diagnostic value of the ResNet50, random forest, and DS ensemble models for papillary thyroid carcinoma and other thyroid nodules

Differential diagnostic value of the ResNet50, random forest, and DS ensemble models for papillary thyroid carcinoma and other thyroid nodules

Artificial Intelligence (AI) in Pathology – A Summary and Challenges

Rapid diagnosis of papillary thyroid carcinoma with machine learning and probe electrospray ionization mass spectrometry

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