A tree-based machine learning model to approach morphologic assessment of malignant salivary gland tumors

López-Janeiro, Álvaro; Cabañuz, Clara; Santana, L. Blasco; Ruiz‐Bravo, Elena

doi:10.1016/j.anndiagpath.2021.151869

Cited by 9 publications

(3 citation statements)

References 27 publications

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“…Researches revealed promising results, although most applications are in developmental phase. However, in one research, mucoepidermoid carcinomas fell in one of the three histological types that easy to be misclassified by the tree-based machine learning model ( 28 ). For this special malignant tumor, more research is still needed.…”

Section: Discussionmentioning

confidence: 99%

Case report: The diagnostic pitfall of Warthin-like mucoepidermoid carcinoma

Yang,

Lei,

Lang

et al. 2024

Front. Oncol.

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Warthin-like mucoepidermoid carcinoma (WL-MEC) is a newly reported variant of mucoepidermoid carcinoma. Its histological feature is easy to confused with metaplastic Warthin Tumor, and its relationship with Warthin tumor in histogenesis is controversial. In this study, we presented two cases of WL-MEC, discussing their clinicopathological and molecular features. Notably, one case was initially misdiagnosed during the first onset of the tumor. Case 1 was a 60-year-old female with a mass in the right parotid gland. Case 2 featured a 29-year-old male who developed a lump at the original surgical site 6 months after a “Warthin tumor” resection from the submandibular gland. Histologically, both tumor exhibited a prominent lymphoid stroma and cystic pattern, accompanied by various amounts of epithelial nests composed of squamoid cells, intermediate cells and mucinous cells. The characteristic eosinophilic bilayer epithelium of Warthin tumor was not typically presented in either case. Both cases tested positive for MAML2 gene rearrangement. To contextualize our findings, we conducted a comprehensive review of forty-eight WL-MEC cases documented in the English literature, aiming to synthesizing a reliable differential diagnostic approach. WL-MEC is a rare yet clinically relevant variant, posing a diagnostic pitfall for pathologists. Our study underscores the importance of a meticulous evaluation of both clinical and histological features, coupled with the detection of MAML2 rearrangement, as a credible method for distinguishing WL-MEC from other benign and malignant lesions, particularly metaplastic Warthin tumor.

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Section: Discussionmentioning

confidence: 99%

Case report: The diagnostic pitfall of Warthin-like mucoepidermoid carcinoma

Yang,

Lei,

Lang

et al. 2024

Front. Oncol.

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“…As for the papers not previously addressed, namely [30][31][32][33][34][35][36][37][38][39][40], it is evident that a significant limitation present in these studies is the lack of comprehensive details regarding the dataset employed. These articles notably omitted crucial information that would otherwise contribute to a more thorough understanding and evaluation of their respective research methodologies and findings.…”

Section: Datasets Used For Ai-based Systems For Dentistry E-healthmentioning

confidence: 99%

“…Continuing with 2021, Alvaro López-Janeiro et al successfully directed the morphological approach to diagnosis by developing a ML algorithm for enhancing the diagnostic performance for malignant salivary gland tumors [35]. Limitations included a small sample size and a lack of external validation.…”

Section: Relevant Work Experiencesmentioning

confidence: 99%

Advancing Dental Diagnostics: A Review of Artificial Intelligence Applications and Challenges in Dentistry

Musleh,

Almossaeed,

Balhareth

et al. 2024

BDCC

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The rise of artificial intelligence has created and facilitated numerous everyday tasks in a variety of industries, including dentistry. Dentists have utilized X-rays for diagnosing patients’ ailments for many years. However, the procedure is typically performed manually, which can be challenging and time-consuming for non-specialized specialists and carries a significant risk of error. As a result, researchers have turned to machine and deep learning modeling approaches to precisely identify dental disorders using X-ray pictures. This review is motivated by the need to address these challenges and to explore the potential of AI to enhance diagnostic accuracy, efficiency, and reliability in dental practice. Although artificial intelligence is frequently employed in dentistry, the approaches’ outcomes are still influenced by aspects such as dataset availability and quantity, chapter balance, and data interpretation capability. Consequently, it is critical to work with the research community to address these issues in order to identify the most effective approaches for use in ongoing investigations. This article, which is based on a literature review, provides a concise summary of the diagnosis process using X-ray imaging systems, offers a thorough understanding of the difficulties that dental researchers face, and presents an amalgamative evaluation of the performances and methodologies assessed using publicly available benchmarks.

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Development and Evaluation of a Convolutional Neural Network for Microscopic Diagnosis Between Pleomorphic Adenoma and Carcinoma Ex‐Pleomorphic Adenoma

Sousa‐Neto,

Nakamura,

Giraldo‐Roldan

et al. 2024

Head & Neck

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AimsTo develop a model capable of distinguishing carcinoma ex‐pleomorphic adenoma from pleomorphic adenoma using a convolutional neural network architecture.Methods and ResultsA cohort of 83 Brazilian patients, divided into carcinoma ex‐pleomorphic adenoma (n = 42) and pleomorphic adenoma (n = 41), was used for training a convolutional neural network. The whole‐slide images were annotated and fragmented into 743 869 (carcinoma ex‐pleomorphic adenomas) and 211 714 (pleomorphic adenomas) patches, measuring 224 × 224 pixels. Training (80%), validation (10%), and test (10%) subsets were established. The Residual Neural Network (ResNet)‐50 was chosen for its recognition and classification capabilities. The training and validation graphs, and parameters derived from the confusion matrix, were evaluated. The loss curve recorded 0.63, and the accuracy reached 0.93. Evaluated parameters included specificity (0.88), sensitivity (0.94), precision (0.96), F1 score (0.95), and area under the curve (0.97).ConclusionsThe study underscores the potential of ResNet‐50 in the microscopic diagnosis of carcinoma ex‐pleomorphic adenoma. The developed model demonstrated strong learning potential, but exhibited partial limitations in generalization, as indicated by the validation curve. In summary, the study established a promising baseline despite limitations in model generalization. This indicates the need to refine methodologies, investigate new models, incorporate larger datasets, and encourage inter‐institutional collaboration for comprehensive studies in salivary gland tumors.

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A tree-based machine learning model to approach morphologic assessment of malignant salivary gland tumors

Cited by 9 publications

References 27 publications

Case report: The diagnostic pitfall of Warthin-like mucoepidermoid carcinoma

Case report: The diagnostic pitfall of Warthin-like mucoepidermoid carcinoma

Advancing Dental Diagnostics: A Review of Artificial Intelligence Applications and Challenges in Dentistry

Development and Evaluation of a Convolutional Neural Network for Microscopic Diagnosis Between Pleomorphic Adenoma and Carcinoma Ex‐Pleomorphic Adenoma

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