Journal of Pathology and Translational Medicine

Original Articles

Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning: Truong Phan-Xuan Nguyen, Minh-Khang Le, Sittiruk Roytrakul, Shanop Shuangshoti, Nakarin Kitkumthorn, Somboon Keelawat; Received July 23, 2024 Accepted September 14, 2024 Published online October 24, 2024; DOI: https://doi.org/10.4132/jptm.2024.09.14 [Epub ahead of print]

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Abstract PDF Supplementary Material: Background
Although the criteria for follicular-pattern thyroid tumors are well-established, diagnosing these lesions remains challenging in some cases. In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. It is crucial to differentiate this variant of papillary thyroid carcinoma from low-risk follicular pattern tumors due to their shared morphological characteristics. Proteomics holds significant promise for detecting and quantifying protein biomarkers. We investigated the potential value of a protein biomarker panel defined by machine learning for identifying the invasive encapsulated follicular variant of papillary thyroid carcinoma, initially using formalin- fixed paraffin-embedded samples.
Methods
We developed a supervised machine-learning model and tested its performance using proteomics data from 46 thyroid tissue samples.
Results
We applied a random forest classifier utilizing five protein biomarkers (ZEB1, NUP98, C2C2L, NPAP1, and KCNJ3). This classifier achieved areas under the curve (AUCs) of 1.00 and accuracy rates of 1.00 in training samples for distinguishing the invasive encapsulated follicular variant of papillary thyroid carcinoma from non-malignant samples. Additionally, we analyzed the performance of single-protein/gene receiver operating characteristic in differentiating the invasive encapsulated follicular variant of papillary thyroid carcinoma from others within The Cancer Genome Atlas projects, which yielded an AUC > 0.5.
Conclusions
We demonstrated that integration of high-throughput proteomics with machine learning can effectively differentiate the invasive encapsulated follicular variant of papillary thyroid carcinoma from other follicular pattern thyroid tumors.

Revisiting the utility of identifying nuclear grooves as unique nuclear changes by an object detector model: Pedro R. F. Rende, Joel Machado Pires, Kátia Sakimi Nakadaira, Sara Lopes, João Vale, Fabio Hecht, Fabyan E. L. Beltrão, Gabriel J. R. Machado, Edna T. Kimura, Catarina Eloy, Helton E. Ramos; J Pathol Transl Med. 2024;58(3):117-126. Published online April 30, 2024; DOI: https://doi.org/10.4132/jptm.2024.03.07

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Abstract PDF: Background
Among other structures, nuclear grooves are vastly found in papillary thyroid carcinoma (PTC). Considering that the application of artificial intelligence in thyroid cytology has potential for diagnostic routine, our goal was to develop a new supervised convolutional neural network capable of identifying nuclear grooves in Diff-Quik stained whole-slide images (WSI) obtained from thyroid fineneedle aspiration.
Methods
We selected 22 Diff-Quik stained cytological slides with cytological diagnosis of PTC and concordant histological diagnosis. Each of the slides was scanned, forming a WSI. Images that contained the region of interest were obtained, followed by pre-formatting, annotation of the nuclear grooves and data augmentation techniques. The final dataset was divided into training and validation groups in a 7:3 ratio.
Results
This is the first artificial intelligence model based on object detection applied to nuclear structures in thyroid cytopathology. A total of 7,255 images were obtained from 22 WSI, totaling 7,242 annotated nuclear grooves. The best model was obtained after it was submitted 15 times with the train dataset (14th epoch), with 67% true positives, 49.8% for sensitivity and 43.1% for predictive positive value.
Conclusions
The model was able to develop a structure predictor rule, indicating that the application of an artificial intelligence model based on object detection in the identification of nuclear grooves is feasible. Associated with a reduction in interobserver variability and in time per slide, this demonstrates that nuclear evaluation constitutes one of the possibilities for refining the diagnosis through computational models.

Deep learning for computer-assisted diagnosis of hereditary diffuse gastric cancer: Sean A. Rasmussen, Thomas Arnason, Weei-Yuarn Huang; J Pathol Transl Med. 2021;55(2):118-124. Published online January 22, 2021; DOI: https://doi.org/10.4132/jptm.2020.12.22

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Abstract PDF

Background
Patients with hereditary diffuse gastric cancer often undergo prophylactic gastrectomy to minimize cancer risk. Because intramucosal poorly cohesive carcinomas in this setting are typically not grossly visible, many pathologists assess the entire gastrectomy specimen microscopically. With 150 or more slides per case, this is a major time burden for pathologists. This study utilizes deep learning methods to analyze digitized slides and detect regions of carcinoma.
Methods
Prophylactic gastrectomy specimens from seven patients with germline CDH1 mutations were analyzed (five for training/validation and two for testing, with a total of 133 tumor foci). All hematoxylin and eosin slides containing cancer foci were digitally scanned, and patches of size 256×256 pixels were randomly extracted from regions of cancer as well as from regions of normal background tissue, resulting in 15,851 images for training/validation and 970 images for testing. A model with DenseNet-169 architecture was trained for 150 epochs, then evaluated on images from the test set. External validation was conducted on 814 images scanned at an outside institution.
Results
On individual patches, the trained model achieved a receiver operating characteristic (ROC) area under the curve (AUC) of 0.9986. This enabled it to maintain a sensitivity of 90% with a false-positive rate of less than 0.1%. On the external validation dataset, the model achieved a similar ROC AUC of 0.9984. On whole slide images, the network detected 100% of tumor foci and correctly eliminated an average of 99.9% of the non-cancer slide area from consideration.
Conclusions
Overall, our model shows encouraging progress towards computer-assisted diagnosis of hereditary diffuse gastric cancer.

Citations

Citations to this article as recorded by

Non-endoscopic Applications of Machine Learning in Gastric Cancer: A Systematic Review
Marianne Linley L. Sy-Janairo, Jose Isagani B. Janairo
Journal of Gastrointestinal Cancer.2024; 55(1): 47. CrossRef
Artificial intelligence applicated in gastric cancer: A bibliometric and visual analysis via CiteSpace
Guoyang Zhang, Jingjing Song, Zongfeng Feng, Wentao Zhao, Pan Huang, Li Liu, Yang Zhang, Xufeng Su, Yukang Wu, Yi Cao, Zhengrong Li, Zhigang Jie
Frontiers in Oncology.2023;[Epub] CrossRef
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Anqi Wang, Jieli Zhou, Gang Wang, Beibei Zhang, Hongyi Xin, Haiyang Zhou
Asian Journal of Surgery.2023; 46(9): 3568. CrossRef
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Yuanqing Yang, Kai Sun, Yanhua Gao, Kuansong Wang, Gang Yu
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Using Deep Learning to Predict Final HER2 Status in Invasive Breast Cancers That are Equivocal (2+) by Immunohistochemistry
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A machine-learning expert-supporting system for diagnosis prediction of lymphoid neoplasms using a probabilistic decision-tree algorithm and immunohistochemistry profile database: Yosep Chong, Ji Young Lee, Yejin Kim, Jingyun Choi, Hwanjo Yu, Gyeongsin Park, Mee Yon Cho, Nishant Thakur; J Pathol Transl Med. 2020;54(6):462-470. Published online August 31, 2020; DOI: https://doi.org/10.4132/jptm.2020.07.11

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126 Download
8 Web of Science
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Abstract PDF Supplementary Material

Background
Immunohistochemistry (IHC) has played an essential role in the diagnosis of hematolymphoid neoplasms. However, IHC interpretations can be challenging in daily practice, and exponentially expanding volumes of IHC data are making the task increasingly difficult. We therefore developed a machine-learning expert-supporting system for diagnosing lymphoid neoplasms.
Methods
A probabilistic decision-tree algorithm based on the Bayesian theorem was used to develop mobile application software for iOS and Android platforms. We tested the software with real data from 602 training and 392 validation cases of lymphoid neoplasms and compared the precision hit rates between the training and validation datasets.
Results
IHC expression data for 150 lymphoid neoplasms and 584 antibodies was gathered. The precision hit rates of 94.7% in the training data and 95.7% in the validation data for lymphomas were not statistically significant. Results in most B-cell lymphomas were excellent, and generally equivalent performance was seen in T-cell lymphomas. The primary reasons for lack of precision were atypical IHC profiles for certain cases (e.g., CD15-negative Hodgkin lymphoma), a lack of disease-specific markers, and overlapping IHC profiles of similar diseases.
Conclusions
Application of the machine-learning algorithm to diagnosis precision produced acceptable hit rates in training and validation datasets. Because of the lack of origin- or disease- specific markers in differential diagnosis, contextual information such as clinical and histological features should be taken into account to make proper use of this system in the pathologic decision-making process.

Citations

Citations to this article as recorded by

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Enhanced Immunohistochemistry Interpretation with a Machine Learning-Based Expert System
Anca Iulia Neagu, Diana Gina Poalelungi, Ana Fulga, Marius Neagu, Iuliu Fulga, Aurel Nechita
Diagnostics.2024; 14(17): 1853. CrossRef
Optimization of diagnosis and treatment of hematological diseases via artificial intelligence
Shi-Xuan Wang, Zou-Fang Huang, Jing Li, Yin Wu, Jun Du, Ting Li
Frontiers in Medicine.2024;[Epub] CrossRef
Real-Life Barriers to Diagnosis of Early Mycosis Fungoides: An International Expert Panel Discussion
Emmilia Hodak, Larisa Geskin, Emmanuella Guenova, Pablo L. Ortiz-Romero, Rein Willemze, Jie Zheng, Richard Cowan, Francine Foss, Cristina Mangas, Christiane Querfeld
American Journal of Clinical Dermatology.2023; 24(1): 5. CrossRef
Validation of a Machine Learning Expert Supporting System, ImmunoGenius, Using Immunohistochemistry Results of 3000 Patients with Lymphoid Neoplasms
Jamshid Abdul-Ghafar, Kyung Jin Seo, Hye-Ra Jung, Gyeongsin Park, Seung-Sook Lee, Yosep Chong
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Dai Chihara, Loretta J. Nastoupil, Christopher R. Flowers
British Journal of Haematology.2023; 202(2): 219. CrossRef
Current Trend of Artificial Intelligence Patents in Digital Pathology: A Systematic Evaluation of the Patent Landscape
Muhammad Joan Ailia, Nishant Thakur, Jamshid Abdul-Ghafar, Chan Kwon Jung, Kwangil Yim, Yosep Chong
Cancers.2022; 14(10): 2400. CrossRef
Recent Application of Artificial Intelligence in Non-Gynecological Cancer Cytopathology: A Systematic Review
Nishant Thakur, Mohammad Rizwan Alam, Jamshid Abdul-Ghafar, Yosep Chong
Cancers.2022; 14(14): 3529. CrossRef
Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation
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Diagnostic Pathology.2021;[Epub] CrossRef

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