Inverted papilloma and nasal polyp classification using a deep convolutional network integrated with an attention mechanism

Li, Xinyao; Zhao, Haoran; Ren, Tao; Tian, Yang; Yan, Aihui; Li, Wei

doi:10.1016/j.compbiomed.2022.105976

Cited by 10 publications

(12 citation statements)

References 23 publications

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Section: Discussionmentioning

confidence: 99%

“…Most applications of AI in rhinology are in diagnosing nasal diseases, including nasal polyps, 14,15,51,82 inverted papilloma, 35,46,90 and other sinonasal tumors 39,69 (Supplemental File 3, available online). Some studies used AI to differentiate anterior ethmoidal artery location, 20 identify middle turbinate pneumatization (concha bullosa), 21 and recognize and calculate the volume of the inferior turbinate and maxillary sinus.…”

Section: Application Of Ai In Rhinologymentioning

confidence: 99%

“…Some studies used AI to differentiate anterior ethmoidal artery location, 20 identify middle turbinate pneumatization (concha bullosa), 21 and recognize and calculate the volume of the inferior turbinate and maxillary sinus. 53 Most studies have combined AI with CT. 20,21,35,44,46,47,53,56,69,80 Other studies have combined AI with MRI, 33,39,40,45 whole-slide imaging (WSI), 14,15,51 endoscopic images, 82,90 or positron emission tomography (PET)-CT. 34 Some studies directly compared AI with specialist physicians and showed equivalence or superiority of AI over specialist physicians 14,69,80,90 ; it was found that AI required much less time to diagnose. 14 Some studies have combined 2 or more algorithms to solve this problem.…”

Section: Application Of Ai In Rhinologymentioning

confidence: 99%

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Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

Wang

Liang

et al. 2023

Otolaryngol.--head neck surg.

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ObjectiveTo update the literature and provide a systematic review of image‐based artificial intelligence (AI) applications in otolaryngology, highlight its advances, and propose future challenges.Data SourcesWeb of Science, Embase, PubMed, and Cochrane Library.Review MethodsStudies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies.ResultsOverall, 686 studies were identified. After screening titles and abstracts, 325 full‐text studies were assessed for eligibility, and 78 studies were included in this systematic review. The studies originated from 16 countries. Among these countries, the top 3 were China (n = 29), Korea (n = 8), the United States, and Japan (n = 7 each). The most common area was otology (n = 35), followed by rhinology (n = 20), pharyngology (n = 18), and head and neck surgery (n = 5). Most applications of AI in otology, rhinology, pharyngology, and head and neck surgery mainly included chronic otitis media (n = 9), nasal polyps (n = 4), laryngeal cancer (n = 12), and head and neck squamous cell carcinoma (n = 3), respectively. The overall performance of AI in accuracy, the area under the curve, sensitivity, and specificity were 88.39 ± 9.78%, 91.91 ± 6.70%, 86.93 ± 11.59%, and 88.62 ± 14.03%, respectively.ConclusionThis state‐of‐the‐art review aimed to highlight the increasing applications of image‐based AI in otorhinolaryngology head and neck surgery. The following steps will entail multicentre collaboration to ensure data reliability, ongoing optimization of AI algorithms, and integration into real‐world clinical practice. Future studies should consider 3‐dimensional (3D)‐based AI, such as 3D surgical AI.

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Section: Discussionmentioning

confidence: 99%

Section: Application Of Ai In Rhinologymentioning

confidence: 99%

Section: Application Of Ai In Rhinologymentioning

confidence: 99%

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Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

Wang

Liang

et al. 2023

Otolaryngol.--head neck surg.

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“…20,21 However, most of these methods are invasive, endoscopic images can also be informative, and the deep learning network processing these images has recently demonstrated its validity. 21 EGFR mutations might be the early event initiating this tumor's development by enhancing epithelial cell proliferation, and numerous factors, including viral infection (such as HPV), smoking, and chronic inflammation, can contribute to nasal inverted papilloma, however, the pathogenesis of the inverted papilloma is still unclear. Recently, some authors demonstrated the distinct inflammatory pattern of the inverted papilloma, implying the oncogenic role of the acute inflammatory cells, neutrophils, in the pathogenesis of the inverted papilloma.…”

Section: Discussionmentioning

confidence: 99%

Unusual presentation and histopathology of bilateral nasal polypi; cavernous hemangioma (right) and inverted papilloma (left)

Hasan,

Mairaj,

Elbadawy

et al. 2023

Clinical Case Reports

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Hemangiomas are common lesions in medical practice, but those arising in the nasal cavity and/or paranasal sinuses are rare. Inverted papilloma is a rare benign tumor with a high incidence rate in both the nasal cavity and sinuses. The presence of both lesions in the same patient is even rarer. Here we present a case of a male patient with an unusual presentation and dual pathologies of cavernous hemangioma and inverted papilloma of the sinonasal tract that underwent endoscopic surgery and showed no recurrence of disease after a 2‐year follow‐up. The association of nasal cavernous hemangioma at one side and inverted papilloma at the other side is a very rare occasion that requires further studies and histopathology is the only diagnostic tool.

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“…In the last 2 years, studies regarding artificial intelligence in IP have gradually become a hot topic (9)(10)(11)(12)(13). In one study, Li et al (14) designed a deep learning framework through convolutional neural networks to automatically identify IP and NP with high AUC values of 0.95. In another study, Ren et al (15) used a deep convolutional neural network (CNN) which combines a densely connected convolutional network (DenseNet) and squeeze-and-excitation network (SENet) to classify IP and NP in CT and achieved a relatively high diagnostic value.…”

Section: Introductionmentioning

confidence: 99%

A new biomarker combining multimodal MRI radiomics and clinical indicators for differentiating inverted papilloma from nasal polyp invaded the olfactory nerve possibly

Yuan

Han

2023

Front. Neurol.

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Background and purposeInverted papilloma (IP) and nasal polyp (NP), as two benign lesions, are difficult to distinguish on MRI imaging and clinically, especially in predicting whether the olfactory nerve is damaged, which is an important aspect of treatment and prognosis. We plan to establish a new biomarker to distinguish IP and NP that may invade the olfactory nerve, and to analyze its diagnostic efficacy.Materials and methodsA total of 74 cases of IP and 55 cases of NP were collected. A total of 80% of 129 patients were used as the training set (59 IP and 44 NP); the remaining were used as the testing set. As a multimodal study (two MRI sequences and clinical indicators), preoperative MR images including T2-weighted magnetic resonance imaging (T2-WI) and contrast-enhanced T1-weighted magnetic resonance imaging (CE-T1WI) were collected. Radiomic features were extracted from MR images. Then, the least absolute shrinkage and selection operator (LASSO) regression method was used to decrease the high degree of redundancy and irrelevance. Subsequently, the radiomics model is constructed by the rad scoring formula. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the model have been calculated. Finally, the decision curve analysis (DCA) is used to evaluate the clinical practicability of the model.ResultsThere were significant differences in age, nasal bleeding, and hyposmia between the two lesions (p < 0.05). In total, 1,906 radiomic features were extracted from T2-WI and CE-T1WI images. After feature selection, using 12 key features to bulid model. AUC, sensitivity, specificity, and accuracy on the testing cohort of the optimal model were, respectively, 0.9121, 0.828, 0.9091, and 0.899. AUC on the testing cohort of the optimal model was 0.9121; in addition, sensitivity, specificity, and accuracy were, respectively, 0.828, 0.9091, and 0.899.ConclusionA new biomarker combining multimodal MRI radiomics and clinical indicators can effectively distinguish between IP and NP that may invade the olfactory nerve, which can provide a valuable decision basis for individualized treatment.

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Inverted papilloma and nasal polyp classification using a deep convolutional network integrated with an attention mechanism

Cited by 10 publications

References 23 publications

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

Unusual presentation and histopathology of bilateral nasal polypi; cavernous hemangioma (right) and inverted papilloma (left)

A new biomarker combining multimodal MRI radiomics and clinical indicators for differentiating inverted papilloma from nasal polyp invaded the olfactory nerve possibly

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